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Social media and fake news in the 2016 election. Journal of Economic Perspectives 31, 2 (2017), 211–36.\nBorukhson, D., Lorenz-Spreen, P., and Ragni, M. When does an individual accept misinformation? In Proceedings of the Annual Meeting of the Cognitive Science Society (2021), vol. 43.\nBrand, D., Riesterer, N. O., and Ragni, M. (n.d.). Unifying models for belief and syllogistic reasoning.\nCinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., and Scala, A. The covid-19 social media infodemic. Scientific Reports 10, 1 (2020), 1–10.\nCrawford, J. R., and Henry, J. D. The positive and negative affect schedule (panas): Construct validity, measurement properties and normative data in a large non-clinical sample. British Journal of Clinical Psychology 43, 3 (2004), 245–265.\nDawson, E., Gilovich, T., and Regan, D. T. Motivated reasoning and performance on the Wason Selection Task. Personality and Social Psychology Bulletin 28, 10 (2002), 1379–1387.\nDel Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., and Quattrociocchi, W. The spreading of misinformation online. Proceedings of the National Academy of Sciences 113, 3 (2016), 554–559.\nFaragó, L., Kende, A., and Krekó, P. We only believe in news that we doctored ourselves. Social Psychology 51, 2 (2019), 77–90.\nFast, E., Chen, B., and Bernstein, M. S. Empath: Understanding topic signals in large-scale text. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (2016), pp. 4647–4657.\nForgas, J. P. Mood effects on cognition: Affective influences on the content and process of information processing and behavior. Emotions and affect in human factors and human-computer interaction (2017), 89–122.\nFrederick, S. Cognitive reflection and decision making. Journal of Economic Perspectives 19, 4 (2005), 25–42.\nFriemann, P., and Ragni, M. Cognitive computational models of spatial relational reasoning: A review. In The 3rd Workshop on Models and Representations in Spatial Cognition (MRSC-3) (2018), Thrash, Kelleher, and Dobnik, Eds.\nFum, D., Del Missier, F., and Stocco, A. The cognitive modeling of human behavior: Why a model is (sometimes) better than 10,000 words. Cognitive Systems Research 8, 3 (2007), 135–142.\nGawronski, B. Partisan bias in the identification of fake news. Trends in Cognitive Sciences 25, 9 (2021), 723–724.\nGigerenzer, G., and Selten, R. Bounded rationality: The adaptive toolbox. MIT press, Cambridge (MA), 2002.\nHerzog, S. M., and von Helversen, B. Strategy selection versus strategy blending: A predictive perspective on single-and multi-strategy accounts in multiple-cue estimation. Journal of Behavioral Decision Making 31, 2 (2018), 233–249.\nKahan, D. M. Ideology, motivated reasoning, and cognitive reflection: An experimental study. Judgment and Decision making 8 (2012), 407–24.\nKahan, D. M., Peters, E., Dawson, E. C., and Slovic, P. Motivated numeracy and enlightened self-government. Behavioural public policy 1, 1 (2017), 54–86.\nKahneman, D. A perspective on judgment and choice: mapping bounded rationality. American Psychologist 58, 9 (2003), 697.\nKahneman, D. Thinking, fast and slow. Farrar, Straus and Giroux, New York, 2011.\nKohlberg, L. Stage and sequence: The cognitive-developmental approach to socialization. Handbook of socialization theory and research 347 (1969), 480.\nKunda, Z. The case for motivated reasoning. Psychological Bulletin 108, 3 (1990), 480.\nLazer, D. M., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., Schudson, M., A. Sloman, S., Sunstein, C. R., Thorson, E. A., Watts, D. J., and Zittrain, J. L. The science of fake news. Science 359, 6380 (2018), 1094–1096.\nLewandowsky, S., Smillie, L., Garcia, D., Hertwig, R., Weatherall, J., Egidy, S., Robertson, R., O’Connor, C., Kozyreva, A., Lorenz-Spreen, P., Blaschke, Y., and Leiser, M. Technology and Democracy: Understanding the influence of online technologies on political behaviour and decision-making. Publications Office of the European Union, Luxembourg, 2020.\nLorenz-Spreen, P., Lewandowsky, S., Sunstein, C. R., and Hertwig, R. How behavioural sciences can promote truth, autonomy and democratic discourse online. Nature Human Behaviour (2020), 1–8.\nLuan, S., Schooler, L. J., and Gigerenzer, G. A signal-detection analysis of fast-and-frugal trees. Psychological Review 118, 2 (2011), 316.\nMartignon, L., Katsikopoulos, K. V., and Woike, J. K. Categorization with limited resources: A family of simple heuristics. Journal of Mathematical Psychology 52, 6 (2008), 352–361.\nMartignon, L., Vitouch, O., Takezawa, M., and Forster, M. R. Naive and yet enlightened: From natural frequencies to fast and frugal decision trees. Thinking: Psychological perspective on reasoning, judgment, and decision making (2003), 189–211.\nOaksford, M., Morris, F., Grainger, B., and Williams, J. M. G. Mood, reasoning, and central executive processes. Journal of Experimental Psychology: Learning, Memory, and Cognition 22, 2 (1996), 476.\nOsmundsen, M., Bor, A., Vahlstrup, P. B., Bechmann, A., and Petersen, M. B. Partisan polarization is the primary psychological motivation behind political fake news sharing on twitter. American Political Science Review (2021), 1–17.\nPennycook, G., Binnendyk, J., Newton, C., and Rand, D. A practical guide to doing behavioural research on fake news and misinformation. PsyArXiv [Preprint]. https:\u002F\u002Fpsyarxiv.com\u002Fg69ha (Accessed 03 April 2021) (2020).\nPennycook, G., Cannon, T. D., and Rand, D. G. Prior exposure increases perceived accuracy of fake news. Journal of Experimental Psychology: General 147, 12 (2018), 1865.\nPennycook, G., Epstein, Z., Mosleh, M., Arechar, A. A., Eckles, D., and Rand, D. G. Shifting attention to accuracy can reduce misinformation online. Nature 592, 7855 (2021), 590–595.\nPennycook, G., and Rand, D. G. Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition 188 (2019), 39–50.\nPennycook, G., and Rand, D. G. Who falls for fake news? the roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking. Journal of personality 88, 2 (2020), 185–200.\nPhillips, N. D., Neth, H., Woike, J. K., and Gaissmaier, W. Fftrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees. Judgment and Decision Making 12, 4 (2017), 344–368.\nPretus, C., Van Bavel, J. J., Brady, W. J., Harris, E. A., Vilarroya, O., and Servin, C. The role of political devotion in sharing partisan misinformation. PsyArXiv [Preprint]. https:\u002F\u002Fpsyarxiv.com\u002F7k9gx (Accessed 03 April 2021) (2021).\nRaab, M., and Gigerenzer, G. The power of simplicity: a fast-and-frugal heuristics approach to performance science. Frontiers in Psychology 6 (2015), 1672.\nRagni, M., Riesterer, N., and Khemlani, S. Predicting individual human reasoning: The PRECORE-Challenge. In Proc. of the 41th CogSci-Conference (2019), A. K. Goel, C. M. Seifert, and C. Freksa, Eds., pp. 9–10.\nRampersad, G., and Althiyabi, T. Fake news: Acceptance by demographics and culture on social media. Journal of Information Technology & Politics 17, 1 (2020), 1–11.\nRathje, S., Roozenbeek, J., Traberg, C., Van Bavel, J., and Van der Linden, S. Letter to the editors of psychological science: Meta-analysis reveals that accuracy nudges have little to no effect for us conservatives: Regarding pennycook et al.(2020). Psychological Science (2022).\nScheibehenne, B., Rieskamp, J., and Wagenmakers, E.-J. Testing adaptive toolbox models: A bayesian hierarchical approach. Psychological review 120, 1 (2013), 39.\nSchwikert, S. R., and Curran, T. Familiarity and recollection in heuristic decision making. Journal of Experimental Psychology: General 143, 6 (2014), 2341.\nTalwar, S., Dhir, A., Kaur, P., Zafar, N., and Alrasheedy, M. Why do people share fake news? associations between the dark side of social media use and fake news sharing behavior. Journal of Retailing and Consumer Services 51 (2019), 72–82.\nThomson, K. S., and Oppenheimer, D. M. Investigating an alternate form of the cognitive reflection test. Judgment and Decision making 11, 1 (2016), 99.\nTodorovikj, S., and Ragni, M. Deductive vs. inductive instructions: Evaluating the predictive powers of cognitive models for conditional reasoning. In Proceedings of the 7th Workshop on Formal and Cognitive Reasoning (2021), vol. 2961, pp. 74–87.\nVan Bavel, J. J., Harris, E. A., Pärnamets, P., Rathje, S., Doell, K., and Tucker, J. A. Political psychology in the digital (mis) information age: A model of news belief and sharing. Social Issues and Policy Review 15 (2020), 84–113.\nVan Bavel, J. J., and Pereira, A. The partisan brain: An identity-based model of political belief. Trends in cognitive sciences 22, 3 (2018), 213–224.\nVosoughi, S., Roy, D., and Aral, S. The spread of true and false news online. Science 359, 6380 (2018), 1146–1151.\nWales, D. J., and Doye, J. P. Global optimization by basin-hopping and the lowest energy structures of lennard-jones clusters containing up to 110 atoms. The Journal of Physical Chemistry A 101, 28 (1997), 5111–5116.\nWatson, D., Clark, L. A., and Tellegen, A. Development and validation of brief measures of positive and negative affect: the panas scales. Journal of personality and social psychology 54, 6 (1988), 1063.\nWatson, D., Wiese, D., Vaidya, J., and Tellegen, A. The two general activation systems of affect: Structural findings, evolutionary considerations, and psychobiological evidence. Journal of Personality and Social Psychology 76, 5 (1999), 820.\nWoike, J. K., Hoffrage, U., and Martignon, L. Integrating and testing natural frequencies, naïve bayes, and fast-and-frugal trees. Decision 4, 4 (2017), 234.\nZhou, X., Jain, A., Phoha, V. V., and Zafarani, R. Fake news early detection: A theory-driven model. Digital Threats: Research and Practice 1, 2 (2020), 1–25.",{"EN":124},"A new phenomenon is the spread and acceptance of misinformation and disinformation on an individual user level, facilitated by social media such as Twitter. So far, state-of-the-art socio-psychological theories and cognitive models focus on explaining how the accuracy of fake news is judged on average, with little consideration of the individual. In this paper, a breadth of core models are comparatively assessed on their predictive accuracy for the individual decision maker, i.e., how well can models predict an individual’s decision before the decision is made. To conduct this analysis, it requires the raw responses of each individual and the implementation and adaption of theories to predict the individual’s response. Building on methods formerly applied on smaller and more limited datasets, we used three previously collected large datasets with a total of 3794 participants and searched for, analyzed and refined existing classical and heuristic modeling approaches. The results suggest that classical reasoning, sentiment analysis models and heuristic approaches can best predict the “Accept” or “Reject” response of a person, headed by a model put together from research by Jay Van Bavel, while other models such as an implementation of “motivated reasoning” performed worse. 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A.M., & Hedetniemi, S.M. (1998). Approximating MAPs for belief networks is NP-hard and other theorems. Artificial Intelligence, 102, 21–38.",{"doi":308},"10.1016\u002FS0004-3702(98)00043-5",{"id":22,"text":310,"url":22,"identifiers":311},"Adams, R., Shipp, S., Friston, K. (2013). Predictions not commands: active inference in the motor system. Brain Structure and Function, 218(3), 611–643.",{"doi":312},"10.1007\u002Fs00429-012-0475-5",{"id":22,"text":314,"url":22,"identifiers":315},"Arora, S., & Barak, B. (2009). Complexity theory: a modern approach. Cambridge: Cambridge University Press.",{"doi":316},"10.1017\u002FCBO9780511804090",{"id":22,"text":318,"url":22,"identifiers":319},"Barlow, H.B. (1961). Possible principles underlying the transformation of sensory messages. In W.A. Rosenblith (Ed.) Sensory Communication, (Vol. 3 pp. 217–234). Cambridge,MA: MIT Press.",{},{"id":22,"text":321,"url":22,"identifiers":322},"Bilmes, J. (2004). On virtual evidence and soft evidence in Bayesian networks. Tech. Rep UWEETR-2004-0016, University of Washington, Department of Electrical Engineering.",{},{"id":22,"text":324,"url":22,"identifiers":325},"Blokpoel, M., Kwisthout, J., van Rooij, I. (2012). When can predictive brains be truly Bayesian? Frontiers in Theoretical and Philosophical Psychology, 3, 406.",{},{"id":22,"text":327,"url":22,"identifiers":328},"Blokpoel, M., Kwisthout, J., van der Weide, T., Wareham, T., van Rooij, I. (2013). A computational-level explanation of the speed of goal inference. Journal of Mathematical Psychology, 57(3-4), 117–133.",{"doi":329},"10.1016\u002Fj.jmp.2013.05.006",{"id":22,"text":331,"url":22,"identifiers":332},"Blokpoel, M., Wareham, H., Haselager, W., Toni, I., van Rooij, I. (2018). Deep analogical inference as the origin of hypotheses. Journal of Problem Solving, 11(1), 3.",{},{"id":22,"text":334,"url":22,"identifiers":335},"Bodlaender, H.L. (1993). A tourist guide through treewidth. Acta Cybernetica, 11, 1–21.",{},{"id":22,"text":337,"url":22,"identifiers":338},"Bossaerts, P., & Murawski, C. (2017). Computational complexity and human decision-making. Trends in Cognitive Sciences, 21(12), 917–929.",{"doi":339},"10.1016\u002Fj.tics.2017.09.005",{"id":22,"text":341,"url":22,"identifiers":342},"Brown, H., & Friston, K. (2012). Free-energy and illusions: the cornsweet effect. Frontiers in Psychology, 3, 43.",{},{"id":22,"text":344,"url":22,"identifiers":345},"Brown, H., Friston, K., Bestmann, S. (2011). Active inference, attention, and motor preparation. Frontiers in Psychology, 2(218), 1–9.",{},{"id":22,"text":347,"url":22,"identifiers":348},"Bruineberg, J., Kiverstein, J., Rietveld, E. (2018). The anticipating brain is not a scientist: the free-energy principle from an ecological-enactive perspective. Synthese, 195(6), 2417–2444.",{"doi":349},"10.1007\u002Fs11229-016-1239-1",{"id":22,"text":351,"url":22,"identifiers":352},"Buesing, L., Bill, J., Nessler, B., Maass, W. (2011). Neural dynamics as sampling: A model for stochastic computation in recurrent networks of spiking neurons. PLoS Computational Biology, 7(11), e1002, 211.",{"doi":353},"10.1371\u002Fjournal.pcbi.1002211",{"id":22,"text":355,"url":22,"identifiers":356},"Castillo, E., Gutiérrez, J., Hadi, A. (1997). Sensitivity analysis in discrete Bayesian networks. IEEE Transactions on Systems Man, and Cybernetics, 27, 412–423.",{"doi":357},"10.1109\u002F3468.594909",{"id":22,"text":359,"url":22,"identifiers":360},"Chater, N., Tenenbaum, J., Yuille, A. (2006). Probabilistic models of cognition: conceptual foundations. Trends in Cognitive Sciences, 107, 287–201.",{"doi":361},"10.1016\u002Fj.tics.2006.05.007",{"id":22,"text":363,"url":22,"identifiers":364},"Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204.",{"doi":365},"10.1017\u002FS0140525X12000477",{"id":22,"text":367,"url":22,"identifiers":368},"Clark, A. (2016). Surfing uncertainty: prediction action, and the embodied mind. Oxford: Oxford University Press.",{"doi":369},"10.1093\u002Facprof:oso\u002F9780190217013.001.0001",{"id":22,"text":371,"url":22,"identifiers":372},"Clementi, A., Rolim, J., Trevisan, L. (1998). Recent advances towards proving P=BPP. In E. Allender, A. Clementi, J. Rolim, L. Trevisan (Eds.) EATCS (p. 64).",{},{"id":22,"text":374,"url":22,"identifiers":375},"Cooper, G.F. (1990). The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence, 42(2), 393–405.",{"doi":376},"10.1016\u002F0004-3702(90)90060-D",{"id":22,"text":378,"url":22,"identifiers":379},"Dagum, P., & Luby, M. (1993). Approximating probabilistic inference in Bayesian belief networks is NP-hard. Artificial Intelligence, 60(1), 141–153.",{"doi":380},"10.1016\u002F0004-3702(93)90036-B",{"id":22,"text":382,"url":22,"identifiers":383},"Darwiche, A. (2009). Modeling and reasoning with Bayesian networks. Cambridge: CU Press.",{"doi":384},"10.1017\u002FCBO9780511811357",{"id":22,"text":386,"url":22,"identifiers":387},"Dayan, P., Hinton, G.E., Neal, R.M. (1995). The helmholtz machine. Neural Computation, 7, 889–904.",{"doi":388},"10.1162\u002Fneco.1995.7.5.889",{"id":22,"text":390,"url":22,"identifiers":391},"Den Ouden, H., Kok, P., De Lange, F. (2012). How prediction errors shape perception, attention, and motivation. Frontiers in Psychology, 3, e548.",{"doi":392},"10.3389\u002Ffpsyg.2012.00548",{"id":22,"text":394,"url":22,"identifiers":395},"Donselaar, N. (2018). Parameterized hardness of active inference. In Proceedings of the international conference on probabilistic graphical models, PMLR, (Vol. 72 pp. 109–120).",{},{"id":22,"text":397,"url":22,"identifiers":398},"Edwards, M., Adams, R., Brown, H., Pare’\u002Fes, I., Friston, K. (2012). A bayesian account of ‘hysteria’. Brain, 135(11), 3495–512.",{"doi":399},"10.1093\u002Fbrain\u002Faws129",{"id":22,"text":401,"url":22,"identifiers":402},"Friston, K. (2002). Functional integration and inference in the brain. Progress in Neurobiology, 590, 1–31.",{},{"id":22,"text":404,"url":22,"identifiers":405},"Friston, K. (2005). A theory of cortical responses. Philosophical Transactions of the Royal Society B, 350, 815–836.",{"doi":406},"10.1098\u002Frstb.2005.1622",{"id":22,"text":408,"url":22,"identifiers":409},"Friston, K. (2008). Hierarchical models in the brain. PLoS Computational Biology, 4(11), e1000,211.",{"doi":410},"10.1371\u002Fjournal.pcbi.1000211",{"id":22,"text":412,"url":22,"identifiers":413},"Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138.",{"doi":414},"10.1038\u002Fnrn2787",{"id":22,"text":416,"url":22,"identifiers":417},"Friston, K., Mattout, J., Trujillo-Barreto, N., Ashburner, J., Penny, W. (2007). Variational free energy and the Laplace approximation. Neuroimage, 34, 220–234.",{"doi":418},"10.1016\u002Fj.neuroimage.2006.08.035",{"id":22,"text":420,"url":22,"identifiers":421},"Friston, K., Adams, R., Perrinet, L., Breakspear, M. (2012). Perceptions as hypotheses: Saccades as experiments. Frontiers in Psychology, 3, e151.",{},{"id":22,"text":423,"url":22,"identifiers":424},"Frixione, M. (2001). Tractable competence. Minds and Machines, 11, 379–397.",{"doi":425},"10.1023\u002FA:1017503201702",{"id":22,"text":427,"url":22,"identifiers":428},"Garey, M., & Johnson, D. (1979). Computers and intractability. A guide to the theory of NP-completeness. W.H Freeman and Co., San Francisco, CA.",{},{"id":22,"text":430,"url":22,"identifiers":431},"Gigerenzer, G. (2008). Why heuristics work. Perspectives in Psychological Science, 3(1), 20–29.",{"doi":432},"10.1111\u002Fj.1745-6916.2008.00058.x",{"id":22,"text":434,"url":22,"identifiers":435},"Gill, J.T. (1977). Computational complexity of probabilistic Turing Machines. SIAM Journal of Computing 6(4), 675–695.",{"doi":436},"10.1137\u002F0206049",{"id":22,"text":438,"url":22,"identifiers":439},"Goldreich, O. (2008). Computational complexity: a conceptual perspective. Cambridge: Cambridge University Press.",{"doi":440},"10.1017\u002FCBO9780511804106",{"id":22,"text":442,"url":22,"identifiers":443},"Griffiths, T., Kemp, C., Tenenbaum, J. (2008). Bayesian models of cognition. In R. Sun (Ed.) The Cambridge handbook of computational cognitive modeling (pp. 59–100): Cambridge University Press.",{},{"id":22,"text":445,"url":22,"identifiers":446},"Griffiths, T., Chater, N., Kemp, C., Perfors, A., Tenenbaum, J. (2010). Probabilistic models of cognition: Exploring representations and inductive biases. Trends in cognitive sciences, 14(8), 357–364.",{"doi":447},"10.1016\u002Fj.tics.2010.05.004",{"id":22,"text":449,"url":22,"identifiers":450},"Griffiths, T., Lieder, F., Goodman, N. (2015). Rational use of cognitive resources: levels of analysis between the computational and the algorithmic. Topics in Cognitive Science, 7, 217–229.",{"doi":451},"10.1111\u002Ftops.12142",{"id":22,"text":453,"url":22,"identifiers":454},"Grush, R. (2004). The emulation theory of representation: Motor control, imagery, and perception. Behavioral and Brain Sciences, 27, 377–442.",{"doi":455},"10.1017\u002FS0140525X04000093",{"id":22,"text":457,"url":22,"identifiers":458},"Habenschuss, S., Jonke, Z., Maass, W. (2013). Stochastic computations in cortical microcircuit models. PLoS Computational Biology, 9(11), e1003, 037.",{"doi":459},"10.1371\u002Fjournal.pcbi.1003311",{"id":22,"text":461,"url":22,"identifiers":462},"Hamming, R. (1950). Error detecting and error correcting codes. Bell System Technical Journal, 29(2), 147–160.",{"doi":463},"10.1002\u002Fj.1538-7305.1950.tb00463.x",{"id":22,"text":465,"url":22,"identifiers":466},"Hobson, J., & Friston, K. (2012). Waking and dreaming consciousness: Neurobiological and functional considerations. Progress in Neurobiology, 98(1), 82–98.",{"doi":467},"10.1016\u002Fj.pneurobio.2012.05.003",{"id":22,"text":469,"url":22,"identifiers":470},"Hohwy, J. (2013). The predictive mind. Oxford: Oxford University Press.",{"doi":471},"10.1093\u002Facprof:oso\u002F9780199682737.001.0001",{"id":22,"text":473,"url":22,"identifiers":474},"Hohwy, J., Roepstorff, A., Friston, K. (2008). Predictive coding explains binocular rivalry: an epistemological review. Cognition, 108(3), 687–701.",{"doi":475},"10.1016\u002Fj.cognition.2008.05.010",{"id":22,"text":477,"url":22,"identifiers":478},"Horga, G., Schatz, K., Abi-Dargham, A., Peterson, B. (2014). Deficits in predictive coding underlie hallucinations in schizophrenia. The Journal of neuroscience, 34(24), 8072–8082.",{"doi":479},"10.1523\u002FJNEUROSCI.0200-14.2014",{"id":22,"text":481,"url":22,"identifiers":482},"Jeffrey, R. (1965). The logic of decision. New York: McGraw-Hill.",{},{"id":22,"text":484,"url":22,"identifiers":485},"Jehee, J., & Ballard, D. (2009). Predictive feedback can account for biphasic responses in the lateral geniculate nucleus. PLoS Computational Biology, 5, 1–10.",{"doi":486},"10.1371\u002Fjournal.pcbi.1000373",{"id":22,"text":488,"url":22,"identifiers":489},"Kant, I. (1999\u002F1787). Critique of pure reason. The Cambridge edition of the Works of Immanuel Kant. Cambridge: Cambridge University Press.",{},{"id":22,"text":491,"url":22,"identifiers":492},"Kiiveri, H., Speed, T.P., Carlin, J.B. (1984). Recursive causal models. Journal of the Australian Mathematical Society Series A Pure mathematics, 36(1), 30–52.",{"doi":493},"10.1017\u002FS1446788700027312",{"id":22,"text":495,"url":22,"identifiers":496},"Kilner, J.M., Friston, K.J., Frith, C.D. (2007a). The mirror-neuron system: a Bayesian perspective. Neuroreport, 18, 619–623.",{"doi":497},"10.1097\u002FWNR.0b013e3281139ed0",{"id":22,"text":499,"url":22,"identifiers":500},"Kilner, J.M., Friston, K.J., Frith, C.D. (2007b). Predictive coding: an account of the mirror neuron system. Cognitive Process, 8, 159–166.",{"doi":501},"10.1007\u002Fs10339-007-0170-2",{"id":22,"text":503,"url":22,"identifiers":504},"Knill, D., & Pouget, A. (2004). The Bayesian brain: the role of uncertainty in neural coding and computation. Trends in Neuroscience, 27(12), 712–719.",{"doi":505},"10.1016\u002Fj.tins.2004.10.007",{"id":22,"text":507,"url":22,"identifiers":508},"Kostopoulos, D. (1991). An algorithm for the computation of binary logarithms. IEEE Transactions on computers, 40(11), 1267–1270.",{"doi":509},"10.1109\u002F12.102831",{"id":22,"text":511,"url":22,"identifiers":512},"Kullback, S., & Leibler, R.A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22, 79–86.",{"doi":513},"10.1214\u002Faoms\u002F1177729694",{"id":22,"text":515,"url":22,"identifiers":516},"Kwisthout, J. (2009). The computational complexity of probabilistic networks. PhD thesis Faculty of Science, Utrecht University, The Netherlands.",{},{"id":22,"text":518,"url":22,"identifiers":519},"Kwisthout, J. (2011). Most probable explanations in Bayesian networks: complexity and tractability. International Journal of Approximate Reasoning, 52(9), 1452–1469.",{"doi":520},"10.1016\u002Fj.ijar.2011.08.003",{"id":22,"text":522,"url":22,"identifiers":523},"Kwisthout, J. (2014). Minimizing relative entropy in hierarchical predictive coding. In L. van der Gaag, & A. Feelders (Eds.) Proceedings of PGM’14, LNCS, (Vol. 8754 pp. 254–270).",{"doi":524},"10.1007\u002F978-3-319-11433-0_17",{"id":22,"text":526,"url":22,"identifiers":527},"Kwisthout, J. (2015). Tree-width and the computational complexity of map approximations in Bayesian networks. Journal of Artificial Intelligence Research, 53, 699–720.",{"doi":528},"10.1613\u002Fjair.4794",{"id":22,"text":530,"url":22,"identifiers":531},"Kwisthout, J. (2018). Approximate inference in Bayesian networks: parameterized complexity results. International Journal of Approximate Reasoning, 93, 119–131.",{"doi":532},"10.1016\u002Fj.ijar.2017.10.029",{"id":22,"text":534,"url":22,"identifiers":535},"Kwisthout, J., & van der Gaag, L. (2008). The computational complexity of sensitivity analysis and parameter tuning. In D. Chickering, & J. Halpern (Eds.) Proceedings of the 24th conference on uncertainty in artificial intelligence (pp. 349–356): AUAI Press.",{},{"id":22,"text":537,"url":22,"identifiers":538},"Kwisthout, J., & van Rooij, I. (2013a). Bridging the gap between theory and practice of approximate Bayesian inference. Cognitive Systems Research, 24, 2–8.",{"doi":539},"10.1016\u002Fj.cogsys.2012.12.008",{"id":22,"text":541,"url":22,"identifiers":542},"Kwisthout, J., & van Rooij, I. (2013b). Predictive coding: intractability hurdles that are yet to overcome [abstract]. In M. Knauff, M. Pauen, N. Sebanz, I. Wachsmuth (Eds.) Proceedings of the 35th annual conference of the cognitive science society Austin, TX: Cognitive Science Society.",{},{"id":22,"text":544,"url":22,"identifiers":545},"Kwisthout, J., Wareham, T., van Rooij, I. (2011). Bayesian intractability is not an ailment approximation can cure. Cognitive Science, 35(5), 779–784.",{"doi":546},"10.1111\u002Fj.1551-6709.2011.01182.x",{"id":22,"text":548,"url":22,"identifiers":549},"Kwisthout, J., Bekkering, H., van Rooij, I. (2017). To be precise, the details don’t matter: On predictive processing, precision, and level of detail of predictions. Brain and Cognition, 112(112), 84–91.",{"doi":550},"10.1016\u002Fj.bandc.2016.02.008",{"id":22,"text":552,"url":22,"identifiers":553},"Lee, T.S., & Mumford, D. (2003). Hierarchical Bayesian inference in the visual cortex. Journal of the Optical Society of America America, 20(7), 1434–1448.",{"doi":554},"10.1364\u002FJOSAA.20.001434",{"id":22,"text":556,"url":22,"identifiers":557},"Lieder, F., & Griffiths, T.L. (2019). Resource-rational analysis: understanding human cognition as the optimal use of limited computational resources. Behavioral and Brain Sciences. \n                  https:\u002F\u002Fdoi.org\u002F10.1017\u002FS0140525X1900061X\n                  \n                .",{"doi":558},"10.1017\u002FS0140525X1900061X",{"id":22,"text":560,"url":22,"identifiers":561},"Littman, M.L., Goldsmith, J., Mundhenk, M. (1998). The computational complexity of probabilistic planning. Journal of Artificial Intelligence Research, 9, 1–36.",{"doi":562},"10.1613\u002Fjair.505",{"id":22,"text":564,"url":22,"identifiers":565},"Maass, W. (2014). Noise as a resource for computation and learning in networks of spiking neurons. Proceedings of the IEEE, 102(5), 860–880.",{"doi":566},"10.1109\u002FJPROC.2014.2310593",{"id":22,"text":568,"url":22,"identifiers":569},"Majithia, J.C., & Levan, D. (1973). A note on base-2 logarithm computations. Proceedings of the IEEE, 61 (10), 1519–1520.",{"doi":570},"10.1109\u002FPROC.1973.9318",{"id":22,"text":572,"url":22,"identifiers":573},"Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information. New York: Freeman.",{},{"id":22,"text":575,"url":22,"identifiers":576},"Otworowska, M., Kwisthout, J., van Rooij, I. (2014). Counter-factual mathematics of counterfactual predictive models. Frontiers in Consciousness Research, 5, 801.",{},{"id":22,"text":578,"url":22,"identifiers":579},"Papadimitriou, CH. (1994). Computational complexity. Reading: Addison-Wesley.",{},{"id":22,"text":581,"url":22,"identifiers":582},"Parberry, I. (1994). Circuit complexity and neural networks. Cambridge: MIT Press.",{"doi":583},"10.7551\u002Fmitpress\u002F1836.001.0001",{"id":22,"text":585,"url":22,"identifiers":586},"Park, J.D., & Darwiche, A. (2004). Complexity results and approximation settings for MAP explanations. Journal of Artificial Intelligence Research, 21, 101–133.",{"doi":587},"10.1613\u002Fjair.1236",{"id":22,"text":589,"url":22,"identifiers":590},"Pearl, J. (1988). Probabilistic reasoning in intelligent systems: networks of plausible inference. Palo Alto: Morgan Kaufmann.",{},{"id":22,"text":592,"url":22,"identifiers":593},"Pearl, J. (2000). Causality: models, reasoning and inference. Cambridge: MIT Press.",{},{"id":22,"text":595,"url":22,"identifiers":596},"Pecevski, D., Bueling, L., Maass, W. (2011). Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons. PLoS Computational Biology, 7(12), 1–25.",{"doi":597},"10.1371\u002Fjournal.pcbi.1002294",{"id":22,"text":599,"url":22,"identifiers":600},"Pink-Hashkes, S., van Rooij, I., Kwisthout, J. (2017). Perception is in the details: a predictive coding account of the psychedelic phenomenon. In Proceedings of the 39th annual meeting of the cognitive science society (pp. 2907–2912).",{},{"id":22,"text":602,"url":22,"identifiers":603},"Rao, R., & Ballard, D. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature neuroscience, 2, 79–87.",{"doi":604},"10.1038\u002F4580",{"id":22,"text":606,"url":22,"identifiers":607},"Rothen, N., Seth, A., Ward, J. (2018). Synesthesia improves sensory memory, when perceptual awareness is high. Vision Research, 153, 1–6.",{"doi":608},"10.1016\u002Fj.visres.2018.09.002",{"id":22,"text":610,"url":22,"identifiers":611},"Seth, A. (2015). Presence, objecthood, and the phenomenology of predictive perception. Cognitive neuroscience, 6(2-3), 111–117.",{"doi":612},"10.1080\u002F17588928.2015.1026888",{"id":22,"text":614,"url":22,"identifiers":615},"Seth, A., & Tsakiris, M. (2018). Being a beast machine: the somatic basis of selfhood. Trends in Cognitive Sciences, 22(11), 969– 981.",{"doi":616},"10.1016\u002Fj.tics.2018.08.008",{"id":22,"text":618,"url":22,"identifiers":619},"Seth, A., Suzuki, K., Critchley, H. (2011). An interoceptive predictive coding model of conscious presence. Frontiers in Psychology, 2, e395.",{},{"id":22,"text":621,"url":22,"identifiers":622},"Shimony, S.E. (1994). Finding MAPs for belief networks is NP-hard. Artificial Intelligence, 68(2), 399–410.",{"doi":623},"10.1016\u002F0004-3702(94)90072-8",{"id":22,"text":625,"url":22,"identifiers":626},"Sterzer, P., Adams, R., Fletcher, P., Frith, C., Lawrie, S., Muckli, L., Petrovic, P., Uhlhaas, P., Voss, M., Corlett, P. (2018). The predictive coding account of psychosis. Biological Psychiatry, 84(9), 634–643.",{"doi":627},"10.1016\u002Fj.biopsych.2018.05.015",{"id":22,"text":629,"url":22,"identifiers":630},"Stockmeyer, L. (1977). The polynomial-time hierarchy. Theoretical Computer Science, 3, 1–22.",{"doi":631},"10.1016\u002F0304-3975(76)90061-X",{"id":22,"text":633,"url":22,"identifiers":634},"Swanson, L. (2016). The predictive processing paradigm has roots in Kant. Frontiers in Systems Neuroscience, 10, 79.",{"doi":635},"10.3389\u002Ffnsys.2016.00079",{"id":22,"text":637,"url":22,"identifiers":638},"Tenenbaum, J.B. (2011). How to grow a mind: statistics, structure, and abstraction. Science, 331, 1279–1285.",{"doi":639},"10.1126\u002Fscience.1192788",{"id":22,"text":641,"url":22,"identifiers":642},"Thagard, P., & Verbeurgt, K. (1998). Coherence as constraint satisfaction. Cognitive Science, 22, 1–24.",{"doi":643},"10.1207\u002Fs15516709cog2201_1",{"id":22,"text":645,"url":22,"identifiers":646},"Thornton, C. (2016). Predictive processing is Turing complete: a new view of computation in the brain.",{},{"id":22,"text":648,"url":22,"identifiers":649},"Torán, J. (1991). Complexity classes defined by counting quantifiers. Journal of the ACM, 38(3), 752–773.",{"doi":650},"10.1145\u002F116825.116858",{"id":22,"text":652,"url":22,"identifiers":653},"Tsotsos, J. (1990). Analyzing vision at the complexity level. Behavioral and Brain Sciences, 13, 423–469.",{"doi":654},"10.1017\u002FS0140525X00079577",{"id":22,"text":656,"url":22,"identifiers":657},"Van de Cruys, S., Evers, K., Van der Hallen, R., Van Eylen, L., Boets, B., de Wit, L., Wagemans, J. (2014). Precise minds in uncertain worlds: Predictive coding in autism. Psychological Review, 121(4), 649–675.",{"doi":658},"10.1037\u002Fa0037665",{"id":22,"text":660,"url":22,"identifiers":661},"van Rooij, I. (2008). The Tractable Cognition Thesis. Cognitive Science, 32, 939–984.",{"doi":662},"10.1080\u002F03640210801897856",{"id":22,"text":664,"url":22,"identifiers":665},"van Rooij, I., Blokpoel, M., Kwisthout, J., Wareham, T. (2019). Cognition and intractability: a guide to classical and parameterized complexity analysis. Cambridge: Cambridge University Press.",{"doi":666},"10.1017\u002F9781107358331",{"id":22,"text":668,"url":22,"identifiers":669},"Vaseghi, S. (2000). Advanced digital signal processing and noise reduction, 2nd. New Jersey: Wiley.",{},{"id":22,"text":671,"url":22,"identifiers":672},"von Helmholtz, H. (1867). Handbuch der Physiologischen Optik. Leipzig: Leopold Voss.",{},{"id":22,"text":674,"url":22,"identifiers":675},"Wagner, K.W. (1986). The complexity of combinatorial problems with succinct input representation. Acta Informatica, 23, 325–356.",{"doi":676},"10.1007\u002FBF00289117",{"id":22,"text":678,"url":22,"identifiers":679},"Weilnhammer, V., Stuke, H., Hesselmann, G., Sterzer, P., Schmack, K. (2017). A predictive coding account of bistable perception-a model-based fMRI study. PLoS Computational Biology, 13(5), e1005, 536.",{"doi":680},"10.1371\u002Fjournal.pcbi.1005536",{"id":682,"createTime":683,"updateTime":683,"relativeEntities":684,"slug":22,"properties":685,"entityType":129,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":694,"fullTextUrl":22,"authors":695,"publicationType":184,"publisherRelationship":711,"citationCount":22,"citationInfo":22,"publishDate":739,"publishYear":740,"citationAnalyzeStatus":21,"lastCitationAnalyze":22,"indexDatabases":22,"openAccess":22,"references":22,"isForceReanalyzing":215},"80e4e048-6bf9-4a95-b64b-a3ac38c94841","2024-02-11T23:30:54.839+00:00",[],{"references":686,"abstract":688,"title":690,"doi":692},{"VOID":687},"Anderson, J. R. (1993). Rules of the mind. Hillsdale: Lawrence Erlbaum Associates.\nBox, G. E. P. (1976). Science and statistics. Journal of the American Statistical Association, 71, 791–799.\nGluck, K. A., & Gunzelmann, G. (2013). Computational process modeling and cognitive stressors: background and prospects for application in cognitive engineering. In J. D. Lee & A. Kirlik (Eds.), The Oxford handbook of cognitive engineering (pp. 424–432). New York: Oxford University Press.\nGluck, K. A., & Pew, R. W. (Eds.). (2005). Modeling human behavior with integrated cognitive architectures: comparison, evaluation, and validation. Psychology Press.\nGunzelmann, G. (2013). Motivations and goals in developing integrative models of human cognition. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 30–31). Austin: Cognitive Science Society.\nLee, M.D., Criss, A.H., Devezer, B. et al. (2019). Robust Modeling in Cognitive Science. Computational Brain and Behavior. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42113-019-00029-y.\nMcClelland, J. L. (2009). The place of modeling in cognitive science. Topics in Cognitive Science, 1(1), 11–38.\nNewell, A. (1973). You can’t play 20 questions with nature and win: projective comments on the papers of this symposium. In W. G. Chase (Ed.), Visual information processing (pp. 283–308). New York: Academic Press.\nNewell, A. (1990). Unified theories of cognition. Cambridge: Harvard University Press.\nRichman, H. B., & Simon, H. A. (1989). Context effects in letter perception: comparison of two theories. Psychological Review, 96(3), 417–432.\nRoberts, S., & Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107, 358–367.\nVeksler, V. D., Myers, C. W., & Gluck, K. A. (2015). Model flexibility analysis. Psychological Review, 122(4), 755–769.\nWalsh, M. M., Gunzelmann, G., & Van Dongen, H. P. A. (2017). Computational cognitive models of the temporal dynamics of fatigue from sleep loss. Psychonomic Bulletin & Review, 24, 1785–1807.",{"EN":689},"Lee et al. (2019) address a critical issue in cognitive science—defining scientific practices that will promote rigor and confidence in our science by ensuring that our mechanisms, models, and theories are adequately described and validated to facilitate replication and to foster trust. They provide a number of concrete suggestions to advance our science along that path. The recommendations emphasize preregistration of models and predictions combined with more comprehensive model evaluation, including published descriptions of exploratory analyses, alternative mechanisms, and model assumptions. These are excellent recommendations, and general adoption of such practices will benefit model assessment and validation methodologies in cognitive science research while improving trust in published reports of computational and mathematical accounts of cognitive phenomena. However, it is unclear that these strategies alone will resolve many other important challenges faced in developing quantitative theories of human cognition and behavior. For example, addressing the crisis of confidence will not, by itself, move the science toward the broader goal of developing more comprehensive and cumulative theories of the nature of the human mind. Cognitive modeling is a critical methodology for achieving that goal. However, to realize the potential will require changes not only to how we evaluate our models, but also to how we measure progress and scientific contribution.",{"EN":691},"Promoting Cumulation in models of the human mind",{"VOID":693},"10.1007\u002Fs42113-019-00060-z","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs42113-019-00060-z",[696],{"id":697,"sortIndex":23,"researcher":22,"roles":698,"affiliations":699,"properties":708},"a5ba8c69-db6b-4942-94e6-d75f75933ac6",[137],[700],{"id":22,"sortIndex":23,"affiliation":701,"properties":22},{"id":702,"createTime":703,"updateTime":703,"relativeEntities":704,"slug":22,"properties":705,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"f89f3a8a-577d-46a1-aa79-cdd3800aff57","2024-02-11T23:30:55.000+00:00",[],{"title":706},{"VI":707},"Warfighter Readiness Research Division, Air Force Research Laboratory, Dayton, USA",{"title":709},{"VI":710},"Glenn Gunzelmann",{"url":694,"publisher":712,"properties":734},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":713,"slug":10,"properties":714,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":719,"manageAffiliations":720,"indexDatabases":721,"url":22,"thumbnailPath":22,"statistic":729,"gsStatistic":22,"type":109,"analyzePriority":22},[],{"issn":715,"eissn":716,"title":717,"url":718},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},[],[],[722],{"id":56,"indexDatabase":723,"url":69,"indexYears":70,"academicFieldIds":728,"indexDatabaseRanking":74},{"id":58,"createTime":59,"updateTime":60,"relativeEntities":724,"label":725,"description":726,"key":66,"publicationTags":727,"standard":22},[],{"EN":63,"VI":63},{"EN":63,"VI":65},[68],[72,73],{"impactFactor":23,"impactFactorByYear":730,"i10Index":82,"i10IndexLast5Year":83,"totalPublication":84,"totalPublicationByYear":731,"totalCitation":93,"totalCitationByYear":732,"totalCitationPerPublication":100,"totalCitationPerPublicationByYear":733,"hindexLast5Year":108,"hindex":108},{"2019":77,"2020":78,"2021":79,"2022":80,"2023":81},{"2018":86,"2019":87,"2020":88,"2021":89,"2022":90,"2023":91,"2024":92},{"2018":95,"2019":96,"2020":97,"2021":98,"2022":83,"2023":99},{"2018":102,"2019":103,"2020":104,"2021":105,"2022":106,"2023":107},{"volume":735,"pages":737},{"VOID":736},"2",{"VOID":738},"157-159","2019-09-10",2019,{"id":742,"createTime":743,"updateTime":743,"relativeEntities":744,"slug":22,"properties":745,"entityType":129,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":754,"fullTextUrl":22,"authors":755,"publicationType":184,"publisherRelationship":790,"citationCount":22,"citationInfo":22,"publishDate":817,"publishYear":740,"citationAnalyzeStatus":21,"lastCitationAnalyze":22,"indexDatabases":22,"openAccess":22,"references":22,"isForceReanalyzing":215},"a3046fec-6046-4a3f-b7fb-934fcb47453c","2023-12-10T23:30:32.160+00:00",[],{"references":746,"abstract":748,"title":750,"doi":752},{"VOID":747},"Breiman, L. (2001). Statistical modeling: the two cultures. Statistical Science, 16, 199–231.\nCavagnaro, D.R., Aranovich, G.J., McClure, S.M., Pitt, M.A., Myung, J.I. (2016). On the functional form of temporal discounting: an optimized adaptive test. Journal of Risk and Uncertainty, 52, 233–254.\nLee, M.D., Criss, A.H., Devezer, B., et al. (2019). Computational Brain & Behavior. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42113-019-00029-y.\nLewis, J., Lee, Y., MacEachern, S. (2012). Robust inference via the blended paradigm. In Proceedings of the Joint Statistical Meetings (pp. 1773–1786).\nRatcliff, R., & Smith, P. (2004). A comparison of sequential sampling model for two-choice reaction time. Psychological Review, 111, 333–367.\nTurner, B., Dennis, S., Van Zandt, T. (2014). Likelihood free Bayesian analysis of memory models. Psychological Review, 120, 667–678.\nVan Zandt, T., Colonius, H., Proctor, R. (2000). A comparison of two response time models applied to perceptual matching. Psychonomic Bulletin Review, 7, 208–256.\nXu, X., Lu, P., MacEachern, S.N., Xu, R. (2019). Calibrated Bayes factors for model comparison. Journal of Statistical Computation and Simulation, 89(4), 591–614.\nYu, Q., MacEachern, S., Peruggia, M. (2011). Bayesian synthesis: combining subjective analyses, with an application to ozone data. Annals of Applied Statistics, 5, 1678–1698.",{"EN":749},"This is a commentary on Lee et al.’s (2019) article encouraging preregistration of model development, fitting, and evaluation. While we are in general agreement with Lee et al.’s characterization of the modeling process, we disagree on whether preregistration of this process will move the scientific enterprise forward. We emphasize the subjective and exploratory nature of model development, and point out that “under-modeling” of data (relying on black-box approaches applied to data without data exploration) is as big a problem as “over-modeling” (fitting noise, resulting in models that generalize poorly). We also note the potential long-run negative impact of preregistration on future generations of cognitive scientists. It is our opinion that preregistration of model development will lead to less, and to less creative, exploratory analysis (i.e., to more under-modeling), and that Lee at al.’s primary goals can be achieved by requiring publication of raw data and code. We conclude our commentary with suggestions on how to move forward.",{"EN":751},"Preregistration of Modeling Exercises May Not Be Useful",{"VOID":753},"10.1007\u002Fs42113-019-00038-x","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs42113-019-00038-x",[756,773],{"id":757,"sortIndex":152,"researcher":22,"roles":758,"affiliations":759,"properties":770},"fc76c09b-1d57-4f48-95ee-ea90da2cdef9",[137],[760],{"id":22,"sortIndex":23,"affiliation":761,"properties":22},{"id":762,"createTime":763,"updateTime":764,"relativeEntities":765,"slug":766,"properties":767,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"8ce6a0da-b490-4feb-8187-30fab5f791b8","2024-01-04T12:48:55.465+00:00","2024-09-27T10:30:35.766+00:00",[],"Department-of-Psychology-The-Ohio-State-University-Columbus-USA",{"title":768},{"VI":769},"Department of Psychology, The Ohio State University, Columbus, USA",{"title":771},{"VI":772},"Trisha Van Zandt",{"id":774,"sortIndex":23,"researcher":22,"roles":775,"affiliations":776,"properties":787},"1007b538-3926-41a4-b1e1-ba4c2722af3e",[137],[777],{"id":22,"sortIndex":23,"affiliation":778,"properties":22},{"id":779,"createTime":780,"updateTime":781,"relativeEntities":782,"slug":783,"properties":784,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"06b1f152-f38d-40d7-b091-9fb3eeae4d30","2023-12-01T17:16:20.383+00:00","2025-06-11T19:45:22.197+00:00",[],"Department-of-Statistics-The-Ohio-State-University-Columbus-USA",{"title":785},{"VI":786},"Department of Statistics, The Ohio State University, Columbus, USA",{"title":788},{"VI":789},"Steven N. MacEachern",{"url":754,"publisher":791,"properties":813},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":792,"slug":10,"properties":793,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":798,"manageAffiliations":799,"indexDatabases":800,"url":22,"thumbnailPath":22,"statistic":808,"gsStatistic":22,"type":109,"analyzePriority":22},[],{"issn":794,"eissn":795,"title":796,"url":797},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},[],[],[801],{"id":56,"indexDatabase":802,"url":69,"indexYears":70,"academicFieldIds":807,"indexDatabaseRanking":74},{"id":58,"createTime":59,"updateTime":60,"relativeEntities":803,"label":804,"description":805,"key":66,"publicationTags":806,"standard":22},[],{"EN":63,"VI":63},{"EN":63,"VI":65},[68],[72,73],{"impactFactor":23,"impactFactorByYear":809,"i10Index":82,"i10IndexLast5Year":83,"totalPublication":84,"totalPublicationByYear":810,"totalCitation":93,"totalCitationByYear":811,"totalCitationPerPublication":100,"totalCitationPerPublicationByYear":812,"hindexLast5Year":108,"hindex":108},{"2019":77,"2020":78,"2021":79,"2022":80,"2023":81},{"2018":86,"2019":87,"2020":88,"2021":89,"2022":90,"2023":91,"2024":92},{"2018":95,"2019":96,"2020":97,"2021":98,"2022":83,"2023":99},{"2018":102,"2019":103,"2020":104,"2021":105,"2022":106,"2023":107},{"volume":814,"pages":815},{"VOID":736},{"VOID":816},"179-182","2019-08-09",{"id":819,"createTime":820,"updateTime":821,"relativeEntities":822,"slug":823,"properties":824,"entityType":129,"verifyStatus":130,"verifyTime":821,"verifyNote":131,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":833,"fullTextUrl":22,"authors":834,"publicationType":184,"publisherRelationship":852,"citationCount":22,"citationInfo":22,"publishDate":878,"publishYear":879,"citationAnalyzeStatus":21,"lastCitationAnalyze":22,"indexDatabases":22,"openAccess":22,"references":22,"isForceReanalyzing":215},"3c704fdb-40ff-44f1-a0fb-6181f13b692a","2023-12-13T11:59:55.206+00:00","2025-01-23T23:21:38.474+00:00",[],"A-Dynamic-Dual-Process-Model-for-Binary-Choices-Serial-Versus-Parallel-Architecture",{"references":825,"abstract":827,"title":829,"doi":831},{"VOID":826},"Alós-Ferrer, C. (2018). A dual-process diffusion model. Journal of Behavioral Decision Making, 31, 203–2018.\nBrocas, I., & Carrillo, J. (2014). Value computation and value modulation: A dual-process theory of self-control (Tech. Rep.).\nBusemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: A dynamic cognition approach to decision making. Psychological Review, 100, 432–459.\nDe Martino, B., Kumaran, D., Seymour, B., & Dolan, R. J. (2006). Frames, biases, and rational decision-making in the human brain. Science, 313(5787), 684–687.\nDeNeys, W. (2021). On dual- and single-process models of thinking. Perspectives on Psychological Science, 16(6), 1413–1427.\nDevaine, M., Waszak, F., & Mamassian, P. (2014). Dual process for intentional and reactive decisions. PLOS Computational Biolog3, 9(4), e1003013.\nDiederich, A., & Colonius, H. (2019). Multisensory integration and exogenous spatial attention: A time-window-of-integration analysis. Journal of Cognitive Neuroscience, 1.\nDiederich, A., & Mallahi-Karai, K. (2018). Stochastic methods for modeling decision-making. H. Batchelder W. Colonius and E.N. Dzhafarov (Eds.), New handbook of mathematical psychology vol. ii modeling and measurement (pp. 1–70). Cambridge University Press.\nDiederich, A., & Oswald, P. (2014). Sequential sampling model for multiattribute choice alternatives with random attention time and processing order. Frontiers in Human Neuroscience, 8(697). https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffnhum.2014.00697\nDiederich, A. (1997). Dynamic stochastic models for decision making with time constraints. Journal of Mathematical Psychology, 41(3), 260–274.\nDiederich, A. (2003). Decision making under conflict: Decision time as a measure of conflict strength. Psychonomic Bulletin & Review, 10(1), 167–176.\nDiederich, A. (2008). A further test on sequential sampling models accounting for payoff effects on response bias in perceptual decision tasks. Perception and Psychophysics, 70(2), 229–256.\nDiederich, A. (2016). A multistage attention-switching model account for payoff effects on perceptual decision tasks with manipulated processing order. Decision, 3(2), 81.\nDiederich, A., & Busemeyer, J. (1999). Conflict and the stochastic-dominance principle of decision making. Psychological science, 10(4), 353–359.\nDiederich, A., & Busemeyer, J. (2006). Modeling the effects of payoff on response bias in a perceptual discrimination task: Threshold-bound, drift rate-change, or two-stage-processing hypothesis. Perception and Psychophysics, 68(2), 194–207.\nDiederich, A., & Colonius, H. (2008). When a high-intensity “distractor\" is better than a low-intensity one: Modeling the effect of an auditory or tactile nontarget stimulus on visual saccadic reaction time. Brain Research, 1242, 219–230.\nDiederich, A., & Colonius, H. (2015). The time window of multisensory integration: Relating reaction times and judgments of temporal order. Psychological Review, 122(2), 232–241.\nDiederich, A., & Colonius, H. (2021). A two-stage diffusion modeling approach to the compelled-response task. Psychological Review, 128(4), 787–802.\nDiederich, A., & Oswald, P. (2016). Multi-stage sequential sampling models with finite or infinite time horizon and variable boundaries. Journal of Mathematical Psychology, 74, 128–145.\nDiederich, A., & Trueblood, J. S. (2018). A dynamic dual process model of risky decision making. Psychological Review, 125(2), 270–292.\nDiederich, A., & Zhao, W. (2019). A dynamic dual process model of intertemporal choice. Spanish Journal of Psychology, 22, 1–14.\nDolan, R. J., & Dayan, P. (2013). Goals and habits in the brain. Neuron, 80(2), 312–325.\nEvans, J. (2006). The heuristic-analytic theory of reasoning: Extension and evaluation. Psychonomic Bulletin & Review, 13(3), 378–395.\nEvans, J. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annual Review of Pychology, 59, 255–278.\nEvans, J., & Stanovich, K. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science, 8(3), 223–241.\nFiala, B., Arico, A., & Nichols, S. (2012). On the psychological origins of dualism: Dual-process cognition and the explanatory gap (pp. 88–110). Creating Consilience: Integrating the Sciences and the Humanities.\nFox, C., & Poldrack, R. (2009). Prospect theory and the brain. P.W. Glimcher, E. Fehr, C. Camerer, and R.A. Poldrack (Eds.), Neuroeconomics: Decision making and the brain (pp. 145–173). London: Academic Press.\nFudenberg, D., & Levine, D. (2006). A dual-self model of impulse control. The American Economic Review, 96(5), 1449–1476.\nGawronski, B., & Creighton, L.A. (2013). Dual process theories. D. Carlston (Ed.), Oxford library of psychology. The oxford handbook of social cognition (pp. 282–312). New York, NY: Oxford University Press.\nGuo, L., Trueblood, J. S., & Diederich, A. (2017). Thinking fast increases framing effects in risky decision making. Psychological Science, 28(4), 530–543. https:\u002F\u002Fdoi.org\u002F10.1177\u002F0956797616689092\nGürçay, B., & Baron, J. (2017). Challenges for the sequential two-system model of moral judgement. Thinking and Reasoning, 23(1), 49–80. https:\u002F\u002Fdoi.org\u002F10.1080\u002F13546783.2016.1216011\nKahneman, D., & Frederick, S. (2002). Representativeness revisited: Attribute substitution in intuitive judgment. T. Gilovich, D. Griffin, and D. Kahneman (Eds.), Heuristics and biases: The psychology of intuitive judgment (pp. 49–81). New York, NY: Cambridge University Press.\nKahneman, D. (2011). Thinking, fast and slow. New York, NY: Farrar, Straus and Giroux.\nKahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision making under risk (pp. 263–291). XLVII: Econometrica.\nKeren, G., & Schul, Y. (2009). Two is not always better than one: A critical evaluation of two-system theories. Perspectives on Psychological Science, 4(6), 533–550.\nKlauer, K., Beller, S., & Hütter, M. (2010). Conditional reasoning in context: A dual-source model of probabilistic inference. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36(2), 298–323.\nKrajbich, I., Bartling, B., Hare, T., & Fehr, E. (2015). Rethinking fast and slow based on a critique of reaction-time reverse inference. Nature Communications, 1–9,. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fncomms8455\nLoewenstein, G., O’Donoghue, T., & Bhatia, S. (2015). Modeling the interplay between affect and deliberation. Decision, 2(2), 55.\nLuce, R. D. (1986). Response times. New York: Oxford University Press.\nMallahi-Karai, K., & Diederich, A. (2019). Decision with multiple alternatives: Geometric models in higher dimensions - The cube model. Journal of Mathematical Psychology, 93,. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jmp.2019.102294\nMallahi-Karai, K., & Diederich, A. (2021). Decision with multiple alternatives: Geometric models in higher dimensions - The disk model. Journal of Mathematical Psychology, 100,\nMilli, S., Lieder, F., & Griffiths, T. L. (2021). A rational reinterpretation of dual-process theories. Cognition, 217, 104881.\nMorin, S., Dube’, L., & Chebat, J.-C. (2007). The role of pleasant music in servicescapes: A test of the dual model of environmental perception. Journal of Retailing, 83(1), 115–130.\nMukherjee, K. (2010). A dual system model of preferences under risk. Psychological Review, 117(1), 243.\nRatcliff, R., Smith, P. L., Brown, S. D., & McKoon, G. (2016). Diffusion decision model: Current issues and history. Trends in Cognitive Sciences, 20(4), 260–281.\nRobinson, H. (2020). Dualism. E.N. Zalta (Ed.), The Stanford encyclopedia of philosophy. (https:\u002F\u002Fplato.stanford.edu\u002Farchives\u002Ffall2020\u002Fentries\u002Fdualism\u002F)\nRoe, R. M., Busemeyer, J. R., & Townsend, J. T. (2001). Multialternative decision field theory: A dynamic connectionist model of decision making. Psychological Review, 108, 370–392.\nSalinas, E., Scerra, V., Hauser, C., Costello, M. G., & Stanford, T. (2014). Decoupling speed and accuracy in an urgent decision-making task reveals multiple contributions to their trade-off. Frontiers in Neuroscience, 8, 85. https:\u002F\u002Fdoi.org\u002F10.3389\u002F2Ffnins.2014.00085\nSalinas, E., Shankar, S., Costello, M. G., Zhu, D., & Stanford, T. (2010). Waiting is the hardest part: Comparison of two computational strategies for performing a compelled-response task. Frontiers in Computational Neuroscience, 4, 153.\nSchneider, W., & Shiffrin, R.M. (1977). Controlled and automatic human information processing: I. detection, search, and attention. Psychological Review, 84(1), 1–66.\nShankar, S., Massoglia, D. P., Zhu, D., Costello, M. G., Stanford, T., & Salinas, E. (2011). Tracking the temporal evolution of a perceptual judgment using a compelled-response task. Journal of Neuroscience, 31, 8406–8421.\nShiffrin, R.M., & Schneider, W. (1977). Controlled and automatic human information processing: Ii.perceptual learning, automaticattending, and a general theory. Psychological Review, 84(2), 127–190.\nSloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119(1), 3.\nSmith, P. L. (2000). Stochastic dynamic models of response time and accuracy: A foundational primer. Journal of Mathematical Psychology, 44(3), 408–463.\nSmith, E. R., & DeCoster, J. (2000). Dual-process models in social and cognitive psychology: Conceptual integration and links to underlying memory systems. Personality and Social Psychology Review, 4(2), 108–131.\nStanford, T., Shankar, S., Massoglia, D. P., Costello, M. G., & Salinas, E. (2010). Perceptual decision making in less than 30 milliseconds. Nature Neuroscience, 13(3), 379–386.\nStanovich, K., & West, R. (2000). Individual differences in reasoning: Implications for the rationality debate? Behavioral and Brain Sciences, 23(5), 645–665.\nTownsend, J.T. (1990). Serial vs. parallel processing: Sometimes they look like Tweedledum and Tweedledee but they can (and should) be distinguished. Psychological Science, 1(1), 46–54.\nTownsend, J. T. (1972). Some results on the identifiability of parallel and serial processes. British Journal of Mathematical and Statistical Psychology, 25, 168–199.\nTrueblood, J., Brown, S., & Heathcote, A. (2014). The multi-attribute linear ballistic accumulator model of context effects in multi-alternative choice. Psychological Review, 121, 179–205.\nTversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453–458.\nTversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323.\nUsher, M., & McClelland, J. L. (2004). Loss aversion and inhibition in dynamical models of multialternative choice. Psychological Review, 111, 757–769.\nvonNeumann, J. V., & Morgenstern, O. (1953). Theory of games and economic behavior (3rd ed.). Princeton, NJ: Princeton University Press.\nWollschläger, L. M., & Diederich, A. (2017). A computational model for constructing preferences for multiple choice options. G. Gunzelmann, A. Howes, T. Tenbrink, and E.J. Davelaar (Eds.), Proceedings of the 39th annual conference of the cognitive science society (pp. 1351–1356). Austin, TX: Cognitive Science Society. Retrieved from https:\u002F\u002Fmindmodeling.org\u002Fcogsci2017\u002Fpapers\u002F0259\u002Fpaper0259.pdf\nWollschläger, L. M., & Diederich, A. (2012). The 2N-ary choice tree model for N-alternative preferential choice. Frontiers in Cognitive Science, 3, 189.\nWollschläger, L. M., & Diederich, A. (2020). Similarity, attraction, and compromise effects: Original findings, recent empirical observations, and computational cognitive process models. American Journal of Psychology, 133, 1–30.",{"EN":828},"Dual process theories have become increasingly popular in psychology, behavioral economics, and neuroscience, assuming that two processes, here generically labeled as System 1 and System 2, have antagonistic characteristics such as automatic versus deliberate, impulsive versus rational, fast versus slow, and more. In decision-making a choice results from an interplay of these two systems. However, most existent dual-process approaches are merely verbal descriptions without providing the means of rigorous testing. The prescribed dynamic dual process model framework is based on stochastic processes and produces testable qualitative and quantitative predictions. In particular, it makes precise predictions regarding choice probability, response time distributions, and the interrelation between these quantities. The focus of the present paper is on the architecture of the two postulated systems: serial versus parallel processing. Using simulation studies, I illustrate how different factors (timing of System 1, time constraint, and architecture) influence model predictions for binary choice situations. The serial and 6 parallel processing versions of the framework are fitted to published data.",{"EN":830},"A Dynamic Dual Process Model for Binary Choices: Serial Versus Parallel Architecture",{"VOID":832},"10.1007\u002Fs42113-023-00186-1","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs42113-023-00186-1",[835],{"id":836,"sortIndex":23,"researcher":22,"roles":837,"affiliations":838,"properties":849},"3f936d6d-a47d-46ee-b5b6-08679db27a04",[137],[839],{"id":22,"sortIndex":23,"affiliation":840,"properties":22},{"id":841,"createTime":842,"updateTime":843,"relativeEntities":844,"slug":845,"properties":846,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"04fdeb52-f924-4079-bc0a-add7227ef719","2024-01-20T10:12:04.416+00:00","2025-06-11T18:12:21.475+00:00",[],"Department-of-Psychology-Carl-Von-Ossietzky-University-Oldenburg-Germany",{"title":847},{"VI":848},"Department of Psychology, Carl Von Ossietzky University Oldenburg, Germany",{"title":850},{"VI":851},"Adele Diederich",{"url":833,"publisher":853,"properties":875},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":854,"slug":10,"properties":855,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":860,"manageAffiliations":861,"indexDatabases":862,"url":22,"thumbnailPath":22,"statistic":870,"gsStatistic":22,"type":109,"analyzePriority":22},[],{"issn":856,"eissn":857,"title":858,"url":859},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},[],[],[863],{"id":56,"indexDatabase":864,"url":69,"indexYears":70,"academicFieldIds":869,"indexDatabaseRanking":74},{"id":58,"createTime":59,"updateTime":60,"relativeEntities":865,"label":866,"description":867,"key":66,"publicationTags":868,"standard":22},[],{"EN":63,"VI":63},{"EN":63,"VI":65},[68],[72,73],{"impactFactor":23,"impactFactorByYear":871,"i10Index":82,"i10IndexLast5Year":83,"totalPublication":84,"totalPublicationByYear":872,"totalCitation":93,"totalCitationByYear":873,"totalCitationPerPublication":100,"totalCitationPerPublicationByYear":874,"hindexLast5Year":108,"hindex":108},{"2019":77,"2020":78,"2021":79,"2022":80,"2023":81},{"2018":86,"2019":87,"2020":88,"2021":89,"2022":90,"2023":91,"2024":92},{"2018":95,"2019":96,"2020":97,"2021":98,"2022":83,"2023":99},{"2018":102,"2019":103,"2020":104,"2021":105,"2022":106,"2023":107},{"pages":876},{"VOID":877},"1-28","2023-12-06",2023,{"id":881,"createTime":882,"updateTime":883,"relativeEntities":884,"slug":885,"properties":886,"entityType":129,"verifyStatus":130,"verifyTime":883,"verifyNote":131,"syncStatus":21,"languages":895,"translateLanguages":22,"viewCount":23,"primaryUrl":896,"fullTextUrl":22,"authors":897,"publicationType":184,"publisherRelationship":969,"citationCount":22,"citationInfo":22,"publishDate":992,"publishYear":214,"citationAnalyzeStatus":21,"lastCitationAnalyze":22,"indexDatabases":22,"openAccess":22,"references":993,"isForceReanalyzing":215},"1ef51659-116d-4e6d-84a5-2b0d24615629","2024-04-11T06:22:06.828+00:00","2024-12-07T23:01:26.644+00:00",[],"Dynamic-Cognitive-States-Explain-Individual-Variability-in-Behavior-and-Modulate-with-EEG-Functional-Connectivity-During-Working-Memory",{"keywords":887,"abstract":889,"title":891,"doi":893},{"EN":888},"",{"EN":890},"Fluctuations in strategy, attention, or motivation can cause large variability in performance across task trials. Typically, this variability is treated as noise, and assumed to cancel out, leaving supposedly stable relationships among behavior, neural activity, and experimental task conditions. Those relationships, however, could change with a participant’s internal cognitive states, and variability in performance may carry important information regarding those states, which cannot be directly measured. Therefore, we used a mathematical, state-space modeling framework to fit internal cognitive states to measured behavioral data, quantifying each participant’s sensitivity to factors such as past errors or distractions, to characterize their underlying fluctuations in reaction time. We show how integrating the states into the modeling framework could help explain trial-by-trial variability in behavior. Further, we identify EEG functional connectivity features that modulate with each state. These results illustrate the potential of this approach and how it could enable quantification of intra- and inter-individual differences and provide insight into their neural bases.",{"EN":892},"Dynamic Cognitive States Explain Individual Variability in Behavior and Modulate with EEG Functional Connectivity During Working Memory",{"VOID":894},"10.1007\u002Fs42113-022-00153-2",[234],"https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs42113-022-00153-2",[898,923,939,955],{"id":899,"sortIndex":102,"researcher":22,"roles":900,"affiliations":901,"properties":920},"ec0c4ed4-cca3-43c3-aefe-02e0adc3e5b2",[],[902,911],{"id":22,"sortIndex":23,"affiliation":903,"properties":22},{"id":904,"createTime":905,"updateTime":905,"relativeEntities":906,"slug":907,"properties":908,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"9b66696f-2331-46be-9774-10d0f8efdd04","2024-04-11T06:22:06.870+00:00",[],"Psychological-and-Brain-Sciences-Department-Johns-Hopkins-University-Baltimore-USA",{"title":909},{"EN":910},"Psychological and Brain Sciences Department, Johns Hopkins University, Baltimore, USA",{"id":22,"sortIndex":23,"affiliation":912,"properties":22},{"id":913,"createTime":914,"updateTime":914,"relativeEntities":915,"slug":916,"properties":917,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"48d3394e-6921-41f6-a319-cd816cef6f8b","2024-04-11T06:22:06.874+00:00",[],"Neuroscience-Department-Johns-Hopkins-University-Baltimore-USA",{"title":918},{"EN":919},"Neuroscience Department, Johns Hopkins University, Baltimore, USA",{"title":921},{"EN":922},"Susan M. 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Sarma",{"id":940,"sortIndex":152,"researcher":22,"roles":941,"affiliations":942,"properties":952},"c8e247e2-91a6-4444-be2b-6f41628783de",[],[943],{"id":22,"sortIndex":23,"affiliation":944,"properties":22},{"id":945,"createTime":946,"updateTime":946,"relativeEntities":947,"slug":948,"properties":949,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"6363b486-41d5-4797-bf0a-7d721dc63194","2024-04-11T06:22:06.849+00:00",[],"U1077-INSERM-EPHE-UNICAEN-Caen-France",{"title":950},{"EN":951},"U1077 INSERM-EPHE-UNICAEN, Caen, France",{"title":953},{"EN":954},"Thomas Hinault",{"id":956,"sortIndex":23,"researcher":22,"roles":957,"affiliations":958,"properties":964},"5b650494-0228-4930-bfae-609f9e916f61",[],[959],{"id":22,"sortIndex":23,"affiliation":960,"properties":22},{"id":929,"createTime":930,"updateTime":930,"relativeEntities":961,"slug":932,"properties":962,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},[],{"title":963},{"EN":935},{"title":965,"email":967},{"EN":966},"Christine Beauchene",{"VOID":968},"cbeauch2@jhu.edu",{"url":22,"publisher":970,"properties":22},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":971,"slug":10,"properties":972,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":977,"manageAffiliations":978,"indexDatabases":979,"url":22,"thumbnailPath":22,"statistic":987,"gsStatistic":22,"type":109,"analyzePriority":22},[],{"issn":973,"eissn":974,"title":975,"url":976},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},[],[],[980],{"id":56,"indexDatabase":981,"url":69,"indexYears":70,"academicFieldIds":986,"indexDatabaseRanking":74},{"id":58,"createTime":59,"updateTime":60,"relativeEntities":982,"label":983,"description":984,"key":66,"publicationTags":985,"standard":22},[],{"EN":63,"VI":63},{"EN":63,"VI":65},[68],[72,73],{"impactFactor":23,"impactFactorByYear":988,"i10Index":82,"i10IndexLast5Year":83,"totalPublication":84,"totalPublicationByYear":989,"totalCitation":93,"totalCitationByYear":990,"totalCitationPerPublication":100,"totalCitationPerPublicationByYear":991,"hindexLast5Year":108,"hindex":108},{"2019":77,"2020":78,"2021":79,"2022":80,"2023":81},{"2018":86,"2019":87,"2020":88,"2021":89,"2022":90,"2023":91,"2024":92},{"2018":95,"2019":96,"2020":97,"2021":98,"2022":83,"2023":99},{"2018":102,"2019":103,"2020":104,"2021":105,"2022":106,"2023":107},"2022-09-28",[994,996,998,1000,1002,1004,1006,1008,1010,1012,1014,1016,1018,1020,1022,1024,1026,1028,1030,1032,1034,1036,1038,1040,1042,1044,1046,1048,1050,1052,1054,1056,1058,1060,1062,1064,1066],{"id":22,"text":995,"url":22,"identifiers":22},"Anderson, B. A. (2013). A value-driven mechanism of attentional selection. Journal of Vision, 13(3), 7–7.",{"id":22,"text":997,"url":22,"identifiers":22},"Anderson, B. A. (2016). The attention habit: How reward learning shapes attentional selection. Annals of the New York Academy of Sciences, 1369(1), 24–39.",{"id":22,"text":999,"url":22,"identifiers":22},"Baddeley, A. (2010). Working memory. Current Biology, 20(4), R136–R140.",{"id":22,"text":1001,"url":22,"identifiers":22},"Baddeley, A. D. & Hitch, G. (1974). Working memory. Psychology of Learning and Motivation (Vol. 8, pp. 47–89). Elsevier.",{"id":22,"text":1003,"url":22,"identifiers":22},"Bell, A. J., & Sejnowski, T. J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129–1159.",{"id":22,"text":1005,"url":22,"identifiers":22},"Breault, M. S., González-Martínez, J. A., Gale, J. T. & Sarma, S. V. (2019). Neural correlates of internal states that capture movement variability. 2019 41st Annual International Conference of the IEEE Engineering In Medicine And Biology Society (EMBC) (pp. 534–537).",{"id":22,"text":1007,"url":22,"identifiers":22},"Castellanos, F. X., Sonuga-Barke, E. J., Milham, M. P., & Tannock, R. (2006). Executive function: is there a central executive? Trends in Cognitive Sciences, 3(10), 117–123.",{"id":22,"text":1009,"url":22,"identifiers":22},"Chang, S., Cunningham, C. A., & Egeth, H. E. (2019). The power of negative thinking: Paradoxical but effective ignoring of salient-but-irrelevant stimuli with a spatial cue. Visual Cognition, 27(3–4), 199–213.",{"id":22,"text":1011,"url":22,"identifiers":22},"Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21.",{"id":22,"text":1013,"url":22,"identifiers":22},"D’Esposito, M., & Postle, B. R. (2015). The cognitive neuroscience of working memory. Annual Review of Psychology, 66, 115–142.",{"id":22,"text":1015,"url":22,"identifiers":22},"Dinstein, I., Heeger, D. J., & Behrmann, M. (2015). Neural variability: friend or foe? Trends in Cognitive Sciences, 19(6), 322–328.",{"id":22,"text":1017,"url":22,"identifiers":22},"ElShafei, H. A., Fornoni, L., Masson, R., Bertrand, O., & Bidet-Caulet, A. (2020). What’s in your gamma? activation of the ventral fronto-parietal attentional network in response to distracting sounds. Cerebral Cortex, 30(2), 696–707.",{"id":22,"text":1019,"url":22,"identifiers":22},"Forster, S., & Lavie, N. (2016). Establishing the attention-distractibility trait. Psychological Science, 27(2), 203–212.",{"id":22,"text":1021,"url":22,"identifiers":22},"Goris, R. L., Movshon, J. A., & Simoncelli, E. P. (2014). Partitioning neuronal variability. Nature Neuroscience, 17(6), 858–865.",{"id":22,"text":1023,"url":22,"identifiers":22},"Hahn, T., Heinzel, S., Dresler, T., Plichta, M. M., Renner, T. J., Markulin, F., & Fallgatter, A. J. (2011). Association between reward-related activation in the ventral striatum and trait reward sensitivity is moderated by dopamine transporter genotype. Human Brain Mapping, 32(10), 1557–1565.",{"id":22,"text":1025,"url":22,"identifiers":22},"Harrell, F. E., Jr., Lee, K. L., Califf, R. M., Pryor, D. B., & Rosati, R. A. (1984). Regression modelling strategies for improved prognostic prediction. Statistics in Medicine, 3(2), 143–152.",{"id":22,"text":1027,"url":22,"identifiers":22},"Harrell, F. E., Jr., Lee, K. L., & Mark, D. B. (1996). Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine, 15(4), 361–387.",{"id":22,"text":1029,"url":22,"identifiers":22},"Hedge, C., Powell, G., & Sumner, P. (2018). The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences. Behavior Research Methods, 50(3), 1166–1186.",{"id":22,"text":1031,"url":22,"identifiers":22},"Hinault, T., Blacker, K., Gormley, M., Anderson, B., & Courtney, S. (2019). Value-driven attentional capture is modulated by the contents of working memory: An EEG study. Cognitive, Affective, & Behavioral Neuroscience, 19(2), 253–267.",{"id":22,"text":1033,"url":22,"identifiers":22},"Hinault, T., Larcher, K., Zazubovits, N., Gotman, J., & Dagher, A. (2019). Spatio-temporal patterns of cognitive control revealed with simultaneous electroencephalography and functional magnetic resonance imaging. Human Brain Mapping, 40(1), 80–97.",{"id":22,"text":1035,"url":22,"identifiers":22},"Jones, K. T., Johnson, E. L., & Berryhill, M. E. (2020). Frontoparietal theta-gamma interactions track working memory enhancement with training and TDCS. Neuroimage, 211, 116615.",{"id":22,"text":1037,"url":22,"identifiers":22},"Jones, K. T., Peterson, D. J., Blacker, K. J., & Berryhill, M. E. (2017). Frontoparietal neurostimulation modulates working memory training benefits and oscillatory synchronization. Brain Research, 1667, 28–40.",{"id":22,"text":1039,"url":22,"identifiers":22},"Lachaux, J. P., Rodriguez, E., Martinerie, J., Varela, F. J., et al. (1999). Measuring phase synchrony in brain signals. Human Brain Mapping, 8(4), 194–208.",{"id":22,"text":1041,"url":22,"identifiers":22},"Makeig, S., Bell, A. J., Jung, T. P., Sejnowski, T. J. et al. (1996). Independent component analysis of electroencephalographic data. Advances in Neural Information Processing Systems, 145–151.",{"id":22,"text":1043,"url":22,"identifiers":22},"Montojo, C. A., & Courtney, S. M. (2008). Differential neural activation for updating rule versus stimulus information in working memory. Neuron, 59(1), 173–182.",{"id":22,"text":1045,"url":22,"identifiers":22},"Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996). A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology, 49(12), 1373–1379.",{"id":22,"text":1047,"url":22,"identifiers":22},"Sacré, P., Kerr, M. S., Kahn, K., Gonzalez-Martinez, J., Bulacio, J., Park, H. J., et al. (2016). Lucky rhythms in orbitofrontal cortex bias gambling decisions in humans. Scientific Reports, 6(1), 1–10.",{"id":22,"text":1049,"url":22,"identifiers":22},"Sacré, P., Kerr, M. S., Subramanian, S., Fitzgerald, Z., Kahn, K., Johnson, M. A., et al. (2019). Risk-taking bias in human decision-making is encoded via a right-left brain push-pull system. Proceedings of the National Academy of Sciences, 116(4), 1404–1413.",{"id":22,"text":1051,"url":22,"identifiers":22},"Sacré, P., Subramanian, S., Kerr, M. S., Kahn, K., Johnson, M. A., Bulacio, J., & Gale, J. T. (2017). The influences and neural correlates of past and present during gambling in humans. Scientific Reports, 7(1), 1–9.",{"id":22,"text":1053,"url":22,"identifiers":22},"Slobodin, O., Cassuto, H., & Berger, I. (2018). Age-related changes in distractibility: Developmental trajectory of sustained attention in ADHD. Journal of Attention Disorders, 22(14), 1333–1343.",{"id":22,"text":1055,"url":22,"identifiers":22},"Stepan, M. E., Fenn, K. M., & Altmann, E. M. (2019). Effects of sleep deprivation on procedural errors. Journal of Experimental Psychology: General, 148(10), 1828.",{"id":22,"text":1057,"url":22,"identifiers":22},"Stoll, F. M., Fontanier, V., & Procyk, E. (2016). Specific frontal neural dynamics contribute to decisions to check. Nature Communications, 7(1), 1–14.",{"id":22,"text":1059,"url":22,"identifiers":22},"Uddin, L. Q. (2020). Bring the noise: Reconceptualizing spontaneous neural activity. Trends in Cognitive Sciences, 24(9), 734–746.",{"id":22,"text":1061,"url":22,"identifiers":22},"VanRullen, R., Busch, N. A., Drewes, J., & Dubois, J. (2011). Ongoing EEG phase as a trial-by-trial predictor of perceptual and attentional variability. Frontiers in Psychology, 2, 60.",{"id":22,"text":1063,"url":22,"identifiers":22},"Voloh, B., Valiante, T. A., Everling, S., & Womelsdorf, T. (2015). Theta-gamma coordination between anterior cingulate and prefrontal cortex indexes correct attention shifts. Proceedings of the National Academy of Sciences, 112(27), 8457–8462.",{"id":22,"text":1065,"url":22,"identifiers":22},"Wu, S., Hitchman, G., Tan, J., Zhao, Y., Tang, D., Wang, L., & Chen, A. (2015). The neural dynamic mechanisms of asymmetric switch costs in a combined stroop-task-switching paradigm. Scientific Reports, 5(1), 1–11.",{"id":22,"text":1067,"url":22,"identifiers":22},"Xu, K. Z., Anderson, B. A., Emeric, E. E., Sali, A. W., Stuphorn, V., Yantis, S., & Courtney, S. M. (2017). Neural basis of cognitive control over movement inhibition: Human FMRI and primate electrophysiology evidence. Neuron, 96(6), 1447–1458.",{"id":1069,"createTime":1070,"updateTime":1070,"relativeEntities":1071,"slug":22,"properties":1072,"entityType":129,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":152,"primaryUrl":1081,"fullTextUrl":22,"authors":1082,"publicationType":184,"publisherRelationship":1134,"citationCount":22,"citationInfo":22,"publishDate":1161,"publishYear":214,"citationAnalyzeStatus":21,"lastCitationAnalyze":22,"indexDatabases":22,"openAccess":22,"references":22,"isForceReanalyzing":215},"11cdf88c-0774-414f-be96-9f98f2bb912b","2024-01-12T22:56:35.344+00:00",[],{"references":1073,"abstract":1075,"title":1077,"doi":1079},{"VOID":1074},"Alexander, W. H., & Brown, J. W. (2010). Computational models of performance monitoring and cognitive control. Topics in Cognitive Science, 2(4), 658–677. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1756-8765.2010.01085.x\nBoag, R., Strickland, L., Loft, S., & Heathcote, A. (2019a). Strategic attention and decision control support prospective memory in a complex dual-task environment. Cognition, 191, 1–24. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cognition.2019.05.011\nBoag, R., Strickland, L., Heathcote, A., Neal, A., & Loft, S. (2019b). Cognitive control and capacity for prospective memory in simulated air traffic control. Journal of Experimental Psychology: General, 148, 2181–2206.\nBogacz, R., Brown, E., Moehlis, J., Holmes, P., & Cohen, J. D. (2006). The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks. Psychological Review, 113(4), 700.\nBrooks, S. P., & Gelman, A. (1998). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7(4), 434–455.\nBrown, S. D., & Heathcote, A. (2008). The simplest complete model of choice response time: Linear ballistic accumulation. Cognitive Psychology, 57(3), 153–178. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cogpsych.2007.12.002\nDamaso, K., Williams, P., & Heathcote, A. (2020). Different types of errors and post-error changes. Psychonomic Bulletin & Review.\nDanielmeier, C., Eichele, T., Forstmann, B. U., Tittgemeyer, M., & Ullsperger, M. (2011). Posterior medial frontal cortex activity predicts post-error adaptations in task-related visual and motor areas. The Journal of Neuroscience, 31(5), 1780–1789. https:\u002F\u002Fdoi.org\u002F10.1523\u002Fjneurosci.4299-10.2011\nDonkin, C., Brown, S. D., & Heathcote, A. (2009). The overconstraint of response time models: Rethinking the scaling problem. Psychonomic Bulletin & Review, 16(6), 1129–1135. https:\u002F\u002Fdoi.org\u002F10.3758\u002Fpbr.16.6.1129\nDonkin, C., Brown, S. D., & Heathcote, A. (2011). Drawing conclusions from choice response time models: a tutorial using the linear ballistic accumulator. Journal of Mathematical Psychology, 55, 140–151.\nDonkin, C. B., & Brown, S. D. (2018). Response times and decision-making. In E.-J. Wagenmakers (Ed.), Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience (4th ed., Vol. 5)\nDutilh, G., Vandekerckhove, J., Forstmann, B. U., Keuleers, E., Brysbaert, M., & Wagenmakers, E.-J. (2012a). Testing theories of post-error slowing. Attention, Perception, & Psychophysics, 74(2), 454–465. https:\u002F\u002Fdoi.org\u002F10.3758\u002Fs13414-011-0243-2\nDutilh, G., van Ravenzwaaij, D., Nieuwenhuis, S., van der Maas, H. L. J., Forstmann, B. U., & Wagenmakers, E.-J. (2012b). How to measure post-error slowing: A confound and a simple solution. Journal of Mathematical Psychology, 56(3), 208–216. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jmp.2012.04.001\nDutilh, G., Forstmann, B. U., Vandekerckhove, J., & Wagenmakers, E.-J. (2013). A diffusion model account of age differences in posterror slowing. Psychology and Aging, 28(1), 64–76. https:\u002F\u002Fdoi.org\u002F10.1037\u002Fa0029875\nGreifeneder, R., Bless, H., & Pham, M. T. (2010). When do people rely on affective and cognitive feelings in judgment? A review. Personality and Social Psychology Review, 15(2), 107–141. https:\u002F\u002Fdoi.org\u002F10.1177\u002F1088868310367640\nGunawan, D. E., Hawkins, G., Kohn, R., Tran, M. N., & Brown, S. D. (in press). Time-evolving psychological processes over repeated decisions. Psychological Review.\nHajcak, G., & Simons, R. F. (2002). Error-related brain activity in obsessive–compulsive undergraduates. Psychiatry Research, 110(1), 63–72. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0165-1781(02)00034-3\nHajcak, G., McDonald, N., & Simons, R. F. (2003). To err is autonomic: Error-related brain potentials, ANS activity, and post-error compensatory behavior. Psychophysiology, 40(6), 895–903. https:\u002F\u002Fdoi.org\u002F10.1111\u002F1469-8986.00107\nHeathcote, A., & Hayes, B. (2012). Diffusion versus linear ballistic accumulation: Different models for response time with different conclusions about psychological mechanisms? Canadian Journal of Experimental Psychology\u002FRevue canadienne de psychologie expérimentale, 66(2), 125–136. https:\u002F\u002Fdoi.org\u002F10.1037\u002Fa0028189\nHeathcote, A., Lin, Y.-S., Reynolds, A., Strickland, L., Gretton, M., & Matzke, D. (2019). Dynamic models of choice. Behavior Research Methods, 51(2), 961–985. https:\u002F\u002Fdoi.org\u002F10.3758\u002Fs13428-018-1067-y\nKing, J. A., Korb, F. M., von Cramon, D. Y., & Ullsperger, M. (2010). Post-error behavioral adjustments are facilitated by activation and suppression of task-relevant and task-irrelevant information processing. The Journal of Neuroscience, 30(38), 12759–12769. https:\u002F\u002Fdoi.org\u002F10.1523\u002Fjneurosci.3274-10.2010\nKlauer, K. C. (2010). Hierarchical multinomial processing tree models: A latent–trait approach. Psychometrika, 75, 70–98.\nLaming, D. R. J. (1968). Information theory of choice-reaction times. Academic Press.\nLaming, D. R. J. (1979). Choice reaction performance following an error. Acta Psychologica, 43(3), 199–224. https:\u002F\u002Fdoi.org\u002F10.1016\u002F0001-6918(79)90026-X\nLeite, F. P., & Ratcliff, R. (2010). Modeling reaction time and accuracy of multiple-alternative decisions. Attention, Perception, & Psychophysics, 72(1), 246–273. https:\u002F\u002Fdoi.org\u002F10.3758\u002Fapp.72.1.246\nLuce, R. D. (1986). Response times: Their role in inferring mental organization. Oxford University Press, Clarendon Press.\nMatzke, D., Dolan, C. V., Batchelder, W. H., & Wagenmakers, E.-J. (2015). Bayesian estimation of multinomial processing tree models with heterogeneity in participants and items. Psychometrika, 80, 205–235.\nNieuwenhuis, S., Ridderinkhof, K. R., Blom, J. O. S., Band, G. P. H., & Kok, A. (2001). Error-related brain potentials are differentially related to awareness of response errors: Evidence from an antisaccade task. Psychophysiology, 38(5), 752–760. https:\u002F\u002Fdoi.org\u002F10.1111\u002F1469-8986.3850752\nNotebaert, W., Houtman, F., Opstal, F. V., Gevers, W., Fias, W., & Verguts, T. (2009). Post-error slowing: An orienting account. Cognition, 111(2), 275–279. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cognition.2009.02.002\nOsth, A. F., Bora, B., Dennis, S., & Heathcote, A. (2017). Diffusion vs. linear ballistic accumulation: Different models, different conclusions about the slope of the zROC in recognition memory. Journal of Memory and Language, 96, 36–61. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jml.2017.04.003\nPurcell, B. A., & Kiani, R. (2016). Neural mechanisms of post-error adjustments of decision policy in parietal cortex. Neuron, 89(3), 658–671. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neuron.2015.12.027\nRabbitt, P. M. A. (1966). How old and young subjects monitor and control responses for accuracy and speed. Journal of Experimental Psychology, 71, 264–272.\nRabbitt, P., & Rodgers, B. (1977). What does a man do after he makes an error? An analysis of response programming. Quarterly Journal of Experimental Psychology, 29(4), 727–743. https:\u002F\u002Fdoi.org\u002F10.1080\u002F14640747708400645\nRae, B., Heathcote, A., Donkin, C., Averell, L., & Brown, S. (2014). The hare and the tortoise: Emphasizing speed can change the evidence used to make decisions. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40(5), 1226.\nRatcliff, R. (1978). A theory of memory retrieval. Psychological Review March, 85(2), 59–108.\nRatcliff, R. (2008). The EZ diffusion method: Too EZ? Psychonomic Bulletin & Review, 15(6), 1218–1228. https:\u002F\u002Fdoi.org\u002F10.3758\u002FPBR.15.6.1218\nRatcliff, R., & McKoon, G. (2008). The diffusion decision model: theory and data for two-choice decision tasks. Neural Computation, 20(4), 873–922. https:\u002F\u002Fdoi.org\u002F10.1162\u002Fneco.2008.12-06-420.\nRatcliff, R., & Rouder, J. N. (1998). Modeling response times for two-choice decisions. Psychological Science, 9(5), 347–356.\nRatcliff, R., & Tuerlinckx, F. (2002). Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability. Psychonomic Bulletin & Review, 9(3), 438–481. https:\u002F\u002Fdoi.org\u002F10.3758\u002FBF03196302\nRatcliff, R., Voskuilen, C., & Teodorescu, A. (2018). Modeling 2-alternative forced-choice tasks_ Accounting for both magnitude and difference effects. Cognitive Psychology, 103, 1–22. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cogpsych.2018.02.002\nRoberts, I. D., & Hutcherson, C. A. (2019). Affect and decision making: Insights and predictions from computational models. Trends in Cognitive Sciences, 23(7), 602–614. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.tics.2019.04.005\nRouder, J. N., & Haaf, J. M. (2019). A psychometrics of individual differences in experimental tasks. Psychonomic Bulletin and Review, 26(2), 452–467. https:\u002F\u002Fdoi.org\u002F10.3758\u002Fs13423-018-1558-y\nSchiffler, B. C., Bengtsson, S. L., & Lundqvist, D. (2017). The sustained influence of an error on future decision-making. Frontiers in Psychology, 8(1077). https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpsyg.2017.01077\nSchouten, J. F., & Bekker, J. A. M. (1967). Reaction time and accuracy. Acta Psychologica, 27, 143–153. https:\u002F\u002Fdoi.org\u002F10.1016\u002F0001-6918(67)90054-6\nSpiegelhalter, D. J., Best, N. G., Carlin, B. P., & van der Linde, A. (2014). The deviance information criterion: 12 years on. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(3), 485–493. https:\u002F\u002Fdoi.org\u002F10.1111\u002Frssb.12062\nStone, M. (1960). Models for choice-reaction time. Psychometrika, 25(3), 251–260.\nStrickland, L., Loft, S., Remington, R. W., & Heathcote, A. (2018). Racing to remember: A theory of decision control in event-based prospective memory. Psychological Review, 125, 851–887.\nTurner, B. M., Sederberg, P. B., Brown, S. D., & Steyvers, M. (2013). A method for efficiently sampling from distributions with correlated dimensions. Psychological Methods, 18(3), 368–384.\nUllsperger, M., Danielmeier, C., & Jocham, G. (2014). Neurophysiology of performance monitoring and adaptive behavior. Physiological Reviews, 94(1), 35–79. https:\u002F\u002Fdoi.org\u002F10.1152\u002Fphysrev.00041.2012\nvan Ravenzwaaij, D., Donkin, C., & Vandekerckhove, J. (2017). The EZ diffusion model provides a powerful test of simple empirical effects. Psychonomic Bulletin & Review, 24, 547–556. https:\u002F\u002Fdoi.org\u002F10.3758\u002Fs13423-016-1081-y\nvan Veen, V., & Carter, C. S. (2006). Error detection, correction, and prevention in the brain: A brief review of data and theories. Clinical EEG and Neuroscience, 37(4), 330–335. https:\u002F\u002Fdoi.org\u002F10.1177\u002F155005940603700411\nWagenmakers, E.-J., Van Der Maas, H. L. J., & Grasman, R. P. P. P. (2007). An EZ-diffusion model for response time and accuracy. Psychonomic Bulletin & Review, 14(1), 3–22. https:\u002F\u002Fdoi.org\u002F10.3758\u002FBF03194023\nWessel, J. R. (2018). An adaptive orienting theory of error processing. Psychophysiology, 55(3), e13041. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fpsyp.13041\nWhite, C. N., & Poldrack, R. A. (2014). Decomposing bias in different types of simple decisions. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40(2), 385–398. https:\u002F\u002Fdoi.org\u002F10.1037\u002Fa0034851\nWhite, C. N., Ratcliff, R., Vasey, M. W., & McKoon, G. (2010). Using diffusion models to understand clinical disorders. Journal of Mathematical Psychology, 54(1), 39–52. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jmp.2010.01.004\nWiecki, T. V., Sofer, I., & Frank, M. J. (2013). HDDM: hierarchical Bayesian estimation of the drift-diffusion model in python. Frontiers in Neuroinformatics, 7. https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffninf.2013.00014\u002Fabstract\nWilliams, P., Heathcote, A., Nesbitt, K., & Eidels, A. (2016). Post-error recklessness and the hot hand. Judgment and Decision Making, 11(2), 174–184.",{"EN":1076},"It has traditionally been assumed that responding after an error is slowed because participants try to improve their accuracy by increasing the amount of evidence required for subsequent decisions. However, recent work suggests a more varied picture of post-error effects, with instances of post-error speeding, and decreases or no change in accuracy. Further, the causal role of errors in these effects has been questioned due to confounds from slow fluctuations in attention caused by factors such as fatigue and boredom. In recognition memory tasks, we investigated both post-error speeding associated with instructions emphasising fast responding and post-error slowing associated with instructions emphasising the accuracy of responding. In order to identify the causes of post-error effects, we fit this data with evidence accumulation models using a method of measuring post-error effects that is robust to confounds from slow fluctuations. When the response-to-stimulus interval between trials was short, there were no post-error effect on accuracy and speeding and slowing were caused by differences in non-decision time (i.e. the time to encode choice stimuli and generate responses). In contrast, when the interval was longer, due to participants providing a confidence rating for their choice, there were also effects on the rate of evidence accumulation and the amount of evidence required for a decision. We discuss the implications of our methods and results for post-error effect research.",{"EN":1078},"What Happens After a Fast Versus Slow Error, and How Does It Relate to Evidence Accumulation?",{"VOID":1080},"10.1007\u002Fs42113-022-00137-2","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs42113-022-00137-2",[1083,1100,1112],{"id":1084,"sortIndex":170,"researcher":22,"roles":1085,"affiliations":1086,"properties":1097},"43ff1f3b-1941-4fb9-808b-784e36ddbd86",[137],[1087],{"id":22,"sortIndex":23,"affiliation":1088,"properties":22},{"id":1089,"createTime":1090,"updateTime":1091,"relativeEntities":1092,"slug":1093,"properties":1094,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"9fb92bcc-17d9-4ccf-8109-a30575f7bcf6","2023-12-30T22:17:12.484+00:00","2025-01-30T15:05:47.361+00:00",[],"Department-of-Psychology-University-of-Amsterdam-Amsterdam-The-Netherlands",{"title":1095},{"VI":1096},"Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands",{"title":1098},{"VI":1099},"Andrew Heathcote",{"id":1101,"sortIndex":23,"researcher":22,"roles":1102,"affiliations":1103,"properties":1109},"ff4331a0-5399-4d10-8f42-f6c874589b00",[137],[1104],{"id":22,"sortIndex":23,"affiliation":1105,"properties":22},{"id":1089,"createTime":1090,"updateTime":1091,"relativeEntities":1106,"slug":1093,"properties":1107,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},[],{"title":1108},{"VI":1096},{"title":1110},{"VI":1111},"Karlye A. 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D. (1984). Direction and orientation selectivity of neurons in visual area MT of the macaque. Journal of Neurophysiology, 52(6), 1106–1130.",{"doi":1280},"10.1152\u002Fjn.1984.52.6.1106",{"id":22,"text":1282,"url":22,"identifiers":1283},"Anderson, J. R. (2007). How can the human mind occur in the physical universe?. New York: Oxford University Press.",{"doi":1284},"10.1093\u002Facprof:oso\u002F9780195324259.001.0001",{"id":22,"text":1286,"url":22,"identifiers":1287},"Andersen, R. A., Brotchie, P. R., Mazzoni, P. (1992). Evidence for the lateral intraparietal area as the parietal eye field. Current Opinion in Neurobiology, 2(6), 840–846.",{"doi":1288},"10.1016\u002F0959-4388(92)90143-9",{"id":22,"text":1290,"url":22,"identifiers":1291},"Anderson, J. R., Byrne, D., Fincham, J. M., Gunn, P. (2008). Role of prefrontal and parietal cortices in associative learning. Cerebral Cortex, 18, 904–914.",{"doi":1292},"10.1093\u002Fcercor\u002Fbhm123",{"id":22,"text":1294,"url":22,"identifiers":1295},"Ashby, F. G., Ennis, J. M., Spiering, B. J. (2007). A neurobiological theory of automaticity in perceptual categorization. Psychological Review, 114(3), 632.",{"doi":1296},"10.1037\u002F0033-295X.114.3.632",{"id":22,"text":1298,"url":22,"identifiers":1299},"Ball, K., & Sekuler, R. (1982). A specific and enduring improvement in visual motion discrimination. Science, 218(4573), 697–698.",{"doi":1300},"10.1126\u002Fscience.7134968",{"id":22,"text":1302,"url":22,"identifiers":1303},"Bamber, D., & Van Santen, J. P. (2000). How to assess a model’s testability and identifiability. Journal of Mathematical Psychology, 44(1), 20–40.",{"doi":1304},"10.1006\u002Fjmps.1999.1275",{"id":22,"text":1306,"url":22,"identifiers":1307},"Beaumont, M. A. (2010). Approximate Bayesian computation in evolution and ecology. Annual Review of Ecology, Evolution, and Systematics, 41, 379–406.",{"doi":1308},"10.1146\u002Fannurev-ecolsys-102209-144621",{"id":22,"text":1310,"url":22,"identifiers":1311},"Birn, R. M., Cox, R. W., Bandettini, P. A. (2002). Detection versus estimation in event-related fMRI: choosing the optimal stimulus timing. Neuroimage, 15(1), 252–264.",{"doi":1312},"10.1006\u002Fnimg.2001.0964",{"id":22,"text":1314,"url":22,"identifiers":1315},"Bogacz, R., Wagenmakers, E. J., Forstmann, B. U., Nieuwenhuis, S. (2010). The neural basis of the speed-accuracy tradeoff. Trends in Neuroscience, 33, 10–16.",{"doi":1316},"10.1016\u002Fj.tins.2009.09.002",{"id":22,"text":1318,"url":22,"identifiers":1319},"Borst, J. P., & Anderson, J. R. (2013). Using model-based functional MRI to locate working memory updates and declarative memory retrievals in the fronto-parietal network. Proceedings of the National Academy of Sciences of the United States, 110, 1628–1633.",{"doi":1320},"10.1073\u002Fpnas.1221572110",{"id":22,"text":1322,"url":22,"identifiers":1323},"Borst, J. P., Taatgen, N. A., Stocco, A., Van Rijn, H. (2010a). The neural correlates of problem states: testing fMRI predictions of a computational model of multitasking. PLoS ONE, 5, e12966.",{"doi":1324},"10.1371\u002Fjournal.pone.0012966",{"id":22,"text":1326,"url":22,"identifiers":1327},"Borst, J. P., Taatgen, N. A., Van Rijn, H. (2010b). The problem state: a cognitive bottleneck in multitasking. Journal of Experimental Psychology: Learning, Memory, & Cognition, 36, 363–382.",{},{"id":22,"text":1329,"url":22,"identifiers":1330},"Boucher, L., Palmeri, T. J., Logan, G. D., Schall, J. D. (2007). Inhibitory control in mind and brain: an interactive race model of countermanding saccades. Psychological Review, 114(2), 376.",{"doi":1331},"10.1037\u002F0033-295X.114.2.376",{"id":22,"text":1333,"url":22,"identifiers":1334},"Britten, K. H., Shadlen, M. N., Newsome, W. T., Movshon, J. A. (1992). The analysis of visual motion: a comparison of neuronal and psychophysical performance. The Journal of Neuroscience, 12(12), 4745–4765.",{"doi":1335},"10.1523\u002FJNEUROSCI.12-12-04745.1992",{"id":22,"text":1337,"url":22,"identifiers":1338},"Britten, K. H., Newsome, W. T., Shadlen, M. N., Celebrini, S., Movshon, J. A. (1996). A relationship between behavioral choice and the visual responses of neurons in macaque MT. Visual Neuroscience, 13(1), 87–100.",{"doi":1339},"10.1017\u002FS095252380000715X",{"id":22,"text":1341,"url":22,"identifiers":1342},"Brown, S., & Heathcote, A. (2005). A ballistic model of choice response time. Psychological Review, 112, 117–128.",{"doi":1343},"10.1037\u002F0033-295X.112.1.117",{"id":22,"text":1345,"url":22,"identifiers":1346},"Brown, S., & Heathcote, A. (2008). The simplest complete model of choice reaction time: linear ballistic accumulation. Cognitive Psychology, 57, 153–178.",{"doi":1347},"10.1016\u002Fj.cogpsych.2007.12.002",{"id":22,"text":1349,"url":22,"identifiers":1350},"Brown, J. W., Hanes, D. P., Schall, J. D., Stuphorn, V. (2008). Relation of frontal eye field activity to saccade initiation during a countermanding task. Experimental Brain Research, 190(2), 135.",{"doi":1351},"10.1007\u002Fs00221-008-1455-0",{"id":22,"text":1353,"url":22,"identifiers":1354},"Buxton, R. B., Wong, E. C., Frank, L. R. (1998). Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. Magnetic Resonance in Medicine, 39(6), 855–864.",{"doi":1355},"10.1002\u002Fmrm.1910390602",{"id":22,"text":1357,"url":22,"identifiers":1358},"Carpenter, R. (1999). Visual selection: neurons that make up their minds. Current Biology, 9(16), R595–R598.",{"doi":1359},"10.1016\u002FS0960-9822(99)80382-0",{"id":22,"text":1361,"url":22,"identifiers":1362},"Carpenter, R. H., & Williams, M. (1995). Neural computation of log likelihood in control of saccadic eye movements. Nature, 377(6544), 59.",{"doi":1363},"10.1038\u002F377059a0",{"id":22,"text":1365,"url":22,"identifiers":1366},"Carpenter, R., Reddi, B., Anderson, A. (2009). A simple two-stage model predicts response time distributions. The Journal of Physiology, 587(16), 4051–4062.",{"doi":1367},"10.1113\u002Fjphysiol.2009.173955",{"id":22,"text":1369,"url":22,"identifiers":1370},"Cassey, P. J., Gaut, G., Steyvers, M., Brown, S. D. (2016). A generative joint model for spike trains and saccades during perceptual decision-making. Psychonomic Bulletin & Review, 23(6), 1757–1778.",{"doi":1371},"10.3758\u002Fs13423-016-1056-z",{"id":22,"text":1373,"url":22,"identifiers":1374},"Celebrini, S., & Newsome, W. T. (1995). Microstimulation of extrastriate area MST influences performance on a direction discrimination task. Journal of Neurophysiology, 73(2), 437–448.",{"doi":1375},"10.1152\u002Fjn.1995.73.2.437",{"id":22,"text":1377,"url":22,"identifiers":1378},"Churchland, A. K., Kiani, R., Shadlen, M. N. (2008). Decision-making with multiple alternatives. Nature Neuroscience, 11(6), 693.",{"doi":1379},"10.1038\u002Fnn.2123",{"id":22,"text":1381,"url":22,"identifiers":1382},"Colby, C. L., & Goldberg, M. E. (1999). Space and attention in parietal cortex. Annual Review of Neuroscience, 22(1), 319–349.",{"doi":1383},"10.1146\u002Fannurev.neuro.22.1.319",{"id":22,"text":1385,"url":22,"identifiers":1386},"Croner, L. J., & Albright, T. D. (1999). Segmentation by color influences responses of motion-sensitive neurons in the cortical middle temporal visual area. Journal of Neuroscience, 19(10), 3935–3951.",{"doi":1387},"10.1523\u002FJNEUROSCI.19-10-03935.1999",{"id":22,"text":1389,"url":22,"identifiers":1390},"Daunizeau, J., Friston, K. J., Kiebel, S. J. (2009). Variational bayesian identification and prediction of stochastic nonlinear dynamic causal models. Physica D: Nonlinear Phenomena, 238(21), 2089–2118.",{"doi":1391},"10.1016\u002Fj.physd.2009.08.002",{"id":22,"text":1393,"url":22,"identifiers":1394},"Daunizeau, J., Adam, V., Rigoux, L. (2014). VBA: a probabilistic treatment of nonlinear models for neurobiological and behavioural data. PLoS Computational Biology, 10(1), e1003441.",{"doi":1395},"10.1371\u002Fjournal.pcbi.1003441",{"id":22,"text":1397,"url":22,"identifiers":1398},"David, O., Kiebel, S. J., Harrison, L. M., Mattout, J., Kilner, J. M., Friston, K. J. (2006). Dynamic causal modeling of evoked responses in EEG and MEG. NeuroImage, 30(4), 1255–1272.",{"doi":1399},"10.1016\u002Fj.neuroimage.2005.10.045",{"id":22,"text":1401,"url":22,"identifiers":1402},"de Hollander, G., Forstmann, B. U., Brown, S. D. (2016). Different ways of linking behavioral and neural data via computational cognitive models. Cognitive Neuroscience and Neuroimaging, 1, 101–109.",{},{"id":22,"text":1404,"url":22,"identifiers":1405},"Ding, L., & Gold, J. I. (2013). The basal ganglia’s contributions to perceptual decision making. Neuron, 79 (4), 640–649.",{"doi":1406},"10.1016\u002Fj.neuron.2013.07.042",{"id":22,"text":1408,"url":22,"identifiers":1409},"Dorris, M. C., Pare, M., Munoz, D. P. (1997). Neuronal activity in monkey superior colliculus related to the initiation of saccadic eye movements. Journal of Neuroscience, 17(21), 8566–8579.",{"doi":1410},"10.1523\u002FJNEUROSCI.17-21-08566.1997",{"id":22,"text":1412,"url":22,"identifiers":1413},"Forstmann, B. U., & Wagenmakers, E. -J. (2015). An introduction to model-based cognitive neuroscience. New York: Springer.",{"doi":1414},"10.1007\u002F978-1-4939-2236-9",{"id":22,"text":1416,"url":22,"identifiers":1417},"Forstmann, B. U., Dutilh, G., Brown, S., Neumann, J., von Cramon, D. Y., Ridderinkhof, K. R., Wagenmakers, E. -J. (2008). Striatum and pre-SMA facilitate decision-making under time pressure. Proceedings of the National Academy of Sciences, 105(45), 17538–17542.",{"doi":1418},"10.1073\u002Fpnas.0805903105",{"id":22,"text":1420,"url":22,"identifiers":1421},"Forstmann, B. U., Anwander, A., Schäfer, A., Neumann, J., Brown, S., Wagenmakers, E. -J., Bogacz, R., Turner, R. (2010). Cortico-striatal connections predict control over speed and accuracy in perceptual decision making. Proceedings of the National Academy of Sciences, 107(36), 15916–15920.",{"doi":1422},"10.1073\u002Fpnas.1004932107",{"id":22,"text":1424,"url":22,"identifiers":1425},"Frigo, M., & Johnson, S. G. (2005). The design and implementation of FFTW3. Proceedings of the IEEE, 93(2), 216–231. special issue on “Program Generation, Optimization, and Platform Adaptation”.",{"doi":1426},"10.1109\u002FJPROC.2004.840301",{"id":22,"text":1428,"url":22,"identifiers":1429},"Friston, K. (2009). Causal modelling and brain connectivity in functional magnetic resonance imaging. PLoS Biology, 7(2), e1000033.",{"doi":1430},"10.1371\u002Fjournal.pbio.1000033",{"id":22,"text":1432,"url":22,"identifiers":1433},"Friston, K. J., Mechelli, A., Turner, R., Price, C. J. (2000). Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics. NeuroImage, 12(4), 466–477.",{"doi":1434},"10.1006\u002Fnimg.2000.0630",{"id":22,"text":1436,"url":22,"identifiers":1437},"Friston, K., Harisson, L., Penny, W. (2003). Dynamic causal modeling. NeuroImage, 19, 1273–1302.",{"doi":1438},"10.1016\u002FS1053-8119(03)00202-7",{"id":22,"text":1440,"url":22,"identifiers":1441},"Friston, K., Preller, K. H., Mathys, C., Cagnan, H., Heinzle, J., Razi, A., Zeidman, P. (2017). Dynamic causal modelling revisited. NeuroImage.",{},{"id":22,"text":1443,"url":22,"identifiers":1444},"Galdo, M., Bahg, G., Turner, B. M. (2019). Variational bayesian methods for cognitive science, in press at Psychological Methods.",{"doi":1445},"10.1037\u002Fmet0000242",{"id":22,"text":1447,"url":22,"identifiers":1448},"Georgiev, D., Rocchi, L., Tocco, P., Speekenbrink, M., Rothwell, J. C., Jahanshahi, M. (2016). Continuous theta burst stimulation over the dorsolateral prefrontal cortex and the Pre-SMA alter drift rate and response thresholds respectively during perceptual decision-making. Brain stimulation, 9(4), 601–608.",{"doi":1449},"10.1016\u002Fj.brs.2016.04.004",{"id":22,"text":1451,"url":22,"identifiers":1452},"Gold, J. I., & Shadlen, M. N. (2001). Neural computations that underlie decisions about sensory stimuli. Trends in Cognitive Sciences, 5(1), 10–16.",{"doi":1453},"10.1016\u002FS1364-6613(00)01567-9",{"id":22,"text":1455,"url":22,"identifiers":1456},"Gold, J. I., & Shadlen, M. N. (2002). Banburismus and the brain: decoding the relationship between sensory stimuli, decisions, and reward. Neuron, 36(2), 299–308.",{"doi":1457},"10.1016\u002FS0896-6273(02)00971-6",{"id":22,"text":1459,"url":22,"identifiers":1460},"Gold, J. I., & Shadlen, M. N. (2007). The neural basis of decision making. Annual Review of Neuroscience, 30, 535–574.",{"doi":1461},"10.1146\u002Fannurev.neuro.29.051605.113038",{"id":22,"text":1463,"url":22,"identifiers":1464},"Graybiel, A. M. (1995). Building action repertoires: memory and learning functions of the basal ganglia. Current Opinion in Neurobiology, 5(6), 733–741.",{"doi":1465},"10.1016\u002F0959-4388(95)80100-6",{"id":22,"text":1467,"url":22,"identifiers":1468},"Hikosaka, O., Takikawa, Y., Kawagoe, R. (2000a). Role of the basal ganglia in the control of purposive saccadic eye movements. Physiological Reviews, 80(3), 953–978.",{"doi":1469},"10.1152\u002Fphysrev.2000.80.3.953",{"id":22,"text":1471,"url":22,"identifiers":1472},"Hikosaka, O., Takikawa, Y., Kawagoe, R. (2000b). Role of the basal ganglia in the control of purposive saccadic eye movements. Physiological Reviews, 80(3), 953–978.",{"doi":1469},{"id":22,"text":1474,"url":22,"identifiers":1475},"Hikosaka, O., Nakamura, K., Nakahara, H. (2006). Basal ganglia orient eyes to reward. Journal of Neurophysiology, 95(2), 567–584.",{"doi":1476},"10.1152\u002Fjn.00458.2005",{"id":22,"text":1478,"url":22,"identifiers":1479},"Ho, T. C., Brown, S., Serences, J. T. (2009). Domain general mechanisms of perceptual decision making in human cortex. Journal of Neuroscience, 29(27), 8675–8687.",{"doi":1480},"10.1523\u002FJNEUROSCI.5984-08.2009",{"id":22,"text":1482,"url":22,"identifiers":1483},"Holmes, W. R. (2015). A practical guide to the probability density approximation (PDA) with improved implementation and error characterization. Journal of Mathematical Psychology, 68, 13–24.",{"doi":1484},"10.1016\u002Fj.jmp.2015.08.006",{"id":22,"text":1486,"url":22,"identifiers":1487},"Houk, J. C., Davis, J. L., Beiser, D.G. (1995). Models of information processing in the basal ganglia. Cambridge: MIT press.",{},{"id":22,"text":1489,"url":22,"identifiers":1490},"Kamitani, Y., & Tong, F. (2005). Decoding the visual and subjective contents of the human brain. Nature Neuroscience, 8(5), 679.",{"doi":1491},"10.1038\u002Fnn1444",{"id":22,"text":1493,"url":22,"identifiers":1494},"Kamitani, Y., & Tong, F. (2006). Decoding seen and attended motion directions from activity in the human visual cortex. Current Biology, 16(11), 1096–1102.",{"doi":1495},"10.1016\u002Fj.cub.2006.04.003",{"id":22,"text":1497,"url":22,"identifiers":1498},"Kiebel, S. J., Garrido, M. I., Moran, R. J., Friston, K. J. (2008). Dynamic causal modelling for EEG and MEG. Cognitive Neurodynamics, 2(2), 121.",{"doi":1499},"10.1007\u002Fs11571-008-9038-0",{"id":22,"text":1501,"url":22,"identifiers":1502},"Kim, J. N., & Shadlen, M. N. (1999). Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque. Nature Neuroscience, 2, 176–185.",{"doi":1503},"10.1038\u002F5739",{"id":22,"text":1505,"url":22,"identifiers":1506},"Korhonen, O., Saarimäki, H., Glerean, E., Sams, M., Saramäki, J. (2017). Consistency of regions of interest as nodes of fMRI functional brain networks. Network Neuroscience, 1(3), 254–274.",{"doi":1507},"10.1162\u002FNETN_a_00013",{"id":22,"text":1509,"url":22,"identifiers":1510},"Kragel, J. E., Morton, N. W., Polyn, S. M. (2015). Neural activity in the medial temporal lobe reveals the fidelity of mental time travel. Journal of Neuroscience, 35(7), 2914–2926.",{"doi":1511},"10.1523\u002FJNEUROSCI.3378-14.2015",{"id":22,"text":1513,"url":22,"identifiers":1514},"Liu, T. T., Frank, L. R., Wong, E. C., Buxton, R. B. (2001). Detection power, estimation efficiency, and predictability in event-related fmri. Neuroimage, 13(4), 759–773.",{"doi":1515},"10.1006\u002Fnimg.2000.0728",{"id":22,"text":1517,"url":22,"identifiers":1518},"Lo, C. -C., & Wang, X. -J. (2006). Cortico–basal ganglia circuit mechanism for a decision threshold in reaction time tasks. Nature Neuroscience, 9(7), 956.",{"doi":1519},"10.1038\u002Fnn1722",{"id":22,"text":1521,"url":22,"identifiers":1522},"Mandeville, J. B., Marota, J. J., Ayata, C., Zaharchuk, G., Moskowitz, M. A., Rosen, B. R., Weisskoff, R. M. (1999). Evidence of a cerebrovascular postarteriole windkessel with delayed compliance. Journal of Cerebral Blood Flow & Metabolism, 19(6), 679–689.",{"doi":1523},"10.1097\u002F00004647-199906000-00012",{"id":22,"text":1525,"url":22,"identifiers":1526},"Mansfield, E. L., Karayanidis, F., Jamadar, S., Heathcote, A., Forstmann, B. U. (2011). Adjustments of response threshold during task switching: a model-based functional magnetic resonance imaging study. J Neurosci, 31(41), 14688–92.",{"doi":1527},"10.1523\u002FJNEUROSCI.2390-11.2011",{"id":22,"text":1529,"url":22,"identifiers":1530},"Marreiros, A. C., Kiebel, S. J., Friston, K. J. (2008). Dynamic causal modelling for fMRI: a two-state model. Neuroimage, 39(1), 269–278.",{"doi":1531},"10.1016\u002Fj.neuroimage.2007.08.019",{"id":22,"text":1533,"url":22,"identifiers":1534},"Maunsell, J. H., & Van Essen, D. C. (1983). Functional properties of neurons in middle temporal visual area of the macaque monkey. I. Selectivity for stimulus direction, speed, and orientation. Journal of Neurophysiology, 49(5), 1127–1147.",{"doi":1535},"10.1152\u002Fjn.1983.49.5.1127",{"id":22,"text":1537,"url":22,"identifiers":1538},"McClelland, J. L. (1993). Toward a theory of information processing in graded, random, interactive networks. In Meyer, D. E., & Kornblum, S. (Eds.) Attention and performance XIV:Synergies in experimental psychology, artificial intelligence and cognitive neuroscience (pp. 655–688). Cambridge: MIT Press.",{},{"id":22,"text":1540,"url":22,"identifiers":1541},"Miletić, S., Turner, B. M., Forstmann, B. U., van Maanen, L. (2017). Parameter recovery for the leaky competing accumulator model. Journal of Mathematical Psychology, 76, 25–50.",{"doi":1542},"10.1016\u002Fj.jmp.2016.12.001",{"id":22,"text":1544,"url":22,"identifiers":1545},"Molloy, M. F., Galdo, M., Bahg, G., Liu, Q., Turner, B. M. (2019). What?s in a response time?: On the importance of response time measures in constraining models of context effects. Decision, 6(2), 171.",{"doi":1546},"10.1037\u002Fdec0000097",{"id":22,"text":1548,"url":22,"identifiers":1549},"Niwa, M., & Ditterich, J. (2008). Perceptual decisions between multiple directions of visual motion. Journal of Neuroscience, 28(17), 4435–4445.",{"doi":1550},"10.1523\u002FJNEUROSCI.5564-07.2008",{"id":22,"text":1552,"url":22,"identifiers":1553},"Norman, K. A., Polyn, S. M., Detre, G. J., Haxby, J. V. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10(9), 424–430.",{"doi":1554},"10.1016\u002Fj.tics.2006.07.005",{"id":22,"text":1556,"url":22,"identifiers":1557},"O’Reilly, R.C. (2006). Biologically based computational models of cortical cognition. Science, 314, 91–94.",{"doi":1558},"10.1126\u002Fscience.1127242",{"id":22,"text":1560,"url":22,"identifiers":1561},"Palestro, J. J., Bahg, G., Sederberg, P. B., Lu, Z.-L., Steyvers, M., Turner, B. M. (2018a). A tutorial on joint models of neural and behavioral measures of cognition. Journal of Mathematical Psychology, 84, 20–48.",{"doi":1562},"10.1016\u002Fj.jmp.2018.03.003",{"id":22,"text":1564,"url":22,"identifiers":1565},"Palestro, J. J., Sederberg, P. B., Osth, A. F., Van Zandt, T., Turner, B. M. (2018b). Likelihood-free methods for cognitive science. Berlin: Springer.",{"doi":1566},"10.1007\u002F978-3-319-72425-6",{"id":22,"text":1568,"url":22,"identifiers":1569},"Penny, W., Ghahramani, Z., Friston, K. (2005). Bilinear dynamical systems. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1457), 983–993.",{"doi":1570},"10.1098\u002Frstb.2005.1642",{"id":22,"text":1572,"url":22,"identifiers":1573},"Pirrone, A., Stafford, T., Marshall, J. A. (2014). When natural selection should optimize speed-accuracy trade-offs. Frontiers in Neuroscience, 8, 73.",{"doi":1574},"10.3389\u002Ffnins.2014.00073",{"id":22,"text":1576,"url":22,"identifiers":1577},"Polyn, S. M., Natu, V. S., Cohen, J. D., Norman, K. A. (2005). Category-specific cortical activity precedes retrieval during memory search. Science, 310(5756), 1963–1966.",{"doi":1578},"10.1126\u002Fscience.1117645",{"id":22,"text":1580,"url":22,"identifiers":1581},"Purcell, B., Heitz, R., Cohen, J., Schall, J., Logan, G., Palmeri, T. (2010). Neurally-constrained modeling of perceptual decision making. Psychological Review, 117, 1113–1143.",{"doi":1582},"10.1037\u002Fa0020311",{"id":22,"text":1584,"url":22,"identifiers":1585},"Ratcliff, R., & Rouder, J. N. (1998). Modeling response times for two-choice decisions. Psychological Science, 9, 347–356.",{"doi":1586},"10.1111\u002F1467-9280.00067",{"id":22,"text":1588,"url":22,"identifiers":1589},"Ratcliff, R., & Smith, P. L. (2004). A comparison of sequential sampling models for two-choice reaction time. Psychological Review, 111, 333–367.",{"doi":1590},"10.1037\u002F0033-295X.111.2.333",{"id":22,"text":1592,"url":22,"identifiers":1593},"Ratcliff, R., Cherian, A., Segraves, M. (2003). A comparison of macaque behavior and superior colliculus neuronal activity to predictions from models of simple two-choice decisions. Journal of Neurophysiology, 90, 1392–1407.",{"doi":1594},"10.1152\u002Fjn.01049.2002",{"id":22,"text":1596,"url":22,"identifiers":1597},"Ratcliff, R., Hasegawa, Y. T., Hasegawa, Y. P., Smith, P. L., Segraves, M. A. (2007). Dual diffusion model for single-cell recording data from the superior colliculus in a brightness-discrimination task. Journal of Neurophysiology, 97, 1756–1774.",{"doi":1598},"10.1152\u002Fjn.00393.2006",{"id":22,"text":1600,"url":22,"identifiers":1601},"Ratcliff, R., Voskuilen, C., Teodorescu, A. (2018). Modeling 2-alternative forced-choice tasks: accounting for both magnitude and difference effects. Cognitive Psychology, 103, 1–22.",{"doi":1602},"10.1016\u002Fj.cogpsych.2018.02.002",{"id":22,"text":1604,"url":22,"identifiers":1605},"Redgrave, P., Prescott, T. J., Gurney, K. N. (1999). The basal ganglia: a vertebrate solution to the selection problem? Neuroscience, 89(4), 1009–1023.",{"doi":1606},"10.1016\u002FS0306-4522(98)00319-4",{"id":22,"text":1608,"url":22,"identifiers":1609},"Rigoux, L., & Daunizeau, J. (2015). Dynamic causal modelling of brain–behaviour relationships. Neuroimage, 117, 202–221.",{"doi":1610},"10.1016\u002Fj.neuroimage.2015.05.041",{"id":22,"text":1612,"url":22,"identifiers":1613},"Roe, R. M., Busemeyer, J. R., Townsend, J. T. (2001). Multialternative decision field theory: a dynamic connectionist model of decision making. Psychological Review, 108, 370–392.",{"doi":1614},"10.1037\u002F0033-295X.108.2.370",{"id":22,"text":1616,"url":22,"identifiers":1617},"Roitman, J., & Shadlen, M. (2002). Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. Journal of Neuroscience, 22(21), 9475–9489.",{"doi":1618},"10.1523\u002FJNEUROSCI.22-21-09475.2002",{"id":22,"text":1620,"url":22,"identifiers":1621},"Ryali, S., Chen, T., Supekar, K., Tu, T., Kochalka, J., Cai, W., Menon, V. (2016). Multivariate dynamical systems-based estimation of causal brain interactions in fMRI: group-level validation using benchmark data, neurophysiological models and human connectome project data. Journal of Neuroscience Methods, 268, 142–153.",{"doi":1622},"10.1016\u002Fj.jneumeth.2016.03.010",{"id":22,"text":1624,"url":22,"identifiers":1625},"Ryali, S., Supekar, K., Chen, T., Menon, V. (2011). Multivariate dynamical systems models for estimating causal interactions in fmri. Neuroimage, 54(2), 807–823.",{"doi":1626},"10.1016\u002Fj.neuroimage.2010.09.052",{"id":22,"text":1628,"url":22,"identifiers":1629},"Ryyppö, E., Glerean, E., Brattico, E., Saramäki, J., Korhonen, O. (2018). Regions of interest as nodes of dynamic functional brain networks. Network Neuroscience, 2(4), 513–535.",{"doi":1630},"10.1162\u002Fnetn_a_00047",{"id":22,"text":1632,"url":22,"identifiers":1633},"Salzman, C. D., & Newsome, W. T. (1994). Neural mechanisms for forming a perceptual decision. Science, 264(5156), 231–237.",{"doi":1634},"10.1126\u002Fscience.8146653",{"id":22,"text":1636,"url":22,"identifiers":1637},"Schall, J. D., Morel, A., King, D. J., Bullier, J. (1995). Topography of visual cortex connections with frontal eye field in macaque: convergence and segregation of processing streams. Journal of Neuroscience, 15(6), 4464–4487.",{"doi":1638},"10.1523\u002FJNEUROSCI.15-06-04464.1995",{"id":22,"text":1640,"url":22,"identifiers":1641},"Schall, J. D. (2003). Neural correlates of decision processes: neural and mental chronometry. Current Opinion in Neurobiology, 12, 182–186.",{"doi":1642},"10.1016\u002FS0959-4388(03)00039-4",{"id":22,"text":1644,"url":22,"identifiers":1645},"Serences, J. T., & Boynton, G. M. (2007a). Feature-based attentional modulations in the absence of direct visual stimulation. Neuron, 55(2), 301–312.",{"doi":1646},"10.1016\u002Fj.neuron.2007.06.015",{"id":22,"text":1648,"url":22,"identifiers":1649},"Serences, J. T., & Boynton, G. M. (2007b). The representation of behavioral choice for motion in human visual cortex. Journal of Neuroscience, 27(47), 12893–12899.",{"doi":1650},"10.1523\u002FJNEUROSCI.4021-07.2007",{"id":22,"text":1652,"url":22,"identifiers":1653},"Shadlen, M. N., & Newsome, W. T. (2001). Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. Journal of Neurophysiology, 86, 1916–1936.",{"doi":1654},"10.1152\u002Fjn.2001.86.4.1916",{"id":22,"text":1656,"url":22,"identifiers":1657},"Shadlen, M. N., Britten, K. H., Newsome, W. T., Movshon, J. A. (1996). A computational analysis of the relationship between neuronal and behavioral responses to visual motion. Journal of Neuroscience, 16(4), 1486–1510.",{"doi":1658},"10.1523\u002FJNEUROSCI.16-04-01486.1996",{"id":22,"text":1660,"url":22,"identifiers":1661},"Silverman, B. W. (1986). Density estimation for statistics and data analysis. London: Chapman & Hall.",{"doi":1662},"10.1007\u002F978-1-4899-3324-9",{"id":22,"text":1664,"url":22,"identifiers":1665},"Simoncelli, E. P., & Heeger, D. J. (1998). A model of neuronal responses in visual area MT. Vision Research, 38(5), 743–761.",{"doi":1666},"10.1016\u002FS0042-6989(97)00183-1",{"id":22,"text":1668,"url":22,"identifiers":1669},"Smith, P. L. (1995). Psychophysically principled models of visual simple reaction time. Psychological Review, 102(3), 567–593.",{"doi":1670},"10.1037\u002F0033-295X.102.3.567",{"id":22,"text":1672,"url":22,"identifiers":1673},"Smith, P. L., & Vickers, D. (1988). The accumulator model of two-choice discrimination. Journal of Mathematical Psychology, 32, 135–168.",{"doi":1674},"10.1016\u002F0022-2496(88)90043-0",{"id":22,"text":1676,"url":22,"identifiers":1677},"Smith, J. F., Pillai, A., Chen, K., Horwitz, B. (2010). Identification and validation of effective connectivity networks in functional magnetic resonance imaging using switching linear dynamic systems. Neuroimage, 52(3), 1027–1040.",{"doi":1678},"10.1016\u002Fj.neuroimage.2009.11.081",{"id":22,"text":1680,"url":22,"identifiers":1681},"Stephan, K. E., Weiskopf, N., Drysdale, P. M., Robinson, P. A., Friston, K. J. (2007). Comparing hemodynamic models with dcm. Neuroimage, 38(3), 387–401.",{"doi":1682},"10.1016\u002Fj.neuroimage.2007.07.040",{"id":22,"text":1684,"url":22,"identifiers":1685},"Stephan, K. E., Kasper, L., Harrison, L. M., Daunizeau, J., den Ouden, H. E., Breakspear, M., Friston, K. J. (2008). Nonlinear dynamic causal models for fMRI. Neuroimage, 42(2), 649–662.",{"doi":1686},"10.1016\u002Fj.neuroimage.2008.04.262",{"id":22,"text":1688,"url":22,"identifiers":1689},"Stephan, K. E., Penny, W. D., Moran, R. J., den Ouden, H. E., Daunizeau, J., Friston, K. J. (2010). Ten simple rules for dynamic causal modeling. Neuroimage, 49(4), 3099–3109.",{"doi":1690},"10.1016\u002Fj.neuroimage.2009.11.015",{"id":22,"text":1692,"url":22,"identifiers":1693},"Stewart, T. C., Choo, X., Eliasmith, C. (2010). Symbolic reasoning in spiking neurons: a model of the cortex\u002Fbasal ganglia\u002Fthalamus loop. In Catrambone, R., & Ohlsson, S. (Eds.) Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 1100–1105). Austin: Cognitive Science Society.",{},{"id":22,"text":1695,"url":22,"identifiers":1696},"Teller, D. Y. (1984). Linking propositions. Vision Research, 24, 1233–1246.",{"doi":1697},"10.1016\u002F0042-6989(84)90178-0",{"id":22,"text":1699,"url":22,"identifiers":1700},"Teodorescu, A. R., & Usher, M. (2013). Disentangling decision models – from independence to competition. Psychological Review, 120, 1–38.",{"doi":1701},"10.1037\u002Fa0030776",{"id":22,"text":1703,"url":22,"identifiers":1704},"Teodorescu, A. R., Moran, R., Usher, M. (2016). Absolutely relative or relatively absolute: violations of value invariance in human decision making. Psychonomic Bulletin & Review, 23(1), 22–38.",{"doi":1705},"10.3758\u002Fs13423-015-0858-8",{"id":22,"text":1707,"url":22,"identifiers":1708},"ter Braak, C. J. F. (2006). A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces. Statistics and Computing, 16, 239–249.",{"doi":1709},"10.1007\u002Fs11222-006-8769-1",{"id":22,"text":1711,"url":22,"identifiers":1712},"Toni, T., Welch, D., Strelkowa, N., Ipsen, A., Stumpf, M. P. (2009). Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. Journal of the Royal Society Interface, 6, 187–202.",{"doi":1713},"10.1098\u002Frsif.2008.0172",{"id":22,"text":1715,"url":22,"identifiers":1716},"Turner, B. M. (2019). Toward a common representational framework for adaptation. In Press at Psychological Review.",{"doi":1717},"10.1037\u002Frev0000148",{"id":22,"text":1719,"url":22,"identifiers":1720},"Turner, B. M., & Sederberg, P. B. (2012). Approximate Bayesian computation with Differential Evolution. Journal of Mathematical Psychology, 56, 375–385.",{"doi":1721},"10.1016\u002Fj.jmp.2012.06.004",{"id":22,"text":1723,"url":22,"identifiers":1724},"Turner, B. M., & Sederberg, P. B. (2014). A generalized, likelihood-free method for parameter estimation. Psychonomic Bulletin and Review, 21, 227–250.",{"doi":1725},"10.3758\u002Fs13423-013-0530-0",{"id":22,"text":1727,"url":22,"identifiers":1728},"Turner, B. M., & Van Zandt, T. (2014). Hierarchical approximate Bayesian computation. Psychometrika, 79, 185–209.",{"doi":1729},"10.1007\u002Fs11336-013-9381-x",{"id":22,"text":1731,"url":22,"identifiers":1732},"Turner, B. M., & Van Zandt, T. (2018). Approximating bayesian inference through model simulation. Trends in Cognitive Sciences.",{"doi":1733},"10.1016\u002Fj.tics.2018.06.003",{"id":22,"text":1735,"url":22,"identifiers":1736},"Turner, B. M., Dennis, S., Van Zandt, T. (2013a). Bayesian analysis of memory models. Psychological Review, 120, 667–678.",{"doi":1737},"10.1037\u002Fa0032458",{"id":22,"text":1739,"url":22,"identifiers":1740},"Turner, B. M., Forstmann, B. U., Wagenmakers, E.-J., Brown, S. D., Sederberg, P. B., Steyvers, M. (2013b). A bayesian framework for simultaneously modeling neural and behavioral data. NeuroImage, 72, 193–206.",{"doi":1741},"10.1016\u002Fj.neuroimage.2013.01.048",{"id":22,"text":1743,"url":22,"identifiers":1744},"Turner, B. M., Sederberg, P. B., Brown, S., Steyvers, M. (2013c). A method for efficiently sampling from distributions with correlated dimensions. Psychological Methods, 18, 368–384.",{"doi":1745},"10.1037\u002Fa0032222",{"id":22,"text":1747,"url":22,"identifiers":1748},"Turner, B. M., Sederberg, P. B., McClelland, J. L. (2015a). Bayesian analysis of simulation-based models. In Press.",{"doi":1749},"10.1016\u002Fj.jmp.2014.10.001",{"id":22,"text":1751,"url":22,"identifiers":1752},"Turner, B. M., Van Maanen, L., Forstmann, B. U. (2015b). Informing cognitive abstractions with neurophysiology: the neural drift diffusion model. Psychological Review, 122, 312–336.",{"doi":1753},"10.1037\u002Fa0038894",{"id":22,"text":1755,"url":22,"identifiers":1756},"Turner, B. M., Rodriguez, C. A., Norcia, T., Steyvers, M., McClure, S. M. (2016). Why more is better: a method for simultaneously modeling EEG, fMRI, and behavior. NeuroImage, 128, 96–115.",{"doi":1757},"10.1016\u002Fj.neuroimage.2015.12.030",{"id":22,"text":1759,"url":22,"identifiers":1760},"Turner, B., Wang, T., Merkle, E. (2017a). Factor analysis linking functions for simultaneously modeling neural and behavioral data. NeuroImage, 153, 28–48.",{"doi":1761},"10.1016\u002Fj.neuroimage.2017.03.044",{"id":22,"text":1763,"url":22,"identifiers":1764},"Turner, B. M., Forstmann, B. U., Love, B. U., Palmeri, T. J., Van Maanen, L. (2017b). Approaches to analysis in model-based cognitive neuroscience. Journal of Mathematical Psychology, 76, 65–79.",{"doi":1765},"10.1016\u002Fj.jmp.2016.01.001",{"id":22,"text":1767,"url":22,"identifiers":1768},"Turner, B. M., Rodriguez, C. A., Liu, Q., Molloy, M. F., Hoogendijk, M., McClure, S. M. (2018). On the neural and mechanistic bases of self-control. Cerebral Cortex, 29(2), 732–750.",{"doi":1769},"10.1093\u002Fcercor\u002Fbhx355",{"id":22,"text":1771,"url":22,"identifiers":1772},"Turner, B. M., Forstmann, B. U., Steyvers, M., et al. (2019a). Joint models of neural and behavioral data. Berlin: Springer.",{"doi":1773},"10.1007\u002F978-3-030-03688-1",{"id":22,"text":1775,"url":22,"identifiers":1776},"Turner, B. M., Palestro, J. J., Miletić, S., Forstmann, B. U. (2019b). Advances in techniques for imposing reciprocity in brain-behavior relations. Neuroscience & Biobehavioral Reviews, 102, 327– 336.",{"doi":1777},"10.1016\u002Fj.neubiorev.2019.04.018",{"id":22,"text":1779,"url":22,"identifiers":1780},"Tversky, A., & Simonson, I. (1993). Context-dependent preferences. Management Science, 39(10), 1179–1189.",{"doi":1781},"10.1287\u002Fmnsc.39.10.1179",{"id":22,"text":1783,"url":22,"identifiers":1784},"Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: the leaky competing accumulator model. Psychological Review, 108, 550–592.",{"doi":1785},"10.1037\u002F0033-295X.108.3.550",{"id":22,"text":1787,"url":22,"identifiers":1788},"van Maanen, L., Brown, S. D., Eichele, T., Wagenmakers, E. -J., Ho, T., Serences, J. (2011). Neural correlates of trial-to-trial fluctuations in response caution. Journal of Neuroscience, 31, 17488–17495.",{"doi":1789},"10.1523\u002FJNEUROSCI.2924-11.2011",{"id":22,"text":1791,"url":22,"identifiers":1792},"van Ravenzwaaij, D., Provost, A., Brown, S. D. (2017). A confirmatory approach for integrating neural and behavioral data into a single model. Journal of Mathematical Psychology, 76, 131–141.",{"doi":1793},"10.1016\u002Fj.jmp.2016.04.005",{"id":22,"text":1795,"url":22,"identifiers":1796},"Vanduffel, W., Fize, D., Mandeville, J. B., Nelissen, K., Van Hecke, P., Rosen, B. R., Tootell, R. B., Orban, G. A. (2001). Visual motion processing investigated using contrast agent-enhanced fMRI in awake behaving monkeys. Neuron, 32(4), 565–577.",{"doi":1797},"10.1016\u002FS0896-6273(01)00502-5",{"id":22,"text":1799,"url":22,"identifiers":1800},"Wagenmakers, E.-J., Farrell, S., Ratcliff, R. (2004). Estimation and interpretation of 1\u002Ffα noise in human cognition. Psychonomic Bulletin and Review, 11, 579–615.",{"doi":1801},"10.3758\u002FBF03196615",{"id":22,"text":1803,"url":22,"identifiers":1804},"Wickens, J. (1997). Basal ganglia: structure and computations. Network: Computation in Neural Systems, 8 (4), R77–R109.",{"doi":1805},"10.1088\u002F0954-898X_8_4_001",{"id":22,"text":1807,"url":22,"identifiers":1808},"Zeki, S. M. (1974). Functional organization of a visual area in the posterior bank of the superior temporal sulcus of the rhesus monkey. The Journal of Physiology, 236(3), 549–573.",{"doi":1809},"10.1113\u002Fjphysiol.1974.sp010452",{"id":1811,"createTime":1812,"updateTime":1813,"relativeEntities":1814,"slug":1815,"properties":1816,"entityType":129,"verifyStatus":130,"verifyTime":1813,"verifyNote":131,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":1825,"fullTextUrl":22,"authors":1826,"publicationType":184,"publisherRelationship":1915,"citationCount":22,"citationInfo":22,"publishDate":1942,"publishYear":1943,"citationAnalyzeStatus":21,"lastCitationAnalyze":22,"indexDatabases":22,"openAccess":22,"references":22,"isForceReanalyzing":215},"3a28a4ae-de60-4db1-a9ca-15724353de94","2024-02-15T02:32:48.590+00:00","2025-02-07T22:45:23.439+00:00",[],"Individual-Differences-in-Cortical-Processing-Speed-Predict-Cognitive-Abilities-a-Model-Based-Cognitive-Neuroscience-Account",{"references":1817,"abstract":1819,"title":1821,"doi":1823},{"VOID":1818},"Baron, R.M., & Kenny, D.A. (1986). The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173.\nBarrouillet, P., Bernardin, S., Camos, V. (2004). Time constraints and resource sharing in adults’ working memory spans. Journal of Experimental Psychology: General, 133(1), 83–100. https:\u002F\u002Fdoi.org\u002F10.1037\u002F0096-3445.133.1.83.\nBasten, U., Hilger, K., Fiebach, C.J. (2015). Where smart brains are different: a quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence, 51, 10–27. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.intell.2015.04.009.\nBazana, P.G., & Stelmack, R.M. (2002). Intelligence and information processing during an auditory discrimination task with backward masking: an event-related potential analysis. Journal of Personality and Social Psychology, 83 (4), 998–1008.\nBentler, P.M., & Chou, C.-P. (1987). Practical issues in structural modeling. Sociological Methods Research, 16(1), 78–117. https:\u002F\u002Fdoi.org\u002F10.1177\u002F0049124187016001004.\nBoehm, U., Marsman, M., Matzke, D., Wagenmakers, E.-J. (2018). On the importance of avoiding shortcuts in applying cognitive models to hierarchical data. Behavior Research Methods. https:\u002F\u002Fdoi.org\u002F10.3758\u002Fs13428-018-1054-3.\nCassidy, S.M., Robertson, I.H., O’Connell, R.G. (2012). Retest reliability of event-related potentials: evidence from a variety of paradigms. Psychophysiology, 49(5), 659–664. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1469-8986.2011.01349.x.\nConway, A.R., Cowan, N., Bunting, M.F., Therriault, D.J., Minkoff, S.R. (2002). A latent variable analysis of working memory capacity, short-term memory capacity, processing speed, and general fluid intelligence. Intelligence, 30(2), 163–183. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0160-2896(01)00096-4.\nDai, T., & Guo, Y. (2017). Predicting individual brain functional connectivity using a bayesian hierarchical model. NeuroImage, 147, 772–787. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neuroimage.2016.11.048.\nDeary, I. (2008). Why do intelligent people live longer? Nature, 456(7219), 175–176. https:\u002F\u002Fdoi.org\u002F10.1038\u002F456175a.\nde Hollander, G., Forstmann, B.U., Brown, S.D. (2016). Different ways of linking behavioral and neural data via computational cognitive models. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1 (2), 101–109. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.bpsc.2015.11.004. Retrieved from http:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2451902215000166.\nDer, G., Batty, G.D., Deary, I.J. (2009). The association between iq in adolescence and a range of health outcomes at 40 in the 1979 us national longitudinal study of youth. Intelligence, 37(6), 573–580. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.intell.2008.12.002.\nDownar, J., Crawley, A.P., Mikulis, D.J., Davis, K.D. (2002). A cortical network sensitive to stimulus salience in a neutral behavioral context across multiple sensory modalities. Journal of Neurophysiology, 87 (1), 615–620. https:\u002F\u002Fdoi.org\u002F10.1152\u002Fjn.00636.2001.\nEngle, R.W., Tuholski, S.W., Laughlin, J.E., Conway, A.R. (1999). Working memory, short-term memory, and general fluid intelligence: a latent-variable approach. Journal of Experimental Psychology: General, 128 (3), 309–331.\nForstmann, B.U., Wagenmakers, E.-J., Eichele, T., Brown, S., Serences, J.T. (2011). Reciprocal relations between cognitive neuroscience and formal cognitive models: opposites attract? Trends in Cognitive Sciences, 15(6), 272–279. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.tics.2011.04.002.\nFrischkorn, G.T., & Schubert, A.-L. (2018). Cognitive models in intelligence research: Advantages and recommendations for their application. Journal of Intelligence 6(3), 1–22. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fjintelligence6030034.\nGelman, A., & Rubin, D.B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4), 457–472. https:\u002F\u002Fdoi.org\u002F10.1214\u002Fss\u002F1177011136.\nGratton, G., Coles, M.G., Donchin, E. (1983). A new method for off-line removal of ocular artifact. Electroencephalography and Clinical Neurophysiology, 55(4), 468–484. https:\u002F\u002Fdoi.org\u002F10.1016\u002F0013-4694(83)90135-9.\nHawkins, G.E., Mittner, M., Boekel, W., Heathcote, A., Forstmann, B.U. (2015). Toward a model-based cognitive neuroscience of mind wandering. Neuroscience, 310, 290–305. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neuroscience.2015.09.053.\nHick, W.E. (1952). On the rate of gain of information. Quarterly Journal of Experimental Psychology, 4(1), 11–26. https:\u002F\u002Fdoi.org\u002F10.1080\u002F17470215208416600.\nHilger, K., Ekman, M., Fiebach, C.J., Basten, U. (2017). Efficient hubs in the intelligent brain: nodal efficiency of hub regions in the salience network is associated with general intelligence. Intelligence, 60(Supplement C), 10–25. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.intell.2016.11.001.\nJäger, A.O., & Süß, H.M. (1997). Berlinger intelligenzstruktur-test form 4. Hogrefe: Göttingen.\nJung, R.E., & Haier, R.J. (2007). The parieto-frontal integration theory (p-fit) of intelligence: converging neuroimaging evidence. Behavioral and Brain Sciences, 30(2), 135–154. https:\u002F\u002Fdoi.org\u002F10.1017\u002FS0140525X07001185.\nKass, R.E., & Raftery, A.E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795. https:\u002F\u002Fdoi.org\u002F10.1080\u002F01621459.1995.10476572.\nKelly, S.P., & O’Connell, R.G. (2013). Internal and external influences on the rate of sensory evidence accumulation in the human brain. Journal of Neuroscience, 33(50), 19434–19441. https:\u002F\u002Fdoi.org\u002F10.1523\u002FJNEUROSCI.3355-13.2013.\nKievit, R.A., Davis, S.W., Griffiths, J., Correia, M.M., Cam-CAN, Henson, R.N. (2016). A watershed model of individual differences in fluid intelligence. Neuropsychologia, 91, 186–198. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neuropsychologia.2016.08.008.\nKovacs, K., & Conway, A.R.A. (2016). Process overlap theory: a unified account of the general factor of intelligence. Psychological Inquiry, 27(3), 151–177. https:\u002F\u002Fdoi.org\u002F10.1080\u002F1047840X.2016.1153946.\nKretzschmar, A., Spengler, M., Schubert, A.-L., Steinmayr, R., Ziegler, M. (2018). The relation of personality and intelligence–what can the brunswik symmetry principle tell us?. Journal of Intelligence 6(3), 1–38. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fjintelligence6030030.\nKyllonen, P.C., & Christal, R.E. (1990). Reasoning ability is (little more than) working-memory capacity?! Intelligence, 14(4), 389–433. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0160-2896(05)80012-1.\nKyllonen, P.C., & Zu, J. (2016). Use of response time for measuring cognitive ability. Journal of Intelligence 4(4), 1–29. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fjintelligence4040014.\nLee, M.D. (2011). How cognitive modeling can benefit from hierarchical bayesian models. Journal of Mathematical Psychology, 55(1), 1–7. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jmp.2010.08.013.\nLee, M.D., & Wagenmakers, E.-J. (2014). Bayesian cognitive modeling: a practical course. Cambridge: Cambridge University Press.\nLee, S.Y., & Song, X.Y. (2004). Evaluation of the bayesian and maximum likelihood approaches in analyzing structural equation models with small sample sizes. Multivariate Behavioral Research, 39(4), 653–686. PMID: 26745462 https:\u002F\u002Fdoi.org\u002F10.1207\u002Fs15327906mbr3904_4.\nLevy, R., & Choi, J. (2013). Bayesian structural equation modeling. In Hancock, G.R., & Mueller, R.O. (Eds.) Structural equation modeling: a second course (pp. 563–623). Information Age: Charlotte and NC.\nLy, A., Boehm, U., Heathcote, A., Turner, B.M., Forstmann, B., Marsman, M., Matzke, D. (2017). A flexible and efficient hierarchical bayesian approach to the exploration of individual differences in cognitive-model-based neuroscience. In Computational models of brain and behavior (pp. 467–479): Wiley-Blackwell. https:\u002F\u002Fdoi.org\u002F10.1002\u002F9781119159193.ch34.\nMarsh, H.W., Hau, K.T., Balla, J.R., Grayson, D. (1998). Is more ever too much? the number of indicators per factor in confirmatory factor analysis. Multivariate Behavioral Research, 33(2), 181–220. https:\u002F\u002Fdoi.org\u002F10.1207\u002Fs15327906mbr3302_1.\nMcGarry-Roberts, P.A., Stelmack, R.M., Campbell, K.B. (1992). Intelligence, reaction time, and event-related potentials. Intelligence, 16(3–4), 289–313. https:\u002F\u002Fdoi.org\u002F10.1016\u002F0160-2896(92)90011-F.\nMejia, A.F., Nebel, M.B., Barber, A.D., Choe, A.S., Pekar, J.J., Caffo, B.S., Lindquist, M.A. (2018). Improved estimation of subject-level functional connectivity using full and partial correlation with empirical bayes shrinkage. NeuroImage, 172, 478–491. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neuroimage.2018.01.029.\nMenon, V., & Uddin, L.Q. (2010). Saliency, switching, attention and control: a network model of insula function. Brain Structure and Function, 214(5), 655–667. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00429-010-0262-0.\nMerkle, E., & Rosseel, Y. (2018). blavaan: Bayesian structural equation models via parameter expansion. Journal of Statistical Software, 85(4), 1–30. https:\u002F\u002Fdoi.org\u002F10.18637\u002Fjss.v085.i04.\nMittner, M., Boekel, W., Tucker, A.M., Turner, B.M., Heathcote, A., Forstmann, B.U. (2014). When the brain takes a break: a model-based analysis of mind wandering. Journal of Neuroscience, 34(49), 16286–16295. https:\u002F\u002Fdoi.org\u002F10.1523\u002FJNEUROSCI.2062-14.2014.\nNeubauer, A.C., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience Biobehavioral Reviews, 33(7), 1004–1023. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neubiorev.2009.04.001.\nNikolaev, B., & McGee, J.J. (2016). Relative verbal intelligence and happiness. Intelligence, 59, 1–7. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.intell.2016.09.002.\nNunez, M.D., Srinivasan, R., Vandekerckhove, J. (2015). Individual differences in attention influence perceptual decision making. Frontiers in Psychology, 8, 18. https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpsyg.2015.00018.\nNunez, M.D., Vandekerckhove, J., Srinivasan, R. (2017). How attention influences perceptual decision making: single-trial eeg correlates of drift-diffusion model parameters. Journal of Mathematical Psychology, 76, 117–130. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jmp.2016.03.003.\nOberauer, K., Lewandowsky, S., Farrell, S., Jarrold, C., Greaves, M. (2012). Modeling working memory: an interference model of complex span. Psychonomic Bulletin Review, 19(5), 779–819. https:\u002F\u002Fdoi.org\u002F10.3758\u002Fs13423-012-0272-4.\nO’Connell, R.G., Dockree, P.M., Kelly, S.P. (2012). A supramodal accumulation-to-bound signal that determines perceptual decisions in humans. Nature Neuroscience, 15(12), 1729–1735. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnn.3248.\nPalmeri, T.J., Love, B.C., Turner, B.M. (2017). Model-based cognitive neuroscience. Journal of Mathematical Psychology. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jmp.2016.10.010.\nPenke, L., Maniega, S.M., Bastin, M.E., Valdes Hernandez, M.C., Murray, C., Royle, N.A., Deary, I.J. (2012). Brain white matter tract integrity as a neural foundation for general intelligence. Molecular Psychiatry, 17(10), 1026–1030. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fmp.2012.66.\nPesta, B.J., McDaniel, M.A., Bertsch, S. (2010). Toward an index of well-being for the fifty u.s. states. Intelligence, 38(1), 160–168. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.intell.2009.09.006.\nPlummer, M. (2003). Jags: a program for analysis of bayesian graphical models using gibbs sampling.\nPolich, J. (2007). Updating p300: an integrative theory of p3a and p3b. Clinical Neurophysiology, 118(10), 2128–2148. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.clinph.2007.04.019.\nRatcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59–108.\nRatcliff, R., & McKoon, G. (2008). The diffusion decision model: theory and data for two-choice decision tasks. Neural Computation, 20(4), 873–922. https:\u002F\u002Fdoi.org\u002F10.1162\u002Fneco.2008.12-06-420.\nRatcliff, R., Philiastides, M.G., Sajda, P. (2009). Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the eeg. Proceedings of the National Academy of Sciences, 106(16), 6539–6544. https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.0812589106.\nRatcliff, R., Sederberg, P.B., Smith, T.A., Childers, R. (2016). A single trial analysis of eeg in recognition memory: tracking the neural correlates of memory strength. Neuropsychologia, 93(Pt A), 128–141. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neuropsychologia.2016.09.026.\nRatcliff, R., Thapar, A., McKoon, G. (2010). Individual differences, aging, and iq in two-choice tasks. Cognitive Psychology, 60(3), 127–157. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cogpsych.2009.09.001.\nRatcliff, R., Thapar, A., McKoon, G. (2011). Effects of aging and iq on item and associative memory. Journal of Experimental Psychology: General, 140(3), 464–487. https:\u002F\u002Fdoi.org\u002F10.1037\u002Fa0023810.\nRaven, J.C., Court, J.H., Raven, J. (1994). Manual for raven’s progressive matrices and mill hill vocabulary scales advanced progressive matrices. Oxford: Oxford University Press.\nRoss, S.M. (2014). Introduction to probability models. New York: Academic Press.\nSchmidt, F.L., & Hunter, J. (2004). General mental ability in the world of work: occupational attainment and job performance. In Work and organisational psychology: Research methodology; assessment and selection; organisational change and development; human resource and performance management; emerging trends: Innovation\u002Fglobalisation\u002Ftechnology. Schmidt, Frank L., Tippie College of Business, University of Iowa, Iowa City, IA, US, 52242 (pp. 35–58): Sage Publications, Inc.\nSchmiedek, F., Oberauer, K., Wilhelm, O., Suss, H.-M., Wittmann, W.W. (2007). Individual differences in components of reaction time distributions and their relations to working memory and intelligence. Journal of Experimental Psychology: General, 136(3), 414–429. https:\u002F\u002Fdoi.org\u002F10.1037\u002F0096-3445.136.3.414.\nSchmitz, F., & Wilhelm, O. (2016). Modeling mental speed: decomposing response time distributions in elementary cognitive tasks and correlations with working memory capacity and fluid intelligence. Journal of Intelligence 4(13), 1–23. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fjintelligence4040013.\nSchubert, A.-L., Frischkorn, G.T., Hagemann, D., Voss, A. (2016). Trait characteristics of diffusion model parameters. Journal of Intelligence 4(7), 1–22. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fjintelligence4030007.\nSchubert, A.-L., Hagemann, D., Frischkorn, G.T. (2017). Is general intelligence little more than the speed of higher-order processing? Journal of Experimental Psychology: General, 146(10), 1498–1512. https:\u002F\u002Fdoi.org\u002F10.1037\u002Fxge0000325.\nSchubert, A.-L., Hagemann, D., Frischkorn, G.T., Herpertz, S.C. (2018). Faster, but not smarter: An experimental analysis of the relationship between mental speed and mental abilities. Intelligence, 71, 66–75. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.intell.2018.10.005.\nSchubert, A.-L., Hagemann, D., Voss, A., Schankin, A., Bergmann, K. (2015). Decomposing the relationship between mental speed and mental abilities. Intelligence, 51, 28–46. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.intell.2015.05.002.\nSebastian, A., Forstmann, B.U., Matzke, D. (2018). Towards a model-based cognitive neuroscience of stopping - a neuroimaging perspective. Neuroscience Biobehavioral Reviews, 90, 130–136. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neubiorev.2018.04.011.\nSeeley, W.W., Menon, V., Schatzberg, A.F., Keller, J., Glover, G.H., Kenna, H., Greicius, M.D. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. Journal of Neuroscience, 27(9), 2349–2356. https:\u002F\u002Fdoi.org\u002F10.1523\u002FJNEUROSCI.5587-06.2007.\nSheppard, L.D., & Vernon, P.A. (2008). Intelligence and speed of information-processing: a review of 50 years of research. Personality and Individual Differences, 44(3), 535–551. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.paid.2007.09.015.\nShiffrin, R.M., Lee, M.D., Kim, W., Wagenmakers, E.-J. (2008). A survey of model evaluation approaches with a tutorial on hierarchical bayesian methods. Cognitive Science, 32(8), 1248–1284. https:\u002F\u002Fdoi.org\u002F10.1080\u002F03640210802414826.\nShou, H., Eloyan, A., Nebel, M. B., Mejia, A., Pekar, J.J., Mostofsky, S., Crainiceanu, C.M. (2014). Shrinkage prediction of seed-voxel brain connectivity using resting state fmri. NeuroImage, 102, 938–944. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neuroimage.2014.05.043.\nSoltani, M., & Knight, R.T. (2000). Neural origins of the p300. Critical Reviews in Neurobiology, 14(3-4), 199–224.\nSpiegelhalter, D.J., Best, N.G., Carlin, B.P., van der Linde, A. (2014). The deviance information criterion: 12 years on. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(3), 485–493. https:\u002F\u002Fdoi.org\u002F10.1111\u002Frssb.12062.\nStone, M. (1960). Models for choice-reaction time. Psychometrika, 25(3), 251–260.\nTroche, S.J., Houlihan, M.E., Stelmack, R.M., Rammsayer, T.H. (2009). Mental ability, p300, and mismatch negativity: analysis of frequency and duration discrimination. Intelligence, 37(4), 365–373. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.intell.2009.03.002.\nTroche, S.J., Indermühle, R., Leuthold, H., Rammsayer, T.H. (2015). Intelligence and the psychological refractory period: a lateralized readiness potential study. Intelligence, 53, 138–144. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.intell.2015.10.003.\nTurner, B.M., Forstmann, B.U., Love, B.C., Palmeri, T.J., van Maanen, L. (2017). Approaches to analysis in model-based cognitive neuroscience. Journal of Mathematical Psychology. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jmp.2016.01.001.\nTurner, B.M., Rodriguez, C.A., Liu, Q., Molloy, M.F., Hoogendijk, M., McClure, S.M. (2018). On the neural and mechanistic bases of self-control. Cerebral cortex. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fcercor\u002Fbhx355.\nTurner, B.M., Wang, T., Merkle, E.C. (2017). Factor analysis linking functions for simultaneously modeling neural and behavioral data. NeuroImage, 153, 28–48. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neuroimage.2017.03.044 Retrieved from http:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1053811917302525.\nUnsworth, N., Fukuda, K., Awh, E., Vogel, E.K. (2014). Working memory and fluid intelligence: capacity, attention control, and secondary memory retrieval. Cognitive Psychology, 71, 1–26. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cogpsych.2014.01.003.\nVandekerckhove, J. (2014). A cognitive latent variable model for the simultaneous analysis of behavioral and personality data. Journal of Mathematical Psychology, 60, 58–71. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jmp.2014.06.004.\nVandekerckhove, J., Tuerlinckx, F., Lee, M.D. (2011). Hierarchical diffusion models for two-choice response times. Psychological Methods, 16(1), 44–62. https:\u002F\u002Fdoi.org\u002F10.1037\u002Fa0021765.\nvan der Maas, H.L.J., Molenaar, D., Maris, G., Kievit, R.A., Borsboom, D. (2011). Cognitive psychology meets psychometric theory: On the relation between process models for decision making and latent variable models for individual differences. Psychological Review, 118(2), 339–356.\nvan Ravenzwaaij, D., Brown, S., Wagenmakers, E.-J. (2011). An integrated perspective on the relation between response speed and intelligence. Cognition, 119(3), 381–393. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cognition.2011.02.002.\nvan Ravenzwaaij, D., Provost, A., Brown, S.D. (2017). A confirmatory approach for integrating neural and behavioral data into a single model. Journal of Mathematical Psychology, 76, 131–141. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jmp.2016.04.005.\nVoss, A., Rothermund, K., Voss, J. (2004). Interpreting the parameters of the diffusion model: an empirical validation. Memory Cognition, 32(7), 1206–1220.\nWabersich, D., & Vandekerckhove, J. (2014). Extending jags: a tutorial on adding custom distributions to jags (with a diffusion model example). Behavior Research Methods, 46(1), 15–28. https:\u002F\u002Fdoi.org\u002F10.3758\u002Fs13428-013-0369-3.\nWestland, J.C. (2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9(6), 476–487. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.elerap.2010.07.003.\nWongupparaj, P., Kumari, V., Morris, R.G. (2015). The relation between a multicomponent working memory and intelligence: the roles of central executive and short-term storage functions. Intelligence, 53(Supplement C), 166–180. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.intell.2015.10.007.\nYap, M.J., Balota, D.A., Sibley, D.E., Ratcliff, R. (2012). Individual differences in visual word recognition: insights from the english lexicon project. Journal of Experimental Psychology: Human Perception and Performance, 38(1), 53–79. https:\u002F\u002Fdoi.org\u002F10.1037\u002Fa0024177.",{"EN":1820},"Previous research has shown that individuals with greater cognitive abilities display a greater speed of higher-order cognitive processing. These results suggest that speeded neural information processing may facilitate evidence accumulation during decision making and memory updating and thus yield advantages in general cognitive abilities. We used a hierarchical Bayesian cognitive modeling approach to test the hypothesis that individual differences in the velocity of evidence accumulation mediate the relationship between neural processing speed and cognitive abilities. We found that a higher neural speed predicted both the velocity of evidence accumulation across behavioral tasks and cognitive ability test scores. However, only a negligible part of the association between neural processing speed and cognitive abilities was mediated by individual differences in the velocity of evidence accumulation. The model demonstrated impressive forecasting abilities by predicting 36% of individual variation in cognitive ability test scores in an entirely new sample solely based on their electrophysiological and behavioral data. Our results suggest that individual differences in neural processing speed might affect a plethora of higher-order cognitive processes, that only in concert explain the large association between neural processing speed and cognitive abilities, instead of the effect being entirely explained by differences in evidence accumulation speeds.",{"EN":1822},"Individual Differences in Cortical Processing Speed Predict Cognitive Abilities: a Model-Based Cognitive Neuroscience Account",{"VOID":1824},"10.1007\u002Fs42113-018-0021-5","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs42113-018-0021-5",[1827,1864,1879,1903],{"id":1828,"sortIndex":102,"researcher":22,"roles":1829,"affiliations":1830,"properties":1861},"17b1003e-13f5-4af5-9af3-339f3c0b2eab",[137],[1831,1841,1851],{"id":22,"sortIndex":23,"affiliation":1832,"properties":22},{"id":1833,"createTime":1834,"updateTime":1835,"relativeEntities":1836,"slug":1837,"properties":1838,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"073e8a21-3565-43b3-aa6b-bb445e6770a9","2024-04-17T23:39:46.097+00:00","2025-06-11T20:17:30.159+00:00",[],"Department-of-Cognitive-Sciences-University-of-California-Irvine-USA",{"title":1839},{"EN":1840},"Department of Cognitive Sciences, University of California, Irvine, USA",{"id":1842,"sortIndex":152,"affiliation":1843,"properties":1850},"b37dffe9-722d-4176-a1b0-3a8cd727416d",{"id":1844,"createTime":1845,"updateTime":1845,"relativeEntities":1846,"slug":22,"properties":1847,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"58ad3f82-5ed2-4248-873a-42a0974f5ff9","2024-01-13T16:37:43.408+00:00",[],{"title":1848},{"VI":1849},"Department of Statistics, University of California, Irvine, USA",{},{"id":1852,"sortIndex":170,"affiliation":1853,"properties":1860},"623a86ad-c5d5-43cf-8fff-1bff513622c1",{"id":1854,"createTime":1855,"updateTime":1855,"relativeEntities":1856,"slug":22,"properties":1857,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"eb733f7d-6eae-433f-9f5e-0264fe2c0460","2024-02-15T02:32:48.641+00:00",[],{"title":1858},{"VI":1859},"Institute of Mathematical Behavioral Sciences, University of California, Irvine, USA",{},{"title":1862},{"VI":1863},"Joachim Vandekerckhove",{"id":1865,"sortIndex":170,"researcher":22,"roles":1866,"affiliations":1867,"properties":1876},"7aff63f9-6048-4abd-9061-d431def17176",[137],[1868],{"id":22,"sortIndex":23,"affiliation":1869,"properties":22},{"id":1870,"createTime":1871,"updateTime":1871,"relativeEntities":1872,"slug":22,"properties":1873,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"ae99dc94-3676-46c1-b623-6c3c9d4e222e","2023-12-25T15:51:41.937+00:00",[],{"title":1874},{"VI":1875},"Institute of Psychology, Heidelberg University, Heidelberg, Germany",{"title":1877},{"VI":1878},"Dirk Hagemann",{"id":1880,"sortIndex":152,"researcher":22,"roles":1881,"affiliations":1882,"properties":1900},"5f8f116a-eb4d-4eb1-9f2b-3f4982a322f1",[137],[1883,1895],{"id":1884,"sortIndex":152,"affiliation":1885,"properties":1893},"80be6dfa-a1a2-416d-bc53-ac05a381fe0a",{"id":1886,"createTime":1887,"updateTime":1887,"relativeEntities":1888,"slug":1889,"properties":1890,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"8394d972-a7ab-4bf4-bf4a-8106e5fa2d3c","2024-09-30T05:28:03.186+00:00",[],"Department-of-Biomedical-Engineering-University-of-California-Irvine-USA",{"title":1891},{"EN":1892},"Department of Biomedical Engineering, University of California, Irvine, USA",{"title":1894},{"VI":1892},{"id":22,"sortIndex":23,"affiliation":1896,"properties":22},{"id":1833,"createTime":1834,"updateTime":1835,"relativeEntities":1897,"slug":1837,"properties":1898,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},[],{"title":1899},{"EN":1840},{"title":1901},{"VI":1902},"Michael D. 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J., Goldwater, S., & Steedman, M. (2017). Bootstrapping language acquisition. Cognition, 164, 116–143.\nAchille, A., Rovere, M., & Soatto, S. (2019). Critical learning periods in deep neural networks. International Conference on Learning Representations (ICLR)\nAchinstein, P. (1964). Models, analogies, and theories. Philosophy of Science, 31(4), 328–350.\nAgassi, J. (2014). Proof, probability or plausibility. In: Mulligan, K., Kijania-Placek, K., & Placek, T. (eds) The History and Philosophy of Polish Logic, History of Analytic Philosophy. London: Palgrave Macmillan, London, pp. 117–127.\nAucher, G., & Schwarzentruber, F. (2013). On the complexity of dynamic epistemic logic. In B. C. Schipper (Ed.), Proceedings of the 14th Conference of Theoretical Aspects of Rationality and Knowledge (TARKXIV) (pp. 19–28). Chennai, India.\nBaltag, A., & Smets, S. (2006). Dynamic belief revision over multi-agent plausibility models. In Proceedings of LOFT (Vol. 6, pp. 11–24). University of Liverpool.\nBaltag, A., & Smets, S. (2008). Probabilistic dynamic belief revision. Synthese, 165, 179–202.\nBartha, P. (2010). By parallel reasoning: the construction and evaluation of analogical arguments. Oxford University Press.\nBarton, G. E., Berwick, R. C., & Ristad, E. S. (1987). Computational complexity and natural language. MIT press.\nBengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157–166.\nBird, A. (2021). Understanding the replication crisis as a base rate fallacy. The British Journal for the Philosophy of Science, 72(4), 965–993.\nBranco, A., Rodrigues, J., Salawa, M., Branco, R., & Saedi, C. (2020). Comparative probing of lexical semantics theories for cognitive plausibility and technological usefulness. In Proceedings of the 28th International Conference on Computational Linguistics (pp. 4004–4019).\nBremnes, H. S., Szymanik, J., & Baggio, G. (2022). Computational complexity explains neural differences in quantifier verification. Cognition, 223, 105013.\nBremnes, H. S., Szymanik, J., & Baggio, G. (2023). The interplay of computational complexity and memory load during quantifier verification. Language, Cognition and Neuroscience on-line first.\nChomsky, N. (1957). Syntactic structures. Mouton & Co.\nChomsky, N. (1959). On certain formal properties of grammars. Information and Control, 2(2), 137–167.\nCichy, R. M., & Kaiser, D. (2019). Deep neural networks as scientific models. Trends in Cognitive Sciences, 23(4), 305–317.\nDe Santo, A., & Drury, J. E. (2019). Encoding and verification effects of generalized quantifiers on working memory. Proceedings from the Annual Meeting of the Chicago Linguistic Society, 55(1), 103–114.\nDe Santo, A., & Rawski, J. (2022). Mathematical linguistics and cognitive complexity. In E. Danesi (Ed.), Handbook of Cognitive Mathematics (pp. 1–38). Springer.\nDror, I. E., & Gallogly, D. P. (1999). Computational analyses in cognitive neuroscience: In defense of biological implausibility. Psychonomic Bulletin & Review, 6(2), 173–182.\nEdelman, S. (1997). Computational theories of object recognition. Trends in cognitive sciences, 1(8), 296–304.\nGarey, M. R., & Johnson, D. S. (1979). Computers and intractability: A guide to the theory of NP-completeness. W.H. Freeman & Co.\nGoudge, T. A. (1966). Plausibility of new hypotheses. The Journal of Philosophy, 63(20), 621–624.\nGraf, T. (2022). Subregular linguistics: Bridging theoretical linguistics and formal grammar. Theoretical Linguistics, 48(3-4), 145–184.\nGrossberg, S. (1987). Competitive learning: From interactive activation to adaptive resonance. Cognitive Science, 11(1), 23–63.\nHeinz, J., Kobele, G. M., & Riggle, J. (2009). Evaluating the complexity of optimality theory. Linguistic Inquiry, 40(2), 277–288.\nHooker, C. A. (1996). The scientific realism of Rom Harré. British Journal for the Philosophy of Science, 47(4).\nHopcroft, J. E., Motwani, R., & Ullman, J. D. (2001). Introduction to automata theory, languages, and computation (3rd ed.). Prentice-Hall.\nJohnson, K. (2015). Notational variants and invariance in linguistics. Mind & Language, 30(2), 162–186.\nKeenan, E. L., & Stabler, E. P. (2010). Language variation and linguistic invariants. Lingua, 120(12), 2680–2685.\nKemp, C., Perfors, A., & Tenenbaum, J. B. (2004). Learning domain structures. Proceedings of the Annual Meeting of the Cognitive Science Society, 26, 672–677.\nKennedy, W. G. (2009). Cognitive plausibility in cognitive modeling, artificial intelligence, and social simulation. In Proceedings of the International Conference on Cognitive Modeling (ICCM) (pp. 24–26).\nLambek, J. (1958). The mathematics of sentence structure. The American Mathematical Monthly, 65(3), 154–170.\nLeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.\nLove, B. C. (2021). Levels of biological plausibility. Philosophical Transactions of the Royal Society B, 376(1815), 20190632.\nLutz, C. (2006). Complexity and succinctness of public announcement logic. In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (pp. 137–143).\nMarr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information. W.H. Freeman & Co.\nMcCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5, 115–133.\nMeehl, P. E. (1992a). Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology. In R. B. Miller (Ed.), The Restoration of Dialogue: Readings in the Philosophy of Clinical Psychology (pp. 523–555). American Psychological Association.\nMeehl, P. E. (1992b). Cliometric metatheory: The actuarial approach to empirical, history-based philosophy of science. Psychological Reports, 71, 339–339.\nMeehl, P. E. (2002). Cliometric metatheory: II. Criteria scientists use in theory appraisal and why it is rational to do so. Psychological Reports, 91(2), 339–404.\nMeehl, P. E. (2004). Cliometric metatheory III: Peircean consensus, verisimilitude and asymptotic method. British Journal for the Philosophy of Science, 55(4).\nMerkx, D., & Frank, S. L. (2021). Human sentence processing: Recurrence or attention? In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (pp. 12–22). Association for Computational Linguistics.\nMewhort, D. J. (1990). Alice in wonderland, or psychology among the information sciences. Psychological Research, 52(2), 158–162.\nMichaelis, J. (2001). Transforming linear context-free rewriting systems into minimalist grammars. In In Proceedings of the 4th International Conference on Logical Aspects of Computational Linguistics (pp. 228–244).\nMichaelis, J. (2004). Observations on strict derivational minimalism. Electronic Notes in Theoretical Computer Science, 53, 192–209.\nMichaelov, J. A., Bardolph, M. D., Coulson, S., & Bergen, B. (2021). Different kinds of cognitive plausibility: Why are transformers better than RNNs at predicting N400 amplitude? Proceedings of the Annual Meeting of the Cognitive Science Society, 43, 300–306.\nMisak, C. J. (2004). Truth and the end of inquiry: A Peircean account of truth. Oxford University Press.\nNefdt, R. M., & Baggio, G. (2023). Notational variants and cognition: The case of dependency grammar. Erkenntnis, 1–31.\nNiiniluoto, I. (1987). Truthlikeness. Springer.\nNyrup, R. (2020). Of water drops and atomic nuclei: Analogies and pursuit worthiness in science. The British Journal for the Philosophy of Science, 71(3), 881–903.\nOota, S. R., Alexandre, F., & Hinaut, X. (2022). Long-term plausibility of language models and neural dynamics during narrative listening. Proceedings of the Annual Meeting of the Cognitive Science Society, 44, 2462–2469.\nPentus, M. (2006). Lambek calculus is NP-complete. Theoretical Computer Science, 357(1-3), 186–201.\nPerconti, P. (2017). The case for cognitive plausibility. In: La Mantia, F., Licata, I., & Perconti, P. (eds) Language in Complexity. Lecture Notes in Morphogenesis. Springer, Cham, pp. 73–79.\nPerfors, A., Tenenbaum, J. B., Griffiths, T. L., & Xu, F. (2011). A tutorial introduction to Bayesian models of cognitive development. Cognition, 120(3), 302–321.\nPhillips, L., & Pearl, L. (2015). The utility of cognitive plausibility in language acquisition modeling: Evidence from word segmentation. Cognitive Science, 39(8), 1824–1854.\nPopper, K. R. (1963). Conjectures and refutations. Routledge.\nPopper, K. R. (1976). A note on verisimilitude. The British Journal for the Philosophy of Science, 27(2), 147–159.\nPsillos, S. (1999). Scientific realism: How science tracks truth. Routledge.\nRamakrishnan, K., Scholte, S., Lamme, V., Smeulders, A., & Ghebreab, S. (2015). Convolutional neural networks in the brain: An fMRI study. Journal of Vision, 15(12), 371–371.\nRichards, B. A., Lillicrap, T. P., Beaudoin, P., et al. (2019). A deep learning framework for neuroscience. Nature Neuroscience, 22(11), 1761–1770.\nRistad, E. S. (1993). The language complexity game. MIT Press.\nRogers, J., & Pullum, G. K. (2011). Aural pattern recognition experiments and the subregular hierarchy. Journal of Logic, Language and Information, 20(3), 329–342.\nRumelhart, D. E. (1989). The architecture of mind: A connectionist approach. In M. I. Posner (Ed.), Foundations of Cognitive Science (pp. 133–159). MIT Press.\nSanborn, A. N., & Chater, N. (2016). Bayesian brains without probabilities. Trends in Cognitive Sciences, 20(12), 883–893.\nSavitch, W. J. (1993). Why it might pay to assume that languages are infinite. Annals of Mathematics and Artificial Intelligence, 8(1-2), 17–25.\nŠešelja, D., & Straßer, C. (2013). Kuhn and the question of pursuit worthiness. Topoi, 32, 9–19.\nShapere, D. (1966). Plausibility and justification in the development of science. The Journal of Philosophy, 63(20), 611–621.\nShaw, J. (2022). On the very idea of pursuitworthiness. Studies in History and Philosophy of Science, 91, 103–112.\nSimon, H. A. (1968). On judging the plausibility of theories. Studies in Logic and the Foundations of Mathematics, 52, 439–459.\nSimon, H. A. (1990). Invariants of human behavior. Annual Review of Psychology, 41(1), 1–20.\nStenning, K., & van Lambalgen, M. (2010). The logical response to a noisy world. In M. Oaksford & N. Chater (Eds.), Cognition and Conditionals: Probability and Logic in Human Thinking (pp. 85–102). Oxford University Press.\nStinson, C. (2020). From implausible artificial neurons to idealized cognitive models: Rebooting philosophy of artificial intelligence. Philosophy of Science, 87(4), 590–611.\nSuppes, P. (2002). Representation and invariance of scientific structures. CSLI Publications.\nSzymanik, J., & Verbrugge, R. (2018). Tractability and the computational mind. In M. Sprevak & M. Colombo (Eds.), The Routledge Handbook of the Computational Mind. Routledge.\nToulmin, S. (1966). The plausibility of theories. The Journal of Philosophy, 63(20), 624–627.\nTrout, J. D. (2002). Scientific explanation and the sense of understanding. Philosophy of Science, 69(2), 212–233.\nTsotsos, J. K. (1993). The role of computational complexity in perceptual theory. Advances in psychology, 99, 261–296.\nvan De Pol, I., Van Rooij, I., & Szymanik, J. (2018). Parameterized complexity of theory of mind reasoning in dynamic epistemic logic. Journal of Logic, Language and Information, 27, 255–294.\nvan Rooij, I. (2008). The tractable cognition thesis. Cognitive Science, 32(6), 939–984.\nvan Rooij, I., & Baggio, G. (2020). Theory development requires an epistemological sea change. Psychological Inquiry, 31(4), 321–325.\nvan Rooij, I., & Baggio, G. (2021). Theory before the test: How to build high-verisimilitude explanatory theories in psychological science. Perspectives on Psychological Science, 16(4), 682–697.\nvan Rooij, I., Blokpoel, M., Kwisthout, J., & Wareham, T. (2019). Cognition and intractability: A guide to classical and parameterized complexity analysis. Cambridge University Press.\nVelázquez-Quesada, F. R. (2014). Dynamic epistemic logic for implicit and explicit beliefs. Journal of Logic, Language and Information, 23, 107–140.\nWareham, H. T. (1996). The role of parameterized computational complexity theory in cognitive modeling. In AAAI-96 Workshop Working Notes: Computational Cognitive Modeling: Source of the Power.\nWareham, T. (1999). Systematic parameterized complexity analysis in computational phonology. Ph.D. thesis, Department of Computer Science, University of Victoria, April 1999. Technical Report ROA-318-0599, Rutgers Optimality Archive.\nYang, G. R., & Wang, X. J. (2020). Artificial neural networks for neuroscientists: A primer. Neuron, 107(6), 1048–1070.\nZollman, K. J. S. (2007). The communication structure of epistemic communities. Philosophy of Science, 74(5), 574–587.\nZollman, K. J. S. (2010). The epistemic benefit of transient diversity. Erkenntnis, 72(1), 17–35.\nZollman, K. J. S. (2013). Network epistemology: Communication in epistemic communities. Philosophy Compass, 8(1), 15–27.",{"EN":1954},"Various notions of plausibility are used in cognitive science to argue for or against the “goodness of theories.” However, plausibility remains poorly understood and difficult to analyze. We review debates in the philosophy of science on uses of plausibility in the assessment of novel scientific theories as well as recent attempts to formalize, reform, or eliminate specific notions of plausibility. Although these discussions highlight important concerns behind plausibility claims, they fail to identify viable notions of plausibility that are sufficiently different from other criteria of “good theory,” such as prior probability or external coherence. We survey uses of plausibility in linguistics and cognitive science, confirming that plausibility is often a proxy for other criteria of good theory. We argue that the need remains for concepts of plausibility that can be employed to assess the quality of proposals at the early stages of theory development when other criteria are not yet applicable. We identify two such notions: one relating to formal constraints on theories and another capturing initial epistemic consensus, if not necessarily convergence on the truth, about the target system in a community of inquiry. We briefly assess the specificity and added value of these notions of plausibility relative to other criteria for good theory.",{"EN":1956},"Plausibility and Early Theory in Linguistics and Cognitive Science",{"VOID":1958},"10.1007\u002Fs42113-024-00196-7","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs42113-024-00196-7",[1961,1976,2003],{"id":1962,"sortIndex":23,"researcher":22,"roles":1963,"affiliations":1964,"properties":1973},"b6684688-677c-48fb-81d5-feab60f0a09c",[137],[1965],{"id":22,"sortIndex":23,"affiliation":1966,"properties":22},{"id":1967,"createTime":1968,"updateTime":1968,"relativeEntities":1969,"slug":22,"properties":1970,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"45d42910-c6ec-4b89-8686-0fa5d7b287b8","2024-02-06T22:34:41.211+00:00",[],{"title":1971},{"VI":1972},"Department of Language and Literature, Norwegian University of Science and Technology, Trondheim, Norway",{"title":1974},{"VI":1975},"Giosuè Baggio",{"id":1977,"sortIndex":170,"researcher":22,"roles":1978,"affiliations":1979,"properties":2000},"b38950fd-874e-48ff-b519-8b493c62fb86",[137],[1980,1990],{"id":22,"sortIndex":23,"affiliation":1981,"properties":22},{"id":1982,"createTime":1983,"updateTime":1984,"relativeEntities":1985,"slug":1986,"properties":1987,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"167e3838-b630-4b25-bda1-2f1fc525cfff","2024-01-09T17:05:39.231+00:00","2024-11-27T14:13:54.550+00:00",[],"Universidad-Panamericana-Mexico-City-Mexico",{"title":1988},{"VI":1989},"Universidad Panamericana, Mexico City, Mexico",{"id":1991,"sortIndex":152,"affiliation":1992,"properties":1999},"b3491fd6-e789-40fa-a4d3-b171d640a8de",{"id":1993,"createTime":1994,"updateTime":1994,"relativeEntities":1995,"slug":22,"properties":1996,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"8b5eeab7-e96d-4b48-87cc-4f5a87456459","2023-12-07T00:52:33.798+00:00",[],{"title":1997},{"VI":1998},"Institute of Philosophy, Czech Academy of Sciences, Prague, Czech Republic",{},{"title":2001},{"VI":2002},"Nancy Abigail Nuñez",{"id":2004,"sortIndex":152,"researcher":22,"roles":2005,"affiliations":2006,"properties":2015},"4c1b958a-f625-410c-a56b-2f7f9f82898b",[137],[2007],{"id":22,"sortIndex":23,"affiliation":2008,"properties":22},{"id":2009,"createTime":2010,"updateTime":2010,"relativeEntities":2011,"slug":22,"properties":2012,"entityType":51,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"92f4643c-2062-4e6f-be8e-5a1904bb47cb","2024-01-29T19:17:30.540+00:00",[],{"title":2013},{"VI":2014},"Department of Linguistics, University of Utah, Salt Lake City, USA",{"title":2016},{"VI":2017},"Aniello De Santo",{"url":1959,"publisher":2019,"properties":2041},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":2020,"slug":10,"properties":2021,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":2026,"manageAffiliations":2027,"indexDatabases":2028,"url":22,"thumbnailPath":22,"statistic":2036,"gsStatistic":22,"type":109,"analyzePriority":22},[],{"issn":2022,"eissn":2023,"title":2024,"url":2025},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},[],[],[2029],{"id":56,"indexDatabase":2030,"url":69,"indexYears":70,"academicFieldIds":2035,"indexDatabaseRanking":74},{"id":58,"createTime":59,"updateTime":60,"relativeEntities":2031,"label":2032,"description":2033,"key":66,"publicationTags":2034,"standard":22},[],{"EN":63,"VI":63},{"EN":63,"VI":65},[68],[72,73],{"impactFactor":23,"impactFactorByYear":2037,"i10Index":82,"i10IndexLast5Year":83,"totalPublication":84,"totalPublicationByYear":2038,"totalCitation":93,"totalCitationByYear":2039,"totalCitationPerPublication":100,"totalCitationPerPublicationByYear":2040,"hindexLast5Year":108,"hindex":108},{"2019":77,"2020":78,"2021":79,"2022":80,"2023":81},{"2018":86,"2019":87,"2020":88,"2021":89,"2022":90,"2023":91,"2024":92},{"2018":95,"2019":96,"2020":97,"2021":98,"2022":83,"2023":99},{"2018":102,"2019":103,"2020":104,"2021":105,"2022":106,"2023":107},{"pages":2042},{"VOID":2043},"1-13","2024-01-17",2024]