Periodic Reporting for period 3 - NEUROABSTRACTION (Abstraction and Generalisation in Human Decision-Making)
Période du rapport: 2020-07-01 au 2021-12-31
This project has major translational potential in two areas. The first is that despite exciting recent progress in artificial ingelligence (AI) research, machine learning researchers have struggled to build agents that learn abstractions or behave flexibly in novel settings. One possibility is that there is something special about how humans learn that allows them to acquire and generalise abstract knowledge. We aim to identify what this might be. Indeed, many of our computational simulations use deep neural networks, the tool of choice in contemporary machine learning, to simulate the learning process. However, unlike most computer scientists, we seek inspiration from neurobiology and cognitive science. A second translational outlet is education. By understanding how humans learn, we can gain insights into how to teach people to acquire information more efficiently and effectively. Our projects ask why, at the level of neural computation, humans learn better from some curricula than others. We are actively seeking opportunities to translate our work in this area.
The overall objectives are 1) to disclose new information about the cognitive mechanisms by which humans learn and make decisions, with a focus on abstract knowedge; to capture the processes by which this occurs in computational simulations involving neural networks; and to compare the representations formed by those neural networks to signals in the human brain, measured with fMRI. Our work seeks to establish and sustain a virtuous circle between psychology/neuroscience and AI/research for the mutual benefit of both fields.
This core work is supplemented by numerous projects (either detailed in the work-package, or growing from new ideas) that focus on reinforcement learning (Juechems et al Neuron 2019; Juechems et al TICS 2019; Juechems et al PNAS under revision), as well as major reviews (Saxe et al, under review Nature Reviews Neuroscience) and numerous additional projects that deal more directly with decision-making (Herce-Castanon, Nature Comms 2019; Cao et al, Neuron 2019; Luyckx et al, Cerebral Cortex 2020)