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Dissociating selectivity adjustments from temporal learning-introducing the context-dependent proportion congruency effect.

Michael SprengelMiriam TomatMike WendtSven KnothThomas Jacobsen
Published in: PloS one (2022)
The list-level proportion congruency effect (PCE) and the context-specific PC (CSPC) effect are typical findings in experimental conflict protocols, which competing explanations attribute to different mechanisms. Of these mechanisms, stimulus-unspecific conflict-induced selectivity adjustments have attracted the most interest, from various disciplines. Recent methodological advances have yielded an experimental procedure for entirely ruling out all stimulus-specific alternatives. However, there is a stimulus-unspecific alternative-temporal learning-which cannot even be ruled out as the sole cause of either effect with any established experimental procedure. That is because it is very difficult to create a scenario in which selectivity adjustments and temporal learning make different predictions-with traditional approaches, it is arguably impossible. Here, we take a step towards solving this problem, and experimentally dissociating the two mechanisms. First, we present our novel approach which is a combination of abstract experimental conditions and theoretical assumptions. As we illustrate with two computational models, given this particular combination, the two mechanisms predict opposite modulations of an as yet unexplored hybrid form of the list-level PCE and the CSPC effect, which we term context-dependent PCE (CDPCE). With experimental designs that implement the abstract conditions properly, it is therefore possible to rule out temporal learning as the sole cause of stimulus-unspecific adaptations to PC, and to unequivocally attribute the latter, at least partially, to selectivity adjustments. Secondly, we evaluate methodological and theoretical aspects of the presented approach. Finally, we report two experiments, that illustrate both the promise of and a potential challenge to this approach.
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