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More than one way to see it: Individual heuristics in avian visual computation

Comparative pattern learning experiments investigate how different species find regularities in sensory input, providing insights into cognitive processing in humans and other animals. Past research has focused either on one species’ ability to process pattern classes or different species’ performan...

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Autores principales: Ravignani, Andrea, Westphal-Fitch, Gesche, Aust, Ulrike, Schlumpp, Martin M., Fitch, W. Tecumseh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4710635/
https://www.ncbi.nlm.nih.gov/pubmed/26113444
http://dx.doi.org/10.1016/j.cognition.2015.05.021
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author Ravignani, Andrea
Westphal-Fitch, Gesche
Aust, Ulrike
Schlumpp, Martin M.
Fitch, W. Tecumseh
author_facet Ravignani, Andrea
Westphal-Fitch, Gesche
Aust, Ulrike
Schlumpp, Martin M.
Fitch, W. Tecumseh
author_sort Ravignani, Andrea
collection PubMed
description Comparative pattern learning experiments investigate how different species find regularities in sensory input, providing insights into cognitive processing in humans and other animals. Past research has focused either on one species’ ability to process pattern classes or different species’ performance in recognizing the same pattern, with little attention to individual and species-specific heuristics and decision strategies. We trained and tested two bird species, pigeons (Columba livia) and kea (Nestor notabilis, a parrot species), on visual patterns using touch-screen technology. Patterns were composed of several abstract elements and had varying degrees of structural complexity. We developed a model selection paradigm, based on regular expressions, that allowed us to reconstruct the specific decision strategies and cognitive heuristics adopted by a given individual in our task. Individual birds showed considerable differences in the number, type and heterogeneity of heuristic strategies adopted. Birds’ choices also exhibited consistent species-level differences. Kea adopted effective heuristic strategies, based on matching learned bigrams to stimulus edges. Individual pigeons, in contrast, adopted an idiosyncratic mix of strategies that included local transition probabilities and global string similarity. Although performance was above chance and quite high for kea, no individual of either species provided clear evidence of learning exactly the rule used to generate the training stimuli. Our results show that similar behavioral outcomes can be achieved using dramatically different strategies and highlight the dangers of combining multiple individuals in a group analysis. These findings, and our general approach, have implications for the design of future pattern learning experiments, and the interpretation of comparative cognition research more generally.
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spelling pubmed-47106352016-02-11 More than one way to see it: Individual heuristics in avian visual computation Ravignani, Andrea Westphal-Fitch, Gesche Aust, Ulrike Schlumpp, Martin M. Fitch, W. Tecumseh Cognition Article Comparative pattern learning experiments investigate how different species find regularities in sensory input, providing insights into cognitive processing in humans and other animals. Past research has focused either on one species’ ability to process pattern classes or different species’ performance in recognizing the same pattern, with little attention to individual and species-specific heuristics and decision strategies. We trained and tested two bird species, pigeons (Columba livia) and kea (Nestor notabilis, a parrot species), on visual patterns using touch-screen technology. Patterns were composed of several abstract elements and had varying degrees of structural complexity. We developed a model selection paradigm, based on regular expressions, that allowed us to reconstruct the specific decision strategies and cognitive heuristics adopted by a given individual in our task. Individual birds showed considerable differences in the number, type and heterogeneity of heuristic strategies adopted. Birds’ choices also exhibited consistent species-level differences. Kea adopted effective heuristic strategies, based on matching learned bigrams to stimulus edges. Individual pigeons, in contrast, adopted an idiosyncratic mix of strategies that included local transition probabilities and global string similarity. Although performance was above chance and quite high for kea, no individual of either species provided clear evidence of learning exactly the rule used to generate the training stimuli. Our results show that similar behavioral outcomes can be achieved using dramatically different strategies and highlight the dangers of combining multiple individuals in a group analysis. These findings, and our general approach, have implications for the design of future pattern learning experiments, and the interpretation of comparative cognition research more generally. Elsevier 2015-10 /pmc/articles/PMC4710635/ /pubmed/26113444 http://dx.doi.org/10.1016/j.cognition.2015.05.021 Text en © 2015 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ravignani, Andrea
Westphal-Fitch, Gesche
Aust, Ulrike
Schlumpp, Martin M.
Fitch, W. Tecumseh
More than one way to see it: Individual heuristics in avian visual computation
title More than one way to see it: Individual heuristics in avian visual computation
title_full More than one way to see it: Individual heuristics in avian visual computation
title_fullStr More than one way to see it: Individual heuristics in avian visual computation
title_full_unstemmed More than one way to see it: Individual heuristics in avian visual computation
title_short More than one way to see it: Individual heuristics in avian visual computation
title_sort more than one way to see it: individual heuristics in avian visual computation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4710635/
https://www.ncbi.nlm.nih.gov/pubmed/26113444
http://dx.doi.org/10.1016/j.cognition.2015.05.021
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