Table Of ContentPhil. Trans.R.Soc. B(2012) 367, 1310–1321
doi:10.1098/rstb.2011.0416
Review
Metacognition in human decision-making:
confidence and error monitoring
Nick Yeung* and Christopher Summerfield
DepartmentofExperimentalPsychology,UniversityofOxford,SouthParksRoad,OxfordOX13UD,UK
People are capable of robust evaluations of their decisions: they are often aware of their mistakes
even without explicit feedback, and report levels of confidence in their decisions that correlate
with objective performance. These metacognitive abilities help people to avoid making the same
mistakestwice,andtoavoidovercommittingtimeorresourcestodecisionsthatarebasedonunreli-
ableevidence.Inthisreview,weconsiderprogressincharacterizingtheneuralandmechanisticbasis
of these related aspects of metacognition—confidence judgements and error monitoring—and
identify crucial points of convergence between methods and theories in the two fields. This con-
vergence suggests that common principles govern metacognitive judgements of confidence and
accuracy; in particular, a shared reliance on post-decisional processing within the systems res-
ponsible for the initial decision. However, research in both fields has focused rather narrowly on
simple, discrete decisions—reflecting the correspondingly restricted focus of current models of
the decision process itself—raising doubts about the degree to which discovered principles will
scale up to explain metacognitive evaluation of real-world decisions and actions that are fluid,
temporally extended, and embedded in the broader context of evolving behavioural goals.
Keywords: decision-making; confidence; metacognition; error monitoring
1. INTRODUCTION andtheroadbecomesslipperyandwet.Theapproach
(a) Metacognition in perceptual choice toeachbendstillrequiresadeftanglingofhandlebars,
Imagine yourself cycling along a narrow, winding but now you are less certain about whether each
countrylaneonasummer’sday.Successfullynegotiat- chosen action is optimized to steer the bicycle in a
ing each twist and turn in the road requires the fashion that appropriately meets the gradient of the
interpretation of a variety of subtle sensory cues and curve and the camber of the road. Action selection
their conversion into appropriate motor commands. proceeds as before, but you are unsure whether the
For example, incoming visual signals that reveal the actions chosen are the most appropriate ones. In
curvature of the road, or motion parallax signals aris- fact, you might even experience an immediate sense
ing from the trees beyond, allows you to gently that you have just made a poor choice. In short, you
adjust the handlebars towards the left or right to are less confident about your decisions.
steer the bicycle smoothly round each bend. Curiously, despite universal agreement that an
Over past decades, psychologists and neuroscien- accompanying sense of confidence is a subjectively
tists have devoted substantial effort to understand salient property of almost all our decisions, there is
the neural and computational mechanisms by which currently little consensus about how we might incor-
actionsareselected on the basis of a streamof incom- porate decision confidence into formal models of
ing sensory information [1]. Because this sort of choice behaviour or explore its biological substrates.
sensorimotor control often requires an observer to Fundamental questions remain unanswered. For
commit to one discrete action from among several example, is the information that gives rise to the
possible candidates, this literature has been informed ‘second-order’estimateofconfidenceinachoiceiden-
by computational models that offer a formal account tical to that determining the ‘first-order’ choice itself?
of how categorical decisions are made. These Why are we generally more sure that we are correct
models,describedinmoredetailbelow,haveprovided than that we have made an error, even for difficult
an excellent account of the decisions and decision choices? Why do we sometimes appear to ‘change
latencies exhibited by observers selecting actions on ourmind’afteramotorprogrammehasbeeninitiated?
the basis of ambiguous sensory information [2–5]. Arethesechangesofmindnecessarilyaccompaniedby
However, now imagine that as you are cycling, the awareness that the initial choice was incorrect?
skydarkensanditbeginstorain—visibilityisreduced, In what follows, we review the literature that has
posed these and related questions about decision con-
fidence in perceptual choice tasks. Subsequently, we
*Authorforcorrespondence([email protected]). highlight links between this work and a literature
that has considered how people monitor whether they
One contribution of 13 to a Theme Issue ‘Metacognition:
computation,neurobiologyandfunction’. have made an error under conditions of uncertainty
1310 Thisjournalisq2012TheRoyalSociety
Review. Confidence and error monitoring N. Yeung & C. Summerfield 1311
time This simple model has much to recommend it both
as a normative and descriptive account of categorical
choice. The DDM has been widely and very suc-
q cessfully applied to decision-making in a range of
cognitive domains—from low-levelperceptualdecisions
a to retrieval of facts from semantic memory, to econo-
e response time for trial 1 mic decision-making under uncertainty [6–8]—and
c
n accounts neatly for several empirical characteristics of
e
d
vi reaction times (RTs) observed for binary choices in
e b response time for trial 2 thesetasks.Firstly,thenaturalgeometryoftheaccumu-
lation-to-boundprocesspredictstheobservedrightward
–q skew in RT distributions. Secondly, varying the bound
offers an elegant account of the economy of speed and
accuracy that characterizes mental chronometry tasks.
Thirdly,byallowingdriftrateandoriginofaccumulation
Figure1.Thedrift-diffusionmodel.Accumulatingevidence tovaryacrosstrials, the model accounts for the relative
(thedecisionvariable,y-axis)overtime(x-axis)isshownfor RTof correct and error trials under conditions where
twoillustrativetrials(markedaandb,greyandblacklines), speed or accuracy are emphasized [8]. Moreover, the
one on which the choiceuis made and the other on which
model implements a sequential probability ratio pro-
thechoice –uismade.Adecisionistriggeredwhenevidence
cedure that optimizes speed for a given error rate, and
reachesuor –u.Grey line, trial 1;blackline, trial 2.
thus takes its place among a family of statistically
optimaldescriptionsofthechoiceprocess[2].
orconflict.Finally,weproposesomepotentiallyfruitful
avenues for research that draw upon common themes 2. DECISION CONFIDENCE
in these two literatures, building on their shared However, as illustrated above, we not only make
strengthsandaddressingtheirsharedlimitations. decisions,butalsoconcurrentlyevaluatethelikelihood
that those decisions will result in favourable or
unfavourable outcomes. How, then, can we incorpor-
(b) Formal models of perceptual choice
ate decision confidence into the formal framework
Even under good viewing conditions, visual infor-
offered by the DDM and other quantitative models
mation is corrupted by multiple sources of noise and
of perceptual choice? In what follows, we review
uncertainty, arising both in the external world and in
recent empirical and modelling work that has
the dynamics of neural processing. One sensible way
attemptedtoextendthisformalframeworktoaccount
to increase the signal-to-noise ratio of visual infor-
formetacognitivejudgements.Muchofthisdebatehas
mation is to sample the external world repeatedly
hinged on a simple question: can confidence be read
and integrate this information over time, making a
outdirectlyfromtheDVatthetimeofchoice(decisio-
decision only when the information is considered to
nal locus model), or do confidence judgements
be of sufficient quality [5]. This idea forms the basis
depend on new information arriving beyond the
of a class of model in which binary choices are
decision point (post-decisional locus models)? [9].
described via an accumulation-to-bound mechanism,
As an aside, we note that this question only arises
with successive samples of information totted up
if one is concerned with modelling the dynamics
until they reach a criterial level or ‘bound’, upon
of the decision process, such as with the DDM or
which a commitment to action is made (figure 1).
relatedevidence-accumulationmodels.Otherdecision-
Different versions of this model make divergent
theoretic models, such as signal detection theory
assumptions about exactly what quantity is integrated
(SDT),havegivendetailedtreatmentofhowtomeasure
en route to a decision—i.e. about precisely how the
observers’ metacognitive sensitivity, specifically, their
‘decisionvariable’(DV)iscomposed.Tosimplifymat-
abilitytodistinguishtheirowncorrectandincorrectjud-
ters, in this review, we focus on one variant of this
gements [10–12]. However, because SDT is a static
model—the ‘drift-diffusion’ model, or DDM—in
model of the decision process in which the temporal
which the DV corresponds to the relative likelihood
dynamicsofevidenceaccumulationareignored,primary
of the two choices being correct, given the stimulus
(‘type I’) and metacognitive (‘type II’) judgements
[3]. In the DDM, the DVon sample v is updated on
t mustnecessarilybe basedonthesameevidence(albeit
each sample t with an increment composed of two
potentially corrupted by different sources of noise
quantities: d, a linear drift term that encodes the rate
[12]), and thusthese models are by definition decision
of evidence accumulation, and cW, Gaussian noise
locusmodels.
with a mean of zero and a variance of c2:
n ¼n þdþcW: ð1:1Þ (a) Decisional locus models
t t(cid:2)1
The very earliest investigations of decision confidence
Decisions are made when the DV exceeds a fixed
revealed that, perhaps unsurprisingly, we are more
deviation from zero, u, such that during evidence certain about our perceptual choices when sensory
accumulation:
inputs are stronger [13], and when we are given
longer tosamplesensoryinformation[14].Itthusfol-
(cid:2)u.v.u: ð1:2Þ
lows that confidence should reflect both the quality of
Phil.Trans.R.Soc.B(2012)
1312 N. Yeung & C. Summerfield Review. Confidence and error monitoring
the evidence (represented by the drift rate,d) and the onlow-confidencetrialsweresubstantiallyattenuated,
quantity of evidence at the choice point (represented falling equidistant between those for the chosen and
bytheabsolutevalueofthedecisionbound,juj).How- unchosen targets. The authors interpret these data as
ever, it is also clear that neither of these quantities showing that confidence is a simple product of quan-
aloneissufficient todescribeanobserver’sconfidence tity and quality of the DV, as proposed by early
intheirchoices.Anydiffusion-to-boundmodelassum- accounts of decision confidence, obviating the need
ing that confidence is directly indexed by the state of for a separate metacognitive monitoring process [20].
evidence at the time of choice inevitably predicts that Kepecs et al. [19]employed an odourdiscrimination
all choiceswill be madewith preciselythe same confi- paradigm in conjunction with single-cell recordings in
dence(correspondingtotheevidencelevelrequiredto the rat orbitofrontal cortex (OFC; see also Kepecs &
reachthebound).Similarly,anymodelproposingthat Mainen [25]). They report an intriguing finding: that
confidence reflects evidence quality implicitly assumes OFC firing rates discriminated between correct judge-
that observers have direct access to this quantity— mentsanderrorsafterthechoicebutbeforetheoutcome
which,iftheyhad,wouldobviatetheneedforasequen- had been revealed, even when objective difficultyof the
tialsamplingapproachinthefirstplace(seePleskac& choiceparadigmwascontrolledfor.Becausevariancein
Busemeyer[15]foranexcellentrecentreview). theseresponsescouldnotbeexplainedbyotherfactors,
Various accounts dating back over 100 years such as the reinforcement history over past trials, the
have therefore proposed that confidence reflects authors suggest that these neurons encode confidence
some interaction between the quantity and quality of estimates associated with the current decision. They
evidence—for example, that under the DDM, confi- showthattheactivityoftheseneuronscanbeexplained
dence scales with the product of d and u [16,17]. byaclassofmodelinwhichtwoevidencetalliesraceto
This account has attractive properties—for example, thedecision bound (rather than a single quantityrepre-
it predicts that when the observer has the option of senting their difference, as in the DDM). Specifically,
terminating evidence accumulation with a choice at neuronal responses reflected the difference between
any point, RTs are faster for high-confidence trials thetwototalsatthetimeofthedecision—makingthisa
[18]. Moreover, this view enjoys support from two decisional locus model, even thoughthe relevant neural
prominent neurophysiological studies detailed in §2b. activity was sustained through the post-response
period—andthisactivityinturnpredictedthelikelihood
thatthechoicewascorrect[19].
(b) Neural substrates of decision confidence At first glance, these two studies offer convincing
Two recent single-cell recording studies, one involv- evidence thatneuronsintheparietalandorbitofrontal
ing rats [19] and the other monkeys [20], claim to cortices encode the subjective confidence associated
have identified neurons encoding subjective decision with achoice: agraded quantitythatcan be estimated
confidence. Kiani et al. [20] recorded from neurons directly fromthe evidence on which the original, first-
inthelateralintraparietalcortex(LIP)ofmacaquesdis- orderchoice was based. In both cases, the authors are
criminating the direction of motion in ambiguous assiduous in attempting to rule out alternative expla-
moving-dot arrays [21] with a saccade to one of two nations of their findings, such as attention or learned
targets placed either side of the motion stimulus. This reward contingencies. However, to return momenta-
experimental approach has previously been used to rily to our discussion of models of categorical choice,
demonstrate that LIP neurons whose receptive field two well-replicated behavioural phenomena cast
overlaps with the chosen target display a characteristic doubt on any model in which subjective confidence
acceleration of spiking activity while the monkey views directly reflects the evidence accumulated up to the
the moving-dot stimulus, whereas those overlapping choice point. First, it has consistently been shown
with the non-chosen target show a relative dampening thatconfidenceincorrectchoicesisstrongerthancon-
of activity [22,23]. This build-up scales with signal fidence in incorrect choices, even when choice
strength, and terminates when an action (saccadic eye difficulty is controlled. As previously pointed out
movement) is selected, prompting the view that LIP [15], this finding cannot be explained by decisional
firing rates encode the level of evidence available for a locus models in which confidence is a mere function
choice—inotherwords,their firing rates form aneural of diffusion model parameters, because these para-
representationoftheDVproposedbytheDDM[1]. meters are invariant across correct and incorrect
To investigate decision confidence, Kiani et al. trials (but see Galvin et al. [10] for relevant analysis
added a new twist to this protocol. Typically, the usingSDT). Second, andcrucially,subjects occasion-
monkey is rewarded for correct but not incorrect ally change their mind between the first-order choice
choices, but here, on a fraction of trials, the monkey and the second-order estimation of decision confi-
was offered a ‘sure bet’ option such that a certain dence. At least on these trials, the decisions must be
but lower-valued reward could be obtained via a sac- influenced by processing that occurs after the first-
cade to a third response option. In ethology, it is order choice point. In §2c, we consider models that
commonly assumed that an animal choosing the safe propose such a mechanism.
option when evidence is scarce must have access to
metacognitive information about the likelihood that
it will make an error on the main task [24]. The (c) Post-decisional locus models and changes
authors found that not only did their monkeys use of mind
thisoptionjudiciously—respondingwhenthestimulus Resulaj et al. [26] report a behavioural experiment in
wasweakorambiguous—butalsothatLIPfiring rates which human subjects indicated the direction of a
Phil.Trans.R.Soc.B(2012)
Review. Confidence and error monitoring N. Yeung & C. Summerfield 1313
random dot motion stimulus by moving a handle to a predicting decision confidence might not be confined
leftwards or rightwards target some 20cm away. This tothestimulationperiodalone.However,onepotential
design allowed the researchers to isolate trials on caveattothisviewisthattheactivityofLIPneuronsis
which subjects began to move towards one target but known to drop off sharply once the eye movement is
then changed their mind and veered-off towards the made [22,23]. Thus, it remains to be shown whether
other. Careful behavioural analyses demonstrated a variation in the post-decisional LIP signal could con-
number of interesting phenomena. Firstly, changes of tribute to a later confidence judgement, or whether a
mind were not symmetric: subjects switched more separaterepresentationoftheevolvingDVissupporting
often from an incorrect to a correct choice. Secondly, theobservedchangesofmind.Thiscaveataside,these
change-of-mind trials tended to occur when, owing emerging findings suggest that post-decisional proces-
to stochasticity in the stimulus display, motion sing plays a crucial role in metacognitive judgements,
energy began by favouring the initial choice, but sub- which can lead to changes of mind or support ratings
sequently came to favour the alternative, switched-to ofconfidenceinaninitialdecision.
option.Becausethemotionstimulusoffsetoncemove-
ment began, subjects must have capitalized on the
balance of information in the immediate pre-decision 3. ERROR MONITORING
period when deciding to change their mind. Notably, People are often aware of their own mistakes; for
although this motion information was available prior example, in choice RT tasks when time pressure is
to the decision, the switch occurred only once move- applied to induce errors in simple judgements. Error
ment initiation began, suggesting that evidence monitoringisthemetacognitiveprocessbywhichweare
accumulation continued beyond the point at which abletodetectandsignalourerrorsassoonasaresponse
the initial choice was made. has been made. This process plays a crucial role in
To account for these and other data, a number of adaptive human behaviour, allowing our actions to be
researchers have proposed models in which, in con- shaped by their outcomes both in the short term, for
trast to the classical DDM, evidence accumulation example,byrespondingmorecautiouslytoavoidfurther
continues even beyond the choice point, with this errors,andinthelongerterm,throughgraduallearning
extra variability in the DV also contributing to esti- ofappropriatestimulus–responsecontingencies.
mates of subjective confidence when probed at a Whereas the studies described above have typically
later time point [9,15]. Resulaj et al. propose that asked subjects to report their confidence that they
their data can be explained by just this type of made the correct choice, studies examining error
model, with changes of mind occurring when latent monitoring have tended to ask subjects the converse
information in the processing pipeline drives the DV question—i.e. to report the likelihood that they made
across a second, ‘change-of-mind’ bound. A related an error. Although these judgements seem like two
account, the two-stage dynamic signal detection sides of the same coin, methods and assumptions in
(2DSD) model [15], likewise proposes that the diffu- the two literatures have often been quite different. For
sion process continues beyond initial choice, with example,decisionconfidencehastypicallybeenstudied
decision confidence reflecting the absolute value of usingtasksthatremainchallengingevenwhenextended
the DVat the post-decision point at which a second- processing times are permitted, such as challenging
orderdecisionisrequired.Thus,incontrasttodiscrete psychophysical discriminations. Under these circum-
changesofmindthatareexpressedasovertcorrections stances, subjects are sometimes sure they responded
ofaninitialresponse,the2DSDmodelallowsforcon- correctlyandsometimesunsurewhether theyare right
tinuously varying levels of confidence that provide an orwrong,butrarelycertainthatthattheymadeamis-
explicit judgement about an earlier response. take [15]. In contrast, research on error monitoring
Behaviourally,thesepost-decisionprocessingmodels has mostly been studied using simple but time-
are able to account for a broad range of findings con- pressured tasks in which subjects are usually aware of
cerning decision confidence. Firstly, they correctly their errors and very rarely unsure whether their
predict that observers will change their mind more decision was right or wrong. Framed in terms of the
often from incorrect to correct responses than vice DDM,thedistinctionconcernswhethererrorsandsen-
versa, because beyond the bound the DV on error sitivitytoprocessingnoisearisebecauseoflowdriftrate,
trials will tend to regress towards the mean, whereas d,inthecaseofperceptualambiguity,oradoptionofa
after correct responses it will continue to grow, driven lowthreshold,u,toengenderfastresponding.Thereis
bythetrue underlying drift rate.This observation also nonetheless obvious similarity between metacognitive
naturally explains another conundrum associated with judgements of confidence and error likelihood, and it
decision confidence: that second-order confidence is isthereforeunsurprisingthat models oferror monitor-
generally higher for correct trials than incorrect trials. ing turn out to complement those more recently
Indeed, reconsidering for a moment the data from developedinperceptualdecision-makingresearch.
Kiani et al., we note that these authors report that
neuralactivityinthedelaybetweenstimulusoffsetand (a)Post-decisionprocessinginerror monitoring
movement execution exerted a separate, independent Rabbittandco-workers’pioneeringworkbeginning in
influence over the decision to choose the ‘sure bet’ the 1960s established that error monitoring relies on
option [20]. Thus, although the authors argue that a post-decision processing. Their experiments showed
mechanistic description of decision confidence does that people can very reliably detect and correct their
notrequireustoinvokeadistinctmetacognitiveprocess own errors without requiring explicit feedback [27],
separate from evidence accumulation, the evidence butthatthisabilityisimpairedwhenstimulusduration
Phil.Trans.R.Soc.B(2012)
1314 N. Yeung & C. Summerfield Review. Confidence and error monitoring
time uncertainty—or conflict—in the decision process after
an initial response [34], or as inconsistency between
the outcomesofparalleldecisionprocessesat different
(a)
processing stages (e.g. perceptual categorization and
q response selection) [35]. While varying in their details
and precise predictions [32,34], common to all pro-
ce (b) qcom posals is the claim that metacognitive accuracy
en judgementsdependonpost-decisionprocessing.
vid (c) In effect, applying the framework provided by the
e
DDM and its variants, one is obliged to assume that
–q error detection reduces to error correction: that errors
are detected whenevercorrective activity reaches a cri-
(d)
teria level—the change-of-mind bound. However, this
conclusion is difficult to reconcile with evidence that
errorcorrectionanddetectionareatleastpartlydissoci-
Figure2.TheoriesoferrordetectionwithintheDDMframe-
able, a finding that was again presciently reported in
work. The drift-diffusion process is illustrated schematically
Rabbitt’sseminalwork.Rabbitt’sstudiesdemonstrated
fortwotrials,oneinwhichdecisionuisthecorrectresponse
that error corrections are a relatively automatic and
andonetrialinwhichthisdecisionisincorrect.Bothdecisions
unreflective consequence of post-decision processing.
occur at the same time point (a). Following the correct
response (grey line), post-decision processing continues to Thus, they can be extremely fast, occurring within
accumulate in favour of the decision just made. Following 10–20msoftheinitialerror[36],andmaybeproduced
errors (black line), the drift rate regresses to its true mean, evenwhensubjectsareinstructedtoavoiddoingso[37].
causing the DV to re-cross the decision bound (b), sub- Incontrast,explicitdetectionandsignallingoferrorsis
sequently cross a change-of-mind bound (c), and finally muchslower,voluntary,more pronetointerference by
crosstheoriginallycorrectdecisionbound, –u(d).Thegrey distractingtasks,andmoresensitivetocognitivedecline
shaded area indicates a period of uncertainty, or conflict,
in normal ageing [29]. Indeed, people sometimes
betweenthere-crossingoftheubound(b)andlatercrossing
remainunawareoferrorsthattheyneverthelesscorrect
ofthe –ubound(d).
[38].Collectively,thesedifferencessuggestthatexplicit
error detection cannot be a mere consequence of
is reduced [28], suggesting its dependence on contin- post-decision correction: further processing or evalu-
ued processing of the stimulus after an initial error ation must intervene between initial correction and
(which is curtailed when stimuli are very briefly pre- later explicit awareness that an error has occurred.
sented). Error monitoring is also impaired when Consistent with this analysis, recent investigations
subsequent stimuli appear soon after the initial have identified dissociable neural correlates of error
response [29], and responses to those later stimuli correctionanddetection.
are themselves postponed following errors [30], con-
sistent with the notion that this monitoring involves (b) Neural substrates of error monitoring
the same mechanisms as the initial decision. Researchinterestinerrormonitoringincreasedsubstan-
Summarizing these findings, Rabbitt likened evi- tially following the discovery of scalp EEG potentials
dence accumulation in decision-making to votes in a that reliably occur time-locked to decision errors.
committee,inwhichincorrectdecisionsaresometimes Most studies have used the Eriksen flanker task, in
made on the basis of incomplete information, but ‘as which subjects perform a speeded categorization of a
subsequent votes come in, a more accurate consensus centraltarget(e.g.HorS),whileignoringflankingdis-
will accumulate and the earlier mistake will become tractorsthataresometimescompatible(e.g.HHH)and
apparent’[28].Thus,errorsarecharacterizedbybipha- sometimes incompatible (e.g. SHS) with that target.
sicevidenceaccumulation,withinitialaccumulationin Withmodestspeed pressure,error rates onincompati-
favour of the incorrect response followed by later drift ble trials can exceed 20 per cent. Following such
towards the correct decision (as the trial-average drift errors,anegativeevent-relatedpotentialoverfrontocen-
rateregressestothetruemean).Bycontrast,continued tral sites is observed within 100ms of the incorrect
evaluation following correct responses tends simply response, followed by a later positive wave peaking
to reinforce the original decision. This model is an 200–400ms later over parietal cortex [31]. Labelled
obvious precursor to more recent accounts of decision theerror-relatednegativity(ERN/Ne)anderrorpositiv-
confidence[15]andchangesofmind[26]. ity(Pe),respectively,theseEEGcomponentshavebeen
All subsequent models of error detection have widelystudiedtoprovideinsightintoerror monitoring
adoptedRabbitt’sbroadframework,withdebatefocus- inhealthyandclinicalpopulations.
ing instead on the precise mechanism by which post- Converging evidence has identified anterior cingu-
decision processing leads to error detection. Figure 2 late cortex (ACC) as the source of the ERN. For
illustrates some key model variants. Within a standard example, in simultaneous EEG–fMRI recordings,
DDMframework,errorscouldbedetectedassuccessive single-trial ERN amplitude correlates reliably only
crossingsofdecisionboundariesforthetwocompeting with activity in a focused ACC source [39]. The
responses [31,32] or as ‘double crossings’ of a single sourceofthePeislesswellcharacterized,butevidence
decision bound [33]—both close relatives of Resulaj that it is a variant of the well-studied P3 component
et al.’s notion of a change-of-mind bound. Errors can [40] would imply widely distributed neural generators
also be detected in terms of the occurrence of inparietalandprefrontalcortex[41].TheP3hasbeen
Phil.Trans.R.Soc.B(2012)
Review. Confidence and error monitoring N. Yeung & C. Summerfield 1315
suggested to reflect the distributed action of norepi- focused on the finding that subjects usually respond
nephrine released by the locus coeruleus brainstem more slowly on trials immediately following errors
nucleus in response to motivationally salient events [27].Althoughthiseffectatleastpartlyreflectsthedis-
during decision-making [42]. tracting occurrence of a rare event [50], as errors
The functional significance of the ERN and Pe is a typically are, a consensus view holds that post-error
matter of ongoing debate. Competing theories of the slowing reflects strategic adaptation to prevent further
ERN propose a role in error detection, reinforcement errors [51]. EEG studies have subsequently shown
learning and conflict monitoring; while theories of that the degree of observed slowing scales with the
the Pe include conscious awareness of errors, affective magnitudeoferror-relatedERN/Peactivity[46].Com-
responses and behavioural adjustments to avoid putational models implementing error-related control
further errors [43]. These debates notwithstanding, over distance-to-bound, juj, account for detailed prop-
it is now clear that the ERN and Pe dissociably map erties of empirically observed post-error slowing. In
onto processes related to error correction and error one class of model, detection of response uncertainty
detection, respectively. Thus, ERN onset coincides (conflict) immediately following error commission
with the onset of error-correcting activity as revealed leads to an increase in the bound—and, hence, more
through EMG recordings [44], typically around the cautious responding—on subsequent trials [52].
time of error commission [31], and its amplitude Recent extensions of this idea suggest that conflict
varies with both the speed [45] and probability [46] detection may also be used to adjust the bound
oferrorcorrection. In contrast, Pe amplitude isinsen- dynamicallyevenasadecisionisbeingmade[51,53].
sitive to the strength of correcting activity when error Error signals not only support subtle adjustments
detection rates are controlled [47]. Conversely, that optimize online decision-making, they also play a
although both the ERN and Pe are found to covary key role in longer term adjustments during learning.
with subjective ratings of response accuracy [46,48], Holroyd & Coles [35], for example, suggest that the
correlations involving the ERN disappear when the ERN reflects reinforcement learning of action values.
two components are carefully dissociated. In antisac- They showed that the ERN migrates in time as new
cade tasks in which subjects correct all of their errors stimulus–responsemappingsarelearned,frominitially
butdetectonlyhalfofthem,ERNamplitudeisequiv- being triggered by environmental feedback to later
alent for aware and unaware errors, whereas the Pe is being driven by internal representations of the learned
robustly observed only when subjects detect their mappings, a pattern that mimics the migration of
errors [38]. Taken together, these findings suggest dopaminergic reward prediction error signals from
that whereas the ERN directly indexes automatic unconditionedrewardstopredictivestimuliduringcon-
post-decision processes leading to rapid error correc- ditioning [54]. Meanwhile, fMRI activity in ACC and
tion, the later Pe is selectively associated with explicit neighbouringcortexatthetimeofanincorrectresponse
detection and signalling of errors. These results thus have been shown to predict response accuracyon later
provide converging evidence for the view that error presentationsoftherelevantstimulus[55,56].
correction and detection reflect distinct processes. Moststudiesofpost-erroradjustmentshavefocused
Arecentstudyhasshedlightonspecificallyhowthe on the ERN and ACC, reflecting wide interest in the
Perelatestoerrordetection[49].Subjectsperformeda role of ACC in reinforcement learning [35,57],
difficultbrightnessdiscriminationunderspeedpressure rather than on the later Pe component. However, the
toinduceamixtureoferrorsresultingfromperceptual ERN and Pe typically covary across conditions and,
ambiguity and decision urgency. After each response, when the two components are dissociated, post-error
they made a binary correct/error judgement, with adjustments are only observed following detected
monetary incentives varied across blocks to encourage errors on which a Pe component is present [38],
either liberal or conservative error signalling. Signal suggesting that the latter may be a more direct corre-
detectionanalysisindicatedthatsubjects’accuracyjud- late of the learning mechanisms by which future
gements could be well fit by assuming that these behaviour is adapted following an error.
judgementsreflectedacontinuumofconfidence(from
sure correct to sure incorrect), with subjects applying
a criterion—a metacognitive u—that varied according 4. INTEGRATIVE MODELS OF DECISION
to the incentive regime. Critically, error signallingper- CONFIDENCE AND ERROR MONITORING
formancewascloselyrelatedtobetween-conditionand The discussion above highlights convergent evolution
trial-by-trial variation in Pe amplitude (but not the in work on decision confidence and error monitoring,
ERN). That is, Pe amplitude appeared to provide a suggesting that common principles govern metacogni-
direct neural index of continuously varying decision tive judgements regardless of whether errors arise
confidenceonwhichsubjectsbasedtheirmetacognitive because of intrinsic task difficulty (low drift rate, d)
judgements,withcategoricalsignallingoferrorsoccur- or because of externally imposed decision urgency
ring when confidence that the response was wrong (low threshold, u). In what follows, we consider the
exceededsomecriteriallevel. implications of this convergence for future research,
both positive (in terms of mutually informative les-
(c) Impact of error monitoring on behaviour sons) and negative (in terms of shared limitations).
Theresearchdescribedabovedocumentsthemechan-
istic and neural basis of error monitoring. A parallel (a) Converging themes
lineofresearchhasconsideredtheimpactoferrormoni- Theories of decision confidence and error monitoring
toring on future behaviour. Much of this work has each emphasize the importance of post-decision
Phil.Trans.R.Soc.B(2012)
1316 N. Yeung & C. Summerfield Review. Confidence and error monitoring
processing, and likewise point to a key dissociation behaviourasaseriesofdiscretedecisions,eachsubject
between, on the one hand, continued processing of toindependentmetacognitivescrutiny,isausefulcon-
stimulus information within the decision-making venience when developing experimental tests and
system (leading to changes of mind and error correc- formal models. However, it remains unclear whether
tions) and, on the other, the formulation and the findings will scale up to explain real-world
expression of explicit judgements of confidence and decisions and actions that are fluid, temporally
accuracy. The literatures have developed these ideas extended and embedded in the broader context of
in complementary directions, such that each literature evolving behavioural goals.
offers valuable lessons for the other. First, whereas Letusrevisitforamomentouropeningexampleof
confidence is typically characterized as varying along cycling along on a winding country lane. Obviously,
a continuum, and formalized as such in accounts we do make occasional discrete categorical
such as the 2DSD model [15], error detection is decisions—for example, when choosing between two
often characterized as an all-or-none process [32,33]. available paths at a fork in the road. However, most
Thus, according to many current theories of error of our decisions and actions unfold gradually, shaped
monitoring, binary yes/no error judgements are an by our interactions with the environment and an
intrinsic feature of the monitoring system rather than ever-changing stream of incoming sensory infor-
a reflection of the arbitrary metacognitive decision mation, as exemplified by the continuous, subtle
thatsubjectsareaskedtomake.Assuch,thesetheories adjustmentsofhandlebarsandbrakesneededtomain-
cannotexplainhowsubjectsareabletoexpressgraded tain direction and balance when cycling. Current
confidence in their accuracy judgements [48,49]. theories of metacognition require a clear division in
Conversely, the preceding review of work on error time between cognitive decision-making and meta-
monitoring identifies important limitations in current cognitive evaluation, with the latter beginning when
theories of decision confidence. This review suggests the former ends (‘post-decision processing’). However,
that the current debate between decisional locus and for continuous and extended actions, there is no
post-decisional models of decision confidence will definable time-point at which a specific decision is
very likely be resolved in favour of the latter, but that finalized and metacognitive evaluation begins. Most
even these post-decisional theories will need modifi- current theories are therefore ill-suited to describing
cation to accommodate evidence that metacognitive thetemporallyextendedactions,comprisinghundreds
awareness (cf. error detection and the Pe) cannot be or thousands of micro-decisions, that characterize
reduced simply to post-decision processing (cf. error everyday behaviour.
correction and the ERN)—the two are at least partly Indeed, decisions that initially appear discrete and
dissociable. Perhaps more informative still will be categorical may turn out, on closer inspection, to be
exploration of the role of confidence judgements in graded and transitory. For example, in the Eriksen
guiding future actions: whereas research on decision flanker task, overt responses occur tightly time-
confidencehaslargelyfocusedonhowconfidenceesti- locked to the point at which lateralized activity in
mates are derived, a major focus of error-monitoring motor cortex exceeds a threshold value [58], just as
research has been on how this kind of metacognitive saccadiceyemovementsappeartobetriggeredaccord-
information might be used to modify behaviour both ing to a threshold firing rate for LIP neurons [1],
in the short- [27,52,53] and long-term [35,55,56]. suggestive of a fixed decision point after which an
Borrowing these insights, we might predict that para- action is initiated. However, fine-grained analyses
metric estimates of confidence could support finer- reveal graded and continuous information flow at
grained control of behaviour than can be achieved every stage, even downstream of motor cortex. Thus,
through binary categorization of responses as correct within a single trial, motor cortex activity may latera-
or incorrect—for example, by allowing parametric lize first towards one response then towards the
variation in post-error slowing or by providing a other; small EMG twitches in one finger may be fol-
scalar predictionerrorsignaltosupportreinforcement lowed by full movements of another; and overt
learning. Thus, confidence estimates could provide actions themselves may vary in force in a graded
useful information in optimizing the rate of learning: manner, for example, with incorrect actions executed
onemightpredictthatpeoplewillpaygreaterattention less forcefully than correct ones [44,58,59]. Mean-
to environmental feedback following responses in while, categorical or economic judgements about
which they have less confidence. visual information are often preceded by exploratory
eye movements, which may themselves constitute
interim decisions en route to the eventual choice [60].
(b) Shared limitations In such systems, there is no single, final decision
As well as sharing complementary strengths, the the- point that could mark the beginning of metacognitive
ories also share common weaknesses. In particular, evaluation. Detailed analyses of neural markers of
like the models of decision-making on which they are metacognition point towards a similar conclusion:
based, current theories of confidence and error moni- error-relatedEEGactivityisnotonlyobservedfollow-
toring have focused almost exclusively on decisions ing overt incorrect actions, but also following ‘partial’
that are discrete and punctate in time: a decision is errors in which the incorrect muscles twitch, but the
made when a boundary is reached [3]; errors are incorrect action is not produced [61] and, crucially,
detected when a second change-of-mind bound is in a graded fashion as a function of the level of sub-
crossed [26]; and confidence is estimated at the time threshold cortical activity favouring an incorrect
of a later metacognitive probe [15]. Characterizing response [34]. Metacognition appears to be graded
Phil.Trans.R.Soc.B(2012)
Review. Confidence and error monitoring N. Yeung & C. Summerfield 1317
and continuous in just the same manner as the revealed that human observers are exquisitely sensi-
underlying decision process. tivity to evidence reliability when sampling from
Human decision-making also has a continuous multiple sources of information [67].
quality whenviewedover longer timescales, with indi- Yet, most currently popular models of perceptual
vidual decisions chained into sequences that serve decisions offer no way of expressing the trust or dis-
longer-term behavioural goals. Thus, actions that trust associated with the evidence accumulated; all
reflect definitive choices at a lower goal level (e.g. sourcesofevidencearecombinedinthecommoncur-
movements of the arm, shifts of posture) may consti- rency of the DV, which then gives the strength of
tute reversible, interim choices at a higher goal level evidence as a simple scalar value (the vertical location
(e.g.turningleftataforkintheroad),andevenacom- ofthediffusingparticleintheDDM,orthemagnitude
mitment to an individual right or left turn might be of the evidence variable in SDT). Once factored into
just a partial commitment to a yet higher level goal the DV in this way, the reliability of evidence has no
(e.g. reaching a specified destination) [62]. This further impact on the decision-making process. This
form of hierarchical structure is built into many feature contrasts with some accounts of economic
recent theories of the computational and neural basis choices, in which sensitivity to risk (i.e. outcome var-
of action selection [63], in which common principles iance) as well as value (i.e. outcome mean) has been
of selection and control are held to operate at each documentedatthebehaviouralandneurophysiological
level of hierarchical abstraction [64]. Recent findings levels [68,69].
indicatethatmetacognitiveprocessesaresimilarlysen- Mathematically, the reliability of perceptual evi-
sitive to this hierarchical structure [65]. For example, dence is orthogonal to its strength, because the mean
errors that are equally discrepant in terms of low- and the variance represent different moments of a
level actions are treated very differently according to probability distribution. Bayesian models that exploit
their impact on global task performance [66]. Little this point—representing evidence as a probability dis-
is currently known about the mechanisms by which tribution with a given mean (evidence strength) and
metacognitive judgements might be embedded in variance (evidence reliability)—have been used to cal-
ongoing higher level behaviour in this way. culate ideal estimates of expected value in economic
Thus, a crucial limitation of current metacognitive choice tasks [70]. Applied to perceptual decision
theoriesisthattheydonotreflectthewaythatourcon- tasks, the notion would be that the diffusing parti-
fidence in our actions contributes to fluid, structured cle of the DDM is more accurately conceived of as a
sensorimotor behaviour. Rather, they consider that probability distribution that evolves across samples
errors are processed in an all-or-nothing fashion, for (figure 3), with the central tendency of that distri-
example,whenapost-decisionprocesscrossesameta- bution analogous to the vertical location of the
cognitivedecisionbound.Inwhatfollows,weconsider particle. Crucially, the variance of this distribution
anotherwayofthinkingaboutdecisionconfidencethat provides additional information—specifically, a rep-
is not subject to these limitations. resentation of evidence reliability in terms of the
precision of the mean—that is not made explicit in
the standard DDM (by precision, we mean the inverse
5. FUTURE DIRECTIONS of the standard deviation).
Addressing the challenges outlined above will require Recently, it has been shown that neural network
the development of new models that consider not modelsinwhichLIPneuronsencodethefullposterior
just discrete, binary choices, but also the kinds of probability distribution associated with a stimulus can
extended, goal-oriented actions that characterize capture behaviour and neural dynamics occurring
everydayhuman behaviour.We conclude by consider- duringpsychophysicaldiscriminationtasksinprimates
ing one promising extension of current decision [71]. This result suggests that precision may be
models, which addresses the issue of the reliability of encoded in the variance of firing rates across a neural
evidence in a manner that opens intriguing new ave- population, thus signalling evidence reliability in
nues for understanding how metacognitive evaluation much the sameway thatmean firing rateof the popu-
might occur for continuous, extended actions. lation encodes evidence strength [72]. Relatedly,
Ratcliff & Starns [73] have recently proposed a
(a) Evidence reliability model of decision confidence in which the decision-
Formal accounts of categorical decisions, such as the relevant evidence on a single trial is not a scalar esti-
DDM, are often illustrated by analogy to court of mate, but a distribution of possible values, allowing
law, in which the jury weighs up evidence favouring parametric estimates of confidence in the choice to
the innocence or guilt of the defendant [1]. However, be calculated asthe integral of this distribution falling
this analogy also highlights an inconsistency between within graded confidence bounds. Together, these
current decision models and choices made in the real findings open promising new avenues for research
world: that in the latter, we usually consider the into the neural basis of categorical choice.
extent to which we trust the evidence relevant for a
decision. For example, in a law court, evidence from
a trustworthy source (for example, official telephone (b) Confidence revisited
records)mightweighmoreheavilyinajury’sdelibera- We propose that evidence strength and reliability are
tions than evidence from an unreliable source (for encoded in parallel during evidence accumulation,
example, a witness with a vested interest). Corre- and that this framework provides an intriguing new
spondingly, careful signal detection analyses have way of thinking about decision confidence—as the
Phil.Trans.R.Soc.B(2012)
1318 N. Yeung & C. Summerfield Review. Confidence and error monitoring
(a)ata) time t time t +1 Swehciochndelyv,idthenecme oqduealliitsyavbalerietso deveesncribweithsiitnuaatiosninsgilne
d
H | trial [71], something that standard models cannot
p ( achieve. In fact, by keeping trackof the likely variabil-
ity of information in the external world, Bayesian
perceptual hypotheses perceptual hypotheses
accounts can optimally distinguish true state changes
(b) in the generative information giving rise to the senses
ses low variability high variability p (H | data) fromnoise[74].Precisionestimatesarethusparticula-
e
h
ot high rly useful in situations where the causes of perceptual
p
y evidence may change unpredictably over time, and as
al h low such may provide a better account of the sort of fluid,
u
pt ongoing sensorimotor integration that characterizes
e
c everydayactivitiessuchasridingabicycle.
er
p The hypothesis also leads to clear and testable
time time
predictions about the sensitivity of human decision-
Figure3.Schematicofamodelinwhichboththemeanand making to evidence reliability. Firstly, it predicts that
variance of information in an array are estimated in a serial variabilityofevidencewithinatrialshouldbothreduce
sampling framework. (a) The left panel shows the posterior accuracy and lengthen RT, because during sequential
probability distribution p (H j data) over a continuous sampling, the precision of the mean increases more
space of possible perceptual hypotheses (e.g. these dots are slowlywhenthesamplesaredrawnfromamorevariable
moving to the right with 30% coherence; this signal is 40% distribution. This prediction has recently been con-
visible; etc.) at a given time, t. This distribution reflectsthe
firmed in an experiment in which observers made
evidence sampled from the stimulus thus far, i.e. between
discrimination judgements on the average feature (e.g.
onset and time t (grey dots). The new sample received at
colour) of an array of multiple elements presented
timetisshowninred.Rightpanel:attimetþ1,thisdistri-
simultaneously on the screen [75]. Critically, this
bution (light grey) is updated in the light of the newly
sampled information, giving rise to anewprobabilitydistri- multi-elementaveragingtaskallowedtheexperimenters
bution.Inthismodel,confidenceisreflectedintheprecision tomanipulatethemeanandthevarianceoftherelevant
oftheposteriordistribution,i.e.thereciprocalofitsstandard featureinanorthogonalfashion.Theresultsshowedthat
deviation.(b)Theevolvingposteriorprobabilitydistribution observers were slower to discriminate more variable
over perceptualhypotheses(y-axis)foreachsuccessivetime arrays,aresultthatispredictedbytheprecisionaccount
point (x-axis; blue–red colourmap; red values indicate but not by standard accumulation models such as the
higher probabilities). The posterior distribution is updated
DDM. Moreover, observers in this study tended to
followingthearrivalofsuccessivesampleswithlowvariance
downweight outlying or otherwise untrustworthy evi-
(left panel) or high variance (right panel). Precision of the
dence,muchlikeastatisticianmightexcludeanoutlier
probabilistic representation of evidence strength increases
from a sample of data. Although this study did not
morerapidlyfor thelow-variabilitysamples.
assess subjects’ second-order confidence in their
decision, the precision accountmakesthe clear predic-
precisionofevidenceaccumulatedduringasingletrial. tion that in the multi-element task, both subjective
Thisrepresentationofconfidencediffersintwocritical confidence and the rate of occurrence of changes of
respects from the models described above: it is based mindwilldependonarrayvariabilitytoagreaterextent
on an intrinsic feature of the decision process—var- thanonitsmean,apredictionripeforfuturetesting.
iance in the evidence encountered—and it is Finally, this conception of decision confidence
available continuously and instantaneously. In con- makes direct contact with broader theories of the
trast, existing models suggest that metacognitive role of metacognitive evaluation in behavioural con-
evaluations are derived indirectly by comparing rep- trol. In particular, because precision is closely related
resentations of the DV from two (or more) discrete to the concept of decision conflict [52]—greater var-
time points. Precision thus provides a more suitable iance in evidence should result in greater conflict
basisfor metacognitiveevaluationinthekindsoftem- between competing response options—the theory can
porally extended tasks discussed above, in which no inherit ideas from research on decision conflict about
discrete decision point divides cognitive decisions how precision estimates might be used to guide both
from metacognitive evaluation. current performance (e.g. through dynamic modu-
This hypothesis, though speculative at this point, lation of decision bounds) [51,53] and future
has several attractive features. Firstly, it is consistent behaviour (e.g. through modulation of learning rate
with the evidence described above on post-decision in relation to environmental signals of success or fail-
processing because instantaneous precision will tend ure) [70]. As such, the precision model not only
to be highly correlated with later variability in the provides a formally specified account of decision con-
DV(thebasisofmostcurrentmodelsofmetacognitive fidence,butalsoleadstoimmediatesuggestionsabout
judgements): variable evidence will give rise to fre- the use of confidence judgements in the optimization
quent errors, and these errors will be accompanied of behaviour.
by low estimates of precision at the time of the initial
response and a high probability of later changes of
mind or error correction. Thus, the precision account 6. CONCLUSION
agrees that existing theories fit the data well, but Formal models such as the DDM have proved extre-
suggests that they may do so for the wrong reasons. mely valuable in understanding human and animal
Phil.Trans.R.Soc.B(2012)
Review. Confidence and error monitoring N. Yeung & C. Summerfield 1319
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in probing the metacognitive processes by which we
Psychon. Bull. Rev. 10, 843–876. (doi:10.3758/BF0
evaluate and express our degree of confidence in our
3196546)
decisions. In particular, significant convergence in
11 Evans,S.&Azzopardi,P.2007Evaluationofa‘bias-free’
methods and theories of decision confidence and error
measure of awareness. Spat. Vis. 20, 61–77. (doi:10.
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governdifferenttypesofmetacognitivejudgements. 12 Maniscalco,B.&Lau,H.2012Asignaldetectiontheor-
There is nonetheless important scope for current etic approach for estimating metacognitive sensitivity
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14 Vickers,D.,Burt,J.,Smith,P.&Brown,M.1985Exper-
behaviour outside the experimental laboratory. We
imental paradigms emphasizing state or process
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peoplearesensitivenotonlytothestrengthofevidence
Psychol.59,129–161.
theyencounterastheymakeadecision,butalsotothe
15 Pleskac, T. J. & Busemeyer, J. R. 2010 Two-stage
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17 Heath, R. A. 1984 Random-walk and accumulator
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18 Vickers,D.&Packer,J.1982Effectsofalternatingsetfor
speedoraccuracyonresponse-time,accuracyandconfi-
N.Y. was supported by a grant from the U.S. National
dence in a unidimensional discrimination task. Acta
Institutes of Health (P50-MH62196). C.S. was supported
by a grant from the Wellcome Trust (WT092646AIA). Psychol. 50, 179–197. (doi:10.1016/0001-6918(82)
Thoughtful commentaries from Vincent de Gardelle and 90006-3)
twoanonymous reviewersaregratefullyacknowledged. 19 Kepecs,A.,Uchida,N.,Zariwala,H.A.&Mainen,Z.F.
2008 Neural correlates, computation and behavioural
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