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The current study presents a methodology to analyze first impressions on the basis of minimal motion information. In order to test the applicability of the approach brief silent video clips of 40 speakers were presented to independent observers (i.e., did not know speakers) who rated them on measures of the Big Five personality traits. The body mov...
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... and the y-coordinate of the other landmarks were subtracted. Mo- tion of the right shoulder itself was derived by subtracting its coordi- nates from the coordinate origin (upper left corner). In conclusion, coordinate data representing the position shifts of the body was translated into successions of horizontal and vertical amplitudes (see Fig. 1). From this data I extracted "motion cues", which are presented ...
Similar publications
Appearance cues and brief displays of behavior are related to people’s personality, to their performance at work and to the outcomes of elections. Thus, people present themselves to others on different communication channels, while their interaction partners form first impressions on the basis of the displayed cues. In the current study we examined...
Citations
... To address RQ1, results illustrating how underlying variables inherent in device designs mediating a listener's embodied interactions with music in 'tuning-in' to 'tune out' are represented in Table 3, with references to participants A1, A2 and Z1. These results have taken into confounding variables like personality differences (Koppensteiner, 2013;Soto & John, 2017); they suggest little variance in how underlying variables are being felt and perceived. ...
... Locomotion speed and style can predict robot personality [2]. Humans can interpret simple motion cues of basic shapes with different personality traits, and these features can be used for assessing the personality of the movement [19]; similarly, in videos, the personality can be predicted using minimal information [20]. Gesturing [21] and hand motion [32] are essential in accurately assessing personality. ...
... Extraversion as most expressible personality factor (e.g., Albright et al., 1988;Kenny et al., 1992;Jiang et al., 2023) captures differences in flexibility, novelty seeking (DeYoung, 2013), and resilience (Oshio et al., 2018), and high scorers were expected (H2a) to show adaptive selforganizing behavior as indicated by more system Entropy (complexity/flexibility), Determinism, Laminarity, and Mean Line. Neuroticism captures more unstable patterns of body motion (Koppensteiner, 2013) and emotion dynamics (Mader et al., 2023). Neuroticism was expected (H2b) to associate with less adaptive dynamic self-organization than other traits, thus lower system Entropy, Laminarity, Determinism, and Mean Line. ...
... Conscientiousness captures orderliness and prioritizing non-immediate goals (DeYoung, 2015), anticipated to be reflected in organized/controlled movement patterns, thus in stronger system Mean Line and Determinism (H2d). High openness to experience is associated with body motion direction and variability (e.g., Koppensteiner, 2013) and dyadic attunement (synchronization, see Tschacher & Ramseyer, 2018); therefore we predicted (H2e) more complex/flexible patterns of movement as reflected in higher Entropy and lower Determinism (predictability), which may be linked to behaviors of exploration and novelty (Gocłowska et al., 2019). All general predictions were pre-registered. ...
... Personality traits showed inverse correlations such as neuroticism with extraversion and agreeableness or positive correlations such as between conscientiousness and extraversion and agreeableness; and agreeableness with openness (see Table 4). The Big Five dimensions are in principle defined as independent personality factors (orthogonal), nevertheless, these associations are commonly reported, as behavior cannot be clearly divided into absolutely independent categories (Koppensteiner, 2013); and also higher-order structures (meta-traits) have been reported in the literature (e.g., DeYoung, 2006). In the context of this study, it is possible to indicate, for example, that the effects of neuroticism (emotional stability) were negatively related to extraversion and conscientiousness, to the extent that a highly emotionally stable individual (low neuroticism) would be likely to score relatively high in extraversion and conscientiousness as well (a "maturity" process, see Bleidorn et al., 2022). ...
... Both kinemes and AUs are found to be equally critical for estimating Conscientiousness, in line with the findings in [60]. Extraversion and Openness are conveyed by exaggerated physical and head movements [56], [61], with different head movement patterns representing high and low Extraversion [62]. While Agreeableness is also positively correlated with head movements [61], [62], empathetic behavior is accurately conveyed via facial expressions as denoted by the higher AU weights. ...
We explore the efficacy of multimodal behavioral cues for explainable prediction of personality and interview-specific traits. We utilize elementary head-motion units named kinemes, atomic facial movements termed action units and speech features to estimate these human-centered traits. Empirical results confirm that kinemes and action units enable discovery of multiple trait-specific behaviors while also enabling explainability in support of the predictions. For fusing cues, we explore decision and feature-level fusion, and an additive attention-based fusion strategy which quantifies the relative importance of the three modalities for trait prediction. Examining various long-short term memory (LSTM) architectures for classification and regression on the MIT Interview and First Impressions Candidate Screening (FICS) datasets, we note that: (1) Multimodal approaches outperform unimodal counterparts; (2) Efficient trait predictions and plausible explanations are achieved with both unimodal and multimodal approaches, and (3) Following the thin-slice approach, effective trait prediction is achieved even from two-second behavioral snippets.
... The interacting partner in a dyad is henceforth understood as the psychological environment in which each individual is engaged, and where individual differences surface (Asendorf, 2017;Leary, 1957;Roche & Cain, 2021;Sullivan et al., 1953). Individuals tend to synchronize more readily when they possess similar features (similarity) which could be perceived from body motion information associated with personality traits (e.g., Koppensteiner, 2013), as behavioral states are theorized to synchronize more readily when smaller system adjustments are required to reach a dyadic attractor state with systemic stability (Nowak et al., 2020;Rivera et al., 2010), which connects dyadic synchronization to improved person-environment fit (e.g., Van Vianen, 2018;Vleugels et al., 2022). ...
... Low-agreeable individuals, in turn, seem to exhibit more rigid, structured, fixated, and less complex movement patterns. This observed link between Agreeableness and complexity (Table S2) aligns with experimental evidence in which Agreeableness is associated with more dynamic movement patterns (e.g., phases of low/high activity/activation and relaxation, Koppensteiner, 2013). Entropy (i.e., unpredictability) can differ depending on the context and process which it expresses, such as mother-infant interactions in which more predictability and therefore low Entropy is desirable (Vanoncini et al., 2022); whether in other environments and systems dynamics, higher levels of Entropy can provide information about the complexity, flexibility of adaptation, learning, and functionality of behavior and processes (e.g., De Jonge-Hoekstra et al., 2020). ...
Objective
In social interactions, humans tend to naturally synchronize their body movements. We investigated interpersonal synchronization in conversations and examined its relationship with personality differences and post‐interaction appraisals.
Method
In a 15‐minute semi‐structured conversation, 56 previously‐unfamiliar dyads introduced themselves, followed by self‐disclosing and argumentative conversations. Their bodily movements were video‐recorded in a standardized room (112 young adults, aged 18–33, mean = 20.54, SD = 2.74; 58% Dutch, 31% German, 11% other). Interpersonal bodily synchronization was estimated as (a) synchronization strength using Windowed Lagged Cross‐Correlations and (b) Dynamic Organization (Determinism/Entropy/Laminarity/Mean Line) using Cross‐Recurrence Quantification Analysis. Bodily synchronization was associated with differences in Agreeableness and Extraversion (IPIP‐NEO‐120) and post‐conversational appraisals (affect/closeness/enjoyment) in mixed‐effect models.
Results
Agreeable participants exhibited higher complexity in bodily synchronization dynamics (higher Entropy) than disagreeable individuals, who also reported more negative affect afterward. Interpersonal synchronization was stronger among extroverts than among introverts and extroverts appraised conversations as more positive and enjoyable. Bodily synchronization strength and dynamic organization were related to the type of conversation (self‐disclosing/argumentative).
Conclusions
Interpersonal dynamics were intimately connected to differences in Agreeableness and Extraversion, varied across situations, and these parameters affected how pleasant, close, and enjoyable each conversation felt.
... Regardless of the motivation, people use their mouse to guide and shape their online experiences. As humans naturally infer internal states from physical motion cues (Koppensteiner, 2013), a similar process can be used to infer an individual's internal states based on online behavioral cues. This research attempts to examine a related topic on the relation between an individual's online behaviors and personality traits. ...
Objective:
In this rapidly digitizing world, it is becoming ever more important to understand people's online behaviors in both scientific and consumer research settings. The current work tests the feasibility of inferring personality traits from mouse movement patterns as a cost-effective means of measuring individual characteristics.
Method:
Mouse movement features (i.e., pauses, fixations, speed, clicks) were collected while participants (N = 791) completed an online image choice task. We compare the results of standard univariate and three forms of multivariate partial least squares (PLS) analyses predicting Big Five traits from mouse movements. We also examine whether mouse movements can predict a proposed measure of task attentiveness (atypical responding), and how these might be related to personality traits.
Results:
Each of the PLS analyses showed significant associations between a linear combination of personality traits (high Conscientiousness, Agreeableness, Openness, low Neuroticism) and several mouse movements associated with slower, more deliberate responding (less unnecessary clicks, more fixations). Additionally, several click-related mouse features were associated with atypical responding on the task.
Conclusions:
As the image choice task itself is not intended to assess personality in any way, our results validate the feasibility of using mouse movements to infer internal traits across experimental contexts.
... Biological motion also correlated to personality traits. For example, with minimal movement information that people relied on to make the first impression, people were able to predict others' perceived personality (Koppensteiner, 2013). Even when the personality traits were inferred from a thin slice of movement, there is a significant correlation with the information provided by knowledgeable informants (Borkenau et al., 2004). ...
The ability to perceive emotional states is a critical part of social interactions, shaping how people understand and respond to each other. In face-to-face communication, people perceive others’ emotions through observing their appearance and behavior. In virtual reality, how appearance and behavior are rendered must be designed. In this study, we asked whether people conversing in immersive virtual reality (VR) would perceive emotion more accurately depending on whether they and their partner were represented by realistic or abstract avatars. In both cases, participants got similar information about the tracked movement of their partners’ heads and hands, though how this information was expressed varied. We collected participants’ self-reported emotional state ratings of themselves and their ratings of their conversational partners’ emotional states after a conversation in VR. Participants’ ratings of their partners’ emotional states correlated to their partners’ self-reported ratings regardless of which of the avatar conditions they experienced. We then explored how these states were reflected in their nonverbal behavior, using a dyadic measure of nonverbal behavior (proximity between conversational partners) and an individual measure (expansiveness of gesture). We discuss how this relates to measures of social presence and social closeness.
... Regardless of the motivation, people use their mouse to guide and shape their online experiences. As humans naturally infer internal states from physical motion cues (Koppensteiner, 2013), a similar process can be used to infer an individual's internal states based on online behavioral cues. This research attempts to examine a related topic on the relation between an individual's online behaviors and personality traits. ...
In this rapidly digitizing world, it is becoming ever more important to understand people’s online behaviors in both scientific and consumer research settings. A cost-effective way to gain a deeper understanding of these behaviors is to examine mouse movement patterns. This research explores the feasibility of inferring personality traits from these mouse movement features (i.e., pauses, fixations, cursor speed, clicks) on a simple image choice task. We compare the results of standard univariate (OLS regression, bivariate correlations) and three forms of multivariate partial least squares (PLS) analyses. This work also examines whether mouse movements can predict task attentiveness, and how these might be related to personality traits. Results of the PLS analyses showed significant associations between a linear combination of personality traits (high Conscientiousness, Agreeableness, and Openness, and low Neuroticism) and several mouse movements associated with slower, more deliberate responding (less unnecessary clicks, more fixations). Additionally, several click-related mouse features were associated with attentiveness to the task. Importantly, as the image choice task itself is not intended to assess personality in any way, our results validate the feasibility of using mouse movements to infer internal traits across experimental contexts, particularly when examined using multivariate analyses and a multiverse approach.
... También el adoptar una postura más enérgica (Naumann et al., 2009) o situarse cerca del interlocutor. Además, realizarán movimientos corporales frecuentes y rápidos (Oberzaucher y Grammer, 2008), presentarán mayores cambios posturales, elevación de hombros y harán uso de gestos variados (Jensen, 2016), tanto ilustradores como adaptadores (González, 2019;Hostetter y Potthoff, 2012;Koppensteiner, 2013). Los extrovertidos preferirán las interacciones cara a cara, un mayor uso de expresiones faciales (Jensen, 2016), tanto a través de un mayor número de sonrisas Duchenne (Mehu et al., 2007;Oberzaucher y Grammer, 2008), como a través de expresiones faciales de tristeza (Keltner, 2005). ...
... Por último, una persona con tendencia al neuroticismo recurrirá a gestos tanto ilustradores como adaptadores (Campbell y Rushton, 1978;González, 2019;Hostetter y Potthoff, 2012, Oberzaucher y Grammer, 2008, presentará menos gestos de expresión en general (Campbell y Rushton, 1978), no devolviendo la sonrisa de forma frecuente en las interacciones sociales (Keltner, 2005), pero sí que realizará un mayor número de movimientos faciales (Harrigan et al., 2004), mayores cambios posturales (González, 2019), y movimientos más rápidos y bruscos (Koppensteiner, 2013), adoptando en ocasiones una postura rígida y tensa (Naumann et al., 2009). También mostrará aversión a la mirada (Campbell y Rushton, 1978;Oberzaucher y Grammer, 2008) lo que dará lugar a miradas evitativas o intermitentes (Jensen, 2016), sonrisas menos genuinas (no Duchenne) (Mehu et al., 2007), mayor número de pausas en general (Campbell y Rushton, 1978), aunque no serán pausas de planificación (González, 2019), mantendrá un tono alto de voz (Jensen, 2016) y no mostrará fluidez en el habla (Gawda, 2007). ...
El objetivo de toda investigación criminal es obtener indicios suficientes para conseguir el esclarecimiento de los hechos. Las entrevistas que realizan los investigadores policiales, tanto a víctimas, como a testigos y sospechosos, son una de las principales fuentes de información de la que disponen. Tal es su importancia que se han desarrollado procedimientos específicos para la toma de declaración a víctimas, como puede ser la prueba preconstituida o la entrevista cognitiva, y lo mismo sucede con los sospechosos, para los que se han desarrollado procedimientos de interrogatorio como el REID y el
PEACE. La entrevista y el interrogatorio deben entenderse como interacciones entre personas (entrevistador-entrevistado), por lo que el entrevistador policial debe crear la estrategia más efectiva en función de la persona que tiene delante. En este sentido es importante la perfilación indirecta y la relación entre personalidad y comportamiento no verbal (CNV), ya que ayudarán al entrevistador a saber qué tipo de persona (personalidad) tiene delante, sin tener que usar cuestionarios autoinformados, evitando algunos de los problemas que plantea la evaluación de personalidad directa. Es por ello que el objetivo del presente trabajo es realizar un estudio piloto en el que se analice la relación entre personalidad y CNV, empleando el modelo PEN de Eysenck, en una muestra penitenciaria, formada por 20 casos de feminicidio. Conseguir perfilar de forma indirecta es solo una parte del proceso, ya que una vez perfilado el sujeto, habría que diseñar una estrategia de entrevista/interrogatorio en función de su personalidad.
... The degree to which these variables can be accurately perceived depends both on whether they are expressed in observable cues (e.g., nonverbal behaviors or features of the appearance) in the target individuals and whether the perceivers (judges) detect and use valid cues to make their judgment (Back and Nestler 2016;Funder 2012). Much research in impression formation has focused on verbal and nonverbal cues (e.g., Hirschmüller et al. 2013;Koppensteiner 2013), on comparing different presentation formats (e.g., pictures, videos, face-to-face interactions; Krzyzaniak et al. 2019), and on what makes a "good" (i.e., judgeable) target (Colvin 1993;Funder 1995;Human and Biesanz 2013). ...
The ability to accurately judge others' personality and the ability to accurately recognize others' emotions are both part of the broader construct of interpersonal accuracy (IPA). However, little research has examined the association between these two IPA domains. Little is also known about the relationship between personality judgment accuracy and other socio-emotional skills and traits. In the present study, 121 participants judged eight traits (Big Five, intelligence, cooperativeness, and empathy) in each of 30 targets who were presented either in a photograph, a muted video, or a video with sound. The videos were 30 second excerpts from negotiations that the targets had engaged in. Participants also completed standard tests of emotion recognition ability, emotion understanding, and trait emotional intelligence. Results showed that personality judgment accuracy, when indexed as trait accuracy and distinctive profile accuracy, positively correlated with emotion recognition ability and was unrelated to emotion understanding and trait emotional intelligence. Female participants were more accurate in judging targets' personality than men. These results provide support for IPA as a set of correlated domain-specific skills and encourage further research on personality judgment accuracy as a meaningful individual difference variable.
