Written by Emil Koch
Meeting Summary:
For all those interested in cognitive science, the latest ARC session has been incredibly enriching. More sessions like this are forthcoming!
It was an honor for us that Nanevie joined us today, offering insights into his journey. He is currently pursuing a master's degree in cognitive science in Grenoble, focusing on artificial and natural cognition. His current specialization lies in cognitive modeling and memory in artificial systems. Concurrently, he is pursuing a master's degree in special education, though he stresses that he is no expert in either field.
Interestingly, he hadn't initially planned to combine education and cognitive science. It was a Yale program description that made him aware of the field of cognitive science, which he didn't even know existed. Inspired by the film Forrest Gump and a desire to engage in something more fulfilling beyond mere software programming, he delved into this field for 2-3 years after completing his initial bachelor's degree in computer science.
Having started a startup focusing on the development of educational video games, his interest in education grew. Currently, he plans to study the relationship between reading and learning words.
How could cognitive science improve our educational systems? Perhaps through brain-computer interfaces that facilitate personalized education? Such methods, including measuring pupil dilation for lie detection, are often utilized in psychology.
Nanevie's journey from computer science to cognitive science through reading papers and taking Coursera courses is inspiring. It exemplifies the lengthy journey of a researcher and, most importantly, underscores how purpose can drive significant achievements.
Additionally, Nanevie suggests that being proactive and reaching out to labs, even as a high school student with intriguing ideas, can catalyze careers.
In particular, in cognitive science, we've learned that modeling can be a powerful tool. It involves simulating processes with certain premises and testing whether they accurately mimic biological pathways. If the outcomes diverge from predictions, it indicates that the hypothesis might be incorrect.
Cognitive science aims to describe, explain, and, when appropriate, simulate the main dispositions and capacities of the human mind -such as language, reasoning, perception, motor coordination, and planning.
While cognitive science investigates the mind and its functionalities, neurobiology studies the molecular and cellular processes of behavior, disease, and health. In this regard, models can save resources and time by simulating whether a hypothesis is worth pursuing further in experiments.
Furthermore, modeling is crucial in various fields, whether for biomarker research, brain processing, economics, or epidemiology.
Artificial intelligence can expedite pattern detection or, in other words, the classification of correlations. Hyperparameters play a vital role in training models. For instance, Principal Component Analysis can reduce the dimensionality of high-throughput data, beneficial in brain imaging with numerous features. However, determining the hyperparameters is often done through computational exhaustive grid search or Bayesian optimizations based on probabilistic models.
Finally, we contemplated whether the brain connectome will be completed anytime soon. Will we be able to construct human brain connectomes aided by the Human Brain Project, akin to what has been achieved with C. elegans? With 80 billion neurons, this presents a colossal task for the foreseeable future.
Interested? More resources will be published here soon!
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