Publications

Bustamante, L. A., Barch D.M., Solis, J., Oshinowo, T., Grahek, I., Konova, A.B., Daw, N.D., & Cohen, J. D. (in press). Anxiety symptoms of major depression associated with increased willingness to exert cognitive, but not physical effort. Psychological Medicine.

Bustamante, L. A., Oshinowo, T., Lee, J.R., Tong, E., Burton, A.R., Shenhav, A., Cohen, J. D., & Daw, N.D. (2023). Effort Foraging Task reveals positive correlation between individual differences in the cost of cognitive and physical effort in humans. Proceedings of the National Academy of the Sciences. https://www.pnas.org/doi/10.1073/pnas.2221510120

Bustamante, L. A., Lieder, F., Musslick, S., Shenhav, A., & Cohen, J. D. (2021). Learning to Overexert Cognitive Control in a Stroop Task. Cognitive, Affective, & Behavioral Neuroscience. https://doi.org/10.3758/s13415-020-00845-x

Preprints

Bustamante, L. A., Barch D.M., Solis, J., Oshinowo, T., Grahek, I., Konova, A.B., Daw, N.D., & Cohen, J. D. (2023). Anxiety symptoms of major depression associated with increased willingness to exert cognitive, but not physical effort. MedRxiv. https://doi.org/10.1101/2024.02.18.24302985

Ongoing Projects

Motivation for Cognitive and Physical Effort in Depression. Study at the Rutgers-Princeton Center for Computational Neuropsychiatry. This project studies the effects of symptoms of depression on decision making. Participants with or without depression complete a behavioral task that measures individual preferences for engaging in cognitive and physical tasks to obtain reward. This study is supported by The New Jersey Alliance for Clinical Translational Science and the New Jersey Health Foundation. 

Dissertation: Quantifying Individual Differences in the Cost of Cognitive and Physical Effort in Humans

Advisors: Jonathan Cohen, Nathaniel Daw, Amitai Shenhav 

Abstract: 

How do people decide which goals to pursue? A substantial body of work has been devoted to understanding how people weigh potential future rewards against effort costs required to achieve those rewards. However, attempts to formalize the mechanisms underlying such effort-based decisions have met challenges both theoretical (e.g., how do people learn when effort is needed?) and methodological (e.g., how can the factors contributing to such decisions be isolated and measured?). This thesis advances theory and measurement of cognitive effort-based decision-making. 

Chapter 2 proposes a theory that the value of cognitive effort is learned through reinforcement based on features, and generalized across situations that share features. We predicted that transfer learning could lead people to overexert effort, even when it harmed performance. In the experiment participants learned whether to give a low- or high- effort response to a stimulus. Consistent with the theory, participants overexerted effort for stimuli that combined features previously rewarded for the high-effort response, but which were rewarded for the low-effort response.

Chapter 3 introduces the Cognitive Effort Tradeoff Task. Participants made explicit choices between performing specified numbers of trials that differed in their cognitive effort requirements. A utility model fit to choices captured the cost of high-effort trials in terms of how many more low-effort trials a participant would complete to avoid high-effort trials. We found that individual differences in cognitive effort costs were related to model-based reinforcement learning strategy, but not to strategic exploration. 

Chapter 4 introduces the Effort Foraging Task, which embedded effort costs into a patch foraging sequential decision task. Participants chose between harvesting a depleting patch, or traveling to a new patch, costing time and effort. Participants' exit thresholds were sensitive to cognitive and physical effort effort demands, consistent with the high-effort tasks having a monetary cost. Cognitive and physical effort costs were positively correlated, suggesting that a unified decision mechanism computes the cost of actions across domains. We found patterns of correlation between both novel tasks and self-reported apathy, anhedonia, depression, anxiety, and effort-seeking.

Professional Activities

Co-organizer of the interdiscplinary Mental Effort Workshop (2020, 2021, 2022)

Tutorial presenter at the 2021 Mental Effort Workshop. Title: Individual Differences Data Analysis With STAN (open-access tutorial)

Co-organizer of the Princeton Neuroscience Institute Retreat (2016).

Teaching

Assistant in Instruction at Princeton University: 

Course title: Laboratory in Principles in Neuroscience (Spring 2019). Instructor for hands-on labs on fMRI Decoding. Professor: Dr. Kenneth Norman.

Course title: Computational Models of Psychological Function (Spring 2018) Professor: Dr. Jonathan Cohen.

Course title: Cognitive Psychology (Spring 2017). Professor: Dr. Jonathan Cohen.

Guest lectures: 

Guest lecture for Topics in Modelling Cognitive Processes. Course for Theoretical and Experimental Psychology Masters students at Ghent University (April 2024). Lecture on  "Foraging theory: models and applications to effort‐based decision‐making”. Authored and lead an open access foraging coding tutorial. 

Introduction to fMRI lecture for Laboratory in Principles of Neuroscience course, at Princeton Neuroscience Institute (Spring 2020).

Introduction to fMRI lecture for Neurotechnologies for Analysis of Neural Dynamics summer course at Princeton Neuroscience Institute (June 2017, June 2018, July 2019).

Teacher for the Prison Teaching Initiative: 

Course title: Introduction to Psychology (Fall 2016, Spring 2017, Fall 2018) Taught at the Edna Mahan Correctional Facility for Women and the Garden State Youth Correctional Facility, lectures on Neuroscience. Through the McGraw Center for Teaching and Learning, Princeton University.