The test of both worlds: identifying feature binding and control processes in congruency sequence tasks by means of action dynamics

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Abstract

Cognitive control processes enable us to act flexibly in a world posing ever-changing demands on our cognitive system. To study cognitive control, conflict tasks and especially congruency sequence effects have been regarded as a fruitful tool. However, for the last decade a dispute has arisen whether or not congruency sequence effects are indeed a valid measure of cognitive control processes. This debate has led to the development of increasingly complex paradigms involving numerous, intricately designed experimental conditions which are aimed at excluding low-level, associative learning mechanisms like feature binding as an alternative explanation for the emergence of congruency sequence effects. Here, we try to go beyond this all-or-nothing thinking by investigating the assumption that both cognitive control processes as well as feature binding mechanisms occur within trials of the same task. Based on a theoretical dual-route-model of behavior under conflict, we show that both classes of cognitive mechanisms should affect behavior at different points of the decision process. By comparing these predictions to continuous mouse movements from an adapted Simon task, we find evidence that control processes and feature binding mechanisms do indeed coexist within the task but that they follow distinct timing patterns. We argue that this dynamic approach to cognitive processing opens up new ways to investigate the diversity of co-existing processes that contribute to the selection of behavior.

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Change history

  • 13 March 2020

    An error in the description of one regressor and in the scaling of beta-weights lead to an incorrect figure and incorrect summary values in a table.

Notes

  1. 1.

    It must be noted that substantial effort has been put into the identification of the temporal characteristics of control versus feature binding influences on congruency sequence effects by analyzing temporal dynamics across trials (e.g., aftereffects of conflict processing in various inter-trial intervals, Egner, Ely, & Grinband, 2010; see also Mayr & Awh, 2009; Notebaert, Gevers, Verbruggen, & Liefooghe, 2006), while our approach focusses on these influences within trials.

  2. 2.

    Note, that this prediction on conflict adaptation across trials is independent of another argument derived from dual route models that refers to conflict solution within the trial: analyses of response time distributions in the Simon task—so called delta plots—show reduced Simon effects for slower trials (Ridderinkhof, van den Wildenberg, Wijnen, & Burle, 2004), which has been interpreted to indicate a temporal lag in the solution of conflict within the current trial, e.g. by inhibitory processes.

  3. 3.

    In the following, complete repetitions will refer to trials in which the compound of stimulus identity (individual digit) and stimulus location (and hence congruency and response) are identical to the previous trial.

  4. 4.

    Since we use a two-response version of the Simon task, one might argue that full switches in a strict sense are impossible. However, due to our combination of eight stimuli and three conditions of congruency (i.e., congruent, neutral, and incongruent), the task already offers a high number of dimensions to switch between, so that full switches will refer to trials that switch in location, demanded response (and hence stimulus) and congruency compared to the previous trial.

  5. 5.

    We apply temporal smoothing for three reasons (similar to spatial smoothing in fMRI analysis; e.g. Mikl et al., 2008): first, it increases the signal to noise ratio. Since the movement angle is a differential measure, it shows a higher level of noise than raw movement data; this noise is reduced by smoothing. Second, smoothing improves the validity of statistical tests since it makes error distributions more normal. Third, it accommodates slight temporal variations between subjects. For temporal smoothing, we used a Gaussian kernel of ten time steps.

  6. 6.

    While a similar analysis could also be performed with multi-level models (see e.g. Mirman, Dixon, & Magnuson, 2008), we use the simpler regression approach as our main interest is in the analysis of within-subject variance. However, we validated our results by an additional hierarchical linear model analysis of decisive time-steps. This analysis showed no qualitative difference in the resulting beta-weights (see supplementary material), supporting our simpler approach.

  7. 7.

    Adding higher order contingencies, e.g. different kinds of partial repetitions or interaction terms, leads to strong correlations (and hence invalid levels of multicollinearity) of these new regressors with conflict adaptation. This is a particular concern the simpler the design of the task and offers typical criticism by proponents of feature binding accounts—we deliberately accepted this limitation as our focus is on unravelling specific influences and their timing patterns based on a theoretical approach instead of explaining all variance that could be present in the data. A consequence of the strong correlations of any additional terms is that performing the analysis with such regressors is methodologically infeasible and does not increase the quality of the statistical model as indicated by a constant R 2.

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Correspondence to Stefan Scherbaum.

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This research was funded by the German Research Foundation (DFG Grant SCH1827/1-1 to S. S.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Scherbaum, S., Frisch, S., Dshemuchadse, M. et al. The test of both worlds: identifying feature binding and control processes in congruency sequence tasks by means of action dynamics. Psychological Research 82, 337–352 (2018). https://doi.org/10.1007/s00426-016-0823-9

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