Abstract
The human brain excels at constructing and using abstractions, such as rules, or concepts. Here, in two fMRI experiments, we demonstrate a mechanism of abstraction built upon the valuation of sensory features. Human volunteers learned novel association rules based on simple visual features. Reinforcement-learning algorithms revealed that, with learning, high-value abstract representations increasingly guided participant behaviour, resulting in better choices and higher subjective confidence. We also found that the brain area computing value signals - the ventromedial prefrontal cortex - prioritised and selected latent task elements during abstraction, both locally and through its connection to the visual cortex. Such a coding scheme predicts a causal role for valuation. Hence, in a second experiment, we used multivoxel neural reinforcement to test for the causality of feature valuation in the sensory cortex, as a mechanism of abstraction. Tagging the neural representation of a task feature with rewards evoked abstraction-based decisions. Together, these findings provide a novel interpretation of value as a goal-dependent, key factor in forging abstract representations.
Highlights
We show that the ventromedial prefrontal cortex (vmPFC) and its connection to the visual cortex construct abstract representations through a goal-dependent valuation process that is implemented as top-down control of sensory cortices
Having established that the vmPFC computes a goal-dependent value signal, we evaluated whether the activity level of this region was sensitive to the strategies that participants used
We found that classification accuracy was significantly higher in Abstract reinforcement learning (RL) trials compared with Feature RL trials in both the hippocampal formation (HPC) and vmPFC (two-sided t-test, HPC: t32 = À2.37, p(FDR) < 0.036, vmPFC: t32 = À2.51, p(FDR) = 0.036, Figure 5C), while the difference was of opposite sign in visual cortex (VC) (t32 = 1.61, p(FDR) = 0.12, Figure 5C)
Summary
The aim of this study is twofold: (i) to demonstrate that abstraction emerges during the course of learning, and (ii) to investigate how the brain, and the vmPFC, uses valuation upon low-level sensory features to forge abstract representations. Our goal was to dissociate neural signatures of these distinct learning strategies in order to show how abstract representations are constructed by the human brain. We aimed to show that arbitration between feature and abstract learning may be achieved using a relatively simple algorithm and proceeded to characterise the neural underpinnings of these two types of learning (i.e. Feature RL and Abstract RL)
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