Different mechanisms underlie implicit visual statistical learning in honey bees and humans

Significance 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. The ability of developing complex internal representations of the environment is considered a crucial antecedent to the emergence of humans’ higher cognitive functions. Yet it is an open question whether there is any fundamental difference in how humans and other good visual learner species naturally encode aspects of novel visual scenes. Using the same modified visual statistical learning paradigm and multielement stimuli, we investigated how human adults and honey bees (Apis mellifera) encode spontaneously, without dedicated training, various statistical properties of novel visual scenes. We found that, similarly to humans, honey bees automatically develop a complex internal representation of their visual environment that evolves with accumulation of new evidence even without a targeted reinforcement. In particular, with more experience, they shift from being sensitive to statistics of only elemental features of the scenes to relying on co-occurrence frequencies of elements while losing their sensitivity to elemental frequencies, but they never encode automatically the predictivity of elements. In contrast, humans involuntarily develop an internal representation that includes single-element and co-occurrence statistics, as well as information about the predictivity between elements. Importantly, capturing human visual learning results requires a probabilistic chunk-learning model, whereas a simple fragment-based memory-trace model that counts occurrence summary statistics is sufficient to replicate honey bees’ learning behavior. Thus, humans’ sophisticated encoding of sensory stimuli that provides intrinsic sensitivity to predictive information might be one of the fundamental prerequisites of developing higher cognitive abilities.