Working Memory Capacity and Cognitive Filtering Predict Demand Avoidance

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In general, optimization of task performance minimizes cognitive demand. For example, when participants can choose freely between task variants, they tend to select the one that minimizes cognitive demand (Kool et al., 2010). Here, we test whether the ability to filter irrelevant information during task performance will reduce cognitive processing demands. Previous research has shown that observers with higher visual working memory (VWM) capacity tend to be more efficient cognitive filterers (Vogel, McCollough & Machizawa, 2005). Consequently, we hypothesize that such demand avoidance arises from individual differences in VWM capacity. To test this hypothesis, we collected psychophysical and electrophysiological measures of VWM capacity and cognitive filtering in a sample of 22 observers. We then correlated these with independent psychophysical measures of demand avoidance. We found that observers with higher VWM capacity tended to select the less demanding of two task alternatives, and that this occurred because filtering irrelevant information increased their sensitivity to our covert demand manipulation. Moreover, reaction times increased significantly when a given trial's instructions switched with respect to the preceding trial's, and this increase tended to be larger among those with greater cognitive filtering ability. Taken together, our results suggest that working memory capacity and cognitive filtering ability contribute to individual differences in demand avoidance. Inefficient cognitive filterers tend to process more irrelevant information and are therefore less sensitive to covert variations in demand. Efficient filterers, on the other hand, can successfully ignore irrelevant information, and are therefore more sensitive. As such, we surmise that individual differences in visual working memory capacity and cognitive filtering predict demand avoidance.


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Attribution-NonCommercial-NoDerivatives Presented at the Vision Sciences Society Seventeenth Annual Meeting, St. Pete Beach, FL, May 19-24, 2017.