Subjects: Psychology >> Cognitive Psychology submitted time 2022-07-06
Abstract：
Curiosity is the main intrinsic motivation driving information-seeking behavior. The curiosity feedback loop model decomposes a curious event into the following six processes: perceived information gap, curiosity generation, value assessment of control, information seeking, curiosity satisfaction, and information integration. These processes create a positive feedback loop that contributes to sustainable knowledge acquisition. The model emphasizes the dynamic and changing nature of curiosity. In addition, this dynamic loop of curiosity is embedded in the lifelong development of the individual, changing as experience is accumulated and the brain develops. The model incorporates the expected value of control model and Bayesian reinforcement learning framework, and integrates research evidence from multiple functional brain systems such as the monitoring system, reward system, and control system. The model provides new ideas for understanding the neurophysiological mechanisms of curiosity.
Peer Review Status:
Awaiting Review
Subjects: Psychology >> Cognitive Psychology submitted time 2022-03-20
Abstract：
Cognitive flexibility is related to one’s level of cognitive ability and creativity, and is an important feature of intelligence. With regard to probabilistic cue learning, whether the level of cognitive flexibility has an impact on the learning process in young adults remains to be studied. We addressed these questions by taking advantage of the event-related potentials (ERP) technique in two rule tasks with the same probability properties, which aimed to see how learners' cognitive flexibility promotes the dynamic process of probabilistic category earning, and its underlying neural mechanisms.
We chose the “number-letter task” as the effective tool to assess learners’ cognitive flexibility level based on previous research and pilot testing. The participants were ranked according to their switch cost. The first 27% (smaller switch cost) were assigned to the high flexibility group, and the last 27% were assigned to the low group. All participants completed the picture selection task and the coin search task in the EEG environment on two occasions with a two week interval in between. The two tasks had the same probability pairs (0-1/3, 0-2/3, 1/3-2/3, 1-1/3), yet were different in form. Leaning curves for different groups, accuracy, latency, and ERPs at different learning stages were recorded and analyzed for each task.
Behavioral results showed that in these two tasks, learners with high flexibility had a higher rule acquisition rate, although the high and low groups did not show any difference in rule acquisition speed. Learners' cognitive flexibility had cross-task advantages in probabilistic cue rule learning. For the ERP results, in the picture selection task there was a marginally significant difference between the two groups in the amplitude of the P300 component under the condition of preacquisiton-high - probability-reward. The advantage of high flexibility in rule learning was mainly due to the higher efficiency of feedback learning. In the coin search task, there was a significant difference between high and low flexibility groups in the amplitude of the FRN component under the conditions of preacquisiton-expectation and the conditions of postacquisition -unexpectation. Furthermore, only the low flexibility group showed a significant difference between the high and low probability conditions in the amplitude of the P300 component.
In conclusion, the study suggests that learners with high cognitive flexibility have a cross-task advantage in probabilistic category learning, which is mainly due to more efficient feedback learning.
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Peer Review Status:Awaiting Review
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