The role of conjunctive representations in prioritizing and selecting planned actions
Abstract
For flexible goal-directed behavior, prioritizing and selecting a specific action among multiple candidates is often important. Working memory has long been assumed to play a role in prioritization and planning, while bridging cross-temporal contingencies during action selection. However, studies of working memory have mostly focused on memory for single components of an action plan, such as a rule or a stimulus, rather than management of all of these elements during planning. Therefore, it is not known how post-encoding prioritization and selection operate on the entire profile of representations for prospective actions. Here, we assessed how such control processes unfold over action representations, highlighting the role of conjunctive representations that nonlinearly integrate task-relevant features during maintenance and prioritization of action plans. For each trial, participants prepared two independent rule-based actions simultaneously, then they were retro-cued to select one as their response. Prior to the start of the trial, one rule-based action was randomly assigned to be high priority by cueing that it was more likely to be tested. We found that both full action plans were maintained as conjunctive representations during action preparation, regardless of priority. However, during output selection, the conjunctive representation of the high priority action plan was more enhanced and readily selected as an output. Further, the strength of the high priority conjunctive representation was associated with behavioral interference when the low priority action was tested. Thus, multiple alternate upcoming actions were maintained as integrated representations and served as the target of post-encoding attentional selection mechanisms to prioritize and select an action from within working memory.
Data availability
Data and analyses are available through OSF (https://https://osf.io/4mx8c/). Specifically, the repository contains trial-by-trial behavioral data files, and all relevant EEG data.
Article and author information
Author details
Funding
National Science Foundation (1734264)
- Ulrich Mayr
National Institute of Mental Health (MH125497)
- David Badre
National Institute of Neurological Disorders and Stroke (NS108380)
- David Badre
Multidisciplinary University Research Initiative (N00014-16-1-2832)
- David Badre
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, Kikumoto et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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