William Alexander

William Alexander

Assistant Professor

Boca Raton, BS-12 310

walexander@fau.edu

http://mocclab.org/index.html

Education

Ph.D., Indiana University, Bloomington, 2006

Research Interests

Computational Modeling

Cognitive Neuroscience

Prefrontal cortex

Cognitive control

Decision-making

Neuroimaging

Research Description

My research investigates the computational mechanisms underlying the function of prefrontal cortex.  Prefrontal cortex is implicated in a wide array of cognitive behaviors, including abstract reasoning, goal-oriented planning, and decision-making.  By developing computational models, my research aims to understand how regions within prefrontal cortex collaborate in order to support high-level cognition.  The goal of this research is to understand both the healthy function of prefrontal cortex, as well as how this function collapses in neuropsychological disorders.

Recent Publications

  • Alexander, W.H., Deraeve, J., and Vassena, E. (in preparation).Dissociation and Integration of Outcome and State Uncertainty Signals in Cognitive Control
  • Alexander, W.H. & Womelsdorf, T. (in preparation). Interactions of medial and lateral prefrontal cortex in hierarchical predictive coding
  • Deraeve, J., Vassena, E. and Alexander, W.H. (in preparation).Conjunction or co-activation? A multi-level MVPA approach to task set representations
  • Cogliatti-Dezza, I., Cleeremans, A., Yu, A., and Alexander, W.H.(submitted) Distinct Value Systems for Reward and Information in Human Prefrontal Cortex
  • Vassena E., Deraeve, J., and Alexander, W.H. (2020). Surprise, value and control in anterior cingulate cortex during speeded decision-making. Nature Human Behavior
  • Cogliatti-Dezza, I., Cleeremans, A., and Alexander, W.H.(2019) Should we control? The interplay between cognitive control and information integration in the resolution of the exploration-exploitation dilemma. Journal of Experimental Psychology: General
  • Vassena, E., Deraeve, J., and Alexander, W.H. (2019). Task-specific prioritization of reward and effort information: Novel insights from behavior and computational modeling. Cognitive, Affective, & Behavioral Neuroscience
  • Deraeve, J. and Alexander, W.H. (2018). Fast, accurate and stable feature extraction using neural networks. Neuroinformatics
  • Alexander, W.H., and Brown, J.W. (2018). Frontal cortex function derives from hierarchical predictive coding. Scientific Reports
  • Cogliatti-Dezza, I., Cleeremans, A., Yu, A., and Alexander, W.H. (2017). Learning the value of information and reward over time when solving exploration-exploitation problems. Scientific Reports
  • Alexander, W.H., Brown, J.W., Collins, A.G.E., Hayden, B.Y., and Vassena, E. (2017). Prefrontal cortex in control: Broadening the scope to identify mechanisms. Journal of Cognitive Neuroscience
  • Alexander, W.H., Vassena, E., Deraeve, J. and Langford, Z.D. (2017). Integrative Modeling of PFC. Journal of Cognitive Neuroscience
  • Vassena, E., Deraeve, J., and Alexander, W.H. (2017). Predicting motivation: computational models of PFC can explain neural coding of motivation and effort-based decision-making in health and disease. Journal of Cognitive Neuroscience
  • Alexander. W.H., and Brown, J.W. (2017). The role of anterior cingulate cortex in prediction error and signaling surprise. Topics in Cognitive Science
  • Brown, J.W., and Alexander, W.H. (2017). Foraging value, risk avoidance, and multiple control signals: How the anterior cingulate cortex controls value-based decision-making. Journal of Cognitive Neuroscience
  • Vassena, E., Holroyd, C. B., & Alexander, W. H. (2017). Computational Models of Anterior Cingulate Cortex: At the Crossroads between Prediction and Effort. Frontiers in Neuroscience, 11.
  • Jahn, A., Nee., D.E., Alexander, W.H., and Brown, J.W. (2016). Distinct regions of pain and prediction error within medial prefrontal cortex. Journal of Neuroscience. 36(49), 12385–12392
  • Alexander, W.H., and Brown, J.W. (2015). Hierarchical Error Representation: A computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Computation, 27, 2354-2410
  • Alexander, W.H., Fukunaga, R., Finn, P., and Brown, J.W. (2015). Reward salience and risk aversion underlie differential ACC activity in substance dependence. Neuroimage: Clinical 8, 59-71.
  • Silvetti, M., Alexander, W.H., Verguts, T., and Brown, J.W. (2014). From conflict management to reward-based decision making: Actors and critics in primate medial frontal cortex. Neuroscience and Behavioral Reviews. 46(1), 44-57.
  • Alexander, W.H. and Brown, J.W. (2014). A general role for medial prefrontal cortex in event prediction. Frontiers in Computational Neuroscience, 8:69
  • Jahn, A., Nee, D.E., Alexander, W.H., and Brown, J.W. (2014). Distinct regions of anterior cingulate cortex signal prediction and outcome evaluation. Neuroimage 95,80-89
  • Zarr, N, Brown, J.W., and Alexander, W.H. (2014). Discounting of reward sequences: a test of competing formal models of hyperbolic discounting. Frontiers in Psychology.
  • Alexander, W.H. and Brown, J.W. (2011). Medial prefrontal cortex as an action-outcome predictor. Nature Neuroscience 14(10), 1338-1344.
  • Alexander, W.H. and Brown, J.W. (2010). Computational models of response-outcome prediction as a basis for cognitive control. Topics in Cognitive Science 2(4), 658-677.
  • Alexander, W.H. and Brown, J.W. (2010). Hyperbolically discounted temporal difference learning. Neural Computation 22(6), 1511-27.
  • Alexander, W.H. and Brown, J.W. (2010). Competition between learned reward and error outcome predictions in anterior cingulate cortex. Neuroimage, 49(5), 3210-3218.
  • Alexander, W.H. (2007). Shifting Attention Using a Temporal Difference Prediction Error and High-Dimensional Input. Adaptive Behavior, 15, 121-133
  • Alexander, W.H. and Sporns, O. (2006). Temporal difference learning with learned attention shifts. Proceedings of the Fifth International Conference on Development and Learning. Bloomington, IN.
  • Alexander, W.H. and Sporns, O. (2004). Interactions of environment, behavior, and synaptic patterns in a neuro-robotic model. In: Animals to Animats 8: Proceedings of the Eighth International Conference on the Simulation of Adaptive Behavior,pp. 13-22, Schaal, S., Ijspeert, A., Billard, A., Vijayakumar, S., Hallam, J., and Meyer, J-A. (Editors). MIT Press: Cambridge, MA.
  • Sporns, O. and Alexander, W.H. (2003). Neuromodulation in a learning robot: Interactions between neural plasticity and behavior. Proceedings of IJCNN 2003, 2789-2794.
  • Alexander, W.H. and Sporns, O. (2003). An Embodied Model of Learning, Plasticity, and Reward. Adaptive Behavior. Vol 10(3-4), Sum 2002, pp. 143-159
  • Sporns, O., and Alexander, W.H. (2002). Neuromodulation and plasticity in an autonomous robot. Neural Networks. Vol 15(4-6), Jun-Jul 2002, pp. 761-774.
  • Alexander, W.H. and Sporns, O (2002). Timed delivery of reward signals in an autonomous robot. In: Animals to Animats 7: Proceedings of the Seventh International Conference on the Simulation of Adaptive Behavior, pp. 195-204, Hallam, B., Floreano, D., Hallam, J., Hayes, G. and Meyer, J-A. (Editors), MIT Press: Cambridge, MA.
  • Sporns, O., and Alexander, W.H. (2002). Dopamine, reward conditioning, and robot behavior. In: Proceedings of the 2nd International Conference on Development and Learning, pp. 265-270, IEEE Computer Society, Los Alamitos, CA.

Scholarly Activities

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