Dynamics Versus Development in Numerosity Estimation: A Computational Model Accurately Predicts a Developmental Reversal

Perceptual judgments result from a dynamic process, but little is known about the dynamics of number-line estimation. A recent study proposed a computational model that combined a model of trial-to-trial changes with a model for the internal scaling of discrete numbers. Here, we tested a surprising prediction of the model—a situation in which children's estimates of numerosity would be better than those of adults. Consistent with the model simulations, task contexts led to a clear developmental reversal: children made more adult-like, linear estimates when to-be-estimated numbers were descending over trials (i.e., backward condition), whereas adults became more like children with logarithmic estimates when numbers were ascending (i.e., forward condition). In addition, adults’ estimates were subject to inter-trial differences regardless of stimulus order. In contrast, children were not able to use the trial-to-trial dynamics unless stimuli varied systematically, indicating the limited cognitive capacity for dynamic updates. Together, the model adequately predicts both developmental and trial-to-trial changes in number-line tasks.     

Adaptations in driver deceleration behaviour with automatic incident detection: A naturalistic driving study

Traffic congestion and crash rates can be reduced by introducing variable speed limits (VSLs) and automatic incident detection (AID) systems. Previous findings based on loop detector measurements have revealed that drivers reduce their speeds while approaching traffic congestion when the AID system is active. Notwithstanding these behavioural effects, most microscopic traffic flow models assessing the impact of VSLs do not describe driver response accurately.This study analyses the main factors that influence driver deceleration behaviour while approaching traffic congestion with and without VSLs. The Dutch VSL database was linked to the driver behaviour data collected in the UDRIVE naturalistic driving study. Driver engagement in secondary tasks and glance behaviour were extracted from the video data. Linear mixed-effects models predicting the characteristics of deceleration events were estimated.The results show that the maximum deceleration is high when approaching a slower leader, when driving at high speeds and short distance headways, and close to the beginning of traffic congestion. The minimum time headway is short when driving at high speeds and changing lanes. Certain drivers showed higher decelerations and shorter time headways than others. Controlled for these main factors, smaller maximum decelerations were found when the VSLs were present and visible, and when the gantries were within close proximity. These factors could be incorporated into microscopic traffic simulations to evaluate the impact of AID systems on traffic congestion more realistically. Further research is needed to clarify the link between engagement in secondary tasks, glance behaviour and deceleration behaviour.     

Trust and intention to use autonomous vehicles: Manufacturer focus and passenger control

Autonomous Vehicle (AV) research has focused on public acceptance of and intention to use AVs, with trust emerging as important to these variables. Research on AV trust has centered on trust in vehicle performance. However, trust evaluation may include AV manufacturers and developers, and regulation pertaining to AVs; thus, we expand our measurement of trust to include beliefs based on manufacturers and regulation. In this experiment, we manipulate information regarding manufacturer focus (i.e., an emphasis on standards, regulations, and research (SRR) versus speed to market) to determine its effect on trust. When information focused on SRR, we found higher levels of trust in AV performance and in manufacturers, compared to when it focused on speed to market. In addition, information regarding passenger control (i.e., the ability to take over for the vehicle and to determine privacy settings) was manipulated to yield either high or low passenger control conditions. Behavioral Intention (BI) to use AVs was lowest when speed to market was emphasized and when passenger control was low. Furthermore, trust was tested as a mediator between perceived risk and BI. Trust in AV performance partially mediated the relationship between perceived AV performance risk and BI. In addition, trust in AV regulation partially mediated the relationship between AV privacy/security risk and BI. Researchers should continue to examine trust beyond the AV itself to encompass trust in manufacturers and regulations. Those designing and marketing AVs should carefully consider decisions that influence manufacturer/developer reputation and passenger control due to their effect on intention to use AVs.     

Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures

The paper presents the formalism of an intelligent decision-making system based on multi-agent neurocognitive architectures, which has an architectural similarity to the human brain. An invariant of the organizational and functional structure of the intellectual decision-making process based on the multi-agent neurocognitive architecture is developed. An algorithm for teaching intelligent decision-making systems based on the self-organization of the invariant of multi-agent neurocognitive architectures is presented. Using this algorithm, an intelligent agent was trained and the architecture of the learning process was built on the basis of an invariant of neurocognitive architecture. Further research is related to training an intelligent agent in more complex behavior and expanding the capabilities of an intelligent decision-making system based on multi-agent neurocognitive architectures.     

Modeling talent development pathways in alpine ski racers

We explore developmental pathways to excellence in sub-elite adolescent alpine ski racing by exploring athletes' engagement in practice activities that characterize sport specialization and/or diversification. Moreover, we extend previous research by using more refined linear mixed-effects models to examine changes in practice and performance data over time rather than relying on the less sensitive repeated measures ANOVAs typically used in previous research. Altogether, 174 individuals (M = 15.9 yrs, women = 91) from professional development academies in the United States completed a sport-specific Practice History Questionnaire (PHQ). We recorded national rankings in technical events to assess performance. Mixed-effects models indicated that hours in ski-related activities generally increased over time (linear β = 1.38, p's < 0.001), except time spent in play, which decreased over time (β = 102.8, p = 0.002). Performance rankings also improved over time (β = 30.99, p < 0.001), with more time spent in ski-activity associated with greater rates of improvement, particularly in younger skiers (β = 9.77, p's < 0.001). The effects were largely driven by time spent in coach-led group practice (β = 2.16, p < 0.001). Overall, an earlier age of first engagement was associated with better rankings (β = −15.40, p = 0.024), and accumulating more hours in other sports was also associated with greater rates of performance improvement (β = −8.87, p = 0.014). Sub-elite alpine ski racers engage early in the sport, with higher-ranked athletes spending more time in ski-specific practice, particularly group practice, during development. However, these athletes generally continued to participate in other sports across development, providing greater support for the early engagement hypothesis rather than early specialization per se.     

多级评分认知诊断模型述评

认知诊断评估旨在探讨个体内部的知识掌握结构,并提供关于学生优缺点的详细诊断信息,以促进个体的全面发展。当前研究者已开发了大量0-1评分的认知诊断模型,但对于多级评分认知诊断模型的研究还比较少。本文对已有的多级评分认知诊断模型进行了归纳,介绍了模型的假设,计量特征以及适用范围,为实际应用者和研究者在多级评分认知诊断模型的比较和选用上提供借鉴和参考。最后,对未来关于多级评分诊断模型的研究方向进行了展望。     

Action representation deficits in adolescents with developmental dyslexia

Developmental dyslexia (DD), a severe and frequent disorder of reading acquisition, is characterized by a diversity of cognitive and motor deficits whose interactions still remain under debate. Although deficits in the automatization of sensorimotor control have been highlighted, internal action representation allowing prediction has never before been investigated. In this study, we considered action representation of 18 adolescents with pure DD and 18 age-matched typical readers. Participants actually and mentally performed a visually guided pointing task involving strong spatiotemporal constraints (speed/accuracy trade-off paradigm). While actual and mental movement times of typical readers were isochronous and both conformed to Fitts’ law, the movement times of dyslexics differed between conditions, and only the actual movement times conformed to Fitts’ law. Furthermore, the quality of motor imagery correlated with word reading abilities. This suggests that the process of action representation is impaired in pure DD and supports the sensorimotor perspective of DD. Theoretical implications are discussed.     

An Ecological Momentary Assessment Study Investigating the Function of Hoarding

This study examined the function of hoarding behaviors and the relations between hoarding and a series of cognitive and affective processes in the moment using ecological momentary assessment. A matched-groups design was used to compare college students with higher hoarding symptoms (n = 31) and matched controls (n = 29). The two groups did not differ in what function they reported acquiring served, and positive automatic reinforcement was the most commonly reported function in both groups. Engaging in hoarding-relevant behaviors did not predict change in positive or negative affect when controlling for previous affect. Emotional reactivity and experiential avoidance in the moment were both elevated in the higher hoarding group compared to controls, while momentary mindfulness and negative affect differentiation were lower. Overall, these findings support the importance of emotion regulation processes in hoarding. They also suggest individuals may not be successfully regulating affect in the moment with hoarding behaviors, despite efforts to do so. It may be useful to evaluate processes such as striving for positive affect in hoarding disorder in the future.     

Crafting Mosaics: Person-Centered Religious Influence and Selection in Adolescent Friendships

This research addresses the intersection of two key domains of adolescents’ lives: religion and peer networks. Religion scholars argue that religion is multi-faceted and better understood by focusing on combinations of indicators (i.e., mosaics), versus a variable-centered approach. We adopt this framework and investigate the interplay between religion and peer networks, both in how religious mosaics are shaped by friends and how religious profiles affect friend selection dynamics. With data from two schools in the National Longitudinal Study of Adolescent Health, we estimate religious mosaics using latent class analysis (LCA) to identify profiles consisting of combinations of commonly available survey-based measures of religious attitudes, behaviors, and identities. Finding evidence of theoretically expected profiles, we then use stochastic actor–based models (SABMs) to investigate network dynamics for these LCA-based religious profiles. We demonstrate how the profile data can be integrated within the SABM framework to evaluate processes of friend selection and influence. Results show evidence of adolescents influencing one another's religious mosaics, but not selecting friends on that basis.     

A Process Model of Causal Reasoning

How do we make causal judgments? Many studies have demonstrated that people are capable causal reasoners, achieving success on tasks from reasoning to categorization to interventions. However, less is known about the mental processes used to achieve such sophisticated judgments. We propose a new process model—the mutation sampler—that models causal judgments as based on a sample of possible states of the causal system generated using the Metropolis–Hastings sampling algorithm. Across a diverse array of tasks and conditions encompassing over 1,700 participants, we found that our model provided a consistently closer fit to participant judgments than standard causal graphical models. In particular, we found that the biases introduced by mutation sampling accounted for people's consistent, predictable errors that the normative model by definition could not. Moreover, using a novel experimental methodology, we found that those biases appeared in the samples that participants explicitly judged to be representative of a causal system. We conclude by advocating sampling methods as plausible process-level accounts of the computations specified by the causal graphical model framework and highlight opportunities for future research to identify not just what reasoners compute when drawing causal inferences, but also how they compute it.