PAM: a photographic affect meter for frequent, in situ measurement of affect

Authors:
John P. Pollak;Phil Adams;Geri Gay
Affiliations:
Cornell University, Ithaca, New York, USA;Cornell University, Ithaca, New York, USA;Cornell University, Ithaca, New York, USA
Venue:
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Year:
2011

Citing 6
Cited 7

An introduction to computerized experience sampling in psychology

Social Science Computer Review
In situ informants exploring an emotional mobile messaging system in their everyday practice

International Journal of Human-Computer Studies
Experimental evaluation of five methods for collecting emotions in field settings with mobile applications

International Journal of Human-Computer Studies
How emotion is made and measured

International Journal of Human-Computer Studies
MyExperience: a system for in situ tracing and capturing of user feedback on mobile phones

Proceedings of the 5th international conference on Mobile systems, applications and services
The disenchantment of affect

Personal and Ubiquitous Computing

Prescriptive persuasion and open-ended social awareness: expanding the design space of mobile health

Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
FEEL: frequent EDA and event logging -- a mobile social interaction stress monitoring system

CHI '12 Extended Abstracts on Human Factors in Computing Systems
Mood meter: counting smiles in the wild

Proceedings of the 2012 ACM Conference on Ubiquitous Computing
CAAT: a discrete approach to emotion assessment

CHI '13 Extended Abstracts on Human Factors in Computing Systems
PIXEE: pictures, interaction and emotional expression

CHI '13 Extended Abstracts on Human Factors in Computing Systems
Demonstrating PIXEE: pictures, interaction and emotional expression

CHI '13 Extended Abstracts on Human Factors in Computing Systems
Managing health with mobile technology

interactions

Quantified Score

Hi-index	0.01

Visualization

Abstract

The assessment of emotion, or affect, is critical for anyone trying to understand human behavior. But there is a problem: affect as a state is frequently changing and difficult to recall and express, yet in research, we typically only assess it via a single questionnaire at the end of a study. This work presents PAM, the Photographic Affect Meter, a novel tool for measuring affect in which users select from a wide variety of photos the one which best suits their current mood. Our findings indicate that PAM-which takes seconds to complete and is designed to run on modern mobile phones and mobile computing devices-demonstrates strong construct validity across two studies and is very well suited for frequent sampling in context. This work provides a tool to researchers in need of frequent assessment of affect and guidance to others interested in developing similar measurement tools.