Mind Your Errors

Abstract

How well people bounce back from mistakes depends on their beliefs about learning and intelligence. For individuals with a growth mind-set, who believe intelligence develops through effort, mistakes are seen as opportunities to learn and improve. For individuals with a fixed mind-set, who believe intelligence is a stable characteristic, mistakes indicate lack of ability. We examined performance-monitoring event-related potentials (ERPs) to probe the neural mechanisms underlying these different reactions to mistakes. Findings revealed that a growth mind-set was associated with enhancement of the error positivity component (Pe), which reflects awareness of and allocation of attention to mistakes. More growth-minded individuals also showed superior accuracy after mistakes compared with individuals endorsing a more fixed mind-set. It is critical to note that Pe amplitude mediated the relationship between mind-set and posterror accuracy. These results suggest that neural mechanisms indexing on-line awareness of and attention to mistakes are intimately involved in growth-minded individuals' ability to rebound from mistakes.

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Psychological Science
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The online version of this article can be found at:
DOI: 10.1177/0956797611419520
2011 22: 1484 originally published online 31 October 2011Psychological Science
Jason S. Moser, Hans S. Schroder, Carrie Heeter, Tim P. Moran and Yu-Hao Lee
Adjustments
Mind Your Errors : Evidence for a Neural Mechanism Linking Growth Mind-Set to Adaptive Posterror
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DOI: 10.1177/0956797611419520
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Whether you think you can or think you can’t—you are
right. (popularly attributed to Henry Ford)
Decades of research by Dweck and her colleagues indicate
that academic and occupational success depend not only on
cognitive ability, but also on beliefs about learning and intel-
ligence (e.g., Dweck, 2006). Dweck’s model of implicit theo-
ries of intelligence (TOIs) distinguishes people who believe
intelligence is unchangeable (i.e., those who have a fixed
mind-set) from people who believe intelligence is malleable
and can be developed through learning (i.e., those who have a
growth mind-set). It is critical to note that these mind-sets are
associated with different reactions to failure. Fixed-minded
individuals view failure as evidence of their own immutable
lack of ability and disengage from tasks when they err; growth-
minded individuals view failure as potentially instructive
feedback and are more likely to learn from their mistakes
(Dweck, 1999; Utman, 1997).
Despite years of work examining the self-report and behav-
ioral correlates of these different mind-sets, little is known
about the neural mechanisms that underlie them—only one
study has examined the neural underpinnings of mind-set. In
that study, Mangels, Butterfield, Lamb, Good, and Dweck
(2006) measured event-related potentials (ERPs)—electrical
brain signals elicited by external or internal events—in college
students endorsing a fixed or growth mind-set while they per-
formed a difficult general knowledge test. They found that
compared with fixed-minded individuals, growth-minded
individuals allocated more attentional resources to corrective
information following error feedback and were more likely to
correct their mistakes on a surprise retest.
Although Mangels et al. (2006) found differences between
individuals with fixed versus growth mind-sets in neural and
behavioral responses to corrective information, they demon-
strated these effects on a task in which performance accuracy
was ambiguous. Participants became aware of their mistakes
only when they were signaled by external feedback. This task
was also quite difficult (success rates were kept at ~40%),
which may have exaggerated differences between the groups
Corresponding Author:
Jason S. Moser, Department of Psychology, Michigan State University, East
Lansing, MI 48824
E-mail: jmoser@msu.edu
Mind Your Errors: Evidence for a Neural
Mechanism Linking Growth Mind-Set to
Adaptive Posterror Adjustments
Jason S. Moser1, Hans S. Schroder1, Carrie Heeter2,
Tim P. Moran1, and Yu-Hao Lee2
1Department of Psychology and 2Department of Telecommunications, Information Studies, and Media,
Michigan State University
Abstract
How well people bounce back from mistakes depends on their beliefs about learning and intelligence. For individuals with a
growth mind-set, who believe intelligence develops through effort, mistakes are seen as opportunities to learn and improve. For
individuals with a fixed mind-set, who believe intelligence is a stable characteristic, mistakes indicate lack of ability. We examined
performance-monitoring event-related potentials (ERPs) to probe the neural mechanisms underlying these different reactions to
mistakes. Findings revealed that a growth mind-set was associated with enhancement of the error positivity component (Pe), which
reflects awareness of and allocation of attention to mistakes. More growth-minded individuals also showed superior accuracy
after mistakes compared with individuals endorsing a more fixed mind-set. It is critical to note that Pe amplitude mediated the
relationship between mind-set and posterror accuracy. These results suggest that neural mechanisms indexing on-line awareness
of and attention to mistakes are intimately involved in growth-minded individuals’ ability to rebound from mistakes.
Keywords
individual differences, electrophysiology, cognitive processes
Received 2/22/11; Revision accepted 7/11/11
Research Report
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Mind-Set and Posterror Adjustments 1485
because of the preponderance of failure. Moreover, the diffi-
culty level of their task is not representative of the sorts of
tasks typically encountered in daily life. Thus, their findings
do not speak to how mind-set affects on-line and immediate
reactions to internally generated errors in simpler, more eco-
logically valid tasks.
In the study reported here, we aimed to extend the findings
of Mangels et al. (2006) by examining response-locked ERPs
that tap into internal performance-monitoring processes elic-
ited by response execution in a speeded reaction time (RT) task.
Specifically, we examined the error-related negativity (ERN)
and the error positivity (Pe), two widely studied ERPs elicited
during error processing that relate to adaptive behavioral adjust-
ments following mistakes. We therefore directly assessed the
relationship between mind-set and the monitoring of one’s own
performance and immediate self-initiated reactions to mistakes.
The ERN is a fronto-centrally maximal negative ERP elicited
approximately 50 ms after an erroneous response (Gehring,
Goss, Coles, Meyer, & Donchin, 1993). Evidence from source-
localization studies indicates that the anterior cingulate cortex
(ACC), a brain region involved in monitoring behavior and
signaling the need for increased cognitive control, is the most
likely generator of the ERN (Carter et al., 1998; Dehaene,
Posner, & Tucker, 1994). The Pe is a centro-parietally maximal
positive ERP occurring between 100 and 600 ms after an erro-
neous response (Ridderinkhof, Ramautar, & Wijnen, 2009).
Research suggests that the Pe also originates in the ACC (van
Veen & Carter, 2002). Current conceptualizations suggest that
the ERN and the Pe are dissociable neural signals involved in
error processing, with the former reflecting conflict between
the correct and the erroneous response and the latter reflecting
awareness of and attention allocation to errors (Hughes &
Yeung, 2011; Nieuwenhuis, Ridderinkhof, Blom, Band, & Kok,
2001; Steinhauser & Yeung, 2010). Consistent with the role of
these ERPs in on-line error monitoring, larger ERN and Pe
amplitudes are associated with adaptive behavioral adjustments,
such as slower and more accurate responses following mistakes
(Compton et al., 2008; Frank, D’Lauro, & Curran, 2007; Hajcak,
McDonald, & Simons, 2003; Themanson, Pontifex, Hillman, &
McAuley, 2011).
In the study reported here, we explored relationships between
mind-set, the ERN and Pe, and behavioral adjustments follow-
ing mistakes—posterror slowing and accuracy—in a simple
two-choice RT task. Given the links between the growth mind-
set and adaptive reactions to mistakes, we predicted that a
growth mind-set would be associated with larger ERN and Pe
amplitudes and greater posterror adjustments than a fixed mind-
set would. We further examined whether these on-line measures
of performance monitoring mediated the relationship between
mind-set and posterror behavioral adjustments.
Method
Twenty-five native-English-speaking undergraduates (20 fe-
male, 5 male; mean age = 20.25 years) participated for course
credit. A letter version of the Eriksen flanker task (Eriksen &
Eriksen, 1974) was administered. Participants were instructed
to click a mouse button to correctly identify the center letter
(target) of a five-letter string in which the target was either con-
gruent (e.g., “MMMMM”) or incongruent (e.g., “NNMNN”)
with the flanker letters. Flanking letters were presented 35 ms
prior to target-letter onset, and all five letters remained on the
screen for a subsequent 100 ms (total trial time was 135 ms). A
fixation cross was presented during the intertrial interval,
which varied between 1,200 and 1,700 ms.
The experimental session consisted of 480 trials grouped
into six blocks of 80 trials each, during which accuracy and
speed were equally emphasized. To elicit a sufficient number
of errors for ERP analysis, we differed the letters making up
the strings by block (e.g., “M” and “N” in Block 1 and “E” and
“F” in Block 2), and mouse button-letter assignments were
reversed at the midpoint of each block (e.g., left mouse-button
click for “M” through 40 trials of Block 1, then right mouse-
button click for “M” for the last 40 trials of Block 1).
Following the flanker task, participants completed a TOI
scale that asked respondents to rate the extent to which they
agreed with four fixed-mind-set statements on a 6-point
Likert-type scale (1 = strongly disagree, 6 = strongly agree).
These statements (e.g., “You have a certain amount of intelli-
gence and you really cannot do much to change it”) were
drawn from previous studies measuring TOI (e.g., Hong, Chiu,
Dweck, Lin, & Wan, 1999). TOI items were reverse-scored so
that higher scores indicated more endorsement of a growth
mind-set, and lower scores indicated more of a fixed
mind-set.
Continuous electroencephalographic activity was recorded
using the ActiveTwo system (BioSemi, Amsterdam, The Neth-
erlands). Recordings were taken from 64 Ag-AgCl electrodes
embedded in a stretch Lycra cap. In addition, two electrodes
were placed on the left and right mastoids. Electrooculogram
activity generated by eye movements and blinks was recorded
at FP1 and three additional electrodes placed inferior to the
left pupil and on the left and right outer canthi. During data
acquisition, the Common Mode Sense active electrode and
Driven Right Leg passive electrode formed the ground. All
signals were digitized at 512 Hz using BioSemi’s ActiView
software.
Off-line analyses were performed using BrainVision Ana-
lyzer (Brain Products, Gilching, Germany). Scalp-electrode
recordings were re-referenced to the mean of the mastoids and
band-pass filtered with cutoffs of 0.1 and 30 Hz (12 dB/octave
roll-off). Ocular artifacts were corrected using the method
developed by Gratton, Coles, and Donchin (1983). Response-
locked data were segmented into individual epochs beginning
200 ms before response execution and continuing for 800 ms
following the response. Physiological artifacts were detected
using a computer-based algorithm, and trials in which the fol-
lowing criteria were met were rejected: a voltage step exceed-
ing 50 μV between contiguous sampling points, a voltage
difference of more than 200 μV within a trial, and a maximum
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1486 Moser et al.
voltage difference less than 0.5 μV within a trial. Trials were
also removed from subsequent analyses if the RT was less than
200 ms or more than 800 ms.
To quantify response-locked ERPs, we subtracted a base-
line equal to the average activity in the 150- to 50-ms prere-
sponse window from each data point subsequent to the
response. The ERN and the corresponding ERP amplitude on
correct trials were defined as the average voltage occurring in
the 0- to 100-ms postresponse time window across five fronto-
central recording sites (Fz, FC1, FCz, FC2, Cz) where the
ERN was maximal (see Fig. S1 in the Supplemental Material
available online). On the basis of previous research suggesting
the presence of an early and a late Pe (Ullsperger & von Cramon,
2006; van Veen & Carter, 2002), we defined the Pe and the
corresponding ERP amplitude on correct trials as the average
voltage occurring in two successive postresponse time win-
dows (150–350 ms and 350–550 ms) across five centro-
parietal recording sites (Cz, CP1, CPz, CP2, Pz) where the Pe
was maximal (see Fig. S1).
Results
Overview of data analyses
Repeated measures analyses of variance (ANOVAs) were
first conducted on behavioral and ERP measures without
regard to individual differences in TOIs in order to establish
baseline experimental effects. ANOVAs conducted on behav-
ioral measures and the ERN included one 2-level factor: accu-
racy (error vs. correct response). The Pe was analyzed using a
2 (accuracy: error vs. correct response) × 2 (time window:
150–350 ms vs. 350–550 ms) ANOVA. Subsequently, TOI
scores were entered into ANOVAs as covariates to assess the
main and interactive effects of mind-set on behavioral and
ERP measures. When significant effects of TOI score were
detected, we conducted follow-up correlational analyses to aid
in the interpretation of results.
Behavioral data
On average, participants were correct on 91.23% (SD = 6%)
of trials. Overall accuracy was not correlated with TOI (r =
.06, p > .79). Participants were also faster on error trials (M =
386.13 ms, SD = 49.14 ms) compared with correct trials (M =
449.30 ms, SD = 43.99 ms), F(1, 24) = 151.50, p < .001,
ηp
2 = .86. When TOI was entered into the ANOVA as a covariate,
there were no significant effects (Fs < 1.78, ps > .19, ηp
2s < .08).
In terms of posterror adjustments, correct responses were
slower on trials immediately following errors (M = 496.34 ms,
SD = 61.47 ms) relative to trials immediately following cor-
rect responses (M = 445.34 ms, SD = 45.78 ms), F(1, 24) =
32.89, p < .001, ηp
2 = .58. When TOI was entered into the
ANOVA as a covariate, there were no significant effects (Fs <
1.15, ps > .29, ηp
2s < .05). Although, overall, participants were
slower on trials immediately following errors, they were
equally accurate on trials immediately following errors (M =
90.70%, SD = 8.31%) and correct responses (M = 91.38%,
SD = 6.20%), F(1, 24) < 1, ηp
2 < .01. When entered into the
ANOVA as a covariate, however, TOI scores interacted with
postresponse accuracy, F(1, 23) = 5.22, p < .05, ηp
2 = .19. Cor-
relational analysis showed that as TOI scores increased, indi-
cating a growth mind-set, so did accuracy on trials immediately
following errors relative to accuracy on trials immediately fol-
lowing correct responses (i.e., posterror accuracy – post-
correct-response accuracy; r = .43, p < .05).
ERPs
As expected, the ANOVA confirmed greater ERP negativity
on error trials (M = –3.43 μV, SD = 4.76 μV) relative to cor-
rect trials (M = –0.23 μV, SD = 4.20 μV), F(1, 24) = 24.05,
p < .001, ηp
2 = .50, in the 0- to 100-ms postresponse time
window. This result is consistent with the presence of an ERN.
There were no significant effects involving TOI (Fs < 1.24,
ps > .27, ηp
2s < .06).
The ANOVA conducted on Pe amplitude confirmed that
errors elicited larger positivity (M = 4.40 μV, SD = 5.56 μV)
than did correct responses (M = –5.43 μV, SD = 3.62 μV), F(1,
24) = 91.24, p < .001, ηp
2 = .79; these results are consistent
with the presence of a Pe. There was also a significant effect of
time window, F(1, 24) = 84.89, p < .001, ηp
2 = .78. These two
main effects were qualified by a significant interaction
between accuracy and time window, F(1, 24) = 7.52, p < .05,
ηp
2 = .24, suggesting that the difference between error and cor-
rect postresponse positivity was larger in the early time win-
dow (M difference = 10.76 μV) than in the late time window
(M difference = 8.88 μV). When entered as a covariate, TOI
showed a significant interaction with accuracy, F(1, 23) =
8.64, p < .01, ηp
2 = .27. Correlational analysis demonstrated
that as TOI scores increased so did positivity on error trials
relative to correct trials averaged across both time windows
(i.e., error activity – correct-response activity; r = .52,1 p < .01;
Fig. 1; see also Table S1 in the Supplemental Material).
Mediation analysis
In addition to significant associations between TOI scores and
Pe (averaged across early and late time windows) and between
TOI scores and posterror accuracy, Pe was also positively cor-
related with posterror accuracy (see Fig. 2). That is, larger Pe
amplitude on error trials (relative to correct trials) was associ-
ated with greater accuracy after errors (versus correct
responses). Therefore, the preconditions for establishing
mediation (Shrout & Bolger, 2002) were met. To test for medi-
ation, we implemented Preacher and Hayes’s (2008) boot-
strapping procedure. As Figure 2 illustrates, controlling for Pe
amplitude significantly attenuated the relationship between
TOI scores and posterror accuracy. The 95% confidence inter-
vals derived from the bootstrapping test did not include zero
(.01–.04), and thus indicated significant mediation.
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Mind-Set and Posterror Adjustments 1487
Discussion
The findings reported here are consistent with previous results
demonstrating that growth mind-sets are associated with adap-
tive responses to mistakes (Dweck, 1999, 2006). We extended
these previous findings by identifying an on-line neural mech-
anism underlying this association. Specifically, a growth
mind-set was associated with enhanced Pe amplitude—a brain
signal reflecting conscious attention allocation to mistakes—
and improved subsequent performance. That the Pe mediated
the relationship between mind-set and posterror performance
further underscores its significance in linking mind-set to
rebounding from mistakes.
Enhanced Pe and posterror performance in growth-minded
individuals is consistent with previous results showing that a
growth mind-set was associated with enhanced attention to
corrective feedback following errors and subsequent error cor-
rection (Mangels et al., 2006). Our findings substantively
extend this prior work by showing that a growth mind-set is
associated with heightened awareness of and attention to
Fixed Mind-Set
–5
0
5
10
15
ERP Amplitude (μV) Pooled
Around CPz
–100 0 100 200 300 400 500 600 700
Time (ms)
Growth Mind-Set
–5
0
5
10
15
ERP Amplitude (μV) Pooled
Around CPz
–100 0 100 200 300 400 500 600 700
Time (ms)
DifferenceError Trials Correct Trials
Fixed Mind-Set Growth Mind-Set
150–550 ms
0 μV13.75 μV
r = .52, p < .01
25
20
15
10
5
0
Pe Difference Amplitude (μv)
23456
TOI Score
Fig. 1. Relationship between the error positivity component (Pe) of the event-related potential (ERP) and theory of intelligence (TOI). The top row
shows grand-average response-locked ERP waveforms pooled from the CPz electrode and four adjacent recording sites, separately for individuals with a
fixed mind-set (left) and individuals with a growth mind-set (right). Waveforms for trials on which responses were correct and trials on which responses
were incorrect, as well as the difference between these waveforms, are shown. Time point 0 is response execution (highlighted by the vertical line). The
fixed mind-set group (TOI scores from 1 to 3) and the growth mind-set group (TOI scores from 4 to 6) were formed on the basis of a median split for
illustrative purposes only. The voltage maps in the bottom panel show the Pe difference amplitude from 150 to 550 ms (average ERP amplitude on error
trials – average ERP amplitude on correct trials) in each of these groups. The scatter plot (with best-fitting regression line) in the bottom right panel
illustrates the relation between Pe difference amplitude (pooled from electrode CPz and four adjacent recording sites) and TOI score.
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1488 Moser et al.
errors as early as 200 ms following error commission. Whereas
Mangels and her colleagues measured neural responses to a
very difficult task in which accuracy was ambiguous prior to
the presentation of external feedback, we found effects of
mind-set on the monitoring of one’s own internally generated
errors and immediate self-generated adjustments following
mistakes in a simple two-choice RT task. We have therefore
shown that growth-minded individuals are characterized by
superior functionality of a very basic self-monitoring and con-
trol system. The finding that mind-set was associated with Pe
and not ERN suggests that a growth mind-set is specifically
associated with enhanced ACC-mediated error processing
(Steinhauser & Yeung, 2010). Together with past findings, the
current results suggest that one reason why a growth mind-set
leads to an increased likelihood of learning from mistakes is
enhanced on-line error awareness. Future studies could manip-
ulate mind-set directly (e.g., Hong et al., 1999) to isolate the
causal role of growth mind-sets in boosting error awareness
and posterror performance.
Overall, the current findings shed new light on the
neural underpinnings of growth mind-sets and their links to
adaptive responses to mistakes and have important implica-
tions for academic and occupational performance. One impli-
cation is that Pe amplitude and posterror adjustments measured
in a simple RT task could serve as indicators of the effective-
ness of programs that train individuals to be more growth
minded. Such programs have been found to improve academic
performance (Aronson, Fried, & Good, 2002; Blackwell,
Trzesniewski, & Dweck, 2007). Implementing the procedure
described here could be an efficient way to provide objective
evidence of the success of programs that have the potential to
produce more highly motivated students and workers.
Acknowledgments
The authors would like to thank Ethan Kross for his extremely help-
ful comments on earlier drafts of this manuscript.
Declaration of Conflicting Interests
The authors declared that they had no conflicts of interest with
respect to their authorship or the publication of this article.
Supplemental Material
Additional supporting information may be found at http://pss.sagepub
.com/content/by/supplemental-data
Note
1. Controlling for trait anxiety (Spielberger, 1983) and achievement
motivation (Elliot & McGregor, 2001) did not affect this relationship
(partial r = .57, p < .01).
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