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Relative Gains, Losses, and Reference Points in Probabilistic Choice in Rats

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  • Andrew T Marshall
  • Kimberly Kirkpatrick

Abstract

Theoretical reference points have been proposed to differentiate probabilistic gains from probabilistic losses in humans, but such a phenomenon in non-human animals has yet to be thoroughly elucidated. Three experiments evaluated the effect of reward magnitude on probabilistic choice in rats, seeking to determine reference point use by examining the effect of previous outcome magnitude(s) on subsequent choice behavior. Rats were trained to choose between an outcome that always delivered reward (low-uncertainty choice) and one that probabilistically delivered reward (high-uncertainty). The probability of high-uncertainty outcome receipt and the magnitudes of low-uncertainty and high-uncertainty outcomes were manipulated within and between experiments. Both the low- and high-uncertainty outcomes involved variable reward magnitudes, so that either a smaller or larger magnitude was probabilistically delivered, as well as reward omission following high-uncertainty choices. In Experiments 1 and 2, the between groups factor was the magnitude of the high-uncertainty-smaller (H-S) and high-uncertainty-larger (H-L) outcome, respectively. The H-S magnitude manipulation differentiated the groups, while the H-L magnitude manipulation did not. Experiment 3 showed that manipulating the probability of differential losses as well as the expected value of the low-uncertainty choice produced systematic effects on choice behavior. The results suggest that the reference point for probabilistic gains and losses was the expected value of the low-uncertainty choice. Current theories of probabilistic choice behavior have difficulty accounting for the present results, so an integrated theoretical framework is proposed. Overall, the present results have implications for understanding individual differences and corresponding underlying mechanisms of probabilistic choice behavior.

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  • Andrew T Marshall & Kimberly Kirkpatrick, 2015. "Relative Gains, Losses, and Reference Points in Probabilistic Choice in Rats," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-33, February.
  • Handle: RePEc:plo:pone00:0117697
    DOI: 10.1371/journal.pone.0117697
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    References listed on IDEAS

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    1. Xiuli Chen & Kieran Mohr & Joseph M Galea, 2017. "Predicting explorative motor learning using decision-making and motor noise," PLOS Computational Biology, Public Library of Science, vol. 13(4), pages 1-33, April.

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