Cognitive diagnostic models show great promise in language assessment for providing rich diagnostic information. The lack of a full understanding of second language listening subskills made model selection difficult. In search of optimal CDM that could provide a better understanding of L2 listening subskills and facilitate accurate classification, this study carried a two-layer model selection. At the test level, A-CDM, LLM, and R-RUM had an acceptable and comparable model fit, suggesting mixed inter-attribute relationships of L2 listening (...) subskills. At the item level, Mixed-CDMs were selected and confirmed the existence of mixed relationships. Mixed-CDMs had better model and person fit than G-DNIA. In addition to statistical approaches, the content analysis provided theoretical evidence to confirm and amend the item-level CDMs. It was found that semantic completeness pertaining to the attributes and item features may influence the attribute relationships. Inexplicable attribute conflicts could be a signal of suboptimal model choice. Sample size and the number of multi-attribute items should be taken into account in L2 listening cognitive diagnostic modeling studies. This study provides useful insights into the model selection and the underlying cognitive process for L2 listening tests. (shrink)

This study applied cognitive diagnostic models to assess students’ learning progressions in energy. A Q-matrix was proposed based on existing literature about learning progressions of energy in the physical science domain and the Trends in International Mathematics and Science Study assessment framework. The Q-matrix was validated by expert review and real data analysis. Then, the deterministic inputs, noisy ‘and’ gate model with hierarchical relations was applied to data from three jurisdictions that had stable, defined science curricula. The (...) results suggested that the hypothesized learning progression was consistent with the observed progression in understanding the energy concept. We also found similarities in students’ attribute mastery across the three jurisdictions. In addition, we examined the instructional sensitivity of the selected item. We discuss several curriculum-related issues and student misconceptions that may affect students’ learning progressions and mastery patterns in different regions of the world. (shrink)

In response to the big data era trend, statistics has become an indispensable part of mathematics education in junior high school. In this study, a pre-test and a post-test were developed for the six attributes of the data distribution characteristic. This research then used the cognitive diagnosis model to learn about the poorly mastered attributes and to verify whether cognitive diagnosis can be used for targeted intervention to improve students' abilities effectively. One hundred two eighth graders participated in (...) the experiment and were divided into two groups. Among them, the intervention materials read by the experimental group students only contained attributes that they could not grasp well. In contrast, the reading materials of the control group were non-targeted. The results of the study showed the following: The variance and the weighted average were poorly mastered by students in the pre-test; compared with the control group, the average test score of the experimental group was significantly improved; in terms of attributes, the experimental group students' mastery of variance and the weighted average was significantly improved than the pre-test, while the control group's mastery was not. Based on this, some teaching suggestions were put forward. (shrink)

A new diagnostic modeling system for automatically synthesizing a deep‐structure model of a student's misconceptions or bugs in his basic mathematical skills provides a mechanism for explaining why a student is making a mistake as opposed to simply identifying the mistake. This report is divided into four sections: The first provides examples of the problems that must be handled by a diagnostic model. It then introduces procedural networks as a general framework for representing the knowledge underlying a skill. (...) The challenge in designing this representation is to find one that facilitates the discovery of misconceptions or bugs existing in a particular student's encoding of this knowledge. The second section discusses some of the pedagogical issues that have emerged from the use of diagnostic models within an instructional system. This discussion is framed in the context of a computer‐based tutoring/gaming system developed to teach students and student teachers how to diagnose bugs strategically as well as how to provide a better understanding of the underlying structure of arithmetic skills. The third section describes our uses of an executable network as a tool for automatically diagnosing student behavior, for automatically generating “diagnostic” tests, and for judging the diagnostic quality of a given exam. Included in this section is a discussion of the success of this system in diagnosing 1300 school students from a data base of 20.000 test items. The last section discusses future research directions. (shrink)

Cognitive diagnostic assessment has been developed rapidly to provide fine-grained diagnostic feedback on students’ subskills and to provide insights on remedial instructions in specific domains. To date, most cognitive diagnostic studies on reading tests have focused on retrofitting a single booklet from a large-scale assessment. Critical issues in CDA involve the scarcity of research to develop diagnostic tests and the lack of reliability and validity evidence. This study explored the development and validation of the (...) Diagnostic Chinese Reading Comprehension Assessment for primary students under the CDA framework. Reading attributes were synthesized based on a literature review, the national curriculum criteria, the results of expert panel judgments, and student think-aloud protocols. Then, the tentative attributes were used to construct three booklets of reading comprehension items for 2–6 graders at three key stages. The assessment was administered to a large population of students in grades 2–6 from 20 schools in a district of Changchun City, China. Q-matrices were compared and refined using the model-data fit and an empirical validation procedure, and five representative cognitive diagnostic models were compared for optimal performance. The fit indices suggested that a six-attribute structure and the G-DINA model were best fitted for the reading comprehension assessment. In addition, diagnostic reliability, construct, internal and external validity results were provided, supporting CDM classifications as reliable, accurate, and useful. Such diagnostic information could be utilized by students, teachers, and administrators of reading programs and instructions. (shrink)

The aim of cognitive diagnosis is to classify respondents' mastery status of latent attributes from their responses on multiple items. Since respondents may answer some but not all items, item-level missing data often occur. Even if the primary interest is to provide diagnostic classification of respondents, misspecification of missing data mechanism may lead to biased conclusions. This paper proposes a joint cognitive diagnosis modeling of item responses and item-level missing data mechanism. A Bayesian Markov chain Monte Carlo (...) method is developed for model parameter estimation. Our simulation studies examine the parameter recovery under different missing data mechanisms. The parameters could be recovered well with correct use of missing data mechanism for model fit, and missing that is not at random is less sensitive to incorrect use. The Program for International Student Assessment 2015 computer-based mathematics data are applied to demonstrate the practical value of the proposed method. (shrink)

While translation competence assessment has been playing an increasingly facilitating role in translation teaching and learning, it still failed to offer fine-grained diagnostic feedback based on certain reliable translation competence standards. As such, this study attempted to investigate the feasibility of providing diagnostic information about students’ translation competence by integrating China’s Standards of English with cognitive diagnostic assessment approaches. Under the descriptive parameter framework of CSE translation scales, an attribute pool was established, from which seven attributes (...) were identified based on students’ and experts’ think-aloud protocols. A checklist comprising 20 descriptors was developed from CSE translation scales, with which 458 students’ translation responses were rated by five experts. In addition, a Q-matrix was established by seven experts. By comparing the diagnostic performance of four widely-used cognitive diagnostic models, linear logistic model was selected as the optimal model to generate fine-grained information about students’ translation strengths and weaknesses. Furthermore, relationships among translation competence attributes were discovered and diagnostic results were shown to differ across high and low proficiency groups. The findings can provide insights for translation teaching, learning and assessment. (shrink)

Cognitive diagnosis models (CDMs) allow classifying respondents into a set of discrete attribute profiles. The internal structure of the test is determined in a Q-matrix, whose correct specification is necessary to achieve an accurate attribute profile classification. Several empirical Q-matrix estimation and validation methods have been proposed with the aim of providing well-specified Q-matrices. However, these methods require the number of attributes to be set in advance. No systematic studies about CDMs dimensionality assessment have been conducted, which contrasts (...) with the vast existing literature for the factor analysis framework. To address this gap, the present study evaluates the performance of several dimensionality assessment methods from the factor analysis literature in determining the number of attributes in the context of CDMs. The explored methods were parallel analysis, minimum average partial, very simple structure, DETECT, empirical Kaiser criterion, exploratory graph analysis, and a machine learning factor forest model. Additionally, a model comparison approach was considered, which consists in comparing the model-fit of empirically estimated Q-matrices. The performance of these methods was assessed by means of a comprehensive simulation study that included different generating number of attributes, item qualities, sample sizes, ratios of the number of items to attribute, correlations among the attributes, attributes thresholds, and generating CDM. Results showed that parallel analysis (with Pearson correlations and mean eigenvalue criterion), factor forest model, and model comparison (with AIC) are suitable alternatives to determine the number of attributes in CDM applications, with an overall percentage of correct estimates above 76% of the conditions. The accuracy increased to 97% when these three methods agreed on the number of attributes. In short, the present study supports the use of three methods in assessing the dimensionality of CDMs. This will allow to test the assumption of correct dimensionality present in the Q-matrix estimation and validation methods, as well as to gather evidence of validity to support the use of the scores obtained with these models. The findings of this study are illustrated using real data from an intelligence test to provide guidelines for assessing the dimensionality of CDM data in applied settings. (shrink)

Multi-level teaching has been proven to be more effective than a one-size-fits-all learning approach. This study aimed to develop and implement a multi-level remedial teaching scheme in various high school classes containing students of a wide range of learning levels and to determine its effect of their learning. The deterministic inputs noisy and gate model of cognitive diagnosis theory was used to classify students at multiple levels according to their knowledge and desired learning outcomes. A total of 680 senior (...) high school students from central provinces in China participated in the initial cognitive diagnostic test, and 1,615 high school sophomores from seven high schools in China participated in a formal cognitive diagnosis test. Thirty-six high school students from Southwestern China participated in the think-aloud protocols, and 258 seniors from three high schools in southwest China participated in the remedial teaching experiment. Through an analysis of students’ think-aloud protocols, cognitive errors of students at all levels were determined, and multi-level remedial teaching programs were designed to address these common cognitive errors. The remedial teaching programs were then implemented in three schools and compared with a control group. The results indicated that the students in the experimental group showed a more significant improvement. In this study, the steps of designing multi-level remedial teaching include assessment, classification, and preparing a teaching scheme, which are feasible and can have remarkable teaching effects. This process can be used for reference by teachers of various subjects. (shrink)

Calibration errors are inevitable and should not be ignored during the estimation of item parameters. Items with calibration error can affect the measurement results of tests. One of the purposes of the current study is to investigate the impacts of the calibration errors during the estimation of item parameters on the measurement accuracy, average test length, and test efficiency for variable-length cognitive diagnostic computerized adaptive testing. The other purpose is to examine the methods for reducing the adverse effects (...) of calibration errors. Simulation results show that calibration error has negative effect on the measurement accuracy for the deterministic input, noisy “and” gate model, and the reduced reparameterized unified model; the average test lengths is shorter, and the test efficiency is overestimated for items with calibration errors; the compensatory reparameterized unified model is less affected by the calibration errors, and the classification accuracy, average test length, and test efficiency are slightly stable in the CRUM framework; methods such as improving the quality of items, using large calibration sample to calibrate the parameters of items, as well as using cross-validation method can reduce the adverse effects of calibration errors on CD-CAT. (shrink)

ObjectiveThis study aimed to develop a diagnostic model of multi-kinematic parameters for patients with amnestic mild cognitive impairment (aMCI).MethodIn this cross-sectional study, 94 older adults were included (33 cognitively normal, CN; and 61 aMCI). We conducted neuropsychological battery tests, such as global cognition and cognitive domains, and collected gait parameters by an inertial-sensor gait analysis system. Multivariable regression models were used to identify the potential diagnostic variables for aMCI. Receiver operating characteristic (ROC) curves were applied (...) to assess the diagnostic accuracy of kinematic parameters in discriminating aMCI from healthy subjects.ResultsMultivariable regression showed that multi-kinematic parameters were the potential diagnostic variables for aMCI. The multi-kinematic parameter model, developed using Timed Up and Go (TUG) time, stride length, toe-off/heel stride angles, one-leg standing (OLS) time, and braking force, showed areas under ROC (AUC), 0.96 [95% confidence interval (CI), 0.905–0.857]; sensitivity, 0.90; and specificity, 0.91. In contrast, a single kinematic parameter’s sensitivity was 0.26–0.95 and specificity was 0.21–0.90. Notably, the separating capacity of multi-kinematic parameters was highly similar to Montreal Cognitive Assessment (MoCA; AUC: 0.96 vs. 0.95). Compared to cognitive domain tests, the separating ability was comparable to Auditory Verbal Learning Test (AVLT) and Boston Naming Test (BNT; AUC: 0.96 vs. 0.97; AUC: 0.96 vs. 0.94).ConclusionWe developed one diagnostic model of multi-kinematic parameters for patients with aMCI in Foshan. (shrink)

The complexity of cognitive emulation of human diagnostic reasoning is the major challenge in the implementation of computer-based programs for diagnostic advice in medicine. We here present an epistemological model of diagnosis with the ultimate goal of defining a high-level language for cognitive and computational primitives. The diagnostic task proceeds through three different phases: hypotheses generation, hypotheses testing and hypotheses closure. Hypotheses generation has the inferential form of abduction (from findings to hypotheses) constrained under the (...) criterion of plausibility. Hypotheses testing is achieved by a deductive inference (from generated hypotheses to expected findings), followed by an eliminative induction, constrained under the criterion of covering, which matches expected findings against patient''s findings to select the best explanation. Hypotheses closure is a deductive-inductive type of inference very similar to the inferences operating in hypotheses testing. In this case induction matches the consequences of the generated hypotheses against the patient''s characteristics or preferences under the criterion of utility. By using the language exploited in this epistemological model, it is possible to describe the cognitive tasks underlying the most influential knowledge-based diagnostic systems. (shrink)

BackgroundThe present study aimed at assessing the diagnostic properties of the Montreal Cognitive Assessment in non-demented ALS patients and at exploring the MoCA administrability according to motor-functional status.MaterialsN = 348 patients were administered the MoCA and Edinburgh Cognitive and Behavioural ALS Screen. Administrability rates and prevalence of defective MoCA scores were compared across King’s and Milano-Torino clinical stages. Regression models were run to test whether the non-administrability of the MoCA and a defective score on it were (...) predicted, net of the ECAS-Total, by disease duration, ALS Functional Rating Scale-Revised and progression rate, computed as /disease duration. Intrinsic and post-test diagnostics were tested against a below-cut-off ECAS-total score.ResultsThe 79.9% of patients successfully underwent the MoCA, whose administrability rates decreased with advanced clinical stages, at variance with its defective score prevalence. The probability of the FAB not being administrable was predicted only by lower ALSFRS-R-bulbar and-upper-limb scores; no motor features, but the ECAS-Total, predicted a defective MoCA performance. The MoCA showed high accuracy and good intrinsic and post-test properties—being slightly more specific than sensitive.DiscussionIn non-demented ALS patients, the MoCA is featured by optimal diagnostics as a screener for cognitive impairment, especially for ruling-out its occurrence, as long as patients are in the early stages of the disease and have sufficiently spared bulbar and upper-limb functions. (shrink)

The interdisciplinary research area Cognitive Interaction Technology (CIT) aims to understand and support interactions between human users and other elements of socio-technical systems. Important reasons for the new interest in understanding CIT in sport psychology are the impressive development of cognitive robotics and advanced technologies such as virtual or augmented reality systems, cognitive glasses or neurotechnology settings. The present article outlines this area of research, addresses ethical issues, and presents an empirical study in the context of a (...) new measurement and assessment system for training in karate. Recent advances in the field of cognitive assistance systems enabled largely automatized assessments of individual mental representation structures for action sequences, such as choreographed movement patterns in dance or martial arts. Empirical investigations with karate practitioners of different skill levels demonstrate that advanced software-based survey and algorithmic analysis procedures based on cognitive models generate individualized performance predictions for a movement sequence from theKanku-dai kata(a pre-defined karate movement sequence), which correlated significantly not only with formal expertise (kyu/danrank) but also with the actual likelihood of mistakes in action execution. This information could prospectively be used to define individual training goals for deliberate practice and incorporated into cognitive interaction technology to provide appropriate feedback. We argue that the development of cognitive interaction systems for sport should explicitly take ethical issues into consideration and present a particular developed engineering approach. The potential benefits of such an assistance system for intermediate and advanced practitioners include more effective and flexible practice, as well as supportive effects, and more flexible training schedules. Furthermore, we argue that researchers from the field of sport psychology can benefit from advances in technological systems that enhance the understanding of mental and motor control in skilled voluntary action. (shrink)

The purpose of cognitive diagnostic modeling is to classify students' latent attribute profiles using their responses to the diagnostic assessment. In recent years, each diagnostic classification model makes different assumptions about the relationship between a student's response pattern and attribute profile. The previous research studies showed that the inappropriate DCMs and inaccurate Q-matrix impact diagnostic classification accuracy. Artificial Neural Networks have been proposed as a promising approach to convert a pattern of item responses into a (...) diagnostic classification in some research studies. However, the ANNs methods produced very unstable and unappreciated estimation unless a great deal of care was taken. In this research, we combined ANNs with two typical DCMs, the deterministic-input, noisy, “and” gate model and the deterministic-inputs, noisy, “or” gate model, within a semi-supervised learning framework to achieve a robust and accurate classification. In both simulated study and real data study, the experimental results showed that the proposed method could achieve appreciated performance across different test conditions, especially when the diagnostic quality of assessment was not high and the Q-matrix contained misspecified elements. This research study is the first time of applying the thinking of semi-supervised learning into CDM. Also, we used the validating test to choose the appropriate parameters for the ANNs instead of using typical statistical criteria. (shrink)

One purpose of cognitive diagnostic model is designed to make inferences about unobserved latent classes based on observed item responses. A heuristic for test construction based on the CDM information index proposed by Henson and Douglas has a far-reaching impact, but there are still many shortcomings. He and other researchers had also proposed new methods to improve or overcome the inherent shortcomings of the CDI test assembly method. In this study, one test assembly method of maximizing the minimum (...) inter-class distance is proposed by using mixed-integer linear programming, which aims to overcome the shortcomings that the CDI method is limited to summarize the discriminating power of each item into a single CDI index while neglecting the discriminating power for each pair of latent classes. The simulation results show that compared with the CDI test assembly and random test assembly, the new test assembly method performs well and has the highest accuracy rate in terms of pattern and attributes correct classification rates. Although the accuracy rate of the new method is not very high under item constraints, it is still higher than the CDI test assembly with the same constraints. (shrink)

Contemporary cognitive science offers limited conceptual tools for describing systematic variation in how individuals maintain coherence under conditions of high informational load. While existing models address attention, working memory, predictive processing, and cognitive style, they do not adequately capture deeper structural differences in how minds compress, integrate, and stabilize representations of reality. This paper proposes a conceptual framework of cognitive compression styles: distinct information-processing architectures that organize perception, meaning-making, and coherence through different compression strategies. The framework (...) identifies seven compression styles. Pattern-sensitive, associative, immersive, sequential, narrative, social-reflective, and integrative. Examining how each exhibits characteristic failure modes under conditions of environmental overload, optimization pressure, and constraint loss. The model is descriptive rather than diagnostic and is intended as a theoretical lens for understanding differential cognitive stability in contemporary high-noise digital environments. (shrink)

Searching for information is critical in many situations. In medicine, for instance, careful choice of a diagnostic test can help narrow down the range of plausible diseases that the patient might have. In a probabilistic framework, test selection is often modeled by assuming that people's goal is to reduce uncertainty about possible states of the world. In cognitive science, psychology, and medical decision making, Shannon entropy is the most prominent and most widely used model to formalize probabilistic uncertainty (...) and the reduction thereof. However, a variety of alternative entropy metrics (Hartley, Quadratic, Tsallis, Rényi, and more) are popular in the social and the natural sciences, computer science, and philosophy of science. Particular entropy measures have been predominant in particular research areas, and it is often an open issue whether these divergences emerge from different theoretical and practical goals or are merely due to historical accident. Cutting across disciplinary boundaries, we show that several entropy and entropy reduction measures arise as special cases in a unified formalism, the Sharma–Mittal framework. Using mathematical results, computer simulations, and analyses of published behavioral data, we discuss four key questions: How do various entropy models relate to each other? What insights can be obtained by considering diverse entropy models within a unified framework? What is the psychological plausibility of different entropy models? What new questions and insights for research on human information acquisition follow? Our work provides several new pathways for theoretical and empirical research, reconciling apparently conflicting approaches and empirical findings within a comprehensive and unified information‐theoretic formalism. (shrink)

The study of creative, diagnostic, visual, spatial, analogical, and temporal reasoning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help only of traditional notions of reasoning such as classical logic. Understanding the contribution of modeling practices to discovery and conceptual change in science requires expanding scientific reasoning to include complex forms of creative reasoning that are not always successful and can lead to incorrect solutions. The study of these (...) heuristic ways of reasoning is situated at the crossroads of philosophy, artificial intelligence, cognitive psychology, and logic; that is, at the heart of cognitive science. There are several key ingredients common to the various forms of model-based reasoning. The term "model" comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations. The models are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. Moreover, in the modeling process, various forms of abstraction are used. Evaluation and adaptation take place in light of structural, causal, and/or functional constraints. Model simulation can be used to produce new states and enable evaluation of behaviors and other factors. Several of the papers in this volume aim at increasing epistemological knowledge about the role of model-based reasoning in various scientific tasks, other papers address fundamental cognitive issues related to model-based reasoning and illustrate novel analyses of cognitive "logical" models of model-based reasoning and of the interplay abduction/model-based reasoning/creative inferences. The volume is based on the papers that were presented at the International Conference Model-Based Reasoning in Science and Engineering: Abduction, Visualization, Simulation, held at the Collegio Ghislieri, University of Pavia, Pavia, Italy, in December 2004. (shrink)

Rapid individual cognitive phenotyping holds the potential to revolutionize domains as wide‐ranging as personalized learning, employment practices, and precision psychiatry. Going beyond limitations imposed by traditional lab‐based experiments, new efforts have been underway toward greater ecological validity and participant diversity to capture the full range of individual differences in cognitive abilities and behaviors across the general population. Building on this, we developed Skill Lab, a novel game‐based tool that simultaneously assesses a broad suite of cognitive abilities while (...) providing an engaging narrative. Skill Lab consists of six mini‐games as well as 14 established cognitive ability tasks. Using a popular citizen science platform (N = 10,725), we conducted a comprehensive validation in the wild of a game‐based cognitive assessment suite. Based on the game and validation task data, we constructed reliable models to simultaneously predict eight cognitive abilities based on the users’ in‐game behavior. Follow‐up validation tests revealed that the models can discriminate nuances contained within each separate cognitive ability as well as capture a shared main factor of generalized cognitive ability. Our game‐based measures are five times faster to complete than the equivalent task‐based measures and replicate previous findings on the decline of certain cognitive abilities with age in our large cross‐sectional population sample (N = 6369). Taken together, our results demonstrate the feasibility of rapid in‐the‐wild systematic assessment of cognitive abilities as a promising first step toward population‐scale benchmarking and individualized mental health diagnostics. (shrink)

The current rise of neurodevelopmental disorders poses a critical need to detect risk early in order to rapidly intervene. One of the tools pediatricians use to track development is the standard growth chart. The growth charts are somewhat limited in predicting possible neurodevelopmental issues. They rely on linear models and assumptions of normality for physical growth data – obscuring key statistical information about possible neurodevelopmental risk in growth data that actually has accelerated, non-linear rates-of-change and variability encompassing skewed distributions. (...) Here, we use new analytics to profile growth data from 36 newborn babies that were tracked longitudinally for 5 months. By switching to incremental (velocity-based) growth charts and combining these dynamic changes with underlying fluctuations in motor performance – as the transition from spontaneous random noise to a systematic signal – we demonstrate a method to detect very early stunting in the development of voluntary neuromotor control and to flag risk of neurodevelopmental derail. (shrink)

With the emergence of large language models and their impressive performance across diverse natural language processing tasks, the question of whether connectionist models can exhibit compositionality without relying on symbolic processing has regained attention in both cognitive science and artificial intelligence. However, interpretability challenges faced by neural networks make it difficult to determine whether they genuinely generalize compositional structures. In this paper, we introduce a targeted evaluation framework designed to directly assess the ability of transformer-based language (...) class='Hi'>models to translate natural language sentences into first-order logic expressions, a task that requires both nuanced linguistic understanding and compositional generalization. To demonstrate our framework, we fine-tune two different sizes of the T5 language model using our dataset, evaluating their performance through three experiments that employ four task-specific evaluation metrics. Our findings reveal that while these models achieve high scores on test data sharing the logical and structural complexity of the training set, their performance drops markedly as sentence length, the number of truth-functional connectives and predicates, and the depth of hierarchical composition increase. More strikingly, the models fail to generalize even when complexity increases solely through repeated applications of a single truth-functional connective. (shrink)

Bayesian networks can be employed to cognitive diagnostic assessment. Most of the existing researches on the BNs for CDA utilized the MCMC algorithm to estimate parameters of BNs. When EM algorithm and gradient descending learning method are adopted to estimate the parameters of BNs, some challenges may emerge in educational assessment due to the monotonic constraints cannot be satisfied in the above two methods. This paper proposed to train the BN first based on the ideal response pattern data (...) contained in every CDA and continue to estimate the parameters of BN based on the EM or the GD algorithm regarding the parameters based on the IRP training method as informative priors. Both the simulation study and realistic data analysis demonstrated the validity and feasibility of the new method. The BN based on the new parameter estimating method exhibits promising statistical classification performance and even outperforms the G-DINA model in some conditions. (shrink)

BackgroundThe aim of this study was to explore the construct validity and diagnostic properties of the Reading the Mind in the Eyes Test in non-demented patients with amyotrophic lateral sclerosis.MaterialsA total of 61 consecutive patients and 50 healthy controls were administered the 36-item RMET. Additionally, patients underwent a comprehensive assessment of social cognition via the Story-Based Empathy Task, which encompasses three subtests targeting Causal Inference, Emotion Attribution, and Intention Attribution, as well as global cognitive [the Edinburgh Cognitive (...) and Behavioral ALS Screen ] and behavioral screening [the Frontal Behavioral Inventory ; the Dimensional Apathy Scale ; the Beck Depression Inventory ; and the State and Trait Anxiety Inventory-Y]. The construct validity of the RMET was tested by regressing it within a stepwise model that encompassed as predictors the abovementioned cognitive and behavioral measures, covarying for demographic and motor confounders. Receiver-operating characteristics analyses allowed exploring intrinsic and post-test properties of the RMET both in discriminating patients from HCs and in identifying patients with a defective SET-EA performance.ResultsThe RMET was solely predicted by the SET-EA and SET-IA. RMET scores showed high accuracy both in discriminating patients from HCs and in identifying patients with a defective SET-EA score, with adequate-to-optimal both intrinsic and post-test properties.DiscussionThe RMET is a convergently and divergently valid measure of affective social cognition in non-demented ALS patients, also featuring optimal intrinsic and post-test diagnostic properties in both case-control and case-finding scenarios. (shrink)

The concept of the tipping point, which describes abrupt and non-linear changes in complex systems, is gaining increasing importance in public administration. However, the relevant academic literature remains fragmented, examining the phenomenon through isolated lenses of system dynamics, governance, or conceptual framing, which hinders a holistic understanding. This article bridges this gap by synthesizing these different approaches through a narrative literature review. As its main contribution, it develops the "Integrated Tipping Point Governance Framework", a new multi-level diagnostic model. The (...) framework posits that the path to a tipping point is not a simple linear process but the result of the interaction of three levels: "Systemic Entrapment" (macro-level structural pressures), "Organizational Inertia" (meso-level institutional lock-ins), and "Cognitive Capture" (micro-level dominant narratives). This framework offers a deeper explanation for the mechanisms that create resistance to change, complementing descriptive theories like Punctuated Equilibrium and providing the necessary diagnostic foundation for applying normative models like Adaptive Governance. Ultimately, the article provides a valuable conceptual tool for analyzing and managing abrupt changes in public policy. (shrink)

Large Language Models (LLMs) exhibit impressive linguistic performance, yet their cognitive and communicative status remains controversial. This paper argues that behavioural fluency alone cannot justify cognitive ascription unless assessed within a design-sensitive framework that clarifies what counts as a minimally adequate cognitive architecture. We distinguish structurally constrained functionalism from unconstrained functionalism and propose a minimal cognitive core – environmentally coupled perception, inference, and action – as a baseline for cognition. Against this baseline, current LLMs fall (...) short: as standalone text-based systems, they lack sensorimotor coupling, integrated inferential responsiveness, and autonomous agency, and thus cannot sustain the forms of world-directed engagement characteristic of even basic cognitive systems. We examine sycophancy and the absence of spontaneity as diagnostic consequences of architectures optimised for user alignment rather than epistemic commitment, showing how these limitations systematically undermine claims of cognitive or communicative competence. Finally, we develop the deprived interface argument: without embodied or interactive access to an environment, LLMs cannot revise or ground their representations, resulting in a derivative form of linguistic idealism – the view according to which an LLM’s world consists of nothing but training data. While future multimodal or embodied architectures may indeed mitigate aspects of interface deprivation, such advances would require structural changes enabling genuine world-directed engagement rather than simply expanding input modalities. Clarifying these distinctions is essential for avoiding category mistakes in AI evaluation and for grounding cognitive ascription in principled criteria rather than surface-level behavioural success. We conclude that LLMs simulate cognition and communication without instantiating the structural conditions required for either, and we outline the implications for AI evaluation and cognitive ascription. (shrink)

An investigation is presented in which a computer simulation model (DIAGNOSER) is used to develop and test predictions for behavior of subjects in a task of medical diagnosis. The first experiment employed a process‐tracing methodology in order to compare hypothesis generation and evaluation behavior of DIAGNOSER with individuals at different levels of expertise (students, trainees, experts). A second experiment performed with only DIAGNOSER identified conditions under which errors in reasoning in the first experiment could be related to interpretation of specific (...) data items. Predictions derived from DIAGNOSER's performance were tested in a third experiment with a new sample of subjects. Data from the three experiments indicated that (1) form of diagnostic reasoning was similar for all subjects trained in medicine and for the simulation model, (2) substance of diagnostic reasoning employed by the simulation model was parable with that of the more expert subjects, and (3) errors in subjects' reasoning were attributable to deficiencies in disease knowledge and the interpretation of specific patient data cues predicted by the simulation model. (shrink)

The Diagnostic and Statistical Manual (DSM) of the American Psychiatric Association, currently in its fourth edition and considered the reference for the characterization and diagnosis of mental disorders, has undergone various developments since its inception in the mid-twentieth century. With the fifth edition of the DSM presently in field trials for release in 2013, there is renewed discussion and debate over the extent of its relative successes - and shortcomings - at iteratively incorporating scientific evidence on the often ambiguous (...) nature and etiology of mental illness. Given the power that the DSM has exerted both within psychiatry and society at large, this essay seeks to analyze variations in content and context of various editions of the DSM, address contributory influences and repercussion of such variations on the evolving landscape of psychiatry as discipline and practice over the past sixty years. Specifically, we document major modifications in the definition, characterization, and classification of mental disorders throughout successive editions of the DSM, in light of shifting trends in the conceptualization of psychopathology within evolving schools of thought in psychiatry, and in the context of progress in behavioral and psychopharmacological therapeutics over time. We touch upon the social, political, and financial environments in which these changes took places, address the significance of these changes with respect to the legitimacy (and legitimization) of what constitutes mental illness and health, and examine the impact and implications of these changes on psychiatric practice, research, and teaching. We argue that problematic issues in psychiatry, arguably reflecting the large-scale adoption of the DSM, may be linked to difficulties in formulating a standardized nosology of psychopathology. In this light, we highlight 1) issues relating to attempts to align the DSM with the medical model, with regard to increasing specificity in the characterization of discrete mental disease entities and the incorporation of neurogenetic, neurochemical and neuroimaging data in its nosological framework; 2) controversies surrounding the medicalization of cognition, emotion, and behavior, and the interpretation of subjective variables as 'normal' or 'abnormal' in the context of society and culture; and 3) what constitutes treatment, enablement, or enhancement - and what metrics, guidelines, and policies may need to be established to clarify such criteria. (shrink)

Although it is not yet officially recognized as a clinical entity which is diagnosable, Internet Gaming Disorder (IGD) has been included in section III for further study in the DSM-5 by the American Psychiatric Association (APA, 2013). This is important because there is increasing evidence that people of all ages, in particular teens and young adults, are facing very real and sometimes very severe consequences in daily life resulting from an addictive use of online games. This article summarizes general aspects (...) of IGD including diagnostic criteria and arguments for the classification as an addictive disorder including evidence from neurobiological studies. Based on previous theoretical considerations and empirical findings, this paper examines the use of one recently proposed model, the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, for inspiring future research and for developing new treatment protocols for IGD. The I-PACE model is a theoretical framework that explains symptoms of Internet addiction by looking at interactions between predisposing factors, moderators, and mediators in combination with reduced executive functioning and diminished decision making. Finally, the paper discusses how current treatment protocols focusing on Cognitive-Behavioral Therapy for Internet addiction (CBT-IA) fit with the processes hypothesized in the I-PACE model. (shrink)

The research described here explores the idea of using Supreme Court oral arguments as pedagogical examples in first year classes to help students learn the role of hypothetical reasoning in law. The article presents examples of patterns of reasoning with hypotheticals in appellate legal argument and in the legal classroom and a process model of hypothetical reasoning that relates them to work in cognitive science and Artificial Intelligence. The process model describes the relationships between an advocate’s proposed test for (...) deciding a case or issue, the facts of the hypothetical and of the case to be decided, and the often conflicting legal principles and policies underlying the issue. The process model of hypothetical reasoning has been partially implemented in a computerized teaching environment, LARGO (“Legal ARgument Graph Observer”) that helps students identify, analyze, and reflect on episodes of hypothetical reasoning in oral argument transcripts. Using LARGO, students reconstruct examples of hypothetical reasoning in the oral arguments by representing them in simple diagrams that focus students on the proposed test, the hypothetical challenge to the test, and the responses to the challenge. The program analyzes the diagrams and provides feedback to help students complete the diagrams and reflect on the significance of the hypothetical reasoning in the argument. The article reports the results of experiments evaluating instruction of first year law students at the University of Pittsburgh using the LARGO program as applied to Supreme Court personal jurisdiction cases. The learning results so far have been mixed. Instruction with LARGO has been shown to help law student volunteers with lower LSAT scores learn skills and knowledge regarding hypothetical reasoning better than a text-based approach, but not when the students were required to participate. On the other hand, the diagrams students produce with LARGO have been shown to have some diagnostic value, distinguishing among law students on the basis of LSAT scores, posttest performance, and years in law school. This lends support to the underlying model of hypothetical argument and suggests using LARGO as a pedagogically diagnostic tool. (shrink)

This study addresses the complex nature of human cognition within a dynamically complex interplay of distributed neural networks, oscillatory activity, and subjective experiential phenomena. Although computational models have made a rich addition to our understanding of memory, attention, and decision-making processes, it is often the case that these models neglect the phenomenological dimensions that make up the lived experience of thought. Accordingly, in the present paper, a neuro-phenomenological framework that synergistically couples EEG-based neural oscillation analysis with first-person (...) class='Hi'>cognitive narratives is introduced for mapping the architecture of consciousness and cognition in a more integrative way. Employing EEG recordings taken during conditions of both rest and task performance, the present study addresses the issue of correlations between oscillatory signatures (i.e., alpha, theta, and gamma bands) and self-reported body variable cognitive states of attention, working memory load, and perceptual switching. These empirical relationships are then interpreted in the context of predictive processing, and explain how the brain builds internal world models and how departures from these world models can bring about cognitive dissonance or attention drift. One or two secondary objectives are to explore the possibility of using artificial intelligence models of intelligence, especially architectures of deep learning style, trained on multimodal data, to simulate or approximate the aforementioned cognitive states. By combining human EEG signatures, and the internal state of representation of artificial neural networks, this work attempts to narrow down the gap between biological and computational understanding. The findings add to the emerging field of discussion in computational cognitive neuroscience, embodied cognition, and cognitive modeling that insists that future brain–behavior mapping efforts do not solely focus on the neural correlates of thought but also its structural and phenomenological "feel". This integrative way of thinking has very important implications for cognitive training, neuroadaptive interfaces, and diagnostics for mental health. (shrink)

Generalized anxiety disorder (GAD) and social phobia (SP) are major anxiety disorders identified by the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV). They are comorbid, overlap in symptoms, yet present with distinct features (worry in GAD and fear of embarrassment in SP). Both have also been explained in terms of conditioning-based models. However, there is little reasoning currently to believe that GAD in adulthood reflects heightened conditionability or heightened threat processing—though patients with SP may show (...) heightened processing of social threat stimuli. Moreover, the computational architectures that maintain these disorders in adulthood are different. For GAD this may reflect the development of an inefficient “worrying” strategy of emotional regulation. For SP this appears to reflect the atypical processing of self-referential information. (shrink)

This article refers to the concept of “epistemia” as a diagnostic category for understanding the epistemological transformations brought about by the use of large language models and generative artificial intelligence. Far from constituting a theory of artificial intelligence or an ontology of cognition, epistemia describes a structural phenomenon emerging from sociotechnical systems: the production of linguistically coherent outputs that lack robust epistemic grounding. This paper argues that this phenomenon results from a deep compatibility between probabilistic text generation and (...) human expectations of meaning, leading to a systematic confusion between plausibility and truth. Furthermore, epistemia is analyzed in relation to cognitive debt and cognitive offloading, highlighting how the externalization of intellectual tasks reshapes habits of judgment, attention, and understanding. A Platonic myth and a Borgesian tale are employed as hermeneutical devices that intensify the perception and analysis of the current phenomenon by offering formal structures that illuminate, by analogy, its ontological and epistemological implications. The analysis situates epistemia within broader debates in philosophy of science and technology, emphasizing its implications for the nature of knowledge, the limits of computational mimesis, and the future of human cognition in AI-mediated environments. (shrink)

How do clinical psychiatrists arrive at their diagnostic conclusions? Little attention has been directed to this question by philosophers of psychiatry. Adrian Kind presents a systematic, in-depth philosophical investigation into this question and argues that psychiatric diagnostic reasoning can be understood as a model-based reasoning procedure analogous to scientific model-based reasoning. To support this, he draws on ideas from the philosophy of science, psychiatry, cognitive science, and artificial intelligence. This study is an invaluable resource for practicing psychiatrists, (...) philosophers interested in psychiatry, and researchers in artificial intelligence or cognitive science interested in medical cognition. (shrink)

Delusions are commonly conceived as false beliefs that are held with certainty and which cannot be corrected. This conception of delusion has been influential throughout the history of psychiatry and continues to inform how delusions are approached in clinical practice and in contemporary schizophrenia research. It is reflected in the full psychosis continuum model, guides psychological and neurocognitive accounts of the formation and maintenance of delusions, and it substantially determines how delusions are approached in cognitive-behavioural treatment. In this Review, (...) we draw on a clinical-phenomenological framework to offer an alternative account of delusion that incorporates the experiential dimension of delusion, emphasising how specific alterations to self-consciousness and reality experience underlie delusions that are considered characteristic of schizophrenia. Against that backdrop, we critically reconsider the current research areas, highlighting empirical and conceptual issues in contemporary delusion research, which appear to largely derive from an insufficient consideration of the experiential dimension of delusions. Finally, we suggest how the alternative phenomenological approach towards delusion could offer new ways to advance current research and clinical practice. (shrink)

Experiences of thought-insertion are a first-rank, diagnostically central symptom of schizophrenia. Schizophrenic patients who undergo such delusional mental states report being first-personally aware of an occurrent conscious thought which is not theirs, but which belongs to an external cognitive agent. Patients seem to be right about what they are thinking but mistaken about who is doing the thinking. It is notoriously difficult to make sense of such delusions. One general approach to explaining the etiology of monothematic delusions has come (...) to be known as the endorsement model. This model claims that the patient holds her delusional belief because she simply trusts her bizarre experience and takes it at face value. The content of the bizarre experience thus plays a central role in the etiology of delusions. Despite being widely discussed with respect to delusions like Capgras and Cotard, an endorsement model of thought-insertion has not yet been formulated. This article seeks to fill this void by fleshing out and defending the endorsement approach to delusions of inserted thoughts. It aims to show that such an approach can be defended against objections that have been raised in the literature. In particular, it will be argued that there is nothing wrong with the idea of being first-personally aware of a thought which is presented in consciousness as being someone else's. The upshot is that with respect to delusions of thought-insertion, the endorsement model turns out to be a viable account of why patients come to believe that someone else is inserting thoughts into their minds. (shrink)

The diffusion of misinformation has garnered considerable attention in our society. As algorithms have been considered one of the major drivers behind the spread and amplification of misinformation, it is useful to understand the effects of these algorithms on misinformation sharing and the manner in which they spread it. This chapter examines the psychological, cognitive, and social factors involved in the processing of misinformation people receive through algorithms and artificial intelligence. Modeling cognitive processes has long been of interest (...) for understanding user reasoning, and many theories from different fields have been formalized into cognitive models. Drawing on theoretical insights from information processing theory with the concept of diagnosticity, it examines how perceived normative values influence a user’s perceived diagnosticity and likelihood of sharing information and whether explainability further moderates this relationship. The findings showed that users with a high heuristic processing of normative values and positive diagnostic perception were more likely to proactively discern misinformation. Users with a high cognitive ability to understand information were more likely to discern it correctly and less likely to share misinformation online. When exposed to misinformation through algorithmic recommendations, users’ perceived diagnosticity of misinformation can be predicted accurately from their understanding of normative values. This perceived diagnosticity would then positively influence the accuracy and credibility of the misinformation. With this focus on misinformation processing, this chapter provides theoretical insights and relevant recommendations for firms to be more resilient in protecting themselves from the detrimental impact of misinformation. (shrink)

The debate about the appropriate methodology of medical education has been (and still is) dominated by the opposing poles of teaching science versus teaching practical skills. I will argue that this conflict between scientific education and practical training has its roots in the underlying, more systematic question about the conceptual foundation of medicine: how far or in what respects can medicine be considered to be a science? By analyzing the epistemological status of medicine I will show that the internal aim (...) of medicine( promoting health through the prevention and treatment of disease ) differs from the internal aim of science ( the methodological and systematic acquisition of knowledge ). Therefore, medicine as a whole discipline should not be considered as a science. However, medicine can be conceptually and methodologically scientific in so much as it is based on scientific knowledge. There is evidence from cognitive science research that diagnostic reasoning not only relies on the application of scientific knowledge but also — especially in routine cases – on a process of pattern recognition, a reasoning strategy based on the memory of previously encountered patients. Hence, medical education must contain both: the imparting of scientific knowledge and the rich exposure to concrete cases during practical training. Hence, the question of teaching science vs. the apprentice model will not be either-or but rather both — but in which proportion? (shrink)

This article presents a computational model of the learning of diagnostic knowledge, based on observations of human operators engaged in real-world troubleshooting tasks. We present a model of problem solving and learning in which the reasoner introspects about its own performance on the problem-solving task, identifies what it needs to learn to improve its performance, formulates learning goals to acquire the required knowledge, and pursues its learning goals using multiple learning strategies. The model is implemented in a computer system (...) which provides a case study based on observations of troubleshooting operators and protocol analysis of the data gathered in the test area of an operational electronics manufacturing plant. The model not only addresses issues in human learning, but, in addition, is computationally justified as a uniform, extensible framework for multistrategy learning. (shrink)

Forensic evidence often involves an evaluation of whether two impressions were made by the same source, such as whether a fingerprint from a crime scene has detail in agreement with an impression taken from a suspect. Human experts currently outperform computer-based comparison systems, but the strength of the evidence exemplified by the observed detail in agreement must be evaluated against the possibility that some other individual may have created the crime scene impression. Therefore, the strongest evidence comes from features in (...) agreement that are also not shared with other impressions from other individuals. We characterize the nature of human expertise by applying two extant metrics to the images used in a fingerprint recognition task and use eye gaze data from experts to both tune and validate the models. The Attention via Information Maximization model quantifies the rarity of regions in the fingerprints to determine diagnosticity for purposes of excluding alternative sources. The CoVar model captures relationships between low-level features, mimicking properties of the early visual system. Both models produced classification and generalization performance in the 75%–80% range when classifying where experts tend to look. A validation study using regions identified by the AIM model as diagnostic demonstrates that human experts perform better when given regions of high diagnosticity. The computational nature of the metrics may help guard against wrongful convictions, as well as provide a quantitative measure of the strength of evidence in casework. (shrink)