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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. 

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. 

Abstract Individuals with autism spectrum disorder (ASD) experience pervasive difficulties in processing social information from faces. However, the behavioral and neural mechanisms underlying social trait judgments of faces in ASD remain largely unclear. Here, we comprehensively addressed this question by employing functional neuroimaging and parametrically generated faces that vary in facial trustworthiness and dominance. Behaviorally, participants with ASD exhibited reduced specificity but increased inter-rater variability in social trait judgments. Neurally, participants with ASD showed hypo-activation across broad face-processing areas. Multivariate analysis based on trial-by-trial face responses could discriminate participant groups in the majority of the face-processing areas. Encoding social traits in ASD engaged vastly different face-processing areas compared to controls, and encoding different social traits engaged different brain areas. Interestingly, the idiosyncratic brain areas encoding social traits in ASD were still flexible and context-dependent, similar to neurotypicals. Additionally, participants with ASD also showed an altered encoding of facial saliency features in the eyes and mouth. Together, our results provide a comprehensive understanding of the neural mechanisms underlying social trait judgments in ASD. 

Abstract Diagnosing language disorders associated with autism is a complex and nuanced challenge, often hindered by the subjective nature and variability of traditional assessment methods. Traditional diagnostic methods not only require intensive human effort but also often result in delayed interventions due to their lack of speed and specificity. In this study, we explored the application of ChatGPT, a state-of-the-art large language model, to overcome these obstacles by enhancing diagnostic accuracy and profiling specific linguistic features indicative of autism. Leveraging ChatGPT's advanced natural language processing capabilities, this research aims to streamline and refine the diagnostic process. Specifically, we compared ChatGPT's performance with that of conventional supervised learning models, including BERT, a model acclaimed for its effectiveness in various natural language processing tasks. We showed that ChatGPT substantially outperformed these models, achieving over 13\% improvement in both accuracy and F1-score in a zero-shot learning configuration. This marked enhancement highlights the model’s potential as a superior tool for neurological diagnostics. Additionally, we identified ten distinct features of autism-associated language disorders that vary significantly across different experimental scenarios. These features, which included echolalia, pronoun reversal, and atypical language usage, were crucial for accurately diagnosing ASD and customizing treatment plans. Together, our findings advocate for adopting sophisticated AI tools like ChatGPT in clinical settings to assess and diagnose developmental disorders. Our approach not only promises greater diagnostic precision but also aligns with the goals of personalized medicine, potentially transforming the evaluation landscape for autism and similar neurological conditions. 

Abstract Faces are among the most important visual stimuli that humans perceive in everyday life. While extensive literature has examined emotional processing and social evaluations of faces, most studies have examined either topic using unimodal approaches. In this review, we promote the use of multimodal cognitive neuroscience approaches to study these processes, using two lines of research as examples: ambiguity in facial expressions of emotion and social trait judgment of faces. In the first set of studies, we identified an event‐related potential that signals emotion ambiguity using electroencephalography and we found convergent neural responses to emotion ambiguity using functional neuroimaging and single‐neuron recordings. In the second set of studies, we discuss how different neuroimaging and personality‐dimensional approaches together provide new insights into social trait judgments of faces. In both sets of studies, we provide an in‐depth comparison between neurotypicals and people with autism spectrum disorder. We offer a computational account for the behavioral and neural markers of the different facial processing between the two groups. Finally, we suggest new practices for studying the emotional processing and social evaluations of faces. All data discussed in the case studies of this review are publicly available. 

Processing social information from faces is difficult for individuals with autism spectrum disorder (ASD). However, it remains unclear whether individuals with ASD make high-level social trait judgments from faces in the same way as neurotypical individuals. Here, we comprehensively addressed this question using naturalistic face images and representatively sampled traits. Despite similar underlying dimensional structures across traits, online adult participants with self-reported ASD showed different judgments and reduced specificity within each trait compared with neurotypical individuals. Deep neural networks revealed that these group differences were driven by specific types of faces and differential utilization of features within a face. Our results were replicated in well-characterized in-lab participants and partially generalized to more controlled face images (a preregistered study). By investigating social trait judgments in a broader population, including individuals with neurodevelopmental variations, we found important theoretical implications for the fundamental dimensions, variations, and potential behavioral consequences of social cognition.