No evidence of task co-representation in a joint Stroop task
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People working together on a task must often represent the goals and salient items of their partner. The aim of the present study was to study the influence of joint task representations in an interference task in which the congruency relies on semantic identity. If task representations are shared between partners in a joint Stroop task (co-representation account), we hypothesized that items in the response set of one partner might influence performance of the other. In Experiment 1, pairs of participants sat side by side. Each participant was instructed to press one of two buttons to indicate which of two colors assigned to them was present, ignoring the text and responding only to the pixel color. There were three types of incongruent distractor words: names of colors from their own response set, names of colors from the other partner’s response set, and neutral words for colors not used as font colors. The results of Experiment 1 showed that when people were doing this task together, distractor words from the partner’s response set interfered more than neutral words and just as much as the words from their own response color set. However, in three follow-up experiments (Experiments 2a, 2b, and 2c), we found an elevated interference for the other response-set words even though no co-actor was present. The overall pattern of results across our study suggests that an alternative response set, regardless of whether it belonged to a co-actor or to a non-social no-go condition, evoked equal amounts of interference comparable to those of the own response set. Our findings are in line with a theory of common coding, in which all events—irrespective of their social nature—are represented and can influence behavior.
The research was funded by the Autonomous Province of Trento, Call “Grandi Progetti 2012”, project “Characterizing and improving brain mechanisms of attention—ATTEND”. The authors report no conflict of interests. We thank Maria Paraskevopoulou and Claudia Bonmassar for the assistance in data collection.
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