A generative adversarial model of intrusive imagery in the human brain

Abstract

The mechanisms underlying the subjective experiences of mental disorders remain poorly understood. This is partly due to long-standing over-emphasis on behavioral and physiological symptoms and a de-emphasis of the patient's subjective experiences when searching for treatments. Here we provide a new perspective on the subjective experience of mental disorders based on findings in neuroscience and artificial intelligence (AI). Specifically, we propose the subjective experience that occurs in visual imagination depends on mechanisms similar to generative adversarial networks that have recently been developed in AI. The basic idea is that a generator network fabricates a prediction of the world, and a discriminator network determines whether it is likely real or not. Given that similar adversarial interactions occur in the two major visual pathways of perception in people, we explored whether we could leverage this AI-inspired approach to better understand the intrusive imagery experiences of patients suffering from mental illnesses such as post-traumatic stress disorder (PTSD) and acute stress disorder. In our model, a nonconscious visual pathway generates predictions of the environment that influence the parallel but interacting conscious pathway. We propose that in some patients, an imbalance in these adversarial interactions leads to an overrepresentation of disturbing content relative to current reality, and results in debilitating flashbacks. By situating the subjective experience of intrusive visual imagery in the adversarial interaction of these visual pathways, we propose testable hypotheses on novel mechanisms and clinical applications for controlling and possibly preventing symptoms resulting from intrusive imagery.

Deep problems with neural network models of human vision

“…For instance, imagery mechanisms could act as the generator of quasi-perceptual experiences, while reality monitoring could serve as the discriminator to distinguish between sensory inputs from real or imagined sources (Gershman, 2019;Lau, 2019). Recent studies investigated involuntary visual experiences using generative neural network models, such as in memory replay (van de Ven, Siegelmann, & Tolias, 2020), intrusive imagery (Cushing et al, 2023), and adversarial dreaming (Deperrois, Petrovici, Senn, & Jordan, 2022). Regarding voluntary visual mental imagery, some key strategies may involve modeling the retrieval process of representations pertaining to semantic information and visual features , and incorporating biologically inspired recurrence in visual imagery processing (Lindsay, Mrsic-Flogel, & Sahani, 2022).…”

Confidence and metacognition

“…Emerging studies have introduced potential computations that underlie perceptual reality monitoring, such as generative adversarial processing (Cushing et al, 2023;Gershman, 2019). Moreover, the frontoparietal regions have been implicated as a neural basis for perceptual reality monitoring, which overlaps with the known neural correlate of confidence (Dijkstra & Fleming, 2023;Lau et al, 2022).…”

Individualistic attitudes in Iterated Prisoner’s Dilemma undermine evolutionary fitness and may drive cooperative human players to extinction

“…So far, how well human players can escape extortion in iterative games has not been investigated by implementing Press and Dyson’s algebraic approach. It is possible that general adversarial models can account for imagining alternative courses of action for escaping extortion [ 29 ]. Furthermore, no previous work attempted to unify IPD and UG in a way that would allow a game-theoretic analysis of evolutionary social-economic fitness associated with human interpersonal cooperation at the stationary state (i.e.…”