Computational Brain & Behavior

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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
A Sequential Sampling Approach to the Integration of Habits and Goals
Computational Brain & Behavior -
Chao Zhang, Arlette van Wissen, Ron Dotsch, Daniël Lakens, Wijnand A. IJsselsteijn
AbstractHabits often conflict with goal-directed behaviors and this phenomenon continues to attract interests from neuroscientists, experimental psychologists, and applied health psychologists. Recent computational models explain habit-goal conflicts as the competitions between two learning systems, arbitrated by a central unit. Based on recent research that combin...... hiện toàn bộ
A Comparison of Approximations for Base-Level Activation in ACT-R
Computational Brain & Behavior - Tập 1 - Trang 228-236 - 2018
Christopher R. Fisher, Joseph Houpt, Glenn Gunzelmann
Cognitive models provide a principled alternative for hypothesis testing and measuring individual differences. However, many cognitive models are computationally intensive to simulate, making their use difficult. Using approximations can make the application of cognitive models more tractable. We compare the standard and hybrid approximations of the base-level activation equation for the ACT-R cog...... hiện toàn bộ
Not all Speed-Accuracy Trade-Off Manipulations Have the Same Psychological Effect
Computational Brain & Behavior - Tập 3 - Trang 252-268 - 2020
Dimitris Katsimpokis, Guy E. Hawkins, Leendert van Maanen
In many domains of psychological research, decisions are subject to a speed-accuracy trade-off: faster responses are more often incorrect. This trade-off makes it difficult to focus on one outcome measure in isolation – response time or accuracy. Here, we show that the distribution of choices and response times depends on specific task instructions. In three experiments, we show that the speed-acc...... hiện toàn bộ
Over-precise Predictions Cannot Identify Good Choice Models
Computational Brain & Behavior - - 2022
Nisheeth Srivastava, Nisheeth Srivastava
Tổng số: 139   
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