The precision of visual working memory is set by allocation of a shared resource

Abstract

The mechanisms underlying visual working memory have recently become controversial. One account proposes a small number of memory "slots," each capable of storing a single visual object with fixed precision. A contrary view holds that working memory is a shared resource, with no upper limit on the number of items stored; instead, the more items that are held in memory, the less precisely each can be recalled. Recent findings from a color report task have been taken as crucial new evidence in favor of the slot model. However, while this task has previously been thought of as a simple test of memory for color, here we show that performance also critically depends on memory for location. When errors in memory are considered for both color and location, performance on this task is in fact well explained by the resource model. These results demonstrate that visual working memory consists of a common resource distributed dynamically across the visual scene, with no need to invoke an upper limit on the number of objects represented.

Figure 1. Precision of visual working memory in a color report task

(a) Subjects were briefly presented with a sample array of 1–6 colored squares; exposure duration was varied across trials (100–2000 ms). After a blank period (900 ms), a test array was presented in which the location of a randomly-selected sample item was highlighted. Subjects reported the remembered color corresponding to the highlighted location by clicking on a color wheel. (b) Precision as a function of the number of items in the sample array (N). Precision is defined as the reciprocal of the standard deviation of the error in subjects’ responses: zero indicates chance performance. Error bars indicate s.e.m. The blue line indicates the best fit to the data of a power law relating precision to the fraction of resources available per item (1/N). (c) Three models for the distribution of responses on the color report task, illustrated for a single trial with a sample array of two items (one red, one green) and a test array that cues the location of the red item. Variability in memory for color alone would predict a gaussian distribution of responses centered on the actual color at the target location (top). In the model proposed by Zhang & Luck (middle), a proportion of responses instead come from a uniform distribution in which colors are chosen at random (shown in green). Alternatively (bottom), variability in memory for location may cause subjects to mistake which item was at the target location on some trials, in which case a proportion of responses (shown in green) will come from a gaussian centered on the non-target color.

Figure 2. Distribution of errors relative to target and non-target colors

(a) Frequency of response as a function of the difference between reported color value and target color value, for varying numbers of items (N) in the sample array. The long tails of the distribution observed for larger sample sizes (e.g. six items, far right) are inconsistent with the simple gaussian model shown in Fig 1c, top, but are consistent with either of the other models shown in Fig 1c. Colored lines indicate the response probabilities predicted by a mixture model combining color error, location error and random components (see main text and Fig 3). (b) Frequency of responses as a function of the difference between reported color value and each non-target color value. The strong central tendency observed for larger numbers of items (N) is not predicted by Zhang & Luck’s model (Fig 1c, middle), but is consistent with errors in memory for location, as illustrated in (Fig 1c, bottom). Colored line indicates the prediction of the three-component model. Error bars indicate s.e.m.

Figure 3. Three sources of error in the report task and the effect of sample duration

(a–c) Subject responses on the memory task were decomposed into three separate components, indicated by the shaded regions: (a) a gaussian distribution with standard deviation σ centered on the target color value (T), corresponding to error in memory for color; (b) gaussian distributions with the same width centered on each non-target color value (NT), corresponding to errors in memory for location; (c) a uniform distribution, capturing random responses unrelated to any of the sample colors. (d–f) Maximum likelihood parameters of the three-component model, as a function of number of items in the sample array (mean across sample durations). (d) The standard deviation (σ) increases with array size, indicating increasing variability in memory for color; (e) the proportion of responses corresponding to non-targets increases with array size, indicating increasing variability in memory for location; (f) the proportion of random responses is shown in black; the grey dashed lines indicate the proportions of random responses expected for a fixed upper limit of 2, 3 or 4 items. (g–i) Effect of sample duration on each parameter of the model: light grey symbols and dotted line, 100 ms; dark grey symbols and dashed line, 500 ms; black symbols and solid line, 2 s. Sample duration does not effect variability in memory for color (g) or location (h) but has a substantial effect on the frequency of random responses (i). Error bars in this figure indicate within-subject s.e.m., as in Zhang and Luck (2008).