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  4. Strategic impatience in Go/NoGo versus forced-choice decision-making
 
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Strategic impatience in Go/NoGo versus forced-choice decision-making

Journal
Advances in Neural Information Processing Systems
Journal Volume
3
Pages
2123-2131
Date Issued
2012
Author(s)
Shenoy P
ANGELA YU-CHEN LIN  
URI
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84877737127&partnerID=40&md5=a40b90836fc08613720214ea1f75d37b
https://scholars.lib.ntu.edu.tw/handle/123456789/625606
Abstract
Two-alternative forced choice (2AFC) and Go/NoGo (GNG) tasks are behavioral choice paradigms commonly used to study sensory and cognitive processing in choice behavior. While GNG is thought to isolate the sensory/decisional component by eliminating the need for response selection as in 2AFC, a consistent tendency for subjects to make more Go responses (both higher hits and false alarm rates) in the GNG task raises the concern that there may be fundamental differences in the sensory or cognitive processes engaged in the two tasks. Existing mechanistic models of these choice tasks, mostly variants of the drift-diffusion model (DDM; [1, 2]) and the related leaky competing accumulator models [3, 4], capture various aspects of behavioral performance, but do not clarify the provenance of the Go bias in GNG.We postulate that this "impatience" to go is a strategic adjustment in response to the implicit asymmetry in the cost structure of the 2AFC and GNG tasks: the NoGo response requires waiting until the response deadline, while a Go response immediately terminates the current trial. We show that a Bayes-risk minimizing decision policy that minimizes not only error rate but also average decision delay naturally exhibits the experimentally observed Go bias. The optimal decision policy is formally equivalent to a DDM with a time-varying threshold that initially rises after stimulus onset, and collapses again just before the response deadline. The initial rise in the threshold is due to the diminishing temporal advantage of choosing the fast Go response compared to the fixed-delay NoGo response. We also show that fitting a simpler, fixed-threshold DDM to the optimal model reproduces the counterintuitive result of a higher threshold in GNG than 2AFC decision-making, previously observed in direct DDM fit to behavioral data [2], although such fixed-threshold approximations cannot reproduce the Go bias. Our results suggest that observed discrepancies between GNG and 2AFC decision-making may arise from rational strategic adjustments to the cost structure, and thus need not imply any other difference in the underlying sensory and cognitive processes.
Other Subjects
Accumulator model; Behavioral performance; Cognitive process; Cognitive processing; Drift-diffusion model; Mechanistic models; Optimal decisions; Response selection; Cognitive systems; Decision making
Type
conference paper

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