Post-training flexibility in category learning.

Exemplar models of categorization, which assume that people make classification decisions based on item information stored in memory, typically assume that all of the exemplars are available and inform decision-making. However, in this study, we hypothesized that people may selectively emphasize subsets of exemplars, giving rise to individual differences in categorization. To verify this hypothesis, we adopted the partial-XOR category structure in Conaway and Kurtz (Psychonomic Bulletin & Review, 24, 1312-1323 2017), which has been evident to be able to induce two major response patterns in the transfer phase: the Proximity and XOR patterns. "Experiment 1" confirmed that these two patterns could be generated if participants were trained with only the exemplars of one category or the other. In "Experiment 2", participants were asked to not only learn the category labels of all exemplars but also memorize the exemplars of only Category A (Condition A), only Category B (Condition B), or two categories (Condition AB) for a recognition test after the training phase of the categorization task. As expected, in the transfer phase, the participants tended to perform the XOR and Proximity patterns, when the exemplars of Category A and Category B were respectively targeted for the recognition test. The parameters of the SDGCM estimated by Bayesian inference for modeling the data of "Experiment 2" showed that the exemplar accessibility of Category A was larger than that of Category B for performing the XOR pattern and vice versa for performing the proximity pattern, hence verifying our hypothesis.

Why Do We Not Wear Masks Anymore during the COVID-19 Wave? Vaccination Precludes the Adoption of Personal Non-Pharmaceutical Interventions: A Quantitative Study of Taiwanese Residents.

Background and Objectives: This study examined whether the decline in people's adoption of personal NPIs (e.g., mask wearing) results from the preclusion by vaccination. This study also incorporates the concepts of risk perception and the risk-as-feelings model to elucidate the possible mechanisms behind this preclusion. Materials and Methods: Two cross-sectional surveys (N = 462 in Survey 1 and N = 505 in Survey 2) were administered before and during the first outbreak of COVID-19 in Taiwan. The survey items were designed to measure participants' perceived severity of COVID-19, worry about COVID-19, intention to adopt personal NPIs, and attitudes toward COVID-19 vaccines. Utilizing the risk perception framework, we conducted multigroup SEM (Structural Equation Modeling) to construct the optimal structural model for both samples. Results and Conclusions: The multigroup SEM results showed that worry (i.e., the emotional component of risk perception) fully mediates the influence of the perceived severity of COVID-19 (i.e., the cognitive component of risk perception) on the intention to adopt NPIs in both surveys [z = 4.03, p < 0.001 for Survey 1 and z = 2.49, p < 0.050 for Survey 2]. Before the outbreak (i.e., Survey 1), people's attitudes toward COVID-19 vaccines showed no significant association with their worry about COVID-19 [z = 0.66, p = 0.508]. However, in Survey 2, following the real outbreak of COVID-19, people's attitudes toward COVID-19 vaccines negatively predicts their worry about COVID-19 [z = -4.31, p < 0.001], indirectly resulting in a negative effect on their intention to adopt personal NPIs. This suggests the occurrence of the Peltzman effect. That is, vaccination fosters a sense of safety, subsequently diminishing alertness to COVID-19, and thus reducing the intention to adopt personal NPIs.

Category variability effect in category learning with auditory stimuli.

The category variability effect refers to that people tend to classify the midpoint item between two categories as the category more variable. This effect is regarded as evidence against the exemplar model, such as GCM (Generalized Context Model) and favoring the rule model, such as GRT (i.e., the decision bound model). Although this effect has been found in conceptual category learning, it is not often observed in perceptual category learning. To figure out why the category variability effect is seldom reported in the past studies, we propose two hypotheses. First, due to sequence effect, the midpoint item would be classified as different categories, when following different items. When we combine these inconsistent responses for the midpoint item, no category variability effect occurs. Second, instead of the combination of sequence effect in different categorization conditions, the combination of different categorization strategies conceals the category variability effect. One experiment is conducted with single tones of different frequencies as stimuli. The collected data reveal sequence effect. However, the modeling results with the MAC model and the decision bound model support that the existence of individual differences is the reason for why no category variability effect occurs. Three groups are identified by their categorization strategy. Group 1 is rule user, placing the category boundary close to the low-variability category, hence inducing category variability effect. Group 2 takes the MAC strategy and classifies the midpoint item as different categories, depending on its preceding item. Group 3 classifies the midpoint item as the low-variability category, which is consistent with the prediction of the decision bound model as well as GCM. Nonetheless, our conclusion is that category variability effect can be found in perceptual category learning, but might be concealed by the averaged data.

Sequential effects in facial expression categorization.

Facial expressions are highly dynamic signals that are rarely categorized as static, isolated displays. However, the role of sequential context in facial expression categorization is poorly understood. This study examines the fine temporal structure of expression-based categorization on a trial-to-trial basis as participants categorized a sequence of facial expressions. The results showed that the local sequential context provided by preceding facial expressions could bias the categorical judgments of current facial expressions. Two types of categorization biases were found: (a) Assimilation effects-current expressions were categorized as close to the category of the preceding expressions, and (b) contrast effects-current expressions were categorized as away from the category of the preceding expressions. The effects of such categorization biases were modulated by the relative distance between the preceding and current expressions, as well as by the different experimental contexts, possibly including the factors of face identity and the range effect. Thus, the present study suggests that facial expression categorization is not a static process. Rather, the temporal relation between the preceding and current expressions could inform categorization, revealing a more dynamic and adaptive aspect of facial expression processing.

Working memory does not dissociate between different perceptual categorization tasks.

Working memory is crucial for many higher level cognitive functions, ranging from mental arithmetic to reasoning and problem solving. Likewise, the ability to learn and categorize novel concepts forms an indispensable part of human cognition. However, very little is known about the relationship between working memory and categorization. This article reports 2 studies that related people's working memory capacity (WMC) to their learning performance on multiple rule-based and information-integration perceptual categorization tasks. In both studies, structural equation modeling revealed a strong relationship between WMC and category learning irrespective of the requirement to integrate information across multiple perceptual dimensions. WMC was also uniformly related to people's ability to focus on the most task-appropriate strategy, regardless of whether or not that strategy involved information integration. Contrary to the predictions of the multiple systems view of categorization, working memory thus appears to underpin performance in both major classes of perceptual category-learning tasks.

A working memory test battery for MATLAB.

We present a battery of four working memory tasks that are implemented using MATLAB and the free Psychophysics Toolbox. The package includes preprocessing scripts in R and SPSS to facilitate data analysis. The four tasks consist of a sentence-span task, an operation-span task, a spatial short-term memory test, and a memory updating task. These tasks were chosen in order to provide a heterogeneous set of measures of working memory capacity, thus reducing method variance and tapping into two content domains of working memory (verbal, including numerical, vs. spatial) and two of its functional aspects (storage in the context of processing and relational integration). The task battery was validated in three experiments conducted in two languages (English and Chinese), involving more than 350 participants. In all cases, the tasks were found to load on a single latent variable. In a further experiment, the latent working memory variable was found to correlate highly but not perfectly with performance on Raven's matrices test of fluid intelligence. We suggest that the battery constitutes a versatile tool to assess working memory capacity with either English- or Chinese-speaking participants. The battery can be downloaded from www.cogsciwa.com ("Software" button).

Knowledge partitioning in categorization: boundary conditions.

Knowledge partitioning refers to the notion that knowledge can be held in independent and non-overlapping parcels. Partitioned knowledge may cause people to make contradictory decisions for identical problems in different circumstances. We report two experiments that explored the boundary conditions of knowledge partitioning in categorization. The studies examined whether or not people would partition their knowledge (1) when categorization rules were or were not verbalizable and (2) when the to-be-categorized stimuli comprised perceptually separable or integral dimensions. When learning difficulty was controlled, partitioning occurred across all combinations of verbalizability and integrality/separability, underscoring the generality of knowledge partitioning. Partitioning was absent only when the task was rapidly learned and people reached a high level of proficiency, suggesting that task difficulty plays a critical role in the emergence of partitioned knowledge.

Knowledge partitioning in categorization: constraints on exemplar models.

The authors present 2 experiments that establish the presence of knowledge partitioning in perceptual categorization. Many participants learned to rely on a context cue, which did not predict category membership but identified partial boundaries, to gate independent partial categorization strategies. When participants partitioned their knowledge, a strategy used in 1 context was unaffected by knowledge demonstrably present in other contexts. An exemplar model, attentional learning covering map, was shown to be unable to accommodate knowledge partitioning. Instead, a mixture-of-experts model, attention to rules and instances in a unified model (ATRIUM), could handle the results. The success of ATRIUM resulted from its assumption that people memorize not only exemplars but also the way in which they are to be classified.

Context-gated knowledge partitioning in categorization.

According to the knowledge partitioning framework, people sometimes master complex tasks by creating multiple independent parcels of partial knowledge. Research has shown that knowledge parcels may contain mutually contradictory information, and that each parcel may be used without regard to knowledge that is demonstrably present in other parcels. This article reports 4 experiments that investigated knowledge partitioning in categorization. When component boundaries of a complex categorization were identified by a context cue, a significant proportion of participants learned partial and independent categorization strategies that were chosen on the basis of context. For those participants, a strategy used in one context was unaffected by knowledge demonstrably present in other contexts, suggesting that knowledge partitioning in categorization can be complete.