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Rats can learn that cocaine and food reinforcers are smaller than expected: A test of a computational model of addiction

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posted on 2023-09-06, 03:05 authored by Katherine R. Marks

According to Redish's (2004) computational model of addiction, drugs of abuse differ from natural rewards (e.g., food) in that they produce a continual, non-correctable overexpectation for the reward. The present experiment tested the model-based prediction that subjects should be incapable of learning that a cocaine reward is smaller than expected, but should be able to learn that a food reward is smaller than expected. This was done by training rats to expect a large cocaine or food reward, subsequently presenting them with a smaller-than-expected reward, and then seeing if they could learn from this discrepancy. Results indicated that subjects were able to learn that a cocaine or food reward was smaller than expected. This outcome suggests that traditional error-correction learning models, where reward expectation decreases after receiving smaller-than-expected rewards instead of boundlessly increasing as it does in Redish's theory, apply to both drug and natural rewards.

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ProQuest

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English

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Thesis (M.A.)--American University, 2010.

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http://hdl.handle.net/1961/thesesdissertations:2863

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application/pdf

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Part of thesis digitization project, awaiting processing.

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