Monitoring Demands for Executive Control: Shared Functions between Human and Nonhuman Primates

Abstract

Fifteen years ago, an influential model proposed that the human dorsal anterior cingulate cortex (dACC) detects conflict and induces adaptive control of behavior. Over the years support for this model has been mixed, in particular due to divergent findings in human versus nonhuman primates. We here review recent findings that suggest greater commonalities across species. These include equivalent behavioral consequences of conflict and similar neuronal signals in the dACC, but also a common failure of dACC lesions to reliably abolish conflict-driven behavior. We conclude that conflict might be one among many drivers of adjustments in executive control and that the ACC might be just one component of overlapping distributed systems involved in context-dependent learning and behavioral control.

Population coding of reward prediction errors through opponent organization in the fronto parietal network

“…The ability to integrate information on longer time scales is a consistent feature of frontal and parietal areas, and these areas have been reported to convey across-trial information about context, rules, the presence of conflict as well as rewards (reviewed in 21 ). In most previous studies, however, the extended memories were adaptive for performing a task, correlating with improved rule-based performance or adaptation to conflict 21 , decision making 32,33 and learning 19,20,34 .…”

“…In frontal cortical areas, however, RPE-like signals have been reported in the ACC, but their specific features are under debate 16,17 . Reward-related activity in the dlPFC has only been reported in complex decision and learning tasks in which expectations and RPEs are difficult to infer (e.g., [18][19][20][21] ). 7A neurons have not been reported to have reward-related activity.…”

Human dorsal anterior cingulate neurons signal conflict by amplifying task-relevant information

“…Our major goal was to go beyond correlating neural activity with task variables, and to instead use targeted dimensionality reduction to determine what specific neuronal computations gave rise to this conflict signal. This method allowed us to directly compare and reject two major hypotheses in the literature, which we call the explicit hypothesis and the epiphenomenal hypothesis (Nakamura et al, 2005; Cole et al, 2009; Schall and Emeric, 2010; Mansouri et al, 2017; Cole et al, 2010; Kolling et al, 2016; Shenhav et al, 2016; Stuphorn et al, 2000). Instead, the data supported a third amplification hypothesis , that the effects of conflict are to amplify the encoding of task-relevant information across populations of neurons.…”

Microscopic and mesoscopic effects of reward uncertainty in monkey fronto-parietal areas

“…The traces from each session were then pooled and subjected to a two-step cleaning procedure to remove outliers in, respectively, the frequency and time domains. For the first step that removed outliers in the frequency domain, we calculated the power spectrum of each LFP trace in the range of 0 -90 Hz (using a multi-taper method with 4 tapers) and characterized each trial with a 5-dimensional vector containing the sum of the logarithm of the power spectrum in 5 frequency bands (0.5-4 Hz, 4-8 Hz, [8][9][10][11][12][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Hz and 30-90 Hz). We then reduced the dimensionality of each session's data set to 2 principal components using principal component analysis, and clustered this 2dimensional data set using Gaussian Mixture Models (GMM; fitgmdist function in the MATLAB statistics and machine learning toolbox).…”

“…A first question concerns the representation of reward variables, as cells sensitive to rewardexpectation have only been reported in the dlPFC [18][19][20][21] but not in 7A. It is unclear whether this difference reflects a true areal specialization or merely a gap in the empirical literature.…”