Capturing the temporal evolution of choice across prefrontal cortex

Abstract

Activity in prefrontal cortex (PFC) has been richly described using economic models of choice. Yet such descriptions fail to capture the dynamics of decision formation. Describing dynamic neural processes has proven challenging due to the problem of indexing the internal state of PFC and its trial-by-trial variation. Using primate neurophysiology and human magnetoencephalography, we here recover a single-trial index of PFC internal states from multiple simultaneously recorded PFC subregions. This index can explain the origins of neural representations of economic variables in PFC. It describes the relationship between neural dynamics and behaviour in both human and monkey PFC, directly bridging between human neuroimaging data and underlying neuronal activity. Moreover, it reveals a functionally dissociable interaction between orbitofrontal cortex, anterior cingulate cortex and dorsolateral PFC in guiding cost-benefit decisions. We cast our observations in terms of a recurrent neural network model of choice, providing formal links to mechanistic dynamical accounts of decision-making.DOI: http://dx.doi.org/10.7554/eLife.11945.001

A distributed, hierarchical and recurrent framework for reward-based choice

“…1b). This signature was subsequently also found in a macaque study of local field potentials (LFPs) from several subregions of the macaque PFC, including the orbitofrontal cortex (OFC), the dorsolateral prefrontal cortex (DLPFC) and the anterior cingulate cortex (ACC), confirming the generality of the mutual inhibition principle across species32.…”

“…1d), the DLPFC32 and the lateral intraparietal cortex (LIP)39,42. Data from studies in the dorsal premotor cortex (PMd) are also consistent with a mutual inhibition process.…”

Excitation and inhibition in anterior cingulate predict use of past experiences

“…Here we found that both decreased levels of GABA and increased levels of glutamate were related to the degree to which a learned task model, as opposed to information displayed on each trial, influenced behavior. This suggests a fundamental difference in function, that dACC represents and regulates the use of a model of the world based on past experiences, rather than that it mediates the integration and selection of all arbitrary types of information during decisions (Hunt et al, 2015). It is particularly in complex environments that monitoring and fine-tuning of how much to use learnt information – as opposed to immediately perceived information - may be crucial.…”

A Spotlight on Reward

“…One simple solution to the Selectivity Problem would be to use a labeled line-each neuron could be designated for a particular option (Padoa-Schioppa, 2011;Hunt et al, 2015). Then comparing values would involve a mutual inhibition between discrete sets of neurons, each set representing the values of its corresponding option.…”

“…Across many studies, it appears that neurons in several reward-sensitive regions compute and signal, through modulations in their firing rates, the values of available options in an evaluation stage. These value signals are then fed into a comparison stage (which may involve the same neurons and may occur simultaneously) that implements competition through mutual inhibition; this competition probably occurs in multiple areas simultaneously (Strait et al, 2016;Hunt et al, 2015).…”