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Abstract How the brain encodes, recognizes, and memorizes general visual objects is a fundamental question in neuroscience. Here, we investigated the neural processes underlying visual object perception and memory by recording from 3173 single neurons in the human amygdala and hippocampus across four experiments. We employed both passive-viewing and recognition memory tasks involving a diverse range of naturalistic object stimuli. Our findings reveal a region-based feature code for general objects, where neurons exhibit receptive fields in the high-level visual feature space. This code can be validated by independent new stimuli and replicated across all experiments, including fixation-based analyses with large natural scenes. This region code explains the long-standing visual category selectivity, preferentially enhances memory of encoded stimuli, predicts memory performance, encodes image memorability, and exhibits intricate interplay with memory contexts. Together, region-based feature coding provides an important mechanism for visual object processing in the human brain. 

Abstract Neurotypical (NT) individuals and individuals with autism spectrum disorder (ASD) make different judgments of social traits from others’ faces; they also exhibit different social emotional responses in social interactions. A common hypothesis is that the differences in face perception in ASD compared with NT is related to distinct social behaviors. To test this hypothesis, we combined a face trait judgment task with a novel interpersonal transgression task that induces measures social emotions and behaviors. ASD and neurotypical participants viewed a large set of naturalistic facial stimuli while judging them on a comprehensive set of social traits (e.g., warm, charismatic, critical). They also completed an interpersonal transgression task where their responsibility in causing an unpleasant outcome to a social partner was manipulated. The purpose of the latter task was to measure participants’ emotional (e.g., guilt) and behavioral (e.g., compensation) responses to interpersonal transgression. We found that, compared with neurotypical participants, ASD participants’ self-reported guilt and compensation tendency was less sensitive to our responsibility manipulation. Importantly, ASD participants and neurotypical participants showed distinct associations between self-reported guilt and judgments of criticalness from others' faces. These findings reveal a novel link between perception of social traits and social emotional responses in ASD. 

Inferring the intentions and emotions of others from behavior is crucial for social cognition. While neuroimaging studies have identified brain regions involved in social inference, it remains unknown whether performing social inference is an abstract computation that generalizes across different stimulus categories or is specific to certain stimulus domain. We recorded single-neuron activity from the medial temporal lobe (MTL) and the medial frontal cortex (MFC) in neurosurgical patients performing different types of inferences from images of faces, hands, and natural scenes. Our findings indicate distinct neuron populations in both regions encoding inference type for social (faces, hands) and nonsocial (scenes) stimuli, while stimulus category was itself represented in a task-general manner. Uniquely in the MTL, social inference type was represented by separate subsets of neurons for faces and hands, suggesting a domain-specific representation. These results reveal evidence for specialized social inference processes in the MTL, in which inference representations were entangled with stimulus type as expected from a domain-specific process. 

IntroductionEarly and accurate diagnosis of autism spectrum disorder (ASD) is crucial for effective intervention, yet it remains a significant challenge due to its complexity and variability. Micro-expressions are rapid, involuntary facial movements indicative of underlying emotional states. It is unknown whether micro-expression can serve as a valid bio-marker for ASD diagnosis. MethodsThis study introduces a novel machine-learning (ML) framework that advances ASD diagnostics by focusing on facial micro-expressions. We applied cutting-edge algorithms to detect and analyze these micro-expressions from video data, aiming to identify distinctive patterns that could differentiate individuals with ASD from typically developing peers. Our computational approach included three key components: (1) micro-expression spotting using Shallow Optical Flow Three-stream CNN (SOFTNet), (2) feature extraction via Micron-BERT, and (3) classification with majority voting of three competing models (MLP, SVM, and ResNet). ResultsDespite the sophisticated methodology, the ML framework's ability to reliably identify ASD-specific patterns was limited by the quality of video data. This limitation raised concerns about the efficacy of using micro-expressions for ASD diagnostics and pointed to the necessity for enhanced video data quality. DiscussionOur research has provided a cautious evaluation of micro-expression diagnostic value, underscoring the need for advancements in behavioral imaging and multimodal AI technology to leverage the full capabilities of ML in an ASD-specific clinical context.