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Action selection appears to rely on conjunctive representations that nonlinearly integrate task-relevant features. Here, we tested a corollary of this hypothesis: that such representations are also intricately involved during attempts to stop an action—a key aspect of action regulation. We tracked both conjunctive representations and those of constituent rule, stimulus, or response features through trial-by-trial representational similarity analysis of the electroencephalogram signal in a combined rule-selection and stop-signal paradigm. Across two experiments with student participants ( N = 57), we found (a) that the strength of decoded conjunctive representations prior to the stop signal uniquely predicted trial-by-trial stopping success (Experiment 1) and (b) that these representations were selectively suppressed following the onset of the stop signal (Experiments 1 and 2). We conclude that conjunctive representations are key to successful action execution and therefore need to be suppressed when an intended action is no longer appropriate. 

Action selection appears to rely on conjunctive representations that nonlinearly integrate task- relevant features (Kikumoto & Mayr, 2020). We test here the flip-side of this hypothesis that such representations are also intricately involved during attempts to stop an action––a key aspect of action regulation. We tracked both conjunctive representations and those of constituent rule, stimulus, or response features through trial-by-trial representational similarity analysis of the EEG signal in a combined, rule-selection and stop-signal paradigm. Across two experiments with student participants (N = 57), we found (a) that the strength of decoded conjunctive representations prior to the stop-signal uniquely predicted trial-by-trial stopping success (Exp. 1) and (b) that these representations were selectively suppressed following the onset of the stop-signal (Exp. 1 and 2). We conclude that conjunctive representations are key to successful action execution and therefore need to be suppressed when an intended action is no longer appropriate. 

People can use abstract rules to flexibly configure and select actions for specific situations, yet how exactly rules shape actions toward specific sensory and/or motor requirements remains unclear. Both research from animal models and human-level theories of action control point to the role of highly integrated, conjunctive representations, sometimes referred to as event files. These representations are thought to combine rules with other, goal-relevant sensory and motor features in a nonlinear manner and represent a necessary condition for action selection. However, so far, no methods exist to track such representations in humans during action selection with adequate temporal resolution. Here, we applied time-resolved representational similarity analysis to the spectral-temporal profiles of electroencephalography signals while participants performed a cued, rule-based action selection task. In two experiments, we found that conjunctive representations were active throughout the entire selection period and were functionally dissociable from the representation of constituent features. Specifically, the strength of conjunctions was a highly robust predictor of trial-by-trial variability in response times and was selectively related to an important behavioral indicator of conjunctive representations, the so-called partial-overlap priming pattern. These results provide direct evidence for conjunctive representations as critical precursors of action selection in humans. 

Despite strong theoretical reasons for assuming that abstract representations organize complex action sequences in terms of subplans (chunks) and sequential positions, we lack methods to directly track such content-independent, hierarchical representations in humans. We applied time-resolved, multivariate decoding analysis to the pattern of rhythmic EEG activity that was registered while participants planned and executed individual elements from pre-learned, structured sequences. Across three experiments, the theta and alpha-band activity coded basic elements and abstract control representations, in particular, the ordinal position of basic elements, but also the identity and position of chunks. Further, a robust representation of higher level, chunk identity information was only found in individuals with above-median working memory capacity, potentially providing a neural-level explanation for working-memory differences in sequential performance. Our results suggest that by decoding oscillatory activity we can track how the cognitive system traverses through the states of a hierarchical control structure.