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Effects of Intrinsic Motivation on Feedback Processing During Learning


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Learning commonly requires feedback about the consequences of one's actions, which can drive learners to modify their behavior. Motivation may determine how sensitive an individual might be to such feedback, particularly in educational contexts where some students value academic achievement more than others. Thus, motivation for a task might influence the value placed on performance feedback and how effectively it is used to improve learning. To investigate the interplay between intrinsic motivation and feedback processing, we used functional magnetic resonance imaging (fMRI) during feedback-based learning before and after a novel manipulation based on motivational interviewing, a technique for enhancing treatment motivation in mental health settings. Because of its role in the reinforcement learning system, the striatum is situated to play a significant role in the modulation of learning based on motivation. Consistent with this idea, motivation levels during the task were associated with sensitivity to positive versus negative feedback in the striatum. Additionally, heightened motivation following a brief motivational interview was associated with increases in feedback sensitivity in the left medial temporal lobe. Our results suggest that motivation modulates neural responses to performance-related feedback, and furthermore that changes in motivation facilitates processing in areas that support learning and memory. Copyright © 2015. Published by Elsevier Inc.
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1Q1 Effects of intrinsic motivation on feedback processing during learning
2Samantha DePasque Swanson , Elizabeth Tricomi
3Department of Psychology, Rutgers University, 101 Warren Street, Newark, NJ 07102, USA
abstract4article info
5Article history:
6Received 16 May 2015
7Accepted 15 June 2015
8Available online xxxx
10 Intrinsic motivation
11 Learning
12 Feedback
13 Striatum
14 Associative memory
15Learning commonly requires feedback about the consequencesof one's actions, which can drive learners to mod-
16ify their behavior. Motivation may determine how sensitive anindividual might be to such feedback, particularly
17in educational contexts where some students value academic achievement more than others. Thus, motivation
18for a task might inuence the value placed on performance feedback and how effectively it is used to improve
19learning. To investigate the interplay between intrinsic motivation and feedback processing, we used functional
20magneticresonance imaging (fMRI) duringfeedback-based learning beforeand after a novel manipulation based
21on motivational interviewing, a technique for enhancing treatment motivation in mentalhealth settings. Because
22of its role in the reinforcement learning system, the striatum is situated to play a signicant role in the modula-
23tion of learning based onmotivation. Consistent with this idea, motivation levels during the task were associated
24with sensitivity to positive versus negative feedback in the striatum. Additionally, heightened motivation follow-
25ing a brief motivational interview was associated with increases in feedback sensitivity in the left medial tempo-
26ral lobe. Our results suggest that motivation modulates neural responses to performance-related feedback, and
27furthermore that changes in motivation facilitate processing in areas that support learning and memory.
28 © 2015 Published by Elsevier Inc.
33 Performance-related feedback is an important part of effortful learn-
34 ing, as information about correct responses and errors can motivate
35 learners to adapt their behaviors. Such feedback engages the striatum,
36 widely regarded as a key region for processing reward-related informa-
37 tion, even in the absence of extrinsically rewarding or punishing out-
38 comes (e.g., Daniel and Pollmann, 2010; Satterthwaite et al., 2012;
39 Tricomi et al., 2006). However, the affective experience of performance-
40 related feedback may be more or less salient depending upon one's
41 motivation to successfully complete the task. For example, positive per-
42 formance feedback may be more reinforcing for a student who values
43 scholastic achievement than for one who sees academics as irrelevant
44 to his or her goals. As a result, it is likely that striatal engagement during
45 feedback processing would be modulated by an individual's motivation
46 to perform well.
47 The striatum serves a critical role in the reinforcement learning
48 system, receiving input from midbrain dopamine neurons that convey
49 information about unexpected rewards, and using information about
50 rewarding consequences to learn to select adaptive behaviors
51 (O'Doherty, 2004). Feedback-related responses in the striatum are pre-
52 sumed to reect the affective value of positive and negative feedback in
53 much the same way that reward responses reect the subjective value
54 of extrinsic rewards such as food or money (Satterthwaite et al.,
55 2012). However, while previous research has established sensitivity to
56contextual inuences in striatal responses to extrinsic rewards
57(e.g., Brosch et al., 2011; Chein et al., 2011; Delgado et al., 2008;
58Guitart-Masip et al., 2010; Nieuwenhuis et al., 2005), it is unclear how
59the learning context might inuence the response of the striatum to
60positive and negative performance feedback. In particular, the motiva-
61tion to perform well on a task may increase the affective salience of
62performance feedback, resulting in exaggerated striatal feedback
64Stable patterns of goal pursuit, assessed by traitmeasures of achieve-
65ment goals, have been found to inuence motivation and performance
66in experimental and academic situations (e.g., Grant and Dweck,
672003; Harackiewicz et al., 1997; Harackiewicz et al., 1998). Such traits
68have been linked with feedback-related activation in the striatum
69(e.g., DePasque Swanson and Tricomi, 2014); however, the relevance
70of a particular goal can also vary over time based on situational factors
71(Covington, 2000). For example, prior experimental work suggests
72that monetary rewards can enhance learning for boring material
73(Murayama and Kuhbandner, 2011). It is not always feasible or desir-
74able to motivate academic performance or health behaviors with pay-
75ments or other extrinsic rewards, which can potentially undermine
76intrinsic motivation for the desired behavior (Deci et al., 1999) or result
77in unintended negative long-term effects on future motivation (Gneezy
78et al., 2011); consequently, it is important to understand the effects of
79task-specic motivation on learning from feedback in the absence of ex-
80trinsic rewards or punishments. We aimed to increase the value of the
81learning goal itself, rather than using rewards that are extrinsic to the
82task to increase goal pursuit.
NeuroImage xxx (2015) xxxxxx
Corresponding author at: De partment of Psychology, Univers ity of California, Los
Angeles, CA 90095, USA.
E-mail address: (S.D. Swanson).
1053-8119/© 2015 Published by Elsevier Inc.
Contents lists available at ScienceDirect
journal homepage:
Please cite this article as: Swanson, S.D., Tricomi, E., Effects of intrinsic motivation on feedback processing during learning, NeuroImage (2015),
83 Intrinsic motivation is characterized by a focus on the inherent satis-
84 faction in performing a particular behavior for its own sake, in contrast
85 with extrinsic motivation, in which the focus is on attaining some sepa-
86 rable outcome (Ryan and Deci, 2000). Behavioral research suggests that
87 a sense of autonomy, or being in control of one's choices, facilitates in-
88 trinsic motivation (Deci and Ryan, 1987). Because we sought to increase
89 our participants' intrinsic motivation for our learning task, we required
90 a manipulation that would support their autonomy at the same time as
91 promoting reection on the value of the task. Motivational interviewing
92 is a strategy for enhancing motivation to change in substance abuse
93 treatment and other health domains, which uses directive questioning
94 to elicit change talk,or self-generated statements in favor of pursuing
95 treatment (Miller and Rollnick, 1991). In this regard, motivational
96 interviewing supports autonomy to enhance intrinsic motivation.
97 Brief interventions based on the principles of motivational
98 interviewing have demonstrated comparable efcacy to longer-term
99 cognitive behavioral therapies for reducing substance abuse (Burke
100 et al., 2003), but specic techniques used within motivational
101 interviewing have rarely been tested experimentally. One notable ex-
102 ception is an fMRI study that found diminished neural responses to alco-
103 hol cues following self-generated change talk in alcohol dependent
104 subjects, suggesting that motivational interviewing can promote the in-
105 hibition of maladaptive reward responses (Feldstein Ewing et al., 2011).
106 Rather than diminishing the value of a maladaptive behavior, we aimed
107 to use the principles of motivational interviewing to enhance motiva-
108 tion and performance on our learning task, by encouraging the partici-
109 pants to generate statements about the value of the learning task.
110 The aim of the present study was to investigate the effect of en-
111 hanced motivation on feedback processing during learning. To achieve
112 this end, we performed two experiments. In the rst, we tested a moti-
113 vational interviewing manipulation that could increase motivation (or
114 attenuate natural decreases in motivation) across two sessions of a
115 learning task. In the second, we capitalized on the motivational variabil-
116 ity within those who experienced the motivational interviewing man ip-
117 ulation and used fMRI to explore neural differences associated with
118 varying motivation levels before and after the interview. In both exper-
119 iments, participants completed two separate sessions of a feedback-
120 based word association learning task. Changes in their motivation for
121 each session were used to investigate motivational effects on learning
122 and feedback processing.
123 General methods
124 To investigate how changesin intrinsic motivation for a learning task
125 inuence performance and neural responses to performance-related
126feedback during learning, we administered two independent sessions
127of a feedback-based learning task before and after a novel motivational
128manipulation. All procedures were approved by the Institutional
129Review Board of Rutgers University, and all participants gave written in-
130formed consent.
131Materials and procedure
132Experimental task
133The participants completed two independent sessions of a word as-
134sociation learning task, adapted from a previous study of feedback pro-
135cessing in the striatum (Tricomi and Fiez, 2008; illustrated in Fig. 1).
136During this feedback-based learning task, the participants learned arbi-
137trary word pairs through trial and error. Each trial required the partici-
138pants to associate one main word with one of two other word choices,
139as in a multiple choice test with two response options. Since the
140words were semantically unrelated, learning was entirely dependent
141on the feedback that followed each response.
142Eighty unique word pairs were learned during the rst task session
143(BEFORE the motivational interviewing manipulation/control rest peri-
144od), and eighty new pairs were learned during the second session
145(AFTER the manipulation/control rest period). Each session consisted
146of two learning phases with feedback, followed by a test phase without
147feedback, in which the same 80 word pairs were presented in random
148order, and the participants chose a match for the main word. During
149learning phase 1, the guesses as to the correct match for the top word
150were arbitrary, so the feedback during learning phase 1 was simply in-
151formative and did not reect personal efcacy on the task. During learn-
152ing phase 2, because the participants had previously been exposed to
153the correct word pairs, the feedback reected the accuracy of the partic-
154ipants' memory in addition to providing information about the correct
155response. The word pairs tested BEFORE the motivational interviewing
156(MI) manipulation included only those pairs that were learned
157BEFORE the MI manipulation, and those tested AFTER the MI manipula-
158tion included only the 80 new word pairs that were introduced AFTER
159the MI manipulation.
160Stimulus presentation and behavioral data collection were imple-
161mented with E-Prime software (Psychology Software Tools, Pittsburgh,
162PA). Each trial during the two learning phases began with a jittered x-
163ation point (16 s), followed by the stimulus screen with the three
164words displayed (4 seconds), during which the participants choose
165one of the two response options, and concluded with the feedback
166screen (2 seconds) which displayed either a green checkmark ()ora
167red x.The self-paced test phase was nearly identical to the learning
Fig. 1. Experimental design. Each participant completed a BEFORE and an AFTER learning session. Each trial required subjects to learn a word pair, with two opportunities to learn each
word pair (learning phase 1 and learning hase 2) followed by a test phase. Each session contained 80 uniqueword pairs. The test phase for each learning session probed recall for only
the word pairs that were learned during that session.
2S.D. Swanson, E. Tricomi / NeuroImage xxx (2015) xxxxxx
Please cite this article as: Swanson, S.D., Tricomi, E., Effects of intrinsic motivation on feedback processing during learning, NeuroImage (2015),
168 phases but did not include performance feedback or jittered inter-trial
169 intervals. We did not include the jitter between the trial and the feed-
170 back screen, because delays of even a few seconds can impact learning
171 strategies and diminish striatal responses to feedback (e.g., Foerde and
172 Shohamy, 2011; Maddox, Ashby, and Bohil, 2003).
173 The word lengths were limited to 12 syllables and 48 letters, and
174 all words were controlled for KuceraFrancis frequency (20650 words
175 per million) and imagibility ratings (score of over 400 in the MRC data-
176 base; Coltheart, 1981). Within each trial, the words were matched for
177 length and frequency, and did not rhyme or begin with the same letter.
178 In addition, the words presented within each trial were rated with a la-
179 tent semantic analysis similarity matrix score below 0.2 to ensure that
180 no preexisting semantic relationships would bias responses toward
181 either option (Landauer et al., 1998).
182 Motivational manipulation
183 Between the rst (BEFORE) and second (AFTER) sessions of the
184 learning task, participants experienced a manipulation that was based
185 on techniques from motivational interviewing. Motivational inter-
186 viewers use an importance rulerto initiate discussion about the im-
187 portance of changing maladaptive behaviors, in which interviewees
188 rate the importance of a particular change on a scale from 0 to 10
189 (Miller and Rollnick, 1991). The interviewer may then ask why they
190 did not indicate a lower number, thus prompting the respondents to
191 generate statements favorable to changing their behavior, even if the
192 original importance rating was low. Self-generated motivational state-
193 ments such as those elicited during motivational interviewing are ex-
194 pected to be more benecial to intrinsic motivation than externally
195 provided reasons for the participant to care about the task (Deci and
196 Ryan, 1987).
197 The participants rst rated their task motivation in response the
198 question: How important would you say it is for you to perform well
199 on this task?After rating their motivation on a sliding scale from 0 to
200 10, the participants were prompted to provide at least two reasons
201 why they gave the rating they did, rather than a lower number. The
202 question was open-ended and therefore allowed subjects to rely on
203 their own values to explain their answers.
204 Manipulation check
205 In a post-experiment questionnaire completed after the learning
206 task, the participants again rated the importance of performing well
207 on the task on a scale from 0 to 10. In addition, to more directly assess
208 whether the participants felt their motivation had changed between
209 sessions, they also completed a motivation change rating,inwhich
210 they reported whether they felt more, less, or equally motivated during
211 the second session of the task as compared with the rst session, on a
212 scale of 1 (a lot less motivated)to5(a lot more motivated). The par-
213 ticipants were also asked to indicate at what point during the study did
214 they become bored or sleepy, on a scale from 1 (right away)to7
215 (never).
216 Behavioral analysis
217 Performance on the task was dened as the percentage of trials with
218 correct responses in each phase BEFORE and AFTER the manipulation.
219 The word pairs presented in the second session of learning did not du-
220 plicate those presented before the manipulation; therefore, any gains
221 in performance reect more efcient learning of the new word associa-
222 tions and cannot be attributed to memory of the associations from the
223 previous session. To test the effect of the motivational manipulation
224 on performance, we examined within-subject changes in test accuracy
225 (BEFORE vs AFTER) and learning phase 2 accuracy (BEFORE vs AFTER),
226 using paired two-tailed t-tests. In addition, to explore individual differ-
227 ences in the effects of changing motivation on task performance,
228difference scores were calculated by subtracting the percent correct
229BEFORE the manipulation from the percent correct AFTER the manipu-
230lation for the learning phase 2 and test phases. We were particularly in-
231terested in the relationship between increasing task motivation and
232task performance, so we conducted bivariate correlations between the
233motivation change rating from the post-experiment manipulation
234check and the performance difference scores from learning phase 2 and
235the test phase.
236Methods: Experiment 1
237We rst completed a behavioral experiment to assess the effects of
238the motivational interviewing manipulation in relation to a control
239condition. The participants completed two sessions of a feedback-
240based word association learning task, one before and one after either a
2415-minute rest period (control group) or a brief motivational
242interviewing manipulation (experimental group).
244Fifty adult participants were recruited from undergraduate courses
245offered by the Rutgers Newark psychology department and received
246course credit in exchange for their participation. Data from eight were
247excluded due to prior experience with the learning task(n= 6 ) and fail-
248ure to complete the entire task (n= 2). Forty-two participants
249remained in the nal sample (13 males). The experimenter randomly
250assigned each participant to either the experimental or control condi-
251tion using a virtual coin ipper (, resulting
252in an experimental group of 21 (10 males), who experienced a motiva-
253tional interviewing manipulation to enhance their intrinsic motivation,
254and a control group of 21 (3males), who experienced a quietrest period
255in place of the manipulation.
256Motivational manipulation
257Twenty-one participants completed the experimental condition,
258which involved the motivational interviewing manipulation described
259in the general methods. On a typed handout, the subjects rated their
260task motivation in response the question: How important would you
261say it is for you to perform well on this task?After indicating their re-
262sponses on a scale from 0 to 10, the participants wrote down at least
263two reasons why they gave the rating they did, rather than a lower
265Control condition
266The twenty-one control participants did not participate in the moti-
267vational manipulation; instead, they sat quietly for approximately ve
268minutes between the rst and second sessions of learning to ensure
269that the spacing between the two learning sessions was comparable to
270that for the experimental group.
271Results: Experiment 1
272Performance: Motivational interviewing group vs control group
273Performance on the learning task is depicted in Fig. 2 for both
274groups. Across both sessions of learning, both groups exhibited perfor-
275mance that was at chance for Phase 1, signicantly improved by Phase
2762 (BEFORE MI t(19) = 4.83, pb0.001; BEFORE Control t(18) = 4.52,
277pb0.001; AFTER MI t(19) = 5.80, pb0.001; AFTER Control t(18) =
2784.98, pb0.001), and improved further in the test phase (BEFORE MI
279t(19) = 4.80, pb0.001; BEFORE Control t(18) = 5.02, pb0.001;
280AFTER MI t(19) = 7.19, pb0.001; AFTER Control t(18) = 3.40, p=
2810.003), demonstrating that all participants successfully learned from
282the feedback during both learning phases. BEFORE the manipulation,
3S.D. Swanson, E. Tricomi / NeuroImage xxx (2015) xxxxxx
Please cite this article as: Swanson, S.D., Tricomi, E., Effects of intrinsic motivation on feedback processing during learning, NeuroImage (2015),
283 the experimental and control groups exhibited highly similar patterns
284 of task performance. AFTER the manipulation, the groups performed
285 similarly in the two learning phases and diverged somewhat in the
286 test phase, although between-group difference in the test phase perfor-
287 mance was not statistically signicant, t(37) = 1.26, p=0.215.Nota-
288 bly, the test phase performance was signicantly greater AFTER the
289 manipulation than BEFORE for the motivational interviewing group,
290 t(19) = 2.71, p= 0.014, but not for the control group, t(18) = 0.421,
291 p= 0.679.
292 Two-tailed independent samples t-tests revealed that, compared to
293 the control group, the experimental group exhibited signicantly great-
294 er increases in their test phase scores from the BEFORE to AFTER
295 sessions (t(40) = 2.234, p= 0.031), but not in their phase 2 scores
296 (t(40) = 0.236, p= 0.815). In other words, the test phase perfor-
297 mance increased more for the motivational interviewing group than
298 the control group, but this was not the case for the learning phase per-
299 formance. The learning phase and test phase performance were highly
300 correlated, both BEFORE, r(37) = 0.56, pb0.001, and AFTER the
301 manipulation, r(37) = 0.79, pb0.001, as were the phase 2 and test
302 phase difference scores, calculated by subtracting the BEFORE session
303 performance from the AFTER session performance, r(37) = 0.46,
304 p= 0.003, so the fact that differences between groups only emerged
305 in the test phase may suggest that motivational benets of the manipu-
306 lation enhanced persistence and promoted learning later into the ses-
307 sion, from the second round of feedback.
308 Intrinsic motivation: Motivational interviewing group vs
309 control group
310 The experimental group exhibited marginal increases in their ratings
311 on the motivational importance rulersfrom the one administered at
312 the midpoint, after the rst session, to the one administered at the
313 end of the study, t(17) = 1.80, p= 0.090. Because the control group
314 did not complete an importance ruler at the midpoint of the study, we
315 compared the participants' perception of how much their motivation
316 changed from BEFORE to AFTER using a single question at the end of
317 the study. These motivation change ratings were positively correlated
318 with the difference between the end and mid-session importance
319 rulers, r(16) = 0.58, p= 0.011. On average, the ratings were close to
320 neutral in both groups, and did not differ signicantly from each
321 other, t(35) = 0.74, p= 0.47. However, the motivational interviewing
322 group expressed ratings non-signicantly above neutral, t(17) = 1.57,
323 p= 0.134; Cohen's d= 0.76, while the control group ratings were sta-
324 tistically indistinguishable from neutral, t(18) = 0.59, p=0.56,
325 Cohen's d= 0.28. Fifty-six percent of the experimental participants
326 expressed increases in motivation, whereas only 37% of their control
327group counterparts did, although this difference was not statistically
328signicant, X
(2, N= 37) = 1.30, p=0.25.
329Relation of motivation to performance
330All participants varied in the extent to which both their phase 2 and
331test phase performance differed after the motivational interview ma-
332nipulation/rest period (% correct AFTER - % correct BEFORE), with an av-
333erage phase 2 difference score of +4.59% (SD = 9.49%) and an average
334test phase difference score of +2.85% (SD = 13.75%). Most importantly,
335individual differences in the motivation change ratings were signicantly
336correlated with changes in both learning phase 2 (r(40) = 0.471, p=
3370.002) and test performance (r(40) = 0.574, pb0.001) from BEFORE
338to AFTER the manipulation/rest period (Fig. 3). Individuals who
339expressed the greatest increases in motivation also evinced greater
340gains in performance from the BEFORE to AFTER sessions. Thus, the im-
341proved learning performance AFTER the manipulation appears to de-
342pend upon the extent to which motivation increased.
343Discussion: Experiment 1
344The administration of a motivational interviewing manipulation re-
345sulted in improvements in task performance that exceeded the training/
346order effect observed in the control group. The fact that these improve-
347ments were signicant only at the test phase, but not during phase 2 of
348learning, may speak to the importance of motivation in enhancing
349sustained effort or persistence at learning even after the rst round of
350feedback. This notion is consistent with previous evidence showing
351that motivation enhances task persistence (Dovis et al., 2012). In the
352context of the present study, it is plausible that even the less motivated
353participants would learn well from the feedback presented upon the
354rst phase of learningbut lose focus later into the task. More motivated
355individuals may expend greater effort in maintaining their attention
356and continuing to learn from the feedback evenafter the second presen-
357tation of the word pairs.
358The motivational interview also yielded a somewhat higher inci-
359dence of increased motivation across the two sessions of the lengthy
360learning task; however, the manipulation was not equally effective
361across all subjects. The degree to which motivation changed varied
362across individuals, and increasing motivation was associated with
363gains in taskperformance. In other words, while the motivational inter-
364view did not result in a robust overall group increase in motivation,
365those individuals whose motivation increased the most after the inter-
366view exhibited the greatest gains in performance. These results
367suggested that an individual differences approach would be more ap-
368propriate for the fMRI study to explore the effects of motivation changes
369on feedback processing in the brain.
370Methods: Experiment 2
372Twenty-six right-handed adult participants (11 males) were recruit-
373ed from the university community to participate in the study. One par-
374ticipant failed to complete the task due to an illness. Twenty-ve
375participants (10 males) remained in the nal sample, which consisted
376of predominantly university studentsand staff with a broad range of de-
377mographics. All participants who completed the study received com-
378pensation of $50 for their time.
379Experimental task
380The participants completed two sessions of the word association
381learning task described in the general methods, using an MRI-
382compatible button box to make their responses on each trial. Both ses-
383sions were completed inside the MRI scanner.
Fig. 2. Experiment 1: Task performance. BEFOREthe manipulation, the experimental and
controlgroups exhibitedhighly similar patternsof task performance.Only the motivation-
al interview (MI) group showed signicantly higher test phase performance AFTER than
BEFORE the manipulation.
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Please cite this article as: Swanson, S.D., Tricomi, E., Effects of intrinsic motivation on feedback processing during learning, NeuroImage (2015),
384 Motivational manipulation
385 While inside the scanner, the participants used a button box to rate
386 their task motivation on a sliding scale from 0 to 10. Next, the experi-
387 menter prompted the participants verbally to state at least two reasons
388 why they gave the rating they did, rather than a lower number. The re-
389 sults of experiment 1 suggested that a manipulation like this one in-
390 creased motivation and task performance for some participants, and
391 further indicated that individual differences in the extent of the elicited
392 motivation changes were correlated with changes in task performance.
393 Focusing on individual differences in the effects of the motivational
394 interviewing manipulation allowed us to explore the relationship be-
395 tween changing motivation, feedback processing, and task performance
396 in a sample that included participants whose motivation increased as
397 well as those whose motivation remained stable or decreased over time.
398 fMRI analysis
399 The scanning took place at the Rutgers University Brain Imaging
400 Center (RUBIC), with a 3 Tesla Siemens Trio scanner and 12-channel
401 head coil. The fMRI data were preprocessed and analyzed using
402 BrainVoyager software version 2.3.1 (Brain Innovation, Maastricht, the
403 Netherlands). Preprocessing included motion correction, spatial
404 smoothing (8 mm, FWHM), voxelwise linear detrending, and high-
405 pass temporal ltering of frequencies (three cycles per time course,
406 equivalent to 0.007 Hz). Preprocessed data were spatially normalized
407 to the Talairach stereotaxic space (Talairach and Tournoux, 1988).
408 After preprocessing, the fMRI data were analyzed using a random-
409 effects general linear model (GLM) to identify voxels throughout the
410 brain that differentiated between the experimental conditions at the
411 time of feedback presentation. The predictors of interest were modeled
412 as events at the time of feedback onset and convolved with a canonical
413 hemodynamic response function. These predictors included activation
414 at the time of positive and negative feedback during each of the four
415 learning phases: learning phase 1 BEFORE, learning phase 2 BEFORE,
416 learning phase 1 AFTER, and learning phase 2 AFTER. Each trial period,
417 beginning with the trial onset and ending at the time of the participant's
418 response, was included in the model as a predictor of no interest. Six
419 motion parameters were also included in the model as predictors of
420 no interest. Clusters of voxels identied by the GLM analysis at an un-
421 corrected statistical threshold of pb0.005 were subjected to a cluster
422 threshold estimator in Brain Voyager, resulting in a corrected threshold
423 of pb0.05. Whole-brain contrasts were used to detect differences in
424 brain responses to positive and negative feedback during the different
425 learning sessions. Imaging data from the self-paced test phases were
426not analyzed, as this phase lacked jittered inter-trial intervals and did
427not include feedback presentation.
428Motivation and feedback processing
429To identify regions in which feedback processing was modulated by
430motivation, a whole-brain analysis of covariance (ANCOVA) was per-
431formed, with self-reported changes in motivation from BEFORE to
432AFTER as a covariate for the contrast representing the change in
433feedback valence sensitivity: AFTER (positive Nnegative) NBEFORE
434(positive Nnegative).
435Task performance
436To identify the regions in which changes in neural processing at the
437time of feedback related to gains in task performance, a whole-brain
438ANCOVA was performed with the test phase performance difference
439score as a covariate for the same contrast of AFTER (positive N
440negative) NBEFORE (positive Nnegative).
441Motivation and subsequent memory
442To explore motivational effects on subsequentmemory for the word
443pairs, a subsequent memory analysis was performed using a second
444GLM analysis. In this analysis we modeled the activation during the en-
445tire trial, from cue onset to feedback offset, during learning phase 1,
446with two predictors of interest: trials that were subsequently answered
447correctly and trials that were answered incorrectly during learning
448phase 2. To identify the brain regions in which the relation between
449brain activity and subsequent memory were affected by motivation,
450an ANCOVA was performed using the motivation change rating as a
451covariate for the change in the strength of the subsequent memory
452effect from BEFORE to AFTER the manipulation, as denedbythe
453contrast: AFTER (subsequent correct Nsubsequent incorrect) NBEFORE
454(subsequent correct Nsubsequent incorrect).
455Results: Experiment 2
456Behavioral results
457Motivation ratings
458At the time of the mid-session motivation manipulation, the motiva-
459tion ratings were already high as indicated on the importance ruler
460(M= 8.00, SD = 1.384, min = 5). The end ratings were similarly high
461(M= 8.00, SD = 1.708, min = 4), and were positively correlated
462with mid-session ratings (r(23) = 0.775, pb0.001). The difference be-
463tween the ratings at the two timepoints was positively correlated with
Fig. 3. Experiment 1: Across all participants, performance improvements in both learning phase 2 and test phase correlated with changes in motivation.
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Please cite this article as: Swanson, S.D., Tricomi, E., Effects of intrinsic motivation on feedback processing during learning, NeuroImage (2015),
464 the motivation change ratings,r(23) = 0.44, p=0.027. On the
465 motivation change rating, approximately half of the participants report-
466 ed increased motivation (n= 13) in contrast to eight who reported de-
467 creased motivation, with four reporting no motivation change after the
468 manipulation. This variability in the effect ofthe manipulation on moti-
469 vation levels allowed us to focus our analyses on individual differences
470 in motivation.
471 Task performance
472 Fig. 4A shows the percentage of the correct responses during each
473 learning and test phase both BEFORE and AFTER the manipulation.
474 The participants performed signicantly better on phase 2 AFTER the
475 manipulation (t(24) = 2.234, p= 0.035). Neither the phase 1 perfor-
476 mance, which was necessarily at chance both BEFORE and AFTER the
477 manipulation, nor the test phase performance, which reected accurate
478 recall of 75.28% of the word pairs, differed signicantly BEFORE versus
479 AFTER the manipulation (t(24) = 0.653, p= 0.520; t(24) = 1.219,
480 p= 0.235, respectively). In other words, the motivational manipulation
481 affected the performance mainly on learning phase 2, which represent-
482 ed the rst opportunity for participants to demonstrate the amount
483 they learned from the study phase.
484 Participants varied in the extent to whichboth their phase 2 and test
485 phase performance differed after the manipulation (% correct AFTER - %
486 correct BEFORE), with an average phase 2 difference score of + 4.89%
487 (SD = 10.93%) and an average test phase difference score of + 2.65%
488 (SD = 10.87%). Most importantly, the individual differences in motiva-
489 tion change ratings were signicantly correlated with the change in
490 phase 2 performance (r(23) = 0.601, p= 0.001; Fig. 4B) and test
491 performance (r(23) = 0.435, p= 0.030) from BEFORE to AFTER the
492 manipulation. Individuals who experienced the greatest increases
493 in motivation also evinced greater gains in performance from the
494 BEFORE to AFTER sessions. Thus, improved learning performance ap-
495 pears to depend upon the extent to which motivation increased after
496 the manipulation.
497 fMRI Results
498 Feedback sensitivity
499 During both learning phases, positive feedback elicited higher acti-
500 vation than negative feedback in many regions that have previously
501 been implicated in reward and feedback processing, including the bilat-
502 eral ventral striatum, head of the right caudate nucleus, ventromedial
503 prefrontal cortex (vmPFC), medial temporal lobes, and posterior cingu-
504 late cortex (PCC; Fig. 5,Table 1). Despite previous ndings that the
505dorsal striatum is more responsive to feedback during the second
506phase of learning, when feedback reects the accuracy of one's memory
507(Tricomi and Fiez, 2008), no areas exhibited signicantly greater sensi-
508tivity to feedback valence during phase 2 compared with phase 1; how-
509ever, the reverse pattern was observed in small regions within the
510inferior frontal gyrus (peak x,y,z= 41, 22, 6; t=3.81,pb0.001) and
511medial frontal gyrus (peak x,y,z= 5, 28, 36; t= 3.47, p=0.002).
512Changes across sessions
513When analyzingchanges in activation across sessions, we compared
514intra-session contrasts of positive Nnegative feedback to control for po-
515tential effects of time or separate scanning sessions on the BOLD signal
516for individual conditions. Therefore, to examine the differences in feed-
517back sensitivity BEFORE and AFTER the manipulation, we compared
518feedback sensitivity (positive Nnegative) BEFORE versus AFTER the ma-
519nipulation using the contrast AFTER (positive Nnegative feedback) N
520BEFORE (positive Nnegative feedback). Valence sensitivity declined
521after the manipulation in the ventral striatum, as well as parts of the oc-
522cipital cortex and cerebellum (Fig. 6A; Table 2), whichis consistent with
523decreases in task engagement that were reported by the subjects (see
524Discussion section).
525To better understand the source of this effect, we examined the pa-
526rameter estimates for positive and negative feedback separately within
527the ventral striatum. The decline in valence sensitivity within the
528ventral striatum was driven more strongly by an attenuated decrease
529in activation following negative feedback, which was marginally higher
530(less negative) AFTER the manipulation than BEFORE, t(24) = 1.75,
531p= 0.093. The decrease in positive feedback activation AFTER the ma-
532nipulation was not signicant, t(24) = 0.63, p= 0.533; however, the
533more subtle decreases in positive feedback combined with nearly signif-
534icant increases in negative feedback activation contributed to a signi-
535cantly reduced valence sensitivity.
536Relationship between feedback sensitivity and motivation
537Within the ventral striatum ROI identied above, the decline in va-
538lence sensitivity was negatively correlated withthe raw motivation rat-
539ings, both from the manipulation between the two learning sessions
540(r(23) = 0.455, p= 0.022; Fig. 6B) and at the end of the study
541(r(23) = 0.426, p= 0.034). Inother words, the mostmotivated subjects
542showed the smallest decline in valence sensitivity over the course of the
543experiment. This pattern suggests that more motivated subjects may
544maintain focus and remain responsive to feedback later during the ex-
545periment, bucking the trend of becoming less attentive due to
Fig. 4. Experiment 2: Behavioral results. A. Thepercentage of correct responsesfor each learningphase is contrastedbetween the learning sessionsBEFORE and AFTER themotivation ma-
nipulation. The percentage of correct responses was signicantlyhigher for learning phase 2 AFTER themanipulation than BEFORE. B. Ratings of the extent to which motivation changed
from BEFORE to AFTER the manipulation were correlated with changes in task performance from BEFORE to AFTER the manipulation, both for learning phase 2 (shown) and test
6S.D. Swanson, E. Tricomi / NeuroImage xxx (2015) xxxxxx
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546 sleepiness or boredom that often occurs later in the experiment. Be-
547 cause the learning task lasted for nearly 90 minutes, it was common
548 for the participants to lose focus later during the experiment, and in
549 fact 21 out of 25 reported becoming bored or sleepy either halfway
550through the experiment (n= 10) or between the start and end of the
551second experimental session (n=11).
552The whole-brain ANCOVA using the motivation change rating as a co-
553variate identied regions in which BEFORE to AFTER changes in valence
554sensitivity correlated withchanges in motivation from BEFORE to AFTER
555the manipulation. One region of the left medial temporal lobe exhibited
556a positive relationship between increasing motivation and increasing
557valence sensitivity, peaking in the anterior parahippocampal gyrus
558(peak x,y,z=22, 20, 24; peak r(23) = 0.692, pb0.05 corrected;
559Fig. 7). In the parahippocampal gyrus, differential activation for
560positive Nnegative feedback increased AFTER the manipulation for par-
561ticipants who also reported increases in motivation AFTER the manipu-
562lation. This relationship remained signicant when subjected to a
563partial correlation to control for the changes in performance from
564BEFORE to AFTER the manipulation: r(22) = 0.671, pb0.001. This re-
565gion has been implicated in associative learning and strength of associ-
566ations during retrieval ( Q2Achim et al., 2002; Spaniol et al., 2009), so this
567pattern of activation may reect feedback-based strengthening of
568memory associations that was enhanced when motivation was high.
569The opposite pattern was observed in a (white-matter) cluster
570near the insula, in which increasing motivation was associated with
571diminishing sensitivity to feedback valence (peak x,y,z=44,17,
57224, peak r(23) = 0.683, pb0.05, corrected).
573Relationship between feedback sensitivity and test phase performance
574A whole-brain ANCOVA using the test phase difference score as a
575covariate identied bilateral posterior cingulate regions in which the
576BEFORE to AFTER changes in valence sensitivity correlated with the
577changes in test phase performance from BEFORE to AFTER the manipu-
578lation (Fig. 8). In PCC, increases in valence sensitivity correlated with in-
579creases in test phase performance, suggesting that heightened feedback
580responses in PCC may contribute to learning. Additional regions, which
581instead demonstrated an inverse correlation between the changes in
582test phase performance and thechanging valence sensitivity, are includ-
583ed in Table 3 (See Table 4). Q3
Fig. 5. Positive NNegativefeedback contrast. Resultsof the conjunctionanalysis performed to identify regions that weremodulated by feedback valence in bothlearning phase 1 andlearn-
ing phase 2. Regions demonstrating sensitivity to feedback valence for both phase 1 feedback and phase 2 feedback included ventral striatum (VS), ventromedial prefrontal cortex
(vmPFC), posterior cingulate cortex (PCC), and the medial temporal lobes (MTL).
t1:1Table 1
t1:2Regions that distinguished between positive and negative feedback during both learning
t1:4Region of activation BA Size
(# voxels)
Peak Talairach
t1:5Conjunction of Ph1 & Ph2
t1:6Positive feedback NNegative feedback
t1:7Inferior Parietal Lobule 40 963 53 38 42 4.065 b0.001
t1:8Superior Temporal Gyrus 21 555 53 512 4.269 b0.001
t1:9Posterior Cingulate/
t1:10 Temporal Cortex
31 35,773 10 50 21 4.954 b0.001
t1:11 Posterior Cingulate
t1:12 Cortex
31 10 50 21 4.954 b0.001
t1:13 Inferior Temporal Gyrus 19 47 56 3 4.947 b0.001
t1:14 Hippocampus 29 26 12 3.985 b0.001
t1:15 Cerebellum (Tuber) 3619 35 68 30 5.199 b0.001
t1:16 Putamen/Insula 604 26 1 15 3.605 0.001
t1:17 Putamen 26 1 15 3.605 0.001
t1:18 Insula 13 32 2 17 3.440 0.002
t1:19 Cerebellum (Tonsil) 933 26 32 33 4.774 b0.001
t1:20 Striatum (bilateral)10,461 13 4 6 6.100 b0.001
t1:21 Ventral Striatum (right) 14 4 9 5.691 b0.001
t1:22 Caudate Head (right) 8 7 3 4.340 b0.001
t1:23 Ventral Striatum (left) 13 4 6 6.100 b0.001
t1:24 Thalamus 454 23 20 3 3.822 b0.001
t1:25 Ventromedial Prefrontal
t1:26 Cortex
32 6659 1 46 3 4.614 b0.001
t1:27 Middle Occipital Gyrus 18 19,960 19 89 6 5.836 b0.001
t1:28 Parahippocampal Gyrus
t1:29 (posterior)
37 925 34 41 9 3.904 b0.001
t1:30 Insula 13 449 34 8 18 3.868 b0.001
t1:31 BA, Brodmann Area.
t1:32 To betteridentify the separatebrain areas encompassed withinthe larger clusters,the
t1:33 threshold was increased until the larger clusters separated into smaller ones and those
t1:34 peaks are also reported.
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584 Relationship between subsequent memory and motivation
585 The ANCOVA using the motivation change ratings as a covariate for
586 the change in subsequent memory effects, i.e., the contrast of AFTER
587 (subsequently correct vs subsequently incorrect) BEFORE (subse-
588 quently correct vs subsequently incorrect) identied clusters of activa-
589 tion in three regions which exhibited increasing subsequent memory
590 effectsfrom BEFORE to AFTER the manipulation as a function of increas-
591 ing motivation: the medial frontal gyrus (BA 8, peak x,y,z= 11, 37, 19,
592 peak r(23) = 0.572, p= 0.003), another in the cerebellum (tonsil, peak
593 x,y,z=1, 53, 42, peak r(23) = 0.676, pb0.001), and a left mid-
594 dle area of the superior temporal sulcus (BA 21, peak x,y,z=46, 2,
595 12, peak r(23) = 0.615, p=0.001)(Fig. 9)Q4 .
596 Discussion: Experiment 2
597 This experiment tested the relationship between the neural re-
598 sponses to feedback during learning and the motivational value of the
599 feedback. The performance increases after a motivational interviewing
600 manipulation was associated with increases in motivation, suggesting
601 that the instructive efcacy of feedback is enhanced when motivation
602 is increased. Additionally, motivation appeared to sustain feedback pro-
603 cessing in the striatum across the duration of the lengthy task, and in-
604 creases in motivation following the manipulation were associated
605 with heightened sensitivity to correct versus incorrect feedback trials
606in the left parahippocampal gyrus. The heightened feedback-related
607activation in the posterior cingulate cortex after the manipulation was
608associated with gains in test phase performance, suggesting that re-
609sponses to feedback in the PCC may play a role in facilitating learning
610during this task. Finally, increasing motivation was associated with a
611stronger relationship between task-related activation during learning
612and subsequent memory in the medial frontal gyrus and the middle
613temporal gyrus. These results suggest that neural processing relating
614to feedback valence is dependent upon the motivation to perform well
615on the task.
616Motivational effects on feedback processing
617In the ventral striatum, the differentiation between positive and
618negative feedback weakened after the manipulation. The participants
619reported that they became bored or sleepy approximately halfway
620through thestudy, and this loss of focus may have contributed to the de-
621cline in feedback sensitivity later in the experiment. However, task-
622specic motivationattenuated the generaltrend for feedback sensitivity
623to decline across the two sessions of the learning task, suggesting that
624the more motivated participants may have remained more attuned to
625the feedback in spite of their fatigue. The ventral striatum has been pre-
626viously implicated in reward processingand learning to predict positive
627outcomes, so its involvement during the feedback-based learning has
628been interpreted as evidence that positive feedback is viewed as a re-
629warding outcome (Satterthwaite et al., 2012). Highly motivated sub-
630jects may be the most likely to replenish their declining interest and
631maintain their valuation of the feedback, thus explaining the modulat-
632ing inuence of motivation on this decline in feedback sensitivity.
633The changes in motivation were associated with changing activation
634in the left medial temporal lobe. The left anterior parahippocampal gyrus
635exhibited increasing valence sensitivity after the manipulation for those
636subjects who reported increases in motivation after the motivational in-
637terview. The motivational modulation of the parahippocampal gyrus in
638this study is consistent with evidence that dopaminergic projections
639from the midbrain to the MTL communicate information about the mo-
640tivational signicance of information (Shohamy and Adcock, 2010). The
641parahippocampal gyrus has been implicated in associative encoding of
642arbitrary pairs of objects (Achim et al., 2007), emotional memory
643encoding (Murty et al., 2010), and memory retrieval, including activa-
644tion during recognition tests that is highest for items that are remem-
645bered with the highest condence (Spaniol et al., 2009). During our
Fig. 6. Decrease in feedback sensitivity from BEFOREto AFTER the manipulation.(A) In the ventral striatum, feedbacksensitivity declinedAFTER the manipulation. (B) Ho wever, through-
out this region, the decline in feedback sensitivity was strongest for those reportingthe lowest levels ofmotivation at the mid-point of the study. Thosewho were most motivatedshowed
an attenuated decline in ventral striatal feedback sensitivity.
t2:1Table 2
t2:2Regions that exhibited an effect of session (BEFORE vs. AFTER themanipulation) on feed-
t2:3back valence sensitivity.
t2:4Region of Activation BA Size
(# voxels)
Peak Talairach
t2:5Comparing learning sessions BEFORE and AFTER manipulation
t2:6BEFORE (pos Nneg) NAFTER (pos Nneg)
t2:7Cuneus 17 259 23 74 6 3.86 b0.001
t2:8Cuneus 19 3329 177 36 4.90 b0.001
t2:9Ventral Striatum 443 14 7 0 3.68 0.001
t2:10 Cerebellum (Pyramis) 13,966 43 74 33 5.01 b0.001
t2:11 Cuneus 19 780 495 24 3.70 0.001
t2:13 AFTER (pos Nneg) NBEFORE (pos Nneg).
t2:14 no regions showed greater valence sensitivity AFTER than BEFORE the manipulation.
t2:15 BA, Brodmann Area.
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646 task, this parahippocampal region may have been involved in the learn-
647 ing and recall of the arbitrary word pairs. When motivation increases,
648 the participants may become more successful at remembering the cor-
649 rect responses with high condence, which have been associated with
650 greater activation in the parahippocampal gyrus.
651 Neural processing supporting task performance
652 While it is important to demonstrate that motivation can inuence
653 feedback related activation, we were also interested in exploring the
654 networks that are involved in translating feedback into learning success.
655 To identify the brain regions that supported learning, we located
656 performance-relevant regions where increases in feedback sensitivity
657 AFTER the manipulation correlated with gains in test performance.
658 The posterior cingulate cortex, which was also identied in the
659 positive Nnegative feedback contrast, showed this pattern. The
660 positive Nnegative activation during learning increased the most after
661 the manipulation for those subjects who showed the largest test
662 phase performance increases. Although the PCC is considered to be a
663 part of thedefault network, it has also shown sensitivity to reward pre-
664 diction error during reinforcement learning (Cohen, 2007), and it has
665 anatomical connections to areas involved in reward, memory, and at-
666 tention (Pearson et al., 2011). Because it has been implicated in salience
667processing, reward value, and attentional shifts (Leech et al., 2011), its
668sensitivity to feedback valence in ourtask may represent a rewardor sa-
669lience reaction that is translated into shifts in attention and enhanced
670task performance.
671Motivational modulation of subsequent memory effects
672In a left middle area of the superior temporal sulcus,a correlation be-
673tween the motivation change ratings and the change in the subsequent
674memory contrast indicated that in this region there is a stronger rela-
675tionship between activation and subsequent memory when motivation
676for the task is higher. The left middle STS may have been recruited due
677to the role of this region in speech processing ( Q5Hein and Knight, 2008),
678which might be engaged when the previously learned word pairs are
679being recalled and/or rehearsed. This strengthening of subsequent
680memory effects during periods of increased motivation could indicate
681that motivation facilitates a stronger link between retrieval/
682maintenance of relevant verbal information during learning and the
683subsequent ability to accurately retrieve the correct word association.
684It is plausible that task-specic motivation would enhance processing
685in the regions relevant to the processing of words (e.g., the STS) and
686the formation of associative memories (e.g., the parahippocampal
687gyrus) during our paired associate word learning task, since previous
Fig. 7. Motivation increase correlates with increasing valence sensitivity in MTL. A whole-brain ANCOVA revealed a region in the left MTL where increasing motivation from BEFORE to
AFTER the manipulationcorrelated with increasing sensitivity to positive Nnegativefeedback from BEFORE to AFTERthe manipulation(both learning phases). The scatterplotuses param-
eter estimates extracted from the entire ROI identied in the whole-brain ANCOVA.
Fig. 8. Changes in feedbackvalence sensitivity in PCCcorrelate with changes in test phase performance. (A) A whole-brainANCOVA identieda cluster in left PCC whereincreasing valence
sensitivity correlated with increasing test phase accuracy. (B) Correlation between test phase difference scores and parameter estimates from thecontrast AFTER (positive Nnegative)
BEFORE (positive Nnegative) in the left PCC cluster.
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688 research has shown similar motivation-related increases in task-
689 relevant processing in cognitive control and visual networks
690 (e.g., Pessoa, 2009).
692Because the BEFORE and AFTER sessions of learning were spaced
693apart in time, it was necessary to control for a potential order effect. Ex-
694periment 1 demonstrated test phase improvements for participants
695who experienced the manipulation and not a behavioral control
696group, suggesting that performance improvements cannot merely be
697attributed to the prior exposure to the experimental paradigm. Addi-
698tionally, to address this confound within the fMRI sample, individual
699conditions were not contrasted between sessions, but rather within-
700session contrasts (e.g., positive Nnegative feedback) were compared
701across the two sessions. Because we were not able to directly compare
702the individual conditions, our results are limited to regions where moti-
703vation or task performance correlated with differential processing of
704positive versus negative feedback. This constraint makes interpretation
705difcult for brain areas that are not typically associated with effects of
706feedback valence per se, such as the MTL, STS and lateral prefrontal cor-
707tex, but was a necessary compromise to rule out global differences in
708signal that may occur across experimental sessions.
709Due to lack of jitter between the feedback and stimulus screens, ac-
710tivationat the time of feedback may include residual activation from the
711trial itself. To address this concern, we included the trial period as a pre-
712dictor of no interest in the model; nonetheless, the results observed
713should not be presumed to be exclusively related to the feedback stim-
714ulus, per se, but are thought to reect processing relating to the experi-
715ence of achieving a correct versus an incorrect response. The nding of
716increased activation following positive relative to negative outcomes in
717the striatum is consistent with the neural responses to unpredictable re-
718wards and punishments in non-learning contexts (e.g. Delgado et al.,
720The motivation change ratings that we used to determine whether
721motivation increased from BEFORE to AFTER the motivational interview
722were collected at the endof the experiment. Retrospective reporting on
723whether motivation increased, decreased, or remained the same across
724the two sessions of learning may have been biased by participants' per-
725ceptions of their performance on the task. The neural results suggest
726that motivation ratings reect motivation beyond merely that bestowed
727by the frequency of positive feedback, since correlations between moti-
728vation and activation in the parahippocampal gyrus remained signi-
729cant when controlling for changes in performance.
730Because time constraints precluded the inclusion of condence
731ratings during the feedback phases, we were not able to investigate
732how neural responses to positive feedback might vary as a function of
733whether it follows a correct guess as opposed to accurate recall.
t3:1Table 3
t3:2Regions in which changes in test phase performance correlate with changes in valence
t3:4Region of Activation BA Size
(# voxels)
Peak Talairach
t3:5Change in Test accuracy correlates with change in valence sensitivity:
t3:6Test % Correct (AFTER - BEFORE) correlates with Positive NNegative (AFTER - BEFORE)
t3:7Positive Correlation
t3:8Posterior Cingulate 30 422 19 50 15 0.70 b0.001
t3:10 Negative Correlation
t3:11 Superior Frontal Gyrus 10 3196 25 46 3 0.69 b0.001
t3:12 Medial Frontal Gyrus 10 1204 14 52 15 0.69 b0.001
t3:13 Cerebellum (Culmen) 439 53 44 27 0.70 b0.001
t3:15 Change in Ph2 accuracy correlates with change in valence sensitivity:
t3:16 Ph2 % Correct (AFTER BEFORE)correlateswithPositiveNNegative (AFTER - BEFORE)
t3:17 Positive Correlation
t3:18 n/a
t3:20 Negative Correlation
t3:21 Inferior Frontal Gyrus 47 284 44 13 90.66 b0.001
t3:22 BA, Brodmann Area.
t4:1Table 4
t4:2Regions in which activation during learning predicts subsequent memory for individual
t4:3word pairs.
t4:4Region of Activation BA Size
(# voxels)
Peak Talairach
t4:5SUBSEQUENT MEMORY (activation during Ph1; classied based on Ph2 accuracy)
t4:6Subsequent Correct NSubsequent Incorrect
t4:7Inferior Frontal Gyrus/
t4:8Middle Frontal Gyrus
9 13,511 43 10 24 5.13 b0.001
t4:9Middle Temporal Gyrus 37 5540 61 47 9 5.41 b0.001
t4:11 Subsequent Incorrect NSubsequent Correct
t4:12 Precentral Gyrus 4 363 59 2 21 3.56 0.002
t4:13 Superior Frontal Gyrus 6 939 17 25 54 3.81 b0.001
t4:14 Anterior Cingulate 24 464 1 28 15 3.99 b0.001
t4:15 Precuneus 7 662 453 48 3.76 b0.001
t4:16 BA, Brodmann Area.
Fig. 9. Subsequent memoryinuenced by motivation. Within the STS, BEFORE to AFTER increases inmotivation were correlated withBEFORE to AFTER increases in the subsequent mem-
ory effect.
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734 Therefore, the precise nature of what positive and negative feedback
735 mean to the participant on any given trial is not fully clear. Because
736 the intrinsic value of feedback may be tied to whether or not a partici-
737 pant feels the feedback reects personal goal achievement as opposed
738 to a random outcome, high-condence responses might show even
739 more sensitivity to differences in intrinsic motivation. This interesting
740 question remains open for further study.
741 One additional limitation is the discrepancy between the ndings in
742 the present experiment and those from previous implementations of
743 this task. Previous research has demonstrated that performance feed-
744 back is only differentially processed in the caudate when that it reects
745 successful goal attainment not when it informs about the accuracy of
746 arbitrary responses (Tricomi and Fiez, 2008). The current experiment
747 did not replicate this pattern, since the caudate was engaged during
748 both the rst and second learning phases. The participants in this sam-
749 ple may have experienced a gambler's fallacy,or a belief that they had
750 the power to choose the correctoption during the rst learning phase.
751 Anecdotally, some participants reported that either the rst or second
752 session seemed easierto learn, in spite of the arbitrary nature of the
753 word pairs. Since even the illusion of agency can result in stronger en-
754 gagement of the caudate during reward processing (Tricomi et al.,
755 2004), it is possible that subtle differences either in the task instructions
756 or the demographics of the samples may have led the current partici-
757 pants to feel a stronger sense of control over their performance during
758 the rst learning phase.
759 Conclusions
760 To best tailor educational practices to the needs of the individual, in-
761 uences on learningand performance other than ability need to be con-
762 sidered. The present study was designed to test the notion that neural
763 responses to feedback during learning reect the motivational value of
764 the feedback. We found evidence that striatal processing of
765 performance-related feedback is modulated by intrinsic motivation,
766 with more motivated participants maintaining a greater differentiation
767 between positive and negative feedback during the second half of the
768 study (after the point that the majority of the subjects begin to feel
769 bored or sleepy). Furthermore, the other brain areas involved in mem-
770 ory and language processing, including the left medial and lateral tem-
771 poral lobe, showed changes in activation that were modulated by
772 increasing motivation. Our ndings indicate that intrinsic motivation
773 is an important factor in learning, which may help to maintain the in-
774 structive efcacy of feedback over time and strengthen the relationship
775 betweenthe neural processingduring learning and the subsequent abil-
776 ity to use this information when it is needed. That performance-related
777 feedback is processed differentially depending on learners' current mo-
778 tivation levels has important implications for educators and other pro-
779 fessionals who wish to shape behavior without resorting to the use of
780 incentives that are extrinsic to the task.
781 Acknowledgements
782 The authors would like to thank Olga Boukrina, Ekaterina
783 Dobryakova, Holly Sullivan Toole, Christina Bejjani, Mauricio Delgado,
784 Luis Rivera, Jennifer Mangels, and William Graves for providing valuable
785 feedback during this project. Thanks are also owed to Rebecca Williams,
786 Michael DeLucca, Stuti Prajapati, Kiranmayee Kurimella, and Onaisa
787 Rizki for their assistance in collecting data. The project described was
788 supported by grants from the National Institute on Drug Abuse
789 (R03 DA029170) and the National Science Foundation (BCS 1150708).
790 The content is solely the responsibility of the authors and does not nec-
791 essarily represent the ofcial views of the National Institute on Drug
792 Abuse, the National Institutes of Health, or the National Science
793 Foundation.
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... The intrinsic motivation to learn is associated with the internal willpower of learners to learn by themselves without external rewards or pressure [33], [34]. There are four main factors that stimulate intrinsic motivation: curiosity, being challenged, control and fantasy [35]. ...
... The motivation of learners can be affected by attractive content or interactive learning materials that are created based on motivational design to enhance learners' motivation to learn [9]. Learners are more stimulated and more positive about learning when an intrinsic motivation strategy is used during the learning process [33]. Implementing intrinsic motivation in mobile applications using AR and multimedia technology has positive impacts on learners' motivation, as shown by several studies [2], [10], [35], [36], [37]. ...
With the rapid evolution of interactive technology, the popularity of mobile augmented reality (MAR) as a learning aid has continued to grow. However, several studies have revealed that research on the impact of AR in the educational domain is both insufficient and in an early phase. More studies are required to evaluate the effectiveness of utilizing MAR in this domain. The purpose of this study was to measure the effect of a mobile training course designed using MAR on trainees’ motivation. We reviewed the associated concepts, highlighted the importance and effectiveness of MAR and explained the benefits and challenges of employing MAR in the educational domain. This study drew on John Keller’s motivational model components and emphasized the significance of intrinsic motivation. We used a quantitative approach and designed a mobile training course that uses MAR to train government employees in Oman. A total of 32 employees were randomly divided into an experimental group and a control group. The experimental group used the designed application, and the control group took a training course online via computers. A motivational survey was conducted, and SPSS statistical software was used for data analysis. The results revealed that there was a significant difference in the mean motivation value for the experimental group: the trainees from the experimental group were more motivated than those from the control group. This study confirms that learners are motivated to participate in mobile training courses designed using MAR, which can contribute to the development of human resources in various domains.
... To achieve lasting changes in driving behaviors, we must consider the role of motivation in behavioral changes. Low levels of motivation can diminish sensitivity to performance-related feedback, whereas high levels of motivation can facilitate learning and may lead to better behavioral outcomes [9]. Because distractions (e.g., reading and texting on a phone) often are intrinsically valuable for the driver, they compete with the motivation to drive more safely. ...
Providing personalized behavioral information as feedback to drivers can lead to safer practices. However, feedback efficacy is likely moderated by the driver's level of motivation towards behavioral change. Gamification of feedback, which is the incorporation of game design elements intended to motivate drivers toward safe behaviors, could potentially reduce unsafe behaviors in the long term. This article assesses a gamified driver feedback design in mitigating driver distraction and enhancing driving performance among younger drivers. A driving simulator study was conducted with 42 drivers, 21–30 years old, comparing: 1) no feedback; 2) real-time feedback; 3) real-time feedback + postdrive feedback; and 4) real-time feedback + postdrive feedback + game design elements to examine their impact on distraction engagement (manual-visual interactions with an in-vehicle display) and driving performance. Groups that received postdrive feedback, both with and without gamification elements, showed reduced distraction engagement and enhanced driving performance compared to no feedback. Between the two types of postdrive feedback, the nongamified feedback provided more benefits in reducing the 95th percentile glance duration to the in-vehicle display, and the one with gamification provided more benefits in reducing the rate of manual interactions with the in-vehicle display. Meanwhile, no benefits were observed with the real-time feedback only condition over no feedback. Despite minor differences in efficacy, both postdrive and gamification feedback appear to be effective countermeasures for distracted driving in the short term. Future research should investigate other game designs for driver feedback and assess the impact of feedback gamification over longer-term exposure.
... An unfortunate implication of the research findings is that, given limited predictive validity of trustworthiness perception (Foo et al., 2022), inefficient trustworthiness learning may heighten the risk of being deceived by "wolves in sheep's clothing" among older adults (Suzuki & Suga, 2010). Importantly, if age-related inefficiency in trustworthiness learning is mediated by motivational factors, as discussed in Study 3, it may be malleable through cognitive interventions that remind older adults of the importance of remembering the "wolves in sheep's clothing" (DePasque & Tricomi, 2015). This possibility is an interesting topic for future research. ...
Humans, as prosocial animals, expose themselves to the risk of exploitation if they fail to determine whether other individuals are trustworthy. People are keen to ascertain the trustworthiness of others that they instantly form initial impressions of others’ trustworthiness from perceptual cues, especially from faces. However, because perceived trustworthiness has low predictive validity, “true” trustworthiness of another individual must be learned from that person’s actual behavior. Considering the increasing societal and scholarly attention to fraud victimization among older adults, we conducted a series of studies comparing the perceptions and learning of other people’s trustworthiness between older and younger adults. The results showed a cross-age similarity in trustworthiness perception (Study 1), but an age-related decline in trustworthiness learning (Study 2). Furthermore, ventral striatal activity was found to be related to older adults’ failure to learn trustworthiness, suggesting the involvement of motivational mechanisms (Study 3).
... Lack of motivation leads to superficial learning, without the learner's willingness to exert effort to achieve a learning goal [1]. There are studies showing that while unmotivated students have lower academic performances, motivated students want to stay involved with their tasks and acquire new skills and competences [2]. ...
Conference Paper
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The development of multimedia tools, as well as web 2.0 technologies coming out, led an increase in the interest for integrating these in education. Initially used for socializing, social networks started to be used more frequently in academic education. Everyday use of smartphones and electronic tablets by students had drawn the attention of teachers. As a result, these devices are seen as potential resources used to improve learning and motivation for students. Using devices during class determines significant changes in the teaching-learning process which requires to reconsider their role in education. Although integrating social networks in education represents a challenge, they are neccessary in order to connect universities to the new generation of students. Even though social platforms were originally designed to connect people, today they constitute important learning resources through which students create content, work collaboratively and constantly interact in order to ease the learning process. The potential of social networks, student motivation for using such platforms for learning, creating online communities for certain didactic activities are themes the academic community is concerned with, and are also frequently approached in literature. Among social networks, Facebook is the most used one. Without having been designed specifically for learning, due to the impact it has on the youth, it can be used in the new social context as a great opportunity through which a collaborative, interactive and attractive learning environment for 21st century students can be created. The present paper aims to analyse the opinion of engineering students regarding the use of social networks in their own learning process. The purpose for which they are used is being analysed and so is their impact on learning.
... Applying effective learning strategies in the context of learning is considered an important way of increasing learners' motivation [17], and the usage of interactive learning theories in a learning environment increases the interaction among learners [59]. Learners are more stimulated and positive when an intrinsic motivation strategy is used during the learning process [24]. Learner motivation can be affected by the usage of attractive content or interactive learning materials by applying a motivational design that enhances learners' motivation toward learning [39]. ...
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Because mobile technology and the widespread usage of mobile devices have swiftly and radically evolved, several training centers have started to offer mobile training (m-training) via mobile devices. Thus, designing suitable m-training course content for training employees via mobile device applications has become an important professional development issue to allow employees to obtain knowledge and improve their skills in the rapidly changing mobile environment. Previous studies have identified challenges in this domain. One important challenge is that no solid theoretical framework serves as a foundation to provide instructional design guidelines for interactive m-training course content that motivates and attracts trainees to the training process via mobile devices. This study proposes a framework for designing interactive m-training course content using mobile augmented reality (MAR). A mixed-methods approach was adopted. Key elements were extracted from the literature to create an initial framework. Then, the framework was validated by interviewing experts, and it was tested by trainees. This integration led us to evaluate and prove the validity of the proposed framework. The framework follows a systematic approach guided by six key elements and offers a clear instructional design guideline checklist to ensure the design quality of interactive m-training course content. This study contributes to the knowledge by establishing a framework as a theoretical foundation for designing interactive m-training course content. Additionally, it supports the m-training domain by assisting trainers and designers in creating interactive m-training courses to train employees, thus increasing their engagement in m-training. Recommendations for future studies are proposed.
... Mahasiswa ada yang pasif menyampaikan argumen dan hanya merespon saat ditanya. Namun pemberian umpan balik dari dosen dapat berperan sebagai penghargaan terhadap capaian mahasiswa sehingga mampu memotivasi mahasiswa untuk berprestasi (Swanson & Tricomi, 2015). ...
The teaching and learning activities at schools and universities have been affected by the Covid-19 pandemic. This article was aimed at describing teachers’ point of view on the implementation of distance learning at PGSD FKIP Unsyiah, especially in mathematics courses. The participants were 3 mathematics teachers at PGSD FKIP Unsyiah. Data was collected through interview and analyzed qualitatively. Results showed that the preparation of learning depended on the completeness of materials uploaded to the university elearning website; courses were conducted using zoom, Whatsapp, elearning website and Youtube channel; evaluation was conducted by giving tasks to the students; lack of IT skills and unstable internet connection were among the obstacles in distance learning. This study implies that the implementation of distance learning could be optimized by overcoming the obstacles.
Motivational deficits in schizophrenia may interact with foundational cognitive processes including learning and memory to induce impaired cognitive proficiency. If such a loss of synergy exists, it is likely to be underpinned by a loss of synchrony between the brains learning and reward sub-networks. Moreover, this loss should be observed even during tasks devoid of explicit reward contingencies given that such tasks are better models of real world performance than those with artificial contingencies. Here we applied undirected functional connectivity (uFC) analyses to fMRI data acquired while participants engaged in an associative learning task without contingencies or feedback. uFC was estimated and inter-group differences (between schizophrenia patients and controls, n = 54 total, n = 28 patients) were assessed within and between reward (VTA and NAcc) and learning/memory (Basal Ganglia, DPFC, Hippocampus, Parahippocampus, Occipital Lobe) sub-networks. The task paradigm itself alternated between Encoding, Consolidation, and Retrieval conditions, and uFC differences were quantified for each of the conditions. Significantly reduced uFC dominated the connectivity profiles of patients across all conditions. More pertinent to our motivations, these reductions were observed within and across classes of sub-networks (reward-related and learning/memory related). We suggest that disrupted functional connectivity between reward and learning sub-networks may drive many of the performance deficits that characterize schizophrenia. Thus, cognitive deficits in schizophrenia may in fact be underpinned by a loss of synergy between reward-sensitivity and cognitive processes.
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The fact that pre-service teachers have self-efficacy, the belief that they can succeed in an academic job, activates their metacognitive thinking skills (MTS). Previous studies have indicated that individuals' academic self-efficacy (ASF) is important for metacognitive thinking. However, variables such as academic motivation (AcM) and career planning might also be effective. In this regard, the present study attempts to examine the role of pre-service teachers’ career plans in the relationship between AcM and MTS and the mediating role of AcM with the structural equation model. To this end, 639 pre-service teachers were employed for the study which adopts a cross-sectional research model. Data analysis indicated positive relationships between AcM, ASF, and MTS. In addition, according to the findings, the career plan acts as a moderator between ASF and MTS. Suggestions for new studies were presented within the scope of the limitations of the study.
There is a growing body of literature demonstrating that language rehabilitation can improve naming impairments for individuals with aphasia. However, there are challenges applying evidence-based research to clinical practice. Well-controlled clinical studies often consist of homogenous samples and exclude individuals who may confound group-level results. Consequently, the findings may not generalize to the diverse clients serviced by speech-language therapists. Within evidence-based guidelines, clinicians can leverage their experiences and theoretical rationale to adapt interventions to meet the needs of individual clients. However, modifications to evidence-based interventions should not alter aspects of treatment that are necessary to produce change within the treatment target. The current discussion paper uses errorless learning, errorful learning, and retrieval practice for naming in aphasia to model how treatment theories can guide clinicians in making theory-informed modifications to interventions. First, we briefly describe the learning mechanisms hypothesized to underlie errorless learning, errorful learning, and retrieval practice. Next, we identify ways clinicians can provide targeted supports to optimize learning for individual clients. The paper ends with a reflection on how well-defined treatment theories can facilitate the generation of practice-based evidence and clinically relevant decision making.
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The authors investigated personality predictors of achievement goals in an introductory psychology class, as well as the consequences of these goals for the motivation and performance of 311 undergraduates. Two dimensions of achievement motivation (workmastery and competitive orientations; J. T. Spence & R. L. Helmreich, 1983) predicted the goals endorsed. Individuals high in workmastery were more likely to adopt mastery goals and less likely to adopt work avoidance goals, whereas competitive individuals were more likely to endorse performance and work avoidance goals. Students adopting mastery goals were more interested in the class, but students adopting performance goals achieved higher levels of performance. These results suggest that both mastery and performance goals can lead to important positive outcomes in college classes.
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The types of goals that students adopt in educational settings and the consequences of those goals on 2 important educational outcomes - performance and intrinsic motivation - are discussed. In the case of performance, we briefly review and evaluate a large body of theory and research conducted by other investigators. In particular, we consider the possibility that some commonly accepted conclusions about the effects of achievement goals are premature. In the case of intrinsic motivation, we describe a theoretical model that has guided our own work on this topic and provide some recent experimental results. We believe that this model and our experimental results can contribute to a more comprehensive understanding of goals and optimal motivation. Finally, we return to the college classroom environment and examine the consequences of goals for both performance and intrinsic motivation, offering a broader analysis of success in college courses.
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The striatum plays a critical role in learning from reward, and it has been implicated in learning from performance-related feedback as well. Positive and negative performance-related feedback is known to engage the striatum during learning by eliciting a response similar to the reinforcement signal for extrinsic rewards and punishments. Feedback is an important tool used to teach new skills and promote healthful lifestyle changes, so it is important to understand how motivational contexts can modulate its effectiveness at promoting learning. While it is known that striatal responses scale with subjective factors influencing the desirability of rewards, it is less clear how expectations and goals might modulate the striatal responses to cognitive feedback during learning. We used functional magnetic resonance imaging to investigate the effects of task difficulty expectations and achievement goals on feedback processing during learning. We found that individuals who scored high in normative goals, which reflect a desire to outperform other students academically, showed the strongest effects of our manipulation. High levels of normative goals were associated with greater performance gains and exaggerated striatal sensitivity to positive versus negative feedback during blocks that were expected to be more difficult. Our findings suggest that normative goals may enhance performance when difficulty expectations are high, while at the same time modulating the subjective value of feedback as processed in the striatum. Electronic supplementary material The online version of this article (doi:10.3758/s13415-014-0269-8) contains supplementary material, which is available to authorized users.
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The authors investigated personality predictors of achievement goals in an introductory psychology class, as well as the consequences of these goals for the motivation and performance of 311 undergraduates. Two dimensions of achievement motivation (workmastery and competitive orientations; J. T. Spence & R. L. Helmreich, 1983) predicted the goals endorsed. Individuals high in workmastery were more likely to adopt mastery goals and less likely to adopt work avoidance goals, whereas competitive individuals were more likely to endorse performance and work avoidance goals. Students adopting mastery goals were more interested in the class, but students adopting performance goals achieved higher levels of performance. These results suggest that both mastery and performance goals can lead to important positive outcomes in college classes. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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First we discuss how extrinsic incentives may come into conflict with other motivations. For example, monetary incentives from principals may change how tasks are perceived by agents, with negative effects on behavior. In other cases, incentives might have the desired effects in the short term, but they still weaken intrinsic motivations. To put it in concrete terms, an incentive for a child to learn to read might achieve that goal in the short term, but then be counterproductive as an incentive for students to enjoy reading and seek it out over their lifetimes. Next we examine the research literature on three important examples in which monetary incentives have been used in a nonemployment context to foster the desired behavior: education; increasing contributions to public goods; and helping people change their lifestyles, particularly with regard to smoking and exercise. The conclusion sums up some lessons on when extrinsic incentives are more or less likely to alter such behaviors in the desired directions.
Intrinsic and extrinsic types of motivation have been widely studied, and the distinction between them has shed important light on both developmental and educational practices. In this review we revisit the classic definitions of intrinsic and extrinsic motivation in light of contemporary research and theory. Intrinsic motivation remains an important construct, reflecting the natural human propensity to learn and assimilate. However, extrinsic motivation is argued to vary considerably in its relative autonomy and thus can either reflect external control or true self-regulation. The relations of both classes of motives to basic human needs for autonomy, competence and relatedness are discussed.
This paper describes a computerised database of psycholinguistic information. Semantic, syntactic, phonological and orthographic information about some or all of the 98,538 words in the database is accessible, by using a specially-written and very simple programming language. Word-association data are also included in the database. Some examples are given of the use of the database for selection of stimuli to be used in psycholinguistic experimentation or linguistic research. © 1981, The Experimental Psychology Society. All rights reserved.
Client ambivalence is a key stumbling block to therapeutic efforts toward constructive change. Motivational interviewing—a nonauthoritative approach to helping people to free up their own motivations and resources—is a powerful technique for overcoming ambivalence and helping clients to get "unstuck." The first full presentation of this powerful technique for practitioners, this volume is written by the psychologists who introduced and have been developing motivational interviewing since the early 1980s. In Part I, the authors review the conceptual and research background from which motivational interviewing was derived. The concept of ambivalence, or dilemma of change, is examined and the critical conditions necessary for change are delineated. Other features include concise summaries of research on successful strategies for motivating change and on the impact of brief but well-executed interventions for addictive behaviors. Part II constitutes a practical introduction to the what, why, and how of motivational interviewing. . . . Chapters define the guiding principles of motivational interviewing and examine specific strategies for building motivation and strengthening commitment for change. Rounding out the volume, Part III brings together contributions from international experts describing their work with motivational interviewing in a broad range of populations from general medical patients, couples, and young people, to heroin addicts, alcoholics, sex offenders, and people at risk for HIV [human immunodeficiency virus] infection. Their programs span the spectrum from community prevention to the treatment of chronic dependence. All professionals whose work involves therapeutic engagement with such individuals—psychologists, addictions counselors, social workers, probations officers, physicians, and nurses—will find both enlightenment and proven strategies for effecting therapeutic change. (PsycINFO Database Record (c) 2012 APA, all rights reserved)