Cognitive Neurocomputational Task Reliability & Clinical Applications Consortium

Summary

In Study 1, investigators will test the ability of each of the seven paradigms to differentiate between schizophrenia (SZ)/schizoaffective disorder (SZA) and matched healthy control subjects (HC) and to predict individual differences in clinical measures of hallucinations and delusions (as well as measures of function). Investigators will also directly compare the tasks in terms of predictive ability and psychometric properties and determine whether the tasks share variance or explain unique variance. This will include determining whether hallucinations and delusions are best explained by separate underlying processes or by a single process. Investigators will also test whether the model-based parameters from these studies are more precise and sensitive than standard behavioral methods for assessing deficits across psychotic disorders and for predicting hallucinations and delusions (Aim 1). Investigators will also validate key model-based parameters against well-characterized neurophysiological measures using electroencephalogram (EEG) (Aim 4). The data from Study 1 will also be used to optimize (e.g., length, floor, and ceiling effects) tasks for Study 2. To generate a sample with sufficient variation in hallucinations and delusions, investigators will recruit the following groups that balance feasibility with clinical relevance: A) healthy controls (HC) \[HC, N=125\]; B) schizophrenia and schizoaffective disorder \[SZ/SZA, N=200\]). For Study 1, investigators will ensure that 40 patients are unmedicated. Investigators anticipate a \~20% screen fail/drop-out rate based on our prior Cognitive Neuroscience Test Reliability and Clinical Applications for Schizophrenia Consortium (CNTRACS) studies. Thus, investigators will run an additional 40 SZ/SZA and 15 HC. All sites have experience recruiting patients in each of these groups as indicated by our past successful studies, and the fact that investigators have been able to recruit patients with significant hallucinations and delusions in all groups. Investigators will also validate key model-based parameters against well-characterized neurophysiological measures using electroencephalogram (EEG) (Aim 4). The data from Study 1 will also be used to optimize (e.g., length, floor, and ceiling effects) tasks for Study 2. To generate a sample with sufficient variation in hallucinations and delusions, investigators will recruit the following groups that balance feasibility with clinical relevance: A) healthy controls (HC) \[HC, N=125\]; B) schizophrenia and schizoaffective disorder \[SZ/SZA, N=200\]). For Study 1, investigators will ensure that 40 patients are unmedicated. Investigators anticipate a \~20% screen fail/drop-out rate based on our prior Cognitive Neuroscience Test Reliability and Clinical Applications for Schizophrenia Consortium (CNTRACS) studies. Thus, investigators will run an additional 40 SZ/SZA and 15 HC. All sites have experience recruiting patients in each of these groups as indicated by our past successful studies, and the fact that investigators have been able to recruit patients with significant hallucinations and delusions in all groups.

Description

Eligibility

Age range

18–59 years

Sex

All

Healthy volunteers

Yes

Interventions

• Behavioral
  Motion Perception Task

  Assesses updating of beliefs from new sensory inputs with prior knowledge via perceiving direction changes in random dot motion stimuli

• Behavioral
  Sine Wave Speech Task

  Measures the degree to which overweighting of prior beliefs impacts processing of sensory inputs (detection of speech).

• Behavioral
  Volatility Task

  Measures the updating of priors about the location of a bag drop from a helicopter when the location changes (prediction errors).

• Behavioral
  Choice Probabilistic Reversal Task

  Assesses prior belief updating when feedback about obtaining positive outcomes changes (prediction errors).

• Behavioral
  Kamin Blocking Task

  Examines the role of mismatches between learned priors and new information (prediction errors) in belief formation about event causes.

• Behavioral
  Minnesota Trust Game

  Assesses the degree to which people distinguish between their priors about others' motivations, such as selfishness and spite, and impersonal chance in one-shot trust game and dictator games.

• Behavioral
  Dynamic Trust Game

  Assesses degree to which people learn and update priors about trusting partners from dynamic social feedback (prediction error) in a social exchange game.

Locations (5)