Abstracts (first author)


Experimental studies of human social learning strategies: exploring sex differences

Author(s): Cross C, Brown G, Morgan T, Laland K


Objectives Culture is an important driver of recent biological evolution in humans. The mechanisms by which information is transmitted between individuals can be studied at the population level – by cultural evolutionists, and at the individual level – by social psychologists. We combined methods from these two approaches to investigate how sex differences in confidence might lead to sex differences in the use of a copy-when-uncertain social learning strategy. Methods Participants (Study 1: N=97; Study 2: N=89) completed a series of two-alternative forced-choice puzzles and reported their confidence in each answer. They then saw the decisions of some previous participants before being asked again for their answer. Social information use was inferred when participants switched their answer to match that of the majority. We modelled the probability of social information use with participant sex, confidence in initial decision, and accuracy of initial decision as predictors. Results Across both studies, confidence had a large effect on social information use, indicative of a copy-when-uncertain strategy. Accuracy predicted confidence, indicating that this strategy is adaptive. Confidence also differed by sex: women reported lower confidence (independent of any small sex differences in accuracy), which in turn increased their probability of using social information. Conclusions Although both sexes appear to use a ‘copy-when-uncertain’ strategy, women are more likely to feel uncertain. This means that a strategy observed to be used in a population (e.g. copy-when-uncertain) can vary according to individual differences in psychological traits. Further integration of these two levels of explanation is therefore needed.


Chairman: Octávio S. Paulo
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XIV Congress of the European Society for Evolutionary Biology

Organization Team
Department of Animal Biology (DBA)
Faculty of Sciences of the University of Lisbon
P-1749-016 Lisbon


Computational Biology & Population Genomics Group