Assaf Harel

Humans are extremely adept at categorizing complex visual environments, an ability supported by a network of scene‐selective cortical areas in occipitotemporal cortex (OTC), primarily parahippocampal‐ and occipital‐place area (PPA, OPA, respectively). Despite increasing knowledge on the development of the scene‐selective network, it is still not well‐understood how experience impacts scene‐related activity in the adult brain. A key question is how activity in scene‐selective cortex changes as people gain experience in categorizing scenes. To address this question, we conducted an fMRI training study focused on the categorization of aerial and terrestrial scenes. Unlike terrestrial scenes, aerial scenes lack the same environmental regularities the brain has adapted to, and thus ideal for testing the impact of experience on scene‐selective cortex. Over six training sessions, 39 participants (19 males and 20 females) were shown scenes of different categories from aerial and terrestrial viewpoints, with half the participants categorizing the scenes at a specific level (e.g., truss bridge/suspension bridge), whereas the other performed an unrelated task on the same images. Both groups were scanned before, during, and after training. We found that categorization training had a group‐specific effect on responses in OPA and PPA, with greater neural sensitivity to viewpoint in the trained versus the untrained group. In contrast, nonscene‐selective regions, such as object‐selective LOC and early visual cortex showed no training effects. Improvements in behavioral performance, including learning transfer, were linked to changes in PPA activity level pre‐ versus‐posttraining. We conclude that scene‐selective cortex can support the learning of novel spatial geometries.

A fractal, a self-similar organic or geometric pattern that repeats at varying scales, is one of the most compelling characteristics found in nature. Previous studies on fractal patterns have demonstrated consistent trends in potential psychological benefits, such as stress reduction. However, we fall short of understanding one of the essential properties of fractals found in nature, i.e., the three-dimensionality of their appearance. In this study, we aimed at understanding the role of the three-dimensionalization of fractal patterns in spatial structures on human subjective perceptual experience. Two hundred seventy three-dimensional spatial prototype models were created for this study, spanning two dimensions: (1) the application of spatial depth (shallow; medium; deep) and (2) fractal complexity (low; medium; high). The participants rated each space on six psychological dimensions (bad–good; stressful–relaxing; ugly–beautiful; boring–interesting; leave–enter; ignore–explore). Significant effects of the application depth of fractals were observed primarily for “boring-interesting”, “ignore-explore”, and “leave-enter” dimensions and were primarily manifested within spaces with medium and high D-values. The results suggest that spatial depth plays a significant role in individuals’ experiences of fractal spaces, arguably by making the space more engaging and interesting.

Throughout evolution, the human visual system has adapted to efficiently encode several environmental constants to deal with the huge complexity involved in representing large-scale spatial environments. Being terrestrial animals, these constants reflect a specific ground-based viewpoint. For example, people show a strong affinity for detecting a perceptual upright layout relative to the horizon. But, what happens when humans leave this terrestrial perspective, for example when taking a flight, or looking down from the Empire State building? Are the mechanisms originally evolved for terrestrial scene recognition also recruited for the recognition of novel large-scale environments people rarely encounter on a daily basis? We propose that studying how people learn to recognize aerial scene images can reveal how the scene recognition system develops through experience and shed light on the putative mechanisms underlying its malleability. We conducted an intensive six-session behavioral training study in which naive participants learned to categorize manmade and natural scenes at a specific-subordinate level (‘suspension bridge’). Scene images depicted real-world places from a terrestrial and an aerial viewpoint, allowing us to establish how people learn to categorize two visually-different images as the same environment. We found that performance constantly improved over the first five training sessions, with greater learning for the aerial compared to the terrestrial scenes. This viewpoint performance gap manifested more for manmade than natural scenes. In addition to memory improvements, we also found learning transfer, the hallmark of perceptual learning. Performance in the sixth session (in which participants categorized scenes they had not been trained with) was significantly better compared to the first session, and equivalent to average performance across training-sessions. Our findings provide novel evidence for the potential mechanisms underlying plasticity in the scene recognition system, showing both memory and perception contribute to experience-based enhancements in scene recognition.

Understanding developmental changes in children's use of specific visual information for recognizing object categories is essential for understanding how experience shapes recognition. Research on the development of face recognition has focused on children's use of low-level information (e.g. orientation sub-bands), or high-level information. In face categorization tasks, adults also exhibit sensitivity to intermediate complexity features that are diagnostic of the presence of a face. Do children also use intermediate complexity features for categorizing faces and objects, and, if so, how does their sensitivity to such features change during childhood? Intermediate-complexity features bridge the gap between low- and high-level processing: they have computational benefits for object detection and segmentation, and are known to drive neural responses in the ventral visual system. Here, we have investigated the developmental trajectory of children's sensitivity to diagnostic category information in intermediate-complexity features. We presented children (5-10 years old) and adults with image fragments of faces (Experiment 1) and cars (Experiment 2) varying in their mutual information, which quantifies a fragment's diagnosticity of a specific category. Our goal was to determine whether children were sensitive to the amount of mutual information in these fragments, and if their information usage is different from adults. We found that despite better overall categorization performance in adults, all children were sensitive to fragment diagnosticity in both categories, suggesting that intermediate representations of appearance are established early in childhood. Moreover, children's usage of mutual information was not limited to face fragments, suggesting the extracting intermediate-complexity features is a process that is not specific only to faces. We discuss the implications of our findings for developmental theories of face and object recognition.

Recent work has reported that the first Event-Related Potential (ERP) component to carry scene-selective information is the posterior P2 component (peaking 220ms post-stimulus onset)(Harel et al., 2016). P2 amplitude was higher in response to scenes than to faces and objects and was also sensitive to the global properties of scenes (e.g. naturalness and openness), suggesting that global scene structure is encoded by 220ms of processing. The present study aimed to extend this finding by testing the impact of scene inversion on the P2 component. Inversion is assumed to disrupt global processing, and as such can be used to test the P2 sensitivity to global information, and more generally establish how early stages of scene perception are impacted by inversion. This is especially important as to this date only a handful of studies have examined scene inversion. We recorded ERPs from participants while they passively viewed upright and inverted color images of faces, objects, and scenes. We used 192 individual images in each category, spanning a wide range of category dimensions and properties to prevent the possibility of a salient image property affecting our results. First, we established that the P2 response was indeed scene-selective, with its amplitude higher in response to scenes than faces and objects, replicating Harel et al.’s original study. Second, we found a P2 inversion effect in the form of a reduced P2 amplitude to inverted compared to upright scenes. Earlier visually-evoked components P1 and N1 did not show significant scene inversion effects. Lastly, faces showed the expected N170 inversion effect as well as P1 and P2 inversion effects, while objects showed no inversion effects for any of the early components. Together, these results suggest that global scene processing can be observed at the P2 time window and support the idea that P2 indexes scene-selective neural processing.

One way in which face recognition develops during infancy and childhood is with regard to the visual information that contributes most to recognition judgments. Adult face recognition depends on critical features spanning a hierarchy of complexity, including low-level, intermediate, and high-level visual information. To date, the development of adult-like information biases for face recognition has focused on low-level features, which are computationally well-defined but low in complexity, and high-level features, which are high in complexity, but not defined precisely. To complement this existing literature, we examined the development of children’s neural responses to intermediate-level face features characterized using mutual information. Specifically, we examined children’s and adults’ sensitivity to varying levels of category diagnosticity at the P100 and N170 components. We found that during middle childhood, sensitivity to mutual information shifts from early components to later ones, which may indicate a critical restructuring of face recognition mechanisms that takes place over several years. This approach provides a useful bridge between the study of low- and high-level visual features for face recognition and suggests many intriguing questions for further investigation.

Uncovering when children learn to use specific visual information for recognizingobject categories is essential for understanding how experience shapes recognition.Research on the development of face recognition has focused on children’s use oflow-level information (e.g. orientation sub-bands), or on children's use of high-levelinformation, namely, configural or holistic information. Do children also useintermediate complexity features for categorizing faces and objects, and if so, atwhat age? Intermediate-complexity features bridge the gap between low- and high- level processing: they have computational benefits for object detection and segmentation, and are known to drive neural responses in the ventral visual system.Here, we asked when children develop sensitivity to diagnostic category information in intermediate-complexity features. We presented children (5-10 years old) and adults with image fragments of faces (Experiment 1) and cars (Experiment 2) varying in their mutual information, which quantities a fragment's diagnosticity of a specific category. Our goal was to determine whether children were sensitive to the amount of mutual information in these fragments, and if their information usage is different from adults’. We found that despite better overall categorization performance in adults, all children were sensitive to fragment diagnosticity in both categories, suggesting that intermediate representations of appearance are established early in childhood. Moreover, children's usage of mutual information was not limited to face fragments, suggesting the extracting intermediate complexity features is a process that is not specific only to faces. We discuss the implications of our findings for developmental theories of face and object recognition.