Literature DB >> 32406716

Strategy use in probabilistic categorization by rhesus macaques (Macaca mulatta) and capuchin monkeys (Cebus [Sapajus] apella).

Will Whitham1, David A Washburn1.   

Abstract

Probabilistic categorization tasks present the learner with a set of possible responses and imperfect cue evidence of which response will be rewarded. A single, optimal integration of all available cues into an optimal response is possible given any set of evidence. In contrast, there are many possible uses of the cues that offer the learner suboptimal (but better than chance) responding. We presented a classic probabilistic categorization task to 3 rhesus macaques (Macaca mulatta) and 13 capuchin monkeys (Cebus [Sapajus] apella) to explore what strategies for integration of imperfectly predictive stimulus information would be used by the animals. Using the strategy analysis models that have been previously used to describe human strategy use in probabilistic categorization tasks, we fit each of thousands of blocks of responses to 25 types of response strategies ranging from complex cognitive strategies (e.g., optimal integration of all evidence) to heuristic strategies (e.g., identify a highly predictive cue and respond based only on its presence or absence) to rote behavior (e.g., choosing the same response every trial). Inferences about strategy use were highly stable within animals and were heterogeneous across animals, with some animals never using cue information and others using it fruitfully. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Entities:  

Year:  2020        PMID: 32406716      PMCID: PMC7993029          DOI: 10.1037/com0000221

Source DB:  PubMed          Journal:  J Comp Psychol        ISSN: 0021-9940            Impact factor:   2.231


  27 in total

1.  How do people solve the "weather prediction" task?: individual variability in strategies for probabilistic category learning.

Authors:  Mark A Gluck; Daphna Shohamy; Catherine Myers
Journal:  Learn Mem       Date:  2002 Nov-Dec       Impact factor: 2.460

2.  A model of hypothesis behavior in discrimination learning set.

Authors:  M LEVINE
Journal:  Psychol Rev       Date:  1959-11       Impact factor: 8.934

Review 3.  Basal ganglia and dopamine contributions to probabilistic category learning.

Authors:  D Shohamy; C E Myers; J Kalanithi; M A Gluck
Journal:  Neurosci Biobehav Rev       Date:  2007-08-10       Impact factor: 8.989

4.  Distinguishing the contributions of implicit and explicit processes to performance of the weather prediction task.

Authors:  Amanda L Price
Journal:  Mem Cognit       Date:  2009-03

5.  Hand preference for a bimanual task in tufted capuchins (Cebus apella) and rhesus macaques (Macaca mulatta).

Authors:  G C Westergaard; S J Suomi
Journal:  J Comp Psychol       Date:  1996-12       Impact factor: 2.231

6.  Prototype abstraction by monkeys (Macaca mulatta).

Authors:  J David Smith; Joshua S Redford; Sarah M Haas
Journal:  J Exp Psychol Gen       Date:  2008-05

Review 7.  Implicit and explicit categorization: a tale of four species.

Authors:  J David Smith; Mark E Berg; Robert G Cook; Matthew S Murphy; Matthew J Crossley; Joseph Boomer; Brian Spiering; Michael J Beran; Barbara A Church; F Gregory Ashby; Randolph C Grace
Journal:  Neurosci Biobehav Rev       Date:  2012-09-11       Impact factor: 8.989

Review 8.  Probabilistic categorization: how do normal participants and amnesic patients do it?

Authors:  M Meeter; G Radics; C E Myers; M A Gluck; R O Hopkins
Journal:  Neurosci Biobehav Rev       Date:  2007-11-19       Impact factor: 8.989

9.  Impaired procedural learning in language impairment: results from probabilistic categorization.

Authors:  Ferenc Kemeny; Agnes Lukacs
Journal:  J Clin Exp Neuropsychol       Date:  2009-06-22       Impact factor: 2.475

10.  Generalized outcome-based strategy classification: comparing deterministic and probabilistic choice models.

Authors:  Benjamin E Hilbig; Morten Moshagen
Journal:  Psychon Bull Rev       Date:  2014-12
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