Psychological Questions Research Articles

The psychological (ab)use of timeouts in professional tennis

ABSTRACT The (ab)use of timeouts as a tool for swinging momentum is a long-standing psychological question in tennis. We provided the first real-world quantitative investigation of this, through an archival analysis of bathroom timeouts taken between sets in professional tennis matches [timeout N = 366 following stringent data preprocessing of confounds]. Our study revealed three key findings. First, the majority of bathroom timeouts were taken by players who lost the prior-set, suggesting that the decision to take a timeout is triggered by negative momentum. Second, the likelihood of recovering from a lost set to win the next set was greater after a bathroom timeout, relative to stringently controlled baseline data where no timeouts were taken. Third, follow-up analyses on the extent to which unforced-errors contributed to the forms of points won and lost, indicated that the timeout advantage is a combination of both the strategist benefitting from the opportunity to personally reset, and a disruption of the rhythm of the opponent, rather than being disproportionally driven by one player. Together, these pioneering findings uniquely inform players and tennis governing bodies about the influence of timeouts and, more broadly, elucidate the role that stoppages can play in sport for altering momentum.

Multidimensional Context Clustering to Analyse Student Engagement in Online Learning Environment

Educational data mining is the application of data mining technology in an educational environment to indicate and resolve various types of issues faced in education. COVID-19 pandemic in 2020 accelerated the shift to emergency remote learning and online learning, which has continued to grow due to its flexibility and cost-effectiveness. However, several challenges exist when adopting the online learning, and which include the access to technology, technology attitude, psychological questions, teacher contact, and quality of assessment. Thus, it becomes important to focus on student engagement as a determinant of success during online learning. Student engagement is a complex construct which is comprised of four aspects which are behavioural, cognitive, emotional and social. In this study, the K-Means clustering approach is chosen to categorise students into clusters according to their active participation in the online learning process. The experiment used produces a silhouette coefficient of 0.71 clustering the datasets into three clusters. Cluster 0 are the disengaged learners who were observed to be least active across all the dimensions, while cluster 1 is composed of passive learners. The cluster 2 comprises the most engaged students with the high level of time management. These results provide information regarding the distinct engagement profiles that may be helpful when the lecturers attempt at student’s interventions.

Learning and transfer of response-effect relations.

Acting means changing the environment according to one's own goals, and this often requires bodily movements as responses. How these responses are selected is a central question in contemporary cognitive psychology. The ideomotor principle offers a simple answer based on two assumptions: An agent first learns an association between a response and its effects. Later, this association can be used in a reverse way: When the agent wants to achieve a desired effect and activates its representation, the associated response representation becomes activated as well. This reversed use of the learned association is considered the means to select the required response. In three experiments, we addressed two questions related to the first assumption: First, we tested whether effect representations generalise to more abstract conceptual knowledge. This is important, because outside the laboratory and in novel situations, effects are variable and not always exactly identical, such that generalisation is necessary for successful actions. Second, the nature of the response-effect relation has been debated recently, and more data are necessary to put theorising on firm empirical ground. Results of our experiments suggest that (a) abstraction to conceptual knowledge seems to occur only under very restricted situations, and (b) it seems that no (implicit) associations between responses and effects are learned, but rather (explicit) propositional knowledge in the form of rules.

Affective experiences from exercise. Youth-adult differences and prediction of exercise behavior

Affective exercise experiences are summative, valenced memories that represent the history of associations between past instances of exercise in an individual's life and pleasant or unpleasant reactions. We used the recently developed Affective Exercise Experiences (AFFEXX) questionnaire to address two important questions in exercise psychology, namely the nature of affective exercise experiences during the childhood-adulthood transition and the relationship between affect and exercise behavior. The first study compared data from 949 adults and 607 children and adolescents, and showed that core affective exercise experiences were associated with different antecedent appraisals in the two groups. Being watched during exercise and perceptions of competence appeared to influence core affective experiences more in children and adolescents than in adults. The second study, using data from a subsample of 94 adults, showed that exercise behavior over 14 days can be predicted by pleasant core affective exercise experiences when they are congruent with strong attraction to exercise. These data highlight the value of theoretically informed research to understand the multifarious affective experiences individuals derive from exercise.

(How) Would You Continue Working? A Comparison of Responses to the Lottery Question and a Basic Income Question

Abstract: The introduction of a basic income (BI) is a controversial topic. A key psychological question is whether most employees would stop or, as suggested by lottery question research, continue working. We addressed this question through two surveys with 268 and 640 employees, examining their plans regarding working if they won the lottery (lottery question) or received a monthly BI (BI question) as well as their reasons for continuing to work. More than 90 % (Study 1) and more than 80 % (Study 2) of respondents planned to continue working in both scenarios. In response to the BI question, more participants indicated they would continue working as before and fewer planned to pursue a different kind of work or to stop working. Financial reasons to continue working were reported more frequently in response to the BI question. Our findings suggest that the generalizability of lottery question research to the BI is limited, which necessitates specific research on the BI.

Amplifying Chinese physicians' emphasis on patients' psychological states beyond urologic diagnoses with ChatGPT-A multi-center cross-sectional study.

Artificial intelligence (AI) technologies, particularly large language models (LLMs), have been widely employed by the medical community. In addressing the intricacies of urology, ChatGPT offers a novel possibility to aid in clinical decision-making. This study aimed to investigate the decision-making ability of LLMs in solving complex urology-related problems and assess its effectiveness in providing psychological support to patients with urological disorders. This study evaluated the clinical and psychological support capabilities of ChatGPT 3.5 and 4.0 in the field of urology. A total of 69 clinical and 30 psychological questions were posed to the AI models, and their responses were evaluated by both urologists and psychologists. As a control, clinicians from Chinese medical institutions provided responses under closed-book conditions. Statistical analyses were conducted separately for each subgroup. In multiple-choice tests covering diverse urological topics, ChatGPT 4.0, performed comparably to the physician group, with no significant overall score difference. Subgroup analyses revealed variable performance, based on disease type and physician experience, with ChatGPT 4.0 generally outperforming ChatGPT 3.5 and exhibiting competitive results against physicians. When assessing the psychological support capabilities of AI, it is evident that ChatGPT4.0 outperforms ChatGPT3.5 across all urology-related psychological problems. The performance of LLMs in dealing with standardized clinical problems and providing psychological support has certain advantages over clinicians. AI stands out as a promising tool for potential clinical aid.

Existential Questions in Perinatal Psychology: Art Therapy Methods in Working with Perinatal Loss

Today, great attention is paid to the topic of perinatal losses, including “recurrent miscarriage” and infertility of unknown origin. Not only the psychological, but also the medical community is sounding the alarm on this issue, since the demographic crisis is also a state threat to our time. The author proposes to consider the main causes and psychological aspects of perinatal loss. The article outlines technologies for supporting in art therapy, to help lients during both the acute stage of experiencing loss and in preparation for subsequent pregnancy. Practical exercises for working with clients using art therapy tools are proposed and signs of successful solving problems of experiencing loss in the perinatal sphere are identified. To solve existential issues in perinatal psychology, art therapy methods show their reliability and effectiveness when accompanying the client at all stages of experiencing loss.

A Neural Decision Signal during Internal Sampling from Working Memory in Humans.

How humans transform sensory information into decisions that steer purposeful behavior is a central question in psychology and neuroscience that is traditionally investigated during the sampling of external environmental signals. The decision-making framework of gradual information sampling toward a decision has also been proposed to apply when sampling internal sensory evidence from working memory. However, neural evidence for this proposal remains scarce. Here we show (using scalp EEG in male and female human volunteers) that sampling internal visual representations from working memory elicits a scalp EEG potential associated with gradual evidence accumulation-the central parietal positivity. Consistent with an evolving decision process, we show how this signal (1) scales with the time participants require to reach a decision about the cued memory content and (2) is amplified when having to decide among multiple contents in working memory. These results bring the electrophysiology of decision-making into the domain of working memory and suggest that variability in memory-guided behavior may be driven (at least in part) by variations in the sampling of our inner mental contents.

COVID-19 and Suicide Tendency: Prediction and Risk Factor Analysis Using Machine Learning and Explainable AI

INTRODUCTION: Pandemics and epidemics have frequently led to a significant increase in the suicide rate in affected regions. However, these unnecessary deaths can be prevented by identifying the risk factors and intervening earlier with those at risk. Numerous empirical studies have exhaustively documented multiple suicide risk factors. In addition, many evidence-based approaches have employed machine learning models to diagnose vulnerable groups, a task that would otherwise be challenging if only human cognition were employed. To date, to the best of our knowledge, no research has been conducted on COVID-19-related suicide prediction.OBJECTIVES: This research, aims to develop a machine-learning model capable of identifying individuals who are contemplating suicide due to COVID-19-related complexities and assessing the potential risk factors.METHODS: We trained a gradient-boosting model based on tree-based learners on 10067 data consisting of 76 features, which were primarily responses to socio-demographic, behavioural, and psychological questions about COVID-19 and suicidal behaviours.RESULTS: The final model predicted individuals at risk with an auROC score of 0.77 and a 95% confidence interval of 0.77 to 0.88. The optimal cutoff produced a sensitivity of 31.37 percent and a specificity of 82.35 percent in predicting suicidal tendencies. However, the auPRC was only 0.26, with a 95 percent confidence interval of 0.13 to 0.38, as the class distribution was extremely unbalanced. Consequently, the scores for precision and recall were 0.35 and 0.31, respectively.CONCLUSION: We investigated the risk factors, the majority of which were associated with sleeping difficulties, fear of COVID-19, social interactions, and other socio-demographic factors. The identified risk factors can be considered when formulating a policy to prevent COVID-19-related suicides, which can impose a long-term economic and health burden on society.

Performance comparison ensemble classifier’s performance in answering frequently asked questions about psychology

In today’s era of digital healthcare transformation, there is a growing demand for swift responses to mental health queries. To meet this need, we introduce an AI-driven chatbot system designed to automatically address frequently asked questions in psychology. Leveraging a range of classifiers including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes, our system extracts insights from expert data sources and employs natural language processing techniques like LDA Topic Modeling and Cosine similarity to generate contextually relevant responses. Through rigorous experimentation, we find that SVM surpasses Naïve Bayes and KNN in accuracy, precision, recall, and F1-score, making it our top choice for constructing the final response system. This research underscores the effectiveness of ensemble classifiers, particularly SVM, in providing accurate and valuable information to enhance mental health support in response to common psychological inquiries.

Deathbots. Discussing the use of Artificial Intelligence in grief / Deathbots. Debatiendo el uso de la Inteligencia Artificial en el duelo

Deathbots, griefbots or thanabots are chatbots based on the digital footprint of the deceased that offer mourners the possibility to ‘talk’ to their loved ones after their death. This Artificial Intelligence–based thanatechnology raises a number of ethical and psychological questions. Drawing on the concept of mediation from cultural psychology and the notion of continuing bonds in bereavement, the article discusses some controversial questions about deathbots, such as the illusion of reality that this technology may generate, its impact on the autonomy of the bereaved, the possible individualization of bereavement, the ethical implications in relation to the deceased and the potential therapeutic uses of this digital tool. We conclude by stressing the need for a non-essentialist perspective when studying the relationship between AI and grief, addressing the mediational role of deathbots not for what they supposedly are but for what they allow us to do.

Remembering religious experience: Reconstruction, reflection, and reliability

This paper explores the relationship between religious belief and religious experience, bringing out a role for episodic memory that has been overlooked in the epistemology of religion. I do so by considering two questions. The first, the “Psychological Question,” asks what psychological role religious experiences play in causally bringing about religious beliefs. The second, the “Reliability Question,” asks: for a given answer to the Psychological Question about how religious beliefs are formed, are those beliefs formed using generally truth-conducive cognitive mechanisms or patterns of reasoning? I argue that the standard way of answering the Psychological Question overlooks the fact that religious beliefs are often formed via reflection on episodic memories of past religious experiences. Furthermore, recognizing this opens up room to make more meaningful progress on answering the Reliability Question.

Crafting a professional teacher learner through models of learning and positive psychological coaching tools

The important question of pedagogy and positive psychology in the 21st-century is how to facilitate educators in becoming professionals with successful self-concepts, capable of supporting and inspiring their students, coping with stress and burnout, and facing the challenges of their careers with autonomous, resilient strategies. Therefore, the main topic of the questionnaire conducted by Károli Gáspár University of the Reformed Church in Hungary is teacher motivation and coaching. This paper discusses a specific aspect of the theoretical and methodological background provided by the literature sources for compiling the questionnaire. Our current analysis encompasses McClelland’s “Theory of Human Motives”, Bateson’s Logical Levels of Learning, and Dilts’ NeuroLogical Levels of Change. Central to our exploration is the significance of reflection in teacher learning, leading to an analysis of Korthagen’s frameworks, such as ALACT, the Onion Model, and the Core Reflection model. Simons and Ruijters’ Model of Professional Learner Identity illuminates the relationship between learning and professionalism in teaching, accentuating the importance of lifelong learning. Ultimately, our endeavour is to comprehend the intricacies of motivating teacher learning by synthesizing insights from various theories, models, and coaching tools to nurture proficient and inspired professional teacher learners.

Primacy of theory? Exploring perspectives on validity in conceptual psychometrics.

Several conceptions of validity have emphasized the contingency of validity on theory. Here we revisit several contributions to the discourse on the concept of validity, which we consider particularly influential or insightful. Despite differences in metatheory, both Cronbach and Meehl's construct validity, and Borsboom, Mellenbergh and van Heerden's early concept of validity regard validity as a criterion for successful measurement and thus, as crucial for the soundness of psychological science. Others, such as Borgstede and Eggert, regard recourses to validity as an appeal to an (unscientific) folk psychology. Instead, they advocate theory-based measurement. It will be demonstrated that these divergent positions converge in their view of psychological theory as indispensable for the soundness of psychological measurement. However, the formulation of the concept (and scope) of scientific theory differs across the presented conceptions of validity. These differences can be at least partially attributed to three disparities in metatheoretical and methodological stances. The first concerns the question of the structure of scientific theories. The second concerns the question of psychology's subject matter. The third regards whether, and if, to which extent, correlations can be indicative of causality and therefore point toward validity. These results indicate that metatheory may help to structure the discourse on the concept of validity by revealing the contingencies the concrete positions rely on.

Supporting the Demand on Mental Health Services with AI-Based Conversational Large Language Models (LLMs)

The demand for psychological counselling has grown significantly in recent years, particularly with the global outbreak of COVID-19, which heightened the need for timely and professional mental health support. Online psychological counselling emerged as the predominant mode of providing services in response to this demand. In this study, we propose the Psy-LLM framework, an AI-based assistive tool leveraging large language models (LLMs) for question answering in psychological consultation settings to ease the demand on mental health professions. Our framework combines pre-trained LLMs with real-world professional questions-and-answers (Q&A) from psychologists and extensively crawled psychological articles. The Psy-LLM framework serves as a front-end tool for healthcare professionals, allowing them to provide immediate responses and mindfulness activities to alleviate patient stress. Additionally, it functions as a screening tool to identify urgent cases requiring further assistance. We evaluated the framework using intrinsic metrics, such as perplexity, and extrinsic evaluation metrics, including human participant assessments of response helpfulness, fluency, relevance, and logic. The results demonstrate the effectiveness of the Psy-LLM framework in generating coherent and relevant answers to psychological questions. This article discusses the potential and limitations of using large language models to enhance mental health support through AI technologies.

Recovering Developmental Bivariate Trajectories in Accelerated Longitudinal Designs with Dynamic Continuous Time Modeling

Accelerated longitudinal designs (ALDs) provide an opportunity to capture long developmental periods in a shorter time framework using a relatively small number of assessments. Prior literature has investigated whether univariate developmental processes can be characterized with data obtained from ALDs. However, many important questions in psychology and related sciences imply working with several variables that are intercorrelated as they unfold over time, such as cognitive and cortical development. Therefore, bivariate developmental models are required. This study aimed to assess the effectiveness of continuous-time bivariate Latent Change Score (CT-BLCS) models for recovering the trajectories of two interdependent developmental processes using data from diverse ALDs. Through a Monte Carlo simulation study, the efficacy of different sampling designs and sample sizes was examined. The study fills a gap in the literature by examining the performance of ALDs in bivariate systems, providing specific recommendations for future application of ALDs for studying interrelated developmental variables.

Interactive structure building in sentence production

How speakers sequence words and phrases remains a central question in cognitive psychology. Here we focused on understanding the representations and processes that underlie structural priming, the speaker’s tendency to repeat sentence structures encountered earlier. Verb repetition from the prime to the target led to a stronger tendency to produce locative variants of the spray-load alternation following locative primes (e.g., load the boxes into the van) than following with primes (e.g., load the van with the boxes). These structural variants had the same constituent structure, ruling out abstract syntactic structure as the source of the verb boost effect. Furthermore, using cleft constructions (e.g., What the assistant loaded into the lift was the equipment), we found that the thematic role order (thematic role-position mappings) of the prime can persist separately from its argument structure (thematic role-syntactic function mappings). Moreover, both priming effects were enhanced by verb repetition and interacted with each other when the construction of the prime was also repeated in the target. These findings are incompatible with the traditional staged model of grammatical encoding, which postulates the independence of abstract syntax from thematic role information. We propose the interactive structure-building account, according to which speakers build a sentence structure by choosing a thematic role order and argument structure interactively based on their prior co-occurrence together with other structurally relevant information such as verbs and constructions.

Evaluating the cost of simplicity in score building: An example from alcohol research.

Building a score from a questionnaire to predict a binary gold standard is a common research question in psychology and health sciences. When building this score, researchers may have to choose between statistical performance and simplicity. A practical question is to what extent it is worth sacrificing the former to improve the latter. We investigated this research question using real data, in which the aim was to predict an alcohol use disorder (AUD) diagnosis from 20 self-reported binary questions in young Swiss men (n = 233, mean age = 26). We compared the statistical performance using the area under the ROC curve (AUC) of (a) a "refined score" obtained by logistic regression and several simplified versions of it ("simple scores"): with (b) 3, (c) 2, and (d) 1 digit(s), and (e) a "sum score" that did not allow negative coefficients. We used four estimation methods: (a) maximum likelihood, (b) backward selection, (c) LASSO, and (d) ridge penalty. We also used bootstrap procedures to correct for optimism. Simple scores, especially sum scores, performed almost identically or even slightly better than the refined score (respective ranges of corrected AUCs for refined and sum scores: 0.828-0.848, 0.835-0.850), with the best performance been achieved by LASSO. Our example data demonstrated that simplifying a score to predict a binary outcome does not necessarily imply a major loss in statistical performance, while it may improve its implementation, interpretation, and acceptability. Our study thus provides further empirical evidence of the potential benefits of using sum scores in psychology and health sciences.

Quand le lieu de travail devient le lieu d’accueil d’un proche atteint de cancer : quelles interactions entre aidant, patient, soignants ?

Three clinical situations explored the interactions between patients treated for cancer in oncology, their family caregivers working as doctors or nurses in the same establishment or service, and the healthcare team providing the patient's care, as well as the repercussions of such a context on these three players. In each situation, the patient, the family caregiver and a member of the team were interviewed using a semi-directive interview guide. The 8 interviews were recorded and transcribed in full, then subjected to thematic content analysis. The tension between "wanting to stay in their place as a relative" and facilitating/accelerating the patient's medical journey was heightened when the patient is being cared for in the institution/service in which the family's caregiver works. The healthcare team reported additional psychological pressure, but few arrangements are made by the team to support the specific nature of these situations. Various factors, such as the severity of the illness, the closeness of the relationship between the caregiver and the patient, existence of a hierarchical link between the caregiver and the team, and the presence of the caregiver on the ward, seemed to potentiate the difficulties felt by the healthcare team and the individual suffering of the caregiver. These situations generated intra- and interpersonal psychological tensions for all concerned, each oscillating between their status as family caregiver and healthcare professional, or as colleague and healthcare professional. These situations have raised ethical and psychological questions for all involved, which need to be anticipated.

Application of facial neuromuscular electrical stimulation (fNMES) in psychophysiological research: Practical recommendations based on a systematic review of the literature

Facial neuromuscular electrical stimulation (fNMES), which allows for the non-invasive and physiologically sound activation of facial muscles, has great potential for investigating fundamental questions in psychology and neuroscience, such as the role of proprioceptive facial feedback in emotion induction and emotion recognition, and may serve for clinical applications, such as alleviating symptoms of depression. However, despite illustrious origins in the 19th-century work of Duchenne de Boulogne, the practical application of fNMES remains largely unknown to today’s researchers in psychology. In addition, published studies vary dramatically in the stimulation parameters used, such as stimulation frequency, amplitude, duration, and electrode size, and in the way they reported them. Because fNMES parameters impact the comfort and safety of volunteers, as well as its physiological (and psychological) effects, it is of paramount importance to establish recommendations of good practice and to ensure studies can be better compared and integrated. Here, we provide an introduction to fNMES, systematically review the existing literature focusing on the stimulation parameters used, and offer recommendations on how to safely and reliably deliver fNMES and on how to report the fNMES parameters to allow better cross-study comparison. In addition, we provide a free webpage, to easily visualise fNMES parameters and verify their safety based on current density. As an example of a potential application, we focus on the use of fNMES for the investigation of the facial feedback hypothesis.