Mother-Infant Interaction Kinect Analysis (MIIKA): An automatic kinematic-based methodology for the investigation of interpersonal distance during early exchanges

Interpersonal distance is a core aspect of mother-child interaction. While conventional measures based on human coders do not fully capture the dynamics of this feature, computational methods provide automatic measures which can detect even small changes and more accurate estimates both spatially and temporally. Using RGB-D sensors (Microsoft Kinect V2), the present study describes a setup to automatically examine interpersonal distance during mother-child interactions, termed Mother-Infant Interaction Kinect Analysis (MIIKA). First, the laboratory setting and the data extraction method are described. By using an ad-hoc algorithm for kinematic data extraction, MIIKA returns three metrics: barycenter position (distance and velocity of approach and separation), movements (number of small, medium and large approaches and separations) and contributions (proportional contributions of mother and child to approaches and separations). Secondly, preliminary MIIKA metrics are described for a non-clinical mother-child dyad as an exemplification of the protocol. As interpersonal distance can be affected by contingent situations, we detected mother-infant full skeleton during three interactional contexts characterized by different kinds of dyadic exchanges: a free play session, a task-oriented activity and an emotionally arousing condition. Results highlighted similarities and differences between the three interactional contexts. MIIKA appears to be a promising setup to automatically examine interpersonal distance in early mother-child interactions.     

Using the EZ-diffusion model to score a Single-Category Implicit Association Test of physical activity

ObjectiveThe Single-Category Implicit Association Test (SC-IAT) has been used as a method for assessing automatic evaluations of physical activity, but measurement artifact or consciously-held attitudes could be confounding the outcome scores of these measures. The objective of these two studies was to address these measurement concerns by testing the validity of a novel SC-IAT scoring technique.DesignStudy 1 was a cross-sectional study, and study 2 was a prospective study.MethodIn study 1, undergraduate students (N = 104) completed SC-IATs for physical activity, flowers, and sedentary behavior. In study 2, undergraduate students (N = 91) completed a SC-IAT for physical activity, self-reported affective and instrumental attitudes toward physical activity, physical activity intentions, and wore an accelerometer for two weeks. The EZ-diffusion model was used to decompose the SC-IAT into three process component scores including the information processing efficiency score.ResultsIn study 1, a series of structural equation model comparisons revealed that the information processing score did not share variability across distinct SC-IATs, suggesting it does not represent systematic measurement artifact. In study 2, the information processing efficiency score was shown to be unrelated to self-reported affective and instrumental attitudes toward physical activity, and positively related to physical activity behavior, above and beyond the traditional D-score of the SC-IAT.ConclusionsThe information processing efficiency score is a valid measure of automatic evaluations of physical activity.     

Modelling asynchrony in automatic speech recognition using loosely coupled hidden Markov models

Hidden Markov models (HMMs) have been successful for modelling the dynamics of carefully dictated speech, but their performance degrades severely when used to model conversational speech. Since speech is produced by a system of loosely coupled articulators, stochastic models explicitly representing this parallelism may have advantages for automatic speech recognition (ASR), particularly when trying to model the phonological effects inherent in casual spontaneous speech. This paper presents a preliminary feasibility study of one such model class: loosely coupled HMMs. Exact model estimation and decoding is potentially expensive, so possible approximate algorithms are also discussed. Comparison of one particular loosely coupled model on an isolated word task suggests loosely coupled HMMs merit further investigation. An approximate algorithm giving performance which is almost always statistically indistinguishable from the exact algorithm is also identified, making more extensive research computationally feasible.     

Innovation,research integrity,and change: A conflict of interest management framework for program developers

Psychology and the social sciences have an important role to play in developing innovative solutions to pressing global mental health and social problems. Programs developed by psychologists and other social scientists have immense potential to alleviate suffering and to promote healthy human development across the lifespan. In order to realise this potential program developers must manage the research and development challenges involved in testing an intervention, evaluating, and then preparing it for wider dissemination and scaling. Particular challenges and conflicts can occur in managing the joint roles of being a program developer and a researcher evaluating an intervention or innovation. This article examines the management of various forms of conflicts of interest that have the potential to produce bias and decrease the confidence of policy makers, funders, practitioners, fellow researchers, and the public in the value of psychological interventions. We argue that best practice guidelines are needed to assist developers negotiate the predictable, sometime unavoidable but challenging conflicts of interest that arise in the research process.     

Subjectively different emotional schematic faces not automatically discriminated from the brain’s bioelectrical responses

The present study investigates how the brain automatically discriminates emotional schematic faces, as indicated by the mismatch responses, and how reliable these brain responses are. Thirty-three healthy volunteers participated in the vMMN EEG experiment with four experimental sets differing from each other by the type of standard (object with scrambled face features) and the type of deviants (Angry, Happy and Neutral schematic faces) presented. Conscious subjective evaluations of valence, arousal and attention catching of the same stimuli showed clear differentiation of emotional expressions. Deviant faces elicited rather similar vMMN at frontal and occipital sites. Bayesian analyses suggest that vMMN does not differ between angry and happy faces. Neutral faces, however, did not yield statistically significant vMMN at occipital leads. Pearson’s correlation and intra-class correlation analyses showed that the brain’s reactions to the stimuli were highly stable within individuals across the experimental sets, whereas the mismatch responses were much more variable.