Word and Face recognition processing based on response times and ex-Gaussian components

[EN] The face is a fundamental feature of our identity. In humans, the existence of specialized processing modules for faces is now widely accepted. However, identifying the processes involved for proper names is more problematic. The aim of the present study is to examine which of the two treatments is produced earlier and whether the social abilities are influent. We selected 100 university students divided into two groups: Spanish and USA students. They had to recognize famous faces or names by using a masked priming task. An analysis of variance about the reaction times (RT) was used to determine whether significant differences could be observed in word or face recognition and between the Spanish or USA group. Additionally, and to examine the role of outliers, the Gaussian distribution has been modified exponentially. Famous faces were recognized faster than names, and differences were observed between Spanish and North American participants, but not for unknown distracting faces. The current results suggest that response times to face processing might be faster than name recognition, which supports the idea of differences in processing nature.Moret-Tatay, C.; Garcia-Ramos, D.; Saiz Mauleón, MB.; Gamermann, D.; Bertheaux, C.; Borg, C. (2021). Word and Face recognition processing based on response times and ex-Gaussian components. Entropy. 23(5):1-17. https://doi.org/10.3390/e23050580S11723

Effect of material properties on emotion: a virtual reality study

IntroductionDesigners know that part of the appreciation of a product comes from the properties of its materials. These materials define the object’s appearance and produce emotional reactions that can influence the act of purchase. Although known and observed as important, the affective level of a material remains difficult to assess. While many studies have been conducted regarding material colors, here we focus on two material properties that drive how light is reflected by the object: its metalness and smoothness. In this context, this work aims to study the influence of these properties on the induced emotional response.MethodWe conducted a perceptual user study in virtual reality, allowing participants to visualize and manipulate a neutral object – a mug. We generated 16 material effects by varying it metalness and smoothness characteristics. The emotional reactions produced by the 16 mugs were evaluated on a panel of 29 people using James Russel’s circumplex model, for an emotional measurement through two dimensions: arousal (from low to high) and valence (from negative to positive). This scale, used here through VR users’ declarative statements allowed us to order their emotional preferences between all the virtual mugs.ResultStatistical results show significant positive effects of both metalness and smoothness on arousal and valence. Using image processing features, we show that this positive effect is linked to the increasing strength (i.e., sharpness and contrast) of the specular reflections induced by these material properties.DiscussionThe present work is the first to establish this strong relationship between specular reflections induced by material properties and aroused emotions

The role of emotion in a sensory design process

L’objet de cette thèse était d’étudier le rôle de l’émotion dans un processus de conception sensorielle. Une première expérimentation a permis de mettre en place une mesure émotionnelle du toucher lors de l’exploration tactile de surfaces. Une seconde expérimentation a permis de mettre en place une mesure émotionnelle visuelle lors de la visualisation d’images IAPS. Ces mesures ont permis d’objectiver la mesure émotionnelle des matériaux et des images en se basant sur deux indicateurs émotionnels, i) la valeur émotionnelle collectée lors de tests déclaratifs et ii) le coefficient de dilatation de la pupille (Bertheaux et al., 2020a).Le processus décisionnaire lors d’un acte d’achat repose sur l’estimation de valeurs utilitaires et hédoniques. Une autre contribution présentée dans ce document réside dans une nouvelle approche du modèle de préférence qui considère les données provenant de l’axe « Sensations » et de l’axe « Emotions ». Par ailleurs, un opérateur d’agrégation permet d’attribuer un score d’acceptabilité aux différentes configurationsdu produit étudié. Ce modèle multidimensionnel de préférence permet de représenter le ressenti émotionnel du panel relatif aux produits (Bertheaux et al., 2018, 2019).Une autre approche a permis de prédire les réactions émotionnelles lors de l’examen d’images de chaises dans un contexte d’achat en ligne. Dans ce cas le modèle multidimensionnel de préférence est utilisé pour estimer une valence basée sur l’évaluation de six descripteurs d’apparence. Une régression non linéaire via un modèle neuronal a montré que la valence « estimée » obtenue par le modèle multidimensionnel de préférence était corrélée à la valence exprimée par le panel (Bertheaux et al., 2020b).The aim of this work was to study the role of emotion in a sensory design process. A first experiment made it possible to set up an emotional measurement of touch during the tactile exploration of surfaces. A second experiment made it possible to set up an emotional measurement during the viewing of IAPS images. The results of these two experiments are based on two emotional indicators, i) the emotional value collected during declarative tests and ii) the coefficient of pupil dilation (Bertheaux et al., 2020a).The decision-making process in an act of purchase is based on the estimation of utility and hedonic values. Another contribution presented in this document is a new approach to the preference model that considers data from "Sensations" and "Emotions" axes. In addition, an aggregation operator makes it possible to assign an acceptability score to the different configurations of the product studied. This multidimensional model allows to represent the emotional feelings of the panel relating to the products (Bertheaux et al., 2018, 2019).Another case study allowed to predict emotional reactions when examining images of chairs in an online shopping context. In this case the multidimensional preference model is used to estimate a valence based on the evaluation of six appearence descriptors. A nonlinear regression via a neuronal model showed that the « estimated » valence obtained by the multidimensional preference model was correlated with the valence expressed by panel (Bertheaux et al., 2020b)

Le rôle de l’émotion dans un processus de conception sensorielle

The aim of this work was to study the role of emotion in a sensory design process. A first experiment made it possible to set up an emotional measurement of touch during the tactile exploration of surfaces. A second experiment made it possible to set up an emotional measurement during the viewing of IAPS images. The results of these two experiments are based on two emotional indicators, i) the emotional value collected during declarative tests and ii) the coefficient of pupil dilation (Bertheaux et al., 2020a).The decision-making process in an act of purchase is based on the estimation of utility and hedonic values. Another contribution presented in this document is a new approach to the preference model that considers data from "Sensations" and "Emotions" axes. In addition, an aggregation operator makes it possible to assign an acceptability score to the different configurations of the product studied. This multidimensional model allows to represent the emotional feelings of the panel relating to the products (Bertheaux et al., 2018, 2019).Another case study allowed to predict emotional reactions when examining images of chairs in an online shopping context. In this case the multidimensional preference model is used to estimate a valence based on the evaluation of six appearence descriptors. A nonlinear regression via a neuronal model showed that the « estimated » valence obtained by the multidimensional preference model was correlated with the valence expressed by panel (Bertheaux et al., 2020b).L’objet de cette thèse était d’étudier le rôle de l’émotion dans un processus de conception sensorielle. Une première expérimentation a permis de mettre en place une mesure émotionnelle du toucher lors de l’exploration tactile de surfaces. Une seconde expérimentation a permis de mettre en place une mesure émotionnelle visuelle lors de la visualisation d’images IAPS. Ces mesures ont permis d’objectiver la mesure émotionnelle des matériaux et des images en se basant sur deux indicateurs émotionnels, i) la valeur émotionnelle collectée lors de tests déclaratifs et ii) le coefficient de dilatation de la pupille (Bertheaux et al., 2020a).Le processus décisionnaire lors d’un acte d’achat repose sur l’estimation de valeurs utilitaires et hédoniques. Une autre contribution présentée dans ce document réside dans une nouvelle approche du modèle de préférence qui considère les données provenant de l’axe « Sensations » et de l’axe « Emotions ». Par ailleurs, un opérateur d’agrégation permet d’attribuer un score d’acceptabilité aux différentes configurationsdu produit étudié. Ce modèle multidimensionnel de préférence permet de représenter le ressenti émotionnel du panel relatif aux produits (Bertheaux et al., 2018, 2019).Une autre approche a permis de prédire les réactions émotionnelles lors de l’examen d’images de chaises dans un contexte d’achat en ligne. Dans ce cas le modèle multidimensionnel de préférence est utilisé pour estimer une valence basée sur l’évaluation de six descripteurs d’apparence. Une régression non linéaire via un modèle neuronal a montré que la valence « estimée » obtenue par le modèle multidimensionnel de préférence était corrélée à la valence exprimée par le panel (Bertheaux et al., 2020b)

Toward an optimal quantitative design method integrating user‐centered qualitative attributes

The determination of product features, which can be seen as design specifications, is a crucial problem that must be carried out upstream to quickly validate the product configuration according to some attributes in relation to the user perception. To this end, the design methods must evolve toward an analysis compatible with various kind of data that can be qualitative or quantitative. In this paper, a new approach is introduced able to take into account various kind of information in order to determine some quantitative design specifications in accordance with the users perception. This is done through a mathematical formulation that exploit different types of data coming from sensory analysis and physical quantities. This mathematical formulation is then used in an optimization procedure that takes into account a preference order over the sensory attributes. The solution of this optimization problem gives thus the best user‐centered specifications that must be used for the conception of the final product

Emotion Measurements Through the Touch of Materials Surfaces

International audienc

Word and Face Recognition Processing Based on Response Times and Ex-Gaussian Components

International audienceThe face is a fundamental feature of our identity. In humans, the existence of specialized processing modules for faces is now widely accepted. However, identifying the processes involved for proper names is more problematic. The aim of the present study is to examine which of the two treatments is produced earlier and whether the social abilities are influent. We selected 100 university students divided into two groups: Spanish and USA students. They had to recognize famous faces or names by using a masked priming task. An analysis of variance about the reaction times (RT) was used to determine whether significant differences could be observed in word or face recognition and between the Spanish or USA group. Additionally, and to examine the role of outliers, the Gaussian distribution has been modified exponentially. Famous faces were recognized faster than names, and differences were observed between Spanish and North American participants, but not for unknown distracting faces. The current results suggest that response times to face processing might be faster than name recognition, which supports the idea of differences in processing nature