The mediational role of motivation in the model of motor development in childhood: A longitudinal study

The aim of this study was twofold: first, to examine the stability of the hypothesized conceptual model of motor development (without and with including various types of motivation) when children are followed up one-year later, and second to examine longitudinally whether changes in one model variable predict changes in other variables, according to the hypothesized pathways in the model. A sample of 361 Spanish students (50.7% girls, 8–11 years old) voluntarily participated in this study. In relation to the first aim, structural equation modeling revealed the expected positive relationship between the model variables in both measurement times. That is: actual motor competence (MC) predicted physical activity (PA) (p < .001), perceived MC mediated the relationship between actual MC and PA (p < .001), and autonomous motivation mediated the relationship between perceived MC and PA (p < .05). Moreover, the comparison of the invariance analysis showed non-practical differences between the unconstrained model and the constrained model, supporting the stability of the model over time. In relation to the second aim, the hypothesized model in Time 2 controlling for Time 1 values showed that changes in children’s actual MC positively predicted changes in their perceived MC (p < .001), which in turn, predicted changes in their autonomous motivation (p < .001), and PA (p < .001) at Time 2. Based on these findings Physical Education teachers are recommended to foster children’s actual and perceived MC as well as their autonomous motivation over time in order to promote PA strategies for lifelong health.     

One-year prediction of cognitive decline following cognitive-stimulation from real-world data

Clinical evidence based on real-world data (RWD) is accumulating exponentially providing larger sample sizes available, which demand novel methods to deal with the enhanced heterogeneity of the data. Here, we used RWD to assess the prediction of cognitive decline in a large heterogeneous sample of participants being enrolled with cognitive stimulation, a phenomenon that is of great interest to clinicians but that is riddled with difficulties and limitations. More precisely, from a multitude of neuropsychological Training Materials (TMs), we asked whether was possible to accurately predict an individual's cognitive decline one year after being tested. In particular, we performed longitudinal modelling of the scores obtained from 215 different tests, grouped into 29 cognitive domains, a total of 124,610 instances from 7902 participants (40% male, 46% female, 14% not indicated), each performing an average of 16 tests. Employing a machine learning approach based on ROC analysis and cross-validation techniques to overcome overfitting, we show that different TMs belonging to several cognitive domains can accurately predict cognitive decline, while other domains perform poorly, suggesting that the ability to predict decline one year later is not specific to any particular domain, but is rather widely distributed across domains. Moreover, when addressing the same problem between individuals with a common diagnosed label, we found that some domains had more accurate classification for conditions such as Parkinson's disease and Down syndrome, whereas they are less accurate for Alzheimer's disease or multiple sclerosis. Future research should combine similar approaches to ours with standard neuropsychological measurements to enhance interpretability and the possibility of generalizing across different cohorts.     

Mixture-modelling-based Bayesian MH-RM algorithm for the multidimensional 4PLM

Several recent works have tackled the estimation issue for the unidimensional four-parameter logistic model (4PLM). Despite these efforts, the issue remains a challenge for the multidimensional 4PLM (M4PLM). Fu et al. (2021) proposed a Gibbs sampler for the M4PLM, but it is time-consuming. In this paper, a mixture-modelling-based Bayesian MH-RM (MM-MH-RM) algorithm is proposed for the M4PLM to obtain the maximum a posteriori (MAP) estimates. In a comparison of the MM-MH-RM algorithm to the original MH-RM algorithm, two simulation studies and an empirical example demonstrated that the MM-MH-RM algorithm possessed the benefits of the mixture-modelling approach and could produce more robust estimates with guaranteed convergence rates and fast computation. The MATLAB codes for the MM-MH-RM algorithm are available in the online appendix.     

Preference Structures and Co-comparability Graphs

Order structures such as linear orders, weak orders, semiorders and interval orders are often considered as models of a decision maker's preferences. In this paper we introduce and study new order structures characterized by their symmetric part belonging to certain classes of co-comparability graphs. We outline possible interpretations and suggest special representations of these structures and we point out their potential use for approximating relations obtained through a multicriteria aggregation procedure. We provide various characterizations of the new structures (as well as of older ones) in terms of minimal forbidden configurations and by algebraic conditions.     

An adaptive approach for modelling multiattribute choice

An adaptive approach for modelling individual-level choice among multiattribute alternatives using the binary logit model is presented. The algorithm involves the collection of paired comparison data. In an effort to maximize the amount of information obtainable from each response, it is based on the experimental design criterion of D-optimality. A simulation study indicates that the proposed algorithm outperforms other sequential selection approaches in terms of estimation accuracy and predictive efficiency under certain circumstances. The results appear to encourage the use of such an adaptive algorithm for individual-level modelling in light of the potential reduction in data requirements without significant loss in predictive accuracy.     

Opium of the people? National identification predicts well-being over time

Social group membership and its social-relational corollaries, for example, social contact, trust, and support, are prophylactic for health. Research has tended to focus on how direct social interactions between members of small-scale groups (i.e., a local sports team or community group) are conducive to positive health outcomes. The current study provides evidence from a longitudinal cross-cultural sample (N = 6,748; 18 countries/societies) that the prophylactic effect of group membership is not isolated to small-scale groups, and that members of groups do not have to directly interact, or in fact know of each other to benefit from membership. Our longitudinal analyses suggest that national identification (strength of association with the country/society of which one is a citizen) predicts lower anxiety and improved health; national identification was in fact almost as positively predictive of health status as anxiety was negatively predictive. The findings indicate that identification with large-scale groups, like small-scale groups, is palliative, and are discussed in terms of globalization and banal nationalism.     

Understanding the impact of heterogeneous rider preferences on a shared autonomous vehicle system

In a ride pooling system, riders may have varied behaviors in seeking pooled or non-pooled rides. It is important to understand the effect of these rider behaviors on the system performance in order to formulate policies to guide ride pooling implementation. Existing literature modeling ride pooling systems using agent-based models only considers the extreme cases in which riders either all participate or not participate in pooling. However, the pooling behaviors could be more complex. This study segments the rides in the system into five types (non-pooling only, non-pooling preferred, indifferent, pooling preferred, and pooling only). We use an agent-based model to simulate these preferences in a system of pooled autonomous vehicles. We use mixture experiments to vary the proportion of riders within these five types and build models to study the interactions among the rider types in terms of the system’s service quality and environmental performance. The results show that higher service level is achieved when all riders in the system are open to pooling, with 30% of pooling only riders and 70% of pooling preferred or indifferent riders providing the maximum value. The results can help formulate incentives and policies to promote ride pooling participation to improve ride pooling system performance.     

计算模型在道德认知研究中的应用

道德认知关注道德心理背后的信息加工。近年来, 研究者开始将计算模型应用于道德认知研究, 以探索道德认知如何在大脑中实现。但目前研究者对道德认知进行计算建模的研究处于起步阶段。计算模型(漂移扩散模型、效用模型、强化学习模型和分层高斯过筛器模型)在道德认知行为和生理研究上的运用量化了道德决策、道德判断和道德推理背后的认知过程和神经机制。此外, 这一新进展对理解反社会行为和精神障碍等有所助益。最后, 计算建模有待完善, 未来研究需要关注其潜在的问题。     

Novice construction of chess memory

Novice acquisition of skilled recall of chess positions was studied in an experiment in which two novices studied a series of five hundred chess positions during a period of several months. They spent fifteen minutes to half an hour a day teaching themselves these positions. As a result their skill in recalling chess positions rose from sixteen percent to somewhere between forty to fifty percent. The learning curve proved to have a shape which indicates that in the beginning learning is very fast but after some 100-150 studied positions the speed of learning decreases substantially. A computer simulation was used to model the results and analyse alternative explanations. Two alternative ways of thinking were tested. In the first, chunk construction was assumed to be based on the neighbourhood of associated pieces. The second model assumed a frequency-based correlative association process. Although the learning curves of the two models are very similar in shape to those of the subjects, the frequency-based associative model gave a better explanation for the data. This is why it is natural to suggest that common co-occurrence in addition to easily recognizable chess-specific characteristics, like colour and type of pieces, guide associative processes during chess players' learning of chess-specific chunks.