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PurposeWe investigated racial disparities in survival by histology in cervical cancer and examined the factors contributing to these disparities.MethodsNon-Hispanic Black and non-Hispanic White (hereafter known as Black and White) patients with stage I-IV cervical carcinoma diagnosed between 2004 and 2017 in the National Cancer Database were studied. Survival differences were compared using Cox modeling to estimate hazard ratio (HR) or adjusted HR (AHR) and 95% confidence interval (CI). The contribution of demographic, socioeconomic and clinical factors to the Black vs White differences in survival was estimated after applying propensity score weighting in patients with squamous cell carcinoma (SCC) or adenocarcinoma (AC).ResultsThis study included 10,111 Black and 43,252 White patients with cervical cancer. Black patients had worse survival than White cervical cancer patients (HR = 1.40, 95% CI = 1.35-1.45). Survival disparities between Black and White patients varied significantly by histology (HR = 1.20, 95% CI = 1.15-1.24 for SCC; HR = 2.32, 95% CI = 2.12-2.54 for AC, interaction p

ObjectiveTo describe the participation of racial and ethnic minority groups (REMGs) in gynecologic oncology trials.MethodsGynecologic oncology studies registered on ClinicalTrials.gov between 2007 and 2020 were identified. Trials with published results were analyzed based on reporting of race/ethnicity in relation to disease site and trial characteristics. Expected enrollment by race/ethnicity was calculated and compared to actual enrollment, adjusted for 2010 US Census population data.Results2146 gynecologic oncology trials were identified. Of published trials (n = 252), 99 (39.3%) reported race/ethnicity data. Recent trials were more likely to report these data (36% from 2007 to 2009; 51% 2013-2015; and 53% from 2016 to 2018, p = 0.01). Of all trials, ovarian cancer trials were least likely to report race/ethnicity data (32.1% vs 39.3%, p = 0.011). Population-adjusted under-enrollment for Blacks was 7-fold in ovarian cancer, Latinx 10-fold for ovarian and 6-fold in uterine cancer trials, Asians 2.5-fold in uterine cancer trials, and American Indian and Alaska Native individuals 6-fold in ovarian trials. Trials for most disease sites have enrolled more REMGs in recent years - REMGs made up 19.6% of trial participants in 2007-2009 compared to 38.1% in 2016-2018 (p

OBJECTIVES: To evaluate outcomes and cost-effectiveness of targeted therapy sequencing for metastatic and recurrent cervical cancer. METHOD: Models were simulated based on phase II and III trials on bevacizumab (bev) from GOG-240, cemiplimab (cemi) from GOG 3016, pembrolizumab (pembro) from KEYNOTE-826, and tisotumab vedotin (tiso) from GOG 3023. Costs were based on IBM Micromedex RED BOOK™ and company listed costs. RESULTS: For [chemo + bev → chemo], total cost was $125,918.04, with median overall survival (mOS) of 21.8 months, and cost-effectiveness ratio (CER) of $119,835.79. For [chemo + bev → cemi], total cost was $187,562.99 with mOS of 28.5 months and CER of $162,039.16. For [chemo + bev + pembro → chemo], total cost was $319,963.78 with mOS 32.9 months and CER of $249,930.10. For [chemo + bev + pembro → tiso], total cost was $455,204.45, with mOS 36.5 months and CER of $320,072.99. CONCLUSION: The combination of immunotherapies and biologics have significantly increased overall survival, but with associated higher costs, primarily related to drug costs.

As society embraces technology to support collaboration anywhere and at anytime, there is a growing opportunity for artificial agents to support such collaboration. However, little seems known about how such agents impact the behavioural performance of human teams. To answer this, we devised a Wizard of Oz study where teams of 3 participants located and corralled targets into a containment area in a virtual desert environment. The Wizard played the role of an artificial intelligent operator who had a map view showing the location of participants, targets and the containment area, and could verbally communicate this information. The Wizard operated under two conditions: they could solely use the map view to decide what responses to utter (non-responsive interaction) or could also listen to participant queries (responsive interaction). The results revealed that participant performance was unaffected by responsive interaction condition, despite having a significantly more favourable perception of a responsive agent.

While communication is an essential part of life, it is not always easy and effortless. This is especially true when talking with someone in a noisy environment. Although communicating in noise is often the rule rather than the exception, very little research has investigated the behavioral processes individuals might use to minimize miscommunication when listening becomes challenged. Here we explored synergistic speech and movement processes that 22 pairs of adults used to hear and be heard when (mis)communicating in noise. The results revealed intricate dynamics both with respect to acoustic optimization of the speech produced and heard, as well as how individuals modulate interpersonal distance and behavioral coordination patterns.

Intention prediction often plays a crucial role in successful social interaction. Previous studies have attempted to understand this skill by focusing on the role of movement kinematics in isolation. However, this approach is limited as the same kinematics typically map to multiple action possibilities (affordances) and as a result, individual's also employ contextual information to predict others' intentions. In this study we present preliminary findings from a qualitative study aimed at investigating intention prediction in naturalistic contexts. Participants viewed an individual reaching for a cup with one of two object-directed intentions: to drink OR to clear the table. A third non-object-directed intention was also included where the observed individual placed their hand on the table next to the cup. For each intention the contextual information was varied by changing the environmental scene between (1) cups full of juice, (2) almost empty cups, and (3) half-empty cups. The findings reveal that participants perceived the cup's functional (most salient) affordance - drink - for the intention so far as the movement kinematics specified an object-directed intention (drink or clear) with a context that clearly afforded it (full and half-full cups). However, participants were also sensitive to the kinematic differences between the object-directed intentions when the context made the functional affordance seem improbable (almost empty cups).

Humans are social animals. Human cognition evolved in a social context. Human cognition develops in a social context. Thus, both the internal mechanisms of cognition and the information we use are social. In this workshop, we aim to extend the boundaries of cognitive sciences beyond individual minds. Following the lead of Dingemanse et al. (2023), we put interaction in focus as a complementary starting point for the study of human cognition.

Perceptual-motor tasks offer redundant solutions to achieve a goal. However, not all solutions are equally robust to error-producing variability and thus, skill learning requires the search for behaviors that are error-tolerant. Throwing a ball to hit a target is a skill that has been studied via the “skittles” task, a simplified 2-dimensional throwing environment. The study investigated whether participants learning the skittles task benefit from task scaffolding via the placements of targets provided by either a human confederate posing as a teacher, or by the participant. These groups were compared to a third sample who received an unstructured curriculum consisting of randomly placed targets. The results demonstrate that participants in the unstructured training condition were better able to perceive the task’s underlying solution geometry, compared to scaffolding provided by a human. Future work will explore methods to design training curricula that are more efficient in facilitating perceptual-motor skill development.

Solving problems with others not only reduces the time required to complete a challenge but may also enable the discovery of novel strategies that qualitatively change how a problem is approached. At the dyadic level, the laboratory-based ‘shepherding task’ demonstrated that, when tasked to contain evasive agents to a centralized location, some participants discover a non-obvious but optimal strategy to solve the task. This paper quantified the interactions between participants engaged in the task using Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA), applied to each participant’s gaze and hand movements. The results demonstrated that strategy discoverers exhibited greater amounts of incidental coupling than non-discoverers prior to discovery. Once discovered, the strategy reduced the strength of coupling between participants, indicating that the strategy also reduced coordination demands. Future work will investigate whether differences in problem-solving can be attributable to differences in the perceptual features participants use which scaffold the discovery of task-optimal solutions.

Collaboration and teaming are critical for solving complex problems. However, little is known about how group dynamics affect teaming behaviours and, ultimately, problem-solving effectiveness. The present study aimed to validate a novel measure of the dynamics of team communication – here termed turn-taking entropy – and to investigate what aspects of those dynamics affect collaborative-problem-solving performance. Thirty-two teams of 4 were asked to complete a simulated crisis-response task in which they had to rank 15 items in order of their importance to their team’s survival (first individually and then as a team). Group responses were better than the aggregated individual responses of team members (suggesting teaming benefits), and were better when team members had task-relevant skills and knowledge. However, response quality was not significantly related to task completion time. Additionally, the proposed entropy measure appeared to capture group communication dynamics, and appeared to differentiate stable an unstable patterns of communication. Implications and directions for future research are discussed.

Modeling human navigation and route selection in complex environments has used path planning in the past and more recently, dynamical models. The current study modeled the navigational movement trajectories of human herder agents tasked with corralling an autonomous target agent into a specified containment zone. Analyses of trajectories of participants completing a first-person herding game revealed that their movement entailed sequential (i) approach and then (ii) corral phases that were invariant to the initial location of the herder and target agents. More importantly, the trajectories of human herders could be captured using a low-dimensional environmentally coupled dynamical model. The novelty of this work is in extending the previous research in modeling navigational behaviors of humans to include moving targets that need to be manipulated into a desired position. Implications of the findings for the development of artificial agents for human-machine herding, as well as more general human-machine teaming are discussed.

Deep reinforcement learning (DRL) is capable of training agents that exceed human-levels of performance in multi-agent tasks. However, the behaviors exhibited by these agents are not guaranteed to be human-like or human-compatible. This poses a problem if the goal is to design agents capable of collaborating with humans or augmenting human actions in cooperative or team-based tasks. Indeed, recommender systems designed to augment human decision-making need to not only recommend actions that align with the task goal, but which also maintain coordinative behaviors between agents. The current study simultaneously explored skill learning performance of human learners when working alongside different artificial agents (AAs) during a collaborative problem-solving task, as well as evaluated the effectiveness of the same AAs as action decision recommender systems to aid learning. The action decisions of the AAs were either modelled by a heuristic model based on human performance or by a deep neural network trained by reinforcement learning using self-play. In addition to evaluating skill learning performance, the current study also tested the congruence between the decisions of the AAs with actual decisions made by humans. Results demonstrate that the performance of humans was significantly worse when working alongside the DRL AA compared to the heuristic AA. Additionally, the action decisions participants made also showed less allignment with the recommendations made by the DRL AA.

Agents trained using deep reinforcement learning (DRL) are capable of meeting or exceeding human-levels of performance in multi-agent tasks. However, the behaviors exhibited by these agents are not guaranteed to be human-like or human-compatible. This poses a problem if the goal is to design agents capable of collaborating with humans in cooperative or team-based tasks. Previous approaches to encourage the development of human-compatible agents have relied on pre-recorded human data during training. However, such data is not available for the majority of everyday tasks. Importantly, research on human perceptual-motor behavior has found that task-directed behavior is often low-dimensional and can be decomposed into a defined set of dynamical perceptual-motor primitives (DPMPs). Accordingly, we propose a hierarchical approach to simplify DRL training by defining the action dynamics of agents using DPMPs at the lower level, while using DRL to train the decision-making dynamics of agents at the higher level. We evaluate our approach using a multi-agent shepherding task used to study human and multi-agent coordination. Our hierarchical, DRL-DPMP approach resulted in agents which trained faster than vanilla, black-box DRL agents. Further, the hierarchical agents reached higher levels of performance not only when interacting with each other during self-play, but also when completing the task alongside agents embodying models of novice and expert human behavior. Finally, the hierarchical DRL-DPMP agents developed decision-making policies that outperformed heuristic-based agents used in previous research in human-agent coordination.

Interpersonal coordination is a key determinant of successful social interaction but is disrupted for people who experience social anxiety. Effective coordination rests on individuals directing their attention toward others, an effect well-documented in previous literature. Yet, little research has considered the concurrent behaviour of interaction partners. Using a novel virtual reality task, we investigated how partner gaze (i.e., direct vs. averted) influenced the emergence of interpersonal coordination. The results revealed two novel effects: (i) spontaneous coordination was diminished in the averted (cf. direct) gaze condition; (ii) spontaneous coordination was positively related to symptoms of social anxiety, but only when partner gaze was averted. This latter finding contrasts the extant literature and points to interaction intensity as a factor governing the social anxiety-coordination association. More broadly, this work provides further evidence that emergent patterns of interpersonal coordination fluctuate as a function of changes in social context and the behaviour of others.

Social cognition is shaped by cultural and social-psychological contexts. Depending on the social cues displayed, the cognitive processes that emerge during embodied social interactions can vary. This study explored the influence of gender diverse social cues on interpersonal coordination and decision-making during dyadic interaction in virtual reality. Participants embodied either a transgender or cisgender avatar and interacted with a transgender confederate (human-controlled virtual agent). Results suggest that transgender embodied women and men displayed more interpersonal affiliation, via their coordination dynamics with the confederate, after an explicit gender identity disclosure. Additionally, cognitive flexibility for ambiguous decisions was influenced by attributions of responsibility made toward the confederate. However, cisgender embodied men did not substantially change their decision-making behaviours or increase their coordination with the confederate. Viscerally taking on the perspective and appearance of a transgender person, achieved through virtual embodiment and interaction, may support positive interactions between cisgender and transgender communities.

Social motor coordination (SMC), defined as the intentional or unintentional coordination of movement between individuals in a social setting, has been linked to greater feelings of rapport and social connectedness. Here, we investigated this relationship using a silent disco paradigm where groups of 3 or 4 individuals danced to either the same music or different music. Visual information was manipulated by initially separating the participants with curtains (2 minutes), after which the curtains were opened (10 minutes). Head movements were recorded with a wireless motion tracking system attached to the silent disco headphones. Rapport and social connectedness measures were obtained using questionnaires completed after participation in the silent disco. Results showed that groups who listened to the same music exhibited a greater degree of SMC than groups that listened to different music. Greater degrees of SMC were also observed when group members were able to see one another. Finally, greater SMC was associated with perceptions of increased self-other overlap and interaction quality.

Older patients and those who require interpreters are least likely to use telehealth for gynecologic oncology care, showing disparities which continued after statewide vaccinations.

Cybersecurity has emerged as one of the most critical issues confronting schools in the 21st century. Computer security is an essential instrument for protecting children, but K-12 schools are considered one of the most attractive targets for data privacy crimes often due to the less-than-effective cybersecurity practices in schools. The human factor is the underlying reason why many attacks on school computers and systems are successful because the uneducated computer user is the weakest link targeted by cyber criminals using social engineering. Formal cyber security awareness is required to mitigate the exploitation of human vulnerabilities by computer hackers and attackers.

Albert Einstein once remarked that the world was characterized by a proliferation of means and a confusion of goals. This certainly characterizes the state of global higher education today. The age of technology and the information society are sweeping educators towards a future dependent upon knowing how more than knowing what. Throughout the world, college and university administrators are attempting to determine global scope coupled with the impact of technology. To many educators higher education is now a place of innovation and change, while to others it is a morass of change and fear. However, the COVID-19 pandemic created change and fear gripped higher education and forced change that was totally unexpected. What are the lessons to be learned from this forced change?

Ciliated cell markers expressed in epithelial ovarian cancers (EOC) are associated with improved survival. We examined the distribution of cells expressing ciliated cell markers in various EOC histologies and stages. Immunohistochemistry and/or multiplex immunofluorescence were used to determine the expression of FOXJ1 and/or CAPS (ciliated cell markers) in tissue microarrays including 4 normal fallopian tubes, 6 normal endometria, 16 cystadenomas, 25 borderline tumors, 21 low-grade carcinomas, and 118 high-grade carcinomas (HGSOC) (46 serous, 29 endometrioid, 30 clear cell, 13 mucinous). CAPS+ cells were observed in normal fallopian tubes and endometria and in ~85% of serous benign and borderline tumors and low-grade carcinomas but only in

With artificial intelligence technologies becoming commonplace today, enhancing the efficiency of human-artificial agent (AA) interactions has become increasingly important. A growing body of research has revealed how dynamic motor primitives (DMPs) of human perceptual-motor behavior can be used to create ‘human-like’ AAs, primarily focusing on cooperative tasks. Using air hockey as a representative task, the current experiment is the first part of a large study aimed at determining the utility of DMP-based models for developing ‘human-like’ competitive AAs. Participants played against a preliminary DMP model and the differences in their behaviors were analyzed. Based on these observed differences, a revised model is proposed, with preliminary results revealing that the new model exhibits behaviors more consistent with those of humans. A major implication of this work is that it presents a framework for creating ‘human-like’ AAs that capture the essential human decision and movement dynamics without requiring large human gameplay datasets.

Joint pick and place tasks occur in many interpersonal scenarios, such as when two people pick up and pass dishes. Previous studies have demonstrated that low-dimensional models can accurately capture the dynamics of pick and place motor behaviors in a controlled 2D environment. The current study models the dynamics of pick-up and pass decisions within a less restrictive virtual reality mediated 3D joint pick and place task. Findings indicate that reach-normalized distance measures, between participants and objects/targets, could accurately predict pick-up and pass decisions. Findings also reveal that participants took longer to pick-up objects where division of labor boundaries were less obvious and tended to pass in locations maximizing the dyad’s efficiency. This study supports the notion that individuals are more likely to engage in interpersonal behavior when a task goal is perceived as difficult or unattainable (i.e., not afforded). Implications of findings for human-artificial agent interactions are discussed.

Perceptual-motor tasks offer redundant solutions to achieve a goal. However, not all solutions are equally robust to error-producing noise or variability and thus, skill learning can be viewed as a search process to identify behaviors that are error-tolerant. Throwing a ball to hit a target is one such example of a complex perceptual-motor skill that has been studied in the laboratory via the virtual “skittles” task, a simplified 2D task involving throwing a tetherball around a pole to hit a target. We implemented the task as a Unity3D environment (code here: https://github.com/ShortFox/SkittlesTaskEnvironment/) which enables participants to complete the task with a computer mouse and replicated key findings from previous research. Our implementation allows for remote data collection and can serve as a pedagogical tool to teach concepts in skill acquisition. Future work will use this task to explore human versus machine skill acquisition by leveraging Unity’s MLAgents reinforcement learning package.

The current study explores the dynamics of interpersonal collision avoidance during an everyday coordination task. The task was a full body adaptation of the collision avoidance arm-movement task employed by Richardson et al. (2015) and required pairs of co-actors to avoid colliding into one another as they moved back and forth within a collaborative workspace. Specifically, participants were required to serve drinks to humanoid customers in a virtual bar where they needed to move back and forth between drink service and refill stations. As expected, stable patterns of movement coordination emerged between co-actors, with one participant in a pair exhibiting a more circular movement trajectory between the service and refill locations, while the second participant in a pair exhibited a more straight-line trajectory between the two locations. Consistent with the results of Richardson et al., (2015), this pattern was observed across nearly all pairs, indicating that the same dynamical processes governing simple hand-arm coordination tasks also regulate more complex, full-body and naturalistic goal-directed interpersonal coordination tasks.

Deep reinforcement learning (Deep RL) methods can train artificial agents (AAs) to reach or exceed human-level performance. However, in multiagent contexts requiring competitive behavior or where the aim is to use AAs for human training, the qualitative behaviors AAs adopt may be just as important as their performance to ensure representative training. This paper compares human behaviors and performance when competing against either a human expert or an AA opponent trained using Deep RL on a 2-dimensional version of Pong. Results show that participants were not sensitive to the movement differences between the human expert and AA. Further, the participants did not alter their behaviors, except to compensate for differences in the environmental states caused by the opponents. The paper concludes with discussion on the potential impacts of AA training on human behavior with regard to representative design in the areas of skill development and team training.

In many team-based activities, members search for information to gain situational awareness and thereby structure their own behavior. The extent to which members are coupled and in control of their surrounding environment can be accessed via the fluctuations of their searching behaviors. To facilitate prospective control, assistive technologies such as a head-up display (HUD) can alleviate the demands of search and facilitate team performance. This study investigated how three-person teams divided their labor and structured their search behavior when playing a multiplayer search-and-retrieval task where first-person visibility and access to a HUD were manipulated. Results showed that access to task relevant information facilitated performance and division of labor, and increased prospective control of searching behaviors, as indexed by detrended fluctuation analysis (DFA). Over multiple sessions, teams learned to use the HUD to structure their behavior to achieve the task goal. Results indicate the potential in using DFA for monitoring prospective control in team contexts.

Altering an individual’s identity during virtual tasks, such that it is incongruent with their own identity, can reduce unconscious biases and increase empathy and prosocial behavior. While such virtual embodiment has been investigated for race, age, and socioeconomic status, the question remains whether immersive perspective taking reduces implicit gender bias. Accordingly, we investigated the effect of gendered embodiment on gender bias related to the underrepresentation of women in science, technology, engineering, and math fields. Undergraduate students (N=65) undertook a simulated virtual interview task, which included the rating and selection of male and female candidates. Females were significantly more empathetic than males. Greater empathy predicted higher candidate ratings, except for competence of the female candidate. Preliminary findings establish foundations for future comparative studies and contribute to elucidating the nature of empathy during and after virtual perspective taking, such that it may have a greater impact on males for modulating gender bias.

Team interaction involves the division of labor and coordination of actions between members to achieve a shared goal. Although the dynamics of interactions that afford effective coordination and performance have been a focus in the cognitive science community, less is known about how to generate these flexible and adaptable coordination patterns. This is important when the goal is to design artificial agents that can augment and enhance team coordination as synthetic teammates. Although previous research has demonstrated the negative impact of model-based agents on the pattern of interactions between members using recurrence quantification methods, more recent work utilizing deep reinforcement learning has demonstrated a promising approach to bootstrap the design of agents to team with humans effectively. This paper explores the impact of artificial agent design on the interaction patterns that are exhibited in human-autonomous agent teams and discusses future directions that can facilitate the design of human-compatible artificial agents.

Superintendents, personnel directors and school boards across the nation are faced with a growing problem of locating high caliber replacements for the exodus of school principals that began as we entered the decade of the 1990s. This departure, which began as natural attrition due to age and retirement, has been accelerated by several other factors including working conditions, educational reform and lack of funding for educators and educational programs. Since principals are critical to school success and student performance an examination of principal selection strategies is critical, particularly the planning needed to find the best applicants. There are several selection strategies that superintendents and school boards can use to recruit and employ the best candidates available, including "grow your own" prospective principals.

Changes in affect over time have been associated with health outcomes. However, previously utilized measurement methods focus on variability of affect (e.g., standard deviation, root mean squared successive difference) and ignore the more complex temporal patterns of affect over time. These patterns may be an important feature in understanding how the dynamics of affect relate to health. Recurrence quantification analysis (RQA) may help alleviate this problem by assessing temporal characteristics unassessed by past methods. RQA metrics, such as determinism and recurrence, can provide a measure of the predictability of affect over time, indexing how often patterns within affective experiences repeat. In Study 1, we first contrasted RQA metrics with commonly used measures of variability to demonstrate that RQA can further differentiate among patterns of affect. In Study 2, we analyzed the associations between these new metrics and health, namely, depressive and somatic symptoms. We found that RQA metrics predicted health above and beyond mean levels and variability of affect over time. The most desirable health outcomes were observed in people who had high mean positive affect, low mean negative affect, low affect variability, and high affect predictability. These studies are the first to demonstrate the utility of RQA for determining how temporal patterns in affective experiences are important for health outcomes. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Interpersonal coordination is essential for successfulcooperative action. Beyond synchronized joint action toachieve a goal such as moving furniture, humans tend tospontaneously coordinate movement in everyday action (i.e.,coordinated limb movement during walking). Furthermore,these actions are said to arise from the interaction dominantdynamics between agents and foment cooperative behavior. Assuch, existing research demonstrates that closer affiliation isassociated with entrainment of physiological signals includingheart beat and rhythmic limb movement. Considering the rolesocial stigmatization plays in disrupting social interaction, thepresent research investigated the impact of concealable stigmadisclosure (depression diagnosis or bisexual identity)—as aperturbation to a nonlinear dynamical system—oninterpersonal coordination and affiliation. Study 1 resultsdemonstrate that depression disclosure may lead to more socialdistancing in a collision avoidance walking task compared tobisexual and neutral disclosures. In study 2, interactionimproved affiliation regardless of disclosure type.

Higher education faces its greatest combinations of challenges: economic uncertainty, accountability and globalization: overlaid by emerging technologies. University leaders face the twin trials of dramatic decreases in public financial support and the increasing cost of resources to avoid technological obsolescence. Technologies continue to evolve that will disrupt higher education in the future. The challenge for traditional universities whose concentration historically has been the production of knowledge in the form of human capital, research, and scholarship is to be able to tap into the expanding need for lifelong learning. Access to higher education will be a necessity for job mobility and economic success. Survival for universities requires modification and adaptation. Traditional educational paradigms have changed and the physical university is now a combination multi-dimensional education model. All these changes demand planning, specifically strategic planning, if higher education institutions are to be competitive and ultimately successful.

The current study utilizes dynamical systems and embodiment theory to better understand how movement dynamics impactdeception detection. While research has consistently revealed humans are often no better than chance at discriminatinga truth from a lie, individuals may reveal more than they know through the dynamic movement of the face and the bodybeyond discrete gestures traditionally examined in deception detection research (e.g., rise of a brow). As expected, thepresent study found that the dynamic stabilities of facial and body movements were significantly influenced by deceptiveintent such that untruthful statements elicited less stability in both the face and upper body. Moreover, despite detectionlevels no greater than chance, the accuracy of observers to detect deceptive intent covaried with these dynamic stabilities.The research presented provides another piece to the illusive puzzle of deception detection, affording researchers andpractitioners a possible tool to tap into deceptive intent.

Adolescence can be a particularly difficult time for youth as they experience the physical, social, and academic changes that accompany maturation. One of the most critical challenges for schools is teaching adolescent students positive social and emotional skills, which are crucial for them to succeed academically and emotionally. "Strong Kids" is a social-emotional learning curriculum designed to reduce students' internalizing symptoms. "Strong Kids" has shown promise in elementary schools; this was the first study to evaluate the newly updated version of the program in a middle level school setting. Two general education teachers implemented the curriculum with eight students at risk for emotional and behavioral disorders. Researchers used a mixed methods design to evaluate outcomes. Findings suggest that "Strong Kids" was effective at improving students' social-emotional knowledge and internalizing symptoms; however, there were no significant changes in their externalizing symptoms.