The journal club is loosely focused on mathematical and models as formal theories of cognition, as well as the neurobiological underpinnings of cognition.
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.”
Beginning in October 2018 we will meet from 12:30-2:00 PM most Fridays in PSYC 336 and go through Simon Farrell and Stephan Lewandowsky’s book, Computational Modeling of Cognition and Behavior. For best results, students should read the chapter we’re covering each week and bring their laptops to the meeting so we can do any exercises in R. This is meant to be very informal and for all levels of expertise.
Links to the articles below are for educational purposes only.
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. If you are not currently on the Journal Club list and would like to be added, or would like to receive the journal club materials (code + chapters) email Darrell Worthy at worthyda@.tamu.edu
10/12/18 – Chapter 2: From Words to Models
10/19/18 – Chapter 3: Basic Parameter Estimation Techniques
11/2/18 – Chapter 4: Maximum Likelihood Parameter Estimation
11/9/18 – Chapter 5: Combining Information from Multiple Participants
11/30/18 – Chapter 6: Bayesian Parameter Estimation
12/7/18 – Chapter 7: Bayesian Parameter Estimation
12/14/18 – Chapter 8: Bayesian Parameter Estimation
1/25/19 – Chapter 9: Multilevel or Hierarchical Modeling
2/1/19 – Chapter 10: Model Comparison
2/8/19 – Chapter 11: Bayesian Model Comparison Using Bayes Factors
2/15/19 – Chapter 12: Using Models in Psychology
2/22/19 – Chapter 13: Neural Network Models
3/1/19 – Chapter 14: Models of Choice Response Time
3/8/19 – Chapter 15: Models in Neuroscience
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. Cognition, 173, 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. Cognition, 173, 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
