September 2019
Volume 19, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2019
Increasingly complex internal visual representations in honeybees, human infants and adults
Author Affiliations & Notes
  • Beáta T Szabó
    Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
    Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary
    Department of Cognitive Science, Central European University, Budapest, Hungary
  • Aurore Avarguès-Weber
    Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, Université de Toulouse, Toulouse, France
  • Gergő Orbán
    Department of Cognitive Science, Central European University, Budapest, Hungary
    Department of Theoretical Physics, Wigner Research Centre for Physics, Hungarian Academy of Sciences, Budapest, Hungary
  • Valerie Finke
    Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, Université de Toulouse, Toulouse, France
  • Márton Nagy
    Department of Cognitive Science, Central European University, Budapest, Hungary
  • Adrian Dyer
    Bio-inspired Digital Sensing Lab, School of Media and Communication, RMIT University, Melbourne, Australia
  • József Fiser
    Department of Cognitive Science, Central European University, Budapest, Hungary
Journal of Vision September 2019, Vol.19, 292c. doi:https://doi.org/10.1167/19.10.292c
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      Beáta T Szabó, Aurore Avarguès-Weber, Gergő Orbán, Valerie Finke, Márton Nagy, Adrian Dyer, József Fiser; Increasingly complex internal visual representations in honeybees, human infants and adults. Journal of Vision 2019;19(10):292c. https://doi.org/10.1167/19.10.292c.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Although some animals such as honeybees (Apis mellifera) are excellent visual learners, little is known about their spontaneously emerging internal representations of the visual environment. We investigated whether learning mechanisms and resulting internal representations are similar across different species by using the same modified visual statistical learning paradigm in honeybees and humans. Observers performed an unrelated discrimination task while being exposed to complex visual stimuli consisting of simple shapes with varying underlying statistical structures. Familiarity tests was used for assessing the emergent internal representation in three conditions exploiting whether each of three different statistics (single shape frequencies, co-occurrence probabilities and conditional probability between neighboring shapes) were sufficient for solving the familiarity task. We found an increasingly complex representation of the visual environment as we moved from honeybees to human infant and to adults. Honeybees automatically learned the joint probabilities of the shapes after extended familiarization but didn’t show sensitivity to the conditional probabilities and they didn’t learn concurrently the single-element frequencies. As we know from previous studies, infants implicitly learn joint- and conditional probabilities, but they aren’t sensitive to concurrent element frequencies either. Adult results in this study were in line with previous results showing that they spontaneously acquired all three statistics. We found that these results could be reproduced by a progression of models: while honeybee behavior could be captured by a learning method based on a simple counting strategy, humans learned differently. Replicating infant’s behavior required a probabilistic chunk learner algorithm. The same model could also replicate the adult behavior, but only if it was further extended by co-representation of higher order chunk and low-level element representations. In conclusion, we’ve found a progression of increasingly complex visual learning mechanisms that were necessary to account for the differences in the honeybee, human infant- and adult behavioral results.

Acknowledgement: This research has been partially supported by the European Union, co-financed by the European Social Fund (EFOP-3.6.3-VEKOP- 16-2017-00002) 
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