Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01z316q475d
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dc.contributor.authorBustamante, Laura Ana
dc.contributor.otherNeuroscience Department
dc.date.accessioned2022-06-16T20:34:03Z-
dc.date.available2022-06-16T20:34:03Z-
dc.date.created2022-01-01
dc.date.issued2022
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01z316q475d-
dc.description.abstractHow 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.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherPrinceton, NJ : Princeton University
dc.relation.isformatofThe Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog: <a href=http://catalog.princeton.edu>catalog.princeton.edu</a>
dc.subjectCognitive control
dc.subjectCognitive effort
dc.subjectComputational Neuroscience
dc.subjectDecision making
dc.subjectMeta-cognition
dc.subjectNeuroeconomics
dc.subject.classificationNeurosciences
dc.subject.classificationCognitive psychology
dc.titleQUANTIFYING INDIVIDUAL DIFFERENCES IN THE COST OF COGNITIVE AND PHYSICAL EFFORT IN HUMANS