Neural architecture of metacognition

Neural architecture of metacognition

Steve Fleming Wellcome Centre for Human Neuroimaging, UCL stephen.fleming@ucl.ac.uk

metacoglab.org

Outline of the course 1. Introduction to metacognition: theory and measurement

2. Origins of metacognition in evolution and development

3. Neural architecture of metacognition

4. Functional roles of metacognition in behavioural control (Rouault)

Where should we look?

Dissociating metacognition from changes in performance Journal of Experimental Psychology: Learnins, Memory, and Cognition 1986, Vol. 12, No. 3, 452-460

Copyright 1986 by the American PsychologicalAs.u~afion, Inc. 0278-7393/86/$00.75

Memory and Metamemory: A Study of the Feeling-of-Knowing Phenomenon in Amnesic Patients Arthur P. Shimamura and Larry R. Squire Veterans Administration Medical Center, San Diego and Department of Psychiatry, University of California, San Diego, School of Medicine Accuracy of the feeling of knowing was tested in patients with Korsakoff's syndrome, patients prescribed electroconvulsive therapy, four other cases of amnesia, and control subjects. In Experiment 1, we tested feeling-of-knowing accuracy for the answers to general information questions that could not be recalled. Subjects were asked to rank nonrecalled questions in terms of how likely they thought they would be able to recognize the answers and were then given a recognition test for these items.

Only patients with Korsakoff's syndrome were impaired in making feeling-of-knowing predictions.

The other amnesic patients were as accurate as control subjects in their feeling-of-knowing predictions. In Experiment 2, we replicated these findings in a sentence memory paradigm that tested newly learned information. The results showed that impaired metamemory is not an obligatory feature of amnesia, because amnesia can occur without detectable metamemory deficits. The impaired metamemory exhibited by patients with Korsakoff's syndrome reflects a cognitive impairment that is not typically observed in other forms of amnesia.

Four groups: healthy controls, alcoholic controls, Korsakoff’s syndrome patients, amnesic patients

LEARN SENTENCES

FEELING OF KNOWING?

Often one experiences a sense or feeling of knowing some information without being able to recall it. In its most frustrating form the information seems to be on the "tip of the tongue."

These experiences illustrate that we have knowledge about what we know, even when complete recall is not possible. Knowledge about one's memory capabilities and knowledge about strategies that can aid memory are termed metamemory (Brown,

RECOGNITION TEST

effects (Diamond & Rozin, 1984; Graf, Squire, & Mandler, 1984; Jacoby & Witherspoon, 1982; Shimamura & Squire, 1984; Squire, Shimamura, & Graf, 1985; for review, see Shimamura, in press).

One possibility is that deficits in metamemory contribute to or cause amnesia. That is, lack of conscious knowledge about what is stored in memory and a lack of awareness about strate-

memory performance as the Korsakoff patients. In fact, the feeling-of-knowing performance of the four (non-Korsakoff) amnesic patients was somewhat above the level obtained by delayed subjects. Patients with Korsakoff's syndrome, however, Planned comparisons showed that delayed control subjects performed more poorly than the delayed control subjects. Thus, Journal of Experimental Psychology: Copyright 1986 by the American PsychologicalAs.u~afion, Inc. and the four amnesic patients exhibited better feeling-of-knowLearnins, Memory, and Cognition 0278-7393/86/$00.75 Korsakoff patients appear to have a particular deficit in meta1986, Vol. 12, No. 3, 452-460 ing accuracy than patients with Korsakoff's syndrome, ts(13) > memory that cannot be explained as a result of their memory 2.2, p < .05. The difference between theand delayed alcoholic sub-A Study impairment.

The finding that the four individual amnesic subMemory Metamemory: of the Feeling-of-Knowing jects and the patients with Korsakoff's syndrome approached jects performed on average somewhat above the expected level Phenomenon in Amnesic Patients statistical significance, t(17) = 1.82, p = .08. Thus, despite the suggests that the metamemory demands for a normal subject fact that both groups of amnesic patients and both groups of tested a long delay may be more difficult than the demands Arthur P. Shimamura and Larryafter R. Squire Veterans Administration Medical Center, San Diego and Department of Psychiatry, University delayed subjects were matched on recognition memory for nonfor an amnesic patient tested after a short delay. Despite this of California, San Diego, School of Medicine recalled items, feeling-of-knowing accuracy differed among the possible advantage in the case of some amnesic patients, pagroups. The four amnesic cases showed good Accuracy of thefeeling-of-knowfeeling of knowing was tested in patients with Korsakoff's syndrome,R. patients pre458 ARTHUR P. SHIMAMURA AND LARRY SQUIRE scribed electroconvulsive therapy, four other cases of amnesia, and control subjects. In Experiment ing accuracy, given their level of memory performance, but the 1, we tested feeling-of-knowing accuracy for the answers to general information questions that could not be recalled.

Subjects were asked to rank of how likely they thoughtF E E L I N G - O F - K N O W I N G patients with Korsakoff's impaired.

Indeed, thenonrecalled questions ina terms R E C O syndrome G N I T I O N OFwere NONRECALLED they would be able to recognize the answers and were then given recognitionperformance test for these items. of other amnesic patients--patients pre knowing PERFORMANC E SENTENCES Only patients with Korsakoff's syndrome were impaired in making feeling-of-knowing predictions. four amnesic somewhat better than .80 80 patients actually performed The other amnesic patients were as accurate as control subjects in their feeling-of-knowing predicscribed bilateral ECT, three patients with amnesia as a resul the two delayed groups, though thesetions. differences marginIn Experimentwere 2, we replicated these findings in a sentence memory paradigm that tested learned information. The results showed that impaired obligatory ofmetamemory an .70 anoxic ischemic episode, and patient N. A.--was a - is not anor ally significant, ts(l 4) < 2.0, ps > .07. newly 70 feature of amnesia, because amnesia can occur without detectable metamemory deficits. The imasa cognitive that of control subjects whose recall and recognition paired metamemory exhibited by patients syndrome reflects impairTable 2 shows recognition performance of nonrecalled sen-with Korsakoff'saccurate ment that is not typically observed in other forms of amnesia. .

6 0 memory performance was matched to the performance of the tences across the four feeling-of-knowing rating categories. Table 2 also shows the average number of nonrecalled sentences amnesic patients. These findings demonstrate that memory and ,50 z 50 one experiences a sense category. or feeling of knowing some effects (Diamond & Rozin, 1984; Squire, & Mandler, that were feeling-of-knowing All subC9 placed in each Often metamemory areGraf,not inextricably linked: Impaired feelin I information without being able to recall it. In its most frustrat1984; Jacoby ,¢ & Witherspoon, 1982; Shimamura & Squire, ject groups, except patients with Korsakoff's syndrome, showed of tu,_1knowing is not anreview, obligatory component of anterograd .40 ing form the information seems to be on the "tip of the tongue."

1984; Squire, Shimamura, & Graf, 1985; for see Shima,,, 4 0 n° These experiences performance illustrate that we haveas knowledge what mura, in press). a general decrease in recognition they about rated n,amnesia. we know, even when complete recall is not possible. Knowledge One possibility is that deficits in metamemory contribute to items from of knowing pure guess.

Thus, asstratein - is, lack of conscious knowledge about one's memoryto capabilities and knowledge about ,,z, 3ohigh feelingabout 8 or cause amnesia.

The.30That feeling-of-knowing impairment exhibited by patient that can aid memory aresyndrome termed metamemory (Brown, what is stored in memory and a lack of awareness about strateExperiment 1, patients gies with Korsakoff's exhibited with Korsakoff's syndrome spanned premorbid semantic mem 1978; Flavell & Wellman, 1977; Gruneberg, 1983). One manigies to aid memory contribute to poor performance on .20could impaired judgments when the judg~- feeling-of-knowing 20 festation of metamemory iseven the feeling-of-knowing phenommemory tests. By this view, amnesic patients should have ory and newly learned (episodic) memory. Because these pa n o n - t h e ability to judge the probability of future success in difficulty in tasks that assess metamemory just as they have ments were based on ane absolute rating technique. ~hance mean.)

-I1

I

tients were the only ones who exhibited amnesia for general se mantic facts, the finding of impaired feeling of knowing in Ex periment 1 could have been attributed to their memory impairment rather than to a specific impairment of meta memory. In Experiment 2, however, we assessed feeling o knowing for newly learned information, and in this case the Figure 4. Recognition memory (seven-alternative, forced-choice) for patients with Korsakoff's syndrome were still the only ones to Metacognition (performance-confidence nonrecalled sentence Task information.

(CON = healthy control subjects; exhibit impaired metamemory. This occurred despite the fac performance whether or not the answer to a nonrecalled question would subALC = alcoholic subjects; KOR = patients with Korsakoff's syndrome; correlation) that other amnesic patients studied were just as impaired in sequently be recognized on a multiple-choice test. Feeling-ofa memory test. Here we investigated the accuracy of the feelingof-knowing experience in amnesic patients.

The study of amnesia could help in explaining the relation between metamemory and memory. Amnesic patients exhibit General Discussion severe recall and CON ALC impairments KOR 4 on tests of CON ALCrecognition--they have deficits inCASES the ability to store, organize, and consciously recollect events that occurred since the onset of amnesia (for Patients with Korsakoff's syndrome impaired mak} 5 min I were L_.1-7 d a y l &in reviews, see Cermak, 1982; Hirst, 1982; Squire Cohen, 1984; delay 1982). ~ this delay ~ informaWeiskrantz, Yet .in about spite of impairment in new learning feeling-of-knowing judgments both general ing capacity (i.e., anterograde amnesia), amnesic patients can tion facts and about newly learned material. andSpecifically, pa-in learn and retain perceptual-motor cognitive skills, often normal fashion (Cohen, 1984; Cohen & Squire, Corkin, tients with Korsakoff's syndrome were unable to 1980; predict 1968; Milner, 1962); and they can demonstrate normal priming

10

.10 - memory tests. Alternatively, the prodifficulty on conventional cesses and brain systems that contribute to metamemory may be independent of the processes and brain systems required to 0establish memory itself. By this view, metamemory could be CON ALC C O N A Labilities C KOR N unaffected in amnesia and metamemory may AbeMpresent whenever amnesic patients perform above chance on a conventional memory test. Finally, only some forms t it~ may 5 be thatrain I ~_1--7 day.~ of amnesia affect metamemory, whereas other forms of amnesia delay - - delay - produce a selective deficit of memory, leaving metamemory abilities intact.

Figure 5 Feeling-of-knowing performance for each subject g r o u p Hirst (1982) suggested that metamemory may be impaired in (CONwith = Korsakoff's healthy control A L C = the alcoholic subjects; K O R = patients syndrome. subjects; Korsakoff's syndrome, best studied form amnesia, occurs as a result of chronic alco- = four other amnesic papatients withof KorsakofFs syndrome; 4 cases hol abuse and nutritional deficiency and is marked by severe tients; bars show _ standard error of the mean.) anterograde amnesia as well as extensive loss of memory for

i

“Double dissociation” of metacognition and performance Memory performance

Metacognition

Classical amnesia Korsakoff’s syndrome Prefrontal lesions

Shimamura & Squire (1986) JEP:LMC; Janowsky, Shimamura & Squire (1989) Psychobiology

Neuropsychologia 42 (2004) 957–966

962

A role for right medial prefrontal cortex in accurate D.M. Schnyer et al. / Neuropsychologia 42 (2004)feeling-of-knowing 957–966 judgments: evidence from patients with lesions to frontal cortex

To evaluate if the observed differences between

David M. Schnyer a,b,∗ , Mieke Verfaellie a , Michael P. Alexander a,c , Ginette LaFleche a , and controls in FOK accuracy were due solely to dif Lindsay Nicholls a , Alfred W. Kaszniak d

in memory performance on the task, recognition performance on the sentence memory task was ent covariate in the between group analysis of FOK ab the gamma measure, while recognition memory a Received 22 April 2003; accepted 17 November 2003 for a significant proportion of the variance betwee (F(1, 28) = 9.35, P < 0.01) the between group e Abstract mained marginally significant (F(1, 28) = 3.35, P In parallel, the Hamann statistic, recognition me The hypothesis that prefrontal cortex plays a critical role in accurate predictions of episodicfor memory performance was tested using the feeling-of-knowing (FOK) paradigm. Fourteen patients with a broad spectrum of damage to the frontal cortex and matched controls counted for a significant proportion of the variance read sentences and later were tested for recall memory, confidence judgments, and FOK accuracy using as cues the sentences with the final word missing. While frontal patients were impaired at recall and recognition memory, they were accurate groups (F(1, 29)able=to make 4.36, P <confidence 0.05) and there r judgments about their recall attempts. By contrast, as a group, the patients were markedly impaired in the accuracy of their prospective marginally significant between group effect (F( FOK judgments. Lesion analysis of frontal patients with clear FOK impairmentarevealed an overlapping region of damage in right medial prefrontal cortex. These findings provide functional and anatomical evidence for a dissociation between recall confidence and prospective 3.73, P < 0.07). a

Memory Disorders Research Center, Boston VA Healthcare System and Boston University School of Medicine (151A), 150 South Huntington Avenue, Boston, MA 02130-4817, USA b Athinoula A. Martinos Center for Biomedical Imaging, MGH/MIT/HMS, Charlestown, MA, USA 964 D.M. Schnyer et al. / Neuropsychologia 42 (2004) 957–966 c Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA d Department of Psychology, University of Arizona, USA

memory monitoring and are discussed in terms of familiarity and access theories of FOK predictions. © 2004 Elsevier Ltd. All rights reserved.

3.3. Correlations of FOK with neuropsychologica measures of memory and frontal function in Fig. 2. Displays confidence (panel a) and FOK accuracy (panel b) using frontal patients both the gamma and indexes. Error bars represent standard error a rating 1. Hamann Introduction about the accuracy of the product of that attempt.

Keywords: Metamemory; Memory monitoring; Frontal injury; Feeling-of-knowing

of the mean.

Researchers have proposed that these retrieval ratings are based on the relative effort put into the retrieval attempt, It has been proposed that the moment to moment funcIn frontal patients, either as judged by reaction time or the “easerelationship with which infor-between both tioning of human memory involves a complex interplay Fig. 3. Lesion convergence4 in four patients with impaired FOK most accuracy. The impaired upper, axially oriented FOK image set, demonstrates the method of lesion patients with mation comes tonumber mind” (Kelley & Lindsay, 1993; Yonelinas, between retrieval and monitoring processes (Koriat overlap & used. The sures of verbal learning separate colors indicate of overlapping subjects, with red (CVLT/AVLT) representing the region where all four long-delay patients overlap. The lower r 3-D volumes indicate the region of right medial prefrontal cortex where there is lesion convergence for four subjects, here represented in transparent relative to controls (see1996).

Fig. Monitoring 2b, F(1,involves 29) =the6.95, < 0.01 accuracy had overlap in vmPFC 2001).

As for prospective monitoring, a classic approach to Goldsmith, onlineP ability to blue. Volume of (a) is the overlap of the patients who are impaired using the Hamann index and volume of (b), those impaired using the gamma index. recognition and the gamma and Hamann measures 3 To this has been the feeling-of-knowing (FOK) paradigm (Hart, on the or potential success of one’s retrieval and F(1, 30) =reflect 8.59, P success < 0.01, respectively). deterevaluate cue-familiarity may be functionally similar to theet al. were tested using bivariate correlations. Th Schnyer (2005) Neuropsychologia 1965).accuracy efforts and can roughly be divided into prospective and retease-of-generation assessment proposed as the basis of con-

ARTICLES

Activations correlating with metamemory

eural systems success. The eriences into ociated with mporal lobe are essential rative memWill you remember this at test? reater during 0 rds2 that will hose that will PFC during 4 t rememberWill you remember this at test? hat PFC cony supporting Time (s) 8 utive operand facilitate 12 ergent behawith frontal suggest that Figure 1 Task design. Scenes and fixations were presented for 4 s. For each scene, participants made judgments of learning by predicting whether or not they would remember the scene in a later recognitiononitoring of memory test. depend on

tudies have implicated PFC as imporwn which specific subregions support

tion (t15 Âź 1.90, P 4 0.05) or latency (t15 Âź 0.91, P 4 0.05) of R versus F responses. In the post-scan recognition test, Kao participants madeNat Neurosci et al. (2005)

ARTICLES

Activations correlating with metamemory Rr Rf Fr Ff

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Second-order report

1.2 1 0.8 0.6 0.4 0.2 0

Right MTL

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Predicted > actual

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activation maps and percent ation maps are rendered onto canonical single-subject epresent the percent signal vation as a function of time, trial types. Bar graphs percent signal change from ulus presentation, for each of Error bars indicate s.e.m. predictions; F, ‘‘will forget’’ remembered; f, later forgotten. est (ROIs) defined from actual ng success contrast. In poral lobe (MTL), only the l encoding success was es above dotted lines). m predicted 4 actual ontrast. Ventromedial MPFC) and dorsomedial MPFC) showed a significant icted encoding success (red es) but not actual encoding he main effect for actual howed a trend toward FC. (c) ROIs defined from dicted encoding success oding success. Left lateral ant main effects for both d encoding success. e 2.

Rr Rf Fr Ff

0.2 0 –0.2 –0.4 –0.6 Trial types

Kao et al. (2005) Nat Neurosci

Neuroscience of metamemory • Neuropsychological studies show a double dissociation between memory and metamemory • Memory performance (recall) depends on integrity of medial temporal lobe; metamemory depends on integrity of a frontoparietal network • Ventromedial PFC activation correlates with prospective second-order reports (“judgments of learning”) • Most studies of metamemory have focused on one type of second-order report (e.g. JOL, FOK) • BUT - unclear what process / computation is engaged by prefrontal activation

Tracking perceptual certainty Threshold 100

Evidence

â&#x2C6;&#x2020;e (confidence)

50

RabbitItem Right Duck Left Item 0 0

40

80

120

Time

Vickers (1970) ; Kepecs et al. (2008)

and the early delay were associated with choosing the sure target later in the trial, as shown by the population average firing rates (Fig. 2D). To quantify this effect in single neurons, we compared activity in the 200-ms period before Ts

ber of a the beh the sign cation t certainty This possibl (i.e., m both LI it seem LIP acti acciden ulus dif based o by both of LIP f a varian in the trial-toto the m strength tions be leverage target (P finding we used 0.015). with su by thei Fig. 1. Postdecision wagering behavior in monkeys is indicative of choice certainty. (A) The sequence of We con events in the task. After acquiring a central fixation point (small red circle), two direction targets (large red Science Kiani & Shadlen (2009) iable di of statesâ&#x20AC;? alternative makes a clear prediction, which is not supported by the data. If the intermediate means were solely mixtures of the responses associated with Tin and Topp choices, then the variance should reflect the dispersion of

Tracking perceptual certainty

monkey would choose Ts (P < 0.01, AN tivity were weakly correlated (r = 0.10, suggesting that Ts was more salient wh each exerted independent leverage on lihood that the monkey would opt wager (P < 0.03) [equation 13 (25)] words, both the evolution of decis activity and the sustained activity in period carry information about choice LIP recordings; lines =reflect opt-out Althoughdashed both quantities the st t in the ever, after the appearance of Ts, these neurons g. 4B, it is easy to dence, variation in the buildup rate a f T with a sure target in their RF became predictive do s e direction decision the amount of time it takes to reach of the xpected chance of postdecision wager. Although it is not from the traces, the visual response in (19â&#x20AC;&#x201C;21, 37, 38), consistent with the lterna-time isobvious cision less view that decision time contributes .on Thisalsimplethe modfirst 200 ms was slightly larger when the certainty (8, 9, 12). avior and success. More monkey would choose Ts (P < 0.01, ANOVA), Indeed, a Bayesian framework tha mprovement in the was more salient when there ce that suggesting that T s rates both evidence and decision tim for trials in which Fig. 2. LIP activity predicts direc- several aspects of the data. As previou odel has only three which were set by tion choices and the postdecision the left-right choices on this task are choices and the wager. (A) Responses from one by the accumulation of evidence favor or trials without Ts neuron on trials in which Ts was the other option (17). This accumulat 2 R = 0.97) and Fig. not presented. Average firing rates we call a decision variable, v(t), is r lishes a prediction for Tin (black) and Topp (gray) by the firing rates of LIP neurons. It b of being correct on choices are shown for all correct neutral value and undergoes a random shown but waived choices (and the 0% coherent drift (also termed drift diffusion) as ev 0.95). The agreemotion strength) during motion cumulates for and against the two dire model and the data viewing and the delay period. natives. The decision terminates natur Bayesian sequenKiani & Shadlen (2009) Science Averages are aligned to motion

location of attention to the Ts location. More ce, and S1 and generally, it provides additional that activity the Smonkey made a decision about evidence the motion 2 d leftward motion ensuing direction in the period preceding the onset of ast term vanishes, activity Ts, even on trials when it opted out of the dithat motion is left out rection task. There is no indication that the ationthe terms implemonkey approached the task as a choice beig. 5B) otion strength.

The malizes belief tween in the three alternatives, Tin, Topp, and Ts. HowUnlike

y 8, 2009

Tracking perceptual certainty

Computational framework for confidence

or

World state (d)

L or R

?

Confidence = P(correct|d, a)

Bayesian estimate of probability correct L or R

Action (a)

PERSPECTIVE

Computing confidence in performance

odel

Statistical inference

V: vestibular information

I: visual information

V: vestibular information

p(I| ): visual likelihood of heading

p(I| ): vestibular likelihood of heading

ic

ction, riable deg

Certainty

p(V| )

hoiceariable

p(I,V| ): visuo-vestibular likelihood of heading Bayesian inference p(z|I,V): posterior distribution over choice-relevant variable

d(I,V): binary choice, left or right

Decision confidence needs access to both belief certainty and choice/response Parietal neurons are candidate neural substrate for tracking perceptual certainty

Confidence

What about choice?

Confidence: p(z = k|d = k,I,V): his posterior belief that choice is correct or, is a is choice -making process. The confidence in this choice, in contrast, is the probability responds to this choice, p (z = k|d = k, I,V). For more details, see Box 1.

Pouget et al. (2016) Nat Neuro

Error signals in PFC 1

Time

…

4 …

Higher or lower than 5?

7

“in this task subjects could detect and correct errors very efficiently without being given any external signal that an error had occurred” Rabbitt (1966) Nature

Results. Of the three stimulus types (with 100%, 50%, and the always correct/always incorrect mappings), performance could improve only on those trials in which the imperative stimuli were mapped with 100% probability to one of the two response options.

Figure 2 shows the accuracy values associated with the 100% mapping condition, averaged across the two stimuli, across blocks, and across participants, for the first and second halves of each block separately. As can be seen, participantsâ&#x20AC;&#x2122; accuracy in this condition improved as the blocks progressed, from 68% during the first half of the blocks to 79% during the second half, t(14) !

"8.75, p # .01. The modelâ&#x20AC;&#x2122;s performance also improved as the blocks progressed, from 64% to 83%, t(14) ! "24.4, p # .01.

Figure 3 illustrates typical ERNs elicited in the experiment.

Shown are the ERPs associated with positive and negative feedback, averaged across trials, across stimuli, and across blocks.

Figure 3A shows the ERN elicited by the feedback in the 50% mapping condition. Confidence intervals (.95) confirmed that the amplitude of the associated difference wave was less than zero (M ! "4.1 !V, SD ! 3.0 !V, interval ! "5.2 !V, "2.2 !V).

The ERN reached peak amplitude about 250 ms following presentation of the negative feedback stimulus (cf. Miltner, Braun, & Coles, 1997). Figure 3B illustrates the ERN associated with the response in the 100% mapping condition. Confidence intervals (.95) also confirmed that the amplitude of the associated difference wave was less than zero (M ! "6.4 !V, SD ! 4.4 !V, interval !

"8.6 !V, "4.2 !V). The ERN reached maximum amplitude about 80 ms following the button press.

Figure 4 illustrates the amplitudes of the ERN associated with the response and with the feedback for each mapping condition.

Shown in each figure are the magnitudes of the difference waves, as determined by averaging the experimental and the simulated data across trials, blocks, stimuli, and participants. Figure 4A illustrates the amplitude of the ERN on trials with 50% mapping probabilities. In this condition, the experimental ERN was elicited primarily by the feedback (M ! "4.1 !V) and not by the response (M ! "0.4 !V), t(14) ! 4.57, p # .01. Conversely, in the 100% mapping condition (Figure 4B), the experimental ERN was elicited

Error signals in PFC

Ridderinkhof et al. 2004 Science

-

error-related negativity (ERN)

Gehring et al. 1993 Psych Sci; Dehaene et al. 1994 Psych Sci

Figure 3. The error-related negativity (ERN) in the probabilistic learning task elicited by feedback stimuli in the 50% mapping condition (A) and the response in the 100% mapping condition (B). The waveforms are more negative on error trials than on correct trials. The ERN peaks about 250 ms after the onset of the feedback stimulus and about 80 ms after the onset of

The ERN reached peak amplitude about 250 ms following presentation of the negative feedback stimulus (cf. Miltner, Braun, & Coles, 1997). Figure 3B illustrates the ERN associated with the response in the 100% mapping condition. Confidence intervals (.95) also confirmed that the amplitude of the associated difference wave was less than zero (M ! "6.4 !V, SD ! 4.4 !V, interval !

"8.6 !V, "4.2 !V). The ERN reached maximum amplitude about 80 ms following the button press.

Figure 4 illustrates the amplitudes of the ERN associated with the response and with the feedback for each mapping condition.

Shown in each figure are the magnitudes of the difference waves, as determined by averaging the experimental and the simulated data across trials, blocks, stimuli, and participants. Figure 4A illustrates the amplitude of the ERN on trials with 50% mapping probabilities. In this condition, the experimental ERN was elicited primarily by the feedback (M ! "4.1 !V) and not by the response (M ! "0.4 !V), t(14) ! 4.57, p # .01. Conversely, in the 100% mapping condition (Figure 4B), the experimental ERN was elicited

Error related negativity (ERN) •

Error-related negativity is observed when participants make errors in choice RT tasks (Dehaene et al., 1994; Gehring et al., 1993)

•

Onset of ERN occurs around time of erroneous muscle activity, peaking at ~80 ms after the response

•

ERN has been attributed to anterior cingulate cortex (ACC) using source localization and fMRI (Dehaene et al., 1994; Braver et al. 2001; Holroyd et al. 2004)

•

tha MS inc sig pat Co 4.8 com con 12 ps

du lar ass als res esp It act the mi

pro Figure 3. The error-related negativity (ERN) in the probabilistic learning RT task elicited by feedback stimuli in the 50% mapping condition (A) and the the response in the 100% mapping condition (B). The waveforms are more com negative on error trials than on correct trials. The ERN peaks about 250 Eri ms after the onset of the feedback stimulus and about 80 ms after the onset of the incorrect response. Waveforms were recorded from channel Cz. pan

per I by ERN is diminished in schizophrenia (e.g. Bates et al. 1 the the primarily by the response (M ! "6.4 !V), and not by the feed-

2002) and elevated in OCD (e.g. Endrass et al.

2008) •

Based on the analyses reported in the previous sections, we can identify two classes of errors: those due to premature responding and those due to data limitations. As we noted, errors due to premature responding are detectable because stimulus evaluation processes, which are incomplete at the time the response is initiated, can lead to a representation of the appropriate response. According to our theory of the ERN, premature responses should be associated with a mismatch between representations of actual and appropriate responses and a large error signal (and a large ERN) should occur. In contrast, errors due to data limitations should be associated with a compromised representation of the appropriate response. In turn, this would create a partial mismatch with the representation of the actual response. A partial mismatch would result in a weaker error signal (and a medium-sized ERN). Such a partial mismatch should also occur on correct trials when processing is data limited.

We tested these ideas by comparing the ERN amplitudes of correct trials rated as "sure correct" with those of correct and incorrect trials rated as "don't know" and incorrect trials rated as "sure incorrect" (see Figure 5). The independent variables were trial type (4), with contrasts between consecutive levels, and compatibility (2). A significant effect of trial type, F(3, 36) = 13.9, p < .001, MSE = 7.6, t = .69, indicated that ERN amplitude was significantly smaller for

Figure 2. Simulated and empirical accuracy data for the probabilistic

learning(2000) task. For both-theERN model and human participants, tracks accuracy in the Scheffers & Coles negatively 100% mapping condition improved in the second half of each block subjective reports accuracy (from sure relative of to theresponse first half. Exp ! Experiment. correct to sure incorrect)

ER back (M ! "1.5 !V), t(14) ! "3.46, p # .01. Lastly, the int experimental ERN in the always correct/always incorrect condiabo tion (Figure 4C) was relatively small for both the response (Mjud !

"1.7 !V) and the feedback (M ! "2.6 !V), and there was am no the significant difference between the two conditions, t(14) ! 1.35, Ge p $ .05. Confidence intervals indicated that the small ERNs in this am condition associated with the response (M ! "1.7 !V, SD ! 1.5

sug mo

-3 1C

Correct Correct Incorrect Incorrect "Sure Correct" "Don't Know" "Don't Know" "Sure Incorrect"

sou con pro res

Implicit vs. explicit error monitoring

ERN can occur even if we are not aware of the error - implicit Late positivity tracks explicit awareness of the error Nieuwenhuis et al. (2001) Psychophysiology

Late positivity also tracks graded confidence Sure error

Sure correct

gure 3. The error-related negativity (ERN) in the probabilistic learning k elicited by feedback stimuli in the 50% mapping condition (A) and the sponse in the 100% mapping condition (B). The waveforms are more gative on error trials than on correct trials. The ERN peaks about 250 ms er the onset of the feedback stimulus and about 80 ms after the onset of e incorrect response. Waveforms were recorded from channel Cz.

Error-related positivity (Pe)

imarily by the response (M ! "6.4 !V), and not by the feedck (M ! "1.5 !V), t(14) ! "3.46, p # .01. Lastly, the perimental ERN in the always correct/always incorrect condion (Figure 4C) was relatively small for both the response (M !

1.7 !V) and the feedback (M ! "2.6 !V), and there was no gnificant difference between the two conditions, t(14) ! 1.35, $ .05. Confidence intervals indicated that the small ERNs in this ndition associated with the response (M ! "1.7 !V, SD ! 1.5

Boldt & Yeung (2015) J Neuro

Explicit vs. implicit metacognition TICS-1294; No. of Pages 8

â&#x20AC;˘ Not all monitoring and control is necessarily consciousOpinion

â&#x20AC;˘ Metacognitive information such as decision uncertainty is used to modulate ongoing thought and behaviour in the absence of awareness

Trends in Cognitive Sciences xxx xxxx

Communication of system 2 metacognitive representations Metacognitive representations

Metacognitive representations

System 2

System 2

Sensory input

â&#x20AC;˘ When metacognitive representations become explicit (conscious), they can be readily used in communication and collaboration

Action

System 1

System 1

Action

TRENDS in Cognitive Sciences

Figure 2. System 2 metacognition for cognitive control across two agents. System 2 metacognitive representations are derived from information in system a form available for verbal report. For example, the reliability of a sensory signal can be reported in terms of confidence. When agents are cooperating, th used to optimise control by, for example, giving more weight to the more confident observer [32]. Via system 2, verbal reports can also have long-te functioning of system 1 [57].

relevant metacognitive representations within system 1 processes in each agent are selected for broadcast to the other agent, so that decisions about which sensorimotor processes to deploy can be taken in a space of shared metacognitive information. This, we suggest, is the distinc-

metacognitive representations when they ar cally coordinating complex actions (e.g., com about confidence used in team sports) [35,3 metacognition can also be used diachronically, Sheaforetpeople al. (2014) TICS to discuss how m making it possible

The ability to make a second-order judgment (on the veracity of a first-order judgment) varies from one person to the other.

It is deteriorated in patients with frontal lobe lesions, in particular in the uppermost frontal section (rostral).

C there Can h be b correlation l i with i h more subtle b l changes h i the in h organization i i off the h brain b i in i normal subjects?

Here, subjects engage in a difficult psychophysical task (detecting a patch of a slightly hi h contrast higher t t between b t 2 screens ). ) This task is maintained close to the threshold to ensure an overall success rate of 71%.

After each trial, participants report their degree of confidence in their first response .

Individual differences in explicit metacognition

Fleming*, Weil* et al. (2010) Science

Individual differences in explicit metacognition

0.75

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0.7 0.65 Metacognitive ability (Aroc) Performance (%)

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20

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Figure 1 | Brodmann area (BA) 10. aâ&#x20AC;&#x201C;d | The location of cytoarchitectonic surface-rendered onto the orbital (a) and medial (b) surface of the human bra of the macaque monkey (c and d). The size of BA 10 (relative to other prefro be significantly larger in the human brain than in the macaque monkey brain permission, from REF. 28 ! John Wiley and Sons Inc. (2003). e | The location lateral convexity has been studied by Petrides and Pandya106. On the lateral incorporates the most anterior parts of the three frontal gyri. AON, anterior o G, gustatory cortex; OB; olfactory bulb; PrCO; precentral opercular area. Re permission, from REF. 106 ! Blackwell Publishing (1994).

BA10 - located at top of prefrontal hierarchy, involved in monitoring other cognitive processes (Koechlin)

Fleming*,

relies heavily on investigations in non To our knowledge, there are no stu activity of frontopolar neurons in Weil*recorded, et al. (2010) Science this area being difficult t

Predictors of metacognitive sensitivity in aPFC

ARTICLES

b Percentage signal change in rRLPFC

12 a

–0.10 0.05

8 | Brain 2014: encouraged Page 8 of 12 rticipants were to use the ade by sliding the cursor using the0 ‘left . The scale cursor was initialized at a –0.05 gs ‘3’ and ‘4’ on each R trial. The confiy = 42 nts’ input for 3 s, followed by a–0.10 change Low t-score to red to confirm the selected rating 3.6 no 2.8 feedback3.2during the main experi-

Figure 4 RLPFC. (a) Brain activity in rRLPFC correlating with decreases Fleming et al. FWE corrected). in subjective confidenceS.(PM. < 0.005, small-volume Coronal section; R, right. (b) Signal in rRLPFC (6-mm sphere MNI space High confidence coordinates (x, y, z) = (39, 41, 16)) showing a main effect of confidence S. M. Fleming et al. from GLM 2; see Online Methods) is shown but not DV. The plot (extracted only to clarify the signal pattern in rRLPFC (that is, absence of main effect of DV). n.s., not significant. (c) Between-subjects regression analysis considering the change in choice accuracy (slope of the logistic fit) between n.s. low- and high-confidence trials (see red double-headed arrow in Fig. 1b) as a covariate for confidence-related activity in rRLPFC (peak (x, y, z) = (27, Low High 44, 16); P < 0.05, small-volume FWE corrected). The scatter plot is not used for statistical inference (which was carried out in the SPM framework); it Figure is shown solely for illustrative purposes. bars represent s.e.m.

Fig. 2. aPFC VBQ findings. shows correlation of metacognitive ability (AROC) and anterior prefrontal Error (aPFC) microstructural measures of white-matter concentration (R Low confidence

n.s.

High DV

Alleninet al. 2017 of |DV| and confidence vmPFC but noNeuroimage interaction between them (2 × 2 ANOVA with factors 419 value, confidence: main effect of value F1,19 = 5.1, P < 0.05; main effect of confidence F1,19 = 7.6, P < 0.05; interaction F1,19 = 0.7, P > 0.5) (Fig. 3c). The absence of an interaction at the neural level is consistent with a theoretical independence between value and noise in the choice process, such that one can have high confidence in a low-value choice and vice versa. Furthermore, the pattern across conditions closely resembles that seen for RT values (Fig. 1d) providing convergent evidence that vmPFC activity is tightly linked to behavior. We also confirmed that the response to confidence was not driven by a categorical response to errors8 (Supplementary Fig. 3).

A

Downloaded from http://brain.oxfordjournals.

Downloaded from http://brain.oxfordjournals.org/ at New York University on August 7, 20

De Martino, Fleming Nat Neuro 5 icipants provided with practiceet al. 2013 c were 4 k there were three practice phases. In 3 were shown with text below the circles 2 1 s in each circle (e.g. ‘40 versus 60’).

0 s completed a series of dot judgements –1 –2 his phase familiarized participants with –3 trate a subject-specific level of !d by –4 R 1 3 4 6 –3 –2 –1 0 2 5 y = 47 re outlined above.

The last phase –4 conChoice accuracy (high confidence–low confidence) at simulated the main task such that Subjects ith indicating their confidence. For the actice trials that simulated the main task in value-based decision-making should also represent subjective conce ratings) without requiring word list Parameter estimates in rRLPFC (high confidence–low confidence)

McCurdy et al. • Memory and Visual Metacognition

1 and MT) across 48 participants. Orientation of crosshairs given below the top left brain in MNI XYZ coordinates. Right side, scatter plots showing peak voxel vs AROC, with least-squares line for illustration purposes. Bottom left, zoomed in view shows overlap of AROC correlation in both MT and R1 maps. Colour bars indicate t-values, blobs displayed on average MT map from our 48 participants. Volume of interest analysis, FWE-peak corrected p < 0.05 within mask generated from previously reported coordinates (Fleming et al., 2010a; McCurdy et al., 2013). See VBQ Analysis for more details.

anterior prefrontal cortex (aPFC)

fidence in a value estimate. In other words, if a brain region involved in value comparison is implementing a process akin to a race model6, Confidence in right rostrolateral prefrontal cortex then activity in that region should be modulated by both initial |DV| A key question is how confidence-related information represented and noise (confidence) on that trial. To test this hypothesis, we con- in vmPFC becomes available for self-report. One computationally structed a general linear model (GLM) of our fMRI4data in which is aslices hierarchical Figure Lesion overlap plausible analysis. hypothesis (A) Coronal throughmodel an wherein confidence in a Figure 5 Task performance and metacognitive accuracy. (A) Performance (% correct) in each domain for each group (HC = healthy olumes from each patient were normaleach trial was modulated by two parametric regressors: |DV| and comparison process is ‘read out’ by an anatomically distinct secondFleming ettemplate al. The 2014 Brain etlesion al. 2013 brain overlap ofMcCurdy normalized maps J Neuro controls; aPFC = anterior PFC lesion group; TL = temporal lobeMNI lesion group). dashed line showing indicates chance (50%) performance.

The 22–24. nor secondary axis orthogonalized shows the average difficulty ofrespect the task (!d) adjusted online for each participant.

Neither performance !d confidence with to |DV|.

We show that activorder network Right rostrolateral prefrontal cortex (rRLPFC) is nstitute (MNI) standard space using FSLperceptual for each patient in the anterior prefrontal cortex lesion (anterior differed across groups. (B) Mean confidence in each domain. Average levels of confidence did not differ between groups. (C) ity in vmPFC was indeed modulated by both value and confidence a likely candidate, as this region is implicated in metacognitive assessRegistration Tool; http://fsl.fmrib.ox.ac.

PFC) group.

Colour bar reflectsaccuracy the proportion of group overlap Metacognitive accuracy scores (meta-d’/d’) for each domain. The dashed line indicates optimal metacognitive (meta-d’/d’ = 1).

9,14,25. Consequently, we tested whether (Fig. 3a,b and Supplementary Table 3; P < 0.05 family-wise error ments of perceptual decisions The Smith, anterior PFC group showed a domain-specific in perceptual metacognitive accuracy. (D) Illustration of the relationship n and 2001).

A two-step regis-impairmentat eachreported voxel.

(B)text.

Anterior PFC (BAgreater 10) regions of interest Perceptual between corrected domain-specific accuracy and pattern the DGI measure in the Hotter this colours reflect aacts DGI score, (FWE) atmetacognitive cluster level).

This is consistent with the region more generally in metacognitive appraisal by enabling ented: (i) a mask was drawn over the the MRIcron atlas and indicating less consistency across domains. Mean metacognitivederived accuracy ( "from standard error) for each group is shown forviewed illustration. in Noteaxial section 1,2 and established function of2)this encoding goal-values explicit reportbetween ofmetacognition confidence in a value comparison. that the groupdefect DGI score is affected by both mean and covariance across domains, whereas differences means g craniotomy to(Table prevent aregion bias ininthe (green, blue).

All patients inonly the anterior PFC group had lesions are apparent here. The anterior PFCthis group had significantly elevated DGI scores compared to the healthy In A–C, black with our hypothesis that region also represents the confidence Wecontrol firstgroup. established that rRLPFC tracked changes in reported con-

alysis

B

Figure 3. Gray matter volume correlations with metacognitive efficien overlay and axial “glass brain,” showing areas in which gray matter volum task. B, Statistical ( T) maps for positive correlation with meta-d!/d! on the brain. The significant cluster was found in the precuneus region. All images w

Comparative anatomy

Wallis (2011) Nat Neuro

Neuron

Comparative anatomy

Human and Monkey Ventrolateral Frontal Cortex

Figure 8. Areas in the Anterior Prefrontal Cortex Resting-state fMRI-derived functional connectivity patterns of human (left) areas 46 (yellow), FPl (ruby), and FPm (pink), and resting-state fMRI-derived functional connectivity patterns of the proposed macaque correspondents (right): area 46 (yellow) and area 10 m (pink). Area FPl could not easily be matched to any macaque vlFC region but had some features of area 46. In the middle, we show spider plots of these regions. Conventions are as in Figure 2.

Neubert et al. (2014) Neuron

Probing circuits for metacognitive evaluation “Implicit” metacognition

“Explicit” metacognition

anterior prefrontal cortex (aPFC)

Fleming et al.

2012 J Neuro

area 46

FPm

FPl Neubert et al. (2014) Neuron Ridderinkhof et al. 2004 Science

2000ms

300ms

1600ms 300ms

High

Time 200ms

3500ms 0 20 40

L or R?

60 80 100

High Confidence?

Med. Med.

Predecision motion

Low

Postdecision motion

Low

Fleming, van der Putten & Daw (2018) Nature Neuroscience

Mathematical model

direction (L/R)

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R

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Xpre

Xpost

+ -k.coh

Xtotal

+k.coh

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Fleming, van der Putten & Daw (2018) Nature Neuroscience

cision Low

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1

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-2

0

change

2

4

Final log-odds correct

Fleming, van der Putten & Daw (2018) Nature Neuroscience

0.2

What PreM PreH PostH

0

people do: PreL PreM PreH PostL

PreL PreM PreH PostM

PreL PreM PreH PostH

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Confidence

0.8 0.6 0.4 0.2

PreM PreH PostH

0

PreL PreM PreH PostL

PreL PreM PreH PostM

PreL PreM PreH PostH

Fleming, van der Putten & Daw (2018) Nature Neuroscience

Post-decision evidence a

1 Cor low Err low Cor med Err med Cor high Err high

BOLD a.u.

1 0.5 0

0

5

10

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1.5

-0.5

BOLD a.u.

0.8

0.5

0.6 0.4 0.2 0 -0.2 -0.4 1

2

PDE bins (

0

Low

Med

3

4

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High

Time (s)

Fleming, van der Putten & Daw (2018) Nature Neuroscience

a Stimulus: PS s1

s2

? s< > b = decision |s – b| = confidence

Boundary: PB b1 b2

e (spikes s–1)

30 20

Uncertainty (s)

Accuracy (%)

0 20 40 60 80 100 Odour mixture (% A)

d

100 75 50

0 0.2 0.4 0.6 0.8 1 Uncertainty (s)

f uracy (%)

c

100 80 60 40 20 0

% choice A

b

100 80

1 0.8 0.6 0.4 0.2 0

Error

Correct

0 20 40 60 80 100 Odour mixture (% A)

Rate (spikes s–1)

“X-pattern” in rodent OFC

outcome varied with the firing rate of indiv and at the population level (Fig. 4h), as p e 30 showed that the highest firing ra analysis also near chance A performance (50% reward pro B these neurons signalled lack of confidence ra 20 formance (0% reward probability; see Me opposite patterns held for the positive outco lation (105/563 neurons for all stimuli poole 10 tion test; Supplementary Fig. 4).

It is possible for the experimenter observi 0 dict individual trial outcomes, but can rat 0 32 44 56 68 100 behaviourally? We tested the ability of rats t Odour mixture (% A) report of confidence using a modified version encouraged rats to give up waiting for uncerh g 0.8 ing the delay to reward delivery and permittin Normalized rate

in the current trial can provide good estimates of the expected decision outcome across trials.

We next looked for specific predictions—patterns of firing rates— that would arise from theoretical confidence estimates. We noticed that, when plotted as a function of stimulus type and trial outcome, decision

0.7

Figure 4 | Confidence estimation in a decision m 0.6 of a model for category decisions. Eac a, Schematic as well as the memory for the category boundary, of values.0.5 In each trial a stimulus, si, and memory drawn from their respective N =133 distributions. A choi 0.4 comparing the two samples (si , bi), and a confid 2 bi | ). calculating their 0 distance 32 44( | si 56 68Incorrect 100 cho represented in theOdour model mixture by the width of (% A)the stimu distributions. See Methods for details. b, Exampl the model, replicating the high choice accuracy o OFC neurons decreased accuracy for mixtures near the impose c, Mean accuracy of model choices as a function D Eo uncertainty estimate, s, is transformed from the stimulus and boundary samples (si 5 1 2 tanh( | s d, Mean decision uncertainty estimates generated of stimulus and trial outcome. Note that the mod et al.

Nature only to a stimulus Kepecs sample and not(2008) the stimulus typ

No signature of post-decision evidence in aPFC ROIs

Fleming, van der Putten & Daw (2018) Nature Neuroscience

Control models PDE

Conf

PDE

Conf

Conf P < 0.05

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n.s.

Conf

46 PDE

FPl

Conf

b FPm

FPl PDE

Post-decision evidence PDE

Conf

Conf

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R y=44

Final confidence

R

L y=44

y=48

y=52

aPFC mediates link between post-decision monitoring and explicit report

Fleming, van der Putten & Daw (2018) Nature Neuroscience

Domain-general or domain-specific?

METACOGNITION

COGNITIVE DOMAIN

DOMAIN-GENERAL

DOMAIN-SPECIFIC

Meta-analysis of sensitivity correlations

Rouault, McWilliams, Allen & Fleming (in press) Personality Neuroscience

5

ated in this experiment for pay. The session lasted around 90 minutes and were paid on average €19.7. We excluded subjects from analysis due to

Neurosci., January(s.d.

30, 2013 • 33(5):1897–1906 ient variation < 0.02) of R-confidence (4 subjects) or P-confidence (4

) for estimation of meta-d’ (see below). The final sample included 39 subjects Fixation Stimuli Fixation Stimuli

ysis.

A

McCurdy et al. • Memory and Visual Metacognition Type I 2AFC task (left or right?)

Type II task (how confident?)

Perceptual decision 1.05s

B

List memorization List memorization

33ms

until response

GratingFixation on L or R?Stimuli and type I 2AFC task Fixation

(left or right?)

Confidence

until response Type II task (how confident?)

1. Experimental Design. (A) The sequence of decisions during a trial. (B) An example . (C) A schematic of the mechanism of probability matching used to reveal subjective ties (see text for further details).

0.5-1.5min (at the beginning of each block)

1s

CHAIR

until response

POLLEN

until response Mnemonic decision

Time

Confidence

periment was conducted using Psychophysics Toolbox version 3 (Brainard,

1. Behavioral tasks. Participants performed both 2-AFC tasks. A, Visual task. Participants viewed two circular stimuli that were presented simultaneously to the left and right of fixation; unning in Matlab.

We use a numerosity task,embedded which isinknown to ulus contained only visual noise, and the otherdiscrimination contained a grating noise. Participants performed a 2-AFC judgment, indicating which stimulus (left or right) contained the Subsequently, participants rated how confident they were that their judgment wasofcorrect using a 4-point scale (not shown on the screen). Participants were constrained to provide enient to fit SDT models (Nieder and Dehaene, 2009).

The 2-AFC stimuli consisted ponses within 5 s (seenumber Materialsofand Methods).

Memory task. At the All beginning of each les with a certain dots in eachB,circle (Figure 1B). dots were ofblock the of 50 trials, participants studied a list of words arranged in 10 rows and 5 columns (an 8 row $ n is shown here for ease of display). In each trial, participants viewed two words presented simultaneously to the left and right of fixation; one word had been presented on the study list and ze and the average distance between dots was kept constant. One of the two r hand not. Participants performed a 2-AFC judgment, indicating which word (left or right) was on the previously studied list. Subsequently, participants rated how confident they were that always contained 50 using dots awhile other dots. thehad to be provided within 5 s.

FC judgment was correct 4-pointthescale (not contained shown on the50+xc screen).

BothBefore responses

Old, L or R?

ent we estimated the value of xc needed to obtain a success rate of 71% using a

physical staircase (Levitt, 1971; see below). We use exactly the same set of

contrast for the grating, using the QUEST threshold estimation proceh measures task performance capacity in a basic task, e.g., for all subjects having the same level of xc (position of the dots, position of the dure (Watson and Pelli, 1983). All stimuli were set to a constant overall discrimination). Thus, although we cannot experimentally level of 90% Michelson contrast. ol for variability in basic task performance, this is easily For the word memory test, English words were generated using the McCurdy et al. J Neuro (2013) cted by normalizing meta-d! by d!.

Medical Research Council Psycholinguistic Database (Wilson, 1988).

McCurdy et al.Metacognition • Memory and Visual Metacognition Curdy et al. • Memory and Visual

A

A

Perceptual metacognition

B

B

Memory metacognition Figure 3.

Gray matter volume correlations with metacognitive efficiency.

Statistical ( T) ma ure 3. Gray matter volume correlations with metacognitive efficiency.

A, Statistical ( T) mapsA,shown for both McCurdy et al. (2013) J Neurosci overlay axial “glass showing areas in whichcorrelates gray matter volumewith correlates positively rlay and axial “glass brain,”andshowing areasbrain,” in which gray matter volume positively meta-d!/d! on task. B,forStatistical ( T) maps for positive correlation with meta-d!/d! onboth the memory for both k. B, Statistical ( T) maps positive correlation with meta-d!/d! on the memory task, for standardtask, overlay and see also Baird et al. (2013) J Neurosci brain. The cluster was found in the precuneus All images thresholded at p in. The significant cluster wassignificant found in the precuneus region.

All images wereregion. thresholded at pwere $ 0.001 uncorrected purposes, circled (in A) the precuneus (in B) pass small for poses, but the circled clustersbut (inthe A) and theclusters precuneus (inand B) pass small volume correction for volume multiplecorrection comparison

Fleming, Ryu, Golfinos & Blackmon Brain (2014)

Performance

Confidence

healthy controls aPFC lesion TL lesion Fleming, Ryu, Golfinos & Blackmon Brain (2014)

healthy controls aPFC lesion TL lesion

Why no metacognitive deficit for memory following aPFC lesion?

CHAIR POLLEN may compensate?

Redundancy? Reorganisation? Intact parietal cortex Need better understanding of the functional architectureâ&#x20AC;Ś Fleming, Ryu, Golfinos & Blackmon Brain (2014)

Per / Mem

A

CR

PN

CR

PN

CR

PN

CR

PN

Within-subject comparison of Wfunctional anatomy S W S C Memory trial

B Perceptual trial +

500ms

ZIPPER + ARTIST

100ms

2000ms per stimulus + 500ms ITI

Old 1900ms max

+

+

New

500ms 2000ms max

+

500ms min Confidence Rating

1 2 3 4 1 2 3 4

Press Number

1 2 3 4 1 2 3 4

500ms min Confidence Rating

2000ms max 500ms min

2 (stimulus) x 2 (domain) block design

1 2 3 4 1 2 3 4

Press Number

1 2 3 4 1 2 3 4

2000ms max 500ms min

Morales, Lau & Fleming (2018) J Neuro

stimulus type for words was abstract shapes ods for details). independently rating the diffierformance exe memory task %; words: perCritically, this task domains ts (perceptual: 0.38, p ! 0.70; performance in shapes/words) ,23) ! 0.15, p !

Morales et al. â&#x20AC;˘ General and Specific Met

A

C

B

D

Morales, Lau & Fleming (2018) J Neuro

MVPA: Confidence-related activity MEMORY

Cross-classification

Cross-validation

PERCEPTION Multivariate pattern analysis (MVPA) Morales et al. â&#x20AC;˘ General and Specific Metacognition in PFC

J. Neurosci

A

B

D

E

Morales, Lau & Fleming (2018) J Neuro

D

ZIPPER + ARTIST

100ms

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1900m max

500m min Confidence Rating

1 2 3 4 1 2 3 4

Press Number

1 2 3 4 1 2 3 4

200 ma

50 m

Morales, Lau & Fleming (2018) J Neuro

E

F Morales, Lau & Fleming (2018) J Neuro

Searchlight - confidence-related activity patterns F

assification design. Pattern vectors (runwise beta images) from one domain were used to train an SVM decoder on two vectors from the other domain (and vice versa). Classification of low (L) and high (H) confidence levels is illustrated. Right,

Morales, Lau & Fleming (2018) J Neuro

Conclusions • Lesions to the frontal and parietal lobes impair metacognitive judgments in a variety of tasks • Brain imaging in healthy volunteers reveals involvement of a frontoparietal network in metacognition of decision-making and memory • Different subregions of this network perform different computations to enable performance monitoring; e.g. pMFC in tracking response accuracy; parietal cortex in tracking perceptual certainty • Anterior prefrontal cortex is particularly well developed in humans; aPFC structure/function is linked to explicit metacognition • There may be domain-specific differences in the neural substrates of metacognition

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Phil. Trans. R. Soc. B | vol. 367 no. 1594 pp. 1279–1438 | 19 May 2012

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ISSN 0962-8436

volume 367

number 1594

pages 1279–1438

In this Issue

Metacognition: computation, neurobiology and function Papers of a Theme Issue organized and edited by Stephen M. Fleming, Raymond J. Dolan, Christopher D. Frith

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Metacognition: computation, neurobiology and function

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The world’s first science journal

19 May 2012

Thank you

metacoglab.org

Neural correlates of confidence reports CONTROL TASK STIMULUS

DECISION CONFIDENCE REPORT

CONFIDENCE > CONTROL

Fleming et al. (2012) J Neuro