Behavioral Sciences

Compensation for mental distress in preschool children is a crucial mechanism for protecting their personality rights, yet current judicial practice in China relies heavily on judicial discretion and lacks child-sensitive standards for determining severity. Following the enactment of the Preschool Education Law of the People’s Republic of China in 2025, the principle of the Best Interests of the Child has placed new behavioral and developmental requirements on decision-making, particularly regarding the recognition of children’s expressive limitations and psychological vulnerability. Drawing on representative judicial cases, this research identifies inconsistencies in current adjudication—primarily between factual presumption and medical proof—and highlights their failure to reflect preschoolers’ developmental characteristics. To address this gap, we construct a child-centered liability determination framework integrating the Lundy model of child participation and Nussbaum’s Capabilities Approach. This framework provides a structured method for incorporating children’s voices into proceedings and offers multidimensional criteria for assessing capability impairment as an indicator of mental distress severity. These findings suggest that the framework can help reduce excessive discretion, strengthen developmental sensitivity, and promote more consistent and equitable adjudication. Beyond the Chinese context, this research offers an analytical lens for advancing international discussions on child-centered mental distress assessment and children’s rights protection.

Play is a critical developmental domain linked to social communication, cognitive growth, and later peer relationships; however, young autistic children often demonstrate delays in their play skills, especially higher level play skills (i.e., symbolic play). Although play-based, parent-mediated interventions show promise in improving parent strategies for engaging their children in play, we know less about how these strategies translate to child play improvement outside of the therapeutic sessions. The current study examined the home activities of 97 caregiver–child dyads following their participation in parent-mediated Joint Attention Symbolic Play Engagement Regulation (JASPER), among families in low socioeconomic circumstances. Naturalistic home observations identified the types of daily activities in which the dyads engaged, including play. Within play contexts, children whose caregivers received JASPER demonstrated a greater change from functional to symbolic play compared to those in a parent education control condition. Additionally, child-level, but not family-level, characteristics predicted greater gains in symbolic play. The results provide insight into the ecological validity of parent-mediated, play-based interventions delivered in home settings and highlight factors associated with variability in play outcomes. These findings have implications for tailoring parent-mediated interventions to better support equitable and sustainable developmental gains for autistic children.

Data-driven approaches have emerged as powerful tools for analyzing process data. This study focuses on two data-driven methods: n-gram chi-square feature selection for extracting key action segments and K-medoids clustering combined with Dynamic Time Warping (DTW) distance for identifying behavioral patterns. To address the limitations that arise when applying these methods to complex tasks where ambiguous raw actions often hinder interpretation, this study introduces distance-based effectiveness indicators to enhance both data-driven methods for analyzing actions in the context of complex problem-solving. The research examines how representing action sequences through state effectiveness ($d_s$) and transition effectiveness ($\mathsf{\Delta }{d}_{s\to s^{\prime }}$) indicators outperforms the use of raw actions alone within the complex collaborative problem-solving Balance Beam task. Results consistently demonstrated that effectiveness indicators significantly improved the sensitivity of n-gram feature selection, the performance of clustering, and the interpretability of both n-grams and resulting clusters. Specifically, state effectiveness representations ($d_s\to d_{s^{\prime }}$) yielded the best outcomes. These findings advocate for the integration of effectiveness indicators into data-driven process analytics to more effectively capture and explain behavioral patterns of problem-solving.