Towards a Translational Model of Decision-Making: Findings from the Web-Surf Task
2017-08
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Towards a Translational Model of Decision-Making: Findings from the Web-Surf Task
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2017-08
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Interventions targeting cognitive disorders often hinge on assumptions that humans and nonhuman animals recruit equivalent cognitive mechanisms during decision- making. Identifying parallel decision systems across species could help bridge gaps between clinical and non-clinical research, and in turn, improve intervention efficacy. The goal of this dissertation is to assess for similar behavioral and neural markers of decision-making across humans and rodents using a sequential foraging paradigm (“The Web-Surf Task”) that was adapted from a rodent spatial neuroeconomic task (“Restaurant Row”). The included studies highlight a functional translational approach that aims to access similar functional constructs via parallel tasks and methodological approaches. The results provide evidence of cross-species behavioral equivalents, such as the ability to detect revealed preferences. Findings from a neuroimaging study suggest that different neural systems track past and forward representations, indicative of human prospection during deliberation (i.e., episodic future thinking). Moreover, neural activation related to difficult decisions is similar to many of the structures that underlie rodent deliberation. Lastly, a risk-variant of the task suggests that regret-instances provide a bridge between our liking and pursuit of rewards. This final study also finds that impulsive individuals may fail to learn from regret. Collectively, this dissertation demonstrates the utility of this novel task for elucidating human deliberative mechanisms and identifying cross-species decision system compatibilities.
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University of Minnesota Ph.D. dissertation.August 2017. Major: Psychology. Advisors: Angus MacDonald, A. Redish. 1 computer file (PDF); viii, 214 pages.
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Abram, Samantha. (2017). Towards a Translational Model of Decision-Making: Findings from the Web-Surf Task. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/200236.
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