In this phase of the project, we have build and tested a new theory of how abstractions are learned. Our theory is grounded in longstanding ideas in cognitive science, but differs in that it is (a) grounded in biologically realistic computational models, using layered networks of neurons; and (b) makes detailed proposals about the neural geometry that arises in such networks, and as such is able to provide an implementational theory of abstraction formation for neuroscience. The theory is supported by new empirical evidence (Luyckx et al 2019, Elife) as well as new work (on the brink of submission; Luyckx et al, in press; Flesch et al, in press) and has been partly described in a review article (Summerfield et al 2020, Prog. Neurobiology). The theory states that potentially high-dimensional information (e.g. visual or auditory signals) is projected onto a low-dimensional representational space in the dorsal stream, in which it is grounded in the actions that agents take with their effectors (in primates, their eyes and limbs). Generalisation between physically dissimilar inputs that obey a common relational structure occurs when information is projected into a common relational format in the parietal cortex. This is supported by the finding that (for example) after learning the reward probability of objects, they are coded with abstract signals measured from a magnitude comparison task involving symbolic number. New work shows that when information with common structure is learned at different times, then neural representations converge to a format that facilitates generalisation, using normalisation processes. Other behaviour/modelling/imaging projects that explore human learning of structured hierarchies, tasks composed of subtasks, and orthogonal tasks are also nearly ready for publication, and as far as we understand, can all be explained under the proposed theory.
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)
