In a pioneering study conducted at the University of Turku in Finland, researchers have demonstrated that artificial intelligence (AI) can analyze and interpret complex social cues from images and videos with an accuracy nearly matching that of humans. Published in the journal Imaging Neuroscience, this research leverages the power of the large multimodal language model GPT-4V, revealing groundbreaking capabilities in AI-driven social perception that extend beyond rudimentary recognition to the nuanced evaluation of human interactions, facial expressions, and social dynamics.
Social perception—the ability to intuitively understand others’ emotions, intentions, and behaviors—is a fundamental human skill central to communication and collaboration. Traditionally considered a uniquely human faculty reliant on empathy and contextual understanding, deciphering social cues presented a profound challenge for AI systems. However, the surge in multimodal models, capable of processing both text and visual input, has presented new opportunities to bridge this divide. The researchers at Turku set out to rigorously test whether GPT-4V’s assessments of social features concur with those made by human observers.
The study involved the model evaluating 138 distinct social attributes extracted from a diverse set of images and video clips. These attributes encompassed a broad spectrum: from minute facial microexpressions and nuanced body language, to more abstract social interaction parameters such as cooperation, hostility, and social engagement. To establish a baseline for comparison, human evaluations totaling over 2,000 assessments were gathered from a large pool of participants, providing a robust dataset against which GPT-4V’s performance was measured.
Remarkably, GPT-4V’s analyses demonstrated a degree of consistency and reliability that rivaled—and in many cases surpassed—the evaluations of individual human observers. The AI’s ratings were not only aligned with general human consensus but also exhibited less variance compared to evaluations from single participants. Although collective assessments from multiple humans remained the gold standard in accuracy, the AI’s stable and reproducible judgments underscore its potential as a dependable proxy in social behavioral analysis.
This breakthrough extends far beyond academic curiosity. By integrating AI-powered social perception into neuroscience research, the study illustrates a practical pathway to accelerate the mapping of social cognition networks in the brain. Functional brain imaging experiments often require exhaustive coding of social content in stimuli, a process traditionally dependent on labor-intensive human annotation. GPT-4V’s ability to swiftly and accurately perform these evaluations can dramatically expedite data processing pipelines, enabling high-throughput experiments that were once prohibitively resource-intensive.
Dr. Severi Santavirta, the postdoctoral researcher spearheading the study, highlights the implications: “Where human evaluations demanded more than 10,000 collective work hours from over 2,000 individuals, the AI accomplished equivalent assessments within mere hours. This scalability can revolutionize social neuroscience by reducing costs, limiting human labor, and maintaining high data quality.”
On a neurological level, the study revealed that brain activation patterns correlated strongly with the social feature evaluations provided by both humans and GPT-4V. When participants viewed social scenes, functional MRI scans indicated that the brain networks engaged during these observations were nearly identical whether the underlying social features were annotated by human raters or the AI model. This remarkable convergence lends neuroscientific credence to the fidelity of AI-based social perception.
The broader societal potential of this research is enormous. Automatic, continuous social situation analysis could find immediate utility in healthcare settings, where monitoring patient well-being and behavioral cues is critical yet often limited by staffing constraints. AI systems could serve as tireless assistants that parse complex social environments around the clock, alerting caregivers to subtle changes indicating distress or recovery.
Moreover, commercial sectors such as marketing stand to benefit considerably. Understanding audience reactions and the perceived social nuances of audiovisual content can reshape how campaigns are designed and targeted. AI-driven analytics might predict viewer engagement or detect unfavorable social signals, enabling brands to optimize content with unprecedented precision.
Security applications present another promising frontier. Surveillance systems augmented with AI capable of interpreting social interactions could foresee potential threats by identifying patterns of hostility or abnormal crowd behavior well before human operators might recognize them. This anticipatory capability promises to enhance public safety without infringing on privacy through automated non-intrusive analysis.
Despite these advances, the researchers caution that AI should not be viewed as a wholesale replacement for human judgment but rather as a powerful complement. The fatigue-free consistency and rapid throughput of AI can enable human experts to focus on confirming critical findings, troubleshooting ambiguous cases, and applying contextual expertise not yet replicable by machines.
Technically, this study pushes the boundary of natural language processing fused with computer vision, highlighting how large multimodal models like GPT-4V capture complex semantic and social information embedded in visual media. This paradigm shift reflects an evolution from simple object recognition toward deep social cognition simulation, enabling machines to understand human experiences more profoundly.
The work also raises intriguing questions about how AI’s internal representation of social features compares to the neural encoding in the human brain, inviting future interdisciplinary research teams to explore the computational neuroscience of social perception. Such inquiries could eventually lead to AI systems that not only interpret but also simulate social behaviors in ways beneficial for education, therapy, and human-computer interaction design.
As these models continue to evolve, the fusion of artificial and human intelligence could forge a new era of social insight, combining vast computational resources with uniquely human empathy. The University of Turku’s study exemplifies this transformative potential, setting a benchmark for AI’s role in decoding the intricacies of human social life.
Subject of Research: People
Article Title: GPT-4V shows human-like social perceptual capabilities at phenomenological and neural levels
News Publication Date: 12-Aug-2025
Web References: 10.1162/IMAG.a.134
Keywords: Artificial intelligence, social perception, GPT-4V, multimodal models, neuroscience, brain imaging, social interaction analysis, functional MRI, human behavior, AI evaluation consistency, computational social cognition
