Computational brain & behavior最新文献

{"title":"Are Semantic Representations Stable? A Bayesian Framework Applied to the Study of Quantifier Meaning.","authors":"Alexandra Sarafoglou, Anne S F Giacobello, Henrik R Godmann, Tamar Johnson, Ingmar Visser, Julia M Haaf, Jakub Szymanik","doi":"10.1007/s42113-026-00302-x","DOIUrl":"10.1007/s42113-026-00302-x","url":null,"abstract":"<p><p>Researchers have begun using Bayesian hierarchical modeling to study semantic representations, for instance, in the context of natural language quantifiers such as <i>most</i>, <i>few</i>, and <i>more than half</i>. Building on previous work, we propose a Bayesian hierarchical model to disentangle three key semantic parameters: the meaning threshold of quantifiers, the vagueness surrounding meaning thresholds, and response noise. We use this model to test the stability of semantic representations over time and across different paradigms. To examine stability over time, we analyzed existing data ([Formula: see text]) from Ramotowska et al. (2023). Contrary to the conclusions drawn by the original authors, we found overwhelming evidence in favor of the hypothesis that semantic representations change over time ([Formula: see text]). At the same time, we found overwhelming evidence that the relative ordering of meaning thresholds within individuals remained stable ([Formula: see text]). Next, we conducted a new experiment ([Formula: see text]) to test stability across paradigms, specifically comparing a linguistic paradigm to a visual one. Here too, we found overwhelming support for differences in between-subject variability in meaning thresholds across paradigms ([Formula: see text]) and for differences in vagueness ([Formula: see text]). Our findings challenge the assumption that semantic representations of logical vocabulary have stable, fixed values, while suggesting that their relative ordering remains stable within individuals. The model we propose provides an effective framework for studying the semantics of quantifiers, detecting individual-level effects, and explicitly accounting for potential instability.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 2","pages":"281-318"},"PeriodicalIF":0.0,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13246847/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148220864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Cognitive Models Reveal Sources of Robust Individual Differences in Conflict Processing.","authors":"M Fiona Molloy, Taraz G Lee, John Jonides, Han Zhang, Jacob Sellers, Andrew Heathcote, Chandra Sripada, Alexander S Weigard","doi":"10.1007/s42113-026-00263-1","DOIUrl":"10.1007/s42113-026-00263-1","url":null,"abstract":"<p><p>Experimental manipulations in conflict tasks, e.g., the Stroop, Flanker, and Simon tasks, lead to systematically poorer performance in \"incongruent\" conditions that feature stimuli that contradict task goals. However, substantial recent debate surrounds whether individual differences in conflict task behavior reflect reliable, trait-like mechanistic processes. Much prior work uses difference scores, contrasting performance between incongruent and congruent trials to index conflict suppression ability, but recent work demonstrates these scores exhibit poor psychometric properties. Formal cognitive process models suggest that individual differences in conflict suppression are driven by task-general processes, as opposed to processes specialized for conflict. However, this prior work separately models cognitive process parameters and their covariation, which fails to adequately account for measurement error. Here, we model distinct mechanisms of conflict task performance and their covariance simultaneously using hierarchical Bayesian joint modeling methods for the first time which improves individual estimation and accounts for error. We fit the conflict linear ballistic accumulator model (LBA) to two large datasets containing multiple conflict tasks and test-retest sessions, and an additional large dataset containing a conflict task and simple perceptual decision-making task. First, within conflict tasks, we found moderate test-retest reliability for both conflict-specific processing mechanisms, and, to a larger degree, task-general mechanisms. Second, task-general, but not conflict-specific, mechanisms were correlated across different conflict tasks. Third, these task-general mechanisms were correlated between conflict tasks and a simple decision-making task without conflict suppression demands. Overall, we found robust individual differences in computational mechanisms underlying general decision-making, but not mechanisms specific to conflict processing.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13004055/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147500944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can Systematic Drift Rate Variability Replace Random Variability in the Diffusion Decision Model?","authors":"Jie Sun, Daniel Feuerriegel, Adam F Osth","doi":"10.1007/s42113-026-00264-0","DOIUrl":"10.1007/s42113-026-00264-0","url":null,"abstract":"<p><p>The full diffusion decision model (DDM) assumes that the rate of evidence accumulation varies across trials, which allows the model to account for slow errors and asymptotic accuracy. This across-trial drift rate variability, however, has been criticised for being difficult to estimate and ad hoc. To examine whether random drift rate variability corresponds to meaningful variations in the quality of decision evidence, we assessed whether the model-estimated drift rate variability parameter can be partitioned by trial-level systematic drift rate information. Using a large recognition memory dataset with electroencephalography (EEG) recordings (<i>n</i> = 132), we systematically linked drift rate to individual trials using exogenous experimental factors-such as word frequency and study-test lag-along with endogenous factors using EEG data. Using simulations, we first demonstrated that, when slow errors arise solely due to across-trial drift rate variability, the random variability can be well partitioned and replaced by systematic variability on the trial-level. However, for the experimental data, the inclusion of systematic variability resulted in little decrease in the random across-trial drift rate variability parameter. These findings indicate that, while the quality of decision-relevant evidence (and hence drift rate) is expected to vary across trials, other mechanisms that produce slow errors are likely present but not implemented in the full DDM.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s42113-026-00264-0.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 2","pages":"261-280"},"PeriodicalIF":0.0,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13293026/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Double-decision Response Time Models of Recall and Recognition Support Resource Accounts of Visual Working Memory.","authors":"Paul M Garrett, Anthony Lua, Daniel Feuerriegel, Daniel R Little, Philip L Smith","doi":"10.1007/s42113-026-00265-z","DOIUrl":"10.1007/s42113-026-00265-z","url":null,"abstract":"<p><p>We use response time decision models to determine whether visual working memory (VWM) is best described by a fixed-capacity 'slot' model or a continuously allocated 'resource' model. We used a double-decision paradigm that combined continuous-outcome recall and two-choice recognition to characterize performance in a VWM task. Participants viewed one to six colored items, recalled the color of a cued target using a color wheel, and then identified the target color from two options in a recognition task. We used continuous-outcome and two-choice EZ diffusion models to analyze the speed and accuracy of the two decisions. The models provided estimates of the latent cognitive parameters, drift rate and boundary separation, with the former providing measures of the memory strengths in the two decisions. We found significant correlations in both drift rate and boundary separation for the two decisions, indicating that continuous recall and two-choice recognition tasks engage similar memory and decision processes. Removing responses within the highly inaccurate 'heavy tails' of the recall distribution reduced, rather than increased, drift rate correlations - especially in the six item condition - implying that the heavy tails of the response distribution represent meaningful low-precision memory traces rather than guesses. Simulations showed that only a small fraction of the heavy tailed memory trace could be accounted for by swap errors. Our results are consistent with variable precision and resource accounts of VWM and inconsistent with slot-based accounts. Our findings underscore the value of joint modeling response time and accuracy data, and highlight the utility of double-decision tasks in clarifying theoretical accounts of memory processes.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 2","pages":"236-260"},"PeriodicalIF":0.0,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13293029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finding Closure: A Closer Look at the Gestalt Law of Closure in Convolutional Neural Networks.","authors":"Yuyan Zhang, Derya Soydaner, Lisa Koßmann, Fatemeh Behrad, Johan Wagemans","doi":"10.1007/s42113-025-00251-x","DOIUrl":"10.1007/s42113-025-00251-x","url":null,"abstract":"<p><p>The human brain has an inherent ability to fill in gaps to perceive figures as complete wholes, even when parts are missing or fragmented. This phenomenon, known as Closure in psychology, is one of the Gestalt laws of perceptual organization. Given the role of Closure in human perception, we investigate whether neural networks exhibit similar functional behavior in object recognition. While the neural substrates of Gestalt principles are thought to involve feedback mechanisms in the brain, convolutional neural networks (CNNs) rely on feedforward architectures. Despite this, we focus on the functional comparison-specifically, object recognition-rather than the underlying mechanisms. We investigate whether CNNs can parallel the human ability to perform Closure. Exploring this crucial visual skill in neural networks can highlight their (dis)similarity to human vision. Recent studies have examined the Closure effect in neural networks, but typically focus on a limited selection of CNNs and yield divergent findings. To address these gaps, we present a systematic framework to investigate Closure. We introduce well-curated datasets designed to test for Closure effects, including both modal and amodal completion. We then conduct experiments on nine CNNs employing different measurements. Our comprehensive analysis reveals that VGG16 and DenseNet-121 exhibit the Closure effect, while other CNNs show variable results. This finding is significant for fields such as AI, Neuroscience, and Psychology, as it bridges understanding across disciplines. By blending insights from psychology and neural network research, we offer a unique perspective that enhances transparency in neural networks.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 1","pages":"104-116"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13035581/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147596474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When Collaboration Falters, Insensitivity to How Our Actions Affect Others Drives Inflated Self-evaluations.","authors":"Michael Moutoussis, Meera Gosalia, Geert-Jan Will, Giles Story, Tobias U Hauser, Aislinn Bowler, Siobhan Edinboro, Gita Prabhu, Raymond Dolan","doi":"10.1007/s42113-025-00250-y","DOIUrl":"10.1007/s42113-025-00250-y","url":null,"abstract":"<p><p>During high-stake interactions, people not only evaluate policies or outcomes, but also themselves and others. Such evaluations may be crucial for long-term outcomes, such as harmonious marriage, confident leadership and indeed mental health. Powerful evaluations occur during interactions, where people can support or let each other down. Thus, we implemented an interactive decision-making game, wherein two real-life participants explicitly evaluated themselves and their play-partner while playing an ecologically framed, probabilistic, iterated prisoner's dilemma. To separate preferences from abilities, participants did not interact with the other directly, but instructed a computer avatar on how to play on their behalf. We tested a range of computational models of participants' person-evaluations. In some, self-evaluation relied on regret or satisfaction regarding one's decisions. However, the winning models relied directly on observed gains and losses. Here, evaluation of the self was proportional to how much one's partner benefited, and vice versa. We found a marked self-positivity bias, which was most prominent in dyads where both partners often defected. Between participants, a self-positivity bias was explained by a reduced weight of one's partner's benefits onto self-evaluation. This suggests that the negative outcomes claimed to attract defensive, external attribution by attribution theorists are one's partner's poor outcomes. Further analysis suggested that a reduced sensitivity to others' outcomes was associated with reduced earnings for the self, hinting at a functional role for person-evaluations in decision-making. Thus, we introduce a novel computational model that provides a concise account of self-serving bias in evaluations, as observed during risky dyadic interactions.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s42113-025-00250-y.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 1","pages":"91-103"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13035595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147596486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Testing the Generalizability of the Priority Heuristic to Two-Stage Decision Making Tasks: A Comparison to Quantum Cognition and Prospect Theory.","authors":"Christopher R Fisher, Othalia Larue, Kevin Schmidt","doi":"10.1007/s42113-025-00260-w","DOIUrl":"10.1007/s42113-025-00260-w","url":null,"abstract":"<p><p>Our goal was to test the ability of the priority heuristic (PH) to generalize to a two-stage decision making task designed to investigate dynamic inconsistency, a phenomenon whereby decision makers deviate from their plans. The PH is a non-compensatory decision strategy in which features are evaluated sequentially in order of importance until one option is determined to be superior. We extended the PH by incorporating reference point dependence into the valuation process and tested it against data from a previously published two-stage decision task (Barkan, R., & Busemeyer, J. R. <i>Journal of Behavioral Decision Making</i>, 16(4), 235-255 2003). Although we demonstrated that the extended PH model can produce dynamic inconsistency, further analysis using a true and error model revealed two deficiencies: (1) the model systematically underestimated risk taking preference, and (2) the data violated a critical inequality derived from the model. In a second analysis, we developed a variant which incorporates individual differences, and compared it to two existing models-one based on prospect theory and another based on quantum cognition. Our results show strong evidence for the quantum cognition model over the extended priority heuristic and the model based on prospect theory. Taken together, our results cast doubt on the ability of the PH to generalize to two-stage decision making.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 2","pages":"213-235"},"PeriodicalIF":0.0,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13292923/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning to Move and Plan like the Knight: Sequential Decision Making with a Novel Motor Mapping.","authors":"Carlos A Velázquez-Vargas, Jordan A Taylor","doi":"10.1007/s42113-025-00245-9","DOIUrl":"10.1007/s42113-025-00245-9","url":null,"abstract":"<p><p>Many skills that humans acquire throughout their lives, such as playing video games or sports, require substantial motor learning and multi-step planning. While both processes are typically studied separately, they are likely to interact during the acquisition of complex motor skills. In this work, we studied this interaction by assessing human performance in a sequential decision-making task that requires the learning of a non-trivial motor mapping. Participants were tasked to move a cursor from start to target locations in a grid world, using a standard keyboard. Notably, the specific keys were arbitrarily mapped to a movement rule resembling the Knight chess piece. In Experiment 1, we showed the learning of this mapping in the absence of planning, led to significant improvements in the task when presented with sequential decisions at a later stage. Computational modeling analysis revealed that such improvements resulted from an increased learning rate about the state transitions of the motor mapping, which also resulted in more flexible planning from trial to trial (less perseveration or habitual responses). In Experiment 2, we showed that incorporating mapping learning into the planning process, allows us to capture (1) differential task improvements for distinct planning horizons and (2) overall lower performance for longer horizons. Additionally, model analysis suggested that participants may limit their search to three steps ahead. We hypothesize that this limitation in planning horizon arises from capacity constraints in working memory, and may be the reason complex skills are often broken down into individual subroutines or components during learning.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"8 4","pages":"535-552"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12995368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147482628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG Activity Predictive of Learning Through Feedback.","authors":"Matthew Danyluik, Sucheta Chakravarty, Jeremy B Caplan","doi":"10.1007/s42113-025-00259-3","DOIUrl":"10.1007/s42113-025-00259-3","url":null,"abstract":"<p><p>Most of what we know about neural mechanisms of incremental learning through feedback comes from descriptive, univariate analyses. Here, we go one step further, seeking brain activity that is not just statistically reliable (potentially small but significant) but can track such learning at the item level, taking a classifier-based approach to narrow in on basic neural encoding processes. Participants ( <math><mrow><mi>N</mi> <mo>=</mo> <mn>45</mn></mrow> </math> ) learned 48 word-value mappings through trial-and-error. First, we checked whether established EEG markers of feedback processing, the feedback-related negativity (FRN) and frontal midline theta activity (FMT), are in fact predictive of trial-to-trial learning of the current item-and they were (above chance, but not by much), validating the behavioural relevance of those features. Next, we asked whether there might be considerably more information about encoding on single trials beyond these statistically robust, regular signals. Indeed, multivariate classifiers (LDA and SVM), incorporating signal-features beyond the FRN and FMT, predicted learning more substantially and exceeded previous performance on episodic recognition using the same basic approach (Chakravarty et al., <i>Journal of Neurophysiology,</i> <i>124</i>(6), 2060-2075, 2020). Time-frequency spectral features produced better classifications (AUC <math><mo>∼</mo></math> 0.7) than time-domain features. Finally, a possible shortcut due to accuracy varying systematically with trial number could not explain away classification success. In sum, FRN and FMT are not just descriptive of feedback-driven learning but also a bit predictive-but are the tip of the iceberg (subject-specific, spatiotemporal features) uncovered by the multivariate classifiers. This extends current classifier-based approaches to brain activity from episodic memory to incremental, feedback-driven learning.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 2","pages":"155-183"},"PeriodicalIF":0.0,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13293034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Parameter Recovery in Computational Models: Employing Outlier-Insensitive Loss Functions.","authors":"Mingqian Guo, Karin Roelofs, Bernd Figner","doi":"10.1007/s42113-025-00261-9","DOIUrl":"10.1007/s42113-025-00261-9","url":null,"abstract":"<p><p>The reliability of parameter estimation is crucial in using computational models for choice data in decision-making tasks, especially so since the cognitive meaningful parameters within these models often are leveraged for further analysis. Typically, model-fitting involves using the model log-likelihood as the loss function to quantify discrepancies between model predictions and observed data. However, outlier data in choice datasets can bias parameter estimation when using log-likelihood. Alternative loss functions that are less sensitive to outliers are available. In this study, we compared a total of 3 such outlier-insensitive loss functions with the log-likelihood function in terms of parameter recovery. We compared their performance in both a reinforcement learning model in a learning paradigm and a hyperbolic model in an intertemporal choice paradigm, in both systematically varying the presence of outliers (ranging from no outliers to 25% of the data being outliers). Our parameter recovery results show that even a small proportion of outlier data can substantially impair parameter identification when using the log-likelihood function, especially for the choice consistency/explore-exploit trade-off parameter. In contrast, outlier-insensitive loss functions markedly improve the recovery of computational model parameters. Moreover, our power analysis further suggests that even a small proportion of outlier trials (e.g., 5%) can potentially undermine the statistical power to detect condition differences, underscoring the importance of accounting for outliers when using cognitive models as measurement tools. Based on our results, we recommend using the outlier-insensitive loss functions for non-hierarchical model estimation as it performed well across both the learning and the intertemporal choice paradigms and under varying degrees of outlier presence.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 2","pages":"196-212"},"PeriodicalIF":0.0,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13293024/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}