The Affective Slider: A Digital Self-Assessment Scale for the Measurement of Human Emotions

PLoS One. 2016 Feb 5;11(2):e0148037. doi: 10.1371/journal.pone.0148037. eCollection 2016.

Abstract

Self-assessment methods are broadly employed in emotion research for the collection of subjective affective ratings. The Self-Assessment Manikin (SAM), a pictorial scale developed in the eighties for the measurement of pleasure, arousal, and dominance, is still among the most popular self-reporting tools, despite having been conceived upon design principles which are today obsolete. By leveraging on state-of-the-art user interfaces and metacommunicative pictorial representations, we developed the Affective Slider (AS), a digital self-reporting tool composed of two slider controls for the quick assessment of pleasure and arousal. To empirically validate the AS, we conducted a systematic comparison between AS and SAM in a task involving the emotional assessment of a series of images taken from the International Affective Picture System (IAPS), a database composed of pictures representing a wide range of semantic categories often used as a benchmark in psychological studies. Our results show that the AS is equivalent to SAM in the self-assessment of pleasure and arousal, with two added advantages: the AS does not require written instructions and it can be easily reproduced in latest-generation digital devices, including smartphones and tablets. Moreover, we compared new and normative IAPS ratings and found a general drop in reported arousal of pictorial stimuli. Not only do our results demonstrate that legacy scales for the self-report of affect can be replaced with new measurement tools developed in accordance to modern design principles, but also that standardized sets of stimuli which are widely adopted in research on human emotion are not as effective as they were in the past due to a general desensitization towards highly arousing content.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Affect*
  • Databases, Factual
  • Female
  • Humans
  • Male
  • Self-Assessment*
  • Semantics

Grants and funding

This work was supported by European Research Council grant 341196 (CDAC).