The nesting of search contexts within natural scenes: evidence from contextual cuing

Abstract

In a contextual cuing paradigm, we examined how memory for the spatial structure of a natural scene guides visual search. Participants searched through arrays of objects that were embedded within depictions of real-world scenes. If a repeated search array was associated with a single scene during study, then array repetition produced significant contextual cuing. However, expression of that learning was dependent on instantiating the original scene in which the learning occurred: Contextual cuing was disrupted when the repeated array was transferred to a different scene. Such scene-specific learning was not absolute, however. Under conditions of high scene variability, repeated search array were learned independently of the scene background. These data suggest that when a consistent environmental structure is available, spatial representations supporting visual search are organized hierarchically, with memory for functional subregions of an environment nested within a representation of the larger scene.

Figure 1

Stimulus manipulations in Experiment 1. In the repeated condition of the training session, each target location was paired with a consistent search array configuration and scene background in every block. In the novel condition of the training session, each target location was paired, randomly, with a search array and scene background in each block. In the transfer session, array-predictive trials paired a learned array and target location with a randomly chosen scene background. Scene-predictive trials paired a learned scene background and target location with a randomly generated search array.

Figure 2

Mean search times for repeated (open circles) and novel (filled circles) trials in the Experiment 1 training session. The best fitting power functions (blue and red lines, respectively) obtained by Nonlinear Mixed Effects Modeling are superimposed onto the training conditions (details in text). Error bars represent standard errors of the means.

Figure 3

Mean search times in the array-predictive (left) and scene-predictive (right) transfer sessions of Experiment 1. Error bars are 95% within-subject confidence intervals based on the error term of the repetition effect for each transfer group.

Figure 4

Mean search times for repeated (open circles) and novel (filled circles) trials in Experiment 2. The best fitting power functions (blue and red lines, respectively) obtained by Nonlinear Mixed Effects Modeling are superimposed onto the training conditions. Error bars represent standard errors of the means.

Figure 5

Mean search times for repeated (open circles) and novel (filled circles) trials in Experiment 3. The best fitting power functions (blue and red lines, respectively) obtained by Nonlinear Mixed Effects Modeling are superimposed onto the training conditions. Error bars represent standard errors of the means.