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How do spatial constraints and meaningful scene regions interact to control overt attention during visual search for objects in real-world scenes? To answer this question, we combined novel surface maps of the likely locations of target objects with maps of the spatial distribution of scene semantic content. The surface maps captured likely target surfaces as continuous probabilities. Meaning was represented by meaning maps highlighting the distribution of semantic content in local scene regions and objects. Attention was indexed by eye movements during search for target objects that varied in the likelihood they would appear on specific surfaces. The interaction between surface maps and meaning maps was analyzed to test whether fixations were directed to meaningful scene regions on target-related surfaces. Overall, meaningful scene regions were more likely to be fixated if they appeared on target-related surfaces than if they appeared on target-unrelated surfaces. These findings suggest that the visual system prioritizes meaningful scene regions on target-related surfaces during visual search in scenes.

We compared the influence of meaning and of salience on attentional guidance in scene images. Meaning was captured by "meaning maps" representing the spatial distribution of semantic information in scenes. Meaning maps were coded in a format that could be directly compared to maps of image salience generated from image features. We investigated the degree to which meaning versus image salience predicted human viewers' spatiotemporal distribution of attention over scenes. Extending previous work, here the distribution of attention was operationalized as duration-weighted fixation density. The results showed that both meaning and image salience predicted the duration-weighted distribution of attention, but that when the correlation between meaning and salience was statistically controlled, meaning accounted for unique variance in attention whereas salience did not. This pattern was observed in early as well as late fixations, fixations including and excluding the centers of the scenes, and fixations following short as well as long saccades. The results strongly suggest that meaning guides attention in real-world scenes. We discuss the results from the perspective of a cognitive-relevance theory of attentional guidance.

From the earliest recordings of eye movements during active scene viewing to the present day, researchers have commonly reported individual differences in eye movement scan patterns under constant stimulus and task demands. These findings suggest viewer individual differences may be important for understanding gaze control during scene viewing. However, the relationship between scan patterns and viewer individual differences during scene viewing remains poorly understood because scan patterns are difficult to analyze. The present study uses a powerful technique called Successor Representation Scanpath Analysis (Hayes, Petrov, & Sederberg, 2011, 2015) to quantify the strength of the association between individual differences in scan patterns during real-world scene viewing and individual differences in viewer intelligence, working memory capacity, and speed of processing. The results of this analysis revealed individual differences in scan patterns that explained more than 40% of the variance in viewer intelligence and working memory capacity measures, and more than a third of the variance in speed of processing measures. The theoretical implications of our findings for models of gaze control and avenues for future individual differences research are discussed.

Eye movements are an important data source in vision science. However, the vast majority of eye movement studies ignore sequential information in the data and utilize only first-order statistics. Here, we present a novel application of a temporal-difference learning algorithm to construct a scanpath successor representation (SR; P. Dayan, 1993) that captures statistical regularities in temporally extended eye movement sequences. We demonstrate the effectiveness of the scanpath SR on eye movement data from participants solving items from Raven's Advanced Progressive Matrices Test. Analysis of the SRs revealed individual differences in scanning patterns captured by two principal components that predicted individual Raven scores much better than existing methods. These scanpath SR components were highly interpretable and provided new insight into the role of strategic processing on the Raven test. The success of the scanpath SR in terms of prediction and interpretability suggests that this method could prove useful in a much broader context.

Perceptual learning was used as a tool for studying motion perception. The pattern of transfer of learning of luminance- (LM) and contrast-modulated (CM) motion is diagnostic of how their respective processing pathways are integrated. Twenty observers practiced fine direction discrimination with either additive (LM) or multiplicative (CM) mixtures of a dynamic noise carrier and a radially isotropic texture modulator. The temporal frequency was 10 Hz, speed was 10 deg/s, and duration was 400 ms, with feedback. Group 1 pre-tested CM for 2 blocks, trained LM for 16 blocks, and post-tested CM for 6 blocks during 6 sessions on separate days. In Group 2, the LM and CM roles were reversed. The d' improved almost twofold in both groups. There seemed to be full transfer from CM to LM but no significant transfer from LM to CM. The pattern of post-switch improvement was asymmetric as well-no further learning during the LM post-test versus rapid relearning during the CM post-test. These strong asymmetries suggest a dual-pathway architecture with Fourier channels sensitive only to LM signals and non-Fourier channels sensitive to both LM and CM. We hypothesize that the channels tuned for the same motion direction but different carriers are integrated using a MAX operation.

Pupil size is correlated with a wide variety of important cognitive variables and is increasingly being used by cognitive scientists. Pupil data can be recorded inexpensively and non-invasively by many commonly used video-based eye-tracking cameras. Despite the relative ease of data collection and increasing prevalence of pupil data in the cognitive literature, researchers often underestimate the methodological challenges associated with controlling for confounds that can result in misinterpretation of their data. One serious confound that is often not properly controlled is pupil foreshortening error (PFE)—the foreshortening of the pupil image as the eye rotates away from the camera. Here we systematically map PFE using an artificial eye model and then apply a geometric model correction. Three artificial eyes with different fixed pupil sizes were used to systematically measure changes in pupil size as a function of gaze position with a desktop EyeLink 1000 tracker. A grid-based map of pupil measurements was recorded with each artificial eye across three experimental layouts of the eye-tracking camera and display. Large, systematic deviations in pupil size were observed across all nine maps. The measured PFE was corrected by a geometric model that expressed the foreshortening of the pupil area as a function of the cosine of the angle between the eye-to-camera axis and the eye-to-stimulus axis. The model reduced the root mean squared error of pupil measurements by 82.5 % when the model parameters were pre-set to the physical layout dimensions, and by 97.5 % when they were optimized to fit the empirical error surface.