Working memory retrieval as a decision process

Abstract

Working memory (WM) is a core cognitive process fundamental to human behavior, yet the mechanisms underlying it remain highly controversial. Here we provide a new framework for understanding retrieval of information from WM, conceptualizing it as a decision based on the quality of internal evidence. Recent findings have demonstrated that precision of WM decreases with memory load. If WM retrieval uses a decision process that depends on memory quality, systematic changes in response time distribution should occur as a function of WM precision. We asked participants to view sample arrays and, after a delay, report the direction of change in location or orientation of a probe. As WM precision deteriorated with increasing memory load, retrieval time increased systematically. Crucially, the shape of reaction time distributions was consistent with a linear accumulator decision process. Varying either task relevance of items or maintenance duration influenced memory precision, with corresponding shifts in retrieval time. These results provide strong support for a decision-making account of WM retrieval based on noisy storage of items. Furthermore, they show that encoding, maintenance, and retrieval in WM need not be considered as separate processes, but may instead be conceptually unified as operations on the same noise-limited, neural representation.

Keywords: decision; precision; response time; retrieval; working memory.

Figure 1

The LATER model in working memory. (A) A decision variable or signal, representing commitment to a particular response, is set off by a stimulus (in this case the memory probe, a response prompt), rises linearly from an initial level (S₀) at a rate picked from a normal distribution with mean μ and SD σ, and initiates a response upon reaching threshold (S_T). The resulting RT distribution is skewed. (B) This is reflected in an asymmetric cumulative density function. However, when plotted on a reciprocal time x axis and probit y axis (a reciprobit plot) it is linear, allowing parameters such as μ and σ to be estimated easily. Increasing memory load reduces the memory resource available per item (Bays & Husain, ; Wilken & Ma, 2004). This could influence the LATER mechanism in two ways: reducing mean rate of rise μ (C), which would be evident in self-parallel shifts of the reciprobit RT distribution (D), or raising decision threshold (E), causing reciprobit swivel (F).

Figure 2

Measuring precision of location and orientation memory. (A) Stimuli and sequence of events on a location-judgment trial. The array featured one, two, four, or six items; here an array size (N) of two items is shown. After the sample display is blanked, subjects' memory for location of a randomly chosen item is tested by redisplaying the item displaced horizontally through distance Δ (0.5°, 2°, or 5°). The subject presses a button to report the direction of displacement. (B) An orientation judgment trial (this time with a set size of four items). A randomly chosen item is redisplayed, rotated through an angle Δ (5°, 20°, or 45°). Red circles indicate gaze position. (C) For each memory load, performance (proportion of responses judging the displacement or rotation as away from fixation) is plotted as a function of the actual displacement or rotation magnitude Δ. Memory precision is measured as the reciprocal of the SD of the fitted cumulative Gaussian.

Figure 3

Set size and memory discrimination difficulty affect response time for both location and orientation tasks. Top: group average of median correct RT for 18 subjects as a function of set size N and displacement/rotation size Δ; error bars represent SEM calculated after excluding variability associated with between-subject differences (Cousineau, 2005). Bottom: group memory precision (1/error function SD, see Figure 2) for 18 subjects as a function of set size N.

Figure 4

Influence of set size and probe displacement on reciprocal RT and the LATER model parameter μ. (A, top) Example RT distributions from two subjects for different set size conditions (all for Δ = 5°) on reciprobit axes, in which reciprocal RT is plotted cumulatively on a probit ordinate. X axis labels show 1/RT (response rate) values and corresponding RTs. (A, bottom) Example RT distributions from two subjects for different displacements (all for N = 1), reciprobit axes. Note that data points in green are identical because they represent the same condition. (B) Values for the decision model parameter μ (measured from the RT distributions in A) for three subjects as a function of array size. Individual subjects' values were scaled relative to the group mean, as described in Cousineau (2005). (C) The same values for μ as a function of subjects' memory precision (1/error function SD, see Figure 2).

Figure 5

Influence of working memory allocation on response time. RT distributions for two subjects in different cueing conditions, on reciprobit axes (reciprocal RT is plotted cumulatively on a probit ordinate). One of the two items was endogenously precued by color, either the item subsequently probed (valid cue) or the other, nontarget item (invalid cue); in 25% of trials the cue was neutral. X axis labels show 1/RT (response rate) values and corresponding RTs.

Figure 6

Influence of delay period on memory precision and response time. (A) Group mean (± SEM) of the standard deviation (precision⁻¹) for subjects' matching responses in a task testing free report from memory of stimulus orientation, as a function of the number of items presented and the delay before the cue to respond. (B) Group median response time for the same conditions, reflecting the combined duration of memory recall and rotation of the dial to the remembered orientation (± within-subject SEM, calculated after excluding variability associated with between-subject differences, as described in Cousineau, 2005).