Performance monitoring by presupplementary and supplementary motor area during an arm movement countermanding task

Agranular frontal cortical microcircuit underlying cognitive control in macaques

The error-related negativity and an N2-component recorded over medial frontal cortex index core functions of cognitive control. While they are known to originate from agranular frontal areas, the underlying microcircuit mechanisms remain elusive. Most insights about microcircuit function have been derived from variations of the so-called canonical microcircuit model. These microcircuit architectures are based extensively on studies from granular sensory cortical areas in monkeys, cats, and rodents. However, evidence has shown striking cytoarchitectonic differences across species and differences in the functional relationships across cortical layers in agranular compared to granular sensory areas. In this minireview, we outline a tentative microcircuit model underlying cognitive control in the agranular frontal cortex of primates. The model incorporates the main GABAergic interneuron subclasses with specific laminar arrangements and target regions on pyramidal cells. We emphasize the role of layer 5 pyramidal cells in error and conflict detection. We offer several specific questions necessary for creating a specific intrinsic microcircuit model of the agranular frontal cortex.

Dorsomedial frontal cortex damage impairs error-based, but not reinforcement-based motor learning in humans

We adapt our movements to new and changing environments through multiple processes. Sensory error-based learning counteracts environmental perturbations that affect the sensory consequences of movements. Sensory errors also cause the upregulation of reflexes and muscle co-contraction. Reinforcement-based learning enhances the selection of movements that produce rewarding outcomes. Although some findings have identified dissociable neural substrates of sensory error- and reinforcement-based learning, correlative methods have implicated dorsomedial frontal cortex in both. Here, we tested the causal contributions of dorsomedial frontal to adaptive motor control, studying people with chronic damage to this region. Seven human participants with focal brain lesions affecting the dorsomedial frontal and 20 controls performed a battery of arm movement tasks. Three experiments tested: (i) the upregulation of visuomotor reflexes and muscle co-contraction in response to unpredictable mechanical perturbations, (ii) sensory error-based learning in which participants learned to compensate predictively for mechanical force-field perturbations, and (iii) reinforcement-based motor learning based on binary feedback in the absence of sensory error feedback. Participants with dorsomedial frontal damage were impaired in the early stages of force field adaptation, but performed similarly to controls in all other measures. These results provide evidence for a specific and selective causal role for the dorsomedial frontal in sensory error-based learning.

Cortico-spinal modularity in the parieto-frontal system: A new perspective on action control

Classical neurophysiology suggests that the motor cortex (MI) has a unique role in action control. In contrast, this review presents evidence for multiple parieto-frontal spinal command modules that can bypass MI. Five observations support this modular perspective: (i) the statistics of cortical connectivity demonstrate functionally-related clusters of cortical areas, defining functional modules in the premotor, cingulate, and parietal cortices; (ii) different corticospinal pathways originate from the above areas, each with a distinct range of conduction velocities; (iii) the activation time of each module varies depending on task, and different modules can be activated simultaneously; (iv) a modular architecture with direct motor output is faster and less metabolically expensive than an architecture that relies on MI, given the slow connections between MI and other cortical areas; (v) lesions of the areas composing parieto-frontal modules have different effects from lesions of MI. Here we provide examples of six cortico-spinal modules and functions they subserve: module 1) arm reaching, tool use and object construction; module 2) spatial navigation and locomotion; module 3) grasping and observation of hand and mouth actions; module 4) action initiation, motor sequences, time encoding; module 5) conditional motor association and learning, action plan switching and action inhibition; module 6) planning defensive actions. These modules can serve as a library of tools to be recombined when faced with novel tasks, and MI might serve as a recombinatory hub. In conclusion, the availability of locally-stored information and multiple outflow paths supports the physiological plausibility of the proposed modular perspective.

Neural mechanisms for executive control of speed-accuracy trade-off

The medial frontal cortex (MFC) plays an important but disputed role in speed-accuracy trade-off (SAT). In samples of neural spiking in the supplementary eye field (SEF) in the MFC simultaneous with the visuomotor frontal eye field and superior colliculus in macaques performing a visual search with instructed SAT, during accuracy emphasis, most SEF neurons discharge less from before stimulus presentation until response generation. Discharge rates adjust immediately and simultaneously across structures upon SAT cue changes. SEF neurons signal choice errors with stronger and earlier activity during accuracy emphasis. Other neurons signal timing errors, covarying with adjusting response time. Spike correlations between neurons in the SEF and visuomotor areas did not appear, disappear, or change sign across SAT conditions or trial outcomes. These results clarify findings with noninvasive measures, complement previous neurophysiological findings, and endorse the role of the MFC as a critic for the actor instantiated in visuomotor structures.

Cortical origin of theta error signals

A multi-scale approach elucidated the origin of the error-related-negativity (ERN), with its associated theta-rhythm, and the post-error-positivity (Pe) in macaque supplementary eye field (SEF). Using biophysical modeling, synaptic inputs to a subpopulation of layer-3 (L3) and layer-5 (L5) pyramidal cells (PCs) were optimized to reproduce error-related spiking modulation and inter-spike intervals. The intrinsic dynamics of dendrites in L5 but not L3 error PCs generate theta rhythmicity with random phases. Saccades synchronized the phases of the theta-rhythm, which was magnified on errors. Contributions from error PCs to the laminar current source density (CSD) observed in SEF were negligible and could not explain the observed association between error-related spiking modulation in L3 PCs and scalp-EEG. CSD from recorded laminar field potentials in SEF was comprised of multipolar components, with monopoles indicating strong electro-diffusion, dendritic/axonal electrotonic current leakage outside SEF, or violations of the model assumptions. Our results also demonstrate the involvement of secondary cortical regions, in addition to SEF, particularly for the later Pe component. The dipolar component from the observed CSD paralleled the ERN dynamics, while the quadrupolar component paralleled the Pe. These results provide the most advanced explanation to date of the cellular mechanisms generating the ERN.

Slip or fallacy? Effects of error severity on own and observed pitch error processing in pianists

Errors elicit a negative, mediofrontal, event-related potential (ERP), for both own errors (error-related negativity; ERN) and observed errors (here referred to as observer mediofrontal negativity; oMN). It is unclear, however, if the action-monitoring system codes action valence as an all-or-nothing phenomenon or if the system differentiates between errors of different severity. We investigated this question by recording electroencephalography (EEG) data of pianists playing themselves (Experiment 1) or watching others playing (Experiment 2). Piano pieces designed to elicit large errors were used. While active participants' ERN amplitudes differed between small and large errors, observers' oMN amplitudes did not. The different pattern in the two groups of participants was confirmed in an exploratory analysis comparing ERN and oMN directly. We suspect that both prediction and action mismatches can be coded in action monitoring systems, depending on the task, and a need-to-adapt signal is sent whenever mismatches happen to indicate the magnitude of the needed adaptation.

Neurophysiological mechanisms of error monitoring in human and non-human primates

Performance monitoring is an important executive function that allows us to gain insight into our own behaviour. This remarkable ability relies on the frontal cortex, and its impairment is an aspect of many psychiatric diseases. In recent years, recordings from the macaque and human medial frontal cortex have offered a detailed understanding of the neurophysiological substrate that underlies performance monitoring. Here we review the discovery of single-neuron correlates of error monitoring, a key aspect of performance monitoring, in both species. These neurons are the generators of the error-related negativity, which is a non-invasive biomarker that indexes error detection. We evaluate a set of tasks that allows the synergistic elucidation of the mechanisms of cognitive control across the two species, consider differences in brain anatomy and testing conditions across species, and describe the clinical relevance of these findings for understanding psychopathology. Last, we integrate the body of experimental facts into a theoretical framework that offers a new perspective on how error signals are computed in both species and makes novel, testable predictions.

On the Comparison Between the Nc/CRN and the Ne/ERN

After the Error Negativity (Ne or ERN) has been described on full-blown errors and on partial error, a smaller Error Negativity-like wave (CRN or Nc) has also been evidenced on correct trials, first in patients with schizophrenia and, later on, in healthy subjects. The functional significance of the Nc as compared to the Ne is of critical importance since most models accounting for the genesis of the Ne on errors and partial errors cannot account for the existence of the Nc if this Nc simply corresponds to a small Ne. On the contrary, if the Nc and the Ne are two completely distinct components, then the existence of a Nc poses no constraint to the existing models. To this end, we examine in the present review the similarities and the differences existing between the Ne and the Nc regarding their functional properties and their anatomical origin.

A Crucial Role of the Frontal Operculum in Task-Set Dependent Visuomotor Performance Monitoring

For adaptive goal-directed action, the brain needs to monitor action performance and detect errors. The corresponding information may be conveyed via different sensory modalities; for instance, visual and proprioceptive body position cues may inform about current manual action performance. Thereby, contextual factors such as the current task set may also determine the relative importance of each sensory modality for action guidance. Here, we analyzed human behavioral, functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG) data from two virtual reality-based hand-target phase-matching studies to identify the neuronal correlates of performance monitoring and error processing under instructed visual or proprioceptive task sets. Our main result was a general, modality-independent response of the bilateral frontal operculum (FO) to poor phase-matching accuracy, as evident from increased BOLD signal and increased source-localized gamma power. Furthermore, functional connectivity of the bilateral FO to the right posterior parietal cortex (PPC) increased under a visual versus proprioceptive task set. These findings suggest that the bilateral FO generally monitors manual action performance; and, moreover, that when visual action feedback is used to guide action, the FO may signal an increased need for control to visuomotor regions in the right PPC following errors.

Hyperscanning EEG and Classification Based on Riemannian Geometry for Festive and Violent Mental State Discrimination

Interactions between two brains constitute the essence of social communication. Daily movements are commonly executed during social interactions and are determined by different mental states that may express different positive or negative behavioral intent. In this context, the effective recognition of festive or violent intent before the action execution remains crucial for survival. Here, we hypothesize that the EEG signals contain the distinctive features characterizing movement intent already expressed before movement execution and that such distinctive information can be identified by state-of-the-art classification algorithms based on Riemannian geometry. We demonstrated for the first time that a classifier based on covariance matrices and Riemannian geometry can effectively discriminate between neutral, festive, and violent mental states only on the basis of non-invasive EEG signals in both the actor and observer participants. These results pave the way for new electrophysiological discrimination of mental states based on non-invasive EEG recordings and cutting-edge machine learning techniques.

Structural and functional abnormalities within sensori-motor and limbic networks underpin intermittent explosive symptoms in Tourette disorder

BACKGROUND

Intermittent explosive outbursts (IEO), manifesting as sudden episodes of verbal or physical aggression, are frequently present in patients with Tourette disorder (TD) and considered as one of the most disabling symptoms by patients and families. The neuronal correlates of these behaviours are poorly understood, and this was the primary objective of the present study.

METHODS

We assessed the presence of IEO in 55 patients with TD and then compared the subgroup of the patients with IEO to those without these manifestations using a multimodal neuroimaging approach.

RESULTS

47% of TD patients presented IEO, which was frequently associated with attention deficit hyperactivity disorder (ADHD). TD patients (without ADHD) with IEO compared to TD without IEO, showed structural changes in the right supplementary motor area as well as in the right hippocampus (increased fractional anisotropy), and in the left orbitofrontal cortex (decreased mean diffusivity). Using these three nodes as seeds for resting state functional connectivity, we showed a lower connectivity within the sensori-motor cortico-basal ganglia network, and an altered connectivity pattern among the orbito-frontal cortex, amygdala and hippocampus.

CONCLUSIONS

Overall, our results indicate that TD with IEO is associated with brain dysfunction related to a less efficient top-down control on action selection, and impairments related to emotional regulation, impulse control and aggressive behaviours.

Single-Neuron Correlates of Error Monitoring and Post-Error Adjustments in Human Medial Frontal Cortex

Humans can self-monitor errors without explicit feedback, resulting in behavioral adjustments on subsequent trials such as post-error slowing (PES). The error-related negativity (ERN) is a well-established macroscopic scalp EEG correlate of error self-monitoring, but its neural origins and relationship to PES remain unknown. We recorded in the frontal cortex of patients performing a Stroop task and found neurons that track self-monitored errors and error history in dorsal anterior cingulate cortex (dACC) and pre-supplementary motor area (pre-SMA). Both the intracranial ERN (iERN) and error neuron responses appeared first in pre-SMA, and ∼50 ms later in dACC. Error neuron responses were correlated with iERN amplitude on individual trials. In dACC, such error neuron-iERN synchrony and responses of error-history neurons predicted the magnitude of PES. These data reveal a human single-neuron correlate of the ERN and suggest that dACC synthesizes error information to recruit behavioral control through coordinated neural activity.

Different population dynamics in the supplementary motor area and motor cortex during reaching

Neural populations perform computations through their collective activity. Different computations likely require different population-level dynamics. We leverage this assumption to examine neural responses recorded from the supplementary motor area (SMA) and motor cortex. During visually guided reaching, the respective roles of these areas remain unclear; neurons in both areas exhibit preparation-related activity and complex patterns of movement-related activity. To explore population dynamics, we employ a novel "hypothesis-guided" dimensionality reduction approach. This approach reveals commonalities but also stark differences: linear population dynamics, dominated by rotations, are prominent in motor cortex but largely absent in SMA. In motor cortex, the observed dynamics produce patterns resembling muscle activity. Conversely, the non-rotational patterns in SMA co-vary with cues regarding when movement should be initiated. Thus, while SMA and motor cortex display superficially similar single-neuron responses during visually guided reaching, their different population dynamics indicate they are likely performing quite different computations.

Investigating common coding of observed and executed actions in the monkey brain using cross-modal multi-variate fMRI classification

Mirror neurons are generally described as a neural substrate hosting shared representations of actions, by simulating or 'mirroring' the actions of others onto the observer's own motor system. Since single neuron recordings are rarely feasible in humans, it has been argued that cross-modal multi-variate pattern analysis (MVPA) of non-invasive fMRI data is a suitable technique to investigate common coding of observed and executed actions, allowing researchers to infer the presence of mirror neurons in the human brain. In an effort to close the gap between monkey electrophysiology and human fMRI data with respect to the mirror neuron system, here we tested this proposal for the first time in the monkey. Rhesus monkeys either performed reach-and-grasp or reach-and-touch motor acts with their right hand in the dark or observed videos of human actors performing similar motor acts. Unimodal decoding showed that both executed or observed motor acts could be decoded from numerous brain regions. Specific portions of rostral parietal, premotor and motor cortices, previously shown to house mirror neurons, in addition to somatosensory regions, yielded significant asymmetric action-specific cross-modal decoding. These results validate the use of cross-modal multi-variate fMRI analyses to probe the representations of own and others' actions in the primate brain and support the proposed mapping of others' actions onto the observer's own motor cortices.

Performance monitoring in the medial frontal cortex and related neural networks: From monitoring self actions to understanding others' actions

Action is a key channel for interacting with the outer world. As such, the ability to monitor actions and their consequences - regardless as to whether they are self-generated or other-generated - is of crucial importance for adaptive behavior. The medial frontal cortex (MFC) has long been studied as a critical node for performance monitoring in nonsocial contexts. Accumulating evidence suggests that the MFC is involved in a wide range of functions necessary for one's own performance monitoring, including error detection, and monitoring and resolving response conflicts. Recent studies, however, have also pointed to the importance of the MFC in performance monitoring under social conditions, ranging from monitoring and understanding others' actions to reading others' mental states, such as their beliefs and intentions (i.e., mentalizing). Here we review the functional roles of the MFC and related neural networks in performance monitoring in both nonsocial and social contexts, with an emphasis on the emerging field of a social systems neuroscience approach using macaque monkeys as a model system. Future work should determine the way in which the MFC exerts its monitoring function via interactions with other brain regions, such as the superior temporal sulcus in the mentalizing system and the ventral premotor cortex in the mirror system.

Neural correlates of visuomotor adjustments during scaling of human finger movements

Visually guided finger movements include online feedback of current effector position to guide target approach. This visual feedback may be scaled or otherwise distorted by unpredictable perturbations. Although adjustments to visual feedback scaling have been studied before, the underlying brain activation differences between upscaling (visual feedback larger than real movement) and downscaling (feedback smaller than real movement) are currently unknown. Brain activation differences between upscaling and downscaling might be expected because within-trial adjustments during upscaling require corrective backwards accelerations, whereas correcting for downscaling requires forward accelerations. In this behavioural and fMRI study we investigated adjustments during up- and downscaling in a target-directed finger flexion-extension task with real-time visual feedback. We found that subjects made longer and more complete within-trial corrections for downscaling perturbations than for upscaling perturbations. The finger task activated primary motor (M1) and somatosensory (S1) areas, premotor and parietal regions, basal ganglia, and cerebellum. General scaling effects were seen in the right pre-supplementary motor area, dorsal anterior cingulate cortex, inferior parietal lobule, and dorsolateral prefrontal cortex. Stronger activations for down- than for upscaling were observed in M1, supplementary motor area (SMA), S1 and anterior cingulate cortex. We argue that these activation differences may reflect differing online correction for upscaling vs. downscaling during finger flexion-extension.

Corticospinal gating during action preparation and movement in the primate motor cortex

During everyday actions there is a need to be able to withhold movements until the most appropriate time. This motor inhibition is likely to rely on multiple cortical and subcortical areas, but the primary motor cortex (M1) is a critical component of this process. However, the mechanisms behind this inhibition are unclear, particularly the role of the corticospinal system, which is most often associated with driving muscles and movement. To address this, recordings were made from identified corticospinal (PTN, n = 94) and corticomotoneuronal (CM, n = 16) cells from M1 during an instructed delay reach-to-grasp task. The task involved the animals withholding action for ~2 s until a GO cue, after which they were allowed to reach and perform the task for a food reward. Analysis of the firing of cells in M1 during the delay period revealed that, as a population, non-CM PTNs showed significant suppression in their activity during the cue and instructed delay periods, while CM cells instead showed a facilitation during the preparatory delay. Analysis of cell activity during movement also revealed that a substantial minority of PTNs (27%) showed suppressed activity during movement, a response pattern more suited to cells involved in withholding rather than driving movement. These results demonstrate the potential contributions of the M1 corticospinal system to withholding of actions and highlight that suppression of activity in M1 during movement preparation is not evenly distributed across different neural populations.

NEW & NOTEWORTHY Recordings were made from identified corticospinal (PTN) and corticomotoneuronal (CM) cells during an instructed delay task. Activity of PTNs as a population was suppressed during the delay, in contrast to CM cells, which were facilitated. A minority of PTNs showed a rate profile that might be expected from inhibitory cells and could suggest that they play an active role in action suppression, most likely through downstream inhibitory circuits.

Coupled Decision Processes Update and Maintain Saccadic Priors in a Dynamic Environment

Much of what we know about how the brain forms decisions comes from studies of saccadic eye movements. However, saccadic decisions are often studied in isolation, which limits the insights that they can provide about real-world decisions with complex interdependencies. Here, we used a serial reaction time (RT) task to show that prior expectations affect RTs via interdependent, normative decision processes that operate within and across saccades. We found that human subjects performing the task generated saccades that were governed by a rise-to-threshold decision process with a starting point that reflected expected state-dependent transition probabilities. These probabilities depended on decisions about the current state (the correct target) that, under some conditions, required the accumulation of information across saccades. Without additional feedback, this information was provided by each saccadic decision threshold, which represented the total evidence in favor of the chosen target. Therefore, the output of the within-saccade process was used, not only to generate the saccade, but also to provide input to the across-saccade process. This across-saccade process, in turn, helped to set the starting point of the next within-saccade process. These results imply a novel role for functional information-processing loops in optimizing saccade generation in dynamic environments.SIGNIFICANCE STATEMENT Saccades are the rapid, ballistic eye movements that we make approximately three times every second to scan the visual scene for interesting things to look at. The apparent ease with which we produce saccades belies their computational sophistication, which can be studied quantitatively in the laboratory to provide insights into how our brain manages the interplay between sensory input and motor output. The present work is important because we show for the first time how this interplay operates both within and across saccades to ensure that these eye movements are guided effectively by learned expectations in dynamic environments. More generally, this study shows how sensory-motor decision processes, typically studied in isolation, interact via functional information-processing loops in the brain to produce complex, adaptive behaviors.

Contextual response time adaptation in the countermanding performance of rats

Humans and non-human primates are known to lengthen their response time (RT) to a go signal when they occasionally must cancel their responses following a stop signal in a countermanding task as well as to adjust their RT adaptively on a trial-by-trial basis. Less is clear regarding the adaptive RT adjustments in the countermanding performance of rodents. To investigate this question, male Wistar rats (N=12) were trained with food reward to press a lever directly below an illuminated light (go signal), but to countermand the lever press subsequent to a tone (stop signal) presented infrequently (25% of trials) at variable delays. Rats were then tested in a standard responding task (0% stop trials) or a countermanding task with a 10-s or 1-s TO interval following errors. Rats exhibited significant RT lengthening in the countermanding task, compared with the standard responding task, and RT shortening following consecutive correct go trials. They also show RT lengthening following both error trials in the standard responding task and unrewarded, non-canceled stop trials in the countermanding task. RT lengthening following erroneous stop trials was observed in sessions with a 10-s TO interval, but not with a 1-s TO interval. Analyses of RT distributions suggest that RT lengthening results largely from reduced sensitivity to the go signal, but also from reduced readiness. These findings indicate that rats exert control in the countermanding task by lengthening RT in anticipation of stop trials to avoid long, unrewarded TO intervals.

Speed of saccade execution and inhibition associated with fractional anisotropy in distinct fronto-frontal and fronto-striatal white matter pathways

Fast cancellation or switching of action plans is a critical cognitive function. Rapid signal transmission is key for quickly executing and inhibiting responses, and the structural integrity of connections between brain regions plays a crucial role in signal transmission speed. In this study, we used the search-step task, which has been used in nonhuman primates to measure dynamic alteration of saccade plans, in combination with functional and diffusion-weighted MRI. Functional MRI results were used to identify brain regions involved in the reactive control of gaze. Probabilistic tractography was used to identify white matter pathways connecting these structures, and the integrity of these connections, as indicated by fractional anisotropy (FA), was correlated with search-step task performance. Average FA from tracts between the right frontal eye field (FEF) and both right supplementary eye field (SEF) and the dorsal striatum were associated with faster saccade execution. Average FA of connections between the dorsal striatum and both right SEF and right inferior frontal cortex (IFC) as well as between SEF and IFC predicted the speed of inhibition. These relationships were largely behaviorally specific, despite the correlation between saccade execution and inhibition. Average FA of connections between the IFC and both SEF and the dorsal striatum specifically predicted the speed of inhibition, and connections between the FEF and SEF specifically predicted the speed of execution. In addition, these relationships were anatomically specific; correlations were observed after controlling for global FA. These data suggest that networks supporting saccade initiation and inhibition are at least partly dissociable. Hum Brain Mapp 37:2811-2822, 2016. © 2016 Wiley Periodicals, Inc.

The Monitoring and Control of Task Sequences in Human and Non-Human Primates

Our ability to plan and execute a series of tasks leading to a desired goal requires remarkable coordination between sensory, motor, and decision-related systems. Prefrontal cortex (PFC) is thought to play a central role in this coordination, especially when actions must be assembled extemporaneously and cannot be programmed as a rote series of movements. A central component of this flexible behavior is the moment-by-moment allocation of working memory and attention. The ubiquity of sequence planning in our everyday lives belies the neural complexity that supports this capacity, and little is known about how frontal cortical regions orchestrate the monitoring and control of sequential behaviors. For example, it remains unclear if and how sensory cortical areas, which provide essential driving inputs for behavior, are modulated by the frontal cortex during these tasks. Here, we review what is known about moment-to-moment monitoring as it relates to visually guided, rule-driven behaviors that change over time. We highlight recent human work that shows how the rostrolateral prefrontal cortex (RLPFC) participates in monitoring during task sequences. Neurophysiological data from monkeys suggests that monitoring may be accomplished by neurons that respond to items within the sequence and may in turn influence the tuning properties of neurons in posterior sensory areas. Understanding the interplay between proceduralized or habitual acts and supervised control of sequences is key to our understanding of sequential task execution. A crucial bridge will be the use of experimental protocols that allow for the examination of the functional homology between monkeys and humans. We illustrate how task sequences may be parceled into components and examined experimentally, thereby opening future avenues of investigation into the neural basis of sequential monitoring and control.

Anticipating conflict: Neural correlates of a Bayesian belief and its motor consequence

Previous studies have examined the neural correlates of proactive control using a variety of behavioral paradigms; however, the neural network relating the control process to its behavioral consequence remains unclear. Here, we applied a dynamic Bayesian model to a large fMRI data set of the stop signal task to address this issue. By estimating the probability of the stop signal - p(Stop) - trial by trial, we showed that higher p(Stop) is associated with prolonged go trial reaction time (RT), indicating proactive control of motor response. In modeling fMRI signals at trial and target onsets, we distinguished activities of proactive control, prediction error, and RT slowing. We showed that the anterior pre-supplementary motor area (pre-SMA) responds specifically to increased stop signal likelihood, and its activity is correlated with activations of the posterior pre-SMA and bilateral anterior insula during prolonged response times. This directional link is also supported by Granger causality analysis. Furthermore, proactive control, prediction error, and time-on-task are each mapped to distinct areas in the medial prefrontal cortex. Together, these findings dissect regional functions of the medial prefrontal cortex in cognitive control and provide system level evidence associating conflict anticipation with its motor consequence.

Inhibitory control in mind and brain 2.0: blocked-input models of saccadic countermanding

The interactive race model of saccadic countermanding assumes that response inhibition results from an interaction between a go unit, identified with gaze-shifting neurons, and a stop unit, identified with gaze-holding neurons, in which activation of the stop unit inhibits the growth of activation in the go unit to prevent it from reaching threshold. The interactive race model accounts for behavioral data and predicts physiological data in monkeys performing the stop-signal task. We propose an alternative model that assumes that response inhibition results from blocking the input to the go unit. We show that the blocked-input model accounts for behavioral data as accurately as the original interactive race model and predicts aspects of the physiological data more accurately. We extend the models to address the steady-state fixation period before the go stimulus is presented and find that the blocked-input model fits better than the interactive race model. We consider a model in which fixation activity is boosted when a stop signal occurs and find that it fits as well as the blocked input model but predicts very high steady-state fixation activity after the response is inhibited. We discuss the alternative linking propositions that connect computational models to neural mechanisms, the lessons to be learned from model mimicry, and generalization from countermanding saccades to countermanding other kinds of responses.

Differentiation of neuronal operations in latent components of event-related potentials in delayed match-to-sample tasks

The goal of this study was to decompose ERPs into latent components associated with hypothetical processes of category discrimination, comparison to working memory and action-related operations. In five variants of the delayed match-to-sample s1-s2 task, instructions were varied for manipulation of the processes. The blind source separation was applied to the collection of ERPs. The category discrimination operation is attributed to three latent components with peak latencies of 130 to 170 ms, which are generated in different parts of the prestriate cortex. The comparison to working memory operation is attributed to a latent component that is generated in the temporal cortex and manifested in a positive deflection with a peak latency of 250 ms after s2. The category discrimination and comparison to working memory effects were dissociated spatially and temporally from attention and action selection effects.

Action monitoring and medial frontal cortex: leading role of supplementary motor area

The capacity to evaluate the outcomes of our actions is fundamental for adapting and optimizing behavior and depends on an action-monitoring system that assesses ongoing actions and detects errors. The neuronal network underlying this executive function, classically attributed to the rostral cingulate zone, is poorly characterized in humans, owing to the limited number of direct neurophysiological data. Using intracerebral recordings, we show that the leading role is played by the supplementary motor area (SMA), which rapidly evaluates successful and erroneous actions. The rostral part of medial prefrontal cortex, driven by the SMA, was activated later and exclusively in the case of errors. This suggests a hierarchical organization of the different frontal regions involved in implementation of action monitoring and error processing.

Neurophysiology of performance monitoring and adaptive behavior

Successful goal-directed behavior requires not only correct action selection, planning, and execution but also the ability to flexibly adapt behavior when performance problems occur or the environment changes. A prerequisite for determining the necessity, type, and magnitude of adjustments is to continuously monitor the course and outcome of one's actions. Feedback-control loops correcting deviations from intended states constitute a basic functional principle of adaptation at all levels of the nervous system. Here, we review the neurophysiology of evaluating action course and outcome with respect to their valence, i.e., reward and punishment, and initiating short- and long-term adaptations, learning, and decisions. Based on studies in humans and other mammals, we outline the physiological principles of performance monitoring and subsequent cognitive, motivational, autonomic, and behavioral adaptation and link them to the underlying neuroanatomy, neurochemistry, psychological theories, and computational models. We provide an overview of invasive and noninvasive systemic measures, such as electrophysiological, neuroimaging, and lesion data. We describe how a wide network of brain areas encompassing frontal cortices, basal ganglia, thalamus, and monoaminergic brain stem nuclei detects and evaluates deviations of actual from predicted states indicating changed action costs or outcomes. This information is used to learn and update stimulus and action values, guide action selection, and recruit adaptive mechanisms that compensate errors and optimize goal achievement.