Neurophysiological mechanisms of error monitoring in human and non-human primates
(Full-text capture 2026-09-21; web artifacts lightly stripped; truncated.)
Zhongzheng Fu
1Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
2Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA.
1,2, Amirsaman Sajad
Amirsaman Sajad
3Center for Integrative & Cognitive Neuroscience, Vanderbilt University, Nashville, TN, USA.
4Vanderbilt Vision Research Center, Vanderbilt University, Nashville, TN, USA.
5Department of Psychology, Vanderbilt University, Nashville, TN, USA.
3,4,5, Steven P Errington
Steven P Errington
3Center for Integrative & Cognitive Neuroscience, Vanderbilt University, Nashville, TN, USA.
4Vanderbilt Vision Research Center, Vanderbilt University, Nashville, TN, USA.
5Department of Psychology, Vanderbilt University, Nashville, TN, USA.
3,4,5, Jeffrey D Schall
Jeffrey D Schall
3Center for Integrative & Cognitive Neuroscience, Vanderbilt University, Nashville, TN, USA.
5Department of Psychology, Vanderbilt University, Nashville, TN, USA.
6Centre for Vision Research, York University, Toronto, Ontario, Canada.
7Vision: Science to Applications (VISTA), York University, Toronto, Ontario, Canada.
8Department of Biology, Faculty of Science, York University, Toronto, Ontario, Canada.
3,5,6,7,8, Ueli Rutishauser
Ueli Rutishauser
1Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
9Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
10Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
1,9,10
- Author information
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1Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
2Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA.
3Center for Integrative & Cognitive Neuroscience, Vanderbilt University, Nashville, TN, USA.
4Vanderbilt Vision Research Center, Vanderbilt University, Nashville, TN, USA.
5Department of Psychology, Vanderbilt University, Nashville, TN, USA.
6Centre for Vision Research, York University, Toronto, Ontario, Canada.
7Vision: Science to Applications (VISTA), York University, Toronto, Ontario, Canada.
8Department of Biology, Faculty of Science, York University, Toronto, Ontario, Canada.
9Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
10Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
✉
Correspondence should be addressed to Zhongzheng Fu, Jeffrey D. Schall or Ueli Rutishauser. zzbrooksfu@gmail.com; schalljd@yorku.edu; rutishauseru@csmc.edu
Author contributions
The authors all contributed to all aspects of preparing the article.
Issue date 2023 Mar.
Reprints and permissions information is available at www.nature.com/reprints.
PMCID: PMC10231843 NIHMSID: NIHMS1902887 PMID: 36707544
The publisher’s version of this article is available at Nat Rev Neurosci
Abstract
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.
Introduction
To survive, all sentient species must adapt to errors1. We can, for example, notice that we pressed a wrong keyboard key without external feedback2, or quickly realize that we have made the wrong turn on our way home or have called an acquaintance by the wrong name. The ability to recognize action errors is essential for learning new tasks, adapting to challenging or changing conditions, and interrupting useless or dangerous courses of action3. Error monitoring is the cognitive process by which we rapidly detect situations in which performance deviates from the intended goal. Error monitoring is an element of performance monitoring, which is a critical aspect of cognitive control whereby we achieve our goals4. What are the neural processes that endow us with the ability to detect whether we have made an error and thereby to gain insight into our own behaviour?
It has long been recognized that performance monitoring is critical for goal-directed behaviour and cognitive control more broadly4. In addition, it is increasingly being appreciated that malfunctioning performance monitoring is a key symptom of some psychiatric disorders, including impulsive behaviour, obsessive–compulsive disorder, addiction and schizophrenia5, 6. Hence, performance monitoring is a central construct in the Research Domain Criteria framework of the US National Institute of Mental Health, 8 and a core element in new computational psychiatry-based approaches to mental health9. As a result, deciphering the neural mechanisms that underlie performance monitoring has become a major interest in both cognitive neuroscience and clinical neuroscience.
The validity of animal model paradigms for higher-level human cognitive processes – in particular, executive functions such as performance monitoring – remains uncertain10, 11. Indeed, although much has been learned about the neural mechanisms of cognitive control in animal models (particularly in the macaque), little is known about the relevance of these findings for understanding human cognition and its disruption in psychiatric disorders. Intracranial recordings in humans provide a rare opportunity to directly compare findings in animal models with those in humans. However, such comparisons are possible only if the animal model system closely mirrors human anatomy, physiology and behaviour.
The aim of this Review is to compare the neuronal mechanisms for self-monitoring of action errors across macaques and humans. Recent work involving similar tasks and assessment of neural activity at the single-neuron level in the medial frontal cortex (MFC) has demonstrated that there are many similarities in error-monitoring processes in humans12 and macaques13. At the same time, experiments possible only in humans reveal the relevance of these processes for cognitive control involved in language, cognitive flexibility and neuropsychiatric disorders. These studies provide converging evidence that error neurons in the MFC constitute single-neuron correlates of the error-related negativity (ERN), an important event-related potential that has spawned a large literature in cognitive neuroscience (see later). Informed by these new findings, we synthesize a model for how errors are computed and propose a set of tasks that are most suitable for multispecies investigation. We conclude by proposing that the neural processes in the MFC can be studied synergistically by combining work in humans and macaques to reveal new insights into the neural mechanisms of cognitive control and its impairment by disease.
Medial frontal cortex
Converging evidence from neuroimaging, electrophysiological recordings and lesion studies in humans and non-human primates indicates that the MFC is essential for performance monitoring4, 12- 15. Two medial frontal regions in particular have been intensely studied ( Fig.1): the first is the supplementary motor complex, which is located within the superior frontal gyrus (SFG), anterior to the motor cortex. This complex includes the supplementary motor area (SMA), the pre-supplementary motor area (pre-SMA)16 and the supplementary eye field (SEF)13, 17. The second is a collection of areas in both banks of the cingulate sulcus and the cingulate gyrus. Habitual descriptions of the ‘anterior cingulate cortex’ (ACC), including ours12, 18, 19, can be replaced by more precise nomenclature reflecting more refined anatomical and comparative studies, which distinguish the middle cingulate cortex (MCC) ventral to the supplementary motor cortex from the ACC around the genu of the corpus callosum20- 22. The rostral ACC contributes more to emotion and autonomic regulation, whereas the MCC contributes more to performance monitoring and cognitive control23, and includes the cingulate motor areas that project to the spinal cord24.
Fig. 1 ∣. Locations of medial frontal lobe areas implicated in performance monitoring and typical recording approaches.
a, Cross sections of the macaque (top) and human (middle and bottom) brains highlighting the relative locations of the pre-supplementary motor area (pre-SMA; green), supplementary motor area (SMA; blue), supplementary eye field (SEF; red), dorsal middle cingulate cortex (dMCC; yellow) and ventral middle cingulate cortex (vMCC; orange). Human brains are illustrated with the paracingulate sulcus absent (middle) or present (bottom). For each species, the approach used for neurophysiological sampling is illustrated. In macaques, intracortical neural signals are sampled with multicontact linear electrode arrays inserted nearly vertically, perpendicular to the cortical layers. In humans, intracortical neural signals are sampled with microwire electrodes. In both species, electroencephalography signals can be recorded from electrodes placed on the cranium (grey cylinder). b, Biophysical contributions of areas in the medial frontal cortex to the error-related negativity. Rostral (left) and caudal (right) sections are presented to highlight variation in contributions from the different cortical areas. Putative dipoles are distinguished by colour for the SEF (red), pre-SMA (green), dMCC (yellow) and vMCC (orange). Dipole orientation varies with cortical folding. The hypothesized vectorial contributions of dipoles in each area ( r e) to medial frontal electroencephalography voltage ( V e) are portrayed through arrows indicating polarity and strength. CS, cingulate sulcus; Fz, frontal midline EEG electrode; FZc, frontal central EEG electrode; PCS, paracingulate sulcus.
One of the most robust findings in the MFC in humans and macaques is its vigorous single-neuron spiking response following erroneous actions12, 13, 19, 25- 28. This signal is also detectable with non-invasive methods such as functional MRI (fMRI)4 and simultaneous fMRI and scalp electroencephalography (EEG)29, 30 in humans. Error-monitoring signals in both the MCC and the SFG are often but not always associated with performance adjustments, such as post-error slowing12, 13. These findings bridge monitoring processes and the engagement of subsequent cognitive control. Together with evidence from lesion and electrical stimulation studies31- 33, this body of research indicates that recording from and manipulating the MFC provides a powerful paradigm to probe the neural substrate of error monitoring.
Invasive single-neuron recordings in the MFC can be obtained in both macaques and humans. In macaques, recordings in the MCC, SEF and SMA, and pre-SMA are routinely performed with microelectrodes or silicon probes ( Fig. 1a). In humans, recordings in the MFC have been performed in two clinical scenarios ( Fig. 1a): first, in patients with epilepsy, areas along the medial wall of the frontal lobe (including the MCC, SMA and pre-SMA) are targeted with depth electrodes to localize focal seizure onset zones or seizure spread patterns34, 35; second, in patients undergoing awake brain surgery for implantation of a deep brain stimulator or targeted resection, the MFC is targeted using microelectrodes36, 37. These neurosurgical scenarios provide opportunities to study the contribution of the MFC to performance monitoring in both macaques and humans with very similar experimental techniques. The aspects of performance monitoring best suited for study in each species differ owing to behavioural and technical constraints, with some aspects approachable only in humans and others approachable only in macaques ( Table 1). We note that although our focus here is on the role of the SFG and MCC in error monitoring, these brain areas contribute importantly to other cognitive functions – such as signalling response conflict14, 17, 38- 40 – that are beyond the scope of this Review.
Table 1 ∣.
Technical considerations linked to and cognitive processes examined in performance monitoring in humans and macaques
| Item | Humans | Macaques |
|---|---|---|
| Technical issues | ||
| Acquisition of task rules | Verbal instruction | Non-verbal conditioning |
| Number of participants | At least 20 | Typically 2 |
| Number of sessions or number of trials per individual | Low | High |
| Motivation | Social reinforcement | Primary reinforcement |
| Brain geometry | Large, highly folded, idiosyncratic cortex | Small, modestly folded, stereotyped cortex |
| EEG electrode placement | On scalp | On scalp; on skull beneath scalp and muscles |
| Typical microelectrode recordings | Brush wire with no layer specificity | Linear electrode arrays allowing layer specificity; square electrode arrays allowing areal sample |
| Possible causal interventions | Electrical stimulation | Electrical stimulation, pharmacology, lesions, optogenetics |
| Potential impact of neurological disease and/or medication | Yes | No |
| Effectors typically studied | Button press with finger | Eye movements, arm movements |
| Study of individual differences (large samples) | Yes | No |
| ERP-single neuron correlates | Yes | Yes |
| Study impact of comorbid neurological disease | Yes | No |
| Cognitive processes examined | ||
| Outcome registration (reward, points) | Yes | Yes |
| Error monitoring | Yes | Yes |
| Expertise level | Naive | High |
| Task switching or multitask comparisons (domain generality) | Yes | No |
| Response inhibition or conflict monitoring (slowing) | Yes | Yes |
| Interference or conflict due to reading | Yes | No |
| Test of awareness of performance | Yes | No |
| Time estimation | Yes | Yes |
| Aversive outcomes | No | Yes |
ALL factors are considered from the point of view of invasive medial frontal cortex recordings. ‘Yes’ and ‘no’ entries indicate topics that, to date, have or have not been studied routinely in the given species, respectively. EEG, electroencephalography; ERP, event-related potential.
The ERN
The MFC has long been thought to be the source of the ERN (also known as the Ne), which is a brief period of greater negative polarization in the electroencephalogram over the MFC when an error is made relative to correct trial performance41- 46 ( Figs. 1b and 2). Although error monitoring has also been investigated with fMRI, the high temporal resolution of EEG offers unique leverage in the investigation of cognitive control processes. Furthermore, the ERN can be measured with a single scalp electrode, thereby providing high translational potential. Consequently, understanding the neural processes that give rise to the ERN has relevance for studying normal human behaviour and as a potential endophenotype for psychiatric disease.
Fig. 2 ∣. Error neurons and error-related negativity in macaques and humans.
Each piece of data is shown for both species for visual comparison. a,b, Examples of single neurons responding to error trials with increased firing rate compared with correct trials. c,d, Average scalp and intracranial error-related negativity (ERN) from individual sessions, revealing a stronger negativity for error than correct trials in both species. AUC, area under the curve; Fz, frontal midline EEG electrode; MCC, middle cingulate cortex; pre-SMA, pre-supplementary motor area; SEF, supplementary eye field. Part a adapted from ref. 13, Springer Nature Limited. Data in part b are from ref. 12. The left panel in part c is adapted from ref. 13, Springer Nature Limited. The middle panel in part c is adapted with permission from ref. 25, APS. The right panel in part d is adapted with permission from ref. 26, APS. The left panel in part d is adapted with permission from ref. 12, Elsevier. The data in the middle and right panels in part d are from ref. 12.
Although the ERN has become a major workhorse in cognitive neuroscience, until recently little was known about the underlying mechanisms that give rise to it. The ERN is different from sensory-evoked potentials or movement-related potentials that are associated with observable events. Rather, the ERN indexes an internal state that can be inferred only indirectly. Therefore, the improved understanding of the ERN that is now emerging (discussed later) can also provide insights into other event-related potentials, such as the contingent negative variation and the readiness potential. The ERN is closely related to the feedback-related negativity (FRN), which is an event-related potential evoked by externally signalled failures (for example, after a correct response receiving less-than-expected reward or sensory feedback indicating an error). The two signals have overlapping scalp voltage distributions47, which has led to the hypothesis that both index the computation of prediction errors48. We discuss the validity of this claim further later. We refer to the terms ‘ERN’ and ‘FRN’ whenever the EEG data are analysed time-locked to the erroneous response onset or feedback onset, respectively. We note that in some studies the term ‘feedback ERN’ is used instead of ‘FRN’ (for example, see ref. 49), which we avoid for clarity.
Understanding how the ERN is generated begins with knowing where it arises. In 1994, using source modelling, researchers showed that a single dipole in the MCC could account for the spatial distribution of the ERN50. Confidence in this conclusion cannot be high, though, because the inverse problem of locating dipoles given a spatial distribution of voltages has no unique solution ( Fig. 1b). More uncertainty arises because the simplified geometric models of the head that were used in the 1990s were poor approximations of the human brain and head. Another uncertainty overlooked by all early and most current studies arises from variation in sulcal morphology across individuals51, which necessarily changes the orientation of dipoles ( Fig. 1b). Of most relevance for the ERN is the absence or presence of a paracinguiate sulcus (PCS), which is located superior to the cingulate sulcus in ~70% of humans52, 53. Such morphological differences in cortical folding patterns produce differences in EEG voltage distributions; for example, a weaker, briefer N400 versus a stronger, longer N400 when response conflict is detected54.
Although the inverse problem of source localization of current locations from voltages is ill-posed and offers indefinite results, forward modelling from current locations to voltage distributions offers definite results. Recent work using this approach has demonstrated that a spatial pattern of voltage resembling the ERN can be produced by a simulated dipole in the SEF of macaques55. The biophysics underlying the relationships between neuronal spiking, synaptic potentials and EEG signals has many complexities. For example, the relationship between current dipoles in the cerebral cortex and surface EEG signals has most commonly been described only in terms of instantaneous electric fields. However, research has demonstrated that the slower diffusion of ions, possibly mediated by glia56, can interact with current dipoles in producing the EEG signal. Relative to granular sensory areas, agranular cortical areas have a higher ratio of glial cells to neurons57, so the contribution of slower ion diffusion to the EEG signal can differ between granular and agranular areas. To our knowledge this has not been investigated.
If both the SFG and the MCC separately signal errors, then the ERN can have at least two sources12. Moreover, if error-related signals are produced in different areas within the MCC, then the number of sources increases further. The voltage measured with an electrode on the head will be the superposition of the voltages produced by current dipoles in each cortical area. The specific character of that superposition depends on the geometry of the cortex, which specifies dipole location, orientation and distance relative to surface electrodes. Voltages from two sources can sum or cancel depending on the orientation of the dipoles. This means that the ERN is a manifestation of multiple neural signals arising in different cortical areas58 ( Fig. 1b). Given the similar anatomical location of the pre-SMA in humans and macaques, its biophysical contribution to the ERN sampled at the midline with EEG electrodes is expected to be similar in both species ( Fig. 1b). However, we expect that dipoles established in the SEF of macaques will contribute more to the ERN than those in the SEF of humans ( Fig. 1b). Previous comparisons of human and macaque MFC functions59 have not incorporated these details of cortical structure.
Spectral decomposition of the EEG signal has provided additional insights into the processes generating the ERN. For example, changes in theta-band and delta-band oscillations along the frontal midline coincide with the ERN60- 62, with both phase resetting and changes of power in ongoing theta oscillations contributing to this signal63, 64. More broadly, theta-band activity in the MFC has been described as a mesoscale neural correlate of cognitive control65. Although they provide powerful insights, these data have not revealed the specific contributions of different parts of the MFC to the ERN. They also do not provide a cellular-level explanation of how the changes in power and phase of theta-band EEG arise following the commission of errors.
In humans, detection of errors sometimes evokes error awareness, which is a metacognitive reflection that enables conscious reasoning about errors. The relationship between error awareness and the ERN, and the positive polarization that follows the ERN (known as the Pe component), is complex and remains an active area of research66, 67. For our purposes it suffices to note that although humans are typically aware of the kind of errors that are accompanied by an ERN in standard tasks, this does not appear to be necessary; an ERN can still be present for unaware errors68. We therefore posit that the mechanisms of error detection we describe are not conditional on awareness.
Intracranial recordings across species
A key goal of this Review is to contrast findings across species ( Figs. 2 and 3). Single neurons that signal errors have been found in the SEF13, pre-SMA, SMA16 and MCC19 in non-human primates and in the pre-SMA and MCC in humans12 ( Fig. 2a, b). The temporal properties of these neurons are similar across species ( Fig. 3c, d): the spike rate is maximally modulated ~100 ms after an erroneous action (saccade, arm movement or finger button press). Two features determine that the error signal reported in these studies is not exclusively of sensory origin. First, the timing of error signals is synchronized with the muscle contraction rather than any sensory events. Second, the error signal arises before the trial outcome is signalled by external feedback about accuracy or the gain or loss of a reward. Also, the magnitude of single-neuron error signals depends weakly, if at all, on the laterality of the motor effector19, 69. It is unknown whether the error-related modulation of single neurons is invariant across motor effectors. One investigation of the topography of the ERN observed after errors committed with the hand or foot indicated a common source70, but a more recent study of errors committed with the eyes or the hand and using more sophisticated modelling of current sources described different sources71.
Fig. 3 ∣. Reliability and latency of error responses in macaques and humans.
Each piece of data is shown for both species for visual comparison. a,b, Single-trial reliability of the error-related negativity (ERN) amplitude at the scalp (left) and intracranial (right) level is high in both species. Each plot is from an individual session. c,d, Single-trial latency of the intracranial ERN (iERN) (left) and error neurons (right). In both species and with both metrics, error signals consistently appear first in the superior frontal gyrus. AUC, area under the curve; CDF, cumulative distribution function; MCC, middle cingulate cortex; pre-SMA, pre-supplementary motor area; SEF, supplementary eye field. Parts a,b are based on reanalysis of data shown in refs. 12, 13. Single-trial amplitudes were extracted using the method described in ref. 12. Part c is adapted with permission from ref. 26, APS. Part d is adapted with permission from ref. 12, Elsevier.
The scalp-recorded ERN reliably occurs ~100 ms after a self-monitored error in macaques and humans ( Fig. 2c, d), despite differences across individuals, task requirements and effectors (eye, hand, foot or vocal)46, 70- 72. The intracranial ERN (iERN) also has a similar peak onset latency between humans and macaques: ~100 ms in the macaque SEF25 and ~130 ms in the macaque MCC after an oculomotor response26; ~100 ms in the human pre-SMA and ~130 ms in the human MCC after a button press using a finger12. In both species, the iERN and the scalp ERN can be estimated reliably on single trials, with the iERN having the highest reliability ( Fig. 3a, b). Two separate human studies have found, on a trial-by-trial basis, that the latency and the amplitude of the iERN in the MCC are correlated with those of the iERN in the pre-SMA12 or the SMA35. In addition, the MCC iERN occurs only when there is a preceding SMA iERN in sessions where both areas are recorded simultaneously35. This is yet to be confirmed in macaques. These results strongly suggest a hierarchical relationship between the MCC and the SFG in error processing across species ( Fig. 3c, d), with action errors being first detected in the SFG and then communicated to the MCC for updating control forward models (see later)12, 35.
In both macaques and humans, two types of error-related neurons have been identified. In the human MFC, ‘type I’ neurons produce more spikes on average on trials in which an error was made (‘error trials’) than on trials with a correct response (‘correct trials’), whereas ‘type II’ neurons do the opposite. The proportion of type II error neurons is notably larger in the pre-SMA (40%) than in the MCC (26%)12. A similar distinction has been made in the macaque SFG, with neurons responding more to error trials than to correct trials being referred to as ‘error cells’ and those with reduced activity in error trials being referred to as ‘reward expectation cells’16. Note that in the human study, no reward but delayed visual feedback on accuracy was provided, suggesting that type II responses cannot be solely attributed to reward expectation.
Given that the latency of error responses in MFC neurons falls in the same range as that of the scalp ERN and iERN, a key unresolved question is how these two signals are related. This question is of particular importance because little is known about how the ERN is generated even though many computational aspects of this signal are well known42. Our macaque and human data12, 13 provide insight into the relation between simultaneously recorded scalp ERN in macaques or iERN in humans and the activity of error neurons. In macaques, simultaneous recording of the ERN and the spiking activity of neurons across all layers of the SEF demonstrates clear associations between the microlevel and macrolevel error signals13. Notably, error neurons in layer 2/3 but not those in layer 5/6 demonstrate this relationship. In humans, the firing rate of error neurons is predicted by the amplitude of the iERN in the pre-SMA and the MCC12. In both species, this relationship exists exclusively for error neurons, suggesting that it is not merely a generic biophysical one but is specific to error computation. Laminar recordings in the human MCC73 and in the macaque SEF13 demonstrate prominent current sinks in layers 2 and 3 during errors, where pyramidal neurons in lower layer 3 and layer 5 extend their dendrites.
One hypothesis for these cross-species findings is that the ERN reflects the highly synchronous and aligned electric dipoles generated within error neurons48. In our view, these dipoles are generated by synaptic inputs from the thalamocortical projections and passive return currents at apical locations, as well as inhibitory actions by interneurons and projections from top-down regions. The larger the ERN amplitude at a given location is, the stronger and more synchronous these synaptic inputs may be, and the more effectively these synaptic inputs can drive the firing of error neurons. These results provide the most direct evidence to date that the ERN is jointly generated in the SFG and the MCC in primates ( Fig. 1b), thereby providing a solid physiological foundation for the many EEG studies that use this signal to study cognitive control.
A second signal of relevance for error monitoring is response conflict74, 75. We differentiate between two types of response conflict-related signals: those occurring during action selection while multiple response options are being considered (ex ante), and those occurring after a response has been made (ex post). Whereas the former calls for online control to resolve conflict proactively, the latter is an ‘after the fact’ evaluative signal. In humans, neurons signalling response conflict ex ante exist in both the MCC and the pre-SMA18, 36, 76. In macaques, no ex ante conflict neurons have been found using the stop-signal task or related conflict tasks, which involve conflict between concurrent go and stop/distractor processes (see below)19, 69, 77- 79. In a task involving visually guided saccades in the presence of a salient distractor, some have interpreted neural spiking in the macaque MCC as ex ante response conflict between the goal-compatible and distractor-driven saccade plans80, but this conclusion is debatable81. The existence of ex ante conflict signals may depend on the task used to probe it. Whether neurons in the human MFC signal ex ante conflict in stop-signal tasks remains an open question. By contrast, neurons signalling ex post conflict exist commonly in both species18, 69, 77, 78. In humans, a subset of neurons in the pre-SMA and the MCC differentiate between whether a correctly performed action was made during high or low conflict, whereas in macaques this signal arises after successfully cancelling a saccade or an arm movement69, 77, 78. In humans, ex ante and ex post conflicts are signalled by different neurons, and multivariate population firing rate patterns do not generalize across these two periods, suggesting that they represent different types of conflict signals18. Although it may seem puzzling why conflict would be signalled ex post, this signal plays a critical role in the model we develop later herein.
Last, we consider how the findings discussed differ from those derived from fMRI and/or scalp EEG. Neither spectral analysis nor fMRI blood oxygenation level-dependent (BOLD)-based analysis has been able to identify that ex ante and ex post conflict signals are distinct because both involve temporal smoothing. Therefore, studies on the relationship between BOLD-based conflict signals and subsequent behavioural adjustments may need to be revised to clarify whether the effect is due to ex ante or ex post conflict signals. Similarly, the insight that errors and ex ante conflict are represented by distinct groups of neurons is uniquely provided by single-neuron studies12.
Anatomical differences across species
The foregoing comparison between humans and macaques indicates that the error-monitoring system is evolutionarily conserved across primates. At the same time, there are notable differences in the mechanisms underlying such monitoring between humans and macaques. Investigation of these differences can reveal new insights into the evolution of cognitive functions.
The human MFC and the macaque MFC exhibit many similar features ( Fig. 1). The agranular areas seem largely homologous in organization and function but differ in their location on the cortical surface82, 83. In both humans and macaques, the SMA is located immediately rostral to the primary motor cortex, and the pre-SMA is located rostral to the SMA ( Fig. 1). In both species, the pre-SMA is found on the medial surface. However, whereas much of the SMA and all of the SEF are located on the dorsal convexity in macaques84, in humans, they are located on the medial wall, with the SEF centred at the paracentral sulcus85.
The folding pattern of sulci in the human MFC is considerably more variable than it is in the macaque MFC. A notable species difference is the PCS86. The PCS is unique to humans and other apes52, 87 but is not present in macaques82. As noted earlier herein, although all humans have a cingulate sulcus, ~70% of humans also have a PCS (most commonly in the left hemisphere but not the right hemisphere)52, 53. The PCS is also more prominent in males51. To our knowledge, no invasive studies have specifically compared the neural activity within versus outside the paracingulate gyrus during performance-monitoring tasks. The PCS is important for understanding performance monitoring for the following reasons. First, its presence dictates where specific neural signals are located86, thereby influencing what the ERN measures ( Fig. 1b). Second, the presence and morphology of the PCS are correlated with interindividual differences in metacognitive abilities such as reality monitoring88, 89 or the ability to perform tasks with response conflict90. Third, properties of the paracingulate gyrus differ in individuals with psychiatric disorders that impair performance monitoring, in particular schizophrenia91 and obsessive–compulsive disorder92.
A final challenge in translating findings between macaques and humans involves the intrinsic composition of the cortical tissue. For example, humans, but not macaques, have large spindle neurons in layer 5 of the cingulate cortex93. However, what difference this makes functionally and for the properties of the local field potential remains unknown. Furthermore, in both species, relatively little is known about how the functional organization of the cingulate sulcus contributes to performance monitoring40. In macaques, the MCC comprises several separate anatomically distinct areas, extending ventrally from the dorsal and ventral banks of the anterior cingulate sulcus, along the medial wall, to the corpus collosum94. Anatomical descriptions of the MCC in macaques note that the cytoarchitecture of the dorsal and ventral banks of the cingulate sulcus differ. Whereas the ventral bank of the MCC is identified as area 24, and as homologous to the MCC in humans95, the dorsal bank is identified as an extension of area F6 caudally or area 9 rostrally ( Fig. 1). Therefore, some researchers argue that the dorsal bank should not be considered part of the cingulate cortex proper95, but others disagree96, 97. In humans, recordings from the MCC are typically pooled across both the dorsal bank and the ventral bank, with no attempts at analysing the two separately. In macaques, recordings have been done mainly in the dorsal bank20. Hence, uncertainty persists about whether the dorsal and ventral banks of the MCC contribute differentially to performance monitoring, and where the boundary is between the dorsal MCC and area F6.
Tasks for performance monitoring
Relating findings across species and neural recording modalities entails understanding the cognitive constructs and demands of different tasks. Establishing equivalences across species, tasks and methods remains a considerable challenge. The validity of such comparisons depends greatly on the details of the tasks used and the instructions given. Guided by models of cognitive control and the Research Domain Criteria framework7, we consider a set of cognitive constructs that jointly allow an animal to monitor its own behaviour. Different tasks engage these constructs to different degrees ( Table 2). Although performance monitoring itself is only one of the eight constructs considered, the other seven constructs are all essential components needed for performance monitoring to be possible. The constructs we consider are as follows: goal maintenance, representing and implementing the instructed goal in a form of working memory98; response inhibition, inhibiting or delaying a motor response; stimulus selection, selecting one of several possible stimuli while ignoring others, which often requires feature or spatial attention; response selection, selecting one of several possible motor responses, which can entail response conflict; performance monitoring, sensitivity to consequences after an action and difficulty during a decision; timekeeping, estimating when an event will occur or reproducing a time interval; stimulus–response mapping, rule specifying which response should be produced in response to which stimulus, which can be more compatible and automatic or more incompatible and challenging; and post-error adjustments, change of performance, typically by delaying responses, to increase accuracy99.
Table 2 ∣.
Cognitive constructs tested with performance-monitoring tasks in humans and macaques
| Cognitive construct | Performance-monitoring task |
|---|---|
| Stop-signal taska | Change-signal task |
| :— | :— |
| Goal maintenanceb | 2 |
| Response inhibitionb | 2 |
| Stimulus selectionb, spatial and feature-based attention | 0 |
| Response selectionb, e | 0 |
| Performance monitoringb | 2 |
| Timekeeping | 1 |
| Stimulus–response incompatibilityf | 1 |
| Post-error adjustmentsg | 2 |
Rating scale: 0, not required to perform the task; 1, required but task not designed to test the cognitive construct indicated; 2, required because task designed to test the cognitive construct indicated. MSIT, multisource interference task.
a
An alternative and equivalent name for the stop-signal task is the ‘countermanding task’.
b
Construct that is part of the Research Domain Criteria framework of the US National Institute of Mental Health.
c
Relates to spatial attention.
d
Relates to feature-based attention.
e
Applies only to tasks in which multiple different actions are possible and also referred to as ‘response conflict’.
f
In some tasks, whether there is a stimulus–response conflict depends on the instructions (for example, word reading versus reporting ink colour in the Stroop task).
g
Restricted to the next trial (not within-trial correction).
Unlike experiments in humans, in which participants can verbalize instructions and choices, experiments with macaques must rely on sensory–motor paradigms in which performance is shaped through operant conditioning. In these tasks, participants express choices through an overt action, typically an eye, forelimb or digit movement. Thus, the use of common sensory–motor tasks can bridge the empirical gap between species.
The stop-signal (and change-signal) task examines response inhibition100 ( Fig. 4a, b and Supplementary Fig. 1a, b). After participants adopt a posture of readiness (for example, fixating), a stimulus is presented that requires an immediate response (‘go’ response; for example, a gaze shift). In some proportion of trials, while the prepotent response is being prepared, a second stimulus (the stop signal) instructs inhibition of the response, which initiates the inhibitory ‘stop’ processes. In the change-signal version, the second stimulus specifies an alternative response. The delay before the stop signal or change signal is presented after the first stimulus (‘stop-signal delay’ or ‘change-signal delay’) is adjusted trial-by-trial by a staircase procedure to allow successful responses on a subset of trials. Performance in the stop-signal task is modelled as the outcome of a race between go and stop processes101. Since the go and stop processes lead to incompatible outcomes, the activation of both of these processes results in response conflict102. Within this framework, the probability of making an error can be inferred from the stop-signal delay, which serves as a proxy for response conflict for successfully cancelling a trial26, 69, 78. A core feature of the stop-signal (change-signal) task is that, on every trial, there is uncertainty about whether the stop (change) instruction will happen103. Because the stop-signal (change-signal) delay is chosen randomly from a certain distribution, participants can develop predictions about the duration of this delay78. Hence, another key aspect of these tasks is timekeeping78, 104. Another timekeeping demand involves maintaining the posture of inhibition (continued fixation of the central stimulus or holding digit or forelimb posture) for a sufficient interval, in order for performance to be qualified as ‘correct’. Thus, two kinds of error are possible. The most common type of error is the failure of inhibition through production of the prepotent response despite the stop (change) signal. This type of error, especially in the case of long stop-signal delays, can result from the fact that there is not enough time for the stopping process to finish, even if it is promptly initiated. In addition, participants can determine that a ‘go’ response is ‘correct’ only by the absence of the stop signal. The less common type of error is the failure to maintain the posture of inhibition (or execution of the changed response). One key advantage of the stop-signal tasks is that the causes of errors are relatively homogeneous, as mentioned earlier, and are well described by mathematical models105. Error rates are well controlled (~50% of stop-signal trials) by adjusting the stop-signal delay using adaptive staircase procedures. A key signature of cognitive control is post-error slowing. In the stop-signal task, post-error slowing is prominent and involves delaying the initiation of a go response after a non-cancelled trial106.
Fig. 4 ∣. Tasks for studying performance monitoring in macaques and humans.
a–f, Summary of tasks that have commonly been used to study performance monitoring; except those requiring reading ( c,e), the tasks are suitable for both macaques and humans. For each task, the sequence of screens shown to the participant (left side) and the timing of critical events during a trial (right side) are shown. a,b, Stop-signal task, which requires participants to stop a movement when the stop signal is shown (red). c–f, Screens for the four human tasks: Stroop task, flanker task, multisource interference task (MSIT) and Simon task, in which participants are prompted to make a response by button press indicating the word colour, unique number, central target orientation or identity of the target, respectively. g, Timing applicable to all tasks shown in parts c–f. See Supplementary Fig. 1 for an illustration of the other three commonly used tasks: change-signal task, go–no-go task and antisaccade task. Table 2 shows the cognitive constructs engaged by these tasks.
Go-no-go and anti-saccade tasks are also used to study response inhibition107 ( Supplementary Fig. 1c- f). Unlike the stop-signal (change-signal) task, however, the cue that determines the type of trial and stimulus–response mapping rule is usually presented before the cue instructing response initiation. Therefore, when the imperative stimulus is presented, there is no uncertainty about the expected response. In the go–no-go task, go trials involve a simple response (gaze shift or button press) usually with direct spatial mapping of the stimulus and the response. On no-go trials, participants must maintain the original posture. In the anti-saccade task, the pro-saccade trials involve a simple orienting response with direct spatial mapping to a visual target. The anti-saccade trials involve inhibition of the reflexive pro-saccade and production of a saccade in the opposite direction. Performance in the anti-saccade task differs according to whether the pro-saccade and anti-saccade trials are intermixed or blocked, with intermixed trials resulting in a higher error rate and longer response time108. Errors made in either the go–no-go task or the anti-saccade task are due to a failure in incorporating the stimulus–response mapping rule and/or a failure in response inhibition107. Of note, participants can make an error by failing to maintain the effector position for a predetermined duration before the trial is considered successful in no-go trials in the go–no-go task.
The Simon task, the Stroop task, the flanker task and the multisource interference task are used to examine response selection, response inhibition, stimulus–response mapping and performance monitoring in humans ( Fig. 4c- g). In these tasks, a single visual stimulus encodes multiple feature dimensions (spatial location, colour and/or word meaning), each of which can prompt a different response. Participants are instructed to respond as quickly as possible to one of the feature dimensions while ignoring the others. This is difficult when the feature dimension to be ignored is mapped to a prepotent, even habitual response. The source of the prepotent response is spatial (Simon task)109, reading (Stroop task)110, irrelevant visual distractors that attract attention (flanker task)111 or mixtures thereof (multisource interference task)112. Trials on which the distractor and target dimensions point to the same response (congruent) engender a minimal level of conflict. A mismatch between the two dimensions (incongruent) engenders response conflict between the target and distractor action representations. Selecting the response associated with the relevant dimension requires inhibiting the prepotent response75, 113, which leads to correct responses with longer response times. Errors in these tasks arise endogenously: participants always have all the information needed to determine the correct response. For the same reasons, errors can be avoided by trading speed for accuracy. Given this property, error rates in these tasks are more difficult to control and generally lower. All four tasks result in post-error slowing.
Errors in the Stroop task and the multisource interference task are due to interference between high-level cognitive processes (related to language; for example, reading a word or number) only available in humans. Nevertheless, Stroop-like effects can be produced in macaques using either extensive training on a particular stimulus–response mapping or through numerosity-based competition114- 116. The extensive training required to produce these effects in macaques highlights one of the key strengths of Stroop-like tasks in humans; that is, they produce interference and errors immediately, showing that the underlying mechanisms do not require training.
Proposed conceptual model
We adopt the framework of forward–inverse modelling to conceptualize the processes underlying performance monitoring and cognitive control117, 118 ( Fig. 5) and to explain the comp
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