Measuring nominal scale agreement between a judge and a known standard

Two designs for comparing a judge's ratings with a known standard are presented and compared. Design A pertains to the situation where the judge is asked to categorize each ofN subjects into one ofr (known) classes with no knowledge of the actual number in each class. Design B is employed when the judge is given the actual number in each class and is asked to categorize the individuals subject to these constraints. The probability distribution of the total number of correct choices is developed in each case. A power comparison of the two procedures is undertaken.     

STIMULUS GENERALIZATION AND EQUIVALENCE CLASSES: A MODEL FOR NATURAL CATEGORIES

Two three-member classes were formed by training AB and BC using a conditional discrimination procedure. The A and B stimuli were nonsense syllables, and the C stimuli were sets of “short” or “long” lines. To test for equivalence, C1 or C2 was presented as a sample with A1 and A2 as comparisons. Once the class-related comparison was chosen consistently, different line lengths were substituted for the training lines in the CA tests. In general, the likelihood of choosing a given comparison was an inverse function of the difference in the length of the test line from the training line. Stimuli in an equivalence class became functionally related not only to each other but also to novel stimuli that resembled a member of the equivalence class. The combination of primary generalization and equivalence class formation, then, can serve as a model to account for the development of naturally occurring categories.     

Cognition, behavior, and the experimental analysis of behavior

A case history illustrates how one research program in the experimental analysis of behavior evolved somewhat differently from the modal research program represented in this journal. A chief issue that seems to be responsible for this difference is the role attributed to theory in behavioral research: Skinner's views on the nature and function of theory and on the nature of observation combine to produce a certain kind of picture of behavior. The classic conception of reinforcement contingencies is tied to this particular picture. But this picture may be incompatible with, and certainly is different from, other possible pictures. Reinforcement contingencies that place greater emphasis on the local temporal patterning of behavior seem tied to some of these alternative pictures of what behavior is. These other pictures encourage a wide range of theoretical approaches, including cognitive ones, various kinds of mathematical analyses, and computer-simulation methods to characterize entire behavior streams. In the future, perhaps the experimental analysis of behavior will accept a somewhat different range of views on the nature and function of theory, a correspondingly different set of experimental methods, and alternative ways of talking about behavior.     

Loss context enhances preferences for generosity but reduces preferences for honesty: Evidence from a combined behavioural-computational approach

The current literature has revealed mixed evidence on whether loss (vs. gain) context promotes or curtails human prosociality. The current study (N=96) aimed to address this issue by examining whether gain/loss context has distinct effects on different prosocial preferences combining computational modelling with Dictator Game and Message Game. These interactive games allow for dissociating preferences for generosity and honesty, which have been respectively associated with intuitive and deliberative systems. Our behavioural and computational modelling results indicate that loss context enhances concerns for generosity but reduces concerns for honesty. These findings support an account under the framework of dual process model asserting that loss facilitates intuitive responses during social decision-making, regardless of whether they are prosocial or proself. The current findings reconcile previous debates on the relationship between loss-gain context and human prosociality and shed light on the design of institutions to promote human prosocial behaviours.     

情绪效价可预测性对时间捆绑效应的影响

时间捆绑效应指的是主观上主动动作和动作结果的时间点相互靠近的现象。其中, 时间捆绑效应可分为动作捆绑和结果捆绑, 分别对应主动动作和动作结果的时间点变化。本研究通过一个混合设计实验考察了情绪效价可预测性对时间捆绑效应的影响。情绪效价可预测性(可预测、不可预测)为被试内因素, 刺激模态(听觉刺激、视觉刺激)为被试间因素。结果发现, 情绪效价为可预测时, 结果捆绑在主动按键后产生听觉刺激或视觉刺激都出现了增强, 而动作捆绑仅在产生视觉刺激时才出现增强。结果表明, 情绪效价为可预测时会增强时间捆绑效应, 但该作用在动作捆绑和结果捆绑中存在差异。由于时间捆绑效应是反映主动控制感的主要指标, 本研究结果对先进驾驶辅助系统的交互设计具有一定的参考意义。     

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.