Abstract
Abstraction can be defined as a cognitive process that identifies common features - abstract variables, or concepts - shared by many examples. Such conceptual knowledge enables subjects to generalize upon encountering new examples, an ability that supports inferential reasoning and cognitive flexibility. To confer the ability to generalize, the brain must represent variables in a particular ‘abstract’ format. Here we show how to construct neural representations that encode multiple variables in an abstract format simultaneously, and we characterize their geometry. Neural representations conforming to this geometry were observed in dorsolateral pre-frontal cortex, anterior cingulate cortex and the hippocampus in monkeys performing a serial reversal-learning task. Similar representations are observed in a simulated multi-layer neural network trained with back-propagation. These findings provide a novel framework for characterizing how different brain areas represent abstract variables that are critical for flexible conceptual generalization.
Footnotes
↵† co-senior authors
We asked whether a neural network trained to perform a simulated version of our experimental task would reveal a geometry similar to the one observed in the experiments. We used Deep Q-learning, a technique that uses a deep neural network representation of the state-action value function of an agent trained with a combination of temporal-difference learning and back-propagation refined and popularized by Mnih et al 2015. There are also many new analyses in the Supplementary Information.
