title: Measurement of latent cognitive abilities involved in concept identification learning.
abstract: BACKGROUND
  We used cognitive and psychometric modeling techniques to evaluate the construct validity and measurement precision of latent cognitive abilities measured by a test of concept identification learning: the Penn Conditional Exclusion Test (PCET).
  
  
  METHODS
  Item response theory parameters were embedded within classic associative- and hypothesis-based Markov learning models and were fitted to 35,553 Army soldiers' PCET data from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).
  
  
  RESULTS
  Data were consistent with a hypothesis-testing model with multiple latent abilities-abstraction and set shifting. Latent abstraction ability was positively correlated with number of concepts learned, and latent set-shifting ability was negatively correlated with number of perseverative errors, supporting the construct validity of the two parameters. Abstraction was most precisely assessed for participants with abilities ranging from 1.5 standard deviations below the mean to the mean itself. Measurement of set shifting was acceptably precise only for participants making a high number of perseverative errors.
  
  
  CONCLUSIONS
  The PCET precisely measures latent abstraction ability in the Army STARRS sample, especially within the range of mildly impaired to average ability. This precision pattern is ideal for a test developed to measure cognitive impairment as opposed to cognitive strength. The PCET also measures latent set-shifting ability, but reliable assessment is limited to the impaired range of ability, reflecting that perseverative errors are rare among cognitively healthy adults. Integrating cognitive and psychometric models can provide information about construct validity and measurement precision within a single analytical framework.
authors.affiliation: a Department of Psychiatry , University of California , San Diego , CA , USA.
authors.forename: Michael L
authors.initials: ML
authors.lastname: Thomas
authors.forename: Gregory G
authors.initials: GG
authors.lastname: Brown
authors.forename: Ruben C
authors.initials: RC
authors.lastname: Gur
authors.forename: Tyler M
authors.initials: TM
authors.lastname: Moore
authors.forename: Virginie M
authors.initials: VM
authors.lastname: Patt
authors.forename: Matthew K
authors.initials: MK
authors.lastname: Nock
authors.forename: James A
authors.initials: JA
authors.lastname: Naifeh
authors.forename: Steven
authors.initials: S
authors.lastname: Heeringa
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Ursano
authors.forename: Murray B
authors.initials: MB
authors.lastname: Stein
mesh_terms: D000328:Adult; D001076:Aptitude / Q000502:physiology*; D003072:Cognition Disorders / Q000175:diagnosis*; D003210:Concept Formation / Q000502:physiology*; D056344:Executive Function / Q000502:physiology*; D005260:Female; D006801:Humans; D007858:Learning / Q000502:physiology*; D008297:Male; D008889:Military Personnel / Q000706:statistics & numerical data; D015233:Models, Statistical; D009483:Neuropsychological Tests / Q000706:statistics & numerical data*; D011594:Psychometrics / Q000379:methods*; D014481:United States; D055815:Young Adult
keywords: Army STARRS; Concept identification learning; Item response theory; Latent variable measurement; Neuropsychology; Penn Conditional Exclusion Test