Computational Brain & Behavior

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When Does an Individual Accept Misinformation? An Extended Investigation Through Cognitive Modeling
Computational Brain & Behavior - Tập 5 - Trang 244-260 - 2022
David Borukhson, Philipp Lorenz-Spreen, Marco Ragni
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 compara...... hiện toàn bộ
Promoting Cumulation in models of the human mind
Computational Brain & Behavior - Tập 2 - Trang 157-159 - 2019
Glenn Gunzelmann
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 emphas...... hiện toàn bộ
Preregistration of Modeling Exercises May Not Be Useful
Computational Brain & Behavior - Tập 2 - Trang 179-182 - 2019
Steven N. MacEachern, Trisha Van Zandt
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 po...... hiện toàn bộ
Extensions of Multivariate Dynamical Systems to Simultaneously Explain Neural and Behavioral Data
Computational Brain & Behavior - - 2020
Qingfang Liu, Alexander Petrov, Zhong-Lin Lü, B. E. Turner
Alleviating the Cold Start Problem in Adaptive Learning using Data-Driven Difficulty Estimates
Computational Brain & Behavior - Tập 4 - Trang 231-249 - 2021
Maarten van der Velde, Florian Sense, Jelmer Borst, Hedderik van Rijn
An adaptive learning system offers a digital learning environment that adjusts itself to the individual learner and learning material. By refining its internal model of the learner and material over time, such a system continually improves its ability to present appropriate exercises that maximise learning gains. In many cases, there is an initial mismatch between the internal model and the learne...... hiện toàn bộ
When Fixed and Random Effects Mismatch: Another Case of Inflation of Evidence in Non-Maximal Models
Computational Brain & Behavior - Tập 6 Số 1 - Trang 84-101 - 2023
João Veríssimo
AbstractMixed-effects models that include both fixed and random effects are widely used in the cognitive sciences because they are particularly suited to the analysis of clustered data. However, testing hypotheses about fixed effects in the presence of random effects is far from straightforward and a set of best practices is still lacking. In the target article, va...... hiện toàn bộ
Transfer of Learned Opponent Models in Zero Sum Games
Computational Brain & Behavior - Tập 5 - Trang 326-342 - 2022
Ismail Guennouni, Maarten Speekenbrink
Human learning transfer abilities take advantage of important cognitive building blocks such as an abstract representation of concepts underlying tasks and causal models of the environment. One way to build abstract representations of the environment when the task involves interactions with others is to build a model of the opponent that may inform what actions they are likely to take next. In thi...... hiện toàn bộ
Commentary on “Robust Modeling in Cognitive Science: Misunderstanding the Goal of Modeling”
Computational Brain & Behavior - Tập 2 - Trang 176-178 - 2019
Richard M. Shiffrin
The article “Robust Modeling in Cognitive Science” (2019) by Lee et al. makes several recommendations about best practices for cognitive science modelers. Many of these are reasonable and will not be discussed in this commentary. I believe several other critically important recommendations either put too much emphasis on less important components of good practice, or are somewhat misguided, and su...... hiện toàn bộ
Cueing Effects in the Attentional Network Test: a Spotlight Diffusion Model Analysis
Computational Brain & Behavior - Tập 1 - Trang 59-68 - 2018
Corey N. White, Ryan Curl
The attentional network test (ANT) uses flanker stimuli with different cue conditions to quantify differences in attentional processing. However, it is unclear precisely how the alerting and orienting cues in the task affect different decision processes. The present study leveraged computational modeling to identify the relationship between attentional cues and decision components. ANT data from a...... hiện toàn bộ
The Experiment is just as Important as the Likelihood in Understanding the Prior: a Cautionary Note on Robust Cognitive Modeling
Computational Brain & Behavior - Tập 2 - Trang 210-217 - 2019
Lauren Kennedy, Daniel Simpson, Andrew Gelman
Cognitive modeling shares many features with statistical modeling, making it seem trivial to borrow from the practices of robust Bayesian statistics to protect the practice of robust cognitive modeling. We take one aspect of statistical workflow—prior predictive checks—and explore how they might be applied to a cognitive modeling task. We find that it is not only the likelihood that is needed to i...... hiện toàn bộ
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