Publications-Periodical Articles
Article View/Open
Publication Export
-
題名 Post-training flexibility in category learning 作者 楊立行
Yang, Lee-Xieng;Chiang, Po-An貢獻者 心理系 關鍵詞 Category learning; Recognition; Category generalization 日期 2024-02 上傳時間 2024-04-11 摘要 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. 關聯 Psychonomic Bulletin & Review 資料類型 article DOI https://doi.org/10.3758/s13423-023-02451-7 dc.contributor 心理系 dc.creator (作者) 楊立行 dc.creator (作者) Yang, Lee-Xieng;Chiang, Po-An dc.date (日期) 2024-02 dc.date.accessioned 2024-04-11 - dc.date.available 2024-04-11 - dc.date.issued (上傳時間) 2024-04-11 - dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/150745 - dc.description.abstract (摘要) 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. dc.format.extent 106 bytes - dc.format.mimetype text/html - dc.relation (關聯) Psychonomic Bulletin & Review dc.subject (關鍵詞) Category learning; Recognition; Category generalization dc.title (題名) Post-training flexibility in category learning dc.type (資料類型) article dc.identifier.doi (DOI) 10.3758/s13423-023-02451-7 dc.doi.uri (DOI) https://doi.org/10.3758/s13423-023-02451-7
