Information-limited parallel processing in difficult heterogeneous covert visual search

Abstract

Difficult visual search is often attributed to time-limited serial attention operations, although neural computations in the early visual system are parallel. Using probabilistic search models (Dosher, Han, & Lu, 2004) and a full time-course analysis of the dynamics of covert visual search, we distinguish unlimited capacity parallel versus serial search mechanisms. Performance is measured for difficult and error-prone searches among heterogeneous background elements and for easy and accurate searches among homogeneous background elements. Contrary to the claims of time-limited serial attention, searches in heterogeneous backgrounds instead exhibited nearly identical search dynamics for display sizes up to 12 items. A review and new analyses indicate that most difficult as well as easy visual searches operate as an unlimited-capacity parallel analysis over the visual field within a single eye fixation, which suggests limitations in the availability of information, not temporal bottlenecks in analysis or comparison. Serial properties likely reflect overt attention expressed in eye movements.

Figure 1

Temporal processing architectures for serial (A) and parallel (C) visual search, corresponding time-course functions for display sizes of 4 and 12 for serial (B) and parallel (D) visual search, and standard single-point response time (E) and accuracy (F). The identical results in a response time paradigm (E and F) are compatible with serial (A, B) or parallel (C, D) architectures of visual analysis.

Figure 2

Stimulus layouts and sample trial sequences. (a) Homogeneous distractor display of size 4. (b) Homogeneous distractor display of size 12; (c) Heterogeneous distractor display of size 12. Display elements appear on an annulus at 4.12 deg of visual angle, grouped with spaces to equate the local interactions of display elements. (d) Trial sequence for response time paradigm. The stimulus appeared for 100 ms or 50 ms (time-limited condition) or until response (unlimited display conditions). (e) Trial sequence for the speed-accuracy tradeoff paradigm.

Figure 3

Average correct RT and error rates as a function of display size for search with free viewing of unlimited-time displays in Experiment 1. (A) RT (left) and proportion errors (right) in target present and target absent conditions of homogeneous and heterogeneous displays for unpracticed observers with free viewing. (B) RT (left) and proportion errors (right) in target present and target absent conditions of homogeneous and heterogeneous displays for practiced (SAT) observers for 50 ms brief displays. (C) RT (left) and errors (right) in target present and target absent conditions of homogeneous and heterogeneous displays for practiced (SAT) observers with free viewing.

Figure 4

Discrimination performance (d′) as a function of total processing time (test onset to response) for display sizes of 4 and 12 from Experiment 2 for (upper left) 100 ms display homogeneous searches; (lower right) 100 ms display heterogeneous searches; (upper right) 50 ms display homogeneous searches, and (lower right) 50 ms display heterogeneous searches. The symbols are observed data points and the smooth curves are best fitting descriptive exponential functions.

Figure 5

Fit of the probabilistic parallel search model to the discrimination data in Figure 4 for 100 ms and 50 ms displays for homogeneous and heterogeneous searches. The symbols are observed data points and the smooth curves are best fitting model functions.

Figure 6

Fit of the probabilistic serial search model to the discrimination data in Figure 4 for 100 ms and 50 ms displays for homogeneous and heterogeneous searches. The symbols are observed data points and the smooth curves are best fitting model functions.

Figure 7

Fit of the probabilistic parallel search model to the percent yes data for 100 ms and 50 ms displays for homogeneous and heterogeneous searches. The symbols are observed data points and the smooth curves are best fitting model functions.

Figure 8

Fit of the probabilistic parallel model to the time-course data of McElree and Carrasco (1999), including a feature and conjunction searches. The symbols are observed data points and the smooth curves are best fitting model functions.