Thresholds for the detection of changing visual features

Dimension-based attention in visual short-term memory

We investigated how dimension-based attention influences visual short-term memory (VSTM). This was done through examining the effects of cueing a feature dimension in two perceptual comparison tasks (change detection and sameness detection). In both tasks, a memory array and a test array consisting of a number of colored shapes were presented successively, interleaved by a blank interstimulus interval (ISI). In Experiment 1 (change detection), the critical event was a feature change in one item across the memory and test arrays. In Experiment 2 (sameness detection), the critical event was the absence of a feature change in one item across the two arrays. Auditory cues indicated the feature dimension (color or shape) of the critical event with 80 % validity; the cues were presented either prior to the memory array, during the ISI, or simultaneously with the test array. In Experiment 1, the cue validity influenced sensitivity only when the cue was given at the earliest position; in Experiment 2, the cue validity influenced sensitivity at all three cue positions. We attributed the greater effectiveness of top-down guidance by cues in the sameness detection task to the more active nature of the comparison process required to detect sameness events (Hyun, Woodman, Vogel, Hollingworth, & Luck, Journal of Experimental Psychology: Human Perception and Performance, 35; 1140-1160, 2009).

Contrasting Predictions of Low- and High-Threshold Models for the Detection of Changing Visual Features

Change blindness is the failure of observers to notice otherwise obvious changes to a visual scene when those changes are masked in some way (eg by blotches or a blanking of the screen). Typically, change blindness is taken as evidence that our representation of the visual world is capacity limited. The locus of this capacity limit is thought to be visual short-term memory (vSTM). The capacity of vSTM is usually estimated with a high-threshold model which assumes that each element in the stimulus array is either fully encoded or not encoded at all, and, furthermore, that false alarms can arise only by guessing, not by noise. Low-threshold models, by contrast, suggest that false alarms can arise by noise at the level of detection/discrimination and/or decision. In this study, we use a well-controlled stimulus display in which a single element changes over a blanking of the screen and contrast predictions from a popular high-threshold model of vSTM with the predictions of a low-threshold model (specifically, the sample-size model) of visual search and vSTM. The data were better predicted by the low-threshold model.