Chinese scientists have achieved new results in consciousness control research
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Recent studies from the Institute of Psychology at the Chinese Academy of Sciences have revealed significant advancements in understanding how conscious control influences familiarity and artificial grammar learning. This research builds upon earlier work by Dienes and colleagues, offering deeper insights into the mechanisms behind implicit and explicit learning processes.
In a two-part experiment, researchers explored whether individuals can consciously regulate their sense of familiarity when exposed to artificial grammars. In the first study, participants were trained on two different grammatical structures three times each, ensuring they developed equal levels of familiarity with both. In the second experiment, one grammar was practiced twice while the other was learned four times, creating a clear difference in the participants' sense of familiarity.
The results showed that participants were not only able to distinguish between the two grammars but also attributed their grammatical decisions to familiarity rather than other factors like guessing, intuition, rule-based reasoning, or memory. Even when asked to identify which grammar had been learned fewer times, they still relied on their sense of familiarity for judgment.
This groundbreaking research is led by Professor Fu Xiaolan, who serves as the director of the Cognitive Psychology Research Office at the Institute of Psychology, Chinese Academy of Sciences. He is also a member of the Young Scientists Group in the Life Sciences and Biotechnology Field. His work focuses on how people become aware of, select, and acquire information from external stimuli. He investigates the cognitive processes involved in interpreting and organizing information, as well as how different formats—such as text or visual graphics—affect knowledge acquisition.
Professor Fu's research also explores ways to transform complex, data-driven knowledge into more understandable formats, such as natural language or graphical representations. This helps people extract meaningful insights from large datasets and uncover new knowledge effectively.