Implications of search accuracy for serial self-terminating models of search

Searching in space and in time

Our conception of attention is intricately linked to limited processing capacity and the consequent requirement to select, in both space and time, what objects and actions will have access to these limited resources. Seminal studies by Treisman (Cognitive Psychology, 12, 97-136, 1980) and Broadbent (Perception and Psychophysics, 42, 105-113, 1987; Raymond et al. Journal of Experimental Psychology: Human Perception and Performance, 18, 849-860, 1992) offered the field tasks for exploring the properties of attention when searching in space and time. After describing the natural history of a search episode we briefly review some of these properties. We end with the question: Is there one attentional "beam" that operates in both space and time to integrate features into objects? We sought an answer by exploring the distribution of errors when the same participant searched for targets presented at the same location with items distributed over time (McLean et al. Quarterly Journal of Experimental Psychology, 35A, 171-186, 1982) and presented all at once with items distributed over space (Snyder Journal of Experimental Psychology; 92, 428-431, 1972). Preliminary results revealed a null correlation between spatial and temporal slippage suggesting separate selection mechanisms in these two domains.

Signal detection evidence for limited capacity in visual search

The nature of capacity limits (if any) in visual search has been a topic of controversy for decades. In 30 years of work, researchers have attempted to distinguish between two broad classes of visual search models. Attention-limited models have proposed two stages of perceptual processing: an unlimited-capacity preattentive stage, and a limited-capacity selective attention stage. Conversely, noise-limited models have proposed a single, unlimited-capacity perceptual processing stage, with decision processes influenced only by stochastic noise. Here, we use signal detection methods to test a strong prediction of attention-limited models. In standard attention-limited models, performance of some searches (feature searches) should only be limited by a preattentive stage. Other search tasks (e.g., spatial configuration search for a "2" among "5"s) should be additionally limited by an attentional bottleneck. We equated average accuracies for a feature and a spatial configuration search over set sizes of 1-8 for briefly presented stimuli. The strong prediction of attention-limited models is that, given overall equivalence in performance, accuracy should be better on the spatial configuration search than on the feature search for set size 1, and worse for set size 8. We confirm this crossover interaction and show that it is problematic for at least one class of one-stage decision models.

Display size effects in visual search: analyses of reaction time distributions as mixtures

In a reanalysis of data from Cousineau and Shiffrin (2004) and two new visual search experiments, we used a likelihood ratio test to examine the full distributions of reaction time (RT) for evidence that the display size effect is a mixture-type effect that occurs on only a proportion of trials, leaving RT in the remaining trials unaffected, as is predicted by serial self-terminating search models. Experiment 1 was a reanalysis of Cousineau and Shiffrin's data, for which a mixture effect had previously been established by a bimodal distribution of RTs, and the results confirmed that the likelihood ratio test could also detect this mixture. Experiment 2 applied the likelihood ratio test within a more standard visual search task with a relatively easy target/distractor discrimination, and Experiment 3 applied it within a target identification search task within the same types of stimuli. Neither of these experiments provided any evidence for the mixture-type display size effect predicted by serial self-terminating search models. Overall, these results suggest that serial self-terminating search models may generally be applicable only with relatively difficult target/distractor discriminations, and then only for some participants. In addition, they further illustrate the utility of analysing full RT distributions in addition to mean RT.