Should metacognition be measured by logistic regression?

Conscious Cogn. 2017 Mar;49:291-312. doi: 10.1016/j.concog.2017.02.007. Epub 2017 Feb 23.


Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria.

Keywords: Cognitive modeling; Generalized linear regression; Logistic regression; Metacognition; Metacognitive sensitivity; Signal detection theory; Type 2 signal detection theory.

MeSH terms

  • Humans
  • Logistic Models
  • Metacognition / physiology*
  • Models, Theoretical*
  • Neuropsychological Tests
  • Regression Analysis*
  • Signal Detection, Psychological / physiology*