What Happens After a Fast Versus Slow Error, and How Does It Relate to Evidence Accumulation?
Tóm tắt
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.
Tài liệu tham khảo
Alexander, W. H., & Brown, J. W. (2010). Computational models of performance monitoring and cognitive control. Topics in Cognitive Science, 2(4), 658–677. https://doi.org/10.1111/j.1756-8765.2010.01085.x
Boag, 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://doi.org/10.1016/j.cognition.2019.05.011
Boag, 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.
Bogacz, 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.
Brooks, S. P., & Gelman, A. (1998). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7(4), 434–455.
Brown, S. D., & Heathcote, A. (2008). The simplest complete model of choice response time: Linear ballistic accumulation. Cognitive Psychology, 57(3), 153–178. https://doi.org/10.1016/j.cogpsych.2007.12.002
Damaso, K., Williams, P., & Heathcote, A. (2020). Different types of errors and post-error changes. Psychonomic Bulletin & Review.
Danielmeier, 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://doi.org/10.1523/jneurosci.4299-10.2011
Donkin, 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://doi.org/10.3758/pbr.16.6.1129
Donkin, 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.
Donkin, 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)
Dutilh, 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://doi.org/10.3758/s13414-011-0243-2
Dutilh, 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://doi.org/10.1016/j.jmp.2012.04.001
Dutilh, 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://doi.org/10.1037/a0029875
Greifeneder, 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://doi.org/10.1177/1088868310367640
Gunawan, D. E., Hawkins, G., Kohn, R., Tran, M. N., & Brown, S. D. (in press). Time-evolving psychological processes over repeated decisions. Psychological Review.
Hajcak, G., & Simons, R. F. (2002). Error-related brain activity in obsessive–compulsive undergraduates. Psychiatry Research, 110(1), 63–72. https://doi.org/10.1016/S0165-1781(02)00034-3
Hajcak, 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://doi.org/10.1111/1469-8986.00107
Heathcote, 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/Revue canadienne de psychologie expérimentale, 66(2), 125–136. https://doi.org/10.1037/a0028189
Heathcote, 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://doi.org/10.3758/s13428-018-1067-y
King, 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://doi.org/10.1523/jneurosci.3274-10.2010
Klauer, K. C. (2010). Hierarchical multinomial processing tree models: A latent–trait approach. Psychometrika, 75, 70–98.
Laming, D. R. J. (1968). Information theory of choice-reaction times. Academic Press.
Laming, D. R. J. (1979). Choice reaction performance following an error. Acta Psychologica, 43(3), 199–224. https://doi.org/10.1016/0001-6918(79)90026-X
Leite, F. P., & Ratcliff, R. (2010). Modeling reaction time and accuracy of multiple-alternative decisions. Attention, Perception, & Psychophysics, 72(1), 246–273. https://doi.org/10.3758/app.72.1.246
Luce, R. D. (1986). Response times: Their role in inferring mental organization. Oxford University Press, Clarendon Press.
Matzke, 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.
Nieuwenhuis, 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://doi.org/10.1111/1469-8986.3850752
Notebaert, 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://doi.org/10.1016/j.cognition.2009.02.002
Osth, 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://doi.org/10.1016/j.jml.2017.04.003
Purcell, B. A., & Kiani, R. (2016). Neural mechanisms of post-error adjustments of decision policy in parietal cortex. Neuron, 89(3), 658–671. https://doi.org/10.1016/j.neuron.2015.12.027
Rabbitt, P. M. A. (1966). How old and young subjects monitor and control responses for accuracy and speed. Journal of Experimental Psychology, 71, 264–272.
Rabbitt, 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://doi.org/10.1080/14640747708400645
Rae, 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.
Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review March, 85(2), 59–108.
Ratcliff, R. (2008). The EZ diffusion method: Too EZ? Psychonomic Bulletin & Review, 15(6), 1218–1228. https://doi.org/10.3758/PBR.15.6.1218
Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: theory and data for two-choice decision tasks. Neural Computation, 20(4), 873–922. https://doi.org/10.1162/neco.2008.12-06-420.
Ratcliff, R., & Rouder, J. N. (1998). Modeling response times for two-choice decisions. Psychological Science, 9(5), 347–356.
Ratcliff, 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://doi.org/10.3758/BF03196302
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. https://doi.org/10.1016/j.cogpsych.2018.02.002
Roberts, 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://doi.org/10.1016/j.tics.2019.04.005
Rouder, J. N., & Haaf, J. M. (2019). A psychometrics of individual differences in experimental tasks. Psychonomic Bulletin and Review, 26(2), 452–467. https://doi.org/10.3758/s13423-018-1558-y
Schiffler, B. C., Bengtsson, S. L., & Lundqvist, D. (2017). The sustained influence of an error on future decision-making. Frontiers in Psychology, 8(1077). https://doi.org/10.3389/fpsyg.2017.01077
Schouten, J. F., & Bekker, J. A. M. (1967). Reaction time and accuracy. Acta Psychologica, 27, 143–153. https://doi.org/10.1016/0001-6918(67)90054-6
Spiegelhalter, 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://doi.org/10.1111/rssb.12062
Stone, M. (1960). Models for choice-reaction time. Psychometrika, 25(3), 251–260.
Strickland, 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.
Turner, 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.
Ullsperger, M., Danielmeier, C., & Jocham, G. (2014). Neurophysiology of performance monitoring and adaptive behavior. Physiological Reviews, 94(1), 35–79. https://doi.org/10.1152/physrev.00041.2012
van 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://doi.org/10.3758/s13423-016-1081-y
van 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://doi.org/10.1177/155005940603700411
Wagenmakers, 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://doi.org/10.3758/BF03194023
Wessel, J. R. (2018). An adaptive orienting theory of error processing. Psychophysiology, 55(3), e13041. https://doi.org/10.1111/psyp.13041
White, 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://doi.org/10.1037/a0034851
White, 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://doi.org/10.1016/j.jmp.2010.01.004
Wiecki, T. V., Sofer, I., & Frank, M. J. (2013). HDDM: hierarchical Bayesian estimation of the drift-diffusion model in python. Frontiers in Neuroinformatics, 7. https://doi.org/10.3389/fninf.2013.00014/abstract
Williams, P., Heathcote, A., Nesbitt, K., & Eidels, A. (2016). Post-error recklessness and the hot hand. Judgment and Decision Making, 11(2), 174–184.