Robert C Wilson
- Interim Director, Cognitive Science
- Associate Professor, Psychology
- Associate Professor, Cognitive Science
- Associate Professor, Cognitive Science - GIDP
- Associate Professor, Evelyn F McKnight Brain Institute
- Associate Professor, Neuroscience - GIDP
- Member of the Graduate Faculty
- Chair, Cognitive Science - GIDP
Contact
- (520) 621-2065
- SPACE SCIENCES, Rm. 165
- TUCSON, AZ 85721-0063
- bob@arizona.edu
Degrees
- Ph.D. Bioengineering
- University of Pennsylvania, Philadelphia, Pennsylvania
- M.S. Bioengineering
- University of Pennsylvania, Philadelphia, Pennsylvania
- B.A. Natural Sciences
- Cambridge University, Cambridge, England
- M.S. Chemistry
- Cambridge University, Cambridge, England
Work Experience
- Princeton University, Princeton, New Jersey (2009 - 2014)
Awards
- Psychonomics Fellow
- Psychonomics society, Fall 2015
Interests
No activities entered.
Courses
2023-24 Courses
-
Directed Research
PSYS 492 (Summer I 2024) -
Directed Research
NSCS 392 (Spring 2024) -
Directed Research
PSY 492 (Spring 2024) -
Directed Research
PSYS 392 (Spring 2024) -
Directed Research
PSYS 492 (Spring 2024) -
Honors Directed Research
NSCS 392H (Spring 2024) -
Honors Independent Study
PSY 299H (Spring 2024) -
Independent Study
PSY 599 (Spring 2024) -
Judgment+Decision Making
PSY 333 (Spring 2024) -
Master's Report
PSY 909 (Spring 2024) -
Modeling the Mind
CGSC 344 (Spring 2024) -
Research
PSY 900 (Spring 2024) -
Directed Research
NSCS 392 (Fall 2023) -
Directed Research
PSYS 392 (Fall 2023) -
Directed Research
PSYS 492 (Fall 2023) -
Honors Directed Research
NSCS 392H (Fall 2023) -
Independent Study
PSY 399 (Fall 2023) -
Independent Study
PSY 499 (Fall 2023) -
Research
PSY 900 (Fall 2023)
2022-23 Courses
-
Directed Research
PSYS 392 (Summer I 2023) -
Independent Study
PSY 399 (Summer I 2023) -
Directed Research
PSYS 392 (Spring 2023) -
Directed Research
PSYS 492 (Spring 2023) -
Dissertation
PSY 920 (Spring 2023) -
Honors Directed Research
PSYS 392H (Spring 2023) -
Independent Study
NSCS 399 (Spring 2023) -
Independent Study
PSY 399 (Spring 2023) -
Independent Study
PSY 499 (Spring 2023) -
Master's Report
PSY 909 (Spring 2023) -
Research
PSY 900 (Spring 2023) -
Directed Research
NROS 392 (Fall 2022) -
Directed Research
PSYS 392 (Fall 2022) -
Directed Research
PSYS 492 (Fall 2022) -
Honors Directed Research
NROS 392H (Fall 2022) -
Honors Directed Research
PSYS 392H (Fall 2022) -
Honors Thesis
PSY 498H (Fall 2022) -
Independent Study
NROS 399 (Fall 2022) -
Independent Study
PSY 399 (Fall 2022) -
Independent Study
PSY 499 (Fall 2022) -
Research
PSY 900 (Fall 2022)
2021-22 Courses
-
Directed Research
PSYS 392 (Summer I 2022) -
Directed Research
PSYS 492 (Summer I 2022) -
Directed Research
PSYS 392 (Spring 2022) -
Directed Research
PSYS 492 (Spring 2022) -
Honors Directed Research
NSCS 392H (Spring 2022) -
Honors Directed Research
NSCS 492H (Spring 2022) -
Honors Thesis
NSCS 498H (Spring 2022) -
Honors Thesis
PSY 498H (Spring 2022) -
Independent Study
NSCS 399 (Spring 2022) -
Independent Study
PSY 399 (Spring 2022) -
Research
PSY 900 (Spring 2022) -
Decisions and the Brain
PSY 433 (Fall 2021) -
Directed Research
NSCS 392 (Fall 2021) -
Directed Research
NSCS 492 (Fall 2021) -
Directed Research
PSYS 392 (Fall 2021) -
Directed Research
PSYS 492 (Fall 2021) -
Dissertation
PSY 920 (Fall 2021) -
Honors Directed Research
NSCS 392H (Fall 2021) -
Honors Directed Research
NSCS 492H (Fall 2021) -
Honors Directed Research
PSYS 392H (Fall 2021) -
Honors Independent Study
NSCS 299H (Fall 2021) -
Honors Thesis
NSCS 498H (Fall 2021) -
Independent Study
NSCS 399 (Fall 2021) -
Independent Study
NSCS 499 (Fall 2021) -
Independent Study
PSY 399 (Fall 2021) -
Modeling the Mind
NSCS 344 (Fall 2021) -
Senior Capstone
NSCS 498 (Fall 2021)
2020-21 Courses
-
Directed Research
PSYS 492 (Summer I 2021) -
Independent Study
PSY 199 (Summer I 2021) -
Directed Research
PSYS 392 (Spring 2021) -
Directed Research
PSYS 492 (Spring 2021) -
Dissertation
PSY 920 (Spring 2021) -
Honors Directed Research
NSCS 492H (Spring 2021) -
Honors Independent Study
NSCS 399H (Spring 2021) -
Honors Thesis
NSCS 498H (Spring 2021) -
Independent Study
NSCS 399 (Spring 2021) -
Independent Study
PSY 199 (Spring 2021) -
Independent Study
PSY 599 (Spring 2021) -
Research
PSY 900 (Spring 2021) -
Systems Neuroscience
NRSC 560 (Spring 2021) -
Directed Research
PSYS 392 (Fall 2020) -
Directed Research
PSYS 492 (Fall 2020) -
Dissertation
PSY 920 (Fall 2020) -
Honors Preceptorship
NSCS 491H (Fall 2020) -
Honors Thesis
NSCS 498H (Fall 2020) -
Independent Study
PSY 499 (Fall 2020) -
Independent Study
PSY 599 (Fall 2020) -
Judgment+Decision Making
PSY 333 (Fall 2020) -
Methods In Neuroscience
NRSC 700 (Fall 2020) -
Modeling the Mind
NSCS 344 (Fall 2020) -
Research
PSY 900 (Fall 2020)
2019-20 Courses
-
Methods In Neuroscience
NRSC 700 (Summer I 2020) -
Directed Research
NSCS 392 (Spring 2020) -
Directed Research
NSCS 492 (Spring 2020) -
Directed Research
PSYS 392 (Spring 2020) -
Directed Research
PSYS 492 (Spring 2020) -
Dissertation
PSY 920 (Spring 2020) -
Honors Directed Research
NSCS 492H (Spring 2020) -
Honors Directed Research
PSYS 392H (Spring 2020) -
Honors Independent Study
NSCS 399H (Spring 2020) -
Independent Study
NSCS 299 (Spring 2020) -
Independent Study
PSY 299 (Spring 2020) -
Independent Study
PSY 399 (Spring 2020) -
Independent Study
PSY 499 (Spring 2020) -
Modeling the Mind
NSCS 344 (Spring 2020) -
Decisions and the Brain
PSY 433 (Fall 2019) -
Directed Research
NSCS 392 (Fall 2019) -
Directed Research
NSCS 492 (Fall 2019) -
Directed Research
PSYS 392 (Fall 2019) -
Directed Research
PSYS 492 (Fall 2019) -
Dissertation
PSY 920 (Fall 2019) -
Honors Directed Research
NSCS 392H (Fall 2019) -
Honors Directed Research
PSYS 392H (Fall 2019) -
Honors Independent Study
NSCS 399H (Fall 2019) -
Independent Study
NSCS 399 (Fall 2019) -
Independent Study
PSY 199 (Fall 2019) -
Independent Study
PSY 399 (Fall 2019) -
Independent Study
PSY 499 (Fall 2019) -
Independent Study
PSY 599 (Fall 2019) -
Judgment+Decision Making
PSY 333 (Fall 2019) -
Research
PSY 900 (Fall 2019)
2018-19 Courses
-
Directed Research
NSCS 492 (Spring 2019) -
Directed Research
PSYS 392 (Spring 2019) -
Directed Research
PSYS 492 (Spring 2019) -
Dissertation
PSY 920 (Spring 2019) -
Honors Directed Research
NSCS 492H (Spring 2019) -
Honors Independent Study
NSCS 399H (Spring 2019) -
Honors Independent Study
NSCS 499H (Spring 2019) -
Honors Independent Study
PSY 499H (Spring 2019) -
Honors Thesis
NSCS 498H (Spring 2019) -
Honors Thesis
PSY 498H (Spring 2019) -
Independent Study
ECOL 299 (Spring 2019) -
Independent Study
NSCS 399 (Spring 2019) -
Independent Study
PSY 399 (Spring 2019) -
Independent Study
PSY 499 (Spring 2019) -
Master's Report
PSY 909 (Spring 2019) -
Modeling the Mind
NSCS 344 (Spring 2019) -
Preceptorship
NSCS 491 (Spring 2019) -
Research
PSY 900 (Spring 2019) -
Directed Research
NSCS 492 (Fall 2018) -
Directed Research
PSIO 492 (Fall 2018) -
Directed Research
PSYS 392 (Fall 2018) -
Directed Research
PSYS 492 (Fall 2018) -
Dissertation
PSY 920 (Fall 2018) -
Honors Independent Study
NSCS 299H (Fall 2018) -
Honors Independent Study
NSCS 399H (Fall 2018) -
Honors Thesis
NSCS 498H (Fall 2018) -
Independent Study
NSCS 299 (Fall 2018) -
Independent Study
NSCS 399 (Fall 2018) -
Independent Study
PSY 199 (Fall 2018) -
Independent Study
PSY 299 (Fall 2018) -
Independent Study
PSY 399 (Fall 2018) -
Independent Study
PSY 499 (Fall 2018) -
Independent Study
PSY 699 (Fall 2018) -
Judgment+Decision Making
PSY 333 (Fall 2018) -
Neuroeconomics
PSY 433 (Fall 2018) -
Research
PSY 900 (Fall 2018)
2017-18 Courses
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Cmptl Cognitive Neurosci
PHIL 544A (Spring 2018) -
Cmptl Cognitive Neurosci
PSY 544A (Spring 2018) -
Directed Research
NSCS 392 (Spring 2018) -
Directed Research
PSYS 392 (Spring 2018) -
Directed Research
PSYS 492 (Spring 2018) -
Dissertation
PSY 920 (Spring 2018) -
Honors Independent Study
NSCS 399H (Spring 2018) -
Honors Independent Study
PSY 399H (Spring 2018) -
Honors Thesis
NSCS 498H (Spring 2018) -
Independent Study
PSY 399 (Spring 2018) -
Modeling the Mind
NSCS 344 (Spring 2018) -
Research
PSY 900 (Spring 2018) -
Senior Capstone
NSCS 498 (Spring 2018) -
Directed Research
NSCS 392 (Fall 2017) -
Directed Research
PSYS 392 (Fall 2017) -
Directed Research
PSYS 492 (Fall 2017) -
Dissertation
PSY 920 (Fall 2017) -
Honors Independent Study
MATH 399H (Fall 2017) -
Honors Independent Study
NSCS 399H (Fall 2017) -
Honors Thesis
NSCS 498H (Fall 2017) -
Independent Study
NSCS 399 (Fall 2017) -
Independent Study
PSIO 399 (Fall 2017) -
Neuroeconomics
PSY 433 (Fall 2017) -
Research
PSY 900 (Fall 2017) -
Senior Capstone
NSCS 498 (Fall 2017)
2016-17 Courses
-
Directed Research
NSCS 392 (Spring 2017) -
Directed Research
NSCS 492 (Spring 2017) -
Directed Research
PSYS 392 (Spring 2017) -
Dissertation
PSY 920 (Spring 2017) -
Honors Independent Study
NSCS 399H (Spring 2017) -
Honors Independent Study
PSIO 499H (Spring 2017) -
Honors Thesis
NSCS 498H (Spring 2017) -
Independent Study
MATH 299 (Spring 2017) -
Independent Study
NSCS 499 (Spring 2017) -
Independent Study
PSY 499 (Spring 2017) -
Master's Report
PSY 909 (Spring 2017) -
Research
PSY 900 (Spring 2017) -
Directed Research
NSCS 392 (Fall 2016) -
Dissertation
PSY 920 (Fall 2016) -
Honors Independent Study
NSCS 399H (Fall 2016) -
Honors Independent Study
NSCS 499H (Fall 2016) -
Honors Independent Study
PSIO 399H (Fall 2016) -
Honors Independent Study
PSY 499H (Fall 2016) -
Honors Thesis
NSCS 498H (Fall 2016) -
Independent Study
NSCS 499 (Fall 2016) -
Independent Study
PSY 499 (Fall 2016) -
Modeling the Mind
NSCS 344 (Fall 2016) -
Neuroeconomics
PSY 433 (Fall 2016) -
Research
PSY 900 (Fall 2016) -
Thesis
PSY 910 (Fall 2016)
2015-16 Courses
-
Directed Research
NSCS 392 (Spring 2016) -
Directed Research
NSCS 492 (Spring 2016) -
Dissertation
PSY 920 (Spring 2016) -
Honors Independent Study
NSCS 299H (Spring 2016) -
Honors Independent Study
NSCS 399H (Spring 2016) -
Honors Independent Study
NSCS 499H (Spring 2016) -
Honors Independent Study
PSIO 399H (Spring 2016) -
Independent Study
NSCS 299 (Spring 2016) -
Independent Study
PSY 299 (Spring 2016) -
Independent Study
PSY 399 (Spring 2016) -
Independent Study
PSY 499 (Spring 2016) -
Modeling the Mind
COGS 344 (Spring 2016) -
Research
PSY 900 (Spring 2016)
Scholarly Contributions
Journals/Publications
- Harootonian, S. K., Ekstrom, A. D., & Wilson, R. C. (2022). Combination and competition between path integration and landmark navigation in the estimation of heading direction. PLOS Computational Biology, 18(2), e1009222.
- Smith, R., Taylor, S., Wilson, R. C., Chuning, A. E., Persich, M. R., Wang, S., & Killgore, W. (2022). Lower Levels of Directed Exploration and Reflective Thinking Are Associated With Greater Anxiety and Depression. Front. Psychiatry, 12, 782136.
- Feng, S. F., Wang, S., Zarnescu, S., & Wilson, R. C. (2021). The dynamics of explore--exploit decisions reveal a signal-to-noise mechanism for random exploration. Scientific reports, 11(1), 1--15.
- Grilli, M. D., McVeigh, K. S., Hakim, Z. M., Wank, A. A., Getz, S. J., Levin, B. E., Ebner, N. C., & Wilson, R. C. (2021). Is this phishing? Older age is associated with greater difficulty discriminating between safe and malicious emails. The Journals of Gerontology: Series B, 76(9), 1711--1715.
- Hakim, Z. M., Ebner, N. C., Oliveira, D. S., Getz, S. J., Levin, B. E., Lin, T., Lloyd, K., Lai, V. T., Grilli, M. D., & Wilson, R. C. (2021). The Phishing Email Suspicion Test (PEST) a lab-based task for evaluating the cognitive mechanisms of phishing detection. Behavior research methods, 53(3), 1342--1352.
- Prat-Carrabin, A., Wilson, R. C., Cohen, J. D., & Silveira, R. (2021). Human inference in changing environments with temporal structure.. Psychological review.
- Wilson, R. C., Bonawitz, E., Costa, V. D., & Ebitz, R. B. (2021). Balancing exploration and exploitation with information and randomization. Current opinion in behavioral sciences, 38, 49--56.
- Grilli, M. D., McVeigh, K. S., Hakim, Z. M., Wank, A. A., Getz, S. J., Levin, B. E., Ebner, N. C., & Wilson, R. C. (2020). Is this phishing? Older age is associated with greater difficulty discriminating between safe and malicious emails. The Journals of Gerontology: Series B.
- Hakim, Z. M., Ebner, N. C., Oliveira, D. S., Getz, S. J., Levin, B. E., Lin, T., Lloyd, K., Grilli, M. D., Lai, V. T., & Wilson, R. C. (2020). The Phishing Email Suspicion Test (PEST) a lab-based task for evaluating the cognitive mechanisms of phishing detection. Behavior Research Methods, 1-11. doi:https://doi.org/10.3758/s13428-020-01495-0
- Harootonian, S. K., Wilson, R. C., Hejtm\'anek, L., Ziskin, E. M., & Ekstrom, A. D. (2020). Path integration in large-scale space and with novel geometries: Comparing vector addition and encoding-error models. PLoS computational biology, 16(5), e1007489.
- Keung, W., Hagen, T. A., & Wilson, R. C. (2020). A divisive model of evidence accumulation explains uneven weighting of evidence over time. Nature communications, 11(1), 1--9.
- Sadeghiyeh, H., Wang, S., Alberhasky, M. R., Kyllo, H. M., Shenhav, A., & Wilson, R. C. (2020). Temporal discounting correlates with directed exploration but not with random exploration. Scientific reports, 10(1), 1--10.
- Sadeghiyeh, H., Wang, S., Kyllo, H. M., Alberhasky, M. R., Savita, S., Kellohen, K. L., & Wilson, R. C. (2020). On the Psychology of the Psychology Subject Pool. Journal of Individual Differences.
- Sundman, M. H., Lim, K., Ton, T. V., Mizell, J., Ugonna, C., Rodriguez, R., Chen, N., Fuglevand, A. J., Liu, Y., Wilson, R. C., & others, . (2020). Transcranial magnetic stimulation reveals diminished homoeostatic metaplasticity in cognitively impaired adults. Brain Communications, 2(2), fcaa203.
- Waltz, J. A., Wilson, R. C., Albrecht, M. A., Frank, M. J., & Gold, J. M. (2020). Differential effects of psychotic illness on directed and random exploration. Computational Psychiatry, 4, 18--39.
- Kane, G. A., Bornstein, A. M., Shenhav, A., Wilson, R. C., Daw, N. D., & Cohen, J. D. (2019). Rats exhibit similar biases in foraging and intertemporal choice tasks. Elife, 8, e48429.
- Keung, W., Hagen, T. A., & Wilson, R. C. (2019). Regulation of evidence accumulation by pupil-linked arousal processes. Nature Human Behaviour, 3(6), 636--645.
- Wilson, R. C., & Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. Elife, 8, e49547.
- Wilson, R. C., Shenhav, A., Straccia, M., & Cohen, J. D. (2019). The eighty five percent rule for optimal learning. Nature communications, 10(1), 1--9.
- Zhang, F., Jaffe-Dax, S., Wilson, R. C., & Emberson, L. L. (2019). Prediction in infants and adults: A pupillometry study. Developmental science, 22(4), e12780.
- Krueger, P. K., Wilson, R. C., & Cohen, J. D. (2017). Strategies for exploration in the domain of losses. Judgement and Decision Making.More infoJoint first author paper
- Somerville, L. H., Sasse, S. F., Garrad, M. C., Drysdale, A. T., Akar, N. A., Insel, C., & Wilson, R. C. (2017). Charting the Expansion of Strategic Exploratory Behavior During Adolescence. Journal of Experimental Psychology: General, 146(2), 155-164.
- Wilson, R. C., Shvartsman, M., Lositsky, O., & Cohen, J. D. (2017). Adaptive response priors in context-dependent decision-making.. Cognitive Science.
- Mortezapouraghdam, Z., Wilson, R. C., Schwabe, L., & Strauss, D. J. (2016). Bayesian modeling of the dynamics of phase modulations and their application to auditory evoked responses at different loudness scales. Frontiers in Computational Neuroscience.More infoJoint first author paper
- Schuck, N. W., Cai, M. B., Wilson, R. C., & Niv, Y. (2016). Human Orbitofrontal Cortex Represents a Cognitive Map of State Space. Neuron, 91(6), 1402-1412.
- Warren, C. M., Eldar, E., Van den Brink, R. L., Tona, K., van der Wee, N. J., Giltay, E., van Noorden, M., Bosch, J., Wilson, R. C., Cohen, J., & Nieuwenhuis, S. (2016). Catecholamine-Mediated Increases in Gain Enhance the Precision of Cortical Representations. Journal of Neuroscience, 36(21), 5699-5708.
- Niv, Y., Daniel, R., Geana, A., Gershman, S. J., Leong, Y. C., Radulescu, A., & Wilson, R. C. (2015). Reinforcement learning in multidimensional environments relies on attention mechanisms. Journal of Neuroscience, 35(21), 8145-8157.
- Wilson, R. C., & Niv, Y. (2015). Is model fitting necessary for model-based fMRI?. PLoS Computational Biology.
- Wilson, R. C., Nassar, M. R., & Gold, J. I. (2013). A mixture of delta-rules approximation to bayesian inference in change-point problems.. PLoS computational biology, 9(7), e1003150. doi:10.1371/journal.pcbi.1003150More infoError-driven learning rules have received considerable attention because of their close relationships to both optimal theory and neurobiological mechanisms. However, basic forms of these rules are effective under only a restricted set of conditions in which the environment is stable. Recent studies have defined optimal solutions to learning problems in more general, potentially unstable, environments, but the relevance of these complex mathematical solutions to how the brain solves these problems remains unclear. Here, we show that one such Bayesian solution can be approximated by a computationally straightforward mixture of simple error-driven 'Delta' rules. This simpler model can make effective inferences in a dynamic environment and matches human performance on a predictive-inference task using a mixture of a small number of Delta rules. This model represents an important conceptual advance in our understanding of how the brain can use relatively simple computations to make nearly optimal inferences in a dynamic world.
- Wilson, R. C., Nassar, M. R., Heasly, B. S., & Gold, J. I. (2010). An approximately Bayesian delta-rule model explains the dynamics of belief updating in a changing environment.. The Journal of neuroscience : the official journal of the Society for Neuroscience, 30(37), 12366-78. doi:10.1523/jneurosci.0822-10.2010More infoMaintaining appropriate beliefs about variables needed for effective decision making can be difficult in a dynamic environment. One key issue is the amount of influence that unexpected outcomes should have on existing beliefs. In general, outcomes that are unexpected because of a fundamental change in the environment should carry more influence than outcomes that are unexpected because of persistent environmental stochasticity. Here we use a novel task to characterize how well human subjects follow these principles under a range of conditions. We show that the influence of an outcome depends on both the error made in predicting that outcome and the number of similar outcomes experienced previously. We also show that the exact nature of these tendencies varies considerably across subjects. Finally, we show that these patterns of behavior are consistent with a computationally simple reduction of an ideal-observer model. The model adjusts the influence of newly experienced outcomes according to ongoing estimates of uncertainty and the probability of a fundamental change in the process by which outcomes are generated. A prior that quantifies the expected frequency of such environmental changes accounts for individual variability, including a positive relationship between subjective certainty and the degree to which new information influences existing beliefs. The results suggest that the brain adaptively regulates the influence of decision outcomes on existing beliefs using straightforward updating rules that take into account both recent outcomes and prior expectations about higher-order environmental structure.
Proceedings Publications
- Hagen, T., & Wilson, R. C. (2017, Summer). Wide-eyed and wrong? Pupil dilation and imperfect evidence accumulation in auditory perceptual decisions. In Reinforcement Learning & Decision Making.
- Keung, W., & Wilson, R. C. (2017, Summer). Regulation of evidence accumulation by pupil-linked noradrenergic system in humans. In Reinforcement Learning & Decision Making.
- Kromenacker, B., & Wilson, R. C. (2017, Summer). Engagement matters: pupil and mental effort mediate depletion effect on subsequent physical tasks. In Reinforcement Learning & Decision Making.
- Sherman, E., Andrada, C., Sikora, C., Long, E., & Wilson, R. C. (2017, Summer). Spontaneous Blink Rate Correlates With Financial Risk Taking. In Reinforcement Learning & Decision Making.
- Wang, S., & Wilson, R. C. (2017, Summer). What is the nature of decision noise in random exploration?. In Reinforcement Learning & Decision Making.
- Zajkowski, W., Kossut, M., & Wilson, R. C. (2017, Summer). A causal role for right frontopolar cortex in directed, but not random, exploration. In Reinforcement Learning and Decision Making (RLDM2017).
- Geana, A., Wilson, R. C., Daw, N. D., & Cohen, J. D. (2016, Summer). Boredom, information-seeking and exploration. In 38th Annual Conference of the Cognitive Science Society, 1, 1751-1756.
- Geana, A., Wilson, R. C., Daw, N. D., & Cohen, J. D. (2016, Summer). Information-Seeking, Learning and the Marginal Value Theorem: A Normative Approach to Adaptive Exploration. In 38th Annual Conference of the Cognitive Science Society, 1, 1793-1798.
- Krueger, P. K., Oliver, A., Cohen, J. D., & Wilson, R. C. (2015, Summer). Directed and random exploration in realistic environments. In Reinforcement Learning and Decision Making (RLDM2015).
- Krueger, P. K., Wilson, R. C., & Cohen, J. D. (2015, Summer). Strategies for exploration in the domain of losses. In Reinforcement Learning and Decision Making (RLDM2015).More infoJoint first author paper
- Lositsky, O., Wilson, R. C., Shvratsman, M., & Cohen, J. D. (2015, Summer). A Drift Diffusion Model of Proactive and Reactive Control in a Context-Dependent Two-Alternative Forced Choice Task. In Reinforcement Learning and Decision Making (RLDM2015).
- Wilson, R. C., & Cohen, J. D. (2015, Summer). Humans tradeoff information seeking and randomness in explore-exploit decisions. In Reinforcement Learning and Decision Making (RLDM2015).
- Wilson, R. C., & Finkel, L. H. (2009). A Neural Implementation of the Kalman Filter. In Neural Information Processing Systems, 22, 2062-2070.More infoRecent experimental evidence suggests that the brain is capable of approximating Bayesian inference in the face of noisy input stimuli. Despite this progress, the neural underpinnings of this computation are still poorly understood. In this paper we focus on the Bayesian filtering of stochastic time series and introduce a novel neural network, derived from a line attractor architecture, whose dynamics map directly onto those of the Kalman filter in the limit of small prediction error. When the prediction error is large we show that the network responds robustly to changepoints in a way that is qualitatively compatible with the optimal Bayesian model. The model suggests ways in which probability distributions are encoded in the brain and makes a number of testable experimental predictions.
Presentations
- Wilson, R. C., Ekstrom, A. D., & Harootonian, S. (2020, September). A sampling model of multimodal spatial orientation. iNav (on-line).
- Wilson, R. C. (2017, February). Making Sound Financial Decisions. Fifth Annual Conference on Successful Aging. University of Arizona.
- Wilson, R. C. (2016, Fall). The explore-exploit dilemma in human reinforcement learning. Google DeepMind - Neuroscience Group. London: Google.
- Wilson, R. C. (2016, Fall). The explore-exploit dilemma in human reinforcement learning. Oxford University - Neurotheory group. Oxford.
- Wilson, R. C. (2016, Fall). The explore-exploit dilemma in human reinforcement learning. University College London, Affective Brain Lab Seminar (via video link). London, via video link.
- Wilson, R. C. (2016, Fall). The explore-exploit dilemma in human reinforcement learning. University of Arizona, Neuroscience Data Blitz. University of Arizona.
- Wilson, R. C. (2016, Summer). The explore-exploit dilemma in human reinforcement learning. Princeton University - Niv lab group meeting. Princeton, NJ.
- Wilson, R. C. (2016, Summer). The explore-exploit dilemma in human reinforcement learning. University College London - Gatsby Group. University College London.
- Wilson, R. C. (2016, Summer). The explore-exploit dilemma in human reinforcement learning. University of Pennsylvania - CNI chalk talk. Philadelphia, PA.
- Wilson, R. C. (2015, April). The explore-exploit dilemma in human reinforcement learning. University of Arizona, Cognitive Science Colloquium. University of Arizona.
- Wilson, R. C. (2015, April). The explore-exploit dilemma in human reinforcement learning. University of Arizona, Psychology Colloquium. University of Arizona.
- Wilson, R. C. (2015, Fall). The explore-exploit dilemma in human reinforcement learning. University of Arizona, Marc Program. University of Arizona.
- Wilson, R. C. (2015, Fall). The explore-exploit dilemma in human reinforcement learning. University of Arizona, Psychology Colloquium New Faculty Data Blitz. University of Arizona.
- Wilson, R. C. (2015, March). The directed-random tradeoff in explore-exploit decision making. Computational and Systems Neuroscience (Cosyne-2015). Salt Lake City.More infoConference workshop presentation
- Wilson, R. C. (2015, Spring). The explore-exploit dilemma in human reinforcement learning. University of Arizona, Economics Department Seminar. University of Arizona.
- Wilson, R. C. (2015, Spring). The explore-exploit dilemma in human reinforcement learning. University of Maryland, Department of Medicine. University of Maryland.
Poster Presentations
- Runyon, J. R., Kromenacker, B. W., Wilson, R. C., Sternberg, E. M., & Hyde, J. (2020, January). Thinking about sweat: Sweat biomarker correlates of physical and mental effort. UArizona COM 4th Annual PI Poster Session. COM: UArizona COM.
- Runyon, J. R., Wilson, R. C., Sternberg, E. M., & Hyde, J. (2019, November). Using sweat and thermography to assess cognitive performance and stress.. UArizona Arthritis Center Annual Conference. UArizona: UArizona COM, Arthritis Center.
- Ekstrom, A. D., Erlenbach, E., Ziskin, E., Wilson, R. C., & Harootonian, S. (2018, November). Modeling path integration in large-scale space and with novel geometries. Society for Neuroscience.
- Wang, S., & Wilson, R. C. (2017, Summer). What is the nature of decision noise in random exploration?. 50th Annual Meeting of the Society for Mathematical Psychology.
- Hagen, T., & Wilson, R. C. (2016, Nov). Wide-eyed and wrong? Pupil dilation correlates with imperfect evidence accumulation in auditory perceptual decisions. Neuroscience 2016. San Diego.
- Kane, G., Vazy, E. M., Wilson, R. C., Shenhav, A., James, M. H., Daw, N. D., Aston-Jones, G. G., & Cohen, J. D. (2016, Nov). Tonic locus coeruleus activity regulates foraging behavior. Neuroscience 2016. San Diego.
- Lositsky, O., Wilson, R. C., Shvartsman, M., & Cohen, J. D. (2016, Nov). Adaptive task representations in context-based decision making. Neuroscience 2016. San Diego.
- Sadeghiyeh, H., Wang, S., & Wilson, R. C. (2016, Nov). The social influence on explore-exploit decisions. Neuroscience 2016. San Diego.
- Sasse, S. F., Garrad, M. C., Drysdale, A. M., Abi Akar, N., Insel, C., Wilson, R. C., & Somerville, L. (2016, Sept). Exploratory decision making becomes more strategic through adolescence. 4th Annual Flux Congress. St Louis.
- Sherman, E., & Wilson, R. C. (2016, Nov). Spontaneous blink rate correlates with financial risk taking. Neuroscience 2016. San Diego.
- Wang, S., Cohen, J. D., & Wilson, R. C. (2016, February). Blink different! Blink rate reflects individual differences in directed exploration. Computational and Systems Neuroscience. Salt Lake City.
- Wilson, R. C., & Cohen, J. D. (2016, February). A unifying theory of explore-exploit decisions. Computational and Systems Neuroscience. Salt Lake City.
- Wilson, R. C., Andrade, C., Carrera, D., Chung, K., Giron, E., Lawwill, A., Low, S., & Vargas, G. (2016, Nov). The NEUrL Project – Neuroscience Education for Urban Learners. Neuroscience 2016. San Diego.
- Krueger, P. K., Wilson, R. C., & Cohen, J. D. (2015, March). Strategies for exploration in the domain of losses. Computational and Systems Neuroscience (Cosyne-2015). Salt Lake City.More infoJoint first author