# A Hearing Test for Hallucinations: Toward Development of Computational Markers for Early Diagnosis

> **NIH NIH K23** · YALE UNIVERSITY · 2022 · $196,839

## Abstract

PROJECT SUMMARY / ABSTRACT
 Early identification of those at clinical high risk of psychosis (CHR) is critical for maximizing outcomes
for those who convert. However, prediction relies largely on subjective symptom reports. Objective biomarkers
are essential. My career goal is to use objective computational neuroscience to predict conversion in CHR.
 In work recently completed with my primary mentor (Dr. Corlett) and published in Science, I examined
whether hallucinations might arise from an over-weighting of prior knowledge in perception. We used sensory
conditioning to elicit hallucinatory experiences. Participants were exposed to repeated pairings of visual and
auditory stimuli and subsequently perceived the auditory stimulus when only the visual was present. We
applied this Conditioned Hallucinations paradigm to four groups: participants with psychosis both with (P+H+)
and without (P+H-) hallucinations, healthy voice-hearers (P-H+), and healthy controls (P-H-). Conditioned
hallucinations were markedly more frequent in those who hallucinate (P+H+ and P-H+) compared with those
who do not (P+H-, P-H-).
 These behavioral data were used to estimate parameters of a Hierarchical Gaussian Filter (HGF)
model with the laboratory of Dr. Stephan (co-mentor). Two different model parameters discriminated between
groups of individuals with and without auditory hallucinations and, orthogonally, with and without a diagnosable
psychotic disorder. On functional imaging analysis, activity in brain regions encoding low-level perceptual belief
(e.g., insula, superior temporal sulcus) differentiated those with and without hallucinations. Activity in brain
regions encoding change sensitivity (e.g., cerebellum) differentiated those with and without psychosis. These
computational and imaging metrics may hasten the detection of conversion in CHR. However, more work is
required. We propose 1) to determine whether these markers relate to risk of conversion in CHR; and 2) to
determine whether they change with symptom severity over time. This research will provide training in the
clinical application of computational models of perception, the evaluation of CHR, and longitudinal data
analysis. Our work will be supported by formal didactics and symposia focused on the theory and practice of
computational modeling.
 To meet my career goal, I must understand more deeply how to construct, alter, and utilize
computational models of perception so that I may capture the subtle abnormalities of information processing
that predate the development of frank hallucinations and psychosis. This proposal will provide me with the
additional training and mentored research experiences necessary to become a fully independent investigator
who brings the tools of computational neuroscience to the service of the early detection of psychosis.

## Key facts

- **NIH application ID:** 10456110
- **Project number:** 5K23MH115252-05
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Albert R Powers
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $196,839
- **Award type:** 5
- **Project period:** 2018-08-23 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10456110

## Citation

> US National Institutes of Health, RePORTER application 10456110, A Hearing Test for Hallucinations: Toward Development of Computational Markers for Early Diagnosis (5K23MH115252-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10456110. Licensed CC0.

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