Inside insight: decoding how insight emerges from competing world models
Inutsuka, K.; Nishioka, T.; Macpherson, T.; Fujiwara, M.; Hikida, T.; Naoki, H.
Show abstract
When and how does insight emerge? We conceptualize insight as a sudden realization arising from restructuring a world model--an internal interpretation linking actions to outcomes. However, this process remains inaccessible even with verbal report. Here we developed inside insight dynamics (IID), a machine-learning framework estimating latent world-model dynamics from behavioral data. We analyzed mouse data from two tasks differing in difficulty and requiring animals to shift from an initial world model to a new one. IID decoded timing of insight-like shifts and evolving reward beliefs within competing world models. We examined how these shifts were acquired through learning. We found that the harder task was better explained by gated learning, in which a new model becomes learnable only after being recognized, whereas the simpler task favored parallel learning, in which candidate models are learned in advance. Thus, IID opens a route to quantifying latent insight dynamics.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.