Why statistical learning researchers should study nonhuman animals.

Abstract

Statistical learning (SL) - the ability to detect patterns in sensory input without explicit instruction - is crucial for building internal models of the environment. In humans, it notably supports language acquisition, including word segmentation and grammar learning. Evidence across primates, songbirds, rodents, and insects indicate that SL is a widely shared evolutionarily conserved ability. The computational complexity of the mechanisms involved; however, varies between species, likely reflecting specific cognitive limitations. Alternatively, specific competences may have evolved to support the emergence of demanding, ecologically relevant, functions such as complex communication systems. These findings challenge the idea of a human-specific SL module while raising key questions about its evolutionary drivers and underlying mechanisms. Addressing these questions requires the expansion of cross-species, cross-modal studies with ecologically valid, unsupervised paradigms. Comparative approaches are indeed essential to uncover shared properties and species-specific SL adaptations. Framing SL as a foundational component of cognition, informed by animal research, offers new insights into brain function, and implicit learning in light of the evolution of sophisticated, emergent cognitive abilities such as complex communication systems.