Mood Impact on Automaticity of Performance: Handwriting as Exemplar

Clara Rispler, Gil Luria, Allon Kahana, Sara Rosenblum

Research output: Contribution to journalArticlepeer-review


The goal of this study was to assess how existing handwriting research can contribute to understanding how moods impact the automatic processing of handwriting performance. We based our hypotheses on extensive research connecting mood with cognitive functions, because handwriting production was shown to be an automated cognitive task impacted by cognitive load. As far as we know, no previous research has examined the direct relationship between affect and handwriting (transcription and text generation when writing by hand). Specifically, evidence exists only for a general relationship between affect and writing (using written words to express ideas or opinions). In this experiment, 62 participants were divided into three mood groups (positive, negative, and neutral). Mood manipulation was conducted according to accepted methods of memory recall and film induction and was evaluated using the PANAS scale. Online measurements of the participants’ handwriting were captured with a tablet and electronic pen. Results showed that the strokes in the negative mood manipulation were shorter in duration and shorter in width and height. The findings presented in this article make a twofold contribution to the cognitive and biologically inspired computational studies: by integrating the study of affect with the study of cognition and by exploring additional objective performance-based evaluation of functional capabilities with the aid of a computerized device. Practical implications are discussed, as are ideas for further research.

Original languageEnglish
Pages (from-to)398-407
Number of pages10
JournalCognitive Computation
Issue number3
StatePublished - 1 Jun 2018

Bibliographical note

Publisher Copyright:
© 2018, Springer Science+Business Media, LLC, part of Springer Nature.


  • Automatic processing
  • Computerized detection tool
  • Handwriting
  • Mood

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Cognitive Neuroscience


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