Neural Correlates of Fixated Low- and High-level Scene Properties during Active Scene Viewing.

During real-world scene perception, viewers actively direct their attention through a scene in a controlled sequence of eye fixations. During each fixation, local scene properties are attended, analyzed, and interpreted. What is the relationship between fixated scene properties and neural activity in the visual cortex? Participants inspected photographs of real-world scenes in an MRI scanner while their eye movements were recorded. Fixation-related fMRI was used to measure activation as a function of lower- and higher-level scene properties at fixation, operationalized as edge density and meaning maps, respectively. We found that edge density at fixation was most associated with activation in early visual areas, whereas semantic content at fixation was most associated with activation along the ventral visual stream including core object and scene-selective areas (lateral occipital complex, parahippocampal place area, occipital place area, and retrosplenial cortex). The observed activation from semantic content was not accounted for by differences in edge density. The results are consistent with active vision models in which fixation gates detailed visual analysis for fixated scene regions, and this gating influences both lower and higher levels of scene analysis.

Cortical control of eye movements in natural reading: Evidence from MVPA.

Language comprehension during reading requires fine-grained management of saccadic eye movements. A critical question, therefore, is how the brain controls eye movements in reading. Neural correlates of simple eye movements have been found in multiple cortical regions, but little is known about how this network operates in reading. To investigate this question in the present study, participants were presented with normal text, pseudo-word text, and consonant string text in a magnetic resonance imaging (MRI) scanner with eyetracking. Participants read naturally in the normal text condition and moved their eyes "as if they were reading" in the other conditions. Multi-voxel pattern analysis was used to analyze the fMRI signal in the oculomotor network. We found that activation patterns in a subset of network regions differentiated between stimulus types. These results suggest that the oculomotor network reflects more than simple saccade generation and are consistent with the hypothesis that specific network areas interface with cognitive systems.

Eye Movements in Real-World Scene Photographs: General Characteristics and Effects of Viewing Task.

The present study examines eye movement behavior in real-world scenes with a large (N = 100) sample. We report baseline measures of eye movement behavior in our sample, including mean fixation duration, saccade amplitude, and initial saccade latency. We also characterize how eye movement behaviors change over the course of a 12 s trial. These baseline measures will be of use to future work studying eye movement behavior in scenes in a variety of literatures. We also examine effects of viewing task on when and where the eyes move in real-world scenes: participants engaged in a memorization and an aesthetic judgment task while viewing 100 scenes. While we find no difference at the mean-level between the two tasks, temporal- and distribution-level analyses reveal significant task-driven differences in eye movement behavior.

Categorical learning revealed in activity pattern of left fusiform cortex.

The brain is organized such that it encodes and maintains category information about thousands of objects. However, how learning shapes these neural representations of object categories is unknown. The present study focuses on faces, examining whether: (1) Enhanced categorical discrimination or (2) Feature analysis enhances face/non-face categorization in the brain. Stimuli ranged from non-faces to faces with two-toned Mooney images used for testing and gray-scale images used for training. The stimulus set was specifically chosen because it has a true categorical boundary between faces and non-faces but the stimuli surrounding that boundary have very similar features, making the boundary harder to learn. Brain responses were measured using functional magnetic resonance imaging while participants categorized the stimuli before and after training. Participants were either trained with a categorization task, or with non-categorical semblance analyzation. Interestingly, when participants were categorically trained, the neural activity pattern in the left fusiform gyrus shifted from a graded representation of the stimuli to a categorical representation. This corresponded with categorical face/non-face discrimination, critically including both an increase in selectivity to faces and a decrease in false alarm response to non-faces. By contrast, while activity pattern in the right fusiform cortex correlated with face/non-face categorization prior to training, it was not affected by learning. Our results reveal the key role of the left fusiform cortex in learning face categorization. Given the known right hemisphere dominance for face-selective responses, our results suggest a rethink of the relationship between the two hemispheres in face/non-face categorization. Hum Brain Mapp 38:3648-3658, 2017. © 2017 Wiley Periodicals, Inc.

Visual Search of Mooney Faces.

Faces spontaneously capture attention. However, which special attributes of a face underlie this effect is unclear. To address this question, we investigate how gist information, specific visual properties and differing amounts of experience with faces affect the time required to detect a face. Three visual search experiments were conducted investigating the rapidness of human observers to detect Mooney face images. Mooney images are two-toned, ambiguous images. They were used in order to have stimuli that maintain gist information but limit low-level image properties. Results from the experiments show: (1) Although upright Mooney faces were searched inefficiently, they were detected more rapidly than inverted Mooney face targets, demonstrating the important role of gist information in guiding attention toward a face. (2) Several specific Mooney face identities were searched efficiently while others were not, suggesting the involvement of specific visual properties in face detection. (3) By providing participants with unambiguous gray-scale versions of the Mooney face targets prior to the visual search task, the targets were detected significantly more efficiently, suggesting that prior experience with Mooney faces improves the ability to extract gist information for rapid face detection. However, a week of training with Mooney face categorization did not lead to even more efficient visual search of Mooney face targets. In summary, these results reveal that specific local image properties cannot account for how faces capture attention. On the other hand, gist information alone cannot account for how faces capture attention either. Prior experience facilitates the effect of gist on visual search of faces; making faces a special object category for guiding attention.