Proactive and reactive control by the medial frontal cortex

Adaptive behavior requires the ability to flexibly control actions. This can occur either proactively to anticipate task requirements, or reactively in response to sudden changes. Recent work in humans has identified a network of cortical and subcortical brain region that might have an important role in proactive and reactive control. However, due to technical limitations, such as the spatial and temporal resolution of the BOLD signal, human imaging experiments are not able to disambiguate the specific function(s) of these brain regions. These limitations can be overcome through single-unit recordings in non-human primates. In this article, we describe the behavioral and physiological evidence for dual mechanisms of control in response inhibition in the medial frontal cortex of monkeys performing the stop signal or countermanding task

Multi-View Broad Learning System for Primate Oculomotor Decision Decoding

Multi-view learning improves the learning performance by utilizing multi-view data: data collected from multiple sources, or feature sets extracted from the same data source. This approach is suitable for primate brain state decoding using cortical neural signals. This is because the complementary components of simultaneously recorded neural signals, local field potentials (LFPs) and action potentials (spikes), can be treated as two views. In this paper, we extended broad learning system (BLS), a recently proposed wide neural network architecture, from single-view learning to multi-view learning, and validated its performance in decoding monkeys' oculomotor decision from medial frontal LFPs and spikes. We demonstrated that medial frontal LFPs and spikes in non-human primate do contain complementary information about the oculomotor decision, and that the proposed multi-view BLS is a more effective approach for decoding the oculomotor decision than several classical and state-of-the-art single-view and multi-view learning approaches

Decomposing Executive Function into Distinct Processes Underlying Human Decision Making.

peer reviewedExecutive function (EF) consists of higher level cognitive processes including working memory, cognitive flexibility, and inhibition which together enable goal-directed behaviors. Many neurological disorders are associated with EF dysfunctions which can lead to suboptimal behavior. To assess the roles of these processes, we introduce a novel behavioral task and modeling approach. The gamble-like task, with sub-tasks targeting different EF capabilities, allows for quantitative assessment of the main components of EF. We demonstrate that human participants exhibit dissociable variability in the component processes of EF. These results will allow us to map behavioral outcomes to EEG recordings in future work in order to map brain networks associated with EF deficits. Clinical relevance- This work will allow us to quantify EF deficits and corresponding brain activity in patient populations in future work

Influence of history on saccade countermanding performance in humans and macaque monkeys

AbstractThe stop-signal or countermanding task probes the ability to control action by requiring subjects to withhold a planned movement in response to an infrequent stop signal which they do with variable success depending on the delay of the stop signal. We investigated whether performance of humans and macaque monkeys in a saccade countermanding task was influenced by stimulus and performance history. In spite of idiosyncrasies across subjects several trends were evident in both humans and monkeys. Response time decreased after successive trials with no stop signal. Response time increased after successive trials with a stop signal. However, post-error slowing was not observed. Increased response time was observed mainly or only after cancelled (signal inhibit) trials and not after noncancelled (signal respond) trials. These global trends were based on rapid adjustments of response time in response to momentary fluctuations in the fraction of stop signal trials. The effects of trial sequence on the probability of responding were weaker and more idiosyncratic across subjects when stop signal fraction was fixed. However, both response time and probability of responding were influenced strongly by variations in the fraction of stop signal trials. These results indicate that the race model of countermanding performance requires extension to account for these sequential dependencies and provide a basis for physiological studies of executive control of countermanding saccade performance

Sequential selection of economic good and action in medial frontal cortex of macaques during value-based decisions.

Value-based decisions could rely either on the selection of desired economic goods or on the selection of the actions that will obtain the goods. We investigated this question by recording from the supplementary eye field (SEF) of monkeys during a gambling task that allowed us to distinguish chosen good from chosen action signals. Analysis of the individual neuron activity, as well as of the population state-space dynamic, showed that SEF encodes first the chosen gamble option (the desired economic good) and only ~100 ms later the saccade that will obtain it (the chosen action). The action selection is likely driven by inhibitory interactions between different SEF neurons. Our results suggest that during value-based decisions, the selection of economic goods precedes and guides the selection of actions. The two selection steps serve different functions and can therefore not compensate for each other, even when information guiding both processes is given simultaneously

Inactivation of Medial Frontal Cortex Changes Risk Preference.

Humans and other animals need to make decisions under varying degrees of uncertainty. These decisions are strongly influenced by an individuals risk preference; however, the neuronal circuitry by which risk preference shapes choice is still unclear [1]. Supplementary eye field (SEF), an oculomotor area within primate medial frontal cortex, is thought to be an essential part of the neuronal circuit underlying oculomotor decision making, including decisions under risk [2-5]. Consistent with this view, risk-related action value and monitoring signals have been observed in SEF [6-8]. However, such activity has also been observed in other frontal areas, including orbitofrontal [9-11], cingulate [12-14], and dorsal-lateral frontal cortex [15]. It is thus unknown whether the activity in SEF causally contributes to risky decisions, or whether it is merely a reflection of neural processes in other cortical regions. Here, we tested a causal role of SEF in risky oculomotor choices. We found that SEF inactivation strongly reduced the frequency of risky choices. This reduction was largely due to a reduced attraction to reward uncertainty and high reward gain, but not due to changes in the subjective estimation of reward probability or average expected reward. Moreover, SEF inactivation also led to increased sensitivity to differences between expected and actual reward during free choice. Nevertheless, it did not affect adjustments of decisions based on reward history

The Decision Path Not Taken

Real-life decisions often involve multiple intermediate choices among competing, interdependent options. Lorteije et al. (2015) introduce a new paradigm for dissecting the neural strategies underlying such decisions

Cortical control and performance monitoring of interrupting and redirecting movements

International audienceVoluntary behaviour requires control mechanisms that ensure our ability to act independently of habitual and innate response tendencies. Electrophysiological experiments, using the stop-signal task in humans, monkeys and rats, have uncovered a core network of brain structures that is essential for response inhibition. This network is shared across mammals and seems to be conserved throughout their evolution. Recently, new research building on these earlier findings has started to investigate the interaction between response inhibition and other control mechanisms in the brain. Here we describe recent progress in three different areas: selectivity of movement inhibition across different motor systems, re-orientation of motor actions and action evaluation