Executive function is a cognitive control system contributes uniquely to resilience (Greenberg, 2006; Obradovic, 2016).This study looked into resilience development during its controversial age period in cognitive perspective, aims to explorehow its components (i.e., cognitive flexibility, inhibitory control, and working memory) affect resilience in different agegroups. Data were collected in middle schools and universities (N=197). Participants were asked to join a series of labexperiments and questionnaires in a psychological lab. Results showed resilience as well as executive function in algorith-mic mind level develop from adolescence to emerging adulthood. Cognitive flexibility plays central role in functioningresilience with various cognitive mechanisms for different populations. With the identification of cognitive mechanismsunderlying the relation between cognitive flexibility subsets (i.e., reactive flexibility and spontaneous flexibility) and re-silience, this study contributes a cognitive perspective for better understanding of resilience before challenging eventshappen.

How do people learn categories and transfer learning? This study addressed this question by examining the roleof attention in the development of category learning and transfer. Participants (adults and 4-year-olds) were trained with twocategories including deterministic and probabilistic features and their attention was directed to either type of features. Aftertraining, participants learned two new categories and their categorization and memory for exemplars were tested. Resultsindicated that adults and 4-year-olds were able to be trained to use either a similarity-based or rule-based strategy. However,adults failed to transfer and went back to their default rule-based strategy in novel situations, whereas 4-year-olds transferredthe learned strategy. Furthermore, in contrast to adults exhibiting better memory for features used in categorization, 4-year-oldsremembered multiple features well regardless of categorization. These results have important implications for understandingthe development of categorization and the role of attention in cognitive development.

Previous research has indicated that the way of learning and thesequence of study influence how we learn and representcategories. However, most studies have focused onclassification learning and it has been rarely studied howlearning sequence influences inference learning. The currentstudy attempted to address this issue. Participants learned fourcategories by classification or inference in both blocked andinterleaved sequence. Then participants completed a transfertask and a feature prediction task. Results showed thatclassification learners encoded characteristic features andformed similarity-based representations in the blocked study,whereas in the interleaved study, they encoded deterministicfeatures and formed rule-based representations. In contrast, forinference learners, the blocked and interleaved study changedtheir learning and representation in the same direction. In bothsequences, inference learners encoded deterministic featuresand formed rule-based representations. These results suggestthat different mechanisms are likely to be involved forinference and classification learning.

Selective attention has been a critical component in many models of categorization and category learning. However, previous developmental studies have found that children under age five exhibited inconsistent patterns of attention in categorization and recognition, which challenges the long-held assumption that categorization is often accompanied by attention optimization. We designed an eye-tracking study to directly investigate this dynamic relationship between attention optimization and categorization using the occlusion-based anticipatory eye movement paradigm. Adult participants were asked to learn artificial categories while their eye movements were recorded and their memory of features was tested. Surprisingly, while their categorization indicated selective attention on features determining category membership, their recognition and gaze data exhibited distributed attention among various features. These results suggest a potential decoupling between attention optimization and categorization.

Category learning is a core competence for minimizing cognitive load and optimizing decision-making. An identical problem can be solved by employing a rule-based or a similarity-based strategy. This work examined whether the use of the two strategies was supported by common or distinct neural substrates. We conducted a category learning experiment with rule-plus-similarity stimuli using EEG-fNIRS fusion methodology. Participants learned two artificial categories using either a rule-based or similarity-based strategy. The results showed a common visual-perceptual-analysis process and distinct decision-making processes between the uses of the two strategies. Larger P300 and N400 amplitudes and Wernicke's area activation indicated that hypotheses testing and verbal rule abstraction processes were critical for rule-based categorization. In contrast, increased frontopolar cortex activity indicated that integration of multiple dimensions was critical for similarity-based categorization. These results were consistent with COVIS theory, implying an explicit system in rule-based category learning whereas an implicit system in similarity-based learning.

How do people tell the difference between random and nonrandom events? What affects peoples understanding of ran-domness? In two experiments, we investigated the role of two characteristics of a sequencealternation rate and numberof occurrencein peoples perception of randomness. We presented participants with a pair of binary sequences of length 6(e.g., OXOXXO vs. XOXXXX) and asked them to evaluate which of the two was more likely to occur. In Experiment1, we examined how participants randomness perception changed as the difference in alternation rate and the differencein the number of occurrence changed. In Experiment 2, we further examined whether participants exhibited differentialreliance on alternation rate and number of outcomes. Results suggest that people exhibit differential reliance on alternationrate and number of occurrence. When the two characteristics are in conflict, people tend to rely more on the alternationrate in their randomness judgement.

Young children often struggle with reasoning based on abstract relations, which is crucial for learning and thinking. Research has shown that children's relational reasoning abilities can be enhanced under certain circumstances. The underlying reasons and mechanisms behind such enhancement, however, remain unclear. This study examined the effectiveness of explanation, a recently discovered method, in enhancing children's relational reasoning abilities. Seventy-one 4- and 5-year-old children participated in a modified Relational Match to Sample (RMTS) task. Some children interacted with an experimenter who demonstrated relational matches and engaged in question-answer sessions, while others completed the task without such interactions. Results indicated that children who observed demonstrations and provided explanations or reports showed a higher proportion of relational matches compared to those who completed the task without such interactions. Furthermore, explanation was more effective than report in promoting children's relational reasoning. These findings suggest that interactive experiences that encourage exploration contribute to the development of children's relational reasoning abilities.