Reading List

In Spring 2021 Dr. Worthy will be teaching a graduate seminar on decision-making on Fridays (PSYC 689).  Syllabus

Below is the reading list for the course:

1-January 29      Probability Learning 1

Shanks, D.R., Tunney, R.J., & McCarthy, J.D. (2002).  A re-examination of probability matching and rational choice.  Journal of Behavioral Decision-Making, 15, 233-250.pdf

Bower, G.H. (1994). A turning point in mathematical learning theory.  Psychological Review, 101(2), 290-300.  .pdf

Vulkan, N. (2000). An economist’s perspective on probability matching.  Journal of Economic Surveys, 14(1), 101-118.  .pdf


Estes, W.K. (1950). Toward a statistical theory of learning.  Psychological Review, 57(2), 94-107..pdf

2-February 5      Probability Learning 2

Kahneman, D., & Tversky, A. (1972). Subjective probability: A judgment of representativeness.  Cognitive Psychology, 3, 430-454.  .pdf

Estes, W.K. (1976).  The cognitive side of probability learning.  Psychological Review, 83(1), 37-64.  .pdf

Bar-Hillel, M. (1980). The base-rate fallacy in probability judgments.  Acta Psychologica, 44, 211-233.  .pdf


Don, H.J., Otto, A.R., Cornwall, A.C., Davis, T., & Worthy, D.A. (2019). Learning reward frequency over reward probability: A tale of two learning rules.  Cognition, 193, 104042.  .pdf

Estes, W.K. (1976). Some functions of memory in probability learning and choice behavior.  Psychology of Learning and Motivation. .pdf

3-February 12    Framing

Tversky, A., & Kahneman, D. (1981).  The framing of decisions and the psychology of choice.  Science, 211, 453-458.  .pdf

De Martino, B., Kumaran, D., Seymour, B., & Dolan, R.J. (2006). Frames, biases, and rational decisión-making in the human brain. Science, 313, 684-687. .pdf

Levin, I.P., Schneider, S.L., & Gaeth, G.J. (1998). All frames are not created equal: A typology and critical analysis of framing effects.  Organizational Behavior and Human Decision Processes, 76, 149-188. .pdf

4-February 19    Judgment and Confidence

Einhorn, H.J., & Hogarth, R.M. (1978). Confidence in Judgment: Persistence of the illusion of validity.  Psychological Review, 85 (5), 395-416. .pdf

Dawes, R.M., Faust, D., & Meehl, P.E. (1989). Clinical versus actuarial judgment.  Science, 243, 1668-1674. .pdf

Dawes, R.M. (1979). The robust beauty of improper linear models in decision-making.  American Psychologist, 34, 571-582. .pdf


Grove, W.M., Zald, D.H., Lebow, B.S., Snitz, B.E., & Nelson, C. (2000). Clinical versus mechanical prediction: A meta-analysis.  Psychological Assessment, 12(1), 19-30.  .pdf

5-February 26    Intertemporal Choice

McClure, S.M., Laibson, D.I., Loewenstein, G., & Cohen, J.D. (2004). Separate neural systems value immediate and delayed monetary rewards. Science, 306, 503-507.  .pdf

Otto, A.R., Markman, A.B., && Love, B.C. (2012). Taking more now: The optimality of impulsive choice hinges on environment structure.  Social Psychological and Personality Sciences 3(2), 131-138.  .pdf

Peters, J., & Buchel, C. (2010).  Episodic future thinking reduces reward delay discounting through and enhancement of prefrontal-mediotemporal interactions.  Neuron, 66(1), 138-148.  .pdf


Figner, B., Knoch, D., Johnson, E.J., Krosch, A.R., Lisanby, S.H., Feher, E., et al. (2010).  Lateral prefrontal cortex and self-control in intertemporal choice.  Nature Neuroscience, 13 (5), 538-539. .pdf

6-March 5          Memory

Gershman, S.J., & Daw, N.D. (2017). Reinforcement learning and episodic memory in humans and animals: An integrative framework.  Annual Review of Psychology, 68, 101-128.  .pdf

Mattar, M., & Daw, N.D. (2018).  Prioritized memory access explains planning and hippocampal replay.  Nature Neuroscience.  .pdf

Shadlen, M.N., & Shohamy, D. (2016). Decision making and sequential sampling from memory.  Neuron, 90, 927-939. .pdf

7-March 12        Habitual versus Goal-Directed Choice

Rangel, A., Camerer, C., & Montague, P.R. (2008). A framework for studying the neurobiology of value-based decision making. Nature Reviews Neuroscience, 9, 545-556.  .pdf

Linnebank, F.E., Kindt, M., & de Wit, S. (2018).  Investigating the balance between goal-directed and habitual control in experimental and real-life settings.  Learning & Behavior, 46(3), 306-319.  .pdf

Chen, Z., Veling, H., Diksterhuis, A. & Holland, R.W. (2016).  How does not responding to appetitive stimuli cause devaluation: Evaluative conditioning or response inhibition?  Journal of Experimental Psychology: General, 145(12), 1687-1701.  .pdf


Sloman, S.A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119, 3-22.  .pdf

Daw, N.D., Niv, Y., & Dayan, P. (2005).  Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nature Neurosciences, 8, 1704-1711.  .pdf

March 19 – Spring Break 

8-March 26         Exploration/Exploitation

Daw, N.D., O’Doherty, J.P., Dayan, P., Seymour, B., & Dolan, R.J. (2006). Cortical substrates for exploratory decisions in humans.  Nature, 441, 876-879.  .pdf

Otto, A.R., Knox, W.B., Markman, A.B., & Love, B.C. (2014). Physiological and behavioral signatures of reflective exploratory choice.  Cognitive, Affective, & Behavioral Neuroscience, 14(4), 1167-1183.  .pdf

Wilson, R.C., Geana, A., White, J.M., Ludvig, E.A., & Cohen, J.D. (2014).  Humans use directed and random exploration to solve the explore-exploit dilemma.  Journal of Experimental Psychology: General, 143(6). 2074-2081.  .pdf


Cohen, J.D., McClure, S.M., & Yu, A.J. (2007). Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Philosophical Transactions of the Royal Society B, 362, ­933-942.  .pdf

Frank, M.J., Doll, B.B., Oas-Terpstra, J., & Moreno, F. (2009). Prefrontal and striatal dopaminergic genes predict individual differences in exploration and exploitation.  Nature Neuroscience, 12, 1062-1068.  .pdf

April 2                         Reading Day, no classes

9-April 9             “Real-world” Decisions

Schulz, E., Bhui, R., Love, B.C., Brier, B., Todd, M.T., & Gershman, S.J. (2019). Structured, uncertainty-driven exploration in real world consumer choice.  Proceedings of the National Academy of Sciences, 116 (28), 13903-13908.  .pdf

Thorstad, R., & Wolff, P. (2018). A big data analysis of the relationship between future thinking and decision-making.  Proceedings of the National Academy of Sciences, 115(8), 1740-1748.  .pdf

Neiman, T., & Loewenstein, Y. (2011). Reinforcement learning in professional basketball players. Nature Communications, 2(1), 569.  .pdf


Otto, A.R., Fleming, S.M., & Glimcher, P.W. (2016). Unexpected but incidental positive outcomes predict real-world gambling.  Psychological Science, 277(3), 299-311. .pdf

10-April 16           Emotion

Bechara, A. (2004). The role of emotion in decision-making: Evidence from neurological patients with orbitofrontal damage.  Brain & Cognition, 55, 30-40.  .pdf

Seymour, B., & Dolan, R. (2008). Emotion, Decision Making, and the Amygdala. Neuron, 58, 662-671.  .pdf

Wu, Y., van Dijk, E., & Clark, L. (2015). Near-wins and near-losses in gambling: A behavioral and facial EMG study.  Psychophysiology, 52(3), 359-366.  .pdf


Loewenstein, G.F., Weber, E.U., Hsee, C.K., & Welch, N. (2001). Risk as feelings. Psychological Bulletin, 127, 267-286.  .pdf

11-April 23           Effort

Westbrook, A., Bosch, R., van den Maata, J.I., Hofmans, L., Papadopetraki, D., Cools, R., & Frank, M.J. (2020). Dopamine promotes cognitive effort by biasing the benefits versus costs of cognitive work.  Science, 367(6484), 1362-1366.  .pdf

Otto, A.R., & Vassena, E. (2020). It’s all relative: Reward-induced cognitive control modulation depends on context.  Journal of Experimental Psychology: General.  .pdf

Chong, T.T.-J., Apps, M., Giehl, K., Sillence, A., Grima, L.L., & Husain, M. (2017).  Neurocomputational mechanisms underlying subjective valuation of effort costs.  PLOS Biology, 15(2), e1002598.  .pdf

Older readings from our journal club from 2017-2019


Estes, W.K. (1976). Some functions of memory in probability learning and choice behavior.  Psychology of Learning and Motivation. .pdf

Wilson, R.C., Shenhav, A., Straccia, M., & Cohen, J.D. (2019).  The eighty five percent rule for optimal learning.  Nature Communications.  .pdf

Lohse, K.R., Miller, M.W., Daou, M., Valerius, W., & Jones, M. (2020).  Dissociating the contributions of reward-prediction errors to trial-level adaptation and long-term learning.  Biological Psychology. .pdf

Radulescu, A., Niv, Y., & Ballard, I. (2019).  Holistic reinforcement learning: The role of structure and attention.  Trends in Cognitive Sciences, 23 (4), 278-292. .pdf

Schedule for Fall 2019:

12/3/19 Chapter 5 of Computational Modeling book

11/19/19 Chapter 4 of Computational Modeling book – sections 4.3 – 4.5

11/5/19 Leong, Y.C., Radulescu, A., Daniel, R., DeWoskin, V., & Niv, Y. (2017).  Dynamic Interaction between Reinforcement Learning and Attention in Multidimensional Environment.  Neuron, 93, 451-463.  .pdf

The Leong et al., article above is the target article.  One thing we will discuss is how (or whether) the model presented in the above article differs from Kruschke’s Alcove model.  They may be very similar, but come from different perspectives – computational neuroscience (Leong) versus cognitive psychology (Kruschke), and focus on slightly different paradigms – decision-making versus category learning.

Kruschke, J.K. (1992).  ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99(1), 22-44.  .pdf

11/4/19 Journal club presentation in Cognoscenti on Compensation, Maintenance, and Reserve.

Cabeza et al. (2018).  Maintenance, reserve, and compensation: the cognitive neuroscience of healthy ageing.  Nature Reviews Neuroscience.  .pdf

Stern et. al (2018). Whitepaper: Defining and investigating cognitive reserve, brain reserve, and brain maintenance.  Alzheimer’s & Dementia.  .pdf

Note: Why does the Stern et al. paper above have no references?  Why would a journal publish a “Review Article” without referencing any of the papers being reviewed? 


Chapter 4 of Computational Modeling book – sections 4.3 – 4.5


Lee, S., Gold, J.I., & Kable, J.W. (2019).  The Human as Delta-Rule Learner. Decision.  .pdf


Chapter 4 of Computational Modeling book – sections 4.1 – 4.2


Inkster, A.B., Mitchell, C.J., Schlegelmilch, R., & Wills, A.J. (2020).  Effect of a context shift on the inverse base-rate effect.  Open Journal of Experimental Psychology and Neuroscience.  .pdf

*note we will read the above journal article instead of the thesis below, because it is not as long and we only have so much time

Inkster, A. (2019). Attention, context, and the inverse base rate effect.  Doctoral Dissertation. University of Plymouth. .pdf 

Don, H.J., Beesley, T., & Livesey, E.J. (2019). Learned predictiveness models predict opposite attention biases in the inverse Base-Rate Effect.  .pdf

Possibly model IBRE data.


Chapter 3 of Computational Modeling book


Hartley, C.A., & Phelps, E.A. (2012). Anxiety and decision-making.  Biological Psychiatry, 72, 113-118.  .pdf

Will possibly model some anxiety-decision-making data


Chapter 2 of Computational Modeling

Iowa Gambling Task or other RL task simulation


Bornstein, A..M., Khaw, M.W., Shohamy, D.., & Daw, N.D. (2017).  Reminders of past choices bias decisions for reward in humans. Nature Communications, 8:15958.  .pdf

Notes on Gershman & Daw, 2017, and Bornstein et al., 2017

Gershman, S.J., & Daw, N.D. (2017).  Reinforcement learning and episodic memory in humans and animals: An Integrative Framework.  Annual Review of Psychology, 68, 101-28. .pdf


Chapter 1 of Computational Modeling of Cognition and Behavior

Iowa Gambling Task Modeling


Hotaling, J.M., Jarvstad, A., Donkin, C., & Newell, B.R. (in press). How to change the weight of rare events in decisions from experience.  Psychological Science.pdf


Chapter 1 of Computational Modeling of Cognition and Behavior

Iowa Gambling Task Modeling

Other articles to read…

Newell, A. (1973).  You can’t play 20 questions with nature and win.  In W.G. Chase (ed.) Visual Information Processing, New York: Academic Press.  .pdf

Kosslyn, S.M. (2006). You can play 20 questions with nature and win: Categorical versus coordinate spatial relations as a case study. Neuropsychologia, 44, 1519-1523. .pdf

Katahira, K. (2018). The statistical structures of reinforcement learning with asymmetric value updates.  Journal of Mathematical Psychology, 87, 31-45.  .pdf

Lefebvre, G., Lebreton, M., Meyniel, F., Bourgeois-Gironde, S., & Palmintieri, S. (2017). Behavioral and neural characterization of optimistic reinforcement learning.  Nature Human Behavior.  .pdf Supp

Notes on Lefebvre 2017 and Katahira 2018, which present conflicting explanations for a “positivity bias” for positive versus negative prediction errors.

Estes, W.K. (1964). All-or-none processes in learning and retention. American Psychologist, 19(1), 16-25. Notes .pdf

Platt, J.R. (1964).  Strong inference. Science 146, 347-353. .pdf  – Nice philosophy of science paper on how not all scientific methods are equal.  “What I am saying is that, in numerous areas that we call science, we have come to like our habitual ways, and our studies that can be continued indefinitely. We measure, we define, we compute, we analyze, but we do not exclude. And this is not the way to use our minds most effectively or to make the fastest progress in solving scientific questions.”

I highly recommend Simon Farrell and Stephan Lewandowsky’s book, Computational Modeling of Cognition and Behavior.  

R code for the book is available here.

A folder with the code from the book and .pdf files for each chapter will be distributed via email.

2017-2018 Readings (reverse ordered):

5/11/18 Gonzalez, C., & Dutt, V. (2011).  Instance-based learning: Integrating sampling and repeated decision from experience.  Psychological Review.  .pdf

5/4/18 Stewart, N., Chater, N., & Brown, G.D.A. (2006).  Decision by sampling.  Cognitive Psychology, 53, 1-26.  .pdf

4/27/18 Kool, W., Gershman, S.J., & Cushman, F.A. (2017).  Cost-benefit arbitration between multiple reinforcement-learning systems.  Psychological Science, 28, 1321-1333. .pdf

4/13/18 Suppes, P.  Estes’ Statistical Learning Theory: Past, Present, and Future.  A.F. Healy, S.M. Kosslyn, R.M. Shiffrin (Eds.), From Learning Theory to Connectionist Theory: Essays in Honor of William K. Estes, Vol.1. Hillsdale, NJ: Lawrence Erlbaum, 1992, pp. 1-20.  .pdf

4/6/18 Rigoli, F., Mathys, C., Friston, K.J., Dolan, R.J. A unifying Bayesian account of contextual effects in value-based choice.  PLOS Computational Biology.  .pdf

3/23/18 Miller, K.J., Botvinick, M.M., & Brody, C.D. (2018). Value representations in orbitofrontal cortex drive learning, but not choice.  bioRxiv. .pdf

Roesch, M.R., Esber, G. R., Li, J., Daw, N.D., & Schoenbaum, G. (2012).  Suprise! Neural correlates of Pearce-Hall and Rescorla-Wagner coexist within the brain.  European Journal of Neuroscience, 35, 1190-1200. .pdf

3/9/18 Sutton, R.S., & Barto, A.G. (1981). Toward a modern theory of adaptive networks: Expectation and prediction. Psychological Review, 88, 135-170. .pdf

3/2/18 Pearce, J.M., & Hall, G. (1980). A model for Pavlovian learning: Variations in the effectiveness of conditioned but not of unconditioned stimuli.  Psychological Review, 87, 532-552. .pdf

Diederen, K.M.J. , Spencer, T., Vestergaard, M.D., Fletcher, P.C., & Schultz, S. (2016). Adaptive prediction error coding in the human midbrain and striatum facilitates behavioral adaptation and learning efficiency. Neuron, 90, 1127-1138. .pdf

2/23/18 Gershman, S.J. (2018). Deconstructing the human algorithms for exploration.  Cognition173, 34-42. .pdf

Shiffrin, R.M. (2010). Perspectives on modeling in cognitive science.  Topics in Cognitive Science, 2, 736-750.  .pdf

2/16/18 Gershman, S.J. (2018). Deconstructing the human algorithms for exploration.  Cognition173, 34-42. .pdf

Shiffrin, R.M. (2010). Perspectives on modeling in cognitive science.  Topics in Cognitive Science, 2, 736-750.  .pdf

(Discussed models and simulations; never got to the readings)

2/9/18 Erev, I., Ert, E., Plonsky, O., Cohen, D., & Cohen, O. (2017). From anomalies to forecasts: Toward a descriptive model of decisions under risk, under ambiguity, and from experience.  Psychological Review, 124 (4), 369-409.  .pdf

2/2/18 Nosofsky, R.M. (1991).  Tests of an exemplar model for relating perceptual classification and recognition memory.  Journal of Experimental Psychology: Human Perception and Performance, 17 (1), 3-27.  .pdf

Shepard, R.N. (1987).  Toward a universal law of generalization in psychological science.  Science, 237 (4820), 1317-1323. .pdf

1/26/18 Peckham, A.D., & Johnson, S.L. (2015).  Spontaneous eye-blink rate as an index of reward responsivity: Validation and links to bipolar disorder.  Clinical Psychological Science.  .pdf

Anderson, B.A. et. al. (2017).  Linking dopaminergic reward signals to the development of attentional bias: A positron emission study. Neuroimage, 157, 27-33. .pdf

12/21/17 Yechiam, E. & Ert, E. (2007).  Evaluating the reliance on past choices in adaptive learning models.  .pdf

12/8/17 Estes, W.K. (1976).  The cognitive side of probability learning.  Psychological Review, 83, (1), 37-64. .pdf

12/1/17 Turner et al. (2017). Approaches to analysis in model-based cognitive neuroscience.  Journal of Mathematical Psychology, 76 Part B, 65-79. .pdf

Love, B.C. (2017).  Cognitive models as bridge between brain and behavior.  Trends in Cognitive Sciences, 1545.  .pdf

11/17/17 Lee et al. (Psycharxiv).  Deep neural networks as Gaussian processes. .pdf

 Bush, R.R., & Mosteller, F. (1951).  A mathematical model for simple learning.  Psychological Review.  .pdf

Aitken, M.R.F., & Dickinson, A. (2005).  Simulations of a modified SOP model applied to retrospective revaluation of human causal learning. Learning & Behavior, 33(2), 147-159. .pdf

11/10/17 Psychonomics – no meeting but we’ll read extra for 11/17

11/3/17 Estes, W.K. (1950).  Toward a Statistical Theory of Learning.  Psychological Review. .pdf

10/27/17 McClaren & Mackintosh (2000).  An elemental model of associative learning: I. Latent inhibition and perceptual learning.  Animal Learning & Behavior, 28 (3), 211-246. .pdf

10/20/17 McClaren & Mackintosh (2002). Associative learning and elemental representation: II Generalization and discrimination.  Animal Learning and Behavior. .pdf

10/13/17 No meeting because of Armadillo conference

10/6/17 Esber & Haselgrove (2011). Reconciling the influence of predictiveness and uncertainty on stimulus salience: A model of attention in associative learning.  Proceedings of the Royal Society B.  SuppMs  .pdf

Mondragon, Alonso, & Kokkola (2017). Associative learning should go deep.  Trends in Cognitive Sciences. .pdf

9/29/17 Nasser, Calu, Schoenbaum, & Sharpe (2017).  The dopamine prediction error: Contributions to associative models of reward learning.  Frontiers in Psychology.  .pdf

9/22/17 Navarro, Newell, & Schultze (2016). Learning and choosing in an uncertain world: An investigation of the explore-exploit dilemma in static and dynamic environments.  Cognitive Psychology.  .pdf