High-variability training does not enhance generalization in the prototype-distortion paradigm.

Classic studies of human categorization learning provided evidence that high-variability training in the prototype-distortion paradigm enhances subsequent generalization to novel test patterns from the learned categories. More recent work suggests, however, that when the number of training trials is equated across low-variability and high-variability training conditions, it is low-variability training that yields better generalization performance. Whereas the recent studies used cartoon-animal stimuli varying along binary-valued dimensions, in the present work we return to the use of prototype-distorted dot-pattern stimuli that had been used in the original classic studies. In accord with the recent findings, we observe that high-variability training does not enhance generalization in the dot-pattern prototype-distortion paradigm when the total number of training trials is equated across the conditions, even when training with very large numbers of distinct instances. A baseline version of an exemplar model captures the major qualitative pattern of results in the experiment, as do prototype models that make allowance for changes in parameter settings across the different training conditions. Based on the modeling results, we hypothesize that although high-variability training does not enhance generalization in the prototype-distortion paradigm, it may do so when participants learn more complex category structures.

Modeling within-session dynamics of categorical and item-memory mechanisms in pigeons.

Past studies have shown that pigeons can learn complex categories and can also remember large numbers of individual objects. In recent work, Cook et al. Psychonomic Bulletin & Review, 28, 548-555, (2021) provided evidence that pigeons may use a dynamic combination of both category-based information and item-specific memorization to solve a categorical variation of the mid-session reversal (MSR) task, which is an influential task for exploring the nature of temporally organized behaviors in animals. To provide greater insight into these pigeons' behaviors, in this article we developed and investigated different computational models and their variations to account for these data. Of these, two models emerged as good candidates. One was a multinomial-processing-tree categorization/memory model, formalizing the two-process mechanism initially proposed by Cook et al. Psychonomic Bulletin & Review, 28, 548-555, (2021). The second was a new object/time-coding model, which posits the storage of object-specific memories with an additional within-session time code and assumes that a basic stimulus generalization process underlies the pigeons' choice behavior. Both provided high-quality fits to the published sets of training and transfer data collected in the categorical MSR task. These computational efforts give deeper insights into the theoretical mechanisms underlying the temporal and sequential structure of behavior in animals and stimulate future empirical research further revealing the organization of the pigeons' cognitive processes.

GAC-SleepNet: A dual-structured sleep staging method based on graph structure and Euclidean structure.

Sleep staging is a precondition for the diagnosis and treatment of sleep disorders. However, how to fully exploit the relationship between spatial features of the brain and sleep stages is an important task. Many current classical algorithms only extract the characteristic information of the brain in the Euclidean space without considering other spatial structures. In this study, a sleep staging network named GAC-SleepNet is designed. GAC-SleepNet uses the characteristic information in the dual structure of the graph structure and the Euclidean structure for the classification of sleep stages. In the graph structure, this study uses a graph convolutional neural network to learn the deep features of each sleep stage and converts the features in the topological structure into feature vectors by a multilayer perceptron. In the Euclidean structure, this study uses convolutional neural networks to learn the temporal features of sleep information and combine attention mechanism to portray the connection between different sleep periods and EEG signals, while enhancing the description of global features to avoid local optima. In this study, the performance of the proposed network is evaluated on two public datasets. The experimental results show that the dual spatial structure captures more adequate and comprehensive information about sleep features and shows advancement in terms of different evaluation metrics.

Category structure and region-specific selective attention.

A fundamental component of human categorization involves learning to attend selectively to relevant dimensions and ignore irrelevant ones. Past research has shown that humans can learn flexible strategies in which the attended dimensions vary depending on the region of feature space in which classification takes place. However, region-specific selective attention (RSA) is often challenging to learn. Here, we test the hypothesis that RSA is facilitated when individual categories are embedded within single regions of stimulus space rather than dispersed across multiple regions. We conduct an experiment that varies across conditions whether categories are embedded within regions, but in which the same RSA strategy would benefit performance across the conditions. To evaluate the hypothesis, we use measures of overall performance accuracy as well as comparisons among formal computational models that do and do not make allowance for RSA. We find strong support for the hypothesis among the upper-median-performing participants in the tested groups. However, even in the condition that promotes the learning of RSA, performance is considerably worse than in comparison conditions in which a single set of dimensions can be attended throughout the entire stimulus space.

Research on application of ReliefF and improved RVM in water quality grade evaluation.

ReliefF algorithm was used to analyze the weight of each water quality evaluation factor, and then based on the Relevance Vector Machine (RVM), Particle Swarm Optimization (PSO) was used to optimize the kernel width factor and hyperparameters of RVM to build a water quality evaluation model, and the experimental results of RVM, PSO-RVM, ReliefF-RVM and PSO-ReliefF-RVM were compared. The results show that ReliefF algorithm, combined with threshold value, selects 5 evaluation factors with significant weight from eight evaluation factors, which reduces the amount of data used in the model, CSI index is used to calculate the separability of each evaluation factor combination. The results show that the overall separability of the combination is best when the evaluation factor with significant weight is reserved. When different water quality evaluation factors were included, the evaluation accuracy of PSO-ReliefF-RVM model reached 95.74%, 14.23% higher than that of RVM model, which verified the effectiveness of PSO algorithm and ReliefF algorithm, and had a higher guiding significance for the study of water quality grade evaluation. It has good practical application value.

Exemplar-model account of categorization and recognition when training instances never repeat.

In a novel version of the classic dot-pattern prototype-distortion paradigm of category learning, Homa et al. (2019) tested a condition in which individual training instances never repeated, and observed results that they claimed severely challenged exemplar models of classification and recognition. Among the results was a dissociation in which participants classified transfer items with high accuracy in the no-repeat condition, yet in old-new recognition tests showed no ability to discriminate between old and new items of the same level of distortion from the prototype. In addition, speed of classification learning was no faster in a condition in which a small set of training instances was repeated continuously compared with the no-repeat condition. Here we show through computer-simulation modeling that exemplar models naturally capture the classification-recognition dissociation in the no-repeat condition, as well as a wide variety of other qualitative effects reported by Homa et al. (2019). We also conduct new conceptual-replication experiments to investigate their reported null effect of repeated versus nonrepeated training instances on speed of classification learning. In contrast to Homa et al. (2019) we find that speed of learning is substantially faster in the repeat condition than in the no-repeat condition, precisely as exemplar models predict. The exemplar model also captures a wide variety of transfer effects observed following the completion of category learning, including the classification-recognition dissociation observed across the repeat and no-repeat conditions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Tacrolimus 0.03% ointment treatment in exfoliative cheilitis: A randomised controlled clinical trial and monitoring blood concentration.

BACKGROUND: Tacrolimus has been used to treat various inflammatory skin diseases, but its safety for topical application on the oral mucosa is unknown. Exfoliative cheilitis (EC) is a chronic inflammatory disorder of the lips characterised by repeated scaling; it is difficult to manage. The aim of this study was to assess the efficacy and safety of tacrolimus 0.03% ointment as a topical treatment in patients with EC. METHODS: In this randomised controlled clinical trial, 40 patients with EC were randomly assigned to receive either tacrolimus 0.03% ointment (experimental group, n = 20) or triamcinolone acetonide 0.1% cream (control group, n = 20) treatment for a 3-week period. Medication was administered in 3, 2 and 1 daily doses during the first, second and third weeks, respectively. The patients with complete healing were followed up for 3 months. The clinical outcomes were measured, including the scores regarding signs (scale, dryness, rhagades and swelling) and symptoms (rough, dry, pain, pruritus and burning sensation) at every visit. Blood concentrations of tacrolimus were assessed. RESULTS: After the 3-week treatment, healing rates of scale in the experimental and control groups were 65% and 10%, respectively (P = .018). Improvement in all signs and two symptoms (rough, pruritus) was much greater in the experimental group (P < .05). The 3-month recurrence rate was higher in the control group (P = .029). Tacrolimus blood concentrations were in the safe range (< 5 ng/mL). CONCLUSION: Topical tacrolimus 0.03% ointment has good short-term efficacy and safety for treating EC.

A case of Melkersson-Rosenthal syndrome with endocrine disorders: Extraordinary efficiency of hydroxychloroquine and mechanism hypothesis.

BACKGROUND AND PURPOSE: Melkersson-Rosenthal syndrome (MRS) is a rare neuro-mucocutaneous disease. In addition to the traditional clinical triad, there is also a diversity of clinical signs, and it may be related to other systemic diseases. METHODS: In the present study, we report a case of MRS with endocrine disorders that exhibits extraordinary therapeutic efficiency by using hydroxychloroquine (HCQ), explore whether there is an internal connection between MRS and endocrine disorders, and discuss the mechanism of the therapeutic efficiency of using HCQ. The hypothesis proposed for the first time is that MRS may essentially be a systemic granulomatous disease. RESULTS: The physical examination revealed orofacial swelling and fissured tongue. The histopathologic examination showed epithelioid granulomas. Combined with the other examination, this case was diagnosed as incomplete MRS. HCQ and local drugs were introduced. The patient achieved clinical recovery and psychological cure by the 18-week follow-up, and the 1-year follow-up found no reactivation of MRS. Moreover, the levels of cortisol and adrenocorticotropic were within normal ranges. CONCLUSIONS: After the drug therapy was targeted at granuloma, not only did all of the symptoms related to MRS disappear, but the endocrine system also returned to normal. It is speculated that the endocrine disorder in this patient may be related to MRS. We further propose the first-time hypothesis that MRS may essentially be a systemic granulomatous disease. It provides a new medication method with high-level efficiency.

Predictive cues reduce but do not eliminate intrinsic response bias.

Predictive cues induce large changes in people's choices by biasing responses towards the expected stimulus category. At the same time, even in the absence of predictive cues, humans often exhibit substantial intrinsic response biases. Despite the ubiquity of both of these biasing effects, it remains unclear how predictive cues interact with intrinsic bias. To understand the nature of this interaction, we examined data across three previous experiments that featured a combination of neutral cues (revealing intrinsic biases) and predictive cues. We found that predictive cues decreased the intrinsic bias to about half of its original size. This result held both when bias was quantified as the criterion location estimated using signal detection theory and as the probability of choosing a particular stimulus category. Our findings demonstrate that predictive cues reduce but do not eliminate intrinsic response bias, testifying to both the malleability and rigidity of intrinsic biases.