Different mechanisms underlie implicit visual statistical learning in honey bees and humans
Abstract
Do animals encode statistical information about visual patterns the same way as humans do? If so, humans’ superior visual cognitive skills must depend on some other factors; if not, the nature of the differences can provide hints about what makes human learning so versatile. We provide a systematic comparison of automatic visual learning in humans and honey bees, showing that while bees do extract statistical information about co-occurrence contingencies of visual scenes, in contrast to humans, they do not automatically encode conditional information. Thus, acquiring implicit knowledge about the statistical properties of the visual environment may be a general mechanism in animals, but the richer representation developed automatically by humans might require specific probabilistic computational faculties.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 28, 2020
- Source ID
- 10.1073/pnas.1919387117
Entities
People
- Adrian G Dyer
- Aurore Avarguès-Weber
- Daniele D’amaro
- József Fiser
- Márton Gáspár Nagy
- Tūnde Szabó
- Valerie Finke
Organizations
- Central European University
- Institute of Cognitive Neuroscience and Psychology
- Johannes Gutenberg University Mainz
- Monash University
- National Institute for Health and Care Research
- Office of Naval Research Global
- Pázmány Péter Catholic University
- RMIT University
- University of Toulouse (1896-1968)