Presentation Information
[C21-01]Development of quantitative decoding methods for “insight”
*Kengo Inutsuka1,2, Tadaaki Nishioka3, Tom Macpherson4, Takatoshi Hikida4, Naoki Honda1,2 (1. Hiroshima University (Japan), 2. Nagoya University (Japan), 3. Icahn School of Medicine at Mount Sinai (United States of America), 4. Osaka University (Japan))
Keywords:
Computational Neuroscience,Quantitative Behavioral Analysis,Generative Modeling
The real world is filled with complex phenomena arising from uncertainty and diverse interactions. In such an environment, higher animals, including humans, exhibit a remarkable adaptive ability—termed “insight”—which enables them to spontaneously generate novel solutions to complex problems through trial and error. This “insight” emerges from the sudden realization of perspectives that differ from conventional approaches and represents a discontinuous process, in contrast to the continuous refinement of solutions. This discontinuous mechanism is believed to promote creative thinking that leads to new solutions. To efficiently address the complex problems of the real world by harnessing “insight” in an applied context, it is imperative to elucidate the discontinuous information processing mechanisms that occur within the brain.
To date, efforts have primarily been made to elucidate the neural basis of “insight” in humans (Jung-Beeman, Mark, et al., 2004). However, because investigations in humans are relatively constrained by the limitations of non-invasive neural activity measurements, the underlying mechanisms remain unresolved. In contrast, analyzing analogous cognitive phenomena in non-human species is invaluable for assessing both the evolutionary foundations and the universality of cognitive functions, and may provide a complementary validation of neural mechanisms not understood in humans. Although the moment of “insight” can be identified through self-report in humans, such determination is challenging in other species. Therefore, advancing the quantitative characterization of the neural substrates of “insight” necessitates the development of time series analysis methods applicable to non-human species.
In this study, we formulate the decision-making process in a mouse behavioral task that requires “insight” within the framework of generative models. Using this formulation, we introduce a machine learning method to decode the time-series data of latent strategies and their switching points from experimentally acquired mouse behavioral data. In the presentation, we will present the results obtained using these methods.
To date, efforts have primarily been made to elucidate the neural basis of “insight” in humans (Jung-Beeman, Mark, et al., 2004). However, because investigations in humans are relatively constrained by the limitations of non-invasive neural activity measurements, the underlying mechanisms remain unresolved. In contrast, analyzing analogous cognitive phenomena in non-human species is invaluable for assessing both the evolutionary foundations and the universality of cognitive functions, and may provide a complementary validation of neural mechanisms not understood in humans. Although the moment of “insight” can be identified through self-report in humans, such determination is challenging in other species. Therefore, advancing the quantitative characterization of the neural substrates of “insight” necessitates the development of time series analysis methods applicable to non-human species.
In this study, we formulate the decision-making process in a mouse behavioral task that requires “insight” within the framework of generative models. Using this formulation, we introduce a machine learning method to decode the time-series data of latent strategies and their switching points from experimentally acquired mouse behavioral data. In the presentation, we will present the results obtained using these methods.