title: Data-driven methodology illustrating mechanisms underlying word list recall: applications to clinical research.
abstract: OBJECTIVE
  Word list learning tasks such as the California Verbal Learning Test (CVLT; Delis, Kramer, Kaplan, & Ober, 1987) are widely used to investigate recall strategies. Participants who recall the most words generally employ semantic techniques, whereas those with poor recall (e.g., patients with schizophrenia) rely on serial techniques. However, these conclusions are based on formulas that assume that categories reflect semantic associations, bind strategy to overall performance, and neglect strategy changes over 5 trials. Therefore, we derived novel measures-independent of recall performance-to compute strategies across trials and identify whether diagnosis predicts recall strategy.
  
  
  METHODS
  Participants were included on the basis of performance on the CVLT (i.e., total words recalled over 5 trials). The 50 highest and 50 lowest performers among healthy volunteers (n = 100) and patients with schizophrenia (n = 100) were selected. Novel measures of recall and transition probability were calculated and analyzed by permutation tests.
  
  
  RESULTS
  Recall patterns and strategies of patients resembled those of controls with similar performance levels: Regardless of diagnosis, low performers were more likely to recall the first 2 and last 4 items from the list; high performers increased engagement of semantically based transitions across the 5 trials, whereas low performers did not.
  
  
  CONCLUSIONS
  Cognitive strategy must be considered independent of overall performance before attributing poor performance to degraded learning processes. Our results demonstrate the importance of departing from global scoring techniques, especially when working with clinical populations such as patients with schizophrenia for whom episodic memory deficits are a hallmark feature.
authors.affiliation: Clinical Brain Disorders Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA.
authors.forename: Julia
authors.initials: J
authors.lastname: Longenecker
authors.forename: Philip
authors.initials: P
authors.lastname: Kohn
authors.forename: Stanley
authors.initials: S
authors.lastname: Liu
authors.forename: Brad
authors.initials: B
authors.lastname: Zoltick
authors.forename: Daniel R
authors.initials: DR
authors.lastname: Weinberger
authors.forename: Brita
authors.initials: B
authors.lastname: Elvevåg
mesh_terms: D000328:Adult; D000465:Algorithms; D000704:Analysis of Variance; D035841:Behavioral Research / Q000379:methods; D016022:Case-Control Studies; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D011939:Mental Recall; D008875:Middle Aged; D011340:Problem Solving; D012016:Reference Values; D012559:Schizophrenia / Q000503:physiopathology*; D012565:Schizophrenic Psychology; D012691:Serial Learning; D014706:Verbal Learning
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