Idiosyncratic, Retinotopic Bias in Face Identification Modulated by Familiarity

The resolution of face perception varies systematically across the visual field

Visual abilities tend to vary predictably across the visual field-for simple low-level stimuli, visibility is better along the horizontal vs. vertical meridian and in the lower vs. upper visual field. In contrast, face perception abilities have been reported to show either distinct or entirely idiosyncratic patterns of variation in peripheral vision, suggesting a dissociation between the spatial properties of low- and higher-level vision. To assess this link more clearly, we extended methods used in low-level vision to develop an acuity test for face perception, measuring the smallest size at which facial gender can be reliably judged in peripheral vision. In 3 experiments, we show the characteristic inversion effect, with better acuity for upright faces than inverted, demonstrating the engagement of high-level face-selective processes in peripheral vision. We also observe a clear advantage for gender acuity on the horizontal vs. vertical meridian and a smaller-but-consistent lower- vs. upper-field advantage. These visual field variations match those of low-level vision, indicating that higher-level face processing abilities either inherit or actively maintain the characteristic patterns of spatial selectivity found in early vision. The commonality of these spatial variations throughout the visual hierarchy means that the location of faces in our visual field systematically influences our perception of them.

Evaluating Machine Learning Stability in Predicting Depression and Anxiety Amidst Subjective Response Errors

Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) pose significant burdens on individuals and society, necessitating accurate prediction methods. Machine learning (ML) algorithms utilizing electronic health records and survey data offer promising tools for forecasting these conditions. However, potential bias and inaccuracies inherent in subjective survey responses can undermine the precision of such predictions. This research investigates the reliability of five prominent ML algorithms-a Convolutional Neural Network (CNN), Random Forest, XGBoost, Logistic Regression, and Naive Bayes-in predicting MDD and GAD. A dataset rich in biomedical, demographic, and self-reported survey information is used to assess the algorithms' performance under different levels of subjective response inaccuracies. These inaccuracies simulate scenarios with potential memory recall bias and subjective interpretations. While all algorithms demonstrate commendable accuracy with high-quality survey data, their performance diverges significantly when encountering erroneous or biased responses. Notably, the CNN exhibits superior resilience in this context, maintaining performance and even achieving enhanced accuracy, Cohen's kappa score, and positive precision for both MDD and GAD. This highlights the CNN's superior ability to handle data unreliability, making it a potentially advantageous choice for predicting mental health conditions based on self-reported data. These findings underscore the critical importance of algorithmic resilience in mental health prediction, particularly when relying on subjective data. They emphasize the need for careful algorithm selection in such contexts, with the CNN emerging as a promising candidate due to its robustness and improved performance under data uncertainties.

Modeling naturalistic face processing in humans with deep convolutional neural networks

Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The ways in which the internal face representations in DCNNs relate to human cognitive representations and brain activity are not well understood. Nearly all previous studies focused on static face image processing with rapid display times and ignored the processing of naturalistic, dynamic information. To address this gap, we developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces). We used this naturalistic dataset to compare representational geometries estimated from DCNNs, behavioral responses, and brain responses. We found that DCNN representational geometries were consistent across architectures, cognitive representational geometries were consistent across raters in a behavioral arrangement task, and neural representational geometries in face areas were consistent across brains. Representational geometries in late, fully connected DCNN layers, which are optimized for individuation, were much more weakly correlated with cognitive and neural geometries than were geometries in late-intermediate layers. The late-intermediate face-DCNN layers successfully matched cognitive representational geometries, as measured with a behavioral arrangement task that primarily reflected categorical attributes, and correlated with neural representational geometries in known face-selective topographies. Our study suggests that current DCNNs successfully capture neural cognitive processes for categorical attributes of faces but less accurately capture individuation and dynamic features.

Polar angle asymmetries in visual perception and neural architecture

Human visual performance changes with visual field location. It is best at the center of gaze and declines with eccentricity, and also varies markedly with polar angle. These perceptual polar angle asymmetries are linked to asymmetries in the organization of the visual system. We review and integrate research quantifying how performance changes with visual field location and how this relates to neural organization at multiple stages of the visual system. We first briefly review how performance varies with eccentricity and the neural foundations of this effect. We then focus on perceptual polar angle asymmetries and their neural foundations. Characterizing perceptual and neural variations across and around the visual field contributes to our understanding of how the brain translates visual signals into neural representations which form the basis of visual perception.

Familiarity Facilitates Detection of Angry Expressions

Personal familiarity facilitates rapid and optimized detection of faces. In this study, we investigated whether familiarity associated with faces can also facilitate the detection of facial expressions. Models of face processing propose that face identity and face expression detection are mediated by distinct pathways. We used a visual search paradigm to assess if facial expressions of emotion (anger and happiness) were detected more rapidly when produced by familiar as compared to unfamiliar faces. We found that participants detected an angry expression 11% more accurately and 135 ms faster when produced by familiar as compared to unfamiliar faces while happy expressions were detected with equivalent accuracies and at equivalent speeds for familiar and unfamiliar faces. These results suggest that detectors in the visual system dedicated to processing features of angry expressions are optimized for familiar faces.

Shared neural codes for visual and semantic information about familiar faces in a common representational space

Our brain processes faces of close others differently than faces of visually familiar individuals. While both types of faces activate similar visual areas, faces of close others activate areas involved in processing social and semantic information. Here, we used between-subject linear classifiers trained on hyperaligned brain data to investigate the neural code for visual and semantic information about familiar others. The identity of both visually and personally familiar faces could be decoded across participants from brain activity in visual areas. Instead, only the identity of personally familiar faces could be decoded in areas involved in social cognition. Our results suggest that individually distinctive information associated with familiar faces is embedded in a neural code that is shared across brains.

Processes evoked by seeing a personally familiar face encompass recognition of visual appearance and activation of social and person knowledge. Whereas visual appearance is the same for all viewers, social and person knowledge may be more idiosyncratic. Using between-subject multivariate decoding of hyperaligned functional magnetic resonance imaging data, we investigated whether representations of personally familiar faces in different parts of the distributed neural system for face perception are shared across individuals who know the same people. We found that the identities of both personally familiar and merely visually familiar faces were decoded accurately across brains in the core system for visual processing, but only the identities of personally familiar faces could be decoded across brains in the extended system for processing nonvisual information associated with faces. Our results show that personal interactions with the same individuals lead to shared neural representations of both the seen and unseen features that distinguish their identities.

Holistic face recognition is an emergent phenomenon of spatial processing in face-selective regions

Spatial processing by receptive fields is a core property of the visual system. However, it is unknown how spatial processing in high-level regions contributes to recognition behavior. As face inversion is thought to disrupt typical holistic processing of information in faces, we mapped population receptive fields (pRFs) with upright and inverted faces in the human visual system. Here we show that in face-selective regions, but not primary visual cortex, pRFs and overall visual field coverage are smaller and shifted downward in response to face inversion. From these measurements, we successfully predict the relative behavioral detriment of face inversion at different positions in the visual field. This correspondence between neural measurements and behavior demonstrates how spatial processing in face-selective regions may enable holistic perception. These results not only show that spatial processing in high-level visual regions is dynamically used towards recognition, but also suggest a powerful approach for bridging neural computations by receptive fields to behavior.

Spatial Heterogeneity in Bistable Figure-Ground Perception

The appearance of visual objects varies substantially across the visual field. Could such spatial heterogeneity be due to undersampling of the visual field by neurons selective for stimulus categories? Here, we show that which parts of a bistable vase-face image observers perceive as figure and ground depends on the retinal location where the image appears. The spatial patterns of these perceptual biases were similar regardless of whether the images were upright or inverted. Undersampling by neurons tuned to an object class (e.g., faces) or variability in general local versus global processing cannot readily explain this spatial heterogeneity. Rather, these biases could result from idiosyncrasies in low-level sensitivity across the visual field.

How familiarity warps representation in the face space

Recognition of familiar as compared to unfamiliar faces is robust and resistant to marked image distortion or degradation. Here we tested the flexibility of familiar face recognition with a morphing paradigm where the appearance of a personally familiar face was mixed with the appearance of a stranger (Experiment 1) and the appearance of one's own face with the appearance of a familiar face and the appearance of a stranger (Experiment 2). The aim of the two experiments was to assess how categorical boundaries for recognition of identity are affected by familiarity. We found a narrower categorical boundary for the identity of personally familiar faces when they were mixed with unfamiliar identities as compared to the control condition, in which the appearance of two unfamiliar faces was mixed. Our results suggest that familiarity warps the representational geometry of face space, amplifying perceptual distances for small changes in the appearance of familiar faces that are inconsistent with the structural features that define their identities.

Size Perception Biases Are Temporally Stable and Vary Consistently Between Visual Field Meridians

The apparent size of visual stimuli depends on where in the visual field they appear. We recently presented a model of how size perception could be biased by stimulus encoding in retinotopic cortex. However, it remains unclear if such perceptual biases are instead trivially related to discrimination ability and if they are temporally stable. An independent test of the model is also still outstanding. Here, I show that perceptual biases are stable across stimulus durations between 50 and 1,000 milliseconds, even though discrimination ability unsurprisingly improves with duration. Furthermore, perceptual biases are stronger along the vertical than the horizontal meridian, which mirrors reported differences in spatial vision and the positional selectivity of early visual cortex. Taken together, these findings support our model of how size is inferred from cortical responses.

Familiarity revealed by involuntary eye movements on the fringe of awareness

Involuntary eye movements during fixation of gaze are typically transiently inhibited following stimulus onset. This oculomotor inhibition (OMI), which includes microsaccades and spontaneous eye blinks, is modulated by stimulus saliency and anticipation, but it is currently unknown whether it is sensitive to familiarity. To investigate this, we measured the OMI while observers passively viewed a slideshow of one familiar and 7 unfamiliar facial images presented briefly at 1 Hz in random order. Since the initial experiments indicated that OMI was occasionally insensitive to familiarity when the facial images were highly visible, and to prevent top-down strategies and potential biases, we limited visibility by backward masking making the faces barely visible or at the fringe of awareness. Under these conditions, we found prolonged inhibition of both microsaccades and eye-blinks, as well as earlier onset of microsaccade inhibition with familiarity. These findings demonstrate, for the first time, the sensitivity of OMI to familiarity. Because this is based on involuntary eye movements and can be measured on the fringe of awareness and in passive viewing, our results provide direct evidence that OMI can be used as a novel physiological measure for studying hidden memories with potential implications for health, legal, and security purposes.