关键词: 类别学习, 交叉重叠类别结构, 交错学习, 集中学习, 自主学习

Abstract:

Previous studies have found that participants benefit more from blocked learning in rule-based category learning but perform better with interleaved learning in information-integration category learning. In interleaved learning, participants need to generate four categories at the same time, which will create a high working memory load if applying a rule-based learning strategy and hence will encourage participants to switch from this sub-optimal strategy to information integration. However, previous studies always require passive conduct of blocked learning or interleaved learning. In real life, people will strategically switch between these two kinds of learning schedules. To grasp a better understanding, we compared passive and proactive learning schedules (blocked, interleaved, self-regulated, random). In addition, the categories used in previous studies are mutually exclusive, which contradicts real life where categories always overlapped each other and cannot be perfectly distinguished according to one or more combinations of features. For mutually exclusive structures, it is easy to confuse rule-based and information-integrated learners, and there is a countable difference in the learning speed of these two category structures. To gain more reliable results, an appropriate overlap level and the number of categories were chosen for this study.

The classical four categories rule-based and information integration task is revised to contain overlapping stimuli. If classified by both two dimensions the highest accuracy was 90%. A 2 × 4 between-subject design was adopted. The dependent variables are accuracy and response time, and the first independent variable was the category structure: rule-based (RB) and information-integration (II). The second variable was the schedule of learning: blocked, interleaved, self-regulated, and random, with random presentation as the baseline condition. 265 college students were paid to participate in the experiment. Each participant should observe and report to which categories the line segment belonged. There were 100 trials each for both the learning phase and the test phase. Each phase comprised 25 trials for each category. For the test phase, a new set of stimuli are used and no feedback is provided. The behavioral data collected fit into a mathematical model to analyze what strategies participants used during tasks.

The results showed a significant main effect of category structure. That is, the classification accuracy of the information-integration task is significantly higher than the rule-based task. The main effect of learning schedules was also significant. That is, the classification accuracy of interleaved, self-regulated, and random learning was significantly higher than that of blocked learning. Post hoc tests showed that the classification accuracy of the blocked learning was significantly lower than that of interleaved, self-regulated, and random learning under rule-based conditions. For the information-integration condition, the classification accuracy of the blocked learning was significantly lower than that of self-regulated. In addition, this study further analyzed learners' self-regulated learning behaviors under the overlapping category structure and found that for both rule-based tasks and information-integration tasks, learners’ average length of blocked learning was significantly negatively correlated with their classification accuracy. A mathematical technique of the “Decision Bound Model” was used to analyze the data from the experiment. The results of model fitting showed that in both rule-based and information-integration tasks, self-regulated learners can use the optimal strategy more frequently.

In conclusion, this study makes up for the deficiency of perfectly classified categories, finds the advantages of self-regulated learning and the disadvantages of blocked learning in category overlap, and preliminarily reveals the self-regulated learning advantages and information processing characteristics of overlapping category learning. It believes that category overlap interferes with the corresponding rules formed by learners for each category under the condition of blocking learning, which is not conducive to the blocked learning of rule-based tasks. In addition, the overlapping category structure will weaken the different information between categories and retain the common information within categories, which is not conducive to the interleaved learning of information-integration tasks. However, compared with passive learning, self-regulated learning has advantages in the learning of the two types of category structure because of its “decision-driven” and “data-driven” effects.

Key words: category learning, category overlap, interleaved learning, blocked learning, self-regulated learning