Facial Expressions In Response Research Articles

Emotional facial expression and perioral motor functions of the human auditory cortex

ObjectiveWe investigated the role of transverse temporal gyrus and adjacent cortex (TTG+) in facial expressions and perioral movements. MethodsIn 31 patients undergoing stereo-electroencephalography monitoring, we describe behavioral responses elicited by electrical stimulation within the TTG+. Task-induced high-gamma modulation (HGM), auditory evoked responses, and resting-state connectivity were used to investigate the cortical sites having different types of responses on electrical stimulation. ResultsChanges in facial expressions and perioral movements were elicited on electrical stimulation within TTG+ in 9 (29%) and 10 (32%) patients, respectively, in addition to the more common language responses (naming interruptions, auditory hallucinations, paraphasic errors). All functional sites showed auditory task induced HGM and evoked responses validating their location within the auditory cortex, however, motor sites showed lower peak amplitudes and longer peak latencies compared to language sites. Significant first-degree connections for motor sites included precentral, anterior cingulate, parahippocampal, and anterior insular gyri, whereas those for language sites included posterior superior temporal, posterior middle temporal, inferior frontal, supramarginal, and angular gyri. ConclusionsMultimodal data suggests that TTG+ may participate in auditory-motor integration. SignificanceTTG+ likely participates in facial expressions in response to emotional cues during an auditory discourse.

237 SeeMe: An Automated Methodology to Detect Early Signs of Recovery After Acute Brain Injury

INTRODUCTION: Assessing the functional state of the brain and predicting recovery after acute brain injury (ABI) remains a major unsolved problem. At present, physicians make predictions using clinical examination, traditional scoring systems, and statistical models, which have significant shortcomings and lack the ability to forecast recovery on an individual basis. METHODS: SeeMe is based on analyzing subtle facial expressions in response to auditory commands (i.e., “open your eyes,” “make a smile”) over time and compares the results to a pre-stimulus baseline. These subtle motions are often not detectable by the naked eye. We enrolled 26 comatose ABI patients and 16 healthy volunteers to test this tool. Command-evoked eye-opening movements were compared to clinical observation and the JFK Coma Recovery Scale-Revised (CRS-R). RESULTS: In our study, 22 of 26 (85%) patients recovered consciousness during hospitalization after ABI. On average, SeeMe detected command-evoked eye opening ∼9 days before clinical assessment using the CRS-R and ∼3 days before blinded human raters who observed the recorded videos using the naked eye.Using the optimally identified decision threshold, SeeMe demonstrated the following performance metrics on ABI patients with no eye opening to command (auditory CRS subscale = 2): sensitivity = 74.1%, specificity = 66.6%, positive predictive value = 16.8%, and negative predictive value = 96.6%. CONCLUSIONS: These findings demonstrate the high predictive performance of SeeMe in assessing consciousness in ABI patients and provide a quantitative tool for studying neural circuits and developing novel therapeutic approaches. SeeMe's ability to detect low-amplitude motor movements opens new avenues for understanding and facilitating recovery of consciousness in patients with ABI.

OVERCOMING DISTANCE: VIRTUAL VISIT OF SIBLINGS IN A NEONATAL ICU IN THE CONTEXT OF THE COVID-19 PANDEMIC: CASE REPORT

Due to the Covid-19 pandemic, several changes were implemented to ensure neonatal care, including restricting access to the Neonatal Intensive Care Unit (NICU) to parents only. This study aimed to discuss the possibility of siblings visiting the NICU through the use of Information and Communication Technologies (ICTs) during the pandemic. The methodology used was a case study, with the participation of two babies admitted to the NICU of a public health unit in Rio de Janeiro. The results showed greater interaction among babies, observed through their body movements and facial expressions in response to visual and auditory stimuli from their siblings, using mobile devices. The visit also allowed the siblings to experience, even remotely, the NICU environment, as they did not expect the new family member to be hospitalized after birth. It was concluded that this type of visit can be used to expand visitation to other family members, such as grandparents, or to those who live far from the hospital. In this way, the virtual visit can function as a health device to facilitate humanization processes, guaranteeing the rights of the baby and his family, even beyond the context of the pandemic.

A Smart Ad Display System

This paper proposes a smart ad display system to provide personalized delivery of video ads. The proposed system records consumers’ facial expression and eye gaze stream data as they watch an ad and analyzes data at the frame level. The recognized facial expression and detected eye gaze are matched to the corresponding frame of the video ad, thereby linking facial expressions to specific visual objects appearing in the ad. By tracking a consumer’s facial expressions in response to various visual objects in real time, the system learns the consumer’s individual preferences toward different ads, searches the ad pool, and selects and subsequently displays a new ad that is most likely to elicit positive attitudinal and behavioral responses. We demonstrate the feasibility and effectiveness of the proposed system with two empirical studies. The results show that by tracking a consumer’s facial responses to only one ad or even part of an ad, our proposed system is able to make reasonably accurate inferences about a consumer’s ad preferences, with or without using information about other consumers. These inferences are used to make personalized recommendations that help enhance consumers’ ad viewing experiences and elicit favorable responses.

The grimace scale: a useful tool for assessing pain in laboratory animals.

Accurately and promptly assessing pain in experimental animals is extremely important to avoid unnecessary suffering of the animals and to enhance the reproducibility of experiments. This is a key concern for veterinarians, animal caretakers, and researchers from the perspectives of veterinary care and animal welfare. Various methods including ethology, immunohistochemistry, electrophysiology, and molecular biology are used for pain assessment. However, the grimace scale, which was developed by taking cues from interpreting pain through facial expressions of non-verbal infants, has become recognized as a very simple and practical method for objectively evaluating pain levels by scoring changes in an animal's expressions. This method, which was first implemented with mice approximately 10 years ago, is now being applied to various experimental animals and is widely used in research settings. This review focuses on the usability of the grimace scale from the "cage-side" perspective, aiming to make it a more user-friendly tool for those involved in animal experiments. Differences in facial expressions in response to pain in various animals, examples of applying the grimace scale, current automated analytical methods, and future prospects are discussed.

Exploring the Function of Expressions in Negotiation: The DyNego-WOZ Corpus

For affective computing to have an impact outside the laboratory, facial expressions must be studied in rich naturalistic situations. We argue negotiations are one such situation as they are ubiquitous in daily life, often evoke strong emotions, and perceived emotion shapes decisions and outcomes. Negotiations are a growing focus in AI research and applications, including agents that negotiate directly with people and attempt to use affective information. We introduce the DyNego-WOZ Corpus, which includes dyadic negotiation between participants and wizard-controlled virtual humans. We demonstrate the value of this corpus to the affective computing community by examining participants' facial expressions in response to a virtual human negotiation partner. We show that people's facial expressions typically co-occur with the end of their partner's speech (suggesting they reflect a reaction to the content of this speech), that these reactions do not correspond to prototypical <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">emotional</i> expressions, and that these reactions can help predict the expresser's subsequent action. We highlight challenges in working with such naturalistic data, including difficulties of expression recognition during speech, and the extreme variability of expressions, both across participants and within a negotiation. Our findings reinforce arguments that facial expressions convey more than emotional state but serve important communicative functions.

Facial asymmetry in dogs with fear and aggressive behaviors towards humans

There is now scientific evidence that, in dogs, distinctive facial actions are produced in response to different emotionally-arousing stimuli suggesting a relationship between lateralized facial expressions and emotional states. Although in humans, relationships between facial asymmetry and both emotional and physiological distress have been reported, there are currently no data on the laterality of dogs’ facial expressions in response to social stimuli with respect to canine behavioral disorders. The aim of the present work was to investigate the facial asymmetries of dogs with fear and aggressive behavior towards humans during two different emotional situations: (1) while the dogs were alone in the presence of their owners and (2) during the approach of an unfamiliar human being. Overall, our results demonstrated high levels of asymmetries in facial expressions of dogs displaying fear and aggressive behaviors towards humans indicating that measuring facial asymmetries in dogs could prove to be a useful non-invasive tool for investigating physiology-based behavioral disorders.

Alterations in facial expressions in individuals at risk for psychosis: a facial electromyography approach using emotionally evocative film clips.

Negative symptoms such as blunted facial expressivity are characteristic of schizophrenia. However, it is not well-understood if and what abnormalities are present in individuals at clinical high-risk (CHR) for psychosis. This experimental study employed facial electromyography (left zygomaticus major and left corrugator supercilia) in a sample of CHR individuals (N = 34) and healthy controls (N = 32) to detect alterations in facial expressions in response to emotionally evocative film clips and to determine links with symptoms. Findings revealed that the CHR group showed facial blunting manifested in reduced zygomatic activity in response to an excitement (but not amusement, fear, or sadness) film clip compared to controls. Reductions in zygomatic activity in the CHR group emerged in response to the emotionally evocative peak period of the excitement film clip. Lower zygomaticus activity during the excitement clip was related to anxiety while lower rates of change in zygomatic activity during the excitement video clip were related to higher psychosis risk conversion scores. Together, these findings inform vulnerability/disease driving mechanisms and biomarker and treatment development.

Face mediated human–robot interaction for remote medical examination

Realtime visual feedback from consequences of actions is useful for future safety-critical human–robot interaction applications such as remote physical examination of patients. Given multiple formats to present visual feedback, using face as feedback for mediating human–robot interaction in remote examination remains understudied. Here we describe a face mediated human–robot interaction approach for remote palpation. It builds upon a robodoctor–robopatient platform where user can palpate on the robopatient to remotely control the robodoctor to diagnose a patient. A tactile sensor array mounted on the end effector of the robodoctor measures the haptic response of the patient under diagnosis and transfers it to the robopatient to render pain facial expressions in response to palpation forces. We compare this approach against a direct presentation of tactile sensor data in a visual tactile map. As feedback, the former has the advantage of recruiting advanced human capabilities to decode expressions on a human face whereas the later has the advantage of being able to present details such as intensity and spatial information of palpation. In a user study, we compare these two approaches in a teleoperated palpation task to find the hard nodule embedded in the remote abdominal phantom. We show that the face mediated human–robot interaction approach leads to statistically significant improvements in localizing the hard nodule without compromising the nodule position estimation time. We highlight the inherent power of facial expressions as communicative signals to enhance the utility and effectiveness of human–robot interaction in remote medical examinations.

Simulating dynamic facial expressions of pain from visuo-haptic interactions with a robotic patient

Medical training simulators can provide a safe and controlled environment for medical students to practice their physical examination skills. An important source of information for physicians is the visual feedback of involuntary pain facial expressions in response to physical palpation on an affected area of a patient. However, most existing robotic medical training simulators that can capture physical examination behaviours in real-time cannot display facial expressions and comprise a limited range of patient identities in terms of ethnicity and gender. Together, these limitations restrict the utility of medical training simulators because they do not provide medical students with a representative sample of pain facial expressions and face identities, which could result in biased practices. Further, these limitations restrict the utility of such medical simulators to detect and correct early signs of bias in medical training. Here, for the first time, we present a robotic system that can simulate facial expressions of pain in response to palpations, displayed on a range of patient face identities. We use the unique approach of modelling dynamic pain facial expressions using a data-driven perception-based psychophysical method combined with the visuo-haptic inputs of users performing palpations on a robot medical simulator. Specifically, participants performed palpation actions on the abdomen phantom of a simulated patient, which triggered the real-time display of six pain-related facial Action Units (AUs) on a robotic face (MorphFace), each controlled by two pseudo randomly generated transient parameters: rate of change beta and activation delay tau . Participants then rated the appropriateness of the facial expression displayed in response to their palpations on a 4-point scale from “strongly disagree” to “strongly agree”. Each participant (n=16, 4 Asian females, 4 Asian males, 4 White females and 4 White males) performed 200 palpation trials on 4 patient identities (Black female, Black male, White female and White male) simulated using MorphFace. Results showed facial expressions rated as most appropriate by all participants comprise a higher rate of change and shorter delay from upper face AUs (around the eyes) to those in the lower face (around the mouth). In contrast, we found that transient parameter values of most appropriate-rated pain facial expressions, palpation forces, and delays between palpation actions varied across participant-simulated patient pairs according to gender and ethnicity. These findings suggest that gender and ethnicity biases affect palpation strategies and the perception of pain facial expressions displayed on MorphFace. We anticipate that our approach will be used to generate physical examination models with diverse patient demographics to reduce erroneous judgments in medical students, and provide focused training to address these errors.

Caught in the Act: Detecting Respondent Deceit and Disinterest in On-Line Surveys. A Case Study Using Facial Expression Analysis

Background Much social marketing research is done on-line recruiting participants through Amazon Mechanical Turk, vetted panel vendors, social media, or community sources. When compensation is offered, care must be taken to distinguish genuine respondents from those with ulterior motives. Focus of the Article We present a case study based on unanticipated empirical observations made while evaluating perceived effectiveness (PE) ratings of anti-tobacco public service announcements (PSAs) using facial expression (FE) analysis (pretesting). Importance to the Social Marketing Field This study alerts social marketers to the risk and impact of disinterest or fraud in compensated on-line surveys. We introduce FE analysis to detect and remove bad data, improving the rigor and validity of on-line data collection. We also compare community (free) and vetted panel (fee added) recruitment in terms of usable samples. Methods We recruited respondents through (Community) sources and through a well-known (Panel) vendor. Respondents completed a one-time, random block design Qualtrics® survey that collected PE ratings and recorded FE in response to PSAs. We used the AFFDEX® feature of iMotions® to calculate respondent attention and expressions; we also visually inspected respondent video records. Based on this quan/qual analysis, we divided 501 respondents (1503 observations) into three groups: (1) Those demonstrably watching PSAs before rating them (Valid), (2) those who were inattentive but completed the rating tasks (Disinterested), and (3) those employing various techniques to game the system (Deceitful). We used one-way analysis of variance (ANOVA) of attention (head positioning), engagement (all facial expressions), and specific facial expressions (FE) to test the likelihood a respondent fell into one of the three behavior groups. Results PE ratings: The Community pool ( N = 92) was infiltrated by Deceitful actors (58%), but the remaining 42% was “attentive” (i.e., no disinterest). The Panel pool ( N = 409) included 11% deceitful and 2% disinterested respondents. Over half of the PSAs change rank order when deceitful responses are included in the Community sample. The smaller proportion of Deceitful and Disinterested (D&amp;D) respondents in the Panel affected 2 (out of 12) videos. In both samples, the effect was to lower the PE ranking of more diverse and “locally made” PSAs. D&amp;D responses clustered tightly to the mean values, believed to be an artefact of “professional” test taking behavior. FE analysis: The combined Valid sample was more attentive (87.2% of the time) compared to Disinterested (51%) or Deceitful (41%) (ANOVA F = 195.6, p &lt; .001). Models using “engagement” and specific Fes (“cheek raise and smirk”) distinguished Valid from D&amp;D responses. Recommendations False PE pretesting scores waste social marketing budgets and could have disastrous results. Risk can be reduced by using vetted panels with a trade-off that community sources may produce more authentically interested respondents. Ways to make surveys more tamper-evident, with and without webcam recording, are provided as well as procedures to clean data. Check data before compensating respondents! Limitations This was an accidental finding in a parent study. The study required computers which potentially biased the pool of survey respondents. The community pool is smaller than the panel group, limiting statistical power.

Analysis of facial expressions in response to basic taste stimuli using artificial intelligence to predict perceived hedonic ratings.

Taste stimuli can induce a variety of physiological reactions depending on the quality and/or hedonics (overall pleasure) of tastants, for which objective methods have long been desired. In this study, we used artificial intelligence (AI) technology to analyze facial expressions with the aim of assessing its utility as an objective method for the evaluation of food and beverage hedonics compared with conventional subjective (perceived) evaluation methods. The face of each participant (10 females; age range, 21–22 years) was photographed using a smartphone camera a few seconds after drinking 10 different solutions containing five basic tastes with different hedonic tones. Each image was then uploaded to an AI application to achieve outcomes for eight emotions (surprise, happiness, fear, neutral, disgust, sadness, anger, and embarrassment), with scores ranging from 0 to 100. For perceived evaluations, each participant also rated the hedonics of each solution from –10 (extremely unpleasant) to +10 (extremely pleasant). Based on these, we then conducted a multiple linear regression analysis to obtain a formula to predict perceived hedonic ratings. The applicability of the formula was examined by combining the emotion scores with another 11 taste solutions obtained from another 12 participants of both genders (age range, 22–59 years). The predicted hedonic ratings showed good correlation and concordance with the perceived ratings. To our knowledge, this is the first study to demonstrate a model that enables the prediction of hedonic ratings based on emotional facial expressions to food and beverage stimuli.

Automatic facial coding versus electromyography of mimicked, passive, and inhibited facial response to emotional faces

ABSTRACT Decoding someone's facial expressions provides insights into his or her emotional experience. Recently, Automatic Facial Coding (AFC) software has been developed to provide measurements of emotional facial expressions. Previous studies provided first evidence for the sensitivity of such systems to detect facial responses in study participants. In the present experiment, we set out to generalise these results to affective responses as they can occur in variable social interactions. Thus, we presented facial expressions (happy, neutral, angry) and instructed participants (N = 64) to either actively mimic, to look at them passively (n = 21), or to inhibit their own facial reaction (n = 22). A video stream for AFC and an electromyogram (EMG) of the zygomaticus and corrugator muscles were registered continuously. In the mimicking condition, both AFC and EMG differentiated well between facial expressions in response to the different emotional pictures. In the passive viewing and in the inhibition condition AFC did not detect changes in facial expressions whereas EMG was still highly sensitive. Although only EMG is sensitive when participants intend to conceal their facial reactions, these data extend previous findings that Automatic Facial Coding is a promising tool for the detection of intense facial reaction.

Expressive suppression to pain in others reduces negative emotion but not vicarious pain in the observer.

Although there are situations where it may be appropriate to reduce one's emotional response to the pain of others, the impact of an observer's emotional expressivity on their response to pain in others is still not well understood. In the present study, we examined how the emotion regulation strategy expressive suppression influences responses to pain in others. Based on prior research findings on expressive suppression and pain empathy, we hypothesized that expressive suppression to pain expression faces would reduce neural representations of negative emotion, vicarious pain, or both. To test this, we applied two multivariate pattern analysis (MVPA)-derived neural signatures to our data, the Picture Induced Negative Emotion Signature (PINES; Chang, Gianaros, Manuck, Krishnan, and Wager (2015)) and a neural signature offacial expression induced vicarious pain (Zhou et al., 2020). In a sample of 60 healthy individuals, we found that viewing pain expression faces increased neural representations of negative emotion and vicarious pain. However, expressive suppression to pain faces reduced neural representations of negative emotion only. Providing support for a connection between neural representations of negative emotion and pain empathy, PINES responses to pain faces were associated with participants' trait-level empathy and the perceived unpleasantness of pain faces. Findings suggest that a consequence of suppressing one's facial expressions in response to the pain of others may be a reduction in the affective aspect of empathy but not the experience of vicarious painitself.

Comparison Between Facilitating and Suppressing Facial Emotional Expressions Using Frontal EEG Asymmetry.

The prefrontal cortex plays a key role in emotional state. Electroencephalography (EEG) studies have reported relationships between frontal asymmetry in the alpha band, emotional state, and emotion-related motivation. The current study investigated whether the positive or negative valence of emotional stimulation or the behavioral intention to either facilitate or suppress one’s facial expression in response to these stimuli is reflected in relevant changes in frontal EEG asymmetry. EEG was recorded while participants either produced a facial expression that was in accord with positive or negative feelings corresponding to image stimuli, or suppressed their facial expressions. The laterality index of frontal alpha power indicated greater relative right frontal activity while participants suppressed facial expression compared with facilitating facial expression during emotional stimulation. However, there was no difference in frontal asymmetry between the presentation of image stimuli showing facial expressions corresponding to positive vs. negative emotions. These results suggested that frontal asymmetry was related to the control of facial emotional expressions rather than the perception of positive vs. negative emotions. Moreover, microstate analysis revealed that the appearance rate of microstate class B with polarity in the left frontal area increased during the suppression of facial expressions. The present results suggested that frontal asymmetry reflects the control of facial emotional expressions, which supports the motivational direction model.

Automated recognition of spontaneous facial expression in individuals with autism spectrum disorder: parsing response variability

BackgroundReduction or differences in facial expression are a core diagnostic feature of autism spectrum disorder (ASD), yet evidence regarding the extent of this discrepancy is limited and inconsistent. Use of automated facial expression detection technology enables accurate and efficient tracking of facial expressions that has potential to identify individual response differences.MethodsChildren and adults with ASD (N = 124) and typically developing (TD, N = 41) were shown short clips of “funny videos.” Using automated facial analysis software, we investigated differences between ASD and TD groups and within the ASD group in evidence of facial action unit (AU) activation related to the expression of positive facial expression, in particular, a smile.ResultsIndividuals with ASD on average showed less evidence of facial AUs (AU12, AU6) relating to positive facial expression, compared to the TD group (p < .05, r = − 0.17). Using Gaussian mixture model for clustering, we identified two distinct distributions within the ASD group, which were then compared to the TD group. One subgroup (n = 35), termed “over-responsive,” expressed more intense positive facial expressions in response to the videos than the TD group (p < .001, r = 0.31). The second subgroup (n = 89), (“under-responsive”), displayed fewer, less intense positive facial expressions in response to videos than the TD group (p < .001; r = − 0.36). The over-responsive subgroup differed from the under-responsive subgroup in age and caregiver-reported impulsivity (p < .05, r = 0.21). Reduced expression in the under-responsive, but not the over-responsive group, was related to caregiver-reported social withdrawal (p < .01, r = − 0.3).LimitationsThis exploratory study does not account for multiple comparisons, and future work will have to ascertain the strength and reproducibility of all results. Reduced displays of positive facial expressions do not mean individuals with ASD do not experience positive emotions.ConclusionsIndividuals with ASD differed from the TD group in their facial expressions of positive emotion in response to “funny videos.” Identification of subgroups based on response may help in parsing heterogeneity in ASD and enable targeting of treatment based on subtypes.Trial registrationClinicalTrials.gov, NCT02299700. Registration date: November 24, 2014

Effects of Emotion Regulation Strategies on Mothers’ Self-Reported, Physiological, and Facial Expressive Responses to Infant Laughing

SYNOPSIS Objective. Mothers’ affective responses to infant laughing are essential in parent-child interaction. This experimental study examined whether instructing mothers to employ emotion regulation strategies can change their self-reported, physiological, and facial expressive responses to infant laughing. Design. Using a within-subjects design, mothers (N = 100, age M = 30.87 years) were exposed to infant laughing sounds while receiving enhancement, suppression, and no emotion regulation instructions. Positive affect, perception of laughing, intended sensitive and insensitive caregiving responses, skin conductance level, and facial expressions in response to infant laughing were measured. Results. Enhancement resulted in increased positive affect, a more positive perception of the laugh, more intended sensitive caregiving responses, and, compared to suppression, fewer intended insensitive caregiving responses. Moreover, enhancement resulted in lower sad and, compared to suppression, higher happy facial expressi-vity. In contrast, suppression resulted in a less positive perception. Enhancement did not affect skin conductance level. Conclusions. Enhancement can have beneficial effects on mothers’ self-reported and facial expressive responses to infant laughing in an experimental setting. Enhancement instructions may be used to increase mothers’ positive feelings in response to infant laughing and to promote sensitive caregiving behaviors and positive facial expressions, which may benefit affective mother-child interchanges.

EP27.10: Fetal facial expression in response to intravaginal emission of different types of music

EP27.10: Fetal facial expression in response to intravaginal emission of different types of music

A pilot study to identify autism related traits in spontaneous facial actions using computer vision

BackgroundIndividuals with autism spectrum disorders (ASD) may be differentiated from typically developing controls (TDC) based on phenotypic features in spontaneous facial expressions. Computer vision technology can automatically track subtle facial actions to gain quantitative insights into ASD related behavioral abnormalities. MethodThis study proposes a novel psychovisual human-study to elicit spontaneous facial expressions in response to a variety of social and emotional contexts. We introduce a markerless facial motion capture and computer vision methods to track spontaneous and subtle activations of facial muscles. The facial muscle activations are encoded into ten representative facial action units (FAU) to gain quantitative, granular, and contextual insights into the psychophysical development of the participating individuals. Statistical tests are performed to identify differential traits in individuals with ASD after comparing those in a cohort of age-matched TDC individuals. ResultsThe proposed framework has revealed significant difference (p < 0.001) in the activation of ten FAU and contrasting activations of FAU between the group with ASD and the TDC group. Unlike the TDC group, the group with ASD has shown unusual prevalence of mouth frown (FAU 15) and low correlations in temporal activations of several FAU pairs: 6–12, 10–12, and 10–20. The interpretation of different FAU activations suggests quantitative evidence of expression bluntness, lack of expression mimicry, incongruent reaction to negative emotions in the group with ASD. ConclusionOur generalized framework may be used to quantify psychophysical traits in individuals with ASD and replicate in similar studies that require quantitative measurements of behavioral responses.

Cryptic Emotions and the Emergence of a Metatheory of Mind in Popular Filmmaking.

Hollywood movies can be deeply engaging and easy to understand. To succeed in this manner, feature‐length movies employ many editing techniques with strong psychological underpinnings. We explore the origins and development of one of these, the reaction shot. This shot typically shows a single, unspeaking character with modest facial expression in response to an event or to the behavior or speech of another character. In a sample of movies from 1940 to 2010, we show that the prevalence of one type of these shots—which we call the cryptic reaction shot—has grown dramatically. These shots are designed to enhance viewers’ emotional involvement with characters. They depict a facial gesture that reflects a slightly negative and slightly aroused emotional state. Their use at the end of conversations, and typically at the end of scenes, helps to leave viewers in a state of speculation about what the character is thinking and what her thoughts may mean for the ongoing narrative.