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03-07-2018 | Original Article | Uitgave 2/2020

Psychological Research 2/2020

When task sharing reduces interference: evidence for division-of-labour in Stroop-like tasks

Psychological Research > Uitgave 2/2020
Roberta Sellaro, Barbara Treccani, Roberto Cubelli
Belangrijke opmerkingen
Roberta Sellaro and Barbara Treccani are shared first authors of this work. Co-first authors are listed in alphabetical order.


Performing a task with another person may either enhance or reduce the interference produced by task-irrelevant information. In three experiments, we employed the joint version of a Stroop-like task (i.e., the picture–word interference—PWI—task) to investigate some of the task features that seem to be critical in determining the effect of task-irrelevant information when the task is shared between two individuals. Participants were asked to perform a PWI task, which required to name a picture while ignoring a distractor word, first individually (in a baseline block of trials) and then co-acting with an alleged partner. Results showed that, compared to the baseline and to a condition in which participants continued to perform the PWI task individually, the belief of co-acting with another individual who was thought to be in charge of the distractor words suppressed the semantic interference effect when these words were in case alternation letters (e.g., “mOuSe”). Conversely, the semantic interference effect persisted when the co-actor was thought to be in charge of the same task as the participant, that is, the co-actor was thought to respond to the pictures. These results are accounted for by assuming that, when the participant knows that another person is in charge of the task-irrelevant information, a division-of-labour between participant and co-actor can be established. Such a division-of-labour may provide the participant with a strategy to oppose the semantic interference effect. Our findings, therefore, suggest that sharing a task with another person in charge of potentially interfering information can enable people to filter out this information from their own task representation.

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