Thought Experiments as an Error Detection and Correction Tool

Learning by thinking in natural and artificial minds

Canonical cases of learning involve novel observations external to the mind, but learning can also occur through mental processes such as explaining to oneself, mental simulation, analogical comparison, and reasoning. Recent advances in artificial intelligence (AI) reveal that such learning is not restricted to human minds: artificial minds can also self-correct and arrive at new conclusions by engaging in processes of 'learning by thinking' (LbT). How can elements already in the mind generate new knowledge? This article aims to resolve this paradox, and in so doing highlights an important feature of natural and artificial minds - to navigate uncertain environments with variable goals, minds with limited resources must construct knowledge representations 'on demand'. LbT supports this construction.

Do reflection prompts promote children's conflict monitoring and revision of misconceptions?

We tested whether reflection prompts enhance conflict monitoring and facilitate the revision of misconceptions. German children (N = 97, Mage = 7.20, 56% female) were assigned to a prediction or a prediction with reflection condition that included reflection prompts. Children in the prediction with reflection condition (1) showed greater error-related response times and pupil dilation responses, indicating better conflict monitoring, and (2) performed closer to an optimal Bayesian learner, indicating better monitoring-based control. However, by the end of the study, all children had similar levels of misconception revision. Thus, reflection prompts can enhance learning from anomalous evidence by improving conflict monitoring, but they may need to be repeated often to sustain their beneficial effects.