Greater Stroop Effect Predicts Better Performance on

THE INTERNATIONAL JOURNAL OF CREATIVITY & PROBLEM SOLVING 2018, 28(2), 27-37

Greater Stroop Effect Predicts Better Performance on Creative Insight Problems, But Not on Divergent Thinking Tasks Viktoria Tidikis and Ivan K. Ash Northern Arizona University, U.S.A., Old Dominion University, U.S.A. Differing lines of thought on how attentional control affects creativity are found throughout the literature. Some researchers have observed a relationship between lower attentional control and creativity, while others have identified that higher attentional control aids creative task performance. In an attempt to clarify these seemingly contradictory findings, we examined the relationship between Stroop task performance and two types of creative problems: divergent thinking and insight tasks. Two differing types of tasks were selected to represent creative thinking’s two distinctive processes. We proposed that success in these problem types would correlate differentially with Stroop performance. Two hundred and forty participants participated in the experiment. Greater Stroop effect was related to a higher number of insight problems solved, but not to divergent thinking task performance. Study results are discussed in terms of Dual Pathway Model of creativity perspective and theories of restructuring in solving insight problems.

In the literature examining the relationship between cognitive control and creativity, two differing predictions have emerged. One view suggests that creative people are notably lacking self-control (e.g., Mendelsohn, 1976), while the other provides evidence for a relationship between greater cognitive control and creativity (e.g., Edl, Benedek, Papousek, Weiss, & Fink, 2014). According to the first view, when solving creative problems, creative people tend to have more defocused attention (i.e., less control over their attention than that exhibited by less creative people). Martindale (1989) also proposed that defocused attention is not a stable characteristic of creative people but is rather task specific. Simply, some creative tasks require focused, controlled attention, while others call for more fluid, defocused attention, and creative people are better at switching their attentional focus based on task demands (Ansburg & Hill, 2003). Such adjustment occurs automatically, without involvement of conscious self-control. Yet another line of research shows support of a relationship between greater attentional control and creativity. Higher cognitive control has been found to be correlated with higher scores on divergent thinking tasks (Benedek, Franz, Heene, & Neubauer, 2012; Edl et al., 2014; Golden, 1975; Groborz & Necka, 2003; Zabelina, Robinson, Council, & Bresin, 2012). Groborz and Necka found that participants scoring higher on measures of cognitive control also scored higher on some aspects of creativity, namely originality, but not on fluency and flexibility. In yet another study, higher cognitive control was related to higher scores on both originality and fluency (Edl et al.). The authors also Correspondence concerning this article should be addressed to Viktoria Tidikis, Department of Psychological Sciences, Northern Arizona University, 1100 S. Beaver St, PO Box 15106, Flagstaff, AZ 86011.USA. E-mail: [email protected]

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found no Stroop interference effect in participants who were enrolled or had recently received a post-secondary degree within study areas having high creativity demands. Still other studies have looked at attentional control as part of central executive functioning. Participants with high executive control, as measured by general working memory (WM) capacity, outperformed participants scoring low on WM in a variety of tasks, including insight problems, musical improvisation and original ideation (De Dreu, Nijstadt, Baas, Wolsink, & Roskes, 2012). Likewise, a study by Benedek et al. (2014) found support for a relationship between executive abilities and divergent thinking. Higher updating and inhibition scores predicted higher divergent thinking scores, while shifting did not. Ash and Wiley (2006) found that a higher ability to control attention (i.e., higher WM scores) was correlated with searching the initial faulty problem space, but not with success on problems that isolated the restructuring phase. Conversely, other research has found that higher WM capacity led to poorer performance due to participants selecting non-optimal cognitive strategies for the task at hand (review by DeCaro & Beilock, 2010). Recent research also showed that higher working memory capacity led to worse performance on insight problem (Stockum & DeCaro, 2016). Still other research has emphasized the role of both associative and executive processes in creative cognition (Beaty, Silvia, Nusbaum, Jauk, & Benedek, 2014). Here, the authors found that higher central executive functioning as well as greater semantic distance among responses related to a higher quality of creative ideas on divergent thinking tasks. Zabelina and Robinson (2010) suggested that creativity is best predicted by an ability to exert flexible control over attention based on the contextual nature of the task. And, as shown by Dorfman, Martindale, Gassimova and Vartanian (2008), the ability to switch between focused and defocused attention is key to creative cognition. Nijstad and colleagues (Nijstad, et al., 2010) conceptualized creativity as a function of both flexibility and persistence, proposing a Dual Pathway Model of creativity. Their model suggests that creative thinking involves two distinctly different types of cognitive processes. One pathway (flexibility), relates to an ability to switch between different perspectives, and is associated with spreading activation mechanisms throughout the semantic network, as well as less conscious control and greater holistic processing. The second pathway (persistence), is associated with greater cognitive control, systematic exploration of alternatives, and data-driven processes. These two pathways have differential correlates in terms of both dispositional characteristics and cognitive processes. On the other hand, Nijstad et al.’s model predicts that cognitive control should have a higher correlation with the persistence pathway than with the flexibility pathway. Beaty et al. (2014) also looked at the role of executive and associative processes in creative cognition and found that both types of processes contribute to creativity as measured with divergent thinking tasks. Duality of the processes involved in creative cognition has also been conceptualized in terms of dual process theories of cognition (e.g., Evans, 2008; Gilhooly & Fioratou, 2009). This approach differentiates between Type 1 and Type 2 cognitive processes. Type 1 processes are fast, automatic, and unconscious, and are involved in automatically accessing semantic or conceptual knowledge through spreading activation (e.g., semantic priming effects). In contrast, Type 2 processes take place in working memory and rely heavily on the central executive component. Type 2 processes are slow, effortful, sequential, and conscious. Thus, the effects of attentional control on creative task performance are characterized by whether the successful completion of a creative task is a result of Type 1 or Type 2 cognitive processes.

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We propose that success on some creative tasks may be more reliant on one or the other of these different processes. Specifically, divergent thinking tasks are more reliant on the automatic spreading activation processes, thus looser attentional control might be beneficial on these types of problems (Benedek et al., 2014). Therefore, we predicted that people scoring lower on an attentional control measure (the Stroop task) will overall do better on divergent thinking tasks. For the insight problems, on the other hand, two competing predictions have emerged from the previous literature. Problem solvers may potentially benefit from greater attentional control during the search stage, as was previously shown (Ash & Wiley, 2006). Alternatively, looser attentional control may help to access distant information in the associative network, which may help with the restructuring process (Stockum & DeCaro, 2016). Consequently, study’s second aim was to test these two opposing predictions simultaneously. By answering the aforementioned questions, we hoped that this study would resolve some of the contradictory finding in the literature regarding the relationship between attentional control and creative problem-solving performance. METHOD Participants A sample of 240 students from a large Southeastern university was recruited to participate in this study (Age M = 20.3, SD = 3.15, 174 females, 66 males). Ethical guidelines as set forth by the American Psychological Association were followed; informed consent was required of all participants. All procedures were in compliance with the IRB at each author’s university. Materials Stroop Task. In the Stroop task, participants are asked to name the color of letters of a word that appears on screen. There are two conditions in this task: congruent, where the meaning of a word and the color of its letters match, and incongruent, where the meaning of a word and the color of its letters do not match. The difference in reaction time (RT) between congruent and incongruent conditions has been widely used as a measure of attentional control; a greater difference between congruent and incongruent conditions indicates a greater Stroop effect, which is indicative of poorer attentional control (e.g., Edl, et al., 2014; Golden, 1975; Kalanthroff, Henik, Derakshan, & Usher, 2016). After completing four practice trails, participants completed 50 test trails, indicating their response by selecting one of four color keys on a computer keyboard. For half of the test trials, letter colors matched the meaning of the word (congruent trials), and for the remaining half the test trials, letter colors and word meaning did not match (incongruent trails). Stimuli presentation was randomized across the participants. Due to a comparatively small number of Stroop trials, we calculated split-half reliability for incongruent and congruent trials to ensure that a stable Stroop effect was achieved. Guttman split-half reliability coefficient for the incongruent trials was r = .82, and for the congruent trials was r = .84, which indicated a stable effect. The coefficients obtained in this study were comparable to the coefficients obtained in other studies (e.g., congruent, r = .71, incongruent, r = 79; Strauss, Allen, Jorgensen, & Cramer, 2005). Creativity Measures. Insight Problems. Researching insight has been complicated due to the overall difficulty of insight problems and their heterogeneity (MacGregor & Cunningham,

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2008). In order to address this inherent challenge, a battery of Rebus puzzles, which require insight to solve, was created. Employing Rebus puzzles provided the additional benefit of fine-grained analysis of the processes responsible for the insight. Rebus puzzles typically require the solver to come up with a common phrase based on verbal or visual cues hidden within the presented material. For example, the solution to “PUNISHMENT” is capital punishment, which requires the solver to notice and explicitly interpret the font characteristics of the presented word. Two types of Rebus puzzles were used (Figure 1): supra-word restructuring (relationship between words) and sub-word restructuring (interpretation of the characteristics of the word itself). A subset of sixteen puzzles was used in this study; half were supra-word problems, and the other half were sub-word problems. The stimuli were selected from a larger set of Rebus puzzles developed and validated by MacGregor and Cunningham. Participants viewed one problem at a time on a computer screen, and they had one minute to solve each puzzle. They recorded their answers in a booklet that had identical instructions to that on screen. Two raters coded participants’ responses for the number of problems correctly solved. The intraclass correlation coefficient (ICC) between two raters for the number of puzzles solved was .98. Sub-Word AGES

BAD wolf

PUNISHMENT

league

(Dark ages)

(Big bad wolf)

(Little league)

R.P.I.

amUous

(Capital punishment) XQQME

(Grave error)

(Ambiguous)

(Excuse me)

(Tea for two)

1t345

Supra-Word rodiamondugh

(Somewhere over the rainbow)

(A home away from home)

(Beating about the bush)

(Diamond in the rough)

(A little on the large side)

(Just between friends)

(Lying down on the job)

(Rock around the clock)

Figure 1. Rebus Puzzles with the Answers in the Parentheses

Open-Ended Problems. There were two types of open-ended problems used: problems requiring the production of multiple divergent ideas [Unusual Uses (UU)] and problems requiring extrapolation from a given situation to a different situation [Consequences (C)]. The UU problem is commonly used as a divergent thinking measure (Smith, Michael, & Hocevar, 1990). For these types of problems, participants were instructed

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to write as many answers to a question as quickly as possible: “List as many as possible uses for a shoe.” The other task measures the logical extrapolation of experiences applied to an impossible situation. Participants were instructed to produce as many ideas as possible for the fictitious situation of “What would happen if one were able to listen to [another] person’s secret thoughts?” Participants had two minutes to complete each problem. Both problem types were adapted from the Minnesota Tests of Creative Thinking which were developed based on Guilford and colleagues’ materials (Guilford, Wilson, Christensen, & Lewis, 1951). The data were coded by two raters for fluency (number of unique responses), originality (average originality rating of all creative answers), and quality of responses. Fluency score was calculated by counting the total number of responses on each measure for each participant. For example, if a participant wrote “wearing” and “plant a flower” for the shoe use problem, fluency would be scored as a “2”. Originality measured how original a response was compared to other responses in the sample. Raters used a 1-10 scale for each response, “1” being not at all original, and “10” being uniquely original. Rating were determined based on statistical frequency of a particular response in the sample. Responses that occurred most frequently received a lower score than responses occurring infrequently (e.g., if less than 10% of the participants provided a particular response, that response would receive a score of “10”, 11-20%, a score of “9”, etc.). For the above example, the originality score for “wearing” was 1, as numerous participants provided the same response (over 90%), and the score for “plant a flower” was 10, as fewer than 2% of participants offered that response. The mean of all originality ratings for each task was determined for each participant. Thus, using the same example above, originality was scored as “5.5.” Instead of using flexibility, which previously showed no relationship with the cognitive control (e.g., Groborz and Necka, 2003), we coded for the quality of responses. We chose to code for quality, as it has been suggested, it may more accurately represent the creative outcome than other measures (Baer, 1993), and this has been successfully used in other studies (e.g., Chen, Kasof, Himsel, Greenberger, Dong, & Xue, 2002). The responses were coded on a 1-10 scale, with a higher score indicating a higher quality of responses. Raters were asked to give a subjective judgement of perceived quality of each response. Coding responses were verified by a third coder. The task of the third coder was to ensure that there were no major discrepancies between the initial two raters. The coding responses of the initial two raters for each problem were averaged for analyses, and mean ratings for each problem were summed to create composite scores for fluency, originality and quality. The interclass correlation coefficient (ICC) between the two raters for the number of ideas produced was .99 for both problem types. The ICC between two raters for originality was .75 for the Unusual Uses (UU) problem and .84 for the Consequences (C) problem. The ICC between two raters for quality was .72 for the Unusual Uses (UU) problem and .74 for the Consequences (C) problem. Procedure This study was part of a larger study that examined the effects of various cognitive processes on creativity (Tidikis, Ash, & Collier, 2017). Each participant attended a one-hour session in a laboratory located on a university campus. Upon arrival, the participants were informed of the study’s purpose and signed a volunteer rights notification form prior to the experiment. Participants used a laboratory personal computer to complete the experiment. Presentation order of the measures was randomized across

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the participants. Each participant was debriefed at the end of their session. Stimuli were presented electronically using E-Prime 1.0 software (Psychology Software Tools, Pittsburgh, PA). The participants were presented with stimuli blocks, each preceded by a set of instructions, followed by practice trials. RESULTS Our analytic strategy was to first create overall composite scores for the Rebus puzzles and divergent thinking problems in order to create more comparable scores. Considering sub-scores separately was the next step. The overall score for Rebus puzzles was created by adding up the number of correct responses for both sub-word and supra-word problems. A composite score for divergent thinking problems was created by adding the scores for fluency, originality, and quality. The mean solution rate for Rebus puzzles was 47% (M = 7.46, SD = 2.95). The Stroop effect was a significant predictor of number of Rebus puzzles solved, b = .16, t (234) = 2.49, p = .01, and it explained a significant proportion of variance in the number of rebus puzzles solved, R2 = .022, F (1, 234) = 6.20, p = .013. That is, participants with poor functioning attentional control were better at solving insight problems. The Stroop effect, on the other hand, did not predict performance on the divergent thinking tasks, b = -0.01, t (226) = -0.10, p =.918, R2 = .00005, F (1, 226) = 0.01, p = .918. We further looked into the relationship between Stroop effect and performance on different problem subtypes (Table 1). For Rebus puzzles, we separated problems by sub-word and supra-word, and for divergent thinking tasks, we looked at fluency (the number of responses), originality, and quality separately. Interestingly, a greater Stroop effect was only related to the number of supra-word Rebus puzzles solved. All other correlation were non-significant. For the means and standard deviations see Table 2. Table 1 Correlations between Stroop Effect And Different Problem Subtypes Variables Stroop Effect Rebus Sub Word Rebus Supra Word Divergent UU Fluency Divergent C Fluency Divergent UU Originality Divergent C Originality Divergent UU Quality Divergent C Quality

Stroop Effect

Rebus Sub

Rebus Supra

UU Fluency

C Fluency

.107

-

.172*

.603**

-

.028

.021

.043

-

.063

.091

.043

.288**

-

-.047

.122

.221**

-.065

-.088

-

-.002

-.087

-.061

-.130

.040

.063

-

.072

-.066

-.165*

-.099

.016

-.077

.045

-

-.027

-.117

-.069

.064

.074

-.080

.241*

.161*

**Correlation is significant at the 0.01 level (2-tailed). *Correlation is significant at the 0.05 level (2-tailed). UU-Unusual Uses divergent thinking task C-Consequences divergent thinking task

UU Originality

C Originality

UU Quality

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Table 2 Means and Standard Deviations for All the Variables Variables Stroop Congruent (ms) Stroop Incongruent (ms) Rebus Sub Word (solved correctly out of 8) Rebus Supra Word (solved correctly out of 8) Divergent UU Fluency (number of responses) Divergent C Fluency (number of responses) Divergent UU Originality (originality rating on 1-10 scale) Divergent C Originality (originality rating on 1-10 scale) Divergent UU Quality (quality rating on 1-10 scale) Divergent C Quality (quality rating on 1-10 scale)

Mean

SD

976.74 1106.94 3.00 4.46 6.86 2.95 5.35 5.73 5.16 5.35

172.05 206.14 1.39 1.90 3.11 1.71 1.22 0.92 0.46 1.23

DISCUSSION In an attempt to clarify discrepancies in previous research in regard to the role of attentional control in creative cognition, we examined the relationship between Stroop effect and performance on two different types of creative problems: divergent thinking and insight. A greater Stroop effect predicted higher solution rates on insight problems, but not divergent thinking task performance. Furthermore, a greater Stroop effect was only related to the number of supra-word Rebus puzzles solved, but not to the number of sub-word Rebus puzzles. Due to the previous findings supporting a relationship (e.g., Benedek, Franz, Heene, & Neubauer, 2012; Edl, et al., 2014; Golden, 1975; Groborz & Necka, 2003; Zabelina, et al., 2012), it was surprising not to find a relationship between greater attentional control, as measured with Stroop task performance, and performance on divergent thinking tasks. However, these results are also consistent with the Dual Pathway Model of creativity perspective (Nijstad, et al., 2010). This theory proposes that both controlled and holistic processing are involved in divergent thinking. Other studies have also found that the relationship between attentional control and success at creativity tasks vary as a function of the type of processed involved in the task. For example, Tidikis, Ash, and Futterman Collier (2017) found that the relationship between attentional breadth and creativity depended on whether creative tasks relied on Type 1 or Type 2 cognitive processes. Broader attentional breadth predicted success on tasks determined by Type 1, or automatic processes, while narrow attentional breadth led to greater success on tasks relying on Type 2, or conscious processes. Moreover, when only measuring the creative outcome, the processes involved in reaching an answer may not be identifiable in the final solution. Finding measures that reflect not only the outcome of the creative process, but that also parse out various processes that lead to the solution, may be paramount for answering the question about the role of different cognitive processes in creativity. Novel results were obtained here, in regard to the relationship between a greater Stroop effect and success on select insight problems (those requiring participants to notice relationships between words, i.e., supra-word Rebus puzzles). Participants with

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lower attentional control had higher solution rates on these problems than did participants with greater attentional control. Insight problems are conceptualized in the literature as problems that require the solver to restructure a problem’s representation in order to solve it (Ash & Wiley, 2006). Solving an insight problem entails a sequence of steps. First, likely approaches based on previous experience are considered. Should old strategies fail, the person reaches an impasse. Then, to overcome impasse, one must restructure their initial problem representation to successfully reach solution (Knoblich, Ohlsson, Haider, & Rhenius, 1999). Looser attentional control may have helped information, first thought to be irrelevant by the solver, to “leak through” their attentional filter. On problems requiring restructuring at the word level (sub-word), such leakage did not add any helpful information towards solution success. However, on puzzles requiring restructuring at the higher, between-word level (supra-word), such leakage allowed to access information about the relationship, that otherwise would not be accessed in a tighter attentional control condition. Ricks, Turley-Ames, and Wiley (2007) similarly found that higher attentional control (as measured by WM capacity) may lead to focusing too much on an incorrect solution and serve to aggravate mental sets while solving the Remote Associates Test (RAT) problems. One limitation of this study may be relatively small number of Stroop task trials. This study used fewer trials than the average number used in the Stroop effect research literature [the number of the Stroop trials varies from less than 20 (MacLeod, 1991) to more than 150 (e.g., Gonthier, Braver, & Bugg, 2016) in the research literature]. However, research studies that used considerably fewer number of trials were also able to establish a stable Stroop effect. For example, Edl, Benedek, Papousek, Weiss, and Fink (2014), in their study of the relationship between Stroop performance and creativity, used between 19-30 trials per condition; Spieler, Balota, and Faust (1996), created a widely used paradigm using 36 trials per condition. Additionally, split-half reliability for each condition in our study was high, indicating a stable Stroop effect. Another limitation is lack of clarity on whether solvers solved Rebus puzzles via insight or via a more analytic strategy. Previous research found that some Rebus or other “convergent thinking” problems can be solved using either of these strategies (e.g., Salvi, Bricolo, Kounios, Bowden, & Beeman, 2016). Whereas most insight solutions involve the process of restructuring, analytic solutions do not; additionally, differing cognitive processes comprise insight and analytic solutions (e.g., Kounios & Beeman, 2009). Therefore, it would be important to question participants how they reached the solution (insight or analysis), in addition to investigating the total number of solutions. It is conceivable that individuals using analytic strategy will be negatively impacted by the lower attentional control, while individuals using insight will benefit from the looser attentional control. Thus, the next step would be to replicate these findings with more traditional insight problems and collect measures indicating whether these problems were solved via insight or analysis. In this study, we further expanded research in the attentional control-creativity paradigm by: 1) using Rebus puzzles as a more process-sensitive measure of insight, and 2) including both insight and divergent thinking problems as measures of creative thinking. Based on our study results, a few implications and suggestions for future research have emerged. First, the study gave further support for the model of creative thinking involving different processes. To simulate success on tasks involving these processes, researchers must consider using tasks adequately geared to represent these

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differing processes (Cunningham, MacGregor, Gibb, & Haar, 2009). To further examine the role of different cognitive processes in creative cognition, more fine-grained measures of both process and outcome are required. Obtaining measures that would not only show success of an outcome (e.g., number of problems solved, quality of ideas produced), but also differentiate between the different processes involved in creative cognition, could possibly provide answers to questions regarding the differential cognitive requirements for the different stages of creativity. Some researchers suggested that people may use different cognitive strategies to arrive at the same solution (Weisberg, 2015). Thus, adopting an individual-differences approach might also be warranted when examining the cognitive processes behind creative cognition. REFERENCES Ansburg, P. I., & Hill, K. (2003). Creative and analytic thinkers differ in their use of attentional resources. Personality and Individual Differences, 34, 1141-1152. doi:10.1016/S0191-8869(02)00104-6 Ash, I. K., & Wiley, J. (2006). The nature of restructuring in insight: An individualdifferences approach. Psychonomic Bulletin & Review, 13, 66-73. doi:10.3758/ BF03193814 Baer, J. (1993). Creativity and divergent thinking: A task-specific approach. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. Beaty, R. E., Silvia, P. J., Nusbaum, E. C., Jauk, E., & Benedek, M. (2014). The roles of associative and executive processes in creative cognition. Memory & Cognition, 42, 1186-1197. doi:10.3758/s13421-014-0428-8 Benedek, M., Franz, F., Heene, M., & Neubauer, A. C. (2012). Differential effects of cognitive inhibition and intelligence on creativity. Personality and Individual Differences, 53, 480–485. Benedek, M., Jauk, E., Sommer, M., Arendasy, M., & Neubauer, A. C. (2014). Intelligence, creativity, and cognitive control: The common and differential involvement of executive functions in intelligence and creativity. Intelligence, 46, 73-83. doi:10.1016/j.intell.2014.05.007 Chen, C., Kasof, J., Himsel, A. J., Greenberger, E., Dong, Q., & Xue, G. (2002). Creativity in drawings of geometric shapes: A cross-cultural examination with the consensual assessment technique. Journal of Cross-Cultural Psychology, 33, 171-187. doi:10.1177/0022022102033002004 Cunningham, J. B., MacGregor, J. N., Gibb, J., & Haar, J. (2009). Categories of insight and their correlates: An exploration of relationships among classic-type insight problems, rebus puzzles, remote associates and esoteric analogies. The Journal of Creative Behavior, 43(4), 262-280. doi:10.1002/j.2162-6057.2009.tb01318.x DeCaro, M. S., & Beilock, S. L. (2010). The benefits and perils of attentional control. In M. Csikszentmihalyi & B. Bruya (Eds.), Effortless attention: A new perspective in the cognitive science of attention and action (pp. 51–73). Cambridge, MA: MIT Press. DeCaro, M. S., Van Stockum, C. J., & Wieth, M. B. (2016). When higher working memory capacity hinders insight. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42, 39-49. doi:10.1037/xlm0000152

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