Co-actors represent each other's task regularity through social statistical learning.

Area of Science:

• Cognitive Psychology
• Social Neuroscience
• Human-Computer Interaction

Background:

• Joint action research shows implicit representation of co-actor's low-level task elements.
• The extent to which abstract, high-level task features are co-represented remains unclear.

Purpose of the Study:

• To investigate whether individuals represent and learn abstract task regularity from a co-actor in joint action.
• To determine if this learned regularity transfers to subsequent individual performance.

Main Methods:

• Participants performed tasks alongside co-actors, responding to interleaved shape sequences.
• Varied practice conditions included joint practice, individual practice with a co-actor present, and individual practice alone.
• Assessed learning of co-actor's sequences (indirect learning) and own sequences (direct learning).

Main Results:

• Participants showed indirect learning of co-actor's sequence regularity when practicing together or believing they were acting together.
• Indirect learning was absent when co-actors were disengaged or tasks lacked shared regularity.
• Direct learning of own sequences remained consistent across conditions.

Conclusions:

• Co-actors represent and learn each other's task regularity through social statistical learning.
• This abstract regularity is transferred to subsequent performances, extending co-representation in joint action.