Experimental Task Research Articles

Machine Learning Classification of Local Environments in Molecular Crystals.

Identifying local structural motifs and packing patterns of molecular solids is a challenging task for both simulation and experiment. We demonstrate two novel approaches to characterize local environments in different polymorphs of molecular crystals using learning models that employ either flexibly learned or handcrafted molecular representations. In the first case, we follow our earlier work on graph learning in molecular crystals, deploying an atomistic graph convolutional network combined with molecule-wise aggregation to enable per-molecule environmental classification. For the second model, we develop a new set of descriptors based on symmetry functions combined with a point-vector representation of the molecules, encoding information about the positions and relative orientations of the molecule. We demonstrate very high classification accuracy for both approaches on urea and nicotinamide crystal polymorphs and practical applications to the analysis of dynamical trajectory data for nanocrystals and solid-solid interfaces. Both architectures are applicable to a wide range of molecules and diverse topologies, providing an essential step in the exploration of complex condensed matter phenomena.

A self-adaptive agent for flexible posture planning in robotic milling system

In recent years, industrial robots are widely used in the milling processes of the fundamental components such as aeronautic parts, aerospace cabins and blades on the marine propellers of the worships, because of their flexible structure, large operating space, and robust capacity of rapid reconfiguration. Along with the robotic machining process, multiple types of geometrical and physical constraints display the time-varying characteristics, which are difficult to be considered simultaneously by the traditional static planning methods on the robotic milling posture. Therefore, a deep reinforcement learning (DRL) enhanced virtual repulsive potential field flexible model is proposed for the robotic milling posture dynamic planning. The preliminary planning of static milling posture is realized based on the fixed setting of the virtual modal parameters in the posture planning model, which is treated as a theory preparation for training the deep reinforcement learning agent. To develop the static planning model, multiple constraints are established by considering the performances of geometric, kinematic and machining precision simultaneously. Furthermore, a reward function is constructed for the dynamic agent, comprehensively combining the long and short reward benefits in nonlinear correlation with virtual repulsive forces. By taking the virtual modal parameters as the action space of the dynamic agent, a DRL enhanced agent VRPF-SAC is created and trained under the dynamic milling environment from virtual repulsive planning field. Four tasks of large size robotic milling simulations and experiments are designed and developed to validate the effectiveness of the flexible planning model. In comparison with the static model, the flexible planning model displays the excellent global effects covering the kinematic, and precision performance, with 65.78 %, 72.04 %, and 70.25 % reduction in velocity, acceleration, and jerk of robotic joints, and ensuring the effective precision performance from −0.365 to 0.283 mm of large size workpiece with large curvature changes. The proposed flexible posture planning architecture can provide a basic for robotic dynamic milling of core parts with large size, narrow space and sharp curvatures of the aeronautic parts, aerospace cabins and worship propellers.

The EQ-5D-5L valuation study for Trinidad and Tobago

PurposeThe 2016 EQ-5D-3L value set for Trinidad and Tobago (T&T) allows for the calculation of EQ-5D-5L values via the crosswalk algorithm. The 2016 value set was based on methods predating the EQ-VT protocol, now considered the gold standard for developing EQ-5D value sets. Furthermore, direct elicitation of EQ-5D-5L is preferred over crosswalked values. This study aimed to produce an EQ-5D-5L value set for T&T.MethodsA representative sample (age, sex, geography) of adults each completed 10 composite Time Trade-Off (cTTO) tasks and 12 Discrete Choice Experiment (DCE) tasks in face-to-face interviews. The cTTO data were analyzed using a Tobit model that corrects for heteroskedasticity. DCE data were analyzed using a mixed logit model. The cTTO and DCE data were combined in hybrid models.ResultsOne thousand and seventy-nine adults completed the valuation interviews. Among the modelling approaches that were explored, the hybrid heteroskedastic Tobit model produced all internally consistent, statistically significant coefficients, and performed best in terms of out-of-sample predictivity for single states. Compared to the existing EQ-5D-5L crosswalk set, the new value set had a higher number of negative values (236 or 7.6% versus 21 or 0.7%). The mean absolute difference was 0.157 and the correlation coefficient between the two sets was 0.879.ConclusionThis study provides a value set for the EQ-5D-5L for T&T using the EQ-VT protocol. We recommend this value set for QALY computations relating to T&T.

Decomposing modal thought.

Cognitive scientists have become increasingly interested in understanding how natural minds represent and reason about possible ways the world could be. However, there is currently little agreement on how to understand this remarkable capacity for modal thought. We argue that the capacity for modal thought is built from a set of relatively simple component parts, centrally involving an ability to consider possible extensions of a part of the actual world. Natural minds can productively combine this ability with a range of other capacities, eventually allowing for the observed suite of increasingly more sophisticated ways of modal reasoning. We demonstrate how our (de)compositional account is supported by both the trajectory of children's developing capacity for reasoning about possible ways the world could be and by what we know about how such modal thought is expressed within and across natural languages. Our approach makes new predictions about which kinds of capacities are required by which kinds of experimental tasks and, as a result, contributes to settling currently open theoretical questions about the development of modal thought and the acquisition of modal vocabulary in children. Our work also provides a more systematic way of understanding possible variation in modal thought and talk, and, more generally, paves the way toward a unified theory that will ultimately allow researchers across disciplines to relate their findings to each other within a framework of shared assumptions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Examining Parental Investment Dynamics in Twin Families: Insights From a Triadic Experimental Task [Conference Presentation Abstract

Examining Parental Investment Dynamics in Twin Families: Insights From a Triadic Experimental Task [Conference Presentation Abstract

Refined cyclic R-curve determination through residual crack tip stress reduction by annealing

The accurate determination of the cyclic R-curve of the threshold of stress intensity factor range is a demanding experimental task. The main challenge concerns the introduction of a pre-crack that is open under tension loading and free of residual stresses. When pre-cracks are generated by cyclic compression-compression loading, a zone of tensile residual stresses up to the material yield strength is generated ahead of the pre-crack tip. These stresses may affect crack propagation at the beginning of fatigue crack growth and should consequently be removed especially for high-strength materials. However, subsequent stress-relief annealing is impractical for numerous metallic material classes due to undesired microstructural alterations at the temperatures required for stress relief. In the present contribution, the above-described issue is addressed for a high speed steel using single-edge notched bending specimens pre-cracked at various stages of a typical high speed steel heat treatment process. It is shown that stress-relieving via a heat treatment route is a significant improvement compared to the conventional compression pre-cracking procedures to measure material-specific cyclic R-curves.

A multimodal fMRI dataset unifying naturalistic processes with a rich array of experimental tasks.

Cognitive neuroscience has advanced significantly due to the availability of openly shared datasets. Large sample sizes, large amounts of data per person, and diversity in tasks and data types are all desirable, but are difficult to achieve in a single dataset. Here, we present an open dataset with N = 101 participants and 6 hours of scanning per participant, with 6 multifaceted cognitive tasks including 2 hours of naturalistic movie viewing. This datasets' combination of ample sample size, extensive data per participant, more than 600 hours worth of data, and a wide range of experimental conditions - including cognitive, affective, social, and somatic/interoceptive tasks - positions it uniquely for probing important questions in cognitive neuroscience.

Social episodic memory in severe alcohol use disorder: Positive encoding bias and negative bias in accessibility of interpersonal information.

Alterations in higher-order social cognition are well documented in individuals with severe alcohol use disorder (SAUD). However, the basic mechanisms underpinning them are not well understood. This knowledge gap hampers the development of targeted therapeutic interventions. Here, we investigated whether individuals with SAUD show abnormalities in social episodic memory processes, which may represent relevant candidate mechanisms for alterations in social cognition. Recently detoxified patients with SAUD and matched healthy controls (HCs) completed two experimental tasks. We first used a Social Recognition Task in 40 SAUD patients and 40 HCs to measure the participants' ability to implicitly memorize the facial identity and emotion of novel interpersonal cues (i.e., dynamic facial expressions of anger and happiness). We then used a Social Memory Accessibility Task in 29 SAUD patients and 30 HCs) to measure participants' access to and fluency for already existing social memories by asking them to retrieve as many specific positive and negative interpersonal events as possible within equal time limits. In the Social Recognition Task, we found that, compared to HCs, patients with SAUD had a globally lower recognition performance for the facial identities of novel social stimuli, but a preserved bias toward positive information. Conversely, in the social memory accessibility task, patients showed greater access to and fluency for negative interpersonal memories than controls (no group differences were observed for positive ones), resulting in a negative accessibility bias. This exploration of episodic social memory in individuals with SAUD showed (1) a preserved bias for the encoding of positive versus negative novel social information, and (2) greater access to negative than positive interpersonal memories. These results enhance our understanding of socio-affective processing in individuals with SAUD and identify social memory alterations that may contribute to social cognition and interpersonal difficulties.

Temporal resolution relates to sensory hyperreactivity independently of stimulus detection sensitivity in individuals with autism spectrum disorder.

Researchers have been focusing on perceptual characteristics of autism spectrum disorder (ASD) in terms of sensory hyperreactivity. Previously, we demonstrated that temporal resolution, which is the accuracy to differentiate the order of two successive vibrotactile stimuli, is associated with the severity of sensory hyperreactivity. We currently examined whether an increase in the perceptual intensity of a tactile stimulus, despite its short duration, is derived from high temporal resolution and high frequency of sensory temporal summation. Twenty ASD and 22 typically developing (TD) participants conducted two psychophysical experimental tasks to evaluate detectable duration of vibrotactile stimulus with same amplitude and to evaluate temporal resolution. The sensory hyperreactivity was estimated using self-reported questionnaire. There was no relationship between the temporal resolution and the duration of detectable stimuli in both groups. However, the ASD group showed severe sensory hyperreactivity in daily life than TD group, and the ASD participants with severe sensory hyperreactivity tended to have high temporal resolution, not high sensitivity of detectable duration. Contrary to the hypothesis, there might be different processing between temporal resolution and sensitivity for stimulus detection. We suggested that the atypical temporal processing would affect to sensory reactivity in ASD.

Phonation Patterns in Spanish Vowels: Spectral and Spectrographic Analysis

This article provides a detailed examination of voice quality in word-final vowels in Spanish. The experimental task involved the pronunciation of words in two prosodic contexts by native Spanish speakers from diverse dialects. A total of 400 vowels (10 participants × 10 words × 2 contexts × 2 repetitions) were analyzed acoustically in Praat. Waveforms and spectrograms were inspected visually for voice, creak, breathy voice, and devoicing cues. In addition, the relative amplitude difference between the first two harmonics (H1–H2) was obtained via FFT spectra. The findings reveal that while creaky voice is pervasive, breathy voice is also common, and devoicing occurs in 11% of tokens. We identify multiple phonation types (up to three) within the same vowel, of which modal voice followed by breathy voice was the most common combination. While creaky voice was more frequent overall for males, modal voice tended to be more common in females. In addition, creaky voice was significantly more common at the end of higher prosodic constituents. The analysis of spectral tilt shows that H1–H2 clearly distinguishes breathy voice from modal voice in both males and females, while H1–H2 values consistently discriminate creaky and modal voice in male participants only.

INCENTIVE MECHANISMS OF AN EXPERIMENTAL RESOURCE-SHARING PLATFORM CONSIDERING REPUTATION EFFECTS FOR MEGAPROJECTS

Participating in megaproject experimental tasks would significantly improve the laboratories’ industry influence and future competitiveness. Thus, this paper introduces the long-term reputation effects of the incentive model of an experimental resource-sharing platform for megaprojects, which could motivate them to consider future benefits and improve their current efforts. The aim is to incentivize laboratories’ resource-sharing behavior more effectively and to increase the amount of resources shared by these laboratories on the platform, thus guaranteeing the long-term sustainability of the platform. It constructs the incentive model by combining dual implicit and explicit incentive mechanisms. It analyses the incentive mechanism of a reputation effect on laboratories compared with the pure explicit mechanism so that the primary conditions for reputation incentives can be obtained to achieve Pareto improvement. Finally, the proposed method is validated in combination with data simulation. The results show that although dual implicit and explicit incentive mechanisms could reduce the information asymmetry between the two sides and increase the efforts of laboratories and the benefits of the platform, the platform should not blindly increase the intensity of these incentives and need to consider the influence of the subsidies of these laboratories’ upfront inputs, the degree of sharing and their informatization capabilities.

The impact of knowledge characteristics on process performance: experimenting with the conversion perspective on knowledge transfer velocity

PurposeWith shorter product cycles and a growing number of knowledge-intensive business processes, time consumption is a highly relevant target factor in measuring the performance of contemporary business processes. This research aims to extend prior research on the effects of knowledge transfer velocity at the individual level by considering the effect of complexity, stickiness, competencies, and further demographic factors on knowledge-intensive business processes at the conversion-specific levels.Design/methodology/approachWe empirically assess the impact of situation-dependent knowledge transfer velocities on time consumption in teams and individuals. Further, we issue the demographic effect on this relationship. We study a sample of 178 experiments of project teams and individuals applying ordinary least squares (OLS) for regression analysis-based modeling.FindingsThe authors find that time consumed at knowledge transfers is negatively associated with the complexity of tasks. Moreover, competence among team members has a complementary effect on this relationship and stickiness retards knowledge transfers. Thus, while demographic factors urgently need to be considered for effective and speedy knowledge transfers, these influencing factors should be addressed on a conversion-specific basis so that some tasks are realized in teams best while others are not. Guidelines and interventions are derived to identify best task realization variants, so that process performance is improved by a new kind of process improvement method.Research limitations/implicationsThis study establishes empirically the importance of conversion-specific influence factors and demographic factors as drivers of high knowledge transfer velocities in teams and among individuals. The contribution connects the field of knowledge management to important streams in the wider business literature: process improvement, management of knowledge resources, design of information systems, etc. Whereas the model is highly bound to the experiment tasks, it has high explanatory power and high generalizability to other contexts.Practical implicationsTeam managers should take care to allow the optimal knowledge transfer situation within the team. This is particularly important when knowledge sharing is central, e.g. in product development and consulting processes. If this is not possible, interventions should be applied to the individual knowledge transfer situation to improve knowledge transfers among team members.Social implicationsFaster and more effective knowledge transfers improve the performance of both commercial and non-commercial organizations. As nowadays, the individual is faced with time pressure to finalize tasks, the deliberated increase of knowledge transfer velocity is a core capability to realize this goal. Quantitative knowledge transfer models result in more reliable predictions about the duration of knowledge transfers. These allow the target-oriented modification of knowledge transfer situations so that processes speed up, private firms are more competitive and public services are faster to citizens.Originality/valueTime consumption is an increasingly relevant factor in contemporary business but so far not been explored in experiments at all. This study extends current knowledge by considering quantitative effects on knowledge velocity and improved knowledge transfers.

Effects of attention on the processing of physical and abstract auditory regularities: An exploratory MMN study

The mismatch-negativity (MMN) component of the auditory event-related potential (ERP) reflects preattentive memory mechanisms encoding various types of regularities in the auditory environment. In an oddball paradigm, two types of deviant stimuli (in separate blocks) were presented among frequent standard stimuli: physical deviants (higher in pitch than the standards) and more complex, “abstract” deviants (tone pairs descending in pitch, presented among ascending standard tone pairs). The attentional load of the participant was manipulated under three conditions, where the participants either (1) watched a silent video, (2) played a computer game (Tetris) or (3) attended to the auditory stimuli and tried to detect infrequent target stimuli which were of lower intensity than the standard stimuli. The goal was to find out, whether the possible attention effects (suppression/enhancement) on the MMN are similar or different to stimuli requiring the extraction of either physical or abstract invariances. Both the physical and abstract deviants elicited in all conditions MMNs but no statistically significant amplitude differences between the conditions were found. The N2b and P3b components were elicited only in the attend condition and only by the soft target tones. The results further confirm that the MMN is a robust response to various types of regularity violations, showing no major effects of attentional manipulations. The results also suggest that the most commonly used primary task in MMN experiments, watching a silent video, usually keeps the participants' attention well enough directed away from the auditory stimuli. However, in cases where a cognitively more demanding but still participant-friendly primary task is needed, a simple computer game such as Tetris can be used, enabling better control of the participants’ attention and vigilance.

Postural Control During Single-Leg Landing in Female Athletes After Anterior Cruciate Ligament Reconstruction.

Secondary anterior cruciate ligament (ACL) injury is a complication of ACL reconstruction (ACLR), which may result from altered neuromuscular control affecting anticipatory postural adjustments (APAs) and compensatory postural adjustments (CPAs) required for maintaining balance during movement. However, it remains unclear how APAs and CPAs differ in single-leg landings post-ACLR compared to healthy subjects. The purpose of this study was to clarify the differences in muscle activities of APAs and CPAs, lower limb kinematics, and kinetics between athletes with a history of ACLR and healthy athletes during single-leg landing. Cross-sectional study. Eighteen female athletes were recruited and divided into ACLR (n = 9) and control groups (n = 9). The experimental task involved a single-leg landing from a 30 cm box. Joint angles and moments were determined using a 3-dimensional motion analysis system, while muscle activity was assessed using surface electromyography. Analysis intervals were divided into two phases: the APA phase (-150 ms to 50 ms) and the CPA phase (50 ms to 250 ms), with initial contact (0 ms) as the reference point. Muscle activity onset time was defined as the time when the baseline exceeded by the sum of mean values and 2 standard deviations. No significant differences were observed in muscle activity or onset time between the ACLR and control groups. However, an increased hip external rotation moment was observed during the CPA phase in the ACLR group. These findings suggest that APAs and CPAs of athletes who returned to sports more than 1 year post-ACLR may be similar. The increased hip external rotation moment in the ACLR group during the CPA phase could represent a specific compensatory strategy to decrease the hip internal rotation angle post-ACLR. III.

A Direct Comparison Between Discrete Choice With Duration and Composite Time Trade-Off Methods: Do They Produce Similar Results?

ObjectivesDiscrete choice experiments including a duration attribute (DCEd) represent a promising candidate method for valuing health-related quality-of-life instruments. However, it has not been established that DCEd can produce similar results as composite time trade-off (cTTO) or EuroQol Valuation Technology (EQ-VT) valuations of the EQ-5D-5L instrument. This study provides a direct comparison between cTTO and EQ-VT, and DCEd valuation methods. MethodsAn EQ-VT study was conducted in Trinidad and Tobago to value the EQ-5D-5L. 1079 respondents each completed 10 cTTO tasks and 12 discrete choice experiments tasks without a duration attribute. A separate sample of 970 respondents each completed 18 split-triplet DCEd tasks. Several regression models were applied to the EQ-VT data, and the DCEd data were analyzed using mixed logit models with an exponential discount rate. The estimated values were compared using scatterplots and Bland-Altman plots. ResultsThe ordering of dimensions was identical in level 5 for cTTO/EQ-VT and DCEd models, with pain/discomfort being the most important dimension and usual activities being least important. cTTO/EQ-VT models produced a value for state 55555 ranging between −0.52 and −0.69, whereas this was −0.543 for the nonlinear mixed logit model for the DCEd data. Scatterplots and Bland-Altman plots suggested excellent agreement between cTTO/EQ-VT and DCEd-based estimates. ConclusionsCTTO/EQ-VT and DCEd valuations produce similar results when correcting DCEd for nonlinear time preferences. The ordering of importance of the dimensions and scale are identical, suggesting that the 2 methods measure the same construct and produce similar results.

Home-Based EEG Hyperscanning for Infant-Caregiver Social Interactions.

Prior hyperscanning studies that record the brain activities of caregivers and children concurrently have primarily been conducted within the confines of the laboratory, thus limiting the generalizability of results to real-life settings. Here, a comprehensive protocol for capturing synchronized electroencephalography (EEG), electrocardiography (ECG), and behavioral recordings from infant-caregiver dyads during various interactive tasks at home is proposed. This protocol demonstrates how to synchronize the different data streams and report EEG data retention rates and quality checks. Additionally, critical issues and possible solutions with respect to the experimental setup, tasks, and data collection in home settings are discussed. The protocol is not limited to infant-caregiver dyads but can be applied to various dyadic constellations. Overall, we demonstrate the flexibility of EEG hyperscanning setups, which allow experiments to be conducted outside of the laboratory to capture participants' brain activities in more ecologically valid environmental settings. Yet, movement and other types of artifacts still constrain the experimental tasks that can be performed in the home setting.

Analysis of the effectiveness of the methodology for designing a cloud-oriented distance learning environment for biology in informal education conditions

The article contains an analysis of research on the design of a cloud-oriented environment for distance learning of biology, teachers' experience in designing cloud-oriented environments contributes to the development of distance learning forms in general secondary education institutions and the widespread implementation of electronic tools in educational practice. When organizing distance learning in natural sciences, it is necessary to take into account the platform's capabilities and services for developing students' natural-science thinking, conducting observations, laboratory and practical experiments, implementing experimental tasks. The combination of cloud services and methods of distance learning in biology allows visualization of biological processes, the research-oriented nature of activities, organizing research projects on a single platform, using media resources, interactive simulators, integrating virtual content with the physical environment, and using programs for quality knowledge monitoring. The aim of the study is to develop a methodology for teachers to design a cloud-oriented educational environment for distance learning of biology for students in informal education conditions, testing its effectiveness based on the results of evaluation using a factor-criterion model, and developing recommendations for improving the qualifications of teachers in designing cloud-oriented environments in informal education conditions. Theoretical and empirical methods were applied to achieve the set goal, involving the study of previous research in the field of designing distance educational environments and the integration of cloud services into the educational process. Mathematical statistical methods were used to analyze the results of the survey of teachers from the focus group. The effectiveness of the developed methodology was tested using a factor-criterion model, which includes four factors: organizational, methodological, procedural, and content. Expert assessment of the effectiveness of the methodology revealed that the effectiveness of designing a cloud-oriented environment for distance learning of biology by biology teachers in general secondary education institutions, who participated in the focus group, is 0.79, indicating a high level

MolPROP: Molecular Property prediction with multimodal language and graph fusion

Pretrained deep learning models self-supervised on large datasets of language, image, and graph representations are often fine-tuned on downstream tasks and have demonstrated remarkable adaptability in a variety of applications including chatbots, autonomous driving, and protein folding. Additional research aims to improve performance on downstream tasks by fusing high dimensional data representations across multiple modalities. In this work, we explore a novel fusion of a pretrained language model, ChemBERTa-2, with graph neural networks for the task of molecular property prediction. We benchmark the MolPROP suite of models on seven scaffold split MoleculeNet datasets and compare with state-of-the-art architectures. We find that (1) multimodal property prediction for small molecules can match or significantly outperform modern architectures on hydration free energy (FreeSolv), experimental water solubility (ESOL), lipophilicity (Lipo), and clinical toxicity tasks (ClinTox), (2) the MolPROP multimodal fusion is predominantly beneficial on regression tasks, (3) the ChemBERTa-2 masked language model pretraining task (MLM) outperformed multitask regression pretraining task (MTR) when fused with graph neural networks for multimodal property prediction, and (4) despite improvements from multimodal fusion on regression tasks MolPROP significantly underperforms on some classification tasks. MolPROP has been made available at https://github.com/merck/MolPROP.Scientific contributionThis work explores a novel multimodal fusion of learned language and graph representations of small molecules for the supervised task of molecular property prediction. The MolPROP suite of models demonstrates that language and graph fusion can significantly outperform modern architectures on several regression prediction tasks and also provides the opportunity to explore alternative fusion strategies on classification tasks for multimodal molecular property prediction.

A chinrest-based approach to measure eye movements and experimental task engagement in macaques with minimal restraint

BackgroundThe use of Rhesus macaques in vision research is crucial due to their visual system's similarity to humans. While invasive techniques have been the norm, there has been a shift towards non-invasive methods, such as facemasks and head molds, to enhance animal welfare and address ethical concerns. New MethodWe present a non-invasive, 3D-printed chinrest with infrared sensors, adapted from canine research, allowing for accurate eye movement measurements and voluntary animal participation in experiments. ResultsThe chinrest method showed a 16% and 28% increase in average trial numbers for Monkey 1 and Monkey 2, respectively, compared to the traditional headpost method. The engagement was high, with monkeys performing over 500 trials per session and initiating a new trial after an average intertrial interval of approximately 1 second. The hit rate improved by about 10% for Monkey 1 in the chinrest condition, and the fixation precision, measured by the standard deviation of gaze positions, was significantly better in the chinrest condition, with Monkey 1 showing a reduction in fixation imprecision from 0.26° to 0.17° in the X-axis. Comparison with Existing MethodsThe chinrest approach showed significant improvements in trial engagement and reduction in aborted trials due to fixation breaks, indicating less stress and potentially improved data quality compared to previous non-invasive methods. ConclusionsThe chinrest method offers a significant advancement in primate cognitive testing by allowing for precise data collection while addressing animal welfare concerns, possibly leading to better scientific outcomes and a paradigm shift in primate research methodologies.

Betting on Your Feelings: The Interplay between Emotion and Cognition in Gambling Affective Task.

Background: Gambling Disorder (GD) is a bio-psycho-social disorder resulting from the interaction of clinical, cognitive, and affective factors. Impulsivity is a crucial factor in addiction studies, as it is closely linked to cognitive distortions in GD by encompassing impulsive choices, motor responses, decision-making, and cognitive biases. Also, emotions, mood, temperament, and affective state are crucial in developing and maintaining GD. Gambling can be used as a maladaptive coping strategy to avoid or escape problems and distress. Methods: The aim of the present study is to explore differences in personality traits and emotion regulation of people suffering from GD, substance-dependent gamblers (SDGs), and healthy controls (HCs). Additionally, the study proposes a new experimental task: the "Gambling Affective Task" (GAT) to investigate the influence of affective priming on risk-taking behaviors. Results: Our findings indicate that participants placed lower bets following positive priming. Additionally, SDGs wagered significantly higher amounts than HCs, regardless of priming type. In general, participants exhibited longer response times after positive priming trials, compared to negative and neutral priming trials. These findings suggest that experiencing positive emotions can act as a protective factor by delaying and lengthening gambling behaviors. By comparing gamblers with and without substance comorbidity, we can gain insight into the exclusive factors of GD and improve our understanding of this disorder. Conclusions: By elucidating the impact of emotional states on risk-taking, the research also provides new insights into the prevention and treatment of GD.