# Suboptimal decision criteria are predicted by subjectively weighted probabilities and rewards

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## Abstract

Subjects performed a visual detection task in which the probability of target occurrence at each of the two possible locations, and the rewards for correct responses for each, were varied across conditions. To maximize monetary gain, observers should bias their responses, choosing one location more often than the other in line with the varied probabilities and rewards. Typically, and in our task, observers do not bias their responses to the extent they should, and instead distribute their responses more evenly across locations, a phenomenon referred to as ‘conservatism.’ We investigated several hypotheses regarding the source of the conservatism. We measured utility and probability weighting functions under Prospect Theory for each subject in an independent economic choice task and used the weighting-function parameters to calculate each subject’s subjective utility (*SU*(*c*)) as a function of the criterion *c*, and the corresponding weighted optimal criteria (*wc* _{ opt }). Subjects’ criteria were not close to optimal relative to *wc* _{ opt }. The slope of *SU*(*c*) and of expected gain *EG*(*c*) at the neutral criterion corresponding to *β* = 1 were both predictive of the subjects’ criteria. The slope of *SU*(*c*) was a better predictor of observers’ decision criteria overall. Thus, rather than behaving optimally, subjects move their criterion away from the neutral criterion by estimating how much they stand to gain by such a change based on the slope of subjective gain as a function of criterion, using inherently distorted probabilities and values.

## Keywords

Decision-making Signal detection theory Spatial Vision## Notes

### Acknowledgments

This work was supported in part by NIH grant EY08266.

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