Iconic faces are not real faces: enhanced emotion detection and altered neural processing as faces become more iconic

No effect of autistic traits on social attention: evidence based on single-cue and conflicting-cues scenarios

Individuals often use others' gaze and head directions to direct their attention. To investigate the influence of autistic traits on social attention, we conducted two experiments comparing groups with high and low autistic traits in single-cue (Experiment 1) and conflicting-cue (Experiment 2) scenarios. Our findings indicate that individuals responded more rapidly to the direction of a single social cue or the consensus of multiple cues. However, we did not observe significant differences in social attention between individuals with high and low autistic traits. Notably, as the stimulus onset asynchrony (SOA) increased, individuals with low autistic traits exhibited greater improvements in reaction speed compared to those with high autistic traits. This suggests that individuals with low autistic traits excel at leveraging temporal information to optimize their behavioral readiness over time, hinting at potential variations in cognitive flexibility related to autistic traits.

Facial icons as indexes of emotions and intentions

Various objects and artifacts incorporate representations of faces, encompassing artworks like portraits, as well as ethnographic or industrial artifacts such as masks or humanoid robots. These representations exhibit diverse degrees of human-likeness, serving different functions and objectives. Despite these variations, they share common features, particularly facial attributes that serve as building blocks for facial expressions-an effective means of communicating emotions. To provide a unified conceptualization for this broad spectrum of face representations, we propose the term "facial icons" drawing upon Peirce's semiotic concepts. Additionally, based on these semiotic principles, we posit that facial icons function as indexes of emotions and intentions, and introduce a significant anthropological theory aligning with our proposition. Subsequently, we support our assertions by examining processes related to face and facial expression perception, as well as sensorimotor simulation processes involved in discerning others' mental states, including emotions. Our argumentation integrates cognitive and experimental evidence, reinforcing the pivotal role of facial icons in conveying mental states.

Meaning above (and in) the head: Combinatorial visual morphology from comics and emoji

Compositionality is a primary feature of language, but graphics can also create combinatorial meaning, like with items above faces (e.g., lightbulbs to mean inspiration). We posit that these “upfixes” (i.e., upwards affixes) involve a productive schema enabling both stored and novel face–upfix dyads. In two experiments, participants viewed either conventional (e.g., lightbulb) or unconventional (e.g., clover-leaves) upfixes with faces which either matched (e.g., lightbulb/smile) or mismatched (e.g., lightbulb/frown). In Experiment 1, matching dyads sponsored higher comprehensibility ratings and faster response times, modulated by conventionality. In Experiment 2, event-related brain potentials (ERPs) revealed conventional upfixes, regardless of matching, evoked larger N250s, indicating perceptual expertise, but mismatching and unconventional dyads elicited larger semantic processing costs (N400) than conventional-matching dyads. Yet mismatches evoked a late negativity, suggesting congruent novel dyads remained construable compared with violations. These results support that combinatorial graphics involve a constrained productive schema, similar to the lexicon of language.

The Influence of Key Facial Features on Recognition of Emotion in Cartoon Faces

Cartoon faces are widely used in social media, animation production, and social robots because of their attractive ability to convey different emotional information. Despite their popular applications, the mechanisms of recognizing emotional expressions in cartoon faces are still unclear. Therefore, three experiments were conducted in this study to systematically explore a recognition process for emotional cartoon expressions (happy, sad, and neutral) and to examine the influence of key facial features (mouth, eyes, and eyebrows) on emotion recognition. Across the experiments, three presentation conditions were employed: (1) a full face; (2) individual feature only (with two other features concealed); and (3) one feature concealed with two other features presented. The cartoon face images used in this study were converted from a set of real faces acted by Chinese posers, and the observers were Chinese. The results show that happy cartoon expressions were recognized more accurately than neutral and sad expressions, which was consistent with the happiness recognition advantage revealed in real face studies. Compared with real facial expressions, sad cartoon expressions were perceived as sadder, and happy cartoon expressions were perceived as less happy, regardless of whether full-face or single facial features were viewed. For cartoon faces, the mouth was demonstrated to be a feature that is sufficient and necessary for the recognition of happiness, and the eyebrows were sufficient and necessary for the recognition of sadness. This study helps to clarify the perception mechanism underlying emotion recognition in cartoon faces and sheds some light on directions for future research on intelligent human-computer interactions.

An artificial nervous system to treat chronic stroke

Despite remarkable advances in the treatment of numerous medical conditions, neurological disease and injury remains an outstanding challenge and cause of disability worldwide. The decreased regenerative capacity and extreme complexity and heterogeneity of nervous tissue, in particular the brain, and the fact that the brain remains the least understood organ, have hampered our ability to provide definitive treatments for prevalent conditions such as stroke. Stroke is the second-leading cause of death worldwide, and the nervous system is intimately involved in other prevalent conditions including ischemic heart disease, diabetes mellitus, and hypertension. Advances in neuromodulation, electroceuticals, microsurgical techniques, optogenetics, brain-computer interfaces, and autologous constructs offer potential solutions to address the otherwise permanent neurological deficits of stroke and other conditions. Here we review these various approaches to build an "artificial nervous system" that could restore function and independence in people living with these conditions. We focus on stroke both because it is the leading cause of neurological disability worldwide and because we anticipate that advances in the reversal of stroke-related deficits will have ripple effects benefiting people with other neurological conditions including spinal cord injury, traumatic brain injury, ALS, and muscular dystrophy.

Electrophysiological Correlates of Processing Warning Signs With Different Background Colors: An Event-Related Potentials Investigation

Warning signs, as a type of safety signs, are widely applied in our daily lives to informing people about potential hazards and prompting safe behavior. Although previous studies have paid attention to the color of warning signs, they are mostly based on surveys and behavioral experiments. The neural substrates underlying the perception of warning signs with different background colors remain not clearly characterized. Therefore, this research is intended to address this gap with event-related potentials (ERPs) technique. Warning signs with three different background colors (i.e., white, yellow, and blue) were used in the experiment. The results showed that the perceptual differences between different warning signs were present in the form of differential ERPs components (P1, N1, P2, N2, and P3) though subjects were not required to explicitly attend to the warning signs.

Understanding cartoon emotion using integrated deep neural network on large dataset

Emotion is an instinctive or intuitive feeling as distinguished from reasoning or knowledge. It varies over time, since it is a natural instinctive state of mind deriving from one’s circumstances, mood, or relationships with others. Since emotions vary over time, it is important to understand and analyze them appropriately. Existing works have mostly focused well on recognizing basic emotions from human faces. However, the emotion recognition from cartoon images has not been extensively covered. Therefore, in this paper, we present an integrated Deep Neural Network (DNN) approach that deals with recognizing emotions from cartoon images. Since state-of-works do not have large amount of data, we collected a dataset of size 8 K from two cartoon characters: ‘Tom’ & ‘Jerry’ with four different emotions, namely happy, sad, angry, and surprise. The proposed integrated DNN approach, trained on a large dataset consisting of animations for both the characters (Tom and Jerry), correctly identifies the character, segments their face masks, and recognizes the consequent emotions with an accuracy score of 0.96. The approach utilizes Mask R-CNN for character detection and state-of-the-art deep learning models, namely ResNet-50, MobileNetV2, InceptionV3, and VGG 16 for emotion classification. In our study, to classify emotions, VGG 16 outperforms others with an accuracy of 96% and F1 score of 0.85. The proposed integrated DNN outperforms the state-of-the-art approaches.

Influence of Attention Control on Implicit and Explicit Emotion Processing of Face and Body: Evidence From Flanker and Same-or-Different Paradigms

Many existing findings indicate that processing of emotional information is pre-attentive, largely immune from attentional control. Nevertheless, inconsistent evidence on the interference of emotional cues on cognitive processing suggests that this influence may be a highly conditional phenomenon. The aim of the present study was twofold: (1) to examine the modulation of attention control on emotion processing using facial expressions (2) explore the very same effect for emotional body expressions. In Experiment 1, participants performed a Flanker task in which they had to indicate either the emotion (happy/fearful) or the gender of the target stimulus while ignoring the distracting stimuli at the side. We found evidence for intrusion of the emotional dimension of a stimulus in both the emotion and gender discrimination performance, thus when either task-relevant or task-irrelevant. To further explore the influence of attention control mechanisms, in Experiment 2 participants performed a same-or-different judgment task in which they were asked to pay attention to both the central and lateral stimuli and indicated whether the central stimulus matched the lateral for emotion or gender. Results showed that emotional features exerted an influence at an implicit level (i.e., during gender judgments) for bodies only. Gender features did not affect emotional processing in either experiments. To rule out the possibility that this effect was driven by postural rather than emotional features of fearful vs. happy stimuli, a control experiment was conducted. In Experiment 3, bodies with an opening/up-ward or closing/down-ward posture but with no emotional valence were presented. Results revealed that the body posture did not influence gender discrimination. Findings suggest that the emotional valence of a face or body stimulus can overpass attention filtering mechanisms, independently from the level of attentional modulation (Experiment 1). However, broadening the focus of attention to include the lateral stimuli led emotional information to intrude on the main task, exerting an implicit, bottom–up influence on gender processing, only when conveyed by bodies (Experiment 2). Results point to different mechanisms for the implicit processing of face and body emotional expressions, with the latter likely having role on action preparation processes.

Artificial Faces Predict Gaze Allocation in Complex Dynamic Scenes

Both low-level physical saliency and social information, as presented by human heads or bodies, are known to drive gaze behavior in free-viewing tasks. Researchers have previously made use of a great variety of face stimuli, ranging from photographs of real humans to schematic faces, frequently without systematically differentiating between the two. In the current study, we used a Generalized Linear Mixed Model (GLMM) approach to investigate to what extent schematic artificial faces can predict gaze when they are presented alone or in competition with real human faces. Relative differences in predictive power became apparent, while GLMMs suggest substantial effects for real and artificial faces in all conditions. Artificial faces were accordingly less predictive than real human faces but still contributed significantly to gaze allocation. These results help to further our understanding of how social information guides gaze in complex naturalistic scenes.

Editors’ Introduction and Review: Visual Narrative Research: An Emerging Field in Cognitive Science

Drawn sequences of images are among our oldest records of human intelligence, appearing on cave paintings, wall carvings, and ancient pottery, and they pervade across cultures from instruction manuals to comics. They also appear prevalently as stimuli across Cognitive Science, for studies of temporal cognition, event structure, social cognition, discourse, and basic intelligence. Yet, despite this fundamental place in human expression and research on cognition, the study of visual narratives themselves has only recently gained traction in Cognitive Science. This work has suggested that visual narrative comprehension requires cultural exposure across a developmental trajectory and engages with domain‐general processing mechanisms shared by visual perception, attention, event cognition, and language, among others. Here, we review the relevance of such research for the broader Cognitive Science community, and make the case for why researchers should join the scholarship of this ubiquitous but understudied aspect of human expression.

Drawn sequences of images, like those in comics and picture stories, are a pervasive and fundamental way that humans have communicated for millennia. Yet, the study of visual narratives has only recently gained traction in Cognitive Science. Here we explore what has held back the study of the cognition of visual narratives, and why researchers should join in scholarship of this ubiquitous aspect of expression.

Contextually-Based Social Attention Diverges across Covert and Overt Measures

Humans spontaneously attend to social cues like faces and eyes. However, recent data show that this behavior is significantly weakened when visual content, such as luminance and configuration of internal features, as well as visual context, such as background and facial expression, are controlled. Here, we investigated attentional biasing elicited in response to information presented within appropriate background contexts. Using a dot-probe task, participants were presented with a face-house cue pair, with a person sitting in a room and a house positioned within a picture hanging on a wall. A response target occurred at the previous location of the eyes, mouth, top of the house, or bottom of the house. Experiment 1 measured covert attention by assessing manual responses while participants maintained central fixation. Experiment 2 measured overt attention by assessing eye movements using an eye tracker. The data from both experiments indicated no evidence of spontaneous attentional biasing towards faces or facial features in manual responses; however, an infrequent, though reliable, overt bias towards the eyes of faces emerged. Together, these findings suggest that contextually-based social information does not determine spontaneous social attentional biasing in manual measures, although it may act to facilitate oculomotor behavior.

Influence of Color on Emotion Recognition Is Not Bidirectional: An Investigation of the Association Between Color and Emotion Using a Stroop-Like Task

The association between color and emotion has been shown, with red facilitating recognition of anger and green facilitating recognition of happiness. However, it has been unclear if emotional stimulus conversely facilitates and/or inhibits recognition of such colors. This study used a Stroop-like task, which required participants to ignore facial expressions and recognize color, in order to investigate the influence of emotion on recognition of color. In addition, this study investigated the association between color and emotion recognition from emoticons, as it was recently suggested that the process of emotion recognition from emoticons was different from that of actual faces. Results revealed that for facial expressions and emoticons, color influenced emotion recognition, in line with previous studies. Conversely, facial expression did not influence recognition of color. The results suggest that in emotion recognition people consider surrounding contextual information and integrate it automatically; however, in color recognition, they do not.

An event-related potential comparison of facial expression processing between cartoon and real faces

Faces play important roles in the social lives of humans. Besides real faces, people also encounter numerous cartoon faces in daily life which convey basic emotional states through facial expressions. Using event-related potentials (ERPs), we conducted a facial expression recognition experiment with 17 university students to compare the processing of cartoon faces with that of real faces. This study used face type (real vs. cartoon), emotion valence (happy vs. angry) and participant gender (male vs. female) as independent variables. Reaction time, recognition accuracy, and the amplitudes and latencies of emotion processing-related ERP components such as N170, VPP (vertex positive potential), and LPP (late positive potential) were used as dependent variables. The ERP results revealed that cartoon faces caused larger N170 and VPP amplitudes as well as a briefer N170 latency than did real faces; that real faces induced larger LPP amplitudes than did cartoon faces. In addition, the results showed a significant difference in the brain regions as reflected in a right hemispheric advantage. The behavioral results showed that the reaction times for happy faces were shorter than those for angry faces; that females showed a higher accuracy than did males; and that males showed a higher recognition accuracy for angry faces than happy faces. Due to the sample size, these results may suggestively but not rigorously demonstrate differences in facial expression recognition and neurological processing between cartoon faces and real faces. Cartoon faces showed a higher processing intensity and speed than real faces during the early processing stage. However, more attentional resources were allocated for real faces during the late processing stage.

The eyes do not have it after all? Attention is not automatically biased towards faces and eyes

It is commonly accepted that attention is spontaneously biased towards faces and eyes. However, the role of stimulus features and task settings in this finding has not yet been systematically investigated. Here, we tested if faces and facial features bias attention spontaneously when stimulus factors, task properties, response conditions, and eye movements are controlled. In three experiments, participants viewed face, house, and control scrambled face-house images in an upright and inverted orientation. The task was to discriminate a target that appeared with equal probability at the previous location of the face, house, or the control image. In all experiments, our data indicated no spontaneous biasing of attention for targets occurring at the previous location of the face. Experiment 3, which measured oculomotor biasing, suggested a reliable but infrequent saccadic bias towards the eye region of upright faces. Importantly, these results did not reflect our specific laboratory settings, as in Experiment 4, we present a full replication of a classic finding in the literature demonstrating reliable social attention bias. Together, these data suggest that attentional biasing for social information is task and context mediated, and less robust than originally thought.