Measuring psychological abilities or traits is trickier than it seems from the published
literature (
1
). We try to study abstract psychological constructs like “inhibition” or “impulsivity,”
but we can only measure these indirectly through behaviors such as reaction times
or self-report ratings. When we apply these measures in clinical and individual differences
research, our goal is typically to understand why a patient group appears to be, for
example, more “impulsive” than healthy control subjects or is to use measures of “inhibition”
to predict a clinical outcome. There are many potential pitfalls and wrong turns in
the path toward achieving this goal. In our recent work, we have shown that our intuitions
about what makes a good cognitive task can sometimes lead us astray (
2
,
3
). Here, we discuss how these issues intersect with the goals of computational psychiatry.To read this article in full you will need to make a payment
Subscribe to Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
Already a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
Flake JK, Fried EI (in press): Measurement schmeasurement: Questionable measurement practices and how to avoid them. Adv Methods Pract Psychol Sci.
- The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences.Behav Res Methods. 2018; 50: 1166-1186
- Low and variable correlation between reaction time costs and accuracy costs explained by accumulation models: Meta-analysis and simulations.Psychol Bull. 2018; 144: 1200-1227
- Studies of interference in serial verbal reactions.J Exp Psychol. 1935; 18: 643-662
- Many labs 3: Evaluating participant pool quality across the academic semester via replication.J Exp Soc Psychol. 2016; 67: 68-82
- Psychometric Theory.2nd ed. McGraw-Hill, New York1978
- Computational modeling applied to the dot-probe task yields improved reliability and mechanistic insights.Biol Psychiatry. 2019; 85: 606-612
- Computational psychiatry as a bridge from neuroscience to clinical applications.Nat Neurosci. 2016; 19: 404-413
- A roadmap for the development of applied computational psychiatry.Biol Psychiatry Cogn Neurosci Neuroimaging. 2016; 1: 386-392
- Improving the reliability of computational analyses: Model-based planning and its relationship with compulsivity.Biol Psychiatry Cogn Neurosci Neuroimaging. 2020; 5: 601-609
Article Info
Publication History
Published online: May 20, 2020
Accepted:
May 12,
2020
Received:
May 1,
2020
Identification
Copyright
© 2020 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.
