Journal Club

Our lab meets each week to discuss experiments, data, and other issues.  We hold a journal club from 12:00 – 1:00 PM in PSYC 336 to talk about new and classic papers and facilitate thought about new experiments, and increase our knowledge of what other labs are doing.  Researchers from other labs are welcome to attend.  The journal club is loosely focused on mathematical and models as formal theories of cognition, as well as the neurobiological underpinnings of cognition.

Links to the articles below are for educational purposes only.

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