The effects of delay duration on visual working memory for orientation

Abstract

We used a delayed-estimation paradigm to characterize the joint effects of set size (one, two, four, or six) and delay duration (1, 2, 3, or 6 s) on visual working memory for orientation. We conducted two experiments: one with delay durations blocked, another with delay durations interleaved. As dependent variables, we examined four model-free metrics of dispersion as well as precision estimates in four simple models. We tested for effects of delay time using analyses of variance, linear regressions, and nested model comparisons. We found significant effects of set size and delay duration on both model-free and model-based measures of dispersion. However, the effect of delay duration was much weaker than that of set size, dependent on the analysis method, and apparent in only a minority of subjects. The highest forgetting slope found in either experiment at any set size was a modest 1.14°/s. As secondary results, we found a low rate of nontarget reports, and significant estimation biases towards oblique orientations (but no dependence of their magnitude on either set size or delay duration). Relative stability of working memory even at higher set sizes is consistent with earlier results for motion direction and spatial frequency. We compare with a recent study that performed a very similar experiment.

Error-correcting dynamics in visual working memory

“…Model fits indicated that random guessing increased with time for human subjects (Fig. S10), consistent with previous literature (3)(4)(5). Guessing decreased with delay, however, for the two monkeys.…”

“…We quantified error as the angular deviation between the target color and the subject's report. As expected (3)(4)(5)(6)(7)(8)(9)(10), the average absolute error increased as a function of delay and working memory load ( Fig. 1b; humans (H): load, F (1, 89) = 147.23, p < 0.001; delay, F (1, 89) = 85.44, p < 0.001; load x delay, F (1, 89) = 13.92, p < 0.001; monkey W (W): load, p < 0.001; delay, p = 0.006; load x delay, p = 0.495; monkey E (E): load, p < 0.001; delay, p = 0.009; load x delay, p = 0.303).…”

“…Yet, it is imperfect; internal noise introduces errors into memory representations (1, 2). Such errors accumulate over time (3)(4)(5) and increase with the number of items simultaneously held in working memory (6-10). Here, we show that error-correcting attractor dynamics mitigate the impact of noise on working memory.…”

Problematic usage of the Zhang and Luck mixture model

“…5. Forego modeling altogether and compare across conditions only model-free summary statistics, such as mean absolute error, circular variance, or circular standard deviation (Van den Berg et al, 2017;Shin et al, 2017;Rademaker et al, 2012;Pertzov et al, 2017;Emrich et al, 2017). This is my best recommendation if the purpose of a study is not model comparison but a characterization of some independent variable on working memory performance.…”

“…Such a convincing win might be rare, though: usually, the VP model fits as well or somewhat better (Van denBerg et al, 2014Berg et al, , 2017.3. Ensure that any inferences drawn from a comparison across conditions are independent of the fitted model(Shin et al, 2017). The disadvantages of this approach are that it is more work and that a decision must be made on how to aggregate results that are not completely consistent across models.…”