A note on computing Louis’ observed information matrix identity for IRT and cognitive diagnostic models

Using Louis’ formula, it is possible to obtain the observed information matrix and the corresponding large-sample standard error estimates after the expectation–maximization (EM) algorithm has converged. However, Louis’ formula is commonly de-emphasized due to its relatively complex integration representation, particularly when studying latent variable models. This paper provides a holistic overview that demonstrates how Louis’ formula can be applied efficiently to item response theory (IRT) models and other popular latent variable models, such as cognitive diagnostic models (CDMs). After presenting the algebraic components required for Louis’ formula, two real data analyses, with accompanying numerical illustrations, are presented. Next, a Monte Carlo simulation is presented to compare the computational efficiency of Louis’ formula with previously existing methods. Results from these presentations suggest that Louis’ formula should be adopted as a standard method when computing the observed information matrix for IRT models and CDMs fitted with the EM algorithm due to its computational efficiency and flexibility.     

Psychosis,symbol, affectivity 1: etiopathogenesis and treatment through analytical psychology

This is the first of two papers concerning our study into an integrated approach to psychotic disorders, conducted at the University of Palermo’s Psychiatry Unit Polyclinic over approximately 15 years. Here we will explore and reflect upon the acute psychotic condition mainly from a theoretical and conceptual perspective, while in the second paper we will explore the clinical perspective. From the point of view of psychopathology, and in the light of C.G. Jung’s conceptualization of analytical psychology, as well as calling on contributions from other authors from the systemic-relational and post-psychoanalytic field, we will clarify the ideas developed over these last few years by our team. From a more speculative perspective we will advance new interpretive hypotheses in an attempt to thoroughly understand the nature of the psychotic condition, both on a psychodynamic relational and a neuroscientific level. The paper describes how we revised our understanding of psychosis from a brain disease to a process aimed at the rearrangement of psychic functioning, as anticipated by John Weir Perry nearly 40 years ago.     

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.     

The Lived Experiences of Addiction Counselors After Marijuana Legalization

This qualitative study examines the perceptions of counselors who work with populations with addictions in states where marijuana has been legalized. Findings include themes related to client experiences, counselor experiences, specific interventions, and lack of understanding.     

Brief Cognitive-Behavioral Therapy for Suicidal Inpatients

Suicide risk is elevated in psychiatric patients following discharge from inpatient care. Despite this vulnerability, there has been limited research investigating suicide prevention protocols that take into account the unique system characteristics of this setting (e.g., short lengths of stay, crisis stabilization treatment model, multidisciplinary team coordination). Cognitive-behavioral therapy (CBT) has demonstrated efficacy for improving suicide risk in outpatients, but has not been validated with inpatients. The current study was a treatment development and pilot feasibility open trial that modified brief cognitive-behavioral therapy (BCBT) for an inpatient setting (BCBT-I). Key treatment modifications included administering up to 10 sessions (depending on patient length of stay), daily, and in a standardized order, with core crisis management skills introduced during the first three sessions. In addition, coordination with the inpatient treatment team was included in BCBT-I implementation. Six adult inpatients with a recent suicide attempt enrolled and completed an average of 4.67 BCBT-I sessions (SD = 1.36). The treatment was highly acceptable (Client Satisfaction Questionnaire total score M = 3.49, SD = 0.73). Pre- to posttreatment effect sizes demonstrated improvements in suicidal ideation (d = 0.97), depression (d = 1.33), and suicidal implicit associations (d = 1.28). All but one of the participants (83%) completed follow-up assessments 1-, 2-, and 3-months postdischarge. Over follow-up, two participants reported suicidal ideation (both without intent), and none reported suicide attempts, preparatory acts or behaviors, or nonsuicidal self-injury. This study provided preliminary evidence supporting the feasibility of CBT to treat suicidal inpatients. Future research is needed to validate BCBT-I in a larger, randomized controlled trial to determine whether BCBT-I reduces suicide risk beyond that afforded by inpatient treatment alone.     

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.     

Comparing computer‐based training and lecture formats to teach visual analysis of baseline‐treatment graphs

Accurate analysis of data is vital to the validation of interventions. As such, there has been a recent increase in studies evaluating visual analysis training procedures. However, past investigations have not evaluated direct and indirect visual analysis training methods with matched instructional content that was systematically designed. Furthermore, training has rarely included assessment of generalization and maintenance of visual analysis skills. The purpose of the current dissertation study was to compare the effectiveness and efficiency of (a) computer‐based training, (b) lecture formats with and (c) without the opportunity to pause, and (d) a no‐training group to teach visual analysis of AB graphs to university students. To make these formats directly comparable, the instructional content was equated by ensuring information and examples were identical across the three training procedures. Eighty‐three students were randomly assigned to one of the four groups. Results showed that all three training formats produced increases in accurate responding compared to the no‐training group. Visual analysis skills generalized to novel graphs and maintained approximately 2 weeks following all trainings. These results suggest that structured approaches that are carefully designed to train visual analysis are effective and lead to gains that generalize and maintain in the absence of training.     

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.