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Learning a Map Under Positive Emotions: How Self-Reported Arousal and Individual Differences Relate to Route and Survey Knowledge (Short Paper)

Authors Veronica Muffato , Laura Miola, Francesca Pazzaglia, Chiara Meneghetti

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Subject Classification

ACM Subject Classification
  • Social and professional topics → User characteristics
Keywords
  • map
  • emotions
  • arousal
  • route knowledge
  • survey knowledge
  • spatial cognition

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Abstract

Digital map learning is a common everyday activity requiring the encoding of landmarks and their spatial relations, and may be related to individual differences. The present study investigated how individual positive emotional states differing in arousal (high vs. low) relate to route and survey knowledge after spatial learning from maps. A total of 156 participants (ongoing data collection) completed an online within-subject procedure including emotionally induced scenarios, map study, and recall tasks assessing route (landmark order) and survey (landmark location task) knowledge. Results showed no direct effect of arousal condition on performance; however, individual increases in arousal predicted lower route knowledge accuracy, while survey knowledge was unaffected. Importantly, trait positive affect positively predicted both route and survey performance. Age negatively predicted both, gender differences emerged only for survey knowledge, whereas spatial anxiety impaired route knowledge. These findings suggest a selective role of arousal in route processing and highlight the importance of considering individual differences in spatial learning from maps.

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Veronica Muffato, Laura Miola, Francesca Pazzaglia, and Chiara Meneghetti. Learning a Map Under Positive Emotions: How Self-Reported Arousal and Individual Differences Relate to Route and Survey Knowledge (Short Paper). In 17th International Conference on Spatial Information Theory (COSIT 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 393, pp. 26:1-26:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.COSIT.2026.26

Author Details

Veronica Muffato
  • Department of General Psychology, University of Padova, Italy
Laura Miola
  • Department of General Psychology, University of Padova, Italy
Francesca Pazzaglia
  • Department of General Psychology, University of Padova, Italy
Chiara Meneghetti
  • Department of General Psychology, University of Padova, Italy

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