Extending the simultaneous-sequential paradigm to measure perceptual capacity for features and words

Abstract

In perception, divided attention refers to conditions in which multiple stimuli are relevant to an observer. To measure the effect of divided attention in terms of perceptual capacity, we introduce an extension of the simultaneous-sequential paradigm. The extension makes predictions for fixed-capacity models as well as for unlimited-capacity models. We apply this paradigm to two example tasks, contrast discrimination and word categorization, and find dramatically different effects of divided attention. Contrast discrimination has unlimited capacity, consistent with independent, parallel processing. Word categorization has a nearly fixed capacity, consistent with either serial processing or fixed-capacity, parallel processing. We argue that these measures of perceptual capacity rely on relatively few assumptions compared to most alternative measures.

Figure A1.

Quantitative predictions for three models in the simultaneous-sequential comparison. Predicted percent correct for each condition is plotted as a function of task difficulty. (a) Predictions of the unlimited-capacity, parallel model. (b) Predictions of the fixed-capacity, parallel model. (c) Predictions of the standard serial model. These predictions were generated using an equal mixture of standard serial models with m values of 2 and 3.

Figure A2.

Comparison of the models and data for the simultaneous and sequential conditions. Percent correct in the sequential condition is plotted against percent correct in the simultaneous condition. Data from the contrast increment and word categorization experiments are plotted with error bars representing 1 SEM.

Figure A3.

Comparison of the models and data for the sequential and repeated conditions. Percent correct in the repeated condition is plotted against percent correct in the sequential condition. Data from the contrast increment and word categorization experiments are plotted with error bars representing 1 SEM. (a) Predictions assuming integration model of information pooling. (b) Predictions assuming independent-decisions model of information pooling.

Figure A4.

Comparison of the models and data for the simultaneous and repeated conditions. Percent correct in the repeated condition is plotted against percent correct in the simultaneous condition. Results from Experiments 1 are shown for the contrast increment and word categorization tasks with error bars representing 1 SEM.

Figure 1.

Sample display from the simultaneous condition of the contrast discrimination task. The three distractors are discs of lower contrast and the target is a single disc of higher contrast. In this illustration, the contrast values have been exaggerated. The stimuli are embedded in dynamic Gaussian noise patches.

Figure 2.

Sample display from the simultaneous condition of the word categorization task. In this illustration, the contrast of the word stimuli has been increased for visibility. The stimuli are embedded in dynamic Gaussian noise patches.

Figure 3.

Display conditions used in this study. (a) Simultaneous condition: All four stimuli are displayed in one 100 ms frame. (b) Sequential condition: Stimuli are divided between two 100 ms frames shown sequentially. Each frame displays two stimuli. (c) Repeated condition: All four stimuli are displayed twice in two 100 ms frames shown sequentially.

Figure 4.

Demonstration of the logic of the extended simultaneous-sequential paradigm. Columns correspond to the two models, rows correspond to the three conditions, so that each cell depicts the performance of each model in each condition. A grey bar represents the presence of a stimulus. Stimuli appear in four locations, indicated by the numbers 1 through 4 arranged vertically. Depending on the condition, stimuli can occur in the first and/or the second frame. The black arrows represent the hypothesized course of perceptual analysis for each model. Overall, the unlimited-capacity model predicts the pattern of performance simultaneous = sequential < repeated, while the fixed-capacity model predicts simultaneous < sequential = repeated.

Figure 5.

Results of Experiment 1: contrast discrimination task. Percent correct performance is plotted for each of the three conditions. Results are consistent with the unlimited-capacity model, which predicts equivalence between the simultaneous and sequential conditions.

Figure 6.

Results of Experiment 2: word categorization task. Percent correct performance is plotted for each of the three conditions. Results are nearly consistent with the fixed-capacity model, which predicts equivalence between the sequential and repeated conditions.