Literature DB >> 35122189

Extrinsic rewards, intrinsic rewards, and non-optimal behavior.

Mousa Karayanni1, Israel Nelken2,3.   

Abstract

The optimality of behavior in experimental settings is usually determined with respect to an extrinsic reward defined by the experimenters. However, actions that do not lead to reward are ubiquitous in many species and in many experimental paradigms. Modern research on decision processes commonly treat non-optimal behaviors as noise, often excluding from analysis animals that do not reach behavioral performance criteria. However, non-optimal behaviors can be a window on important brain processes. Here we explore the evidence that non-optimal behaviors are the consequence of intrinsically motivated actions, related to drives that are different from that of obtaining extrinsic reward. One way of operationally characterizing these drives is by postulating intrinsic rewards associated with them. Behaviors that are apparently non-optimal can be interpreted as the consequence of optimal decisions whose goal is to optimize a combination of intrinsic and extrinsic rewards. We review intrinsic rewards that have been discussed in the literature, and suggest ways of testing their existence and role in shaping animal behavior.
© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Curiosity; Decision making; Exploration; Intrinsic motivation; Optimal behaviour; Reward processing

Mesh:

Year:  2022        PMID: 35122189     DOI: 10.1007/s10827-022-00813-z

Source DB:  PubMed          Journal:  J Comput Neurosci        ISSN: 0929-5313            Impact factor:   1.621


  21 in total

1.  Monkeys pay per view: adaptive valuation of social images by rhesus macaques.

Authors:  Robert O Deaner; Amit V Khera; Michael L Platt
Journal:  Curr Biol       Date:  2005-03-29       Impact factor: 10.834

2.  An Analysis of Decision under Risk in Rats.

Authors:  Christine M Constantinople; Alex T Piet; Carlos D Brody
Journal:  Curr Biol       Date:  2019-05-30       Impact factor: 10.834

3.  Cortical substrates for exploratory decisions in humans.

Authors:  Nathaniel D Daw; John P O'Doherty; Peter Dayan; Ben Seymour; Raymond J Dolan
Journal:  Nature       Date:  2006-06-15       Impact factor: 49.962

4.  Neural circuitry of information seeking.

Authors:  Ethan S Bromberg-Martin; Ilya E Monosov
Journal:  Curr Opin Behav Sci       Date:  2020-08-25

5.  Orbitofrontal cortex uses distinct codes for different choice attributes in decisions motivated by curiosity.

Authors:  Tommy C Blanchard; Benjamin Y Hayden; Ethan S Bromberg-Martin
Journal:  Neuron       Date:  2015-01-22       Impact factor: 17.173

6.  Midbrain dopamine neurons signal preference for advance information about upcoming rewards.

Authors:  Ethan S Bromberg-Martin; Okihide Hikosaka
Journal:  Neuron       Date:  2009-07-16       Impact factor: 17.173

7.  Rats and humans can optimally accumulate evidence for decision-making.

Authors:  Bingni W Brunton; Matthew M Botvinick; Carlos D Brody
Journal:  Science       Date:  2013-04-05       Impact factor: 47.728

8.  Model-based influences on humans' choices and striatal prediction errors.

Authors:  Nathaniel D Daw; Samuel J Gershman; Ben Seymour; Peter Dayan; Raymond J Dolan
Journal:  Neuron       Date:  2011-03-24       Impact factor: 17.173

9.  Lateral habenula neurons signal errors in the prediction of reward information.

Authors:  Ethan S Bromberg-Martin; Okihide Hikosaka
Journal:  Nat Neurosci       Date:  2011-08-21       Impact factor: 24.884

10.  Value-complexity tradeoff explains mouse navigational learning.

Authors:  Nadav Amir; Reut Suliman-Lavie; Maayan Tal; Sagiv Shifman; Naftali Tishby; Israel Nelken
Journal:  PLoS Comput Biol       Date:  2020-12-11       Impact factor: 4.475

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