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Principal Investigator: Dobromir Rahnev
Organization: GEORGIA INSTITUTE OF TECHNOLOGY
Fiscal Year: 2024
Award: $369,995
Funding agency: National Institute of Mental Health
Project Summary. Metacognition is the ability to reflect on and evaluate one’s behavior. Impaired metacognition
has been proposed as a major symptom for a number of psychiatric disorders such as schizophrenia, depression,
generalized anxiety disorder, obsessive-compulsive disorder, and even substance abuse. Deficits in metacog-
nition have further been found after brain lesions. Such deficits can have severe detrimental effects on patients;
treating them requires insight into the computational and neural substrates of metacognition. However, efforts in
this direction have been hampered by a lack of an overarching framework that can inform computational models
of confidence, the measurement of metacognitive ability, and the investigation of the neural bases of metacog-
nition. This proposal will advance a novel theoretical framework based on the concept of hierarchical noise ar-
chitecture. Perceptual decision making will be used as a model system but the architecture is perfectly general
and expected to apply across all domains of metacognition. According to the hierarchical noise architecture (1)
sensory noise corrupts the decision-level representation of the stimulus thus affecting both the perceptual and
confidence judgments, whereas (2) an additional metacognitive noise corrupts confidence but not the perceptual
judgment. The proposed architecture makes a novel prediction – namely, that higher sensory noise should be
more detrimental to the perceptual decisions than the confidence rating – that is supported by strong preliminary
data. More importantly, this architecture can be used to extract a new, model-based measure of metacognitive
ability with desirable psychometric properties such as being independent from participants’ bias for high or low
confidence. This new measure can therefore be used to examine the effectiveness of treatments on patients’
metacognitive abilities: for example, if a patient with deficiency in metacognition changes her strategy and starts
using high confidence more, the new measure – but not previous measures – will remain unchanged. Beyond
predicting new behavioral phenomena and leading to an improved measure of metacognitive ability, the hierar-
chical noise architecture can also elucidate the neural bases of metacognition. Specifically, the functional roles
postulated by the hierarchical noise model can be linked directly to the functions of large-scale brain networks
and especially the central executive and the salience networks. These links will be established using a variety of
techniques such as causally interfering with different nodes of these networks, correlating metacognitive ability
with the connectivity within these networks, and examining the dynamics of the inter-network communication
during confidence generation. The insights gained by this proposal will have a direct link to work in the clinic by
providing researchers a better tool to assess the metacognitive deficits of patients (including the effectiveness
of proposed treatments) and link such dysfunction to specific brain circuits. This work is thus expected to benefit
patients suffering a range of diseases from schizophrenia to depression to substance abuse.
Terms: <Affect><Aging><Architecture><Back><Bayesian Modeling><Bayesian adaptive designs><Bayesian adaptive models><Bayesian belief network><Bayesian belief updating model><Bayesian framework><Bayesian hierarchical model><Bayesian network model><Bayesian nonparametric models><Bayesian spatial data model><Bayesian spatial image models><Bayesian spatial models><Bayesian statistical models><Bayesian tracking algorithms><Behavior><Behavioral><Biologic Models><Biological Models><Brain><Brain Nervous System><Clinic><Communication><Computer Models><Computerized Models><Data><Decision Making><Dependence><Disease><Disorder><Dorsum><Dysfunction><Effectiveness><Encephalon><Engineering / Architecture><Functional disorder><Generalized Anxiety Disorder><Generations><Goals><Human><Impairment><Investigation><Investigators><Judgment><Knowledge><Lesion><Link><Measurement><Measures><Mental Depression><Mental disorders><Mental health disorders><Middle Frontal Gyrus><Middle frontal gyrus structure><Mission><Model System><Modeling><Modern Man><NIH><National Institutes of Health><Noise><Obsessive-Compulsive Disorder><Obsessive-Compulsive Neurosis><Outcome><Participant><Patients><Persons><Physiopathology><Position><Positioning Attribute><Property><Psychiatric Disease><Psychiatric Disorder><Psychometrics><Public Health><Research><Research Personnel><Researchers><Risk Behaviors><Risky Behavior><Role><Schizophrenia><Schizophrenic Disorders><Sensory><Source><Stimulus><Structure><Substance abuse problem><Symptoms><Techniques><Testing><Treatment Effectiveness><United States National Institutes of Health><Validation><Visual><Work><abuse of substances><adjudication><adjudicative process and procedure><assess effectiveness><at risk behavior><base><bases><computational modeling><computational models><computer based models><computerized modeling><dementia praecox><depression><determine effectiveness><disability><effectiveness assessment><effectiveness evaluation><evaluate effectiveness><examine effectiveness><generalized anxiety><improved><insight><mental illness><metacognition><neural><novel><pathophysiology><psychiatric illness><psychological disorder><schizophrenic><social role><substance abuse><system architecture><tool><validations>