Contributions of shape and reflectance information to social judgments from faces

Oh, DongWon; Dotsch, Ron; Todorov, Alexander

doi:10.1016/j.visres.2019.10.010

Cited by 15 publications

(14 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, many works have argued (the multiple motive hypothesis ) that the evaluation of facial attractiveness is a complex process, influenced by the prior inference of semantic personality traits (such as dominance, extroversion or trustworthiness) that we consensually attribute to specific shape and luminance patterns in others’ face ( Oh, Dotsch & Todorov, 2019 ; Oh, Grant-Villegas & Todorov, 2020 ; Abir et al, 2017 ; Walker & Vetter, 2016 ; Adolphs et al, 2016 ; Galantucci et al, 2014 ; Little, 2014 ; Todorov & Oosterhof, 2011 ; Oosterhof & Todorov, 2008 ; Edler, 2001 ; Cunningham et al, 1995 ). According to this scenario, facial attractiveness is influenced by the single-subject relative inclination towards some fundamental personality traits.…”

Section: Introductionmentioning

confidence: 99%

“…According to this scenario, facial attractiveness is influenced by the single-subject relative inclination towards some fundamental personality traits. In the word of Oh, Dotsch & Todorov (2019) , individuals who highly value a personality trait, such as dominance, are likely to perceive faces that appear to possess the trait as attractive . This implies, in particular, that (A) the single subject preferred faces are expected to be, to some extent, distinguishable if characterised or inferred with sufficient accuracy, and (B) they are expected to reflect meaningful information regarding the subject.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unsupervised inference approach to facial attractiveness

Ibáñez-Berganza

Amico

Lancia

et al. 2020

PeerJ

View full text Add to dashboard Cite

The perception of facial attractiveness is a complex phenomenon which depends on how the observer perceives not only individual facial features, but also their mutual influence and interplay. In the machine learning community, this problem is typically tackled as a problem of regression of the subject-averaged rating assigned to natural faces. However, it has been conjectured that this approach does not capture the complexity of the phenomenon. It has recently been shown that different human subjects can navigate the face-space and “sculpt” their preferred modification of a reference facial portrait. Here we present an unsupervised inference study of the set of sculpted facial vectors in such experiments. We first infer minimal, interpretable and accurate probabilistic models (through Maximum Entropy and artificial neural networks) of the preferred facial variations, that encode the inter-subject variance. The application of such generative models to the supervised classification of the gender of the subject that sculpted the face reveals that it may be predicted with astonishingly high accuracy. We observe that the classification accuracy improves by increasing the order of the non-linear effective interaction. This suggests that the cognitive mechanisms related to facial discrimination in the brain do not involve the positions of single facial landmarks only, but mainly the mutual influence of couples, and even triplets and quadruplets of landmarks. Furthermore, the high prediction accuracy of the subjects’ gender suggests that much relevant information regarding the subjects may influence (and be elicited from) their facial preference criteria, in agreement with the multiple motive theory of attractiveness proposed in previous works.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unsupervised inference approach to facial attractiveness

Ibáñez-Berganza

Amico

Lancia

et al. 2020

PeerJ

View full text Add to dashboard Cite

show abstract

“…According to this scenario, facial attractiveness is influenced by the single-subject relative inclination towards some fundamental personality traits. In the word of Oh et al (2019a), individuals who highly value a personality trait, such as dominance, are likely to perceive faces that appear to possess the trait as attractive. This implies, in particular, that (A) the single subject preferred faces are expected to be, to some extent, distinguishable if characterised or inferred with sufficient accuracy, and (B) they are expected to reflect meaningful information regarding the subject.…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Unsupervised inference approach to facial attractiveness (v0.1)"

2020

View full text Add to dashboard Cite

The perception of facial attractiveness is a complex phenomenon which depends on how the observer perceives not only individual facial features, but also their mutual influence and interplay. In the machine learning community, this problem is typically tackled as a problem of regression of the subject-averaged rating assigned to natural faces. However, it has been conjectured that this approach does not capture the complexity of the phenomenon. A recent original experiment (Ibáñez-Berganza et al., Scientific Reports 9, 8364, 2019) allowed different human subjects to navigate the face-space and "sculpt" their preferred modification of a reference facial portrait. Here we present an unsupervised inference study of the set of sculpted facial vectors in this experiment. We first infer minimal, interpretable and accurate probabilistic models (through Maximum Entropy and artificial neural networks) of the preferred facial variations, that encode the inter-subject variance. The application of such generative models to the supervised classification of the gender of the subject that sculpted the face reveals that it may be predicted with astonishingly high accuracy. We observe that the classification accuracy improves by increasing the order of the non-linear effective interaction. This suggests that the cognitive mechanisms related to facial discrimination in the brain do not involve the positions of single facial landmarks only, but mainly the mutual influence of couples, and even triplets and quadruplets of landmarks. Furthermore, the high prediction accuracy of the subjects' gender suggests that much relevant information regarding the subjects may influence (and be elicited from) their facial preference criteria, in agreement with the multiple motive theory of attractiveness proposed in previous works.

show abstract

“…We acknowledge that facial shape and surface are often operationalized differently in the literature. For example, shape can be defined as second-order spatial relations among facial features in a two-dimensional (Andrews et al, 2016) or three-dimensional space (Jiang et al, 2009; Oh et al, 2019). Also, surface is often referred to as facial color (Russell et al, 2006); reflectance (Russell et al, 2007) or texture (O’Toole et al, 1999), and it typically encompasses information from all such different properties.…”

mentioning

confidence: 99%

Image reconstruction reveals the impact of aging on face perception.

Chang¹,

Nemrodov²,

Drobotenko³

et al. 2021

Journal of Experimental Psychology: Human Perception and Perfor

View full text Add to dashboard Cite

Extensive work has demonstrated an age-related decline in face recognition, but the nature and the extent of aging-related alterations in face representations remain unclear. Here, we address these issues using an image reconstruction approach to reveal the content of visual representations. Healthy young and older adults provided similarity judgments for pairs of face images. Facial shape and surface features were subsequently derived and combined into image reconstructions of facial appearance. Both objective and experimental evaluations revealed that reconstructions were successful for every participant. Critically, shape and surface properties, such as eye shape and skin tone, were less accurately represented in older than young individuals. Yet, age-related differences in face representations, though significant, were less pronounced than those due to individual variability. Our results provide novel insights into age-related changes in visual perception and demonstrate the utility of image reconstruction to uncovering internal representations across a variety of populations. Public Significance StatementIt is well-documented that human face perception abilities deteriorate with age, but how exactly healthy aging impacts these abilities remains largely unknown. In the current study, we show that visual processing of facial shape and surface information is compromised in older adults but that age-related effects are less prominent than those due to individual differences. Our work also demonstrates that a data-driven approach can pinpoint subtle differences in face representations, and it further validates the applicability of image reconstruction to a wider population.

show abstract

Contributions of shape and reflectance information to social judgments from faces

Abstract: This paper was accepted for publication in Vision Research. This is a non-final and non-copyedited version of the paper.

Cited by 15 publications

References 73 publications

Unsupervised inference approach to facial attractiveness

Unsupervised inference approach to facial attractiveness

Peer Review #1 of "Unsupervised inference approach to facial attractiveness (v0.1)"

Image reconstruction reveals the impact of aging on face perception.

Contact Info

Product

Resources

About