Abstract:

Hallucinations, defined as perceptual experiences occurring in the absence of external stimuli, remain a central challenge in psychiatry and neuroscience. They are prevalent across multiple disorders, including schizophrenia, Parkinson’s disease, and affective psychoses, and often predict poor prognosis and treatment resistance. While significant progress has been made in identifying brain regions and networks associated with hallucinatory states, the precise neural and computational mechanisms underlying their emergence are still not fully understood. This review synthesizes recent empirical findings and theoretical developments to provide an integrated account of the neurobiological circuits, computational models, and therapeutic interventions related to hallucinations, while highlighting critical challenges and directions for future research.

At the neural systems level, accumulating evidence implicates a distributed network rather than isolated regions in the genesis of hallucinations. Key structures such as the prefrontal cortex, temporoparietal junction, insula, hippocampus, and parahippocampal gyrus have been consistently linked to hallucinatory phenomena. However, the precise contribution of each region and the nature of their dynamic interactions remain unclear. A major theme emerging from current research is that hallucinations are likely the product of aberrant communication within large-scale cortical-subcortical networks, rather than dysfunction in a single locus. This distributed view calls for multimodal neuroimaging approaches that integrate structural, functional, and temporal information to characterize how interactions among regions give rise to hallucinatory experiences.

Computational psychiatry has provided valuable tools for conceptualizing hallucinations in terms of predictive coding and Bayesian inference. Within this framework, perception is modeled as the integration of bottom-up sensory evidence with top-down priors. Hallucinations may result from an imbalance in this process, characterized by overweighted priors or diminished sensory precision, leading to misattribution of internally generated signals as external perceptions. Empirical studies support this account, showing that individuals prone to hallucinations exhibit abnormally high reliance on prior beliefs during perceptual decision-making. Neurochemical findings further indicate that striatal dopamine plays a central role in modulating the weighting of prediction errors, with hyperdopaminergic states biasing the system toward prior-driven inferences. The development of hierarchical Gaussian filter models has expanded these insights by allowing simultaneous modeling of priors, learning rates, and belief stability, offering a more fine-grained computational account of hallucinatory states. Importantly, this modeling framework may help distinguish between subtypes of hallucinations, providing a basis for more individualized clinical interventions.

Non-pharmacological interventions have also become a focus of translational research. Cognitive-behavioral therapy can help patients reinterpret and manage hallucinatory experiences, while electroconvulsive therapy has demonstrated efficacy in some treatment-resistant cases. Among neuromodulatory methods, transcranial magnetic stimulation has shown promise, particularly when applied to the temporoparietal junction. However, clinical outcomes remain inconsistent, reflecting heterogeneity in patient subgroups, stimulation protocols, and neural targets. Recent work highlights the importance of individualized targeting using neuroimaging-guided navigation and computational modeling to optimize stimulation parameters. Such approaches underscore a growing recognition that hallucinations are not homogeneous phenomena and that effective intervention must account for differences in neural circuitry and symptom profiles across patients.

Looking ahead, several challenges define the next stage of research. The first concerns the need to move beyond region-specific accounts toward a network-based understanding of hallucinations, leveraging multimodal imaging and computational modeling to characterize distributed interactions. Another challenge lies in the lack of standardized paradigms for cross-modal comparison, which hinders efforts to identify shared versus modality-specific mechanisms across auditory, visual, and somatic hallucinations. Progress will require the development of unified experimental frameworks and the incorporation of cross-diagnostic samples to disentangle disease-specific and modality-specific contributions. Finally, establishing causal evidence remains a pressing need. Most current findings are correlational, and future research must employ causal intervention strategies—such as intracranial stimulation combined with behavioral paradigms and computational modeling—to determine the necessity and sufficiency of specific circuits in hallucinatory states.

In summary, hallucinations emerge from the interplay of distributed neural networks, altered neurochemical modulation, and computational distortions in predictive processing. By integrating empirical neuroscience, computational modeling, and clinical intervention research, this review demonstrates the value of moving toward a more mechanistic and individualized understanding of hallucinatory phenomena. Advances in this direction have the potential not only to refine theoretical models but also to inform the development of targeted therapeutic strategies, ultimately improving outcomes for patients and reducing the societal burden associated with these profoundly disruptive symptoms.

Key words: hallucinations, conditioned hallucinations, neural circuits, predictive coding, neuromodulation