Academic journal article Memory & Cognition

Recognition and Position Information in Working Memory for Visual Textures

Academic journal article Memory & Cognition

Recognition and Position Information in Working Memory for Visual Textures

Article excerpt

In three experiments, we examined connections between item-recognition memory and memory for item-position information. With sequences of compound gratings as study and probe items, subjects made either item-position judgments (Experiments 1 and 2), by identifying the serial position of the study item that matched the probe, or recognition judgments (Experiment 3), by judging whether the probe had or had not been presented in the study series. Integrating a summed-similarity account of recognition into a signal detection framework shows that the variance of summed similarities on lure trials (probe not present in the study series) exceeds the variance on target trials (probe present in the study series). This prediction is borne out by the empirical zROC functions, all of which had slopes that were greater than 1. Additionally, about 25% of correct recognitions were accompanied by incorrect item position identification. Misidentifications of item position arose from two sources-structural similarity and positional similarity-which combined in an approximately additive fashion.

Working 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 Tand 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-l 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).

On L trials, variability in the summed-similarity signal arises from n study items that do not replicate p (by definition, there is no item in the study list that replicates p). …

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.