The effect of working memory maintenance on long-term memory

Abstract

Initially inspired by the Atkinson and Shiffrin model, researchers have spent a half century investigating whether actively maintaining an item in working memory (WM) leads to improved subsequent long-term memory (LTM). Empirical results have been inconsistent, and thus the answer to the question remains unclear. We present evidence from 13 new experiments as well as a meta-analysis of 61 published experiments. Both the new experiments and meta-analysis show clear evidence that increased WM maintenance of a stimulus leads to superior recognition for that stimulus in subsequent LTM tests. This effect appears robust across a variety of experimental design parameters, suggesting that the variability in prior results in the literature is probably due to low power and random chance. The results support theories on which there is a close link between WM and LTM mechanisms, while challenging claims that this relationship is specific to verbal memory and evolved to support language acquisition.

Keywords: Change detection; Incidental memory; Language acquisition; Long-term memory; Verbal working memory; Visual working memory.

Figure 1.

Funnel plot of raw effects included in meta-analysis. Regression test finds no evidence of asymmetry (z = −0.25, p = 0.81).

Figure 2.

All experiments began with a change-detection test, in which WM maintenance was manipulated. After an irrelevant filler task, participants engaged in a surprise recognition test, which compared recognition of stimuli as a function of previous WM maintenance.

Figure 3.

A schematic illustration of a change-detection trial, depicting the Chinese character stimuli from Experiments 3 & 7. On each trial, three stimuli are presented. The first stimulus is to be viewed only (passively viewed object). The second is the item to be maintained in WM (stored object). The final stimulus is the probe (attended object), which the participant must judge to be same or different as the stored object. Thus, this object must be attended but need not be held in WM. By requiring the participant to wait at least 1000 ms before responding, we ensured that the participant viewed the attended object at least as long as the stored and passively-viewed objects.

Figure 4.

Examples of the Fribbles, Greebles, and 3D shapes used in Experiments 3–10.

Figure 5.

Percent correct in the surprise recognition test, collapsing across experiments. Error bars represent standard errors of the mean.

Figure 6.

Percent correct in the surprise recognition test, Exps. 1–8. Error bars represent standard errors of the mean.

Figure 7.

Percent correct in the surprise recognition test, for a replication of Exp. 3 (left; N=90) and Exp. 4 (middle; N=91), and Exp. 8 (right; N=38). For the first two, the only difference from the original was that the filled delay video (142s clip from “Kiwi”; used with permission, Dony Permedi, donysanimation.com); the pattern of significance was identical to the original experiments. The replication of Exp. 8 was run in-lab with Harvard undergraduates as participants, and the pattern of significance was identical to Exp. 4 (the short-delay Fribbles experiment). Error bars represent standard errors of the mean.

Figure 8.

A schematic illustration of Experiment 12. In the change-detection task (top), participants determined whether the upper-case probe matched the lower-case stimulus previously presented in that location. In the recognition task (bottom), participants decided whether they had ever seen that item before; examples of the five trial types are shown (compare with change-detection stimuli). Note that the use of capital letters in the change-detection task probe and in the surprise recognition test necessitates verbal encoding of stimuli, minimizing the role of visual memory.

Figure 9.

Percent correct in the surprise memory test for the four critical conditions. Error bars represent ±1 standard error.