The journal club is loosely focused on mathematical and models as formal theories of cognition, as well as the neurobiological underpinnings of cognition. Sometimes this becomes a sort of virtual journal club when we are not been meeting regularly. For some articles I will post notes on what I think are some interesting things about a given paper.
Links to the articles below are for educational purposes only. Notes are just my opinions.
For journal club meetings that will resume in August 2019 – if you would like to be added to the email list contact Darrell Worthy at worthyda@.tamu.edu
Schedule for Fall 2019:
11/12/19 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???!!! What is going on? Why would a journal publish a “Review Article” without referencing any of the papers being reviewed? (The answer may be that this will increase the journal’s impact factor).
10/29/19
Chapter 4 of Computational Modeling book – sections 4.3 – 4.5
10/22/19
Lee, S., Gold, J.I., & Kable, J.W. (2019). The Human as Delta-Rule Learner. Decision. .pdf
10/15/19
Chapter 4 of Computational Modeling book – sections 4.1 – 4.2
10/8/19
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.
10/1/19
Chapter 3 of Computational Modeling book
9/24/19
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
9/17/19
Chapter 2 of Computational Modeling
Iowa Gambling Task or other RL task simulation
9/10/19
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
9/3/19
Chapter 1 of Computational Modeling of Cognition and Behavior
Iowa Gambling Task Modeling
8/27/19
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
9/3/19
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. 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
