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2018


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Rational metareasoning and the plasticity of cognitive control

Lieder, F., Shenhav, A., Musslick, S., Griffiths, T. L.

PLOS Computational Biology, 14(4):e1006043, Public Library of Science, April 2018 (article)

Abstract
The human brain has the impressive capacity to adapt how it processes information to high-level goals. While it is known that these cognitive control skills are malleable and can be improved through training, the underlying plasticity mechanisms are not well understood. Here, we develop and evaluate a model of how people learn when to exert cognitive control, which controlled process to use, and how much effort to exert. We derive this model from a general theory according to which the function of cognitive control is to select and configure neural pathways so as to make optimal use of finite time and limited computational resources. The central idea of our Learned Value of Control model is that people use reinforcement learning to predict the value of candidate control signals of different types and intensities based on stimulus features. This model correctly predicts the learning and transfer effects underlying the adaptive control-demanding behavior observed in an experiment on visual attention and four experiments on interference control in Stroop and Flanker paradigms. Moreover, our model explained these findings significantly better than an associative learning model and a Win-Stay Lose-Shift model. Our findings elucidate how learning and experience might shape people’s ability and propensity to adaptively control their minds and behavior. We conclude by predicting under which circumstances these learning mechanisms might lead to self-control failure.

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Rational metareasoning and the plasticity of cognitive control DOI Project Page Project Page [BibTex]

2018


Rational metareasoning and the plasticity of cognitive control DOI Project Page Project Page [BibTex]


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Over-Representation of Extreme Events in Decision Making Reflects Rational Use of Cognitive Resources

Lieder, F., Griffiths, T. L., Hsu, M.

Psychological Review, 125(1):1-32, January 2018 (article)

Abstract
People’s decisions and judgments are disproportionately swayed by improbable but extreme eventualities, such as terrorism, that come to mind easily. This article explores whether such availability biases can be reconciled with rational information processing by taking into account the fact that decision-makers value their time and have limited cognitive resources. Our analysis suggests that to make optimal use of their finite time decision-makers should over-represent the most important potential consequences relative to less important, put potentially more probable, outcomes. To evaluate this account we derive and test a model we call utility-weighted sampling. Utility-weighted sampling estimates the expected utility of potential actions by simulating their outcomes. Critically, outcomes with more extreme utilities have a higher probability of being simulated. We demonstrate that this model can explain not only people’s availability bias in judging the frequency of extreme events but also a wide range of cognitive biases in decisions from experience, decisions from description, and memory recall.

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DOI [BibTex]

DOI [BibTex]


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A physical model for efficient ranking in networks

De Bacco, C., Larremore, D. B., Moore, C.

Science Advances, 4(7), American Association for the Advancement of Science, 2018 (article)

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Code Preprint link (url) DOI Project Page [BibTex]

Code Preprint link (url) DOI Project Page [BibTex]


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AreWater Smart Landscapes’ Contagious? An epidemic approach on networks to study peer effects

Brelsford, C., De Bacco, C.

Networks and Spatial Economics (NETS), pages: 1572-9427, 2018 (article)

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Preprint link (url) [BibTex]

Preprint link (url) [BibTex]


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The Computational Challenges of Pursuing Multiple Goals: Network Structure of Goal Systems Predicts Human Performance

Reichman, D., Lieder, F., Bourgin, D. D., Talmon, N., Griffiths, T. L.

PsyArXiv, 2018 (article)

Abstract
Extant psychological theories attribute people’s failure to achieve their goals primarily to failures of self-control, insufficient motivation, or lacking skills. We develop a complementary theory specifying conditions under which the computational complexity of making the right decisions becomes prohibitive of goal achievement regardless of skill or motivation. We support our theory by predicting human performance from factors determining the computational complexity of selecting the optimal set of means for goal achievement. Following previous theories of goal pursuit, we express the relationship between goals and means as a bipartite graph where edges between means and goals indicate which means can be used to achieve which goals. This allows us to map two computational challenges that arise in goal achievement onto two classic combinatorial optimization problems: Set Cover and Maximum Coverage. While these problems are believed to be computationally intractable on general networks, their solution can be nevertheless efficiently approximated when the structure of the network resembles a tree. Thus, our initial prediction was that people should perform better with goal systems that are more tree-like. In addition, our theory predicted that people’s performance at selecting means should be a U-shaped function of the average number of goals each means is relevant to and the average number of means through which each goal could be accomplished. Here we report on six behavioral experiments which confirmed these predictions. Our results suggest that combinatorial parameters that are instrumental to algorithm design can also be useful for understanding when and why people struggle to pursue their goals effectively.

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DOI [BibTex]

DOI [BibTex]

2017


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Strategy selection as rational metareasoning

Lieder, F., Griffiths, T. L.

Psychological Review, 124, pages: 762-794, American Psychological Association, November 2017 (article)

Abstract
Many contemporary accounts of human reasoning assume that the mind is equipped with multiple heuristics that could be deployed to perform a given task. This raises the question of how the mind determines when to use which heuristic. To answer this question, we developed a rational model of strategy selection, based on the theory of rational metareasoning developed in the artificial intelligence literature. According to our model people learn to efficiently choose the strategy with the best cost–benefit tradeoff by learning a predictive model of each strategy’s performance. We found that our model can provide a unifying explanation for classic findings from domains ranging from decision-making to arithmetic by capturing the variability of people’s strategy choices, their dependence on task and context, and their development over time. Systematic model comparisons supported our theory, and 4 new experiments confirmed its distinctive predictions. Our findings suggest that people gradually learn to make increasingly more rational use of fallible heuristics. This perspective reconciles the 2 poles of the debate about human rationality by integrating heuristics and biases with learning and rationality. (APA PsycInfo Database Record (c) 2017 APA, all rights reserved)

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DOI Project Page [BibTex]

2017


DOI Project Page [BibTex]


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Empirical Evidence for Resource-Rational Anchoring and Adjustment

Lieder, F., Griffiths, T. L., Huys, Q. J. M., Goodman, N. D.

Psychonomic Bulletin \& Review, 25, pages: 775-784, Springer, May 2017 (article)

Abstract
People’s estimates of numerical quantities are systematically biased towards their initial guess. This anchoring bias is usually interpreted as sign of human irrationality, but it has recently been suggested that the anchoring bias instead results from people’s rational use of their finite time and limited cognitive resources. If this were true, then adjustment should decrease with the relative cost of time. To test this hypothesis, we designed a new numerical estimation paradigm that controls people’s knowledge and varies the cost of time and error independently while allowing people to invest as much or as little time and effort into refining their estimate as they wish. Two experiments confirmed the prediction that adjustment decreases with time cost but increases with error cost regardless of whether the anchor was self-generated or provided. These results support the hypothesis that people rationally adapt their number of adjustments to achieve a near-optimal speed-accuracy tradeoff. This suggests that the anchoring bias might be a signature of the rational use of finite time and limited cognitive resources rather than a sign of human irrationality.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Community detection, link prediction, and layer interdependence in multilayer networks

De Bacco, C., Power, E. A., Larremore, D. B., Moore, C.

Physical Review E, 95(4):042317, APS, 2017 (article)

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Code Preprint link (url) Project Page [BibTex]

Code Preprint link (url) Project Page [BibTex]


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A computerized training program for teaching people how to plan better

Lieder, F., Krueger, P. M., Callaway, F., Griffiths, T. L.

PsyArXiv, 2017 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Toward a rational and mechanistic account of mental effort

Shenhav, A., Musslick, S., Lieder, F., Kool, W., Griffiths, T., Cohen, J., Botvinick, M.

Annual Review of Neuroscience, 40, pages: 99-124, Annual Reviews, 2017 (article)

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Project Page [BibTex]

Project Page [BibTex]


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The anchoring bias reflects rational use of cognitive resources

Lieder, F., Griffiths, T. L., Huys, Q. J. M., Goodman, N. D.

Psychonomic Bulletin \& Review, 25, pages: 762-794, Springer, 2017 (article)

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[BibTex]

[BibTex]