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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
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
Mind Your Errors : Evidence for a Neural Mechanism Linking Growth Mind-Set to Adaptive Posterror
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DOI: 10.1177/0956797611419520
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
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
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.
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.
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
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
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).
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,
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).
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,
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
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
ERP Amplitude (μV) Pooled
Around CPz
–100 0 100 200 300 400 500 600 700
Time (ms)
Growth Mind-Set
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
Pe Difference Amplitude (μv)
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.
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
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β = 0.62**
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... The error positivity (Pe)-an ERP signal that denotes conscious attention allocation to an error (Falkenstein et al., 2000)-has been linked to the emotional significance of errors (Falkenstein et al., 2000;Ridderinkhof et al., 2009). A growth mindset has been associated with a greater Pe amplitude (Moser et al., 2011;Schroder et al., 2017), suggesting these individuals are particularly cognizant of having made a mistake. However, mindsets that are experimentally induced show different effects-inducing a fixed mindset by emphasizing the importance of genetics led to a greater Pe amplitude. ...
During the past 60 years, perceptions about the origins of mental illness have shifted toward a biomedical model, depicting depression as a biological disorder caused by genetic abnormalities and/or chemical imbalances. Despite benevolent intentions to reduce stigma, biogenetic messages promote prognostic pessimism, reduce feelings of agency, and alter treatment preferences, motivations, and expectations. However, no research has examined how these messages influence neural markers of ruminative activity or decision-making, a gap this study sought to fill. In this pre-registered, clinical trial (NCT03998748), 49 participants with current or past depressive experiences completed a sham saliva test and were randomly assigned to receive feedback that they either have (gene-present; n = 24) or do not have (gene-absent; n = 25) a genetic predisposition to depression. Before and after receiving the feedback, resting-state activity and neural correlates of cognitive control (error-related negativity [ERN] and error positivity [Pe]) were measured using high-density electroencephalogram (EEG). Participants also completed self-report measures of beliefs about the malleability and prognosis of depression and treatment motivation. Contrary to hypotheses, biogenetic feedback did not alter perceptions or beliefs about depression, nor did it alter EEG markers of self-directed rumination nor neurophysiological correlates of cognitive control. Explanations of these null findings are discussed in the context of prior studies.
... People instinctively consider errors as unpleasant experiences and avoid them. However, studies on brain analysis suggest that errors, though unpleasant, are necessary for effective learning (Moser et al., 2011;Boaler, 2015). Therefore, going further than avoiding errors, one should develop a positive perspective about them in order to render learning more meaningful. ...
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The teacher must, first of all, have a good knowledge of mathematics to detect the errors in the student's mathematical knowledge and to identify the general situation in the classroom. This depends on the mathematical knowledge gained in the teacher training schools. General mathematics topics occupying an important place in the departments of mathematics and science education form the basis for a better understanding and comprehension of subsequent mathematics and science topics (Gökçek & Açıkyıldız, 2015).In this study, approaches of teacher candidates to errors in the questions on limits, derivatives, integrals, and asymptotes were determined as the case of the research. Perspectives regarding this case were examined in detail in line with the candidates' answers to the questions "what, how, why" and presented to the reader (Yin, 1984).
Bu çalışmanın amacı Dweck (1986, 1999, 2006) ve öğrencileri (Blackwell vd. 2007; Paunesku vd., 2015; Yeager & Dweck, 2012) tarafından teorileştirilen gelişim odaklı zihniyet inançları konusuna ilişkin, ortaokul öğrencileri üzerinde psikometrik özellikleri araştırılmış bir ölçek geliştirmektir. Ölçek verileri; 2020-2021 eğitim öğretim yılında Türkiye’nin Batı Karadeniz Bölümü’nde bulunan bir ilin dört farklı ortaokulunda öğrenim gören toplam 1213 öğrenciden elde edilmiştir. Açımlayıcı faktör analizi sürecinde (AFA) 556, doğrulayıcı faktör analizi (DFA) sürecinde 657 katılımcı ortaokul öğrencisi yer almıştır. AFA ön analizlerine göre KMO değerinin .848 ve Bartlett küresellik testinin ise .01 düzeyinde anlamlı olduğu görülmüştür (χ2=1649,016, df= 45, p=.00). Açımlayıcı faktör analizi sonuçlarına göre ortaya çıkan iki faktörlü yapı toplam varyansın %55,233’ünü açıklayabilmiştir. Analizler sonucunda sabit ve gelişim zihniyeti kavramlarıyla etiketlenen 9 maddelik iki faktörlü yapı DFA çalışmaları sonucunda doğrulanmıştır. DFA’ya ilişkin uyum iyiliği indeksleri incelendiğinde iki faktörlü 9 maddelik ölçek modelinin PCMIN/DF (=2,452), GFI (=.979), RMSEA (=.047), CFI (=.984), NFI (=.974), IFI (=.984) ile mükemmel; RMR (=.058) ile de iyi bir uyuma sahip olduğu saptanmıştır. Başka bir ifadeyle AFA ile ortaya konulan iki faktörlü hipotez modeli DFA ile doğrulanmıştır. Ölçeğin tamamı için Cronbach alpha güvenirlik katsayısı .85; birinci alt boyut (gelişim zihniyeti) için .85, ikinci alt boyut (sabit zihniyet) için ise .81 olarak hesaplanmıştır. Mevcut bulgular ışığında psikometrik özellikleri incelenmiş, sabit zihniyet ve gelişim zihniyeti olarak isimlendirilen, 9 maddelik 6’lı likert formu olan “Gelişim Odaklı Zihniyet İnançları Ölçeği”nin ortaokul öğrencilerinin “örtük zihniyet inançları”nı ölçebilecek geçerli ve güvenilir bir ölçme aracı olduğu söylenebilir.
In this chapter, we argue that to understand intelligence one must understand motivation. In the past, intelligence was often cast as an entity unto itself, relatively unaffected by motivation. In our chapter, we spell out how motivational factors determine (1) whether individuals initiate goals relating to the acquisition and display of intellectual skills, (2) how persistently they pursue those goals, and (3) how effectively they pursue those goals, that is, how effectively they learn and perform in the intellectual arena. As will be seen, motivational factors can have systematic and meaningful effects on intellectual ability, performance, and accomplishment over time. Our discussion emphasizes that heritability is not incompatible with the malleability of intelligence and that motivation is the vehicle through which intellectual skills are successfully acquired, expressed, and built upon.
Failure is the pathway to transformation. Employing reflective strategies within the three primary domains of knowledge enhances the learning process amidst failure. Where the suffering of failure can seem traumatic, systematic reflection enables learners to comprehensively analyze their behavior and evaluate its contribution to performance outcomes, resulting in posttraumatic growth and transformative results. Failure, of any kind, need not have the final word.
Guided by the implicit theories of intelligence (ITI) and the cognitive–motivational–relational theory of emotion and coping, the current cross-sectional study aimed to test the effects of students’ incremental view of intelligence (i.e., growth mindset) in coping with academic underachievement and the potential mediating role of the fear of failure (FOF). A total of 444 Chinese undergraduate students, aged 18 to 25 years old ( M = 19.76, SD = 1.48, 53.4% were female), voluntarily completed the paper-and-pencil questionnaire. A partial mediational model showed good fit with the survey data. Growth mindset had a positive direct effect on problem-focused coping (PFC) and a negative effect on FOF. FOF had a positive effect on emotion-focused coping (EFC) but not PFC. The bootstrapping results showed that growth mindset had an indirect negative effect on EFC via FOF. Our findings provide further evidence that ITI can affect different coping styles, specifically in the domain of academic failure. Growth mindset directly promoted remedial coping and prevented disengagement-oriented coping in the context of negative academic outcomes through lessening the fear of subsequent aversive consequences of failure.
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Growth mindset, the belief that one’s abilities can improve through cognitive effort, is an important psychological construct with broad implications for enabling children to reach their highest potential. However, surprisingly little is known about malleability of growth mindset in response to cognitive interventions in children and its neurobiological underpinnings. Here we address critical gaps in our knowledge by investigating behavioral and brain changes in growth mindset associated with a four-week training program designed to enhance foundational, academically relevant, cognitive skills in 7–10-year-old children. Cognitive training significantly enhanced children’s growth mindset. Cross-lagged panel analysis of longitudinal pre- and post-training data revealed that growth mindset prior to training predicted cognitive abilities after training, providing support for the positive role of growth mindset in fostering academic achievement. We then examined training-induced changes in brain response and connectivity associated with problem solving in relation to changes in growth mindset. Children’s gains in growth mindset were associated with increased neural response and functional connectivity of the dorsal anterior cingulate cortex, striatum, and hippocampus, brain regions crucial for cognitive control, motivation, and memory. Plasticity of cortico-striatal circuitry emerged as the strongest predictor of growth mindset gains. Taken together, our study demonstrates that children’s growth mindset can be enhanced by cognitive training, and elucidates the potential neurobiological mechanisms underlying its malleability. Findings provide important insights into effective interventions that simultaneously promote growth mindset and learning during the early stages of cognitive development.
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After experiencing years of procedural teaching in K-12 mathematics classrooms, many students arrive at college with ideas about, and approaches towards, mathematics that are not helpful to their learning. Students’ prior experiences and misconceptions can then negatively impact their experiences in university STEM courses. This paper describes a short course in the “big ideas” of calculus, that offered students an approach of problem-based learning, combined with mindset messages, otherwise known as a “mathematical mindset approach”. The mixed-method study considered how a ‘mathematical mindset’ teaching intervention impacted the learning, achievement, and beliefs of incoming college students, finding that the intervention significantly changed students’ ideas about mathematics, their own potential, and the value of collaboration. At the end of the course students also significantly improved their achievement on assessments of problem solving and collaboration. Importantly the course allowed students to believe in their own potential and to approach mathematics with a growth mindset, suggesting a role for such courses in students’ mathematics pathways.
In this article, I explore the impact of receiving mentorship into research and theory of the field that was guided by a social constructivist learning lens. I reflect on the ways that my conception of research and research agenda were framed subconsciously by early experiences investigating my secondary mathematics teaching practice under the mentorship of Dr. Terry Wood. Terry mentored through listening, posing questions, creating cognitive conflict, and encouraging my autonomous exploration. What stood out from this process are the parallels between her words about constructivism, applied to the elementary mathematics classroom, and her ways of mentoring me into the space of mathematics education research, theory, and practice. Her patient and student-centered mentorship had a profound impact on my ways of framing and studying teacher learning.
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Students, staff, and faculty in higher education are facing unprecedented challenges due to the COVID-19 pandemic. Recent data revealed that a good number of academic activities and opportunities were disrupted as a result of the COVID-19 pandemic and its variants. While much uncertainty remains for the next academic year, how higher education institutions and their students might improve responses to the rapidly changing situation matters. This systematic review and framework proposal aim to update previous empirical work and examine the current evidence for the effectiveness of growth mindset interventions in young adults. To this end, a systematic search identified 20 empirical studies involving 5, 805 young adults. These studies examined growth mindset within ecologically valid educational contexts and various content areas. Generally, these findings showed that brief messages of growth mindset can improve underrepresented students' academic performance and facilitate other relevant psychological constructs. In addition, we argue, although growth mindset has been identified as a unitary concept, it is comprised of multiple interdependent skills, such as self-control, self-efficacy, and self-esteem. Understanding the nature of growth mindset may contribute to successful mindset implementation. Therefore, this article presents a practical framework to help educators in higher education rethink the multidimensionality of growth mindset and to provide their students with alternative routes to achieve their goals. Finally, additional articles were discussed to help evaluate growth mindset interventions in higher education.
This research sought to integrate C. S. Dweck and E. L. Leggett's (1988) model with attribution theory. Three studies tested the hypothesis that theories of intelligence-the belief that intelligence is malleable (incremental theory) versus fixed (entity theory)-would predict (and create) effort versus ability attributions, which would then mediate mastery-oriented coping. Study 1 revealed that, when given negative feedback, incremental theorists were more likely than entity theorists to attribute to effort. Studies 2 and 3 showed that incremental theorists were more likely than entity theorists to take remedial action if performance was unsatisfactory. Study 3, in which an entity or incremental theory was induced, showed that incremental theorists' remedial action was mediated by their effort attributions. These results suggest that implicit theories create the meaning framework in which attributions occur and are important for understanding motivation.
An unresolved question in neuroscience and psychology is how the brain monitors performance to regulate behavior. It has been proposed that the anterior cingulate cortex (ACC), on the medial surface of the frontal lobe, contributes to performance monitoring by detecting errors. In this study, event-related functional magnetic resonance imaging was used to examine ACC function. Results confirm that this region shows activity during erroneous responses. However, activity was also observed in the same region during correct responses under conditions of increased response competition. This suggests that the ACC detects conditions under which errors are likely to occur rather than errors themselves.
African American college students tend to obtain lower grades than their White counterparts, even when they enter college with equivalent test scores. Past research suggests that negative stereotypes impugning Black students' intellectual abilities play a role in this underperformance. Awareness of these stereotypes can psychologically threaten African Americans, a phenomenon known as “stereotype threat” (Steele & Aronson, 1995), which can in turn provoke responses that impair both academic performance and psychological engagement with academics. An experiment was performed to test a method of helping students resist these responses to stereotype threat. Specifically, students in the experimental condition of the experiment were encouraged to see intelligence—the object of the stereotype—as a malleable rather than fixed capacity. This mind-set was predicted to make students' performances less vulnerable to stereotype threat and help them maintain their psychological engagement with academics, both of which could help boost their college grades. Results were consistent with predictions. The African American students (and, to some degree, the White students) encouraged to view intelligence as malleable reported greater enjoyment of the academic process, greater academic engagement, and obtained higher grade point averages than their counterparts in two control groups.
A 2 × 2 achievement goal framework comprising mastery-approach, mastery-avoidance, performance approach, and performance-avoidance goals was proposed and tested in 3 studies. Factor analytic results supported the independence of the 4 achievement goal constructs. The goals were examined with respect to several important antecedents (e.g., motive dispositions, implicit theories, socialization histories) and consequences (e.g., anticipatory test anxiety, exam performance, health center visits), with particular attention allocated to the new mastery-avoidance goal construct. The results revealed distinct empirical profiles for each of the achievement goals; the pattern for mastery-avoidance goals was, as anticipated, more negative than that for mastery-approach goals and more positive than that for performance avoidance goals. Implications of the present work for future theoretical development in the achievement goal literature are discussed. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Humans can monitor actions and compensate for errors. Analysis of the human event-related brain potentials (ERPs) accompanying errors provides evidence for a neural process whose activity is specifically associated with monitoring and compensating for erroneous behavior. This error-related activity is enhanced when subjects strive for accurate performance but is diminished when response speed is emphasized at the expense of accuracy. The activity is also related to attempts to compensate for the erroneous behavior.