How Virtual Agents Can Learn to Synchronize: an Adaptive Joint Decision-Making Model of Psychotherapy

Joint decision-making can be seen as the synchronization of actions and emotions, usually via nonverbal interaction between people while they show empathy. The aim of the current paper was (1) to develop an adaptive computational model for the type of synchrony that can occur in joint decision-making for two persons modeled as agents, and (2) to visualize the two persons by avatars as virtual agents during their decision-making. How to model joint decision-making computationally while taking into account adaptivity is rarely addressed, although such models based on psychological literature have a lot of future applications like online coaching and therapeutics. We used an adaptive network-oriented modelling approach to build an adaptive joint decision-making model in an agent-based manner and simulated multiple scenarios of such joint decision-making processes using a dedicated software environment that was implemented in MATLAB. Programming in the Unity 3D engine was done to virtualize this process as nonverbal interaction between virtual agents, their internal and external states, and the scenario. Although our adaptive joint decision model has general application areas, we have selected a therapeutic session as example scenario to visualize and interpret the example simulations

Имитационное моделирование технологии управления процессом производства

Предложено использование комплекса имитационного моделирования для получения информации при контроле функционирования и управлении технологическим процессом производства.Запропоновано використання комплексу імітаційного моделювання для одержання інформації при контролюванні функціонування та управління технологічним процесом виробництва.Complex of simulation modeling for obtaining information when checking an operation and control of technological process of production is offered to use

FGFR Family Members Protein Expression as Prognostic Markers in Oral Cavity and Oropharyngeal Squamous Cell Carcinoma

Introduction Fibroblast growth factor receptor family member proteins (FGFR1-4) have been identified as promising novel therapeutic targets and prognostic markers in a wide spectrum of solid tumors. The present study investigates the expression and prognostic value of four FGFR family member proteins in a large multicenter oral cavity squamous cell carcinoma (OCSCC) and oropharyngeal squamous cell carcinoma (OPSCC) cohort. Methods Protein expression of FGFR1-4 was determined by immunohistochemistry on tissue microarrays containing 951 formalin-fixed paraffin embedded OCSCC and OPSCC tissues from the University Medical Center Utrecht and University Medical Center Groningen. Protein expression was correlated to overall survival using Cox regression models, and bootstrapping was performed as internal validation. Results FGFR proteins were highly expressed in 39-64 % of OCSCC and 63-79 % of OPSCC. Seventy-three percent (299/412) of OCSCC and 85 % (305/357) of OPSCC highly co-expressed two or more FGFR family member proteins. FGFR1 protein was more frequently highly expressed in human papillomavirus (HPV)-negative OPSCC than HPV-positive OPSCC (82 vs. 65 %; p = 0.008). Furthermore, protein expression of FGFR family members was not related to overall survival in OCSCC or OPSCC (p . 0.05). Conclusion FGFR family members are frequently highly expressed in OCSCC and OPSCC. These FGFR family member proteins are therefore potential targets for novel therapies that are urgently required to improve survival of OCSCC and OPSCC patients

GATE : a simulation toolkit for PET and SPECT

Monte Carlo simulation is an essential tool in emission tomography that can assist in the design of new medical imaging devices, the optimization of acquisition protocols, and the development or assessment of image reconstruction algorithms and correction techniques. GATE, the Geant4 Application for Tomographic Emission, encapsulates the Geant4 libraries to achieve a modular, versatile, scripted simulation toolkit adapted to the field of nuclear medicine. In particular, GATE allows the description of time-dependent phenomena such as source or detector movement, and source decay kinetics. This feature makes it possible to simulate time curves under realistic acquisition conditions and to test dynamic reconstruction algorithms. A public release of GATE licensed under the GNU Lesser General Public License can be downloaded at the address http://www-lphe.epfl.ch/GATE/

A Multisite Preregistered Paradigmatic Test of the Ego-Depletion Effect

We conducted a preregistered multilaboratory project (k = 36; N = 3,531) to assess the size and robustness of ego-depletion effects using a novel replication method, termed the paradigmatic replication approach. Each laboratory implemented one of two procedures that was intended to manipulate self-control and tested performance on a subsequent measure of self-control. Confirmatory tests found a nonsignificant result (d = 0.06). Confirmatory Bayesian meta-analyses using an informed-prior hypothesis (δ = 0.30, SD = 0.15) found that the data were 4 times more likely under the null than the alternative hypothesis. Hence, preregistered analyses did not find evidence for a depletion effect. Exploratory analyses on the full sample (i.e., ignoring exclusion criteria) found a statistically significant effect (d = 0.08); Bayesian analyses showed that the data were about equally likely under the null and informed-prior hypotheses. Exploratory moderator tests suggested that the depletion effect was larger for participants who reported more fatigue but was not moderated by trait self-control, willpower beliefs, or action orientation.</p

A computational model for flexibility in emotion regulation

Emotion regulation is a vital psychological process that allows people to manage their own emotional states. Recent psychological research has highlighted the importance of flexibility in emotion regulation, such that people can alternative between different emotion regulation strategies. A strategy is chosen depending upon the demands of the situation. This means that healthy emotion regulation is context-sensitive. This paper presents a computational model which models this form of flexible adaptation in emotion regulation in a simplified scenario in which the person has to switch between expressive suppression and attention modulation in managing anger in different work situations. Simulation results are reported that illustrate the capacity of the model to display adaptivity in emotion regulation across different contexts

Modeling Emerging Interpersonal Synchrony and its Related Adaptive Short-Term Affiliation and Long-Term Bonding:A Second-Order Multi-Adaptive Neural Agent Model

When people interact, their behavior tends to become synchronized, a mutual coordination process that fosters short-term adaptations, like increased affiliation, and long-term adaptations, like increased bonding. This paper addresses for the first time how such short-term and long-term adaptivity induced by synchronization can be modeled computationally by a second-order multi-adaptive neural agent model. It addresses movement, affect and verbal modalities and both intrapersonal synchrony and interpersonal synchrony. The behavior of the introduced neural agent model was evaluated in a simulation paradigm with different stimuli and communication-enabling conditions. Moreover, in this paper, mathematical analysis is also addressed for adaptive network models and their positioning within the landscape of adaptive dynamical systems. The first type of analysis addressed shows that any smooth adaptive dynamical system has a canonical representation by a self-modeling network. This implies theoretically that the self-modeling network format is widely applicable, which also has been found in many practical applications using this approach. Furthermore, stationary point and equilibrium analysis was addressed and applied to the introduced self-modeling network model. It was used to obtain verification of the model providing evidence that the implemented model is correct with respect to its design specifications.</p

Testing the effects of a virtual reality game for aggressive impulse management: A preliminary randomized controlled trial among forensic psychiatric outpatients

Prior laboratory experiments among healthy samples found that training avoidance movements to angry faces may lower anger and aggression, especially people high in trait anger. To enrich this training and make it more suitable for clinical applications, the present researchers developed it into a Virtual Reality Game for Aggressive Impulse Management (VR-GAIME). The current study examined the effects of this training in a randomized controlled trial among forensic psychiatric outpatients with aggression regulation problems (N = 30). In addition to the aggression replacement training, patients played either the VR-GAIME or a control game. Aggressive behavior was measured pre-, half-way, and post-treatment via self-report and clinicians ratings. No difference was found between the VR-GAIME and the control game. However, the participants reported gaining more insight into their own behavior and that of others. Future VR intervention tools in clinical settings may capitalize more on their benefits for self-reflection within interpersonal settings