Towards computational models of intention detection and intention prediction

Intention recognition is one of the core components of mindreading, an important process in social cognition. Human beings, from age of 18 months, have been shown to be able to extrapolate intentions from observed actions, even when the performer failed at achieving the goal. Existing accounts of intention recognition emphasize the use of an intent (plan) library, which is matched against observed actions for recognition. These therefore cannot account for recognition of failed sequences of actions, nor novel actions. In this paper, we begin to tackle these open questions by examining computational models for components of human intention recognition, which emphasize the ability of humans to detect and identify intentions in a sequence of observed actions, based solely on the rationality of movement (its efficiency). We provide a high-level overview of intention recognition as a whole, and then elaborate on two components of the model, which we believe to be at its core, namely, those of intention detection and intention prediction. By intention detection we mean the ability to discern whether a sequence of actions has any underlying intention at all, or whether it was performed in an arbitrary manner with no goal in mind. By intention prediction we mean the ability to extend an incomplete sequence of actions to its most likely intended goal. We evaluate the model, and these two components, in context of existing literature, and in a number of experiments with more than 140 human subjects. For intention detection, our model was able to attribute high levels of intention to those traces perceived by humans as intentional, and vice versa. For intention prediction as well, our model performed in a way that closely matched that of humans. The work highlights the intimate relationship between the ability to generate plans, and the ability to recognize intentions.     

Specifying Ability Growth Models Using a Multidimensional Item Response Model for Repeated Measures Categorical Ordinal Item Response Data

When categorical ordinal item response data are collected over multiple timepoints from a repeated measures design, an item response theory (IRT) modeling approach whose unit of analysis is an item response is suitable. This study proposes a few longitudinal IRT models and illustrates how a popular compensatory multidimensional IRT model can be utilized to formulate such longitudinal IRT models, which permits an investigation of ability growth at both individual and population levels. The equivalence of an existing multidimensional IRT model and those longitudinal IRT models is also elaborated so that one can make use of an existing multidimensional IRT model to implement the longitudinal IRT models.     

Data Analytic Advances That Bridge the Scientist-Practitioner Gap

In this commentary, I advance the view that the scientist-practitioner gap is partly due to the research designs commonly used in psychotherapy research. Specifically, I believe that randomized controlled trials, which are important for establishing treatment efficacy and as leverage when making the case for the value of psychotherapy in relation to various stakeholders, are limited for further development of clinical theories. Instead, I find recent advances in cross-lagged panel modeling to be both clinically intuitive and stronger for causal inference than most other nonexperimental designs. In addition to discussing causal inference and clinical interpretation of cross-lagged panel models, I discuss the potential of improving mediation analysis, personalization of research, and studying issues of clinical timing. Finally, I briefly discuss some limitations of cross-lagged panel models. It is my belief that the use of these data analytic advances can make empirical research better live up to the innovations in Beck’s work.     

Compositional insights on the association between physical activity and sedentary behavior on momentary mood in daily life

ObjectiveGiven the increasing number of mental disorders, a growing body of studies now focuses on the relationship between physical activity (PA), sedentary behavior (SB), and mental health. However, momentary mechanisms and interrelatedness between PA, SB, and mood in daily life are highly understudied.MethodsTo investigate whether the composition of light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), and SB influences mood, we conducted an Ambulatory Assessment study in the everyday life of 103 university students over five days. We measured PA and SB via accelerometers and assessed mood up to six times each day on smartphone diaries. We combined compositional data analyses (CoDA) with multilevel modeling to analyze within-person effects of the behavioral composition on mood.ResultsHigher ratio of the SB to LPA and MVPA composition within the 60 min prior to a diary rating negatively influenced valence (p = 0.006) and energetic arousal (p < 0.001). Furthermore, a higher ratio of LPA to SB and MVPA positively influenced energetic arousal (p < 0.001) and a higher ratio of MVPA to SB and LPA positively influenced valence (p = 0.004) and energetic arousal (p = 0.022). Simulation analyses revealed that replacing 20 min of SB with PA can influence mood rating up to 3.39 units [range from 0–100].ConclusionsFindings suggest that minutely replacements of SB with PA may lead to mood enhancements. Given the high prevalence of mental disorders, more studies are warranted to deepen the understanding of momentary compositional mechanisms between SB, PA, and mood. Applying CoDA to intensive longitudinal data can serve as a starting point to identify the optimal composition of SB, LPA, and MVPA for mood enhancements in everyday life.     

国内中介效应的方法学研究

中介效应可以分析自变量对因变量的影响过程和作用机制, 已成为分析多个变量之间关系的一种重要统计方法。最近20年, 中介效应成了研究方法的一个热点。从中介效应的检验方法、效应量、类别变量的中介效应检验、纵向数据的中介效应检验和模型拓展(包括多重中介、多层中介、有调节的中介和有中介的调节模型)五个方面系统总结了国内中介效应的方法学研究的发展历程。最后对中介效应的国外方法学研究进展和中介效应的未来研究方向做了讨论和拓展。