Abstract

Sensitivity to the regularities and structure contained within sequential, goal-directed actions is an important building block for generating expectations about the actions we observe. Until now, research on statistical learning for actions has solely focused on individual action sequences, but many actions in daily life involve multiple actors in various interaction contexts. The current study is the first to investigate the role of statistical learning in tracking regularities between actions performed by different actors, and whether the social context characterizing their interaction influences learning. That is, are observers more likely to track regularities across actors if they are perceived as acting jointly as opposed to in parallel? We tested adults and toddlers to explore whether social context guides statistical learning and—if so—whether it does so from early in development. In a between-subjects eye-tracking experiment, participants were primed with a social context cue between two actors who either shared a goal of playing together (‘Joint’ condition) or stated the intention to act alone (‘Parallel’ condition). In subsequent videos, the actors performed sequential actions in which, for certain action pairs, the first actor’s action reliably predicted the second actor’s action. We analyzed predictive eye movements to upcoming actions as a measure of learning, and found that both adults and toddlers learned the statistical regularities across actors when their actions caused an effect. Further, adults with high statistical learning performance were sensitive to social context: those who observed actors with a shared goal were more likely to correctly predict upcoming actions. In contrast, there was no effect of social context in the toddler group, regardless of learning performance. These findings shed light on how adults and toddlers perceive statistical regularities across actors depending on the nature of the observed social situation and the resulting effects.

Highlights

  • IntroductionStatistical learning refers to the fundamental ability to extract regularities from continuous sensory input

  • Statistical learning of action sequencesStatistical learning refers to the fundamental ability to extract regularities from continuous sensory input

  • The range and scope of this mechanism has led to the view that statistical learning forms part of the basic cognitive skill set necessary for language acquisition [6] and understanding of mental states [7]

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Summary

Introduction

Statistical learning refers to the fundamental ability to extract regularities from continuous sensory input. These skills support learning in multiple domains and across development from early in infancy throughout adulthood [1,2,3]. Statistical learning in social action contexts extract regularities from sequences of visual shapes [4], auditory tones [5], linguistic items, and grammatical structures (for an in-depth discussion, see [6]). Humans are sensitive to the regularities and structure contained within sequential, goal-directed actions they observe others perform [8,9]. Even 10to 11-month-old infants notice when familiar action streams, such as someone cleaning a kitchen, are interrupted with pauses that disrupt the known structure of the sequence [10]

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