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Auditory spatial attention detection (ASAD) seeks to determine which speaker in a surround sound field a listener is focusing on based on the one's brain biosignals. Although existing studies have achieved ASAD from a single-trial electroencephalogram (EEG), the huge inter-subject variability makes them generally perform poorly in cross-subject scenarios. Besides, most ASAD methods do not take full advantage of topological relationships between EEG channels, which are crucial for high-quality ASAD. Recently, some advanced studies have introduced graph-based brain topology modeling into ASAD, but how to calculate edge weights in a graph to better capture actual brain connectivity is worthy of further investigation. To address these issues, we propose a new ASAD method in this paper. First, we model a multi-channel EEG segment as a graph, where differential entropy serves as the node feature, and a static adjacency matrix is generated based on inter-channel mutual information to quantify brain functional connectivity. Then, different subjects' EEG graphs are encoded into a shared embedding space through a total variation graph neural network. Meanwhile, feature distribution alignment based on multi-kernel maximum mean discrepancy is adopted to learn subject-invariant patterns. Note that we align EEG embeddings of different subjects to reference distributions rather than align them to each other for the purpose of privacy preservation. A series of experiments on open datasets demonstrate that the proposed model outperforms state-of-the-art ASAD models in cross-subject scenarios with relatively low computational complexity, and feature distribution alignment improves the generalizability of the proposed model to a new subject.

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The study focusses on risk related generalization beliefs, i.e., the belief that the risk of a specific agent can be generalized across various conditions. These conditions are: G1: across the frequency of usage (from often to rare); G2: across exposure modalities (hot to cold); G3: across exposure routes (oral to dermal), and G4: across detrimental outcomes (specific detrimental endpoint to various detrimental endpoints). We examined how different risk descriptions impact those generalization beliefs using the risks of bamboo tableware for consumers as an example. The research followed a 2x2 between-subjects design with repeated measurements, and the test subjects were non-experts. The first factor, disclosure format, refers to the disclosure (yes/no) of risk generalization limitation. Half of the study participants were informed that bamboo tableware only poses a health risk if it is frequently used for hot beverages or foods. In contrast, the other half received no information about the risk restrictions regarding bamboo tableware use. The second factor referred to the agent description, either described by a particular unfamiliar term (formaldehyde) or a generic, more familiar term (plastics). Furthermore, we tested whether subjects who were initially not informed about the limits of risk generalizations altered their risk generalization beliefs G1 - G4 when they were informed that only frequent hot food and beverage consumption in bamboo tableware causes risks. It was found that respondents' four risk generalization beliefs G1 - G4 were statistically significantly lower for those who were informed about the risk generalization limitations. Additionally, the generalization beliefs G1 - G3 of subjects who were initially not informed, but received the information about the restrictions later, were statistically significantly lower than their initial beliefs, except for generalization across endpoints (G4). We discussed the findings in terms of their implications for risk communication.

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Identifying the origins of moral sensitivities, and their elaboration, within infancy and early childhood is a challenging task, given inherent limitations in infants' behavior. Here, I argue for a multi-pronged, multi-method approach that involves cleaving the moral response at its joints. Specifically, I chart the emergence of infants' moral expectations, evaluations, generalization and enforcement, demonstrating that while many moral sensitivities are present in the second year of life, these sensitivities are closely aligned with, and likely driven by, infants' everyday experience. Moreover, qualitative differences exist between the moral responses that are present in infancy and those of later childhood, particularly in terms of enforcement (i.e., a lack of punishment in infancy). These findings set the stage for addressing outstanding critical questions regarding moral development, that include identifying discrete causal inputs to early moral cognition, identifying whether moral cognition is distinct from social cognition early in life, and explaining gaps that exist between moral cognition and moral behavior in development.

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Chronic social defeat stress (CSDS), a widely used rodent model of stress, reliably leads to decreased social interaction in stress susceptible animals. Here, we investigate a role for fear learning in this response using male 129 Sv/Ev mice, a strain that is more vulnerable to CSDS than the commonly used C57BL/6 strain. We first demonstrate that defeated 129 Sv/Ev mice avoid a CD-1 mouse, but not a conspecific, indicating that motivation to socialize is intact in this strain. CD-1 avoidance is characterized by approach behavior that results in running in the opposite direction, activity that is consistent with a threat response. We next test whether CD-1 avoidance is subject to the same behavioral changes found in traditional models of Pavlovian fear conditioning. We find that associative learning occurs across 10 days CSDS, with defeated mice learning to associate the color of the CD-1 coat with threat. This leads to the gradual acquisition of avoidance behavior, a conditioned response that can be extinguished with 7 days of repeated social interaction testing (5 tests/day). Pairing a CD-1 with a tone leads to second-order conditioning, resulting in avoidance of an enclosure without a social target. Finally, we show that social interaction with a conspecific is a highly variable response in defeated mice that may reflect individual differences in generalization of fear to other social targets. Our data indicate that fear conditioning to a social target is a key component of CSDS, implicating the involvement of fear circuits in social avoidance.

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The delicate balance between discrimination and generalization of responses is crucial for survival in our ever-changing environment. In particular, it is important to understand how stimulus discrimination affects the level of stimulus generalization. For example, when we use non-differential training for Pavlovian eyeblink conditioning to investigate generalization of cerebellar-related eyelid motor responses, we find generalization effects on amount, amplitude and timing of the conditioned responses. However, it is unknown what the generalization effects are following differential training. We trained mice to close their eyelids to a 10 kHz tone with an air-puff as the reinforcing stimulus (CS+), while alternatingly exposing them to a tone frequency of either 4 kHz, 9 kHz or 9.5 kHz without the air-puff (CS-) during the training blocks. We tested the generalization effects during the expression of the responses after the training period with tones ranging from 2 kHz to 20 kHz. Our results show that the level of generalization tended to positively correlate with the difference between the CS+ and the CS- training stimuli. These effects of generalization were found for the probability, amplitude but not for the timing of the conditioned eyelid responses. These data indicate the specificity of the generalization effects following differential versus non-differential training, highlighting the relevance of discrimination learning for stimulus generalization.

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Accurate detection and tracking of animals across diverse environments are crucial for studying brain and behavior. Recently, computer vision techniques have become essential for high-throughput behavioral studies; however, localizing animals in complex conditions remains challenging due to intra-class visual variability and environmental diversity. These challenges hinder studies in naturalistic settings, such as when animals are partially concealed within nests. Moreover, current tools are laborious and time-consuming, requiring extensive, setup-specific annotation and training procedures. To address these challenges, we introduce the 'Detect-Any-Mouse-Model' (DAMM), an object detector for localizing mice in complex environments with minimal training. Our approach involved collecting and annotating a diverse dataset of single- and multi-housed mice in complex setups. We trained a Mask R-CNN, a popular object detector in animal studies, to perform instance segmentation and validated DAMM's performance on a collection of downstream datasets using zero-shot and few-shot inference. DAMM excels in zero-shot inference, detecting mice and even rats, in entirely unseen scenarios and further improves with minimal training. Using the SORT algorithm, we demonstrate robust tracking, competitive with keypoint-estimation-based methods. Notably, to advance and simplify behavioral studies, we release our code, model weights, and data, along with a user-friendly Python API and a Google Colab implementation.

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Generalized fear is a maladaptive behavior in which non-threatening stimuli elicit a fearful response. The ventral tegmental area (VTA) has been demonstrated to play important roles in fear response and fear memory generalization, but the precious neural circuit mechanism is still unclear. Here, we demonstrated that VTA-zona incerta (ZI) glutamatergic projection is involved in regulating high-intensity threatening training induced generalization and anxiety. Combining calcium signal recording and chemogentics, our work reveals that VTA glutamatergic neurons respond to closed arm entering in the model of PTSD. Inhibition of VTA glutamatergic neurons or the glutamatergic projection to ZI could both relieve fear generalization and anxiety. Together, our study proves the VTA - ZI glutamatergic circuit is involved in mediating fear generalization and anxiety, and provides a potential target for treating post-traumatic stress disorder.

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This paper proposes an advanced deep learning model that integrates the Diffusion-Transformer structure and parallel attention mechanism for the tasks of growth estimation and disease detection in jujube forests. Existing methods in forestry monitoring often fall short in meeting the practical needs of large-scale and highly complex forest areas due to limitations in data processing capabilities and feature extraction precision. In response to this challenge, this paper designs and conducts a series of benchmark tests and ablation experiments to systematically evaluate and verify the performance of the proposed model across key performance metrics such as precision, recall, accuracy, and F1-score. Experimental results demonstrate that compared to traditional machine learning models like Support Vector Machines and Random Forests, as well as common deep learning models such as AlexNet and ResNet, the model proposed in this paper achieves a precision of 95%, a recall of 92%, an accuracy of 93%, and an F1-score of 94% in the task of disease detection in jujube forests, showing similarly superior performance in growth estimation tasks as well. Furthermore, ablation experiments with different attention mechanisms and loss functions further validate the effectiveness of parallel attention and parallel loss function in enhancing the overall performance of the model. These research findings not only provide a new technical path for forestry disease monitoring and health assessment but also contribute rich theoretical and experimental foundations for related fields.

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Background: Atrial fibrillation (AFib) detection via mobile ECG devices is promising, but algorithms often struggle to generalize across diverse datasets and platforms, limiting their real-world applicability. Objective: This study aims to develop a robust, generalizable AFib detection approach for mobile ECG devices using crowdsourced algorithms. Methods: We developed a voting algorithm using random forest, integrating six open-source AFib detection algorithms from the PhysioNet Challenge. The algorithm was trained on an AliveCor dataset and tested on two disjoint AliveCor datasets and one Apple Watch dataset. Results: The voting algorithm outperformed the base algorithms across all metrics: the average of sensitivity (0.884), specificity (0.988), PPV (0.917), NPV (0.985), and F1-score (0.943) on all datasets. It also demonstrated the least variability among datasets, signifying its highest robustness and effectiveness in diverse data environments. Moreover, it surpassed Apple's algorithm on all metrics and showed higher specificity but lower sensitivity than AliveCor's Kardia algorithm. Conclusions: This study demonstrates the potential of crowdsourced, multi-algorithmic strategies in enhancing AFib detection. Our approach shows robust cross-platform performance, addressing key generalization challenges in AI-enabled cardiac monitoring and underlining the potential for collaborative algorithms in wearable monitoring devices.

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A Brain-computer interface (BCI) system establishes a novel communication channel between the human brain and a computer. Most event related potential-based BCI applications make use of decoding models, which requires training. This training process is often time-consuming and inconvenient for new users. In recent years, deep learning models, especially participant-independent models, have garnered significant attention in the domain of ERP classification. However, individual differences in EEG signals hamper model generalization, as the ERP component and other aspects of the EEG signal vary across participants, even when they are exposed to the same stimuli. This paper proposes a novel One-source domain transfer learning method based Attention Domain Adversarial Neural Network (OADANN) to mitigate data distribution discrepancies for cross-participant classification tasks. We train and validate our proposed model on both a publicly available OpenBMI dataset and a Self-collected dataset, employing a leave one participant out cross validation scheme. Experimental results demonstrate that the proposed OADANN method achieves the highest and most robust classification performance and exhibits significant improvements when compared to baseline methods (CNN, EEGNet, ShallowNet, DeepCovNet) and domain generalization methods (ERM, Mixup, and Groupdro). These findings underscore the efficacy of our proposed method.

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Background: The identification of compound-protein interactions (CPIs) is crucial for drug discovery and understanding mechanisms of action. Accurate CPI prediction can elucidate drug-target-disease interactions, aiding in the discovery of candidate compounds and effective synergistic drugs, particularly from traditional Chinese medicine (TCM). Existing in silico methods face challenges in prediction accuracy and generalization due to compound and target diversity and the lack of largescale interaction datasets and negative datasets for model learning. Methods: To address these issues, we developed a computational model for CPI prediction by integrating the constructed large-scale bioactivity benchmark dataset with a deep learning (DL) algorithm. To verify the accuracy of our CPI model, we applied it to predict the targets of compounds in TCM. An herb pair of Astragalus membranaceus and Hedyotis diffusaas was used as a model, and the active compounds in this herb pair were collected from various public databases and the literature. The complete targets of these active compounds were predicted by the CPI model, resulting in an expanded target dataset. This dataset was next used for the prediction of synergistic antitumor compound combinations. The predicted multi-compound combinations were subsequently examined through in vitro cellular experiments. Results: Our CPI model demonstrated superior performance over other machine learning models, achieving an area under the Receiver Operating Characteristic curve (AUROC) of 0.98, an area under the precision-recall curve (AUPR) of 0.98, and an accuracy (ACC) of 93.31% on the test set. The model's generalization capability and applicability were further confirmed using external databases. Utilizing this model, we predicted the targets of compounds in the herb pair of Astragalus membranaceus and Hedyotis diffusaas, yielding an expanded target dataset. Then, we integrated this expanded target dataset to predict effective drug combinations using our drug synergy prediction model DeepMDS. Experimental assay on breast cancer cell line MDA-MB-231 proved the efficacy of the best predicted multi-compound combinations: Combination I (Epicatechin, Ursolic acid, Quercetin, Aesculetin and Astragaloside IV) exhibited a half-maximal inhibitory concentration (IC50) value of 19.41 µM, and a combination index (CI) value of 0.682; and Combination II (Epicatechin, Ursolic acid, Quercetin, Vanillic acid and Astragaloside IV) displayed a IC50 value of 23.83 µM and a CI value of 0.805. These results validated the ability of our model to make accurate predictions for novel CPI data outside the training dataset and evaluated the reliability of the predictions, showing good applicability potential in drug discovery and in the elucidation of the bioactive compounds in TCM. Conclusion: Our CPI prediction model can serve as a useful tool for accurately identifying potential CPI for a wide range of proteins, and is expected to facilitate drug research, repurposing and support the understanding of TCM.

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Transfer learning, the reuse of newly acquired knowledge under novel circumstances, is a critical hallmark of human intelligence that has frequently been pitted against the capacities of artificial learning agents. Yet, the computations relevant to transfer learning have been little investigated in humans. The benefit of efficient inductive biases (meta-level constraints that shape learning, often referred as priors in the Bayesian learning approach), has been both theoretically and experimentally established. Efficiency of inductive biases depends on their capacity to generalize earlier experiences. We argue that successful transfer learning upon task acquisition is ensured by updating inductive biases and transfer of knowledge hinges upon capturing the structure of the task in the inductive bias that can be reused in novel tasks. To explore this, we trained participants on a non-trivial visual stimulus sequence task (Alternating Serial Response Times, ASRT); during the Training phase, participants were exposed to one specific sequence for multiple days, then on the Transfer phase, the sequence changed, while the underlying structure of the task remained the same. Our results show that beyond the acquisition of the stimulus sequence, our participants were also able to update their inductive biases. Acquisition of the new sequence was considerably sped up by earlier exposure but this enhancement was specific to individuals showing signatures of abandoning initial inductive biases. Enhancement of learning was reflected in the development of a new internal model. Additionally, our findings highlight the ability of participants to construct an inventory of internal models and alternate between them based on environmental demands. Further, investigation of the behavior during transfer revealed that it is the subjective internal model of individuals that can predict the transfer across tasks. Our results demonstrate that even imperfect learning in a challenging environment helps learning in a new context by reusing the subjective and partial knowledge about environmental regularities.

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Faces can acquire emotional meaning by learning to associate individuals with specific behaviors. Here, we investigated emotional evaluation and brain activations toward faces of persons who had given negative or positive evaluations to others. Furthermore, we investigated how emotional evaluations and brain activation generalize to perceptually similar faces. Valence ratings indicated learning and generalization effects for both positive and negative faces. Brain activation, measured with functional magnetic resonance imaging (fMRI), showed significantly increased activation in the fusiform gyrus (FG) to negatively associated faces but not positively associated ones. Remarkably, brain activation in FG to faces to which emotional meaning (negative and positive) was successfully generalized was decreased compared to neutral faces. This suggests that the emotional relevance of faces is not simply associated with increased brain activation in visual areas. While, at least for negative conditions, faces paired with negative feedback behavior are related to potentiated brain responses, the opposite is seen for perceptually very similar faces despite generalized emotional responses.

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OBJECTIVE: We recently demonstrated that learning abilities among school-age children vary following frequency discrimination (FD) training, with some exhibiting mature adult-like learning while others performing poorly (non-adult-like learners). This study tested the hypothesis that children's post-training generalisation is related to their learning maturity. Additionally, it investigated how training duration influences children's generalisation, considering the observed decrease with increased training in adults. DESIGN: Generalisation to the untrained ear and untrained 2000 Hz frequency was assessed following single-session or nine-session 1000 Hz FD training, using an adaptive forced-choice procedure. Two additional groups served as controls for the untrained frequency. STUDY SAMPLE: Fifty-four children aged 7-9 years and 59 adults aged 18-30 years. RESULTS: (1) Only adult-like learners generalised their learning gains across frequency or ear, albeit less efficiently than adults; (2) As training duration increased children experienced reduced generalisation, similar to adults; (3) Children's performance in the untrained tasks correlated strongly with their trained task performance after the first training session. CONCLUSIONS: Auditory skill learning and its generalisation do not necessarily mature contemporaneously, although mature learning is a prerequisite for mature generalisation. Furthermore, in children, as in adults, more practice makes rather specific experts. These findings should be considered when designing training programs.

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Introduction: Listeners can rapidly adapt to English speech produced by non-native speakers of English with unfamiliar accents. Prior work has shown that the type and number of talkers contained within a stimulus set may impact rate and magnitude of learning, as well as any generalization of learning. However, findings across the literature have been inconsistent, with relatively little study of these effects in populations of older listeners. Methods: In this study, adaptation and generalization to unfamiliar talkers with familiar and unfamiliar accents are studied in younger normal-hearing adults and older adults with and without hearing loss. Rate and magnitude of adaptation are modelled using both generalized linear mixed effects regression and generalized additive mixed effects modelling. Results: Rate and magnitude of adaptation were not impacted by increasing the number of talkers and/or varying the consistency of non-native English accents across talkers. Increasing the number of talkers did strengthen generalization of learning for a talker with a familiar non-native accent, but not for an unfamiliar accent. Aging alone did not diminish adaptation or generalization. Discussion: These findings support prior evidence of a limited benefit for talker variability in facilitating generalization of learning for non-native accented speech, and extend the findings to older adults.

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Background: The CAPS-5 is a reliable instrument for assessing PTSD symptoms, demonstrating strong consistency, validity, and reliability after a traumatic event. However, further research is warranted to explore the divergent validity of the CAPS-5 and its adaptation to diverse cultural contexts. Objective: In this meta-analysis, we endeavoured to comprehensively evaluate the reliability generalization of the CAPS-5 across diverse populations and clinical contexts. Methods: A reliability generalization meta-analysis on the psychometric properties of CAPS-5 was conducted, encompassing 15 studies. The original versions' psychometric properties were systematically retrieved from databases including PubMed, PsychNet, Medline, CHAHL, ScienceDirect, Scopus, Web of Science, and Google Scholar, with a focus on studies published between 2013 and 2023. Two independent investigators evaluated study quality using QUADAS-2 and COSMIN RB, pre-registering the protocol in the Prospero database for transparency and minimizing bias risk. Results: Meta-analysis reveals CAPS-5 global reliability (α = 0.92, 95% CI [0.90, 0.94]), z = 99.44, p < 0.05 across 15 studies, supporting consistent internal consistency. Subscale analysis shows variability in Reexperiencing (α = 0.82), Avoidance (α = 0.68), Cognition and Mood (α = 0.82), and Hyperarousal (α = 0.74), with an overall estimate of 0.77 (95% CI [0.70;0.83]). Language-dependent analysis highlights reliability variations (α range: 0.83 to 0.92) across Brazilian-Portuguese, Dutch, English, French, German, Korean, and Portuguese. Test-retest reliability demonstrates stability (r = 0.82, 95% CI [0.79; 0.85]), with overall convergent validity (r = 0.59, 95% CI [0.50;0.68]). Conclusion: The meta-analysis affirms CAPS-5's robust global and subscale reliability across studies and languages, with stable test-retest results. Moderator analysis finds no significant impact, yet substantial residual heterogeneity remains unexplained. Our findings contribute intricate insights into the psychometric properties of this instrument, offering a more complete understanding of its utility in PTSD assessment. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023483748.

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Background: Generalized anxiety disorder (GAD) and social anxiety disorder (SAD) are distinguished by whether anxiety is limited to social situations. However, reports on the differences in brain functional networks between GAD and SAD are few. Our objective is to understand the pathogenesis of GAD and SAD by examining the differences in resting brain function between patients with GAD and SAD and healthy controls (HCs). Methods: This study included 21 patients with SAD, 17 patients with GAD, and 30 HCs. Participants underwent psychological assessments and resting-state functional magnetic resonance imaging. Whole-brain analyses were performed to compare resting-state functional connectivity (rsFC) among the groups. In addition, logistic regression analysis was conducted on the rsFC to identify significant differences between GAD and SAD. Results: Patients with SAD and GAD had significantly higher rsFC between the bilateral postcentral gyri and bilateral amygdalae/thalami than HCs. Compared with patients with SAD, those with GAD had significantly higher rsFC between the right nucleus accumbens and bilateral thalami and between the left nucleus accumbens and right thalamus. rsFC between the left nucleus accumbens and right thalamus positively correlated with state anxiety in patients with SAD and GAD, respectively. In addition, logistic regression analysis revealed that the right nucleus accumbens and the right thalamus connectivity could distinguish SAD from GAD. Conclusions: GAD and SAD were distinguished by the right nucleus accumbens and the right thalamus connectivity. Our findings offer insights into the disease-specific neural basis of SAD and GAD. Clinical Trial Registration Number: M10545. Impact Statement This study is the first to identify a resting state functional connectivity that distinguishes social anxiety disorder (SAD) from generalized anxiety disorder (GAD) and to clarify a common connectivity in both disorders. We found that the connectivity between the right nucleus accumbens and the right thalamus differentiated SAD from GAD. Furthermore, these rsFC differences suggest an underlying basis for fear overgeneralization. Our findings shed light on the pathophysiology of these conditions and could be used as a basis for further studies to improve outcomes for such patients.

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Fear over-generalization as a core symptom of anxiety disorders is manifested by fear responses even to safe stimuli that are very dissimilar to the original dangerous stimulus. The present study investigated the effects of two separate conditioned stimuli-unconditioned stimuli (CS-US) pairing procedures on fear acquisition and generalization using a perceptual discrimination fear-conditioning paradigm, with US expectancy ratings and skin conductance response (SCR) as indicators. One group accepted continuous followed by partial CS-US pairings (C-P group); the other group accepted partial followed by continuous CS-US pairings (P-C group). It was found that compared to the P-C group, the C-P group showed stronger perceptual discrimination of CS+ and CS- in the fear acquisition and showed weaker SCRs and stronger extinction of US expectancy in the generalization. These findings emphasize that CS-US pairings significantly influence fear acquisition and generalization and suggest that continuous-following partial CS-US pairings promote individual discrimination of threat and safety signals and inhibit the generalization of conditioned fear. The results of this study have implications for clinical interventions for patients experiencing negative events.

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Brain magnetic resonance imaging (MRI) is widely used in clinical practice for disease diagnosis. However, MRI scans acquired at different sites can have different appearances due to the difference in the hardware, pulse sequence, and imaging parameter. It is important to reduce or eliminate such cross-site variations with brain MRI harmonization so that downstream image processing and analysis is performed consistently. Previous works on the harmonization problem require the data acquired from the sites of interest for model training. But in real-world scenarios there can be test data from a new site of interest after the model is trained, and training data from the new site is unavailable when the model is trained. In this case, previous methods cannot optimally handle the test data from the new unseen site. To address the problem, in this work we explore domain generalization for brain MRI harmonization and propose Site Mix (SiMix). We assume that images of travelling subjects are acquired at a few existing sites for model training. To allow the training data to better represent the test data from unseen sites, we first propose to mix the training images belonging to different sites stochastically, which substantially increases the diversity of the training data while preserving the authenticity of the mixed training images. Second, at test time, when a test image from an unseen site is given, we propose a multiview strategy that perturbs the test image with preserved authenticity and ensembles the harmonization results of the perturbed images for improved harmonization quality. To validate SiMix, we performed experiments on the publicly available SRPBS dataset and MUSHAC dataset that comprised brain MRI acquired at nine and two different sites, respectively. The results indicate that SiMix improves brain MRI harmonization for unseen sites, and it is also beneficial to the harmonization of existing sites.

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The concept of career adaptability emerged from the broader field of career development theory and has received increasing attention in the past two decades. To measure career adaptability, there are different scale development studies in the literature, but the most widely used and preferred one is the Career Adapt-abilities Scale developed by Savickas and Porfeli. Therefore, in the present study, the general reliability of the Career Adapt-abilities Scale was measured through meta-analysis. One hundred forty nine study (N = 82519) were included in the analyses. For the CAAS overall score, the average reliability coefficient among the 171 reliability estimations was high. However, reliability estimates in the studies included in the research show high heterogeneity. As a result of the moderator analysis, it was concluded that reliability estimates of these scores differ by item level, culture, language, category of items, different forms of CAAS, age, and SD. Regarding the application of the CAAS in research, the consequences of these findings are discussed in light of the relevant literature.