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Teamwork is indispensable in human societies. However, due to the complexity of studying ecologically valid synchronous team actions, requiring multiple members and a range of subjective and objective measures, the mechanism underlying the impact of synchrony on team performance is still unclear. In this paper, we simultaneously measured groups of nine-participants' (total N = 180) fronto-temporal activations during a drum beating task using functional near infrared spectroscopy (fNIRS)-based hyperscanning and multi-brain network modeling, which can assess patterns of shared neural synchrony and attention/information sharing across entire teams. Participants (1) beat randomly without considering others' drumming (random condition), (2) actively coordinated their beats with the entire group without other external cue (team-focus condition), and (3) beat together based on a metronome (shared-focus condition). Behavioral data revealed higher subjective and objective measures of drum-beat synchronization in the team-focus condition, as well as higher felt interdependence. The fNIRS data revealed that participants in the team-focus condition also showed higher interpersonal neural synchronization (INS) and higher Global Network Efficiency in their left TPJ and mPFC. Higher left TPJ Global Network Efficiency also predicted higher actual synchrony in the team-focus condition, with an effect size roughly 1.5 times that of subjective measures, but not in the metronome-enabled shared-focus condition. This result suggests that shared mental representations with high efficiency of information exchange across the entire team may be a key component of synchrony, adding to the understanding of the actual relation to team work.

Previous studies have demonstrated that the neural basis of deception involves a network of regions including the medial frontal cortex (MFC), superior temporal sulcus (STS), temporo-parietal junction (TPJ), etc. However, to test the actual activity of the brain in the act of deceptive practice itself, existing studies have mainly adopted paradigms of passive deception, where participants are told to lie in certain conditions, and have focused on intra-brain mechanisms in single participants. In order to examine the neural substrates underlying more natural, spontaneous deception in real social interactions, the present study employed a functional near-infrared spectroscopy (fNIRS) hyperscanning technique to simultaneously measure pairs of participants’ fronto-temporal activations in a two-person gambling card-game. We demonstrated higher TPJ activation in deceptive compared to honest acts. Analysis of participants’ inter-brain correlation further revealed that the STS is uniquely involved in deception but not in honesty, especially in females. These results suggest that the STS may play a critical role in spontaneous deception due to mentalizing requirements relating to modulating opponents’ thoughts. To our knowledge, this study was the first to investigate such inter-brain correlates of deception in real face-to-face interactions, and thus is hoped to provide a new path for future complex social behavior research.

We review a relatively new method for studying the developing brain in children and infants with Autism Spectrum Disorder (ASD). Despite advances in behavioral screening and brain imaging, due to paradigms that do not easily allow for testing of awake, very young, and socially-engaged children—i.e., the social and the baby brain—the biological underpinnings of this disorder remain a mystery. We introduce an approach based on functional near-infrared spectroscopy (fNIRS), which offers a noninvasive imaging technique for studying functional activations by measuring changes in the brain's hemodynamic properties. This further enables measurement of brain activation in upright, interactive settings, while maintaining general equivalence to fMRI findings. We review the existing studies that have used fNIRS for ASD, discussing their promise, limitations, and their technical aspects, gearing this study to the researcher who may be new to this technique and highlighting potential targets for future research. •We review fNIRS approaches to studying developing baby brain in individuals with ASD.•fNIRS can measure especially brain activation of babies in their first year of life.•fNIRS can measure brain activation of babies in natural social contexts.•We highlight potential targets for future study, marking a critical window into ASD.

The last decade has witnessed a renaissance of empirical and psychological approaches to art study, especially regarding cognitive models of art processing experience. This new emphasis on modeling has often become the basis for our theoretical understanding of human interaction with art. Models also often define areas of focus and hypotheses for new empirical research, and are increasingly important for connecting psychological theory to discussions of the brain. However, models are often made by different researchers, with quite different emphases or visual styles. Inputs and psychological outcomes may be differently considered, or can be under-reported with regards to key functional components. Thus, we may lose the major theoretical improvements and ability for comparison that can be had with models. To begin addressing this, this paper presents a theoretical assessment, comparison, and new articulation of a selection of key contemporary cognitive or information-processing-based approaches detailing the mechanisms underlying the viewing of art. We review six major models in contemporary psychological aesthetics. We in turn present redesigns of these models using a unified visual form, in some cases making additions or creating new models where none had previously existed. We also frame these approaches in respect to their targeted outputs (e.g., emotion, appraisal, physiological reaction) and their strengths within a more general framework of early, intermediate, and later processing stages. This is used as a basis for general comparison and discussion of implications and future directions for modeling, and for theoretically understanding our engagement with visual art.

The idea that simple visual elements such as colors and lines have specific, universal associations-for example red being warm-appears rather intuitive. Such associations have formed a basis for the description of artworks since the 18th century and are still fundamental to discourses on art today. Art historians might describe a painting where red is dominant as \"warm,\" \"aggressive,\" or \"lively,\" with the tacit assumption that beholders would universally associate the works' certain key forms with specific qualities, or \"aesthetic effects\". However, is this actually the case? Do we actually share similar responses to the same line or color? In this paper, we tested whether and to what extent this assumption of universality (sharing of perceived qualities) is justified. We employed-for the first time-abstract artworks as well as single elements (lines and colors) extracted from these artworks in an experiment in which participants rated the stimuli on 14 \"aesthetic effect\" scales derived from art literature and empirical aesthetics. To test the validity of the assumption of universality, we examined on which of the dimensions there was agreement, and investigated the influence of art expertise, comparing art historians with lay people. In one study and its replication, we found significantly lower agreement than expected. For the whole artworks, participants agreed on the effects of warm-cold, heavy-light, and happy-sad, but not on 11 other dimensions. Further, we found that the image type (artwork or its constituting elements) was a major factor influencing agreement; people agreed more on the whole artwork than on single elements. Art expertise did not play a significant role and agreement was especially low on dimensions usually of interest in empirical aesthetics (e.g., like-dislike). Our results challenge the practice of interpreting artworks based on their aesthetic effects, as these effects may not be as universal as previously thought.

Art research has long aimed to unravel the complex associations between specific attributes, such as color, complexity, and emotional expressiveness, and art judgments, including beauty, creativity, and liking. However, the fundamental distinction between attributes as inherent characteristics or features of the artwork and judgments as subjective evaluations remains an exciting topic. This paper reviews the literature of the last half century, to identify key attributes, and employs machine learning, specifically Gradient Boosted Decision Trees (GBDT), to predict 13 art judgments along 17 attributes. Ratings from 78 art novice participants were collected for 54 Western artworks. Our GBDT models successfully predicted 13 judgments significantly. Notably, judged creativity and disturbing/irritating judgments showed the highest predictability, with the models explaining 31% and 32% of the variance, respectively. The attributes emotional expressiveness, valence, symbolism, as well as complexity emerged as consistent and significant contributors to the models’ performance. Content-representational attributes played a more prominent role than formal-perceptual attributes. Moreover, we found in some cases non-linear relationships between attributes and judgments with sudden inclines or declines around medium levels of the rating scales. By uncovering these underlying patterns and dynamics in art judgment behavior, our research provides valuable insights to advance the understanding of aesthetic experiences considering visual art, inform cultural practices, and inspire future research in the field of art appreciation.

Creativity is a compelling yet elusive phenomenon, especially when manifested in visual art, where its evaluation is often a subjective and complex process. Understanding how individuals judge creativity in visual art is a particularly intriguing question. Conventional linear approaches often fail to capture the intricate nature of human behavior underlying such judgments. Therefore, in this study, we employed interpretable machine learning to probe complex associations between 17 subjective art-attributes and creativity judgments across a diverse range of artworks. A cohort of 78 non-art expert participants assessed 54 artworks varying in styles and motifs. The applied Random Forests regressor models accounted for 30% of the variability in creativity judgments given our set of art-attributes. Our analyses revealed symbolism, emotionality, and imaginativeness as the primary attributes influencing creativity judgments. Abstractness, valence, and complexity also had an impact, albeit to a lesser degree. Notably, we observed non-linearity in the relationship between art-attribute scores and creativity judgments, indicating that changes in art-attributes did not consistently correspond to changes in creativity judgments. Employing statistical learning, this investigation presents the first attribute-integrating quantitative model of factors that contribute to creativity judgments in visual art among novice raters. Our research represents a significant stride forward building the groundwork for first causal models for future investigations in art and creativity research and offering implications for diverse practical applications. Beyond enhancing comprehension of the intricate interplay and specificity of attributes used in evaluating creativity, this work introduces machine learning as an innovative approach in the field of subjective judgment.

Interaction is defined as “individual's simultaneous or sequential actions that affect the immediate and future outcomes of the other individuals involved in the situation” (Johnson and Johnson, 2005). [...]Liu and Pelowski (in press) have categorized social interaction into two structures: concurrent interaction requiring body-movement synchrony between two people (i.e., the same behavior with or without time-delay, such as pair Olympic diving) and turn-based interaction that relies primarily on mind-set synchrony—i.e., holding representations of one's own intention and that of others simultaneously for complementary or contrary behavior, such as in a game of chess. Social psychology literature has consistently demonstrated that different types of interaction may involve different cognitive processes and behaviors (Johnson et al., 1981; Johnson and Johnson, 1989). [...]in order to fully understand neural mechanisms underlying human behavior, it is important to clearly separate and examine each type of interaction itself. According to the proposed classification system (Figure 1), there are generally eight interaction types, which we will now consider. [...]resulting synchronization in paradigms relating to cooperation, but not competition, was argued to be one of the clearest indications of interplay or sharing of information between brains.

In empirical art research, understanding how viewers judge visual artworks as beautiful is often explored through the study of attributes—specific inherent characteristics or artwork features such as color, complexity, and emotional expressiveness. These attributes form the basis for subjective evaluations, including the judgment of beauty. Building on this conceptual framework, our study examines the beauty judgments of 54 Western artworks made by native Japanese and German speakers, utilizing an extreme randomized trees model—a data-driven machine learning approach—to investigate cross-cultural differences in evaluation behavior. Our analysis of 17 attributes revealed that visual harmony, color variety, valence, and complexity significantly influenced beauty judgments across both cultural cohorts. Notably, preferences for complexity diverged significantly: while the native Japanese speakers found simpler artworks as more beautiful, the native German speakers evaluated more complex artworks as more beautiful. Further cultural distinctions were observed: for the native German speakers, emotional expressiveness was a significant factor, whereas for the native Japanese speakers, attributes such as brushwork, color world, and saturation were more impactful. Our findings illuminate the nuanced role that cultural context plays in shaping aesthetic judgments and demonstrate the utility of machine learning in unravelling these complex dynamics. This research not only advances our understanding of how beauty is judged in visual art—considering self-evaluated attributes—across different cultures but also underscores the potential of machine learning to enhance our comprehension of the aesthetic evaluation of visual artworks.

Art, as a prestigious cultural commodity, concerns aesthetic and monetary values, personal tastes, and social reputation in various social contexts—all of which are reflected in choices concerning our liking, or in other contexts, our actual willingness-to-pay for artworks. But, how do these different aspects interact in regard to the concept of social reputation and our private versus social selves, which appear to be essentially intervening, and potentially conflicting, factors driving choice? In our study, we investigated liking and willingness-to-pay choices using—in art research—a novel, forced-choice paradigm. Participants (N = 123) made choices from artwork-triplets presented with opposing artistic quality and monetary value-labeling, thereby creating ambiguous choice situations. Choices were made in either private or in social/public contexts, in which participants were made to believe that either art-pricing or art-making experts were watching their selections. A multi-method design with eye-tracking, neuroendocrinology (testosterone, cortisol), and motivational factors complemented the behavioral choice analysis. Results showed that artworks, of which participants were told were of high artistic value were more often liked and those of high monetary-value received more willingness-to-pay choices. However, while willingness-to-pay was significantly affected by the presumed observation of art-pricing experts, liking selections did not differ between private/public contexts. Liking choices, compared to willingness-to-pay, were also better predicted by eye movement patterns. Whereas, hormone levels had a stronger relation with monetary aspects (willingness-to-pay/ art-pricing expert). This was further confirmed by motivational factors representative for reputation seeking behavior. Our study points to an unexplored terrain highlighting the linkage of social reputation mechanisms and its impact on choice behavior with a ubiquitous commodity, art.