Volume 6 • Issue 2 • PP: 14–18 • 2026
The Role of GANs, VAEs, and Autoregressive Models in Neurological and Psychological Research: A Comprehensive Review
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Generative models, including GANs, VAEs, and autoregressive models, have drastically improved neurological and psychological analyses. They allow for the formation of complex data representations that imitate cognitive processes in a human brain and thus help to understand the workings of the brain and mental diseases. Thus, GANs trained with adversarial mechanisms can synthesize nearly photorealistic fake samples, which can mimic neurological disorders or assess the effectiveness of treatment. VAEs are known to give a formidably well founded method of learning the hidden representations of psychological states, therefore allowing researchers to study the potential causes of mental health problems. Autoregressive models, in contrast, are most applicable in time series data, which is highly important when the neurological signal or behavior under investigation needs to be studied over time. This broad survey discusses the assets and liabilities of these generative approaches, emphasizing their usability in simulating elaborate psychological processes and deploying evidence-based observations to understand the assessment and therapy of mental disorders. Therefore, this work aims to reveal the significant connections between these methodologies and their further potential for investigating human cognition and behavior through the coordinated usage of highly effective computational methods.
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