Recognition and position information in working memory for visual textures

Abstract

282Working within a framework of exemplar-similarity models of memory (see, e.g., Estes, 1994;Kahana & Sekuler, 2002;Kahana, Zhou, Geller, & Sekuler, 2007;Medin & Schaffer, 1978;Nosofsky, 1986;Nosofsky & Kantner, 2006), we used sinusoidal luminance gratings as stimuli in a modified Sternberg (1966) recognition task. The metric properties of the grating stimuli were exploited to test a novel prediction generated by combining the exemplar-similarity approach with an explicit, signal detection account of decision making (Wickens, 2002).In exemplar-similarity models of recognition memory, it is assumed that a summed-similarity computation is a basic component of subjects' recognition judgment. This computation sums-over all study items-the p's similarity to each of the study items. According to the model, when this sum reaches or exceeds some critical value, the subject will say "yes," judging that the p had been among the n study items that had just been seen. Following convention, we will use the term target (T ) to designate trials on which p replicated a study item and the term lure (L) to designate trials on which p did not replicate any of the study items. On average, the value of summed similarity on T trials will exceed that on L trials, which means that P(yes) responses on T trials will be higher than those on L trials. The nature of the elements entering into the computation will also tend to produce a systematic difference in the variances of summed-similarity values on T and on L trials, which leads to an unexpected prediction for the slope of z-transformed receiver operating characteristics(zROCs).On T trials, values of summed similarity arise from two quantitatively different sources that differ in their respective variability. The first, far larger source of variability reflects the contribution of the n 1 study items that are not replicated by p. Random selection of study items from a stimulus pool means that some of n 1 study items will be similar to p, and that others will be very different from p. As a result of this random divergence, these n 1 nonmatching study items will contribute a highly variable amount of similarity to the summed-similarity signal for any trial. The second, smaller source of variability in summed similarity on T trials reflects the contribution of the one study item that the p does replicate. Over trials, this study item's representation will tend to be perceptually similar to p-even with the memorial noise postulated by the model. Because that study item and p are physically identical, they are likely to be perceptually similar, despite the random noise associated with the study item's memorial representation. As a result, similarity between this study item and p will vary over a narrow range clustered near 1.0 (Zhou, Kahana, & Sekuler, 2004). In three experiments, we examined connections between item-recognition memory and memory for itemposition information. With sequences of compound gratings as study and probe items, subjects made either itemposition judgments (Experim...

Similarity-based distortion of visual short-term memory is due to perceptual averaging

“…This suggests that the variance in items’ features is also an important determinant of recognition responses. Furthermore, it has been shown that subjects’ memory for serial position of individual study items is more accurate when when homogeneity of the study items is lower (Yotsumoto et al, 2008). …”

Multiple Time Intervals of Visual Events Are Represented as Discrete Items in Working Memory

“…Our results showed the same patterns of the similarity effect for lure intervals as for lure textures, and thus confirmed that the item representations of time intervals were similar to those of visual textures. The similarity effect for visual items has been well investigated in previous studies (e.g., Kahana and Sekuler, 2002 ; Zhou et al, 2004 ; Yotsumoto et al, 2008 ), but that for temporal items was examined for the first time in the present study.…”

“…Stimuli for each trial were drawn from a pool of two-dimensional (2D) sinusoidal gratings that are often employed in studying recognition/working memory (e.g., Kahana and Sekuler, 2002 ; Yotsumoto et al, 2008 ). Gratings were of 25 different spatial frequencies, derived by combining five vertical and five horizontal frequencies of 1.51, 1.75, 2, 2.25, and 2.49 cycles per degree (CPD), similar to those used by Yotsumoto et al (2008) . The gratings were presented at 10 different time intervals from 0.25 to 0.97 s, with a linear increment of 0.08 s. The numbers of different visual or temporal stimuli rendered the probe prediction impossible.…”

“…Because similarity increases monotonically with decreasing distance ( Kahana and Sekuler, 2002 ), summed similarity can be determined by the summed probe-study items’ distance (PSD), that is, the summation of the 2D Euclidean distances between the probe and all of the study items ( Yotsumoto et al, 2008 ). The summed similarity is inversely proportional to the summed PSD.…”

Frontotemporal function]al connectivity and executive functions contribute to episodic memory performance

“…Episodic memory operates via encoding and retrieval processes, and can be measured using recognition tasks (Diana, Yonelinas, & Ranganath, 2007; Haist, Shimamura, & Squire, 1992; Yotsumoto, McLaughlin, Kahana, & Sekuler, 2008). Recognition is the ability to determine if a currently presented item had been encountered previously (Yonelinas, 2002).…”