Küster, D;
Krumhuber, EG;
Steinert, L;
Ahuja, A;
Baker, M;
Schultz, T;
(2020)
Opportunities and Challenges for Using Automatic Human Affect Analysis in Consumer Research.
Frontiers in Neuroscience
, 14
, Article 400. 10.3389/fnins.2020.00400.
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Abstract
The ability to automatically assess emotional responses via contact-free video recording taps into a rapidly growing market aimed at predicting consumer choices. If consumer attention and engagement are measurable in a reliable and accessible manner, relevant marketing decisions could be informed by objective data. Although significant advances have been made in automatic affect recognition, several practical and theoretical issues remain largely unresolved. These concern the lack of cross-system validation, a historical emphasis of posed over spontaneous expressions, as well as more fundamental issues regarding the weak association between subjective experience and facial expressions. To address these limitations, the present paper argues that extant commercial and free facial expression classifiers should be rigorously validated in cross-system research. Furthermore, academics and practitioners must better leverage fine-grained emotional response dynamics, with stronger emphasis on understanding naturally occurring spontaneous expressions, and in naturalistic choice settings. We posit that applied consumer research might be better situated to examine facial behavior in socio-emotional contexts rather than decontextualized, laboratory studies, and highlight how AHAA can be successfully employed in this context. Also, facial activity should be considered less as a single outcome variable, and more as a starting point for further analyses. Implications of this approach and potential obstacles that need to be overcome are discussed within the context of consumer research.
| Type: | Article |
|---|---|
| Title: | Opportunities and Challenges for Using Automatic Human Affect Analysis in Consumer Research |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fnins.2020.00400 |
| Publisher version: | https://doi.org/10.3389/fnins.2020.00400 |
| Language: | English |
| Additional information: | Copyright © 2020 Küster, Krumhuber, Steinert, Ahuja, Baker and Schultz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | automatic human affect analysis (AHAA), machine learning, facial expression, spontaneous expressions, dynamic responses, consumer research |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Experimental Psychology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10096893 |
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