The temporal dynamics of insight problem solving – restructuring might not always be sudden

Insight problems are likely to trigger an initial, inappropriate mental representation, which needs to be restructured in order to find the solution. Despite the widespread theoretical assumption that this restructuring process happens suddenly, which leads to the typical Aha! experience, the evidence is inconclusive. Among the reasons for this lack of clarity is a reluctance to measure solvers’ subjective experience of the solution process. Here, we overcome previous methodological problems by measuring the dynamics of the solution process using eye movements in combination with the subjective Aha! experience. Our results demonstrate that in a problem that requires restructuring of the initial mental representation, paying progressively more attention to the crucial elements of the problem often preceded the finding of the solution. Most importantly, the sooner solvers started paying attention to the crucial elements, the less sudden and surprising the solution felt to them. The close link between the eye movement patterns and self-reported Aha! experience in the present study underlines the necessity of measuring both the cognitive and the affective components of insight to capture the essence of this phenomenon

Large data and Bayesian modeling—aging curves of NBA players

Researchers interested in changes that occur as people age are faced with a number of methodological problems, starting with the immense time scale they are trying to capture, which renders laboratory experiments useless and longitudinal studies rather rare. Fortunately, some people take part in particular activities and pastimes throughout their lives, and often these activities are systematically recorded. In this study, we use the wealth of data collected by the National Basketball Association to describe the aging curves of elite basketball players. We have developed a new approach rooted in the Bayesian tradition in order to understand the factors behind the development and deterioration of a complex motor skill. The new model uses Bayesian structural modeling to extract two latent factors, those of development and aging. The interaction of these factors provides insight into the rates of development and deterioration of skill over the course of a player’s life. We show, for example, that elite athletes have different levels of decline in the later stages of their career, which is dependent on their skill acquisition phase. The model goes beyond description of the aging function, in that it can accommodate the aging curves of subgroups (e.g., different positions played in the game), as well as other relevant factors (e.g., the number of minutes on court per game) that might play a role in skill changes. The flexibility and general nature of the new model make it a perfect candidate for use across different domains in lifespan psychology

The interplay of sensory and motoric information on processing emotions in narrative texts

This study examines the comprehension of characters’ emotional states in written narrative texts. Recent theories of embodied cognition suggest that emotions are grounded in somatosensory information involving ‘reexperiencing’ of actions and perceptions. In addition, views of graded embodied cognition suggest that there are levels of the embodiment of language, focusing on how perceptual and motor information interact during text comprehension. In the present experiment, one-paragraph stories were written to express six basic emotions: fear, sadness, anger, disgust, happiness, and surprise. Four stories were written for each emotion and for each story four critical sentences were composed. The critical sentences comprised a combination of emotion- and action-based components that matched or mismatched the story. Participants read the stories and the critical sentences. Their task was to respond to a question about the emotional state of the story’s main character, while we measured response latencies and errors. The results of the error rate analyses suggest that, while reading texts, sensory knowledge about characters’ emotional states is activated, but this knowledge is significantly moderated by action-based knowledge. A computational model was used to further confirm these results. The model was trained to predict emotion and action words from critical sentences using linguistic context from stories. Both, the action and emotional words activations showed a distinct effect on participants’ comprehension accuracy

Modelling paralinguistic properties in conversational speech to detect bipolar disorder and borderline personality disorder

Bipolar disorder (BD) and borderline personality disorder (BPD) are two chronic mental health conditions that clinicians find challenging to distinguish based on clinical interviews, due to their overlapping symptoms. In this work, we investigate the automatic detection of these two conditions by modelling both verbal and non-verbal cues in a set of interviews. We propose a new approach of modelling short-term features with visibility-signature transform, and compare it with widely used high-level statistical functions. We demonstrate the superior performance of our proposed signature-based model. Furthermore, we show the role of different sets of features in characterising BD and BPD

Loneliness, social isolation, and effects on cognitive decline in patients with dementia:A retrospective cohort study using natural language processing

INTRODUCTION: The study aimed to compare cognitive trajectories between patients with reports of social isolation and loneliness and those without. METHODS: Reports of social isolation, loneliness, and Montreal Cognitive Assessment (MoCA) scores were extracted from dementia patients' medical records using natural language processing models and analyed using mixed-effects models. RESULTS: Lonely patients (n = 382), compared to controls (n = 3912), showed an average MoCA score that was 0.83 points lower at diagnosis (P = 0.008) and throughout the disease. Socially isolated patients (n = 523) experienced a 0.21 MoCA point per year faster rate of cognitive decline in the 6 months before diagnosis (P = 0.029), but were comparable to controls before this period. This led to average MoCA scores that were 0.69 MoCA points lower at diagnosis (P = 0.011). DISCUSSION: Lower cognitive levels in lonely and socially isolated patients suggest that these factors may contribute to dementia progression. HIGHLIGHTS: Developed Natural Language Processing model to detect social isolation and loneliness in electronic health records. Patients with loneliness reports have lower Montreal Cognitive Assessment (MoCA) scores than other patients. Social isolation was related to the faster decline in MoCA scores before diagnosis. Social isolation and loneliness are promising targets for slowing cognitive decline

Validation of UK Biobank data for mental health outcomes : a pilot study using secondary care electronic health records

The study was funded by the MRC Pathfinder Grant (MC_PC_17215); the National Institute for Health Research’s (NIHR) Oxford Health Biomedical Research Centre (BRC-1215-20005) and the Virtual Brain Cloud from European Commission (grant no. H2020SC1-DTH-2018-1). This work was supported by the UK Clinical Record Interactive Search (UK-CRIS) system funded by the National Institute for Health Research (NIHR) and the Medical Research Council, with the University of Oxford, using data and systems of the NIHR Oxford Health Biomedical Research Centre (BRC-1215-20005).UK Biobank (UKB) is widely employed to investigate mental health disorders and related exposures; however, its applicability and relevance in a clinical setting and the assumptions required have not been sufficiently and systematically investigated. Here, we present the first validation study using secondary care mental health data with linkage to UKB from Oxford - Clinical Record Interactive Search (CRIS) focusing on comparison of demographic information, diagnostic outcome, medication record and cognitive test results, with missing data and the implied bias from both resources depicted. We applied a natural language processing model to extract information embedded in unstructured text from clinical notes and attachments. Using a contingency table we compared the demographic information recorded in UKB and CRIS. We calculated the positive predictive value (PPV, proportion of true positives cases detected) for mental health diagnosis and relevant medication. Amongst the cohort of 854 subjects, PPVs for any mental health diagnosis for dementia, depression, bipolar disorder and schizophrenia were 41.6%, and were 59.5%, 12.5%, 50.0% and 52.6%, respectively. Self-reported medication records in UKB had general PPV of 47.0%, with the prevalence of frequently prescribed medicines to each typical mental health disorder considerably different from the information provided by CRIS. UKB is highly multimodal, but with limited follow-up records, whereas CRIS offers a longitudinal high-resolution clinical picture with more than ten years of observations. The linkage of both datasets will reduce the self-report bias and synergistically augment diverse modalities into a unified resource to facilitate more robust research in mental health.Peer reviewe

COVID-19 partial school closures and mental health problems: A cross-sectional survey of 11,000 adolescents to determine those most at risk.

Funder: NIHR Applied Research Collaboration Oxford and Thames ValleyFunder: The Westminster FoundationBACKGROUND: Understanding adolescents' mental health during lockdown and identifying those most at risk is an urgent public health challenge. This study surveyed school pupils across Southern England during the first COVID-19 school lockdown to investigate situational factors associated with mental health difficulties and how they relate to pupils' access to in-school educational provision. METHODS: A total of 11,765 pupils in years 8-13 completed a survey in June-July 2020, including questions on mental health, risk indicators and access to school provision. Pupils at home were compared to those accessing in-school provision on risk and contextual factors and mental health outcomes. Multilevel logistic regression analyses compared the effect of eight risk and contextual factors, including access to in-school provision, on depression, anxiety and self-reported deterioration in mental wellbeing. RESULTS: Females, pupils who had experienced food poverty and those who had previously accessed mental health support were at greatest risk of depression, anxiety and a deterioration in wellbeing. Pupils whose parents were going out to work and those preparing for national examinations in the subsequent school year were also at increased risk. Pupils accessing in-school provision had poorer mental health, but this was accounted for by the background risk and contextual factors assessed, in line with the allocation of in-school places to more vulnerable pupils. CONCLUSIONS: Although the strongest associations with poor mental health during school closures were established risk factors, further contextual factors of particular relevance during lockdown had negative impacts on wellbeing. Identifying those pupils at greatest risk for poor outcomes is critical for ensuring that appropriate educational and social support can be given to pupils either at home or in-school during subsequent lockdowns

Systematic review of health economic models for assessment and diagnosis of dementia

INTRODUCTION: Timely diagnosis of dementia is a public health priority to enable risk modification and treatment access. This study systematically identifies and critically appraises health economic models of dementia assessment and diagnosis. METHODS: Inclusion criteria were: any dementia stage; evaluated strategy(ies) for initial assessment/diagnosis of dementia; health economic evaluation using decision modeling. Ten databases were searched for 2000–2024. Philips checklist was applied for quality assessment. Narrative synthesis appraised methodological features and issued decision‐making recommendations. RESULTS: Thirty‐two studies were included. Six evaluated cerebrospinal fluid (CSF); 11 neuroimaging including amyloid‐targeting positron emission tomography; three blood‐based biomarkers; two genetic testing; and 10 early assessment/diagnosis strategies. Methodological limitations included non‐consideration of capacity constraints. Decision‐making recommendations generally affirmed current clinical guidelines: for example, CSF to confirm Alzheimer's disease is cost‐effective (incremental cost‐effectiveness ratio of £10,150 per quality‐adjusted life‐year gained vs. no use). DISCUSSION: Methodological appraisal and decision‐making recommendations should assist model development and evidence‐based dementia diagnosis

Chess databases as a research vehicle in psychology : modeling large data

The game of chess has often been used for psychological investigations, particularly in cognitive science. The clear-cut rules and well-defined environment of chess provide a model for investigations of basic cognitive processes, such as perception, memory, and problem solving, while the precise rating system for the measurement of skill has enabled investigations of individual differences and expertise-related effects. In the present study, we focus on another appealing feature of chess—namely, the large archive databases associated with the game. The German national chess database presented in this study represents a fruitful ground for the investigation of multiple longitudinal research questions, since it collects the data of over 130,000 players and spans over 25 years. The German chess database collects the data of all players, including hobby players, and all tournaments played. This results in a rich and complete collection of the skill, age, and activity of the whole population of chess players in Germany. The database therefore complements the commonly used expertise approach in cognitive science by opening up new possibilities for the investigation of multiple factors that underlie expertise and skill acquisition. Since large datasets are not common in psychology, their introduction also raises the question of optimal and efficient statistical analysis. We offer the database for download and illustrate how it can be used by providing concrete examples and a step-by-step tutorial using different statistical analyses on a range of topics, including skill development over the lifetime, birth cohort effects, effects of activity and inactivity on skill, and gender differences