Development and validation of the Mental-Physical Verb Norms (MPVN): A text analysis measure of mental state attribution

Ram Isaac Orr, Michael Gilead

Research output: Contribution to journalArticlepeer-review

Abstract

Attribution of mental states to self and others, i.e., mentalizing, is central to human life. Current measures are lacking in the ability to directly gauge the extent to which individuals engage in spontaneous mentalizing. Focusing on natural language use as an expression of inner psychological processes, we developed the Mental-Physical Verb Norms (MPVN). These norms are participant-derived ratings of the extent to which common verbs reflect mental (vs physical) activities and occurrences, covering a majority of verbs appearing in a given English text. Content validity was assessed against existing expert-compiled dictionaries of mental states and cognitive processes, as well as against normative ratings of verb concreteness. Criterion Validity was assessed through natural text analysis of both experimental data, and natural language use in a real-world online setting. Finally, incremental validity was assessed through a classification analysis. Results indicate the unique contribution of the MPVN ratings as a measure of the degree to which individuals adopt the intentional stance in describing targets, by describing both self and others in mental, opposite physical, terms. We discuss potential uses for future research across various psychological and neurocognitive disciplines, as well as theoretical implications regarding the use of mentalizing language within spontaneous contexts.

Original languageEnglish
JournalBehavior Research Methods
DOIs
StateAccepted/In press - 2022

Keywords

  • Clinical psychology
  • Cognitive neuroscience
  • Developmental psychology
  • Mentalizing
  • Norms
  • Psycholinguistics
  • Social cognition
  • Text analysis

Fingerprint

Dive into the research topics of 'Development and validation of the Mental-Physical Verb Norms (MPVN): A text analysis measure of mental state attribution'. Together they form a unique fingerprint.

Cite this