Skip to Main Content
We solicit papers that show approaches to bridging macro- and micro-level behavioral research on the "social interaction loop" that supports early learning. By "social interaction loop" we mean action sequences during interactions between learners and teachers, and the content and qualities of those interactions. For example, how is the information available for a new learner selected and shaped by a parent or teacher? How do learners display their knowledge or ability, and how do teachers pick up on this information, and adapt to it? The phenomena of interest prototypically focus on human infants and parents, but the same questions can be asked about non-human juvenile-adult dyads, or robot learners with human teachers. A major focus is to precisely quantify and describe what the interaction provides - that is - the specific events and mechanisms that support social learning and adaptation. The contributions should exemplify diverse approaches to studying learning through real-time, contingent, reciprocal interaction (or "co-action"). The focus should be on bridging macro- and micro-level data, analysis, and/or explanation. Macro-level investigations use broad categories of behaviors that unfold over second or minutes, and/or gross changes over developmental or training time (weeks; months). Micro-level investigations describe the details of precise behaviors (or physiological changes), measured in fractions of seconds, that "shape" the (macro-level) interactions. This special issue is intended to show new approaches to "closing the loop" between macro- and micro-level observations of interactions.