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Simple minds: a qualified defence of associative learning
Author(s) -
Cecilia Heyes
Publication year - 2012
Publication title -
philosophical transactions of the royal society b biological sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.753
H-Index - 272
eISSN - 1471-2970
pISSN - 0962-8436
DOI - 10.1098/rstb.2012.0217
Subject(s) - simple (philosophy) , associative property , simplicity , spell , psychology , animal cognition , associative learning , association (psychology) , cognition , cognitive science , cognitive psychology , blindness , epistemology , comparative cognition , sociology , neuroscience , philosophy , mathematics , medicine , anthropology , optometry , pure mathematics
Using cooperation in chimpanzees as a case study, this article argues that research on animal minds needs to steer a course between 'association-blindness'--the failure to consider associative learning as a candidate explanation for complex behaviour--and 'simple-mindedness'--the assumption that associative explanations trump more cognitive hypotheses. Association-blindness is challenged by the evidence that associative learning occurs in a wide range of taxa and functional contexts, and is a major force guiding the development of complex human behaviour. Furthermore, contrary to a common view, association-blindness is not entailed by the rejection of behaviourism. Simple-mindedness is founded on Morgan's canon, a methodological principle recommending 'lower' over 'higher' explanations for animal behaviour. Studies in the history and philosophy of science show that Morgan failed to offer an adequate justification for his canon, and subsequent attempts to justify the canon using evolutionary arguments and appeals to simplicity have not been successful. The weaknesses of association-blindness and simple-mindedness imply that there are no short-cuts to finding out about animal minds. To decide between associative and yet more cognitive explanations for animal behaviour, we have to spell them out in sufficient detail to allow differential predictions, and to test these predictions through observation and experiment.

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