Expert and novice performance in solving physics problems

Elucidating cognitive processes in cardiac arrest team leaders: a virtual reality-based cued-recall study of experts and novices

BACKGROUND

Team leadership during medical emergencies like cardiac arrest resuscitation is cognitively demanding, especially for trainees. These cognitive processes remain poorly characterized due to measurement challenges. Using virtual reality simulation, this study aimed to elucidate and compare communication and cognitive processes-such as decision-making, cognitive load, perceived pitfalls, and strategies-between expert and novice code team leaders to inform strategies for accelerating proficiency development.

METHODS

A simulation-based mixed methods approach was utilized within a single large academic medical center, involving twelve standardized virtual reality cardiac arrest simulations. These 10- to 15-minutes simulation sessions were performed by seven experts and five novices. Following the simulations, a cognitive task analysis was conducted using a cued-recall protocol to identify the challenges, decision-making processes, and cognitive load experienced across the seven stages of each simulation.

RESULTS

The analysis revealed 250 unique cognitive processes. In terms of reasoning patterns, experts used inductive reasoning, while novices tended to use deductive reasoning, considering treatments before assessments. Experts also demonstrated earlier consideration of potential reversible causes of cardiac arrest. Regarding team communication, experts reported more critical communications, with no shared subthemes between groups. Experts identified more teamwork pitfalls, and suggested more strategies compared to novices. For cognitive load, experts reported lower median cognitive load (53) compared to novices (80) across all stages, with the exception of the initial presentation phase.

CONCLUSIONS

The identified patterns of expert performance - superior teamwork skills, inductive clinical reasoning, and distributed cognitive strategiesn - can inform training programs aimed at accelerating expertise development.

ChatGPT vs. sleep disorder specialist responses to common sleep queries: Ratings by experts and laypeople

BACKGROUND

Many individuals use the Internet, including generative artificial intelligence like ChatGPT, for sleep-related information before consulting medical professionals. This study compared responses from sleep disorder specialists and ChatGPT to common sleep queries, with experts and laypersons evaluating the responses' accuracy and clarity.

METHODS

We assessed responses from sleep medicine specialists and ChatGPT-4 to 140 sleep-related questions from the Korean Sleep Research Society's website. In a blinded study design, sleep disorder experts and laypersons rated the medical helpfulness, emotional supportiveness, and sentence comprehensibility of the responses on a 1-5 scale.

RESULTS

Laypersons rated ChatGPT higher for medical helpfulness (3.79 ± 0.90 vs. 3.44 ± 0.99, p < .001), emotional supportiveness (3.48 ± 0.79 vs. 3.12 ± 0.98, p < .001), and sentence comprehensibility (4.24 ± 0.79 vs. 4.14 ± 0.96, p = .028). Experts also rated ChatGPT higher for emotional supportiveness (3.33 ± 0.62 vs. 3.01 ± 0.67, p < .001) but preferred specialists' responses for sentence comprehensibility (4.15 ± 0.74 vs. 3.94 ± 0.90, p < .001). When it comes to medical helpfulness, the experts rated the specialists' answers slightly higher than the laypersons did (3.70 ± 0.84 vs. 3.63 ± 0.87, p = .109). Experts slightly preferred specialist responses overall (56.0%), while laypersons favored ChatGPT (54.3%; p < .001). ChatGPT's responses were significantly longer (186.76 ± 39.04 vs. 113.16 ± 95.77 words, p < .001).

DISCUSSION

Generative artificial intelligence like ChatGPT may help disseminate sleep-related medical information online. Laypersons appear to prefer ChatGPT's detailed, emotionally supportive responses over those from sleep disorder specialists.

An Improved 21st Century Judicial System with Environmental Science Expertise is Needed for Resolving Interstate Water Conflicts

As stresses on groundwater resources increase due to growing population and climate change, water litigation, such as the recently decided Mississippi (MS) vs Tennessee (TN) lawsuit, will become more common. In the United States, lawsuits between states can be heard only by the Supreme Court of the United States (SCOTUS). These lawsuits are expensive and lengthy, often requiring highly specialized technical expertise. In the MS vs TN case, the Court unanimously held that an interstate aquifer is subject to equitable apportionment. Although this appears to be a sound resolution, a careful examination of the SCOTUS hearing transcript revealed that the Justices had several egregious misconceptions about the groundwater system. These misconceptions arose in part due to the failure of technical experts to communicate groundwater concepts in understandable terms and in part due to the Justices' lack of expertise in groundwater science. To address these issues, we first explore methods for improving scientific communication in courtrooms. Second, we propose ideas for reforming the legal system and provide compelling arguments for using the lower courts to hear such cases. We also explore the possibility of creating specialized federal water courts to resolve water disputes.

A general nonparametric classification method for multiple strategies in cognitive diagnostic assessment

Cognitive diagnosis models (CDMs) have been used as psychometric tools in educational assessments to estimate students' strengths and weaknesses in terms of cognitive skills learned and skills that need study. In practice, it is not uncommon that questions can often be solved using more than one strategy, which requires CDMs capable of accommodating multiple strategies. However, existing parametric multi-strategy CDMs need a large sample size to produce a reliable estimation of item parameters and examinees' proficiency class memberships, which obstructs their practical applications. This article proposes a general nonparametric multi-strategy classification method with promising classification accuracy in small samples for dichotomous response data. The method can accommodate different strategy selection approaches and different condensation rules. Simulation studies showed that the proposed method outperformed the parametric CDMs when sample sizes were small. A set of real data was analyzed as well to illustrate the application of the proposed method in practice.

Evaluating website quality: which decision criteria do consumers use to evaluate website quality?

Purpose

The purpose of this paper is to determine which design and operational attributes of e-commerce websites consumers use to assess website quality. Cue utilization theory is used to examine the explanatory power, robustness and relevance of the WebQual model. Results indicate which WebQual dimensions are the most relevant and salient to website users. These dimensions are categorized by their perceived and confidence values. A second study is conducted about how website users evaluate and utilize the WebQual dimensions.

Design/methodology/approach

Survey methodology was utilized to provide insight into the nomological validity of the WebQual model by examining it through a cue utilization lens.

Findings

The first study categorizes the WebQual dimensions on their ability to provide a diagnostic measure of website quality, and consumer confidence in their ability to use these cues when judging the website's overall quality. The second study presents results of each dimension in relation to the quality evaluation of an actual e-commerce website. Additional analysis also revealed gender differences in cue utilization.

Originality/value

This study provided insight into WebQual-based research and identified original differences in cue utilization across genders. Results suggest that it may be beneficial for brand managers to focus on a subset of quality dimensions, rather than assume that consumers are comfortable using all website attributes to formulate quality judgments. These, results contribute to multiple literatures by providing a model that developers can utilize to focus on the deterministic characteristics of overall website quality. Further, the cue utilization perspective provides additional avenues for fruitful further research into consumer decision-making in the e-commerce context.

Reimagining peer review as an expert elicitation process

Journal peer review regulates the flow of ideas through an academic discipline and thus has the power to shape what a research community knows, actively investigates, and recommends to policymakers and the wider public. We might assume that editors can identify the 'best' experts and rely on them for peer review. But decades of research on both expert decision-making and peer review suggests they cannot. In the absence of a clear criterion for demarcating reliable, insightful, and accurate expert assessors of research quality, the best safeguard against unwanted biases and uneven power distributions is to introduce greater transparency and structure into the process. This paper argues that peer review would therefore benefit from applying a series of evidence-based recommendations from the empirical literature on structured expert elicitation. We highlight individual and group characteristics that contribute to higher quality judgements, and elements of elicitation protocols that reduce bias, promote constructive discussion, and enable opinions to be objectively and transparently aggregated.

Moral Reasoning Enables Developmental and Societal Change

Moral reasoning is an essential part of how humans develop and a fundamental aspect of how human societies change over time. On a developmental timescale, reasoning about interpersonal disagreements and dilemmas spurs age-related changes in moral judgments from childhood to adulthood. When asked to distribute resources among others, even young children strive to balance competing concerns with equality, merit, and need. Over the course of development, reasoning and judgments about resource distribution and other moral issues become increasingly sophisticated. From childhood to adulthood, individuals not only evaluate acts as right or wrong but also take the extra steps to rectify inequalities, protest unfair norms, and resist stereotypic expectations about others. The development of moral reasoning also enables change on a societal timescale. Across centuries and communities, ordinary individuals have called for societal change based on moral concerns with welfare, rights, fairness, and justice. Individuals have effectively employed reasoning to identify and challenge injustices. In this article, we synthesize recent insights from developmental science about the roles of moral reasoning in developmental and societal change. In the concluding section, we turn to questions for future research on moral reasoning and change.

Becoming an Expert Teacher: Assessing Expertise Growth in Peer Feedback Video Recordings by Lexical Analysis

Teacher education enables students to grow from ‘novice’ into ‘starting expert’ teachers. In this study, students’ textual peer feedback on video recordings of their teaching practice was analysed to determine the growth of their expertise in relation to blended curriculum design. The degree to which curriculum and literature influenced their feedback was assessed by semantic network analysis of prominent words from the literature that was studied, as well as the lexical richness and semantic cohesion of students’ peer feedback and reflections. The lexical richness and the semantic cohesion increased significantly by the end of the semester. This means that students incorporated new vocabulary and maintained semantic consistency while using the new words. Regarding the semantic network analysis, we found stronger connections between the topics being discussed by the students at the end of the semester. Active use of video and peer feedback enhances students’ active knowledge base, thus furthering effective teaching.

Schematic memory persistence and transience for efficient and robust continual learning

Continual learning is considered a promising step toward next-generation Artificial Intelligence (AI), where deep neural networks (DNNs) make decisions by continuously learning a sequence of different tasks akin to human learning processes. It is still quite primitive, with existing works focusing primarily on avoiding (catastrophic) forgetting. However, since forgetting is inevitable given bounded memory and unbounded task loads, 'how to reasonably forget' is a problem continual learning must address in order to reduce the performance gap between AIs and humans, in terms of (1) memory efficiency, (2) generalizability, and (3) robustness when dealing with noisy data. To address this, we propose a novel ScheMAtic memory peRsistence and Transience (SMART)¹ framework for continual learning with external memory that builds on recent advances in neuroscience. The efficiency and generalizability are enhanced by a novel long-term forgetting mechanism and schematic memory, using sparsity and 'backward positive transfer' constraints with theoretical guarantees on the error bound. Robust enhancement is achieved using a novel short-term forgetting mechanism inspired by background information-gated learning. Finally, an extensive experimental analysis on both benchmark and real-world datasets demonstrates the effectiveness and efficiency of our model.

Evidence-based medicine's curious path: From clinical epidemiology to patient-centered care through decision analysis

Evidence-based medicine (EBM), one of the most important movements in health care, has been a lightning rod for controversy. Conflicts about the meaning and value of EBM are owing in part to lack of clarity about basic questions regarding its development, the importance of expertise and intuition, and the role of evidence in clinical decision making. These issues have persisted in part because of unclarity at the outset, but also because of how EBM evolved, why it was introduced when it was, and how it was modified following its introduction. This paper traces the evolution of EBM from clinical epidemiology (CE) and the internal dispute that precipitated the developers to establish EBM as a distinct approach to clinical practice. The paper proposes that health care industrialization also had a significant role in EBM's emergence and that industrialization influenced the decision to merge EBM with the method of normative decision making known as decision analysis (DA). The paper discusses the impact of this merger, in particular how it led to EBM's identification with managed care and has added momentum to the effort at forging a connection between a normative decision model and clinical judgement. This effort would turn clinical decision making into a conduit for bringing administrative rules and regulations into the consulting room and would result in expertise becoming a surplus skill. The paper closes by discussing a challenge yet unmet by EBM's advocates and critics-to chronicle the dangers that EBM in the framework of DA during the current era of industrialization poses to health and health care, and discover ways of unhinging the relationship between model and judgement.

Enhancing clinical judgement in virtual care for complex chronic disease

The COVID-19 pandemic has transformed traditional in-person care into a new reality of virtual care for patients with complex chronic disease (CCD), but how has this transformation impacted clinical judgement? I argue that virtual specialist-patient interaction challenges clinical reasoning and clinical judgement (clinical reasoning combined with statistical reasoning). However, clinical reasoning can improve by recognising the abductive, deductive, and inductive methods that the clinician employs. Abductive reasoning leading to an inference to the best explanation or invention of an explanatory hypothesis is the default response to unfamiliar or confusing situations. Deductive reasoning supports a previously established goal, but deductive accuracy requires sound premises leading to a valid conclusion. Inductive reasoning uses efficient data sorting, data interpretation, and plan creation without a previously established goal, and allows assessing inferential accuracy over time. In all cases, communication remains the backbone of the clinical encounter. Virtual care for CCD challenges clinical judgement by reducing available information, so even experienced specialists who use induction might default to deduction or abduction. The visit might shorten, decreasing narrative competence and in-turn management quality. Clinical judgement in virtual encounters can be enhanced by allowing sufficient time, employing allied health staff, using an advance script, avoiding dogmatic commitment to either virtual or in-person encounters, special training in virtual care, and conscious awareness of abductive, deductive, and inductive reasoning processes. Clinical judgement in virtual encounters especially calls for Gestalt cognition to assess a situational pattern irreducible to its parts and independent of its particulars, so that efficient data interpretation and self-reflection are enabled. Gestalt cognition integrates abduction, deduction, and induction, appropriately divides the time and effort spent on each, and can compensate for reduced available information. Evaluating one's clinical judgement for those components especially vulnerable to compromise can help optimize the delivery of virtual care for patients with CCD.

Chunking and data compression in verbal short-term memory

Short-term verbal memory is improved when words can be chunked into larger units. Miller (1956) suggested that the capacity of verbal short-term memory is determined by the number of chunks that can be stored in memory, rather than by the number of items or the amount of information. But how does the improvement due to chunking come about, and is memory really determined by the number of chunks? One possibility is that chunking is a form of data compression. It allows more information to be stored in the available capacity. An alternative is that chunking operates primarily by redintegration. Chunks exist only in long-term memory, and enable the corresponding items in short-term memory to be reconstructed more reliably from a degraded trace. We review the data favoring each of these views and discuss the implications of treating chunking as data compression. Contrary to Miller, we suggest that memory capacity is primarily determined both by the amount of information that can be stored but also by the underlying representational vocabulary of the memory system. Given the limitations on the representations that can be stored in verbal short-term memory, chunking can sometimes allow the information capacity of short-term memory to be exploited more efficiently. (202 words).

How good is our diagnostic intuition? Clinician prediction of bacteremia in critically ill children

Background

Clinical intuition and nonanalytic reasoning play a major role in clinical hypothesis generation; however, clinicians’ intuition about whether a critically ill child is bacteremic has not been explored. We endeavored to assess pediatric critical care clinicians’ ability to predict bacteremia and to evaluate what affected the accuracy of those predictions.

Methods

We conducted a retrospective review of clinicians’ responses to a sepsis screening tool (“Early Sepsis Detection Tool” or “ESDT”) over 6 months. The ESDT was completed during the initial evaluation of a possible sepsis episode. If a culture was ordered, they were asked to predict if the culture would be positive or negative. Culture results were compared to predictions for each episode as well as vital signs and laboratory data from the preceding 24 h.

Results

From January to July 2017, 266 ESDTs were completed. Of the 135 blood culture episodes, 15% of cultures were positive. Clinicians correctly predicted patients with bacteremia in 82% of cases, but the positive predictive value was just 28% as there was a tendency to overestimate the presence of bacteremia. The negative predictive value was 96%. The presence of bandemia, thrombocytopenia, and abnormal CRP were associated with increased likelihood of correct positive prediction.

Conclusions

Clinicians are accurate in predicting critically ill children whose blood cultures, obtained for symptoms of sepsis, will be negative. Clinicians frequently overestimate the presence of bacteremia. The combination of evidence-based practice guidelines and bedside judgment should be leveraged to optimize diagnosis of bacteremia.

Functional, anatomical and diffusion tensor MRI study of radiology expertise

BACKGROUND

Repeated practice to acquire expertise could result in the structural and functional changes in relevant brain circuits as a result of long-term potentiation, neurogenesis, glial genesis, and remodeling.

PURPOSE

The goal of this study is to use task fMRI to study the brain of expert radiologists performing a diagnosis task where a series of medical images were presented during fMRI acquisition for 12s and participants were asked to choose a diagnosis. Structural and diffusion-tensor MRI were also acquired.

METHODS

Radiologists (N = 12, 11M, 38.2±10.3 years old) and non-radiologists (N = 17, 15M, 30.6±5.5 years old) were recruited with informed consent. Medical images were presented for 12 s and three multiple choices were displayed and the participants were asked to choose a diagnosis. fMRI, structural and diffusion-tensor MRI were acquired. fMRI analysis used FSL to determine differences in fMRI responses between groups. Voxel-wise analysis was performed to determine if subcortical volume, cortical thickness and fractional anisotropy differed between groups. Correction for multiple comparisons used false discovery rate.

RESULTS

Radiologists showed overall lower task-related brain activation than non-radiologists. Radiologists showed significantly lower activation in the left lateral occipital cortex, left superior parietal lobule, occipital pole, right superior frontal and precentral gyri, lingual gyrus, and the left intraparietal sulcus (p<0.05). There were no significant differences between groups in cortical thickness, subcortical volume and fractional anisotropy (p>0.05).

CONCLUSIONS

Radiologists and non-radiologists had no significant difference in structural metrics. However, in diagnosis tasks, radiologists showed markedly lower task-related brain activations overall as well as a number of high-order visual and non-visual brain regions than non-radiologists. Some brain circuits appear to be uniquely associated with differential-diagnosis paradigm expertise that are not involved in simpler object-recognition cases. Improved understanding of the brain circuitry involved in acquisition of expertise might be used to design optimal training paradigms.

Sequence matters: patterns in task-based clinical reasoning

Background

The cognitive pathways that lead to an accurate diagnosis and efficient management plan can touch on various clinical reasoning tasks (1). These tasks can be employed at any point during the clinical reasoning process and though the four distinct categories of framing, diagnosis, management, and reflection provide some insight into how these tasks map onto clinical reasoning, much is still unknown about the task-based clinical reasoning process. For example, when and how are these tasks typically used? And more importantly, do these clinical reasoning task processes evolve when patient encounters become complex and/or challenging (i.e. with contextual factors)?

Methods

We examine these questions through the lens of situated cognition, context specificity, and cognitive load theory. Sixty think-aloud transcripts from 30 physicians who participated in two separate cases – one with a contextual factor and one without – were coded for 26 clinical reasoning tasks (1). These tasks were organized temporally, i.e. when they emerged in their think-aloud process. Frequencies of each of the 26 tasks were aggregated, categorized, and visualized in order to analyze task category sequences.

Results

We found that (a) as expected, clinical tasks follow a general sequence, (b) contextual factors can distort this emerging sequence, and (c) the presence of contextual factors prompts more experienced physicians to clinically reason similar to that of less experienced physicians.

Conclusions

These findings add to the existing literature on context specificity in clinical reasoning and can be used to strengthen teaching and assessment of clinical reasoning.

Tracing the evolution of AI: conceptualization of artificial intelligence in mass media discourse

Purpose

This study/paper aims to understand the public perceptions of AI through mass media discourse. In the past few years, significant progress has been made in the field of artificial intelligence (AI). The benefits of AI are obvious, but there is still huge uncertainty and controversy over the public perception of AI. How does the mass media conceptualize AI?

Design/methodology/approach

In this paper, the authors analyze the evolution of AI covered by five major news media outlets in the past 30 years from 7 dimensions: scientific subject, keyword, country, institution, people, topic and opinion polarity.

Findings

First of all, different subjects are competing for and dividing up the right to speak of AI, leading to the gradual fragmentation of the concept of AI. Second, reporting on AI often includes reference to commercial institutions and scientists, showing a successful integration of science and business. Moreover, the result of topic modeling shows that news media mainly defines AI from three perspectives: an imagination, a commercial product and a field of scientific research. Finally, negative reports have focused on various issues relating to AI ethics.

Originality/value

The results can help bridge various conversations surrounding AI and promote richer discussions, increase the participation of scientists, businesses, governments and the public and provide more perspectives on the functions, prospects and pitfalls of AI.

Brain activity links performance in science reasoning with conceptual approach

Understanding how students learn is crucial for helping them succeed. We examined brain function in 107 undergraduate students during a task known to be challenging for many students-physics problem solving-to characterize the underlying neural mechanisms and determine how these support comprehension and proficiency. Further, we applied module analysis to response distributions, defining groups of students who answered by using similar physics conceptions, and probed for brain differences linked with different conceptual approaches. We found that integrated executive, attentional, visual motion, and default mode brain systems cooperate to achieve sequential and sustained physics-related cognition. While accuracy alone did not predict brain function, dissociable brain patterns were observed when students solved problems by using different physics conceptions, and increased success was linked to conceptual coherence. Our analyses demonstrate that episodic associations and control processes operate in tandem to support physics reasoning, offering potential insight to support student learning.

Reasoning processes in clinical reasoning: from the perspective of cognitive psychology

Clinical reasoning is considered a crucial concept in reaching medical decisions. This paper reviews the reasoning processes involved in clinical reasoning from the perspective of cognitive psychology. To properly use clinical reasoning, one requires not only domain knowledge but also structural knowledge, such as critical thinking skills. In this paper, two types of reasoning process required for critical thinking are discussed: inductive and deductive. Inductive and deductive reasoning processes have different features and are generally appropriate for different types of tasks. Numerous studies have suggested that experts tend to use inductive reasoning while novices tend to use deductive reasoning. However, even experts sometimes use deductive reasoning when facing challenging and unfamiliar problems. In clinical reasoning, expert physicians generally use inductive reasoning with a holistic viewpoint based on a full understanding of content knowledge in most cases. Such a problem-solving process appears as a type of recognition-primed decision making only in experienced physicians' clinical reasoning. However, they also use deductive reasoning when distinct patterns of illness are not recognized. Therefore, medical schools should pursue problem-based learning by providing students with various opportunities to develop the critical thinking skills required for problem solving in a holistic manner.

A protocol analysis of use of forward and backward reasoning during valuation problem solving

Purpose

Behavioural studies of valuers have suggested that valuers rely on a number of cognitive strategies involving reasoning and intuition when undertaking a valuation task. However, there are few studies of the actual reasoning mechanisms in valuation. In other fields, much attention has been paid to forward and backward reasoning, as this shows the choices and decisions that are made in undertaking a complex task. This paper studied this during a valuation task. The purpose of this paper is twofold: first, to develop a methodological approach for empirical research on valuers’ reasoning, and, second, to report expert-novice differences on valuers’ use of forward and backward reasoning during a valuation problem solving.

Design/methodology/approach

The study utilised a verbal protocol analysis (VPA) to elicit think-aloud data from a purposive sample of a group of valuers of different levels of expertise undertaking a commercial-valuation task. Through a content analysis interpretive strategy, the transcripts were analysed into different cognitive segments identifying the forward and backward reasoning strategies.

Findings

The findings showed that valuers accomplished the valuation task by dividing the overall problem into sub-problems. These sub-problems are thereafter solved by integrating available data with existing knowledge by relying more on forward reasoning than backward reasoning. However, there were effects associated with the level of expertise in the way the processes of forward and backward reasoning are used, with the expert and intermediate valuers being more thorough and comprehensive in their reasoning process than the novices.

Research limitations/implications

This study explores the possibility that forward and backward reasoning play an important role in commercial valuation problem solving using a limited sample of valuers. Given this, data cannot be generalised to all valuation practice settings but may motivate future research that examines the effectiveness of forward and backward reasoning in diverse valuation practice settings and develops a holistic model of valuation reasoning.

Practical implications

The findings of this study are applicable to valuation practice. Future training efforts need to evaluate the usefulness of teaching problem solving and explicitly recognise forward and backward reasoning, along with other problem-solving strategies uncovered in this study, as standard training strategies for influencing the quality of valuation decisions.

Originality/value

By adopting VPA, this study employs an insightful and rich dataset which allows an interpretation of thoughts of valuers into cognitive reasoning strategies that provide a deeper level of understanding of how valuers solve valuation problem; this has not been possible in previous related valuation studies.

THE INTERACTION OF INTUITION AND RATIONALITY DURING ESCALATED NPD DECISIONS: AN INVESTIGATION OF DECISION-MAKERS’ AFFECTIVE STATES

Decision-makers often struggle to terminate unsuccessful new product development (NPD) projects, so that escalating commitment occurs. Although research shows that rational and intuitive decision-making styles (DMS) as well as a decision-maker’s affective state determines the performance of NPD decisions, little is known about their influences on escalating commitment. By applying the affect infusion model in an experimental study, we investigate how a decision-maker’s affective state influence their escalating commitment by focusing on their use of a rational and an intuitive DMS. Our findings, based on 366 respondents, show that a rational DMS is unable to reduce commitment escalation. Surprisingly, an intuitive DMS is able to reduce a decision-maker’s commitment in the case of a positive affect, whereas a rational DMS increases their commitment in the case of a negative affect. Thus, our interdisciplinary research on affect and decision-making extends and contributes to research into decision-making during the NPD process as well as into escalating commitment.

Mathematical expertise modulates the architecture of dorsal and cortico-thalamic white matter tracts

To what extent are levels of cognitive expertise reflected in differential structural connectivity of the brain? We addressed this question by analyzing the white matter brain structure of experts (mathematicians) versus non-experts (non-mathematicians) using probabilistic tractography. Having mathematicians and non-mathematicians as participant groups enabled us to directly compare profiles of structural connectivity arising from individual levels of expertise in mathematics. Tracking from functional seed regions activated during the processing of complex arithmetic formulas revealed an involvement of various fiber bundles such the inferior fronto-occipital fascicle, arcuate fasciculus/superior longitudinal fasciculus (AF/SLF), cross-hemispheric connections of frontal lobe areas through the corpus callosum and cortico-subcortical connectivity via the bilateral thalamic radiation. With the aim of investigating expertise-dependent structural connectivity, the streamline density was correlated with the level of expertise, defined by automaticity of processing complex mathematics. The results showed that structural integrity of the AF/SLF was higher in individuals with higher automaticity, while stronger cortico-thalamic connectivity was associated with lower levels of automaticity. Therefore, we suggest that expertise in the domain of mathematics is reflected in plastic changes of the brain's white matter structure, possibly reflecting a general principle of cognitive expertise.

Cognitive diagnosis models for multiple strategies

Cognitive diagnosis models (CDMs) have been used as psychometric tools in educational assessments to estimate students' proficiency profiles. However, most CDMs assume that all students adopt the same strategy when approaching problems in an assessment, which may not be the case in practice. This study develops a generalized multiple-strategy CDM for dichotomous response data. The proposed model provides a unified framework to accommodate various condensation rules (e.g., conjunctive, disjunctive, and additive) and different strategy selection approaches (i.e., probability-matching, over-matching, and maximizing). Model parameters are estimated using the marginal maximum likelihood estimation via expectation-maximization algorithm. Simulation studies showed that the parameters of the proposed model can be adequately recovered and that the proposed model was relatively robust to some types of model misspecifications. A set of real data was analysed as well to illustrate the use of the proposed model in practice.