Concurrent visual working memory bias in sequential integration of approximate number

Abstract

Previous work has shown bidirectional crosstalk between Working Memory (WM) and perception such that the contents of WM can alter concurrent percepts and vice versa. Here, we examine WM-perception interactions in a new task setting. Participants judged the proportion of colored dots in a stream of visual displays while concurrently holding location- and color information in memory. Spatiotemporally resolved psychometrics disclosed a modulation of perceptual sensitivity consistent with a bias of visual spatial attention towards the memorized location. However, this effect was short-lived, suggesting that the visuospatial WM information was rapidly deprioritized during processing of new perceptual information. Independently, we observed robust bidirectional biases of categorical color judgments, in that perceptual decisions and mnemonic reports were attracted to each other. These biases occurred without reductions in overall perceptual sensitivity compared to control conditions without a concurrent WM load. The results conceptually replicate and extend previous findings in visual search and suggest that crosstalk between WM and perception can arise at multiple levels, from sensory-perceptual to decisional processing.

Figure 1

Experimental paradigm. (a) Schematic outline of a trial in the WM interference condition. Left, the to-be-maintained WM sample was a single dot (red or blue) presented at a random position on an invisible circular path around fixation. Middle, During WM maintenance, a stream of 6 random dots displays was presented. Each display contained 20 dots, a variable number of which was blue, the others red. Participants were asked to evaluate whether the stream contained relatively more blue or red dots (see “Methods” for details). Right, both the color and location of the WM sample were to be reproduced from memory at the end of the trial. (b) Within-subjects control conditions. In Control 1, the WM task elements were omitted. In Control 2, the WM- and decision task elements were rearranged such that the two tasks were not concurrent.

Figure 2

Spatial weighting analysis. (a) Spatial weighting of the decision displays before rotational alignment, collapsed across all trials (WM and control conditions) and displays (1–6). Positive values indicate overweighting, negative values underweighting, relative to an unbiased observer model fitted to each individual (see “Methods”). Transparent mask indicates significant regional over- or underweighting (p < 0.05, two-tailed, FDR-corrected across pixels). (b) Spatial distribution of WM sample positions reported on WM recall (cf. Fig. 1a, right) after rotational alignment (cf. e), aggregated across all participants. White circle indicates true position (rotation-aligned) of the WM sample. (c) Spatial weighting on WM trials after rotational alignment (cf. e), same conventions as in b. Purple dot indicates (rotation-aligned) location of the WM sample. (d) Pie masks for angular tuning analysis. Spatial weights within each segment were averaged and examined as a function of the absolute angular distance from the WM sample (see Fig. 3b below for results). (e) Rotational alignment of trials. Displays were rotated offline such that the trial-specific WM sample positions matched the same (virtual) reference location (arbitrarily set to 45°, cf. purple markers).

Figure 3

Regional weight concentration—time course. (a) spatial weighting on WM trials after rotational alignment as in Fig. 2c but shown separately for each of the six displays in the decision stream. A significant regional gain concentration (p < 0.05, two-tailed, FDR-corrected, indicated by transparent mask) was observed in display 1 only. (b) Purple: angular tuning of spatial weighting in terms of mean angular distance from the WM sample (cf. Fig. 2d). Significant tuning was evident exclusively in display 1. Yellow curves show analogue analysis of control trials (pooled over control conditions 1 & 2) pseudo-aligned to the same WM-locations as the WM-trials.

Figure 4

Bidirectional biases of WM and perceptual decisions—color information. (a) Psychometric weighting functions averaged over the six displays in the decision task, plotted separately for trials where the concurrently maintained WM sample was red or blue, and for control conditions without a concurrent WM task (cf. Fig. 1). (b) Bias terms (intercepts) derived from logistic regression of choice. (c) Choice sensitivity (slopes) to the red-blue dot composition in each of the six displays in the decision stream. (d) Memory recall bias. Probability of (erroneous) blue/red report in WM recall as a function of the blue-red composition of the intervening decision displays. Small inset plot shows the time-course of this relation over the six stream displays in terms of logistic regression coefficients. Error bars in all panels show standard error of the mean.

Figure 5

Averaging or gambling—color information. (a) WM-induced choice bias relative to pooled control conditions (cf. Fig. 4b) plotted separately for “averaging” (left bars, n = 35) and “gambling” variants (right bars, n = 33) of the decision task. (b) choice sensitivity (slopes) for each of the six displays in the decision stream (cf. Fig. 4c), plotted separately for the averaging and gambling tasks. Yellow curves show pooled control conditions. (c) WM recall bias (cf. inset in Fig. 4d) plotted separately for the averaging and gambling conditions. Small inset plot shows proportion correct color recall. Error bars in all panels show standard error of the mean.

Figure 6

Averaging or gambling—spatial WM precision. Spatial distribution of WM positions reported on WM recall (rotation-aligned as in Fig. 2b), shown separately for the averaging- (left) and gambling (middle) variants of the intermittent decision task. Right panel shows statistical map of the difference between the two, thresholded at p < 0.05 (two-tailed, uncorrected). White circles indicate true original position (rotation-aligned) of the WM sample. Participants in the averaging condition tended to report more locations near to the target and fewer locations afar from it, compared to the gambling condition.