research-article

An Accessible Tool to Measure Implicit Approach-Avoidance Tendencies Towards Food Outside the Lab

Authors:

Jasper J. van Beers,

Daisuke Kaneko,

Ivo V. Stuldreher,

Hilmar G. Zech,

Anne-Marie BrouwerAuthors Info & Claims

ICMI '20 Companion: Companion Publication of the 2020 International Conference on Multimodal Interaction

Pages 307 - 311

https://doi.org/10.1145/3395035.3425647

Published: 27 December 2020 Publication History

Abstract

Implicit approach-avoidance tendencies can be measured by the approach-avoidance task (AAT). The emergence of mobile variants of the AAT enable its use for both in-the-lab and in-the-field experiments. Within the food domain, use of the AAT is concentrated in research on eating disorders or healthy eating and is seldom used as an implicit measure of food experience. Given the prevalence of explicit measures in this field, the AAT may provide additional valuable insights into food experience. To facilitate the use of the AAT as an implicit measure, a processing tool and accompanying graphical user interface (GUI) have been developed for a mobile smartphone variant of the AAT. This tool improves upon the existing processing framework of this mobile AAT by applying more robust filtering and introduces additional sensor correction algorithms to improve the quality of sensor data. Along with refined estimates of reaction time (RT) and reaction force (RF), these processing improvements introduce a new metric: the response distance (RD). The capabilities of the tool, along with the potential added value of calculating RF and RD, are explained in this paper through the processing of pilot data on molded and unmolded food. In particular, the RF and RD may be indicative of participants' arousal. The tool developed in this paper is open source and compatible with other experiments making use of the mobile AAT within, and beyond, the domain of food experience.

References

[1]

Andrew K. Solarz, 1960. "Latency of instrumental responses as a function of compatibility with the meaning of eliciting verbal signs," Journal of Experimental Psychology 59, 4, 239--245.

[2]

R. Hans Phaf, Sören E. Mohr, Mark Rotteveel and Jelte M. Wicherts, 2014. "Approach, avoidance, and affect: a meta-analysis of approach-avoidance tendencies in manual reaction time tasks," Frontiers in Psychology 5, 378.

[3]

Mark Chen and John A. Bargh, 1999. "Consequences of Automatic Evaluation: Immediate Behavioral Predispositions to Approach or Avoid the Stimulus," Personality and social psychology bulletin 25, 2, 215--224.

[4]

Mike Rinck and Eni S. Becker, 2007. "Approach and avoidance in fear of spiders," Journal of Behavior Therapy and Experimental Psychiatry 38, 2, 105--120.

[5]

Hilmar G. Zech, Mark Rotteveel, Wilco W. van Dijk and Lotte F. van Dillen, 2020. "A mobile approach-avoidance task," Behavior Research Methods.

[6]

Karin Mogg, Matt Field and Brendan P. Bradley, 2005. "Attentional and approach biases for smoking cues in smokers: an investigation of competing theoretical views of addiction," Psychopharmacology 180, 333--341.

[7]

Mauro Bianchi, Andrea Carnaghi and Soraya E. Shamloo, 2018. "Intergroup attitudes accessibility and motor approach-avoidance responses in White and Black individuals in Portugal.," Psicologia sociale 13, 2, 147--164.

[8]

Theresa M. Marteau, Gareth J. Hollands and Paul C. Fletcher, 2012. "Changing Human Behavior to Prevent Disease: The Importance of Targetting Automatic Processes," Science 337, 6101, 1492--1495.

[9]

Daniela Becker, Nils B. Jostmann, Reinout W. Wiers and Rob W. Holland, 2014. "Approach avoidance training in the eating domain: Testing the effectiveness across three single session studies," Appetite 85, 58--65.

[10]

Eva Kemps, Marika Tiggemann, Rachel Martin and Mecia Elliott, 2013. "Implicit Approach-Avoidance Associations for Craved Food Cues," Journal of Experimental Psychology: Applied 19, 1, 30--38.

[11]

Timo Brockmeyer, Carolyn Hahn, Christina Reetz, Ulrike Schmidt and Hans-Christoph Friederich, 2015. "Approach bias and cue reactivity towards food in people with high versus low levels of food craving," Appetite 95, 197--202.

[12]

Adrian Meule, Anja Lender, Anna Richard, Radomir Dinic and Jens Blechert, 2019. "Approach-avoidance tendencies towards food: Measurement on a touchscreen and the role of attention and food craving," Appetite 137, 145--151.

[13]

Georgios Paslakis, Simone Kühn, Anke Schaubschläger, Katharina Schieber, Kathrin Röder, Elisabeth Rauh and Yesim Erim, 2016. "Explicit and implicit approach vs. avoidance tendencies towards high vs. low calorie food cues in patients with anorexia nervosa and healthy controls," Appetite 107, 171--179.

[14]

Betina Piqueras-Fiszman, Alexandra A. Kraus and Charles Spence, 2014. "'Yummy' versus 'Yucky'! Explicit and implicit approach--avoidance motivations towards appealing and disgusting foods," Appetite 78, 193--202.

[15]

Beate Seibt, Michael Häfner and Roland Deutsch, 2007. "Prepared to eat: how immediate affective and motivational responses to food cues are influenced by food deprivation," European Journal of Social Psychology 37, 2.

[16]

Sofie Lagast, Xavier Gellynck, Joachim J. Schouteten, Veerle De Herdt and Hans De Steur, 2017. "Consumers' emotions elicited by food: A systematic review of explicit and implicit methods," Trends in Food Science & Technology 69, A, 172--189.

[17]

Daisuke Kaneko, Alexander Toet, Anne-Marie Brouwer, Victor Kallen and Jan B. F. van Erp, 2018. "Methods for Evaluating Emotions Evoked by Food Experiences: A Literature Review," Frontiers in Psychology 9, 911.

[18]

Vinod Venkatraman, Angelika Dimoka, Paul A. Pavlou, Khoi Vo, William Hampton, Byran Bollinger, Hal E. Hershfield, Masakazu Ishihara and Russell S. Winer, 2015. "Predicting Advertising success beyond Traditional Measures: New Insights from Neurophysiological Methods and Market Response Modeling," Journal of Marketing Research 52, 4, 436--452.

[19]

Anton Kos and Anton Umek, 2016. "Evaluation of Smartphone Inertial Sensor Performance for Cross-Platform Mobile Applications," Sensors 16, 4, 477.

[20]

Alexander Toet, Daisuke Kaneko, Inge de Kruijf, Shota Ushiama, Martin G. van Schaik, Anne-Marie Brouwer, Victor Kallen and Jan B. van Erp, 2019. "CROCUFID: A Cross-Cultural Food Image Database for Research on Food Elicited Affective Responses," Frontiers in psychology 10, 58.

[21]

Daisuke Kaneko, Alexander Toet, Shota Ushiama, Anne-Marie Brouwer, Victor Kallen and Jan B. van Erp, 2019. "EmojiGrid: A 2D pictorial scale for cross-cultural emotion assessment of negatively and positively valenced food," Food Research International 115, 541--551.

Cited By

严巧(2023)“Bigger Eyes, Smaller Belly”: Food Approach-Avoidance Bias and the Classical ParadigmAdvances in Social Sciences10.12677/ASS.2023.12104312:01(308-316)Online publication date: 2023
https://doi.org/10.12677/ASS.2023.121043
Aulbach Mvan Alebeek HKahveci SBlechert J(2023)Testing the effectiveness of a mobile approach avoidance intervention and measuring approach biases in an ecological momentary assessment context: study protocol for a randomised-controlled trialBMJ Open10.1136/bmjopen-2022-07044313:4(e070443)Online publication date: 25-Apr-2023
https://doi.org/10.1136/bmjopen-2022-070443
Brouwer Avan Beers JSabu PStuldreher IZech HKaneko D(2021)Measuring Implicit Approach–Avoidance Tendencies towards Food Using a Mobile Phone outside the LabFoods10.3390/foods1007144010:7(1440)Online publication date: 22-Jun-2021
https://doi.org/10.3390/foods10071440
Show More Cited By

Index Terms

An Accessible Tool to Measure Implicit Approach-Avoidance Tendencies Towards Food Outside the Lab
1. Applied computing
  1. Law, social and behavioral sciences
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

ICMI '20 Companion: Companion Publication of the 2020 International Conference on Multimodal Interaction

October 2020

548 pages

ISBN:9781450380027

DOI:10.1145/3395035

General Chairs:
Khiet Truong
University of Twente, the Netherlands
,
Dirk Heylen
University of Twente, the Netherlands
,
Mary Czerwinski
Microsoft Research, USA
,
Program Chairs:
Nadia Berthouze
University College London, United Kingdom
,
Mohamed Chetouani
Sorbonne University, France
,
Mikio Nakano
C4A Research Institute, Japan

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 December 2020

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Kikkoman Europe R&D Laboratory B.V.

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ICMI '20

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SIGCHI

ICMI '20: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

October 25 - 29, 2020

Virtual Event, Netherlands

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Overall Acceptance Rate 453 of 1,080 submissions, 42%

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Cited By

严巧(2023)“Bigger Eyes, Smaller Belly”: Food Approach-Avoidance Bias and the Classical ParadigmAdvances in Social Sciences10.12677/ASS.2023.12104312:01(308-316)Online publication date: 2023
https://doi.org/10.12677/ASS.2023.121043
Aulbach Mvan Alebeek HKahveci SBlechert J(2023)Testing the effectiveness of a mobile approach avoidance intervention and measuring approach biases in an ecological momentary assessment context: study protocol for a randomised-controlled trialBMJ Open10.1136/bmjopen-2022-07044313:4(e070443)Online publication date: 25-Apr-2023
https://doi.org/10.1136/bmjopen-2022-070443
Brouwer Avan Beers JSabu PStuldreher IZech HKaneko D(2021)Measuring Implicit Approach–Avoidance Tendencies towards Food Using a Mobile Phone outside the LabFoods10.3390/foods1007144010:7(1440)Online publication date: 22-Jun-2021
https://doi.org/10.3390/foods10071440
Kaneko DStuldreher IReuten AToet Avan Erp JBrouwer A(2021)Comparing Explicit and Implicit Measures for Assessing Cross-Cultural Food ExperienceFrontiers in Neuroergonomics10.3389/fnrgo.2021.6462802Online publication date: 22-Mar-2021
https://doi.org/10.3389/fnrgo.2021.646280

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