The architecture of working memory: Features from multiple remembered objects produce parallel, coactive guidance of attention in visual search

Abstract

Theories of working memory (WM) differ in their claims about the number of items that can be maintained in a state that directly interacts with other, ongoing cognitive operations (termed the focus of attention). A similar debate has arisen in the literature on visual working memory (VWM), focused on the number of items that can simultaneously interact with attentional priority. In 3 experiments, we used a redundancy-gain paradigm to provide a comprehensive test of the latter question. Participants searched for 2 cued features (e.g., a color and a shape) within a search array. The cued feature values changed on a trial-by-trial basis, requiring VWM. The target (when present) could match 1 of the cued features (single-target trials) or both cued features (redundant-target trials). We tested whether response time distributions contained a substantial proportion of trials with redundant-target responses that were faster than predicted by 2 independent guidance processes operating in parallel (i.e., violations of the race-model inequality). Violations are consistent with a coactive architecture in which both cued values guide attention in parallel and sum on the priority map. Robust violations were observed in all cases predicted by the hypothesis that multiple items in VWM can guide attention simultaneously, and these results were inconsistent with the hypothesis that guidance is limited to a single item simultaneously. When considered in the larger context of the literature on VWM and attention, the present results are consistent with a model of WM architecture in which the focus of attention can maintain multiple, independent representations. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Figure 1.

A) Sequence of events in a trial of Experiments 1 and 2. Participants first saw a search cue consisting of two feature values (a color and a shape) that randomly changed on a trial-by-trial basis. Next, they searched for a target object in the search array that matched either of the two cued feature values. The search task was present/absent, except in Experiment 2B (internal bar orientation report). Note that the embedded horizontal or vertical bars were present in all sub-experiments but were task relevant only in Experiment 2B. B) Search conditions and other manipulations. The right side of Panel B illustrates the main search conditions. There were three target-present conditions. In the two single-target conditions, the target object matched one of the two cued feature values but not the other. In the redundant-target condition, the target matched both feature values. Finally, on target-absent trials, neither feature value was present (there were no target-absent trials in Experiment 2B). Note that in Experiment 1A, 2A, and 2B, when redundant target features were present, they were associated with the same object in the search array. In Experiment 1B, they were associated with different objects in the search array. Finally, the left side of Panel B illustrates the cue manipulation. In Experiment 1 the two cued feature values were presented as part of a single object (integrated cue). In Experiment 2, these values were presented as parts of two separate objects (separated cue).

Figure 2.

An illustration of the possible implementation of coactive guidance from multiple items in VWM on the priority map used to guide attention. The left-side of the figure illustrates guidance on single-target trials. When searching for a red item or a square item, a green square in the display will generate a larger salience signal than other items given its match to the cued shape, allowing attention to be oriented to the target location. The right-side of the figure illustrates guidance on redundant-target trials. A redundantly matching red square will create a salience signal that is larger than could have been generated by any single-feature match, because top-down guidance from the two feature values sum at the target location, allowing more efficient orientating to the target than could have been generated in the single-target condition. In other words, guidance from the two feature values coactivate at the target location.

Figure 3.

A) Hypothetical cumulative distribution functions (CDFs) illustrating a data pattern the violates the race model inequality (RMI). Lines indicate the cumulative probability of response by a particular time, t, for the three target-present conditions. Gray bars indicate the sum of the single-target probabilities for values of t corresponding to the 5^th to the 95^th percentile of the redundant target distribution. Violations of the RMI (i.e., higher probability of response for the redundant target condition than the sum of the single-target probabilities) are present for lower percentiles (i.e., faster trials). B) Hypothetical CDFs illustrating a data pattern that does not violate the RMI. Note that at no point does the probability of response in the redundant target condition exceed the sum of the single-target probabilities.

Figure 4.

A) Violation plots for Experiment 1. Mean violations of the race model inequality for the integrated-cue condition as a function of whether the redundant-target features were associated with the same search target object (Experiment 1A) or with two, separate search target objects (Experiment 1B). Error bars are condition-specific, within-subject 95% confidence intervals (Morey, 2008). Note that negative mean differences scores (as observed across the range of response percentiles in Experiment 1B) are plotted as ≤ 0, since tests of the RMI are inherently one sided. B) Aggregate cumulative distribution functions (CDFs) for the single-target conditions and the redundant-target condition. The orange line shows the sum of the two single-target CDFs. For this plot, participant RT values were averaged across corresponding percentiles in the participants’ RT distributions (i.e., Vincentized). In the violation plots in Panel A, difference scores were first calculated for each participant and then averaged. Thus, there may be subtle variations between the CDFs reported in Panel B and the mean difference scores reported in Panel A. The individual participant CDFs, from which the difference scores in the violation plots were calculated, are reported in supplementary materials.

Figure 5.

A) Violation plots for Experiment 2. Mean violations of the race model inequality for the separated cue condition as a function of whether the response was a present/absent decision (Experiment 2A) or report of a secondary feature of the target object (Experiment 2B). Error bars are condition-specific, within-subject 95% confidence intervals (Morey, 2008). B) Aggregate cumulative distribution functions (CDFs) for the single-target conditions and the redundant-target condition. The orange line shows the sum of the two single-target CDFs. Given differences in the method of aggregation, there may be subtle variations between the CDFs reported in Panel B and the mean difference scores reported in Panel A.

Figure 6.

A) Illustration of the sequence of events in Experiment 3. B) Illustration of the three target-present conditions and the target-absent condition for this example trial. C) Possible configurations of colors within the two-color objects in the search display.

Figure 7.

A) Mean violations of the race model inequality for the two-color search task of Experiment 3. Error bars are condition-specific, within-subject 95% confidence intervals (Morey, 2008). B) Aggregate cumulative distribution functions (CDFs) for the single-target conditions and the redundant-target condition. The orange line shows the sum of the two single-target CDFs. Given differences in the method of aggregation, there may be subtle variations between the CDFs reported in Panel B and the mean difference scores reported in Panel A.