Many of the choices that we make every day are based on noisy information, generated in environments that are complex and subject to change. In the face of such sources of uncertainty, it can be of vital importance to evaluate the accuracy of these choices. Estimating confidence in our decisions and detecting mistakes even without external feedback allows us to identify when future behavior should be altered; for example, by taking a more cautious approach to future decisions when we think we have just made an error.

How the brain achieves this feat of performance monitoring without feedback has been a fruitful topic of research in recent decades, and related physiological signals have been identified across several measurement modalities. The first of these came in the form of deflections in the human event-related potential (ERP) that occur following responses on various kinds of choice tasks and were found to be sensitive to whether the executed choice was correct or erroneous (Falkenstein et al., 1991; Falkenstein et al., 2000; Gehring et al., 1993). It was later observed that these signals are sensitive not only to error commission, but also error detection (or ‘error awareness’; Dhar et al., 2011; Endrass et al., 2007; Nieuwenhuis et al., 2001; O'Connell et al., 2007; Steinhauser and Yeung, 2010; Wessel et al., 2011) – that is, whether a committed error was subsequently recognised as such by the participant – and inferred (Scheffers and Coles, 2000; Shalgi and Deouell, 2012) or self-reported (Boldt and Yeung, 2015) decision confidence. Human neuroimaging studies using similar experimental paradigms have identified brain areas where blood-oxygen-level-dependent (BOLD) activity exhibits similar sensitivities (Carter et al., 1998; Fleming et al., 2018; Harsay et al., 2018; Hester et al., 2005; Klein et al., 2007). In animal models, post-decisional but pre-feedback firing rates of single neurons in regions of frontal cortex have been shown to encode the preceding decision (Tsujimoto et al., 2009), its accuracy (Stuphorn et al., 2000), and the tendency to opt-out following initial decisions (Middlebrooks and Sommer, 2012) or wait for a delayed reward (Kepecs et al., 2008; both proxies for decision confidence).

The rich collection of findings summarized above suggests that the brain is well-endowed with signals that can be exploited for performance monitoring when feedback from the environment is not available. However, much of this work has been primarily descriptive, highlighting relationships of the neural signals in question to conditions (e.g. correct vs. error), quantities (e.g. graded confidence judgements) and consequences (e.g. next-trial behavioral adjustment) relevant to monitoring. While some authors have begun to interpret these phenomena and antecedents in mechanistic and sometimes also computational terms (e.g. Fleming and Daw, 2016; Kepecs et al., 2008; Yeung and Summerfield, 2012), our aim is to construe an overarching, mechanistically explicit framework that allows us to capture and predict such processes in a more systematic and unifying fashion.

In the current work, we seek to provide such a framework. We pay particular attention to recent findings pertaining to a slow positive wave ERP with a centro-parietal scalp distribution and a peak typically between 200 and 600 ms after choices. Since a cardinal characteristic of this signal is that its amplitude is larger after errors than correct choices (Falkenstein et al., 1991), it is usually referred to as the error positivity, or Pe. We argue that the Pe reflects the computational process of post-decisional evidence accumulation: a proposal that accounts not only for the classical association of this signal with error detection (Dhar et al., 2011; Endrass et al., 2007; Nieuwenhuis et al., 2001; O'Connell et al., 2007; Steinhauser and Yeung, 2010; Wessel et al., 2011), but also specific aspects of its morphology (Murphy et al., 2015) and more recent links with graded confidence judgments (Boldt and Yeung, 2015) and future behavioral adjustments (e.g. Desender et al., 2019a). In turn, we suggest that the Pe – a signal with thus far unique properties in cognitive neuroscience in that it appears to track the dynamic trajectory of a selective ‘metacognitive’ decision variable – provides the critical link to a computational framework for performance monitoring within which the functional significance of related brain signals can be readily understood.

In order for the reader to fully appreciate the details of our proposal, we will start with a brief overview of evidence accumulation models and associated neural signatures. Then, we will explain how post-decisional evidence accumulation provides a mechanistic framework for understanding the Pe. We will end by outlining novel predictions and future research directions arising from our proposed framework.

Convergent lines of theoretical and empirical work, across psychology and neuroscience, suggest that many kinds of decision can be well explained in terms of the accumulation of evidence for different alternatives over time (Gold and Shadlen, 2007; Ratcliff and McKoon, 2008). According to this framework, in the simplest case of a task with two discrete choice alternatives, an agent is assumed to set two decision boundaries, corresponding to each choice option (see Figure 1A). When presented with noisy evidence (e.g. samples of sensory information in the case of a perceptual choice), the agent then accumulates the evidence for each option, and the option for which the accumulated evidence first transgresses the associated decision boundary is selected, indicating choice commitment. Algorithmic models incorporating these features have been highly successful in fitting choice behavior across many domains (Forstmann et al., 2016; Ratcliff et al., 2016; Shadlen and Kiani, 2013).

Figure 1

Download asset Open asset

A key prediction of models of decision-making through evidence accumulation is that the momentary accumulated evidence – also known as the decision variable (DV) – is represented in the brain. Indeed, signals that appear to track the evolution of the DV have been observed in rodents (Hanks et al., 2014), monkeys (Gold and Shadlen, 2007), and humans (Donner et al., 2009; Heekeren et al., 2004; O'Connell et al., 2012). Of particular relevance here, using scalp electroencephalographic (EEG) recordings, it has been shown that a centro-parietal positivity (CPP) that emerges during choice formation closely resembles a form of the DV (Kelly and O'Connell, 2013; O'Connell et al., 2012; O'Connell et al., 2018). Specifically, in two-alternative decision-making tasks, the CPP bears hallmarks of an unsigned (i.e. not choice-selective) DV that can be read out from evidence accumulation models by taking the absolute of the accumulated evidence (Kelly and O'Connell, 2013; Twomey et al., 2016). Three key dynamical properties of this measure are also seen in the CPP on such tasks: (i) a gradual ramping over time, the slope of which depends on absolute evidence strength and correlates with response time (RT); (ii) a stereotyped peak amplitude that is independent of the evidence strength (putatively reflecting a termination of the accumulation process upon boundary crossing); and (iii) a close correspondence between peak latency and RT (putatively reflecting the timing of the boundary crossing). The CPP on these tasks is observed irrespective of the modality of the evidence (e.g. visual vs. auditory) and the method of commitment (e.g. overtly, with a motor response, or covertly without a measurable motor response), suggesting it is a supramodal, effector-independent decision signal.

Another salient characteristic of the CPP manifests in paradigms requiring detection of faint sensory stimuli (as opposed to discrimination of features of high-intensity stimuli). Here, the CPP exhibits the properties described above only on trials when the stimulus is judged to be present (‘hits’ and, to a less clear extent, ‘false alarms’; O'Connell et al., 2012; Twomey et al., 2015). When the stimulus is presented but not detected (‘misses’), however, the signal is diminished in amplitude or even absent entirely. In these respects, as well as in topographic distribution, there are clear similarities between the CPP and the classic P300 component (Hillyard et al., 1971; Squires et al., 1973; Twomey et al., 2015). The diminution of these signals on miss trials can be easily explained in the context of evidence accumulation models as a case in which a ‘stimulus present’ decision boundary remains unmet due to insufficient evidence. By contrast, this family of signals has not been found to ramp up as clearly when participants are asked to actively confirm the absence of the target stimulus (‘correct rejections’; Hillyard et al., 1971; Squires et al., 1973), suggesting the CPP may not reflect accumulation of internally generated ‘evidence for absence’ (Deco et al., 2007) in detection tasks.

In sum, decision-making has been successfully modelled as the accumulation of noisy evidence over time, and a centro-parietal positivity in the EEG has been proposed to reflect key aspects of this process.

Having described how the process of evidence accumulation provides a plausible account of choice formation in brain and behavior, we now turn to the question of monitoring of choice accuracy in the absence of external feedback. How are decision-makers able to detect their own mistakes without explicit feedback from the environment, and provide fine-grained estimates of confidence in the adequacy of the preceding choice? In order to account for performance monitoring of choices within the same evidence accumulation framework described above, Pleskac and Busemeyer, 2010 proposed that an evidence accumulation process can take place in the post-decisional period and be used to inform estimates of choice confidence (see Figure 1A). To intuit how such a model explains monitoring of choice accuracy, consider a series of choices made with moderately high evidence strength. For the majority of trials, the DV will reach the boundary corresponding to the correct choice, and post-decisional accumulation of samples from the same evidence source will, on average, furnish accumulated evidence that corroborates the selected choice. This corroborating evidence in turn produces a sense of high confidence. For the few incorrect choices, by contrast, post-decisional accumulation will, on average, furnish accumulated evidence that conflicts with the selected choice. This conflicting evidence in turn results in reduced confidence, doubt, or even a change of mind (van den Berg et al., 2016; Resulaj et al., 2009). Thus, post-decisional evidence accumulation is a candidate mechanism through which performance monitoring can occur. At this point, it is important to clarify that post-decisional processing is not necessarily restricted to the period following an overt behavioral response, but can in principle also take place before any response is executed. This is because there is usually a small delay between a boundary crossing corresponding to initial decision commitment, and subsequent motor execution – a delay that constitutes one component of the so-called non-decision time parameter T_er in evidence accumulation models (visualized by the gray box in Figure 1A; the other component being stimulus encoding time). It has been shown that if the delay between initial boundary crossing and subsequent response completion is sufficiently long (for example, on tasks requiring response execution via protracted limb movement), then post-decisional accumulation during this delay can produce changes of mind before the initially chosen response is completed (Resulaj et al., 2009).

Because ‘performance monitoring’ is a rather broad concept, let us explore further how the simple mechanism of post-decisional accumulation might account for different expressions of performance monitoring (Yeung and Summerfield, 2012). First, post-decisional accumulation can explain categorical expressions of error detection. For example, in many studies of performance monitoring participants are required to signal (e.g. via button-press) when they think that they made an error with a preceding choice (for detailed discussion, see Wessel, 2012). Such error signalling responses can readily be modelled in the context of post-decisional accumulation by imposing a boundary on the post-decisional process (which is distinct from the boundary corresponding to the initial choice; Figure 1B): Only when the accumulated post-decisional evidence crosses that boundary is an error-signalling response given (Pleskac and Busemeyer, 2010). As alluded to above, the same principle can also be applied to generate overt changes of mind, when an initial choice (corresponding to the initial boundary crossing) is subsequently overturned in favour of a different alternative (corresponding to a secondary boundary crossing; Resulaj et al., 2009). Second, post-decisional accumulation also naturally explains graded levels of confidence in the accuracy of a choice (Figure 1C). Human participants can typically provide well-calibrated estimates of their own performance (Aitchison et al., 2015; Fleming et al., 2018; Sanders et al., 2016). To explain such behavior, it suffices to assume that confidence directly reflects (a transformation of) the accumulated post-decisional evidence. Such an account can explain various patterns that are apparent in empirically observed human confidence judgments (Moran, 2015; Pleskac and Busemeyer, 2010; Yu et al., 2015). For example, it explains why confidence decreases monotonically as difficulty increases (i.e. because post-decisional evidence will, on average, strongly favour the initial, usually correct choice when evidence strength is high), why the accuracy of confidence judgments is better under speed pressure (i.e., because speed pressure increases the probability of ‘fast errors’ even for high evidence strength, which are in turn more likely to generate conflicting evidence in the post-decisional period and be correctly detected; Desender et al., 2021; Desender et al., 2020), and why the accuracy of confidence judgments increases when more time elapses between the first decision and the confidence judgment (Yu et al., 2015). Thus, post-decisional accumulation explains key expressions of performance monitoring (error detection and confidence) and accounts for a range of related findings.

The preceding summary of post-decisional evidence accumulation and its explanatory power vis-à-vis empirically-observed performance monitoring behavior is predicated on the assumption that the post-decisional process operates largely on the same evidence source as the initial choice (Calder-Travis et al., 2020; Pleskac and Busemeyer, 2010; Resulaj et al., 2009; van den Berg et al., 2016). This effective continuation of the initial accumulation is possible in perceptual decision tasks, for example, if the stimulus display remains accessible in the post-decisional period (Fleming et al., 2018), or through accumulation of sensory information that is still in the processing pipeline (e.g. in a sensory buffer) if the stimulus is removed upon choice (Resulaj et al., 2009). Under such a view, the quality of the evidence (or ‘drift rate’) informing the initial and post-decisional accumulation processes will be approximately the same (excluding possible choice-induced biases in evidence weighting; e.g. Rollwage et al., 2020; Talluri et al., 2018; Yu et al., 2015). However, post-decisional accumulation may not be restricted only to the same evidence source that informed the initial choice. Several scenarios are possible where the initial evidence source is no longer available, and other sources of information can be consulted to inform the post-decisional accumulation process. One likely candidate is a short-term memory trace of the stimulus (Smith and Ratcliff, 2009; Vlassova and Pearson, 2013). These traces are known to persist for several seconds after stimulus offset (Wimmer et al., 2015), making them well positioned to furnish input to a deliberative post-decisional accumulation process. Because memory is subject to decay over time, this could produce a case where drift rates can differ between the initial and post-decisional accumulation processes. Post-decisional accumulation could also incorporate new, qualitatively different sources of information that might carry evidence about whether an error was just made. These sources could include an internally generated conflict signal (Yeung et al., 2004), interoceptive signals (Ullsperger et al., 2010), an estimate of elapsed decision time (Desender et al., 2017; Kiani et al., 2014), new information that comes to mind from memory (Nelson and Dunlosky, 1991), or irrelevant signals such as stimulus familiarity (Glenberg et al., 1982) and previous exposure to the same stimulus (Metcalfe and Finn, 2008).

Another important dimension of any post-decisional accumulation process is what we refer to as its reference frame: effectively, what propositions a DV furnished by the accumulation process will serve to arbitrate between. Generally speaking, pre-decisional evidence accumulation will tend to be concerned with mapping accumulated (sensory) evidence to the available choice alternatives (a ‘stimulus/response reference frame’), and it is possible that post-decisional accumulation is simply a continuation of this process. This scenario, which we illustrate schematically in Figure 1, entails that the mapping of the post-decisional DV onto an internal estimate of confidence will depend on the sign of the initial choice, and so would require an additional transformation before the DV can be used to inform explicit confidence or error detection reports. Alternatively, post-decisional accumulation could take the form of an entirely distinct accumulation process, with a different reference frame that specifically pertains to the accuracy of the preceding choice (Pouget et al., 2016). In this scenario, evidence samples consistent with the initial choice will always drive the post-decisional DV in one direction, while those inconsistent with the initial choice will drive it in the opposite direction. Such a change in reference frames for the post-decisional process could confer certain advantages. It would ensure that the resulting DV can be directly used to inform confidence and error detection, without requiring a transformation. It would also mean that the post-decisional process could in principle exploit additional evidence sources, such as those that we outline above, that are specifically informative about the accuracy of the preceding choice. This scope for multiplexing of multiple sources of ‘error evidence’ could serve to increase the accuracy of confidence and error detection judgements. Importantly, we show through simulations (Appendix 1 - Appendix 1—figure 1 and 2) that a distinct post-decisional accumulation process with a ‘choice accuracy reference frame’ generates confidence and error detection behavior with equivalent sensitivity, and is subject to the same effects of experimental factors, as a continuation of the initial accumulation process in a stimulus/response reference frame – even when the former is selectively sensitive to evidence samples that conflict with the initial decision (a possible feature of post-decisional accumulation; Yu et al., 2015). Thus, a switch in reference frames, even coupled with a more selective accumulation of the post-decisional evidence, incurs no associated cost in the accuracy of performance monitoring. This question of the reference frame of post-decisional processing is the subject of ongoing interest (Fleming, 2016; Fleming et al., 2018) and one that we will return to later in this review.

In light of the above insight that post-decisional accumulation accounts for a wide variety of findings related to performance monitoring, an emerging question is whether a neural signature of this process can be identified. As outlined earlier, the CPP has been proposed to reflect the evolution of the DV (i.e. accumulated evidence) during the process of choice formation (O'Connell et al., 2018). Here, we argue that another ramping centro-parietal ERP, the Pe, reflects a functionally equivalent evidence accumulation process that takes place after the choice is made, thus providing a neural readout of post-decisional accumulation. In the following, we discuss the evidence in favour of this proposal with a focus on how the morphology of the Pe is consistent with that of a post-decisional DV, and how this neural signal relates to error detection and decision confidence.

Morphology and functional characteristics of the Pe

In the previous section, we described how the CPP exhibits three key dynamical properties of a DV in bounded evidence accumulation models: gradual ramping with a rate proportional to RT (and absolute evidence strength); a stereotyped pre-response amplitude; and a close correspondence between peak latency and RT. Importantly, models that include post-decisional evidence accumulation predict the same three signatures for the post-decisional DV if it too is subject to a decision boundary – as is expected to be the case for tasks requiring self-initiated error signalling (see also Moran et al., 2015).

Although the apparent similarity between pre-decisional centro-parietal positivities (the P300 and, by extension, the CPP; Twomey et al., 2015) and the Pe has been appreciated before (Ridderinkhof et al., 2009), it was only recently that the same three morphological signatures described above for the CPP were also observed for the Pe (see Figure 2). In a difficult Go/No-Go task, Murphy et al., 2015 asked participants to signal commission errors by pressing a designated ‘error detection’ button as fast as possible after realizing they had made an error. The three key signatures predicted for a post-decisional evidence accumulation signal were indeed found in the Pe component that was observed between the initial erroneous response and the subsequent error signalling response. First, the build-up rate of the Pe was proportional to the speed of error detection, such that errors that were detected faster were associated with steeper ramping of the Pe (note that here an objective measure of evidence strength was not available, so directly testing an association with evidence strength was not possible). Second, the Pe reached a stereotyped pre-error-response amplitude on detected error trials that was independent of the detection RT. Third, there was a close correspondence between peak Pe latency and the detection RT. Thus, the Pe exhibited all the properties expected of a post-decisional DV in a speeded, categorical error detection scenario. Accordingly, the simulated DV from an algorithmic model of the error detection process driven by evidence accumulation was found to match the trajectory of the Pe remarkably well (Murphy et al., 2015). Aside from these striking morphological similarities, we note that the Pe also shares functional characteristics with the pre-decisional CPP. In the aforementioned study (Murphy et al., 2015), the authors conducted a second experiment where participants did not explicitly signal their errors. A reliable Pe was still observed, indicating that the Pe, like the CPP (O'Connell et al., 2012), is not tied to motor responding. Also like the CPP (O'Connell et al., 2012), the Pe is independent of the sensory modality in which stimuli are presented (Falkenstein et al., 2000).

Figure 2

Download asset Open asset

Importantly, although these three morphological signatures are a first step in relating the Pe to underlying computational principles, such qualitative assessment of these signatures provides a rather indirect approach to do so. A complementary approach to link both aspects would be through model-based EEG analysis, as was previously successfully employed for perceptual decisions (Berberyan et al., 2021; Philiastides and Sajda, 2006; van Vugt et al., 2012) and associated confidence judgments (Pereira et al., 2020). Potential approaches could be to include single-trial Pe amplitudes as regressors when estimating parameters of post-decisional evidence accumulation models (for a related approach, see: Desender et al., 2019b), or to jointly model behavioral data and single-trial Pe amplitudes (Bahg et al., 2020).

Together, these morphological and functional similarities suggest that the CPP and Pe may reflect the same functional process (cf. Ridderinkhof et al., 2009), and thus emphasize the credentials of the Pe as a candidate signature of post-decisional evidence accumulation.

As outlined at the outset of our review, the ultimate goal of performance monitoring is to facilitate adjustments to behavior that are adaptive (Botvinick et al., 2001; Fernandez-Duque et al., 2000; Ridderinkhof et al., 2004; Shimamura, 2008): If a monitoring system detects that several errors have been made in a row, this likely calls for a change in the decision policy or model of the world that produced those errors (Purcell and Kiani, 2016a; Sarafyazd and Jazayeri, 2019). Given the close theoretical link between monitoring and adaptation, we now turn our focus to the possible motivational and functional significance of the Pe and post-decisional accumulation process. Harsay et al., 2012 suggested that motivational salience is a useful concept to link performance monitoring and error detection with adaptive behavior. Notable parallels have been observed between both the neural processing and behavioral consequences of errors and other kinds of salient event (such as deviant or novel stimuli; e.g., Notebaert et al., 2009; Ullsperger et al., 2010; Wessel, 2018). Erroneous choices are motivationally significant events because of their usually infrequent occurrence, their usefulness as learning signals, and their utility in triggering behavioral adaptation.

We suggest that the motivational salience of a preceding choice can be read out from the DV of a post-decisional accumulation process that accumulates evidence that that choice was incorrect – that is, precisely the process we propose to be reflected in the Pe. In other words, the accumulation process captured by the Pe furnishes a scalar quantity that can be directly leveraged to determine the desired magnitude of adjustments to future behavior. One possible course of adaptive action is to employ a more cautious decision policy after an error, instantiated by raising the decision boundary on the following trial (Dutilh et al., 2012; Purcell and Kiani, 2016b). This boundary raising will lead to slower decisions and, critically, will also increase the probability of being correct. According to our proposal, the magnitude of such post-error slowing (Rabbitt, 1966) should be predicted by the magnitude of the post-decisional DV on the preceding trial (possibly mixed with residual ‘orienting’ effects to the preceding error; Notebaert et al., 2009), which can be read out via the amplitude of the Pe. Consistent with this notion, some early studies showed a relation between Pe amplitude and post-error slowing through between-subject correlations (Hajcak et al., 2003; Nieuwenhuis et al., 2001). However, according to our framework such adaptive slowing should also manifest at the trial-by-trial level. Moreover, it should not be restricted to the categorical contrast of errors vs corrects, but rather should depend on the amount of accumulated post-decisional error evidence (i.e. Pe amplitude) in a graded manner. Consistent with this, Desender et al., 2019a, recently showed that Pe amplitude strongly and linearly predicts the trade-off between speed and accuracy on the next trial (see Figure 1D). These findings are in line with our proposal that the Pe furnishes a scalar quantity that can be adaptively used to alter subsequent decision policy.

While post-error slowing is perhaps the most commonly investigated form of adaptive behavior, other forms can be observed as well and these too may be determined by the outcome of a post-decisional accumulation process. For instance, post-decisional evidence accumulation might also underlie the decision to transition from an exploitative to an explorative mode, or vice versa, in foraging tasks (e.g. Kolling et al., 2012). Although such tasks usually involve veridical feedback about the reward that was just gained, thereby obviating the need for internal computations of choice accuracy, there is some evidence that confidence about the upcoming choice being the most rewarding modulates the trade-off between exploitation and exploration (Boldt et al., 2019). As another example, Desender et al., 2019b showed that Pe amplitude after an initial choice could be used to predict whether or not participants chose to sample more information from a noisy sensory evidence source before confirming or changing that choice. Thus, presumably via simple comparison to a boundary, the post-decisional DV reflected in the Pe can be used to guide choices about whether or not to seek additional information.

We have argued that the Pe reflects post-decisional evidence accumulation. Our proposal not only reconciles a variety of previous research findings; it also opens up exciting avenues of future research. In the remainder of this review, we discuss how our proposal relates to hierarchical accounts of performance monitoring and ‘metacognition’, possible neural origins of the Pe, and the implications of our proposal for the onset of awareness.

1. 1. Aitchison L
   2. Bang D
   3. Bahrami B
   4. Latham PE

   (2015) Doubly bayesian analysis of confidence in perceptual Decision-Making

   PLOS Computational Biology 11:e1004519.

   https://doi.org/10.1371/journal.pcbi.1004519

   • PubMed
   • Google Scholar
2. 1. Bahg G
   2. Evans DG
   3. Galdo M
   4. Turner BM

   (2020) Gaussian process linking functions for mind, brain, and behavior

   PNAS 117:29398–29406.

   https://doi.org/10.1073/pnas.1912342117

   • PubMed
   • Google Scholar
3. 1. Bang JW
   2. Shekhar M
   3. Rahnev D

   (2019) Sensory noise increases metacognitive efficiency

   Journal of Experimental Psychology: General 148:437–452.

   https://doi.org/10.1037/xge0000511

   • Google Scholar
4. 1. Berberyan HS
   2. van Maanen L
   3. van Rijn H
   4. Borst J

   (2021) EEG-based identification of evidence accumulation stages in Decision-Making

   Journal of Cognitive Neuroscience 33:510–527.

   https://doi.org/10.1162/jocn_a_01663

   • PubMed
   • Google Scholar
5. 1. Bogacz R
   2. Brown E
   3. Moehlis J
   4. Holmes P
   5. Cohen JD

   (2006) The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks

   Psychological Review 113:700–765.

   https://doi.org/10.1037/0033-295X.113.4.700

   • PubMed
   • Google Scholar
6. 1. Bogacz R
   2. Wagenmakers EJ
   3. Forstmann BU
   4. Nieuwenhuis S

   (2010) The neural basis of the speed-accuracy tradeoff

   Trends in Neurosciences 33:10–16.

   https://doi.org/10.1016/j.tins.2009.09.002

   • PubMed
   • Google Scholar
7. 1. Boldt A
   2. de Gardelle V
   3. Yeung N

   (2017) The impact of evidence reliability on sensitivity and Bias in decision confidence

   Journal of Experimental Psychology: Human Perception and Performance 43:1520–1531.

   https://doi.org/10.1037/xhp0000404

   • Google Scholar
8. 1. Boldt A
   2. Blundell C
   3. De Martino B

   (2019) Confidence modulates exploration and exploitation in value-based learning

   Neuroscience of Consciousness 2019:niz004.

   https://doi.org/10.1093/nc/niz004

   • PubMed
   • Google Scholar
9. 1. Boldt A
   2. Yeung N

   (2015) Shared neural markers of decision confidence and error detection

   Journal of Neuroscience 35:3478–3484.

   https://doi.org/10.1523/JNEUROSCI.0797-14.2015

   • Google Scholar
10. 1. Botvinick MM
    2. Braver TS
    3. Barch DM
    4. Carter CS
    5. Cohen JD

    (2001) Conflict monitoring and cognitive control

    Psychological Review 108:624–652.

    https://doi.org/10.1037/0033-295X.108.3.624

    • PubMed
    • Google Scholar
11. Preprint

    1. Calder-Travis J
    2. Bogacz R
    3. Yeung N

    (2020) Bayesian confidence for drift diffusion observers in dynamic stimuli tasks

    bioRxiv.

    https://doi.org/10.1101/2020.02.25.965384

    • Google Scholar
12. 1. Carter CS
    2. Braver TS
    3. Barch DM
    4. Botvinick MM
    5. Noll D
    6. Cohen JD

    (1998) Anterior cingulate cortex, error detection, and the online monitoring of performance

    Science 280:747–749.

    https://doi.org/10.1126/science.280.5364.747

    • PubMed
    • Google Scholar
13. 1. Cavanagh JF
    2. Frank MJ

    (2014) Frontal theta as a mechanism for cognitive control

    Trends in Cognitive Sciences 18:414–421.

    https://doi.org/10.1016/j.tics.2014.04.012

    • PubMed
    • Google Scholar
14. 1. Cohen MX

    (2014) A neural microcircuit for cognitive conflict detection and signaling

    Trends in Neurosciences 37:480–490.

    https://doi.org/10.1016/j.tins.2014.06.004

    • PubMed
    • Google Scholar
15. 1. Debener S
    2. Ullsperger M
    3. Siegel M
    4. Fiehler K
    5. von Cramon DY
    6. Engel AK

    (2005) Trial-by-trial coupling of concurrent electroencephalogram and functional magnetic resonance imaging identifies the dynamics of performance monitoring

    Journal of Neuroscience 25:11730–11737.

    https://doi.org/10.1523/JNEUROSCI.3286-05.2005

    • PubMed
    • Google Scholar
16. 1. Deco G
    2. Pérez-Sanagustín M
    3. de Lafuente V
    4. Romo R

    (2007) Perceptual detection as a dynamical bistability phenomenon: a neurocomputational correlate of sensation

    PNAS 104:20073–20077.

    https://doi.org/10.1073/pnas.0709794104

    • PubMed
    • Google Scholar
17. 1. Desender K
    2. Van Opstal F
    3. Van den Bussche E

    (2017) Subjective experience of difficulty depends on multiple cues

    Scientific Reports 7:44222.

    https://doi.org/10.1038/srep44222

    • PubMed
    • Google Scholar
18. 1. Desender K
    2. Boldt A
    3. Verguts T
    4. Donner TH

    (2019a) Confidence predicts speed-accuracy tradeoff for subsequent decisions

    eLife 8:e43499.

    https://doi.org/10.7554/eLife.43499

    • PubMed
    • Google Scholar
19. 1. Desender K
    2. Murphy P
    3. Boldt A
    4. Verguts T
    5. Yeung N

    (2019b) A postdecisional neural marker of confidence predicts Information-Seeking in Decision-Making

    The Journal of Neuroscience 39:3309–3319.

    https://doi.org/10.1523/JNEUROSCI.2620-18.2019

    • PubMed
    • Google Scholar
20. Preprint

    1. Desender K
    2. Vermeylen L
    3. Verguts T

    (2020) Dynamic influences on static measures ofmetacognition

    bioRxiv.

    https://doi.org/10.1101/2020.10.29.360453

    • Google Scholar
21. 1. Desender K
    2. Donner TH
    3. Verguts T

    (2021) Dynamic expressions of confidence within an evidence accumulation framework

    Cognition 207:104522.

    https://doi.org/10.1016/j.cognition.2020.104522

    • PubMed
    • Google Scholar
22. Software

    1. Desender K

    (2021) NeuralSignalsOfPerformanceMonitoring, version swh:1:rev:892bf43d65e1adec7a5872f4cb911eb9d28d8633

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:8da3150be602261a3ba7c76e0a1a0c81ce2d46fd;origin=https://github.com/kdesende/NeuralSignalsOfPerformanceMonitoring;visit=swh:1:snp:a9bd03b9b4cb2684ac122f0c3578b9fc22789024;anchor=swh:1:rev:892bf43d65e1adec7a5872f4cb911eb9d28d8633
23. 1. Dhar M
    2. Wiersema JR
    3. Pourtois G

    (2011) Cascade of neural events leading from error commission to subsequent awareness revealed using EEG source imaging

    PLOS ONE 6:e19578.

    https://doi.org/10.1371/journal.pone.0019578

    • PubMed
    • Google Scholar
24. 1. Di Gregorio F
    2. Maier ME
    3. Steinhauser M

    (2018) Errors can elicit an error positivity in the absence of an error negativity: evidence for independent systems of human error monitoring

    NeuroImage 172:427–436.

    https://doi.org/10.1016/j.neuroimage.2018.01.081

    • PubMed
    • Google Scholar
25. 1. Di Gregorio F
    2. Maier ME
    3. Steinhauser M

    (2020) Are errors detected before they occur? early error sensations revealed by metacognitive judgments on the timing of error awareness

    Consciousness and Cognition 77:102857.

    https://doi.org/10.1016/j.concog.2019.102857

    • PubMed
    • Google Scholar
26. 1. Donner TH
    2. Siegel M
    3. Fries P
    4. Engel AK

    (2009) Buildup of choice-predictive activity in human motor cortex during perceptual decision making

    Current Biology 19:1581–1585.

    https://doi.org/10.1016/j.cub.2009.07.066

    • PubMed
    • Google Scholar
27. 1. Dotan D
    2. Meyniel F
    3. Dehaene S

    (2018) On-line confidence monitoring during decision making

    Cognition 171:112–121.

    https://doi.org/10.1016/j.cognition.2017.11.001

    • PubMed
    • Google Scholar
28. 1. Dotan D
    2. Pinheiro-Chagas P
    3. Al Roumi F
    4. Dehaene S

    (2019) Track it to crack it: dissecting processing stages with finger tracking

    Trends in Cognitive Sciences 23:1058–1070.

    https://doi.org/10.1016/j.tics.2019.10.002

    • PubMed
    • Google Scholar
29. 1. Dutilh G
    2. Vandekerckhove J
    3. Forstmann BU
    4. Keuleers E
    5. Brysbaert M
    6. Wagenmakers E-J

    (2012) Testing theories of post-error slowing

    Attention, Perception, & Psychophysics 74:454–465.

    https://doi.org/10.3758/s13414-011-0243-2

    • Google Scholar
30. 1. Endrass T
    2. Reuter B
    3. Kathmann N

    (2007) ERP correlates of conscious error recognition: aware and unaware errors in an antisaccade task

    European Journal of Neuroscience 26:1714–1720.

    https://doi.org/10.1111/j.1460-9568.2007.05785.x

    • Google Scholar
31. 1. Falkenstein M
    2. Hohnsbein J
    3. Hoormann J
    4. Blanke L

    (1991) Effects of crossmodal divided attention on late ERP components. II. error processing in choice reaction tasks

    Electroencephalography and Clinical Neurophysiology 78:447–455.

    https://doi.org/10.1016/0013-4694(91)90062-9

    • PubMed
    • Google Scholar
32. 1. Falkenstein M
    2. Hoormann J
    3. Christ S
    4. Hohnsbein J

    (2000) ERP components on reaction errors and their functional significance: a tutorial

    Biological Psychology 51:87–107.

    https://doi.org/10.1016/S0301-0511(99)00031-9

    • PubMed
    • Google Scholar
33. 1. Fernandez-Duque D
    2. Baird JA
    3. Posner MI

    (2000) Executive attention and metacognitive regulation

    Consciousness and Cognition 9:288–307.

    https://doi.org/10.1006/ccog.2000.0447

    • PubMed
    • Google Scholar
34. 1. Fleming SM

    (2016) Changing our minds about changes of mind

    eLife 5:e14790.

    https://doi.org/10.7554/eLife.14790

    • PubMed
    • Google Scholar
35. 1. Fleming SM
    2. van der Putten EJ
    3. Daw ND

    (2018) Neural mediators of changes of mind about perceptual decisions

    Nature Neuroscience 21:617–624.

    https://doi.org/10.1038/s41593-018-0104-6

    • PubMed
    • Google Scholar
36. 1. Fleming SM
    2. Daw ND

    (2016) Self-evaluation of decision performance: a general bayesian framework for metacognitive computation

    Psychological Review 124:1–59.

    https://doi.org/10.1037/rev0000045

    • Google Scholar
37. 1. Forstmann BU
    2. Ratcliff R
    3. Wagenmakers EJ

    (2016) Sequential sampling models in cognitive neuroscience: advantages, applications, and extensions

    Annual Review of Psychology 67:641–666.

    https://doi.org/10.1146/annurev-psych-122414-033645

    • PubMed
    • Google Scholar
38. 1. Gehring WJ
    2. Goss B
    3. Coles MGH
    4. Meyer DE
    5. Donchin E

    (1993) A neural system for error detection and compensation

    Psychological Science 4:385–390.

    https://doi.org/10.1111/j.1467-9280.1993.tb00586.x

    • Google Scholar
39. 1. Gherman S
    2. Philiastides MG

    (2015) Neural representations of confidence emerge from the process of decision formation during perceptual choices

    NeuroImage 106:134–143.

    https://doi.org/10.1016/j.neuroimage.2014.11.036

    • PubMed
    • Google Scholar
40. 1. Gherman S
    2. Philiastides MG

    (2018) Human VMPFC encodes early signatures of confidence in perceptual decisions

    eLife 7:e38293.

    https://doi.org/10.7554/eLife.38293

    • PubMed
    • Google Scholar
41. 1. Glenberg AM
    2. Wilkinson AC
    3. Epstein W

    (1982) The illusion of knowing: failure in the self-assessment of comprehension

    Memory & Cognition 10:597–602.

    https://doi.org/10.3758/BF03202442

    • Google Scholar
42. 1. Gold JI
    2. Shadlen MN

    (2007) The neural basis of decision making

    Annual Review of Neuroscience 30:535–574.

    https://doi.org/10.1146/annurev.neuro.29.051605.113038

    • PubMed
    • Google Scholar
43. 1. Hajcak G
    2. McDonald N
    3. Simons RF

    (2003) To err is autonomic: error-related brain potentials, ANS activity, and post-error compensatory behavior

    Psychophysiology 40:895–903.

    https://doi.org/10.1111/1469-8986.00107

    • PubMed
    • Google Scholar
44. 1. Hanks T
    2. Kiani R
    3. Shadlen MN

    (2014) A neural mechanism of speed-accuracy tradeoff in macaque area LIP

    eLife 3:e02260.

    https://doi.org/10.7554/eLife.02260

    • Google Scholar
45. 1. Harsay HA
    2. Spaan M
    3. Wijnen JG
    4. Ridderinkhof KR

    (2012) Error awareness and salience processing in the oddball task: shared neural mechanisms

    Frontiers in Human Neuroscience 6:6.

    https://doi.org/10.3389/fnhum.2012.00246

    • Google Scholar
46. 1. Harsay HA
    2. Cohen MX
    3. Spaan M
    4. Weeda WD
    5. Nieuwenhuis S
    6. Ridderinkhof KR

    (2018) Error blindness and motivational significance: shifts in networks centering on anterior insula co-vary with error awareness and pupil dilation

    Behavioural Brain Research 355:24–35.

    https://doi.org/10.1016/j.bbr.2017.10.030

    • PubMed
    • Google Scholar
47. 1. Heekeren HR
    2. Marrett S
    3. Bandettini PA
    4. Ungerleider LG

    (2004) A general mechanism for perceptual decision-making in the human brain

    Nature 431:859–862.

    https://doi.org/10.1038/nature02966

    • PubMed
    • Google Scholar
48. 1. Herrmann MJ
    2. Römmler J
    3. Ehlis AC
    4. Heidrich A
    5. Fallgatter AJ

    (2004) Source localization (LORETA) of the error-related-negativity (ERN/Ne) and positivity (Pe)

    Cognitive Brain Research 20:294–299.

    https://doi.org/10.1016/j.cogbrainres.2004.02.013

    • PubMed
    • Google Scholar
49. 1. Hester R
    2. Foxe JJ
    3. Molholm S
    4. Shpaner M
    5. Garavan H

    (2005) Neural mechanisms involved in error processing: a comparison of errors made with and without awareness

    NeuroImage 27:602–608.

    https://doi.org/10.1016/j.neuroimage.2005.04.035

    • PubMed
    • Google Scholar
50. 1. Hillyard SA
    2. Squires KC
    3. Bauer JW
    4. Lindsay PH

    (1971) Evoked potential correlates of auditory signal detection

    Science 172:1357–1360.

    https://doi.org/10.1126/science.172.3990.1357

    • PubMed
    • Google Scholar
51. 1. Hughes G
    2. Yeung N

    (2011) Dissociable correlates of response conflict and error awareness in error-related brain activity

    Neuropsychologia 49:405–415.

    https://doi.org/10.1016/j.neuropsychologia.2010.11.036

    • PubMed
    • Google Scholar
52. 1. Iannaccone R
    2. Hauser TU
    3. Staempfli P
    4. Walitza S
    5. Brandeis D
    6. Brem S

    (2015) Conflict monitoring and error processing: new insights from simultaneous EEG-fMRI

    NeuroImage 105:395–407.

    https://doi.org/10.1016/j.neuroimage.2014.10.028

    • PubMed
    • Google Scholar
53. 1. Kelly SP
    2. O'Connell RG

    (2013) Internal and external influences on the rate of sensory evidence accumulation in the human brain

    Journal of Neuroscience 33:19434–19441.

    https://doi.org/10.1523/JNEUROSCI.3355-13.2013

    • PubMed
    • Google Scholar
54. 1. Kepecs A
    2. Uchida N
    3. Zariwala HA
    4. Mainen ZF

    (2008) Neural correlates, computation and behavioural impact of decision confidence

    Nature 455:227–231.

    https://doi.org/10.1038/nature07200

    • PubMed
    • Google Scholar
55. 1. Kiani R
    2. Corthell L
    3. Shadlen MN

    (2014) Choice certainty is informed by both evidence and decision time

    Neuron 84:1329–1342.

    https://doi.org/10.1016/j.neuron.2014.12.015

    • PubMed
    • Google Scholar
56. 1. Kiani R
    2. Shadlen MN

    (2009) Representation of confidence associated with a decision by neurons in the parietal cortex

    Science 324:759–764.

    https://doi.org/10.1126/science.1169405

    • PubMed
    • Google Scholar
57. 1. Klein TA
    2. Endrass T
    3. Kathmann N
    4. Neumann J
    5. von Cramon DY
    6. Ullsperger M

    (2007) Neural correlates of error awareness

    NeuroImage 34:1774–1781.

    https://doi.org/10.1016/j.neuroimage.2006.11.014

    • PubMed
    • Google Scholar
58. 1. Koizumi A
    2. Maniscalco B
    3. Lau H

    (2015) Does perceptual confidence facilitate cognitive control?

    Attention, Perception, & Psychophysics 77:1295–1306.

    https://doi.org/10.3758/s13414-015-0843-3

    • PubMed
    • Google Scholar
59. 1. Kolling N
    2. Behrens TE
    3. Mars RB
    4. Rushworth MF

    (2012) Neural mechanisms of foraging

    Science 336:95–98.

    https://doi.org/10.1126/science.1216930

    • PubMed
    • Google Scholar
60. Book

    1. Luck SJ

    (2014)

    An Introduction to the Event-Related Potential Technique

    MIT Press.

    • Google Scholar
61. 1. Luck SJ
    2. Gaspelin N

    (2017) How to get statistically significant effects in any ERP experiment (and why you shouldn't)

    Psychophysiology 54:146–157.

    https://doi.org/10.1111/psyp.12639

    • PubMed
    • Google Scholar
62. 1. Maniscalco B
    2. Peters MA
    3. Lau H

    (2016) Heuristic use of perceptual evidence leads to dissociation between performance and metacognitive sensitivity

    Attention, Perception, & Psychophysics 78:923–937.

    https://doi.org/10.3758/s13414-016-1059-x

    • PubMed
    • Google Scholar
63. 1. Maniscalco B
    2. Lau H

    (2012) A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings

    Consciousness and Cognition 21:422–430.

    https://doi.org/10.1016/j.concog.2011.09.021

    • PubMed
    • Google Scholar
64. 1. Metcalfe J
    2. Finn B

    (2008) Evidence that judgments of learning are causally related to study choice

    Psychonomic Bulletin & Review 15:174–179.

    https://doi.org/10.3758/PBR.15.1.174

    • PubMed
    • Google Scholar
65. 1. Middlebrooks PG
    2. Sommer MA

    (2012) Neuronal correlates of metacognition in primate frontal cortex

    Neuron 75:517–530.

    https://doi.org/10.1016/j.neuron.2012.05.028

    • PubMed
    • Google Scholar
66. 1. Moran R

    (2015) Optimal decision making in heterogeneous and biased environments

    Psychonomic Bulletin & Review 22:38–53.

    https://doi.org/10.3758/s13423-014-0669-3

    • PubMed
    • Google Scholar
67. 1. Moran R
    2. Teodorescu AR
    3. Usher M

    (2015) Post choice information integration as a causal determinant of confidence: novel data and a computational account

    Cognitive Psychology 78:99–147.

    https://doi.org/10.1016/j.cogpsych.2015.01.002

    • PubMed
    • Google Scholar
68. 1. Murphy PR
    2. Robertson IH
    3. Allen D
    4. Hester R
    5. O'Connell RG

    (2012) An electrophysiological signal that precisely tracks the emergence of error awareness

    Frontiers in Human Neuroscience 6:6.

    https://doi.org/10.3389/fnhum.2012.00065

    • Google Scholar
69. 1. Murphy PR
    2. Robertson IH
    3. Harty S
    4. O'Connell RG

    (2015) Neural evidence accumulation persists after choice to inform metacognitive judgments

    eLife 4:e11946.

    https://doi.org/10.7554/eLife.11946

    • PubMed
    • Google Scholar
70. 1. Murphy PR
    2. Boonstra E
    3. Nieuwenhuis S

    (2016) Global gain modulation generates time-dependent urgency during perceptual choice in humans

    Nature Communications 7:7.

    https://doi.org/10.1038/ncomms13526

    • Google Scholar
71. 1. Murphy PR
    2. Wilming N
    3. Hernandez-Bocanegra DC
    4. Prat-Ortega G
    5. Donner TH

    (2021) Adaptive circuit dynamics across human cortex during evidence accumulation in changing environments

    Nature Neuroscience 24:987–997.

    https://doi.org/10.1038/s41593-021-00839-z

    • PubMed
    • Google Scholar
72. 1. Nelson TO
    2. Dunlosky J

    (1991) When people's Judgments of Learning (JOLs) are Extremely Accurate at Predicting Subsequent Recall: The “Delayed-JOL Effect”

    Psychological Science 2:267–271.

    https://doi.org/10.1111/j.1467-9280.1991.tb00147.x

    • Google Scholar
73. 1. Nieuwenhuis S
    2. Ridderinkhof KR
    3. Blom J
    4. Band GP
    5. Kok A

    (2001) Error-related brain potentials are differentially related to awareness of response errors: evidence from an antisaccade task

    Psychophysiology 38:752–760.

    https://doi.org/10.1111/1469-8986.3850752

    • PubMed
    • Google Scholar
74. 1. Notebaert W
    2. Houtman F
    3. Opstal FV
    4. Gevers W
    5. Fias W
    6. Verguts T

    (2009) Post-error slowing: an orienting account

    Cognition 111:275–279.

    https://doi.org/10.1016/j.cognition.2009.02.002

    • PubMed
    • Google Scholar
75. 1. O'Connell RG
    2. Dockree PM
    3. Bellgrove MA
    4. Kelly SP
    5. Hester R
    6. Garavan H
    7. Robertson IH
    8. Foxe JJ

    (2007) The role of cingulate cortex in the detection of errors with and without awareness: a high-density electrical mapping study

    European Journal of Neuroscience 25:2571–2579.

    https://doi.org/10.1111/j.1460-9568.2007.05477.x

    • PubMed
    • Google Scholar
76. 1. O'Connell RG
    2. Dockree PM
    3. Robertson IH
    4. Bellgrove MA
    5. Foxe JJ
    6. Kelly SP

    (2009) Uncovering the neural signature of lapsing attention: electrophysiological signals predict errors up to 20 s before they occur

    Journal of Neuroscience 29:8604–8611.

    https://doi.org/10.1523/JNEUROSCI.5967-08.2009

    • PubMed
    • Google Scholar
77. 1. O'Connell RG
    2. Dockree PM
    3. Kelly SP

    (2012) A supramodal accumulation-to-bound signal that determines perceptual decisions in humans

    Nature Neuroscience 15:1729–1735.

    https://doi.org/10.1038/nn.3248

    • PubMed
    • Google Scholar
78. 1. O'Connell RG
    2. Shadlen MN
    3. Wong-Lin K
    4. Kelly SP

    (2018) Bridging neural and computational viewpoints on perceptual Decision-Making

    Trends in Neurosciences 41:838–852.

    https://doi.org/10.1016/j.tins.2018.06.005

    • PubMed
    • Google Scholar
79. 1. Pasquali A
    2. Timmermans B
    3. Cleeremans A

    (2010) Know thyself: metacognitive networks and measures of consciousness

    Cognition 117:182–190.

    https://doi.org/10.1016/j.cognition.2010.08.010

    • PubMed
    • Google Scholar
80. 1. Pereira M
    2. Faivre N
    3. Iturrate I
    4. Wirthlin M
    5. Serafini L
    6. Martin S
    7. Desvachez A
    8. Blanke O
    9. Van De Ville D
    10. Millán JDR

    (2020) Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging

    PNAS 117:8382–8390.

    https://doi.org/10.1073/pnas.1918335117

    • PubMed
    • Google Scholar
81. 1. Peters MAK
    2. Thesen T
    3. Ko YD
    4. Maniscalco B
    5. Carlson C
    6. Davidson M
    7. Doyle W
    8. Kuzniecky R
    9. Devinsky O
    10. Halgren E
    11. Lau H

    (2017) Perceptual confidence neglects decision-incongruent evidence in the brain

    Nature Human Behaviour 1:139.

    https://doi.org/10.1038/s41562-017-0139

    • Google Scholar
82. 1. Philiastides MG
    2. Sajda P

    (2006) Temporal characterization of the neural correlates of perceptual decision making in the human brain

    Cerebral Cortex 16:509–518.

    https://doi.org/10.1093/cercor/bhi130

    • PubMed
    • Google Scholar
83. 1. Pleskac TJ
    2. Busemeyer JR

    (2010) Two-stage dynamic signal detection: a theory of choice, decision time, and confidence

    Psychological Review 117:864–901.

    https://doi.org/10.1037/a0019737

    • PubMed
    • Google Scholar
84. 1. Pouget A
    2. Drugowitsch J
    3. Kepecs A

    (2016) Confidence and certainty: distinct probabilistic quantities for different goals

    Nature Neuroscience 19:366–374.

    https://doi.org/10.1038/nn.4240

    • PubMed
    • Google Scholar
85. 1. Purcell BA
    2. Kiani R

    (2016a) Hierarchical decision processes that operate over distinct timescales underlie choice and changes in strategy

    PNAS 113:E4531–E4540.

    https://doi.org/10.1073/pnas.1524685113

    • PubMed
    • Google Scholar
86. 1. Purcell BA
    2. Kiani R

    (2016b) Neural mechanisms of Post-error adjustments of decision policy in parietal cortex

    Neuron 89:658–671.

    https://doi.org/10.1016/j.neuron.2015.12.027

    • PubMed
    • Google Scholar
87. 1. Rabbitt PM

    (1966) Errors and error correction in choice-response tasks

    Journal of Experimental Psychology 71:264–272.

    https://doi.org/10.1037/h0022853

    • PubMed
    • Google Scholar
88. 1. Rafiei F
    2. Rahnev D

    (2021) Qualitative speed-accuracy tradeoff effects that cannot be explained by the diffusion model under the selective influence assumption

    Scientific Reports 11:1–19.

    https://doi.org/10.1038/s41598-020-79765-2

    • PubMed
    • Google Scholar
89. 1. Ratcliff R
    2. Smith PL
    3. Brown SD
    4. McKoon G

    (2016) Diffusion decision model: current issues and history

    Trends in Cognitive Sciences 20:260–281.

    https://doi.org/10.1016/j.tics.2016.01.007

    • PubMed
    • Google Scholar
90. 1. Ratcliff R
    2. McKoon G

    (2008) The diffusion decision model: theory and data for two-choice decision tasks

    Neural Computation 20:873–922.

    https://doi.org/10.1162/neco.2008.12-06-420

    • PubMed
    • Google Scholar
91. 1. Resulaj A
    2. Kiani R
    3. Wolpert DM
    4. Shadlen MN

    (2009) Changes of mind in decision-making

    Nature 461:263–266.

    https://doi.org/10.1038/nature08275

    • PubMed
    • Google Scholar
92. 1. Ridderinkhof KR
    2. Ullsperger M
    3. Crone EA
    4. Nieuwenhuis S

    (2004) The role of the medial frontal cortex in cognitive control

    Science 306:443–447.

    https://doi.org/10.1126/science.1100301

    • PubMed
    • Google Scholar
93. 1. Ridderinkhof KR
    2. Ramautar JR
    3. Wijnen JG

    (2009) To P(E) or not to P(E): a P3-like ERP component reflecting the processing of response errors

    Psychophysiology 46:531–538.

    https://doi.org/10.1111/j.1469-8986.2009.00790.x

    • PubMed
    • Google Scholar
94. 1. Rollwage M
    2. Loosen A
    3. Hauser TU
    4. Moran R
    5. Dolan RJ
    6. Fleming SM

    (2020) Confidence drives a neural confirmation Bias

    Nature Communications 11:6.

    https://doi.org/10.1038/s41467-020-16278-6

    • PubMed
    • Google Scholar
95. 1. Sanders JI
    2. Hangya B
    3. Kepecs A

    (2016) Signatures of a statistical computation in the human sense of confidence

    Neuron 90:499–506.

    https://doi.org/10.1016/j.neuron.2016.03.025

    • PubMed
    • Google Scholar
96. 1. Sarafyazd M
    2. Jazayeri M

    (2019) Hierarchical reasoning by neural circuits in the frontal cortex

    Science 364:eaav8911.

    https://doi.org/10.1126/science.aav8911

    • PubMed
    • Google Scholar
97. 1. Scheffers MK
    2. Coles MGH

    (2000) Performance monitoring in a confusing world: error-related brain activity, judgments of response accuracy, and types of errors

    Journal of Experimental Psychology: Human Perception and Performance 26:141–151.

    https://doi.org/10.1037/0096-1523.26.1.141

    • PubMed
    • Google Scholar
98. 1. Selimbeyoglu A
    2. Keskin-Ergen Y
    3. Demiralp T

    (2012) What if you are not sure? electroencephalographic correlates of subjective confidence level about a decision

    Clinical Neurophysiology 123:1158–1167.

    https://doi.org/10.1016/j.clinph.2011.10.037

    • PubMed
    • Google Scholar
99. 1. Shadlen MN
    2. Kiani R

    (2013) Decision making as a window on cognition

    Neuron 80:791–806.

    https://doi.org/10.1016/j.neuron.2013.10.047

    • PubMed
    • Google Scholar
100. 1. Shalgi S
     2. Deouell LY

     (2012) Is any awareness necessary for an ne?

     Frontiers in Human Neuroscience 6:124.

     https://doi.org/10.3389/fnhum.2012.00124

     • PubMed
     • Google Scholar
101. Book

     1. Shimamura AP

     (2008) A neurocognitive approach to metacognitive monitoring and control

     In: Dunlosky J, Bjork R. A, editors. Handbook of Metamemory and Memory. Psychology Press. pp. 373–390.

     https://doi.org/10.4324/9780203805503.CH19

     • Google Scholar
102. 1. Smith PL
     2. Ratcliff R

     (2009) An integrated theory of attention and decision making in visual signal detection

     Psychological Review 116:283–317.

     https://doi.org/10.1037/a0015156

     • PubMed
     • Google Scholar
103. 1. Squires KC
     2. Hillyard SA
     3. Lindsay PH

     (1973) Vertex potentials evoked during auditory signal detection: relation to decision criteria

     Perception & Psychophysics 14:265–272.

     https://doi.org/10.3758/BF03212388

     • Google Scholar
104. 1. Steinemann NA
     2. O'Connell RG
     3. Kelly SP

     (2018) Decisions are expedited through multiple neural adjustments spanning the sensorimotor hierarchy

     Nature Communications 9:1–50.

     https://doi.org/10.1038/s41467-018-06117-0

     • PubMed
     • Google Scholar
105. 1. Steinhauser M
     2. Yeung N

     (2010) Decision processes in human performance monitoring

     Journal of Neuroscience 30:15643–15653.

     https://doi.org/10.1523/JNEUROSCI.1899-10.2010

     • PubMed
     • Google Scholar
106. 1. Steinhauser M
     2. Yeung N

     (2012) Error awareness as evidence accumulation: effects of speed-accuracy trade-off on error signaling

     Frontiers in Human Neuroscience 6:6.

     https://doi.org/10.3389/fnhum.2012.00240

     • PubMed
     • Google Scholar
107. 1. Stuphorn V
     2. Taylor TL
     3. Schall JD

     (2000) Performance monitoring by the supplementary eye field

     Nature 408:857–860.

     https://doi.org/10.1038/35048576

     • PubMed
     • Google Scholar
108. 1. Talluri BC
     2. Urai AE
     3. Tsetsos K
     4. Usher M
     5. Donner TH

     (2018) Confirmation Bias through selective overweighting of Choice-Consistent evidence

     Current Biology 28:3128–3135.

     https://doi.org/10.1016/j.cub.2018.07.052

     • PubMed
     • Google Scholar
109. 1. Tsujimoto S
     2. Genovesio A
     3. Wise SP

     (2009) Monkey orbitofrontal cortex encodes response choices near feedback time

     Journal of Neuroscience 29:2569–2574.

     https://doi.org/10.1523/JNEUROSCI.5777-08.2009

     • PubMed
     • Google Scholar
110. 1. Twomey DM
     2. Murphy PR
     3. Kelly SP
     4. O'Connell RG

     (2015) The classic P300 encodes a build-to-threshold decision variable

     European Journal of Neuroscience 42:1636–1643.

     https://doi.org/10.1111/ejn.12936

     • Google Scholar
111. 1. Twomey DM
     2. Kelly SP
     3. O'Connell RG

     (2016) Abstract and Effector-Selective decision signals exhibit qualitatively distinct dynamics before delayed perceptual reports

     Journal of Neuroscience 36:7346–7352.

     https://doi.org/10.1523/JNEUROSCI.4162-15.2016

     • PubMed
     • Google Scholar
112. 1. Ullsperger M
     2. Harsay HA
     3. Wessel JR
     4. Ridderinkhof KR

     (2010) Conscious perception of errors and its relation to the anterior insula

     Brain Structure and Function 214:629–643.

     https://doi.org/10.1007/s00429-010-0261-1

     • PubMed
     • Google Scholar
113. 1. Usher M
     2. McClelland JL

     (2001) The time course of perceptual choice: the leaky, competing accumulator model

     Psychological Review 108:550–592.

     https://doi.org/10.1037/0033-295X.108.3.550

     • PubMed
     • Google Scholar
114. 1. van den Berg R
     2. Anandalingam K
     3. Zylberberg A
     4. Kiani R
     5. Shadlen MN
     6. Wolpert DM

     (2016) A common mechanism underlies changes of mind about decisions and confidence

     eLife 5:e12192.

     https://doi.org/10.7554/eLife.12192

     • PubMed
     • Google Scholar
115. 1. van Vugt MK
     2. Simen P
     3. Nystrom LE
     4. Holmes P
     5. Cohen JD

     (2012) EEG oscillations reveal neural correlates of evidence accumulation

     Frontiers in Neuroscience 6:6.

     https://doi.org/10.3389/fnins.2012.00106

     • PubMed
     • Google Scholar
116. 1. Vlassova A
     2. Pearson J

     (2013) Look before you leap: sensory memory improves decision making

     Psychological Science 24:1635–1643.

     https://doi.org/10.1177/0956797612474321

     • PubMed
     • Google Scholar
117. 1. Wang XJ

     (2002) Probabilistic decision making by slow reverberation in cortical circuits

     Neuron 36:955–968.

     https://doi.org/10.1016/S0896-6273(02)01092-9

     • PubMed
     • Google Scholar
118. 1. Wessel JR
     2. Danielmeier C
     3. Ullsperger M

     (2011) Error awareness revisited: accumulation of multimodal evidence from central and autonomic nervous systems

     Journal of Cognitive Neuroscience 23:3021–3036.

     https://doi.org/10.1162/jocn.2011.21635

     • PubMed
     • Google Scholar
119. 1. Wessel JR

     (2012) Error awareness and the error-related negativity: evaluating the first decade of evidence

     Frontiers in Human Neuroscience 6:6.

     https://doi.org/10.3389/fnhum.2012.00088

     • PubMed
     • Google Scholar
120. 1. Wessel JR

     (2018) An adaptive orienting theory of error processing

     Psychophysiology 55:e13041.

     https://doi.org/10.1111/psyp.13041

     • Google Scholar
121. 1. Wimmer K
     2. Compte A
     3. Roxin A
     4. Peixoto D
     5. Renart A
     6. de la Rocha J

     (2015) Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT

     Nature Communications 6:7177.

     https://doi.org/10.1038/ncomms7177

     • PubMed
     • Google Scholar
122. 1. Wokke ME
     2. Cleeremans A
     3. Ridderinkhof KR

     (2017) Sure I'm sure: prefrontal oscillations support metacognitive monitoring of decision making

     The Journal of Neuroscience 37:781–789.

     https://doi.org/10.1523/JNEUROSCI.1612-16.2016

     • PubMed
     • Google Scholar
123. 1. Yeung N
     2. Botvinick MM
     3. Cohen JD

     (2004) The neural basis of error detection: conflict monitoring and the error-related negativity

     Psychological Review 111:931–959.

     https://doi.org/10.1037/0033-295X.111.4.931

     • PubMed
     • Google Scholar
124. 1. Yeung N
     2. Summerfield C

     (2012) Metacognition in human decision-making: confidence and error monitoring

     Philosophical Transactions of the Royal Society B: Biological Sciences 367:1310–1321.

     https://doi.org/10.1098/rstb.2011.0416

     • Google Scholar
125. 1. Yu S
     2. Pleskac TJ
     3. Zeigenfuse MD

     (2015) Dynamics of postdecisional processing of confidence

     Journal of Experimental Psychology: General 144:489–510.

     https://doi.org/10.1037/xge0000062

     • Google Scholar
126. 1. Zylberberg A
     2. Barttfeld P
     3. Sigman M

     (2012) The construction of confidence in a perceptual decision

     Frontiers in Integrative Neuroscience 6:6.

     https://doi.org/10.3389/fnint.2012.00079

     • PubMed
     • Google Scholar
127. 1. Zylberberg A
     2. Fetsch CR
     3. Shadlen MN

     (2016) The influence of evidence volatility on choice, reaction time and confidence in a perceptual decision

     eLife 5:e17688.

     https://doi.org/10.7554/eLife.17688

     • PubMed
     • Google Scholar

Author details

1. Kobe Desender

   1. Brain and Cognition, KU Leuven, Leuven, Belgium
   2. Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

   Contribution

   Conceptualization, Software, Visualization, Writing - original draft

   For correspondence

   kobe.desender@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5462-4260

2. K Richard Ridderinkhof

   1. Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
   2. Amsterdam center for Brain and Cognition (ABC), University of Amsterdam, Amsterdam, Netherlands

   Contribution

   Conceptualization, Writing - review and editing

   Competing interests

   No competing interests declared

3. Peter R Murphy

   1. Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
   2. Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland

   Contribution

   Conceptualization, Writing - review and editing

   Competing interests

   No competing interests declared

• 1,650

  views

• 251

  downloads

• 37

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.