A multi-lab test of the facial feedback hypothesis by the Many Smiles Collaboration

Coles, Nicholas A.; March, David S.; Marmolejo‐Ramos, Fernando; Larsen, Jeff T.; Arinze, Nwadiogo Chisom; Ndukaihe, Izuchukwu Lawrence Gabriel; Willis, Megan L.; Foroni, Francesco; Reggev, Niv; Mokady, Aviv; Forscher, Patrick S.; Hunter, John F.; Kaminski, Gwenaël; Yüvrük, Elif; Kapucu, Aycan; Nagy, Tamás; Hajdú, Nándor; Tejada, Julián; Freitag, Raquel Meister Ko.; Zambrano, Danilo; Som, Bidisha; Aczél, Balázs; Barzykowski, Krystian; Adamus, Sylwia; Filip, Katarzyna; Yamada, Yuki; Ikeda, Ayumi; Eaves, Daniel; Levitan, Carmel; Leiweke, Sydney; Parzuchowski, Michał; Butcher, Natalie; Pfuhl, Gerit; Basnight-Brown, Dana M.; Hinojosa, José Antonio; Montoro, Pedro R.; Delgado, Lady Grey Javela; Vezirian, Kévin; IJzerman, Hans; Trujillo, Natalia; Pressman, Sarah D.; Gygax, Pascal; Özdoğru, Asil Ali; Fernández, Susana Ruiz; Ellsworth, Phoebe C.; Gaertner, Lowell; Strack, Fritz; Marozzi, Marco; Liuzza, Marco Tullio

doi:10.1038/s41562-022-01458-9

Cited by 53 publications

(18 citation statements)

References 73 publications

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“…The hypothesis debates that the matched facial movement leads to a matching emotion in the perceiver, which leads to emotional recognition (Hatfield et al, 2009). This is in line with the facial feedback hypothesis, which argues that facial expression directly triggers one's emotional experience (Coles et al, 2022). The hypothesis is also associated with an empathy model by Lipps that emphasises "inner imitation" in understanding others' emotions (Montag et al, 2008).…”

Section: Introductionmentioning

confidence: 60%

Facial mimicry depends on the emotion we recognize in others’ faces

Nakamura¹,

Tanaka²

2023

Preprint

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Rapid facial reaction (RFR), also known as facial mimicry, plays an important role in the synchronisation of expressions and associated emotions among individuals. RFR was originally understood as a form of pure motion mimicry (matched motor hypothesis), while there is counterevidence suggesting that RFR does not merely involve an imitation of the motion. We investigated the existence of a top-down effect on RFR by measuring the motion in facial muscles with electromyography while perceiving faces with different emotions in their upper and lower half (Experiment 1). In the conditions of upper anger (anger in the upper half of the face) and lower happiness (happiness in the lower half of the face), the participants frowned more when they recognised the ambiguous face as angry, while they smiled more when recognising the face as happy. This result suggests a top-down effect of emotional recognition on RFR. We also measured RFR when seeing upper-only or lower-only facial images (Experiment 2). While the result supported the top-down effect in the occurrence of RFR, it also suggested that the mouth regions, and not the eye regions, are key to inducing RFR. These findings aid in further understanding social interaction at an unconscious and physical level.

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Section: Introductionmentioning

confidence: 60%

Facial mimicry depends on the emotion we recognize in others’ faces

Nakamura¹,

Tanaka²

2023

Preprint

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“…Similarly, large teams can be beneficial when it comes to the design and development of procedures and materials. For example, despite spending nearly a year designing a study with a smaller group of experts, feedback from a broader set of collaborators on the early design for the Many Smiles Collaboration led to substantial refinements [19]. As another example, the second project run by ManyBabies brought together researchers with competing theoretical orientations to design a maximally informative test of outstanding debates in developmental psychology about whether or not infants possess a representational theory of mind [20].…”

Section: Determining Where To Leverage the Power Of Big Teamsmentioning

confidence: 99%

“…In some cases, this can simply be the team leaders if that is the process for conflict mediation outlined in the collaboration agreement. A third approach, developed in the Many Smiles Collaboration [19] is to deploy a majority-minority opinion model. In this model, the main text of the paper reflects the majority opinion among collaborators, and special sections (e.g., supplemental materials) are developed where dissenting opinions are described.…”

Section: Navigating Disagreementsmentioning

confidence: 99%

How to build up big team science: A practical guide for large-scale collaborations

Baumgartner¹,

Alessandroni²,

Byers‐Heinlein³

et al. 2023

Preprint

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The past decade has witnessed a proliferation of Big Team Science (BTS), endeavours where a comparatively large number of researchers pool their intellectual and/or material resources in pursuit of a common goal. Despite this burgeoning interest, there exists little guidance on how to create, manage, and participate in these collaborations. In this paper, we integrate insights from a multidisciplinary set of BTS initiatives to provide a how-to guide for BTS. We first discuss initial considerations for launching a BTS project, such as building the team, identifying leadership, governance, tools, and open science approaches. We then turn to issues related to running and completing a BTS project, such as study design, ethical approvals, and issues related to data collection, management, and analysis. Finally, we address topics that present special challenges for BTS, including authorship decisions, collaborative writing, and team decision making.

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“…It also branches out to different muscles located on the face. Traditionally, emotions are divided by two categories: voluntary and involuntary [37][38][39]. Since the brainstem is the oldest part of the brain area-it is responsible for involuntary/unconscious facial expressions and the motor cortex is specifically active in consciously controlled/intentional facial expressions.…”

Section: Experimental Design Approachmentioning

confidence: 99%

The Study of Facial Muscle Movements for Non-Invasive Thermal Discomfort Detection via Bio-Sensing Technology. Part I: Development of the Experimental Design and Description of the Collected Data

et al. 2020

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In the time of climate change, as heat waves become a more regular occurrence, indoor thermal comfort is an important factor in day to day life. Due to such circumstances, many researchers have focused their studies on finding an effective solution that will not only enable thermal comfort, but also increase satisfaction within the indoor environment and, as a result, productivity. The fast development of the biometrical field encouraged the study focused on the investigation of how bio-markers, in combination with artificial intelligence algorithms, can be collected within an experimental setting to create a new approach for non-invasive thermal discomfort detection. The developed experimental design provides synergy between automatic facial coding, pulse, and galvanic skin response measurements via iMotions software in a controlled environment. The iMotions software has built-in machine vision algorithms, and with Shimmer sensors and a post-processing tool through Affectiva AFFDEX, is able to collect facial action data through detection of the facial muscle movements and various bio-markers. The Zero Emission Building (ZEB) Test Cell laboratory was used as the control environment and transformed to imitate an office space for the data collection campaign at NTNU in Trondheim. The given experimental design provides an opportunity to create an immense database with bio-markers that are linked to the subcortical level of the brain, indoor parameters, and direct feedback on the comfort level of occupants within an office-like environment. In total, 111 data collection sessions were registered with iMotions. The discomfort button was pressed 240 times and 1080 planned indoor comfort evaluation surveys were held during experiment. The discomfort button was pressed 49 times to indicate that participant felt discomfort due to low temperature and 52 due to high temperature. Collected data revealed a big deviation in the discomfort temperature values for experiment participants with respect to performed temperature ramps. While it is common to use the same predefined temperature range for facility management, it became clear that the complexity of the task is greater and should not be approached on a human computational level. Implementation of AI can potentially provide higher value accuracy within thermal discomfort detection and enable unique personal user experience at the workplace.

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A multi-lab test of the facial feedback hypothesis by the Many Smiles Collaboration

Cited by 53 publications

References 73 publications

Facial mimicry depends on the emotion we recognize in others’ faces

Facial mimicry depends on the emotion we recognize in others’ faces

How to build up big team science: A practical guide for large-scale collaborations

The Study of Facial Muscle Movements for Non-Invasive Thermal Discomfort Detection via Bio-Sensing Technology. Part I: Development of the Experimental Design and Description of the Collected Data

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