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A Novel Approach to Investigate the Impact of Mindset and Physiology on the Choice to Invest Effort during an Arithmetic Task

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Title: A Novel Approach to Investigate the Impact of Mindset and Physiology on the Choice to Invest Effort during an Arithmetic Task
Language: English
Authors: Nieuwenhuis, Smiddy (ORCID 0000-0002-7930-2866), Janssen, Tieme W. P., van der Mee, Denise J., Rahman, Farah A., Meeter, Martijn, van Atteveldt, Nienke M. (ORCID 0000-0002-3387-6151)
Source: Mind, Brain, and Education. May 2023 17(2):123-131.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 9
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Undergraduate Students, Physiology, World Views, Individual Development, Decision Making, Intention, Motivation, Learning Activities, Failure
DOI: 10.1111/mbe.12356
ISSN: 1751-2271
1751-228X
Abstract: Growth mindset, the belief that personal attributes such as intelligence are malleable, has previously been related to more effort investment. Here, we investigated how undergraduates' mindset (N = 114) relates to the choice to invest effort during an arithmetic task, indexed by whether they make low vs. high effort-related choices. Social cognitive theory suggests that past performance experiences (mastery vs. failure) and physiological state are important sources for competence self-evaluations. Therefore, in addition to mindset, we also investigated how effort-related choices are influenced more dynamically, by failures and physiological responses during the task. Growth mindset and physiological effort mobilization did not predict effort-related choices but making mistakes did predict lower effort choices in the subsequent round. This study further supports the importance of mastery experiences for effort investment and provides a novel approach for integrating different levels of influence on effort-related choices during an educationally-relevant task.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1379040
Database: ERIC
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  Value: <anid>AN0163976527;[309x]01may.23;2023May31.06:59;v2.2.500</anid> <title id="AN0163976527-1">A Novel Approach to Investigate the Impact of Mindset and Physiology on the Choice to Invest Effort During an Arithmetic Task </title> <p>Growth mindset, the belief that personal attributes such as intelligence are malleable, has previously been related to more effort investment. Here, we investigated how undergraduates' mindset (N = 114) relates to the choice to invest effort during an arithmetic task, indexed by whether they make low vs. high effort‐related choices. Social cognitive theory suggests that past performance experiences (mastery vs. failure) and physiological state are important sources for competence self‐evaluations. Therefore, in addition to mindset, we also investigated how effort‐related choices are influenced more dynamically, by failures and physiological responses during the task. Growth mindset and physiological effort mobilization did not predict effort‐related choices but making mistakes did predict lower effort choices in the subsequent round. This study further supports the importance of mastery experiences for effort investment and provides a novel approach for integrating different levels of influence on effort‐related choices during an educationally‐relevant task.</p> <p>Students vary in how far they believe abilities can change (growth mindset) or not (fixed mindset). We investigated whether students' (growth) mindset, making mistakes and a physiological measure of effort influenced their choices for easy or difficult arithmetic problems. We found that mistakes in the previous round influenced the choice to invest effort in the next round. Against our expectations, (growth) mindset and physiological effort were not related to the choice to invest effort.</p> <p>Mahatma Gandhi once said, "<emph>satisfaction lies in the effort, not in the attainment, full effort is full victory</emph>" ([<reflink idref="bib26" id="ref1">26</reflink>], p. 154). As many teachers recognize, such an attitude is not found in all students. Students with stronger growth mindsets consider their intelligence and abilities to be something that can be improved and developed through effort (Dweck & Leggett, [<reflink idref="bib17" id="ref2">17</reflink>]). Notably, growth‐mindset‐oriented individuals show more adaptive self‐regulation skills, such as adopting mastery‐oriented rather than helpless‐oriented strategies, and focusing on future expectations of success rather than negative emotions (Burnette, O'Boyle, VanEpps, Pollack, & Finkel, [<reflink idref="bib10" id="ref3">10</reflink>]). In contrast, individuals with stronger fixed mindsets consider intelligence largely unchangeable. They are inclined to view effort as evidence for a lack of ability, and therefore avoid challenging situations (Blackwell, Trzesniewski, & Dweck, [<reflink idref="bib6" id="ref4">6</reflink>]; Hong, Chiu, Dweck, Lin, & Wan, [<reflink idref="bib21" id="ref5">21</reflink>]).</p> <p>According to Hong et al. ([<reflink idref="bib21" id="ref6">21</reflink>]), growth‐mind‐oriented individuals may focus more on efforts to increase their ability. By contrast, fixed mindset‐oriented individuals weigh the role of ability more heavily in terms of performance. This difference can be reflected in the choices they make concerning easy or more challenging tasks, with fixed‐mindset‐oriented individuals avoiding more challenging tasks to avoid making mistakes. In real life, this means that mindset may influence effort‐related choices that students make at school, as students are in control over the activities and courses of action they choose in school. Supporting this hypothesis, Hong et al. ([<reflink idref="bib21" id="ref7">21</reflink>]) found that growth‐mindset‐oriented individuals more often chose to take a remedial course when faced with unsatisfactory performance than fixed‐mindset‐oriented individuals. Yeager et al. ([<reflink idref="bib41" id="ref8">41</reflink>]) found that a year after a growth mindset intervention, students were more willing to attend an advanced math course. Both studies suggest that students with a growth mindset are more willing to seek challenges and invest effort, as measured by their future intention.</p> <p>Although there is evidence that mindset influences effort‐related beliefs and intentions, its impact on actual effort investment during learning remains largely unexplored. Therefore, we adapted an existing arithmetic task (Engle‐Friedman et al., [<reflink idref="bib18" id="ref9">18</reflink>]) to measure both the mobilization of effort (physiological) and the choice to invest effort (behavioral). This is important because, in addition to relatively stable self‐beliefs such as mindset, motivated behaviors such as effort‐related choices are likely to depend on more dynamic state‐level influences (Vu et al., [<reflink idref="bib39" id="ref10">39</reflink>]). Bandura's social cognitive theory suggests past performance experiences (mastery vs. failure) and physiological state are important dynamic sources for competence self‐evaluations (Bandura, [<reflink idref="bib1" id="ref11">1</reflink>]; Bandura, Freeman, & Lightsey, [<reflink idref="bib3" id="ref12">3</reflink>]; Bandura, [<reflink idref="bib2" id="ref13">2</reflink>]) which may affect effort‐related choices (Schunk & DiBenedetto, [<reflink idref="bib34" id="ref14">34</reflink>]). In the present study, we operationalize effort at the behavioral level as <emph>choice to invest effort</emph> during a challenging arithmetic task in which students can choose their own difficulty level of preference (Beck, [<reflink idref="bib4" id="ref15">4</reflink>]). We define the physiological component of effort as <emph>effort mobilization</emph>, which can be seen as an objective state that reflects how the body reacts to arithmetic problems at the chosen difficulty level.</p> <p>To date, most evidence for the impact of mindset comes from behavioral studies. To our knowledge, no studies have focused on the autonomic nervous system (ANS) in relation to the mindset. ANS measures are specifically suitable to extend our knowledge about effort mobilization during ecologically valid tasks (Richter, Gendolla, & Wright, [<reflink idref="bib32" id="ref16">32</reflink>]), as ANS measures are more objective than self‐report measures and can be continuously measured without interrupting the task. A useful ANS index of effort mobilization is sympathetic nervous system (SNS) activity (e.g., Harper, Eddington, & Silvia, [<reflink idref="bib20" id="ref17">20</reflink>]). Most research on effort mobilization is conducted in the context of motivational intensity theory, in which indicators of sympathetic impact on the heart are used (Richter et al., [<reflink idref="bib32" id="ref18">32</reflink>]). To date, the pre‐ejection period (PEP), which is the systolic interval between ventricular depolarization and the opening of the aortic valve (Gendolla, Wright, & Richter, [<reflink idref="bib19" id="ref19">19</reflink>]), is the best‐known cardiac indicator of SNS activity (Richter & Gendolla, 2009). Faster PEP responses have been suggested to correlate with higher effort engagement (Kelsey, [<reflink idref="bib24" id="ref20">24</reflink>]); therefore, PEP might provide an autonomic indicator of effort. In addition, greater PEP reactivity indicates more SNS activity in reaction to a task than at the baseline (reflecting more effort).</p> <p>Here, we aimed to investigate the underlying mechanisms of how mindset and dynamic factors, such as committing mistakes and physiological state, impact the choice to invest effort in an arithmetic task. We first hypothesized that students with a stronger growth mindset choose higher difficulty levels, as they are more focused on challenging learning activities (e.g., Hong et al., [<reflink idref="bib21" id="ref21">21</reflink>]; Yeager et al., [<reflink idref="bib41" id="ref22">41</reflink>]). We also hypothesized, based on the social cognitive theory, that making mistakes and PEP reactivity relate to the choice to invest effort more dynamically during the task. More specifically, we predicted that making mistakes in a round would relates to lower difficulty level choices in the subsequent round, and that this would be more apparent in students with a stronger fixed mindset. Furthermore, we hypothesized that more negative PEP reactivity (reflecting more effort mobilization) would be associated with choosing higher difficulty levels in the subsequent round, particularly in students with a stronger growth mindset. For students with a stronger fixed mindset, we hypothesized that more effort mobilization would be associated with choosing lower difficulty levels, as they may view exerting effort as a lack of ability (Blackwell et al., [<reflink idref="bib6" id="ref23">6</reflink>]; Hong et al., [<reflink idref="bib21" id="ref24">21</reflink>]).</p> <hd id="AN0163976527-2">METHODS</hd> <p></p> <hd id="AN0163976527-3">Participants</hd> <p>The sample consisted of 114 undergraduate psychology students (95%) and pedagogy students aged 18–26 years (<emph>M</emph> = 20.36, <emph>SD</emph> = 1.94; 75% females). The students were recruited via an online recruitment system (SonaSystem) and flyers. Each participant was compensated for participation (monetary or course credit). This study was conducted in accordance with the Declaration of Helsinki and the local ethics committee.</p> <hd id="AN0163976527-4">Procedure</hd> <p>After signing up and prior to the experiment, two active informed consent sessions were conducted. Participants were required to fill out a questionnaire with motivational constructs at home. Non‐Dutch speaking students (61%) received an English questionnaire and instructions, Dutch‐speaking students the same in Dutch.</p> <p>After the participants were welcomed, electrocardiography (ECG) and impedance cardiography (ICG) were performed to continuously record physiological responses throughout the entire experiment. The experiment started with a PEP baseline measurement at rest in which students watched the first 10 min of a nature documentary about plants without audio.</p> <p>After the PEP baseline measurement, the arithmetic task started. The keyboard was placed on the participant's lap to answer the equations and to avoid unnecessary bodily movements. See Figure 1 for the order and phases of the experiment (total duration 20 min). This study was not preregistered.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/309X/01may23/mbe12356-fig-0001.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="mbe12356-fig-0001.jpg" title="1 Schematic overview study design and experimental task. PEP = pre‐ejection period." /> </p> <p></p> <hd id="AN0163976527-6">Materials</hd> <p></p> <hd id="AN0163976527-7">Arithmetic Task</hd> <p>For the arithmetic task we used an adapted version of the Math Effort Task of Engle‐Friedman et al. ([<reflink idref="bib18" id="ref25">18</reflink>]), programmed in OpenSesame (Mathôt, Schreij, & Theeuwes, [<reflink idref="bib27" id="ref26">27</reflink>]). The task started with three practice trials and a round to assess the baseline arithmetic skills, which we called the arithmetic estimation round (AER). Participants were required to solve arithmetic equations at each difficulty level (five levels), presented in ascending order. We calculated baseline arithmetic skills as the number of correct answers on all 20 equations, divided by 4 (number of equations per level) in order to fit to the same scale as the chosen difficulty level (score between 1–5). This enabled us to investigate whether the students chose difficulty levels below or above their measured abilities. The task then continued with five choice rounds of 10 arithmetic equations. At the beginning of each round, the participant chose the difficulty level (1–5) that they preferred for each round.</p> <p>Each equation was a combination of two or more arithmetic operations: summation, subtraction, multiplication, and/or division; the level of difficulty depended on the combination of these operations (see Table 1). The equations and difficulty of the operations were derived from a study by Dedovic et al. ([<reflink idref="bib14" id="ref27">14</reflink>]).</p> <p>1 Table Overview Difficulty Level Rules, Examples, and Grading Manipulation of the Arithmetic Task</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Level</th><th align="center">Rules</th><th align="center">Example</th><th align="center">Grading</th></tr></thead><tbody valign="top"><tr><td>Level 1</td><td>Three, 1‐digit integers (0–9), only addition and subtraction</td><td>3 + 3–4 =</td><td>Number correct * 0.6</td></tr><tr><td>Level 2</td><td>Three, 1 or 2‐digits integers (0–19), addition and subtraction, with negative numbers</td><td>7–13 + 12 =</td><td>Number correct * 0.7</td></tr><tr><td>Level 3</td><td>Three, 1–2 (double) digits integers (0–99), addition, subtraction, and multiplication</td><td>62–59 * 3 =</td><td>Number correct * 0.8</td></tr><tr><td>Level 4</td><td>Four, 1–2 (double) digits integers (0–99), addition, subtraction, and multiplication</td><td>(12 * 4) + 4–46) =</td><td>Number correct * 0.9</td></tr><tr><td>Level 5</td><td>Four, 1–4 (double) digits integers (0–99), multiplication and dividing are a must, addition and subtraction are used to complete the arithmetic equations</td><td>28 * 25 / 50–12 =</td><td>Number correct * 1.0</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note</emph>. Rules and equations were derived from the study of Dedovic et al. ([<reflink idref="bib14" id="ref28">14</reflink>]). Grading manipulation and control over own difficulty level were based on the study of Engle‐Friedman et al. ([<reflink idref="bib18" id="ref29">18</reflink>]). The grading manipulation was only used in the choice rounds.</p> <p>Participants received the following instruction: "In order to estimate your basic arithmetic skills, we start the experiment with four equations from each difficulty level." Each problem had to be completed within 10 s by pressing a number on the keyboard. Students were not informed of their AER scores.</p> <p>After the AER, the choice rounds started. Participants received short instructions: "From now on there will be five rounds with 10 equations. At the start of each round, you can choose your own difficulty level. The highest score in a round will count as your final grade. Try to do your best." Example equations on each difficulty level were presented to the participants. After answering, participants received performance feedback (correct, incorrect, or late). Based on Engle‐Friedman et al. ([<reflink idref="bib18" id="ref30">18</reflink>]) we also used a grading manipulation. Students were instructed that after each round, they would receive a grade ranging from 0 to 10. But the grade depended on the difficulty level they chose (see Table 1). From the arithmetic task we derived the following variables: AER score, chosen difficulty level in each round (choice), and the percentage of mistakes in each round.</p> <hd id="AN0163976527-8">Questionnaires</hd> <p></p> <hd id="AN0163976527-9">Growth mindset</hd> <p>Growth mindset was measured using the revised self‐theory scale designed by De Castella and Byrne ([<reflink idref="bib13" id="ref31">13</reflink>]). This questionnaire consists of eight items, four items concerning fixed mindset ("<emph>To be honest, I don't think I can really change how intelligent I am</emph>") and four items concerning growth mindset (<emph>"I believe I can always substantially improve on my intelligence"</emph>). Each item was scored on a Likert scale from 1 (<emph>totally disagree</emph>) to 6 (<emph>totally agree</emph>). The fixed mindset items were reversed and summed up with the growth mindset items to create one mindset variable. Higher scores indicated a stronger growth mindset. The combined growth mindset scale had an internal consistency of α = .93.</p> <hd id="AN0163976527-10">Physiological Recordings</hd> <p>Physiology was measured by combining ECG and ICG using the VU University Ambulatory Monitoring System (VU‐AMS5fs). Five adhesive 55 mm Kendall H98SG hydrogel ECG electrodes (Medtronic, Heerlen, Netherlands) were placed on the subject's torso to record ECG (1 kHz) and ICG signals (250 Hz). Data were analyzed using the VU‐DAMS suite version 5.3.1. Data were manually labeled with the experimental phases (resting baseline, AER, and five experimental rounds). Data quality was assessed based on the presence of artifacts in the R‐peaks (ECG) and impedance signals (ICG) as detected by the embedded DAMS algorithms. When necessary, additional artifacts that were not captured by the algorithm (e.g., missed R‐peaks, noise to body movement, and irregular respiration) were manually removed.</p> <hd id="AN0163976527-11">Pre‐ejection‐period</hd> <p>For each round in the task, a single average ICG value was calculated using a VU‐DAMS algorithm by means of ensemble averaging of the ICG signal over all R‐peaks in the specific rounds (Riese et al., [<reflink idref="bib33" id="ref32">33</reflink>]). Given its sensitivity for movement artifacts, ICG signal was filtered using a 60 Hz low pass filter (Hurwitz et al., [<reflink idref="bib22" id="ref33">22</reflink>]). Each ICG was inspected visually, and B‐point scoring was adjusted using guidelines proposed in preceding studies if necessary (Nederend, Ten Harkel, Blom, Berntson, & de Geus, [<reflink idref="bib31" id="ref34">31</reflink>]; Lozano et al., [<reflink idref="bib25" id="ref35">25</reflink>]; Sherwood et al., [<reflink idref="bib36" id="ref36">36</reflink>]; Willemsen, De Geus, Klaver, Van Doornen, & Carrofl, [<reflink idref="bib40" id="ref37">40</reflink>]). PEP was calculated as time interval (in <emph>ms</emph>) between electrical depolarization of the ventricle at the Q‐onset in the ECG and the onset of blood outflow into the aorta as B‐point in the ICG.</p> <p>PEP was subjected to a data cleaning procedure in which (<reflink idref="bib1" id="ref38">1</reflink>) PEP was plotted against heart rate (HR) within participants to detect possible deviant values, (<reflink idref="bib2" id="ref39">2</reflink>) PEP was checked against the corresponding ranges within the observed HR according to the VU‐AMS manual and (<reflink idref="bib3" id="ref40">3</reflink>) the distribution was checked over all participants to determine possible outliers. In case a deviant PEP value was identified the scoring of the data in the original data file was checked and adjusted if necessary. The preprocessing process was all performed before hypothesis testing.</p> <p>Physiology was continuously measured during the resting baseline round and the task AER and the five choice rounds. PEP was calculated from the beginning to the end of the rounds, resulting in seven PEP measurements that are used in the current study. Common practice in analyzing PEP is by investigating its reactivity (Matthews et al., [<reflink idref="bib28" id="ref41">28</reflink>]). This can be achieved by subtracting the PEP value calculated during the resting baseline from the PEP value in each of the five rounds (i.e., Boyce et al., [<reflink idref="bib9" id="ref42">9</reflink>]; Yeager, Lee, & Jamieson, [<reflink idref="bib42" id="ref43">42</reflink>]). For the AER we did not use the reactivity score. The internal consistency across rounds of both the absolute PEP scores (α = .99) and the PEP reactivity scores (α = .98) were high.</p> <hd id="AN0163976527-12">Data Analyses</hd> <p></p> <hd id="AN0163976527-13">Validation Analyses</hd> <p>To assess the validity of PEP as index of effort mobilization, we performed a repeated‐measures (RM)ANOVA, using a within‐subject factor Physiology Phase (PEP during: resting baseline; AER; choice rounds). In this analysis, PEP reflects a participant's amount of Sympathetic Nervous System (SNS) activity. Faster PEP responses reflect higher effort mobilization (more SNS activity), while slower PEP responses reflect lower effort mobilization (less SNS activity). This is illustrated in Figure 2.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/309X/01may23/mbe12356-fig-0002.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="mbe12356-fig-0002.jpg" title="2 Schematic overview of Pre‐Ejection Period interpretation. The y‐axis represents Pre‐Ejection‐Period in milliseconds." /> </p> <p></p> <p>Using another (RM)ANOVA, we analyzed whether students chose higher or lower difficulty levels compared to AER. We used one within‐subject factor Experimental Phase (AER; average chosen difficulty). This analysis was performed to validate the expected task effects.</p> <hd id="AN0163976527-15">Hypothesis Testing</hd> <p>To investigate predictors of the choice to invest effort in more detail, we performed a Linear Mixed Model analysis. The chosen difficulty level for each round was the dependent variable. The independent variables were rounds, mistakes, and PEP reactivity in the previous round (as dynamic, repeated round‐level predictors) and growth mindset (as more stable individual differences). The AER score as entered as covariate to control for the possible effects of baseline arithmetic skills. The first round was excluded because we could not estimate the number of mistakes and PEP reactivity from the previous round. In addition, we included two interaction terms in the analysis: (<reflink idref="bib1" id="ref44">1</reflink>) between mistakes and growth mindset, and (<reflink idref="bib2" id="ref45">2</reflink>) between PEP reactivity and growth mindset. Mistakes and PEP reactivity were centered around the individual mean across the five rounds. Growth mindset and baseline arithmetic skills were centered around the grand mean.</p> <p>We specified a random intercept model with no predictors (to determine the covariance structure) and two levels: repeated rounds as level 1 (mistakes and PEP reactivity) and student as level 2 (growth mindset). We used the first‐order autoregressive (AR1) covariance structure for the repeated measures because it captured the shared variance of adjacent measurements and provided a better fit than the other covariance types. We used the scaled identity covariance structure for the random intercept at level 2.</p> <p>We used a <emph>p</emph>‐value of < .05 as an indicator of significance. Effect sizes were considered small (η<sups>2</sups> = 0.01), medium (η<sups>2</sups> = 0.06) or large (η = 0.14). All analyses were performed in IBM SPSS Statistics for Windows (version 26).</p> <hd id="AN0163976527-16">RESULTS</hd> <p>Descriptive statistics and correlations for all relevant variables are presented in Table 2. To assess whether PEP is a valid index of effort mobilization, a (RM)ANOVA was performed using a within‐subject factor Physiology Phase, with three PEP levels: resting baseline, AER, and choice rounds. The model yielded a main effect of Physiology Phase (<emph>F</emph>(<reflink idref="bib2" id="ref46">2</reflink>, 226) = 28.62, <emph>p</emph> < .001) with a large effect size (η<sups>2</sups> = 0.20). As shown in Figure 3, repeated contrasts showed that PEP increased from the resting baseline to the AER (<emph>F</emph>(<reflink idref="bib1" id="ref47">1</reflink>,<reflink idref="bib113" id="ref48">113</reflink>) = 36.88, <emph>p</emph> < .001, η<sups>2</sups> = 0.25), which indicated more effort mobilization during the AER. However, repeated contrasts also showed that PEP became slower from AER to the choice rounds (<emph>F</emph>(<reflink idref="bib1" id="ref49">1</reflink>, 113) = 34.23, <emph>p</emph> < .001, η<sups>2</sups> = 0.24). This indicates that less effort was mobilized during the choice rounds compared to the AER. Furthermore, simple contrasts showed that PEP was faster during the choice rounds than during the rest (<emph>F</emph>(<reflink idref="bib1" id="ref50">1</reflink>,<reflink idref="bib113" id="ref51">113</reflink>) = 17.09, <emph>p</emph> < .001, η<sups>2</sups> = 0.13), which indicated more effort mobilization during the choice rounds. In summary, SNS activity, as indexed by PEP, was successfully elicited by the task. The mean of the absolute PEP values across experimental rounds were only used in the validation analysis. For all other analyses, PEP reactivity was used as a repeated measure, separately for each round. Table 2 only presents PEP reactivity (averaged across the five rounds).</p> <p>2 Table Descriptives and Correlations Across All Relevant Constructs</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="center"><italic>M</italic></th><th align="center"><italic>SD</italic></th><th>1.</th><th>2.</th><th>3.</th><th>4.</th><th align="center">5.</th></tr></thead><tbody valign="top"><tr><td>1. Mean choice (difficulty)</td><td> 2.85</td><td> 0.80</td><td>–</td><td align="center" /><td align="center" /><td align="center" /><td /></tr><tr><td>2. Baseline arithmetic skills</td><td> 3.28</td><td> 0.58</td><td>  .47**</td><td>–</td><td align="center" /><td align="center" /><td /></tr><tr><td>3. Average mistakes (% incorrect)</td><td>25.51</td><td>11.40</td><td>.13</td><td>    .26**</td><td>–</td><td align="center" /><td /></tr><tr><td>4. Mean PEP reactivity</td><td>−2.81</td><td> 7.26</td><td>−.18</td><td>−.09</td><td>−.06</td><td>–</td><td /></tr><tr><td>5. Growth mindset</td><td>35.05</td><td> 7.32</td><td>.11</td><td>−.01</td><td>−.03</td><td>.05</td><td>–</td></tr></tbody></table> </ephtml> </p> <ulist> <item>2 <emph>Note</emph>. *<emph>p</emph> < .05, **<emph>p</emph> < .001.</item> <item>3 Abbreviation: PEP = pre‐ejection period.</item> </ulist> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/309X/01may23/mbe12356-fig-0003.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="mbe12356-fig-0003.jpg" title="3 Validation of sympathetic arousal as indexed by Pre‐ejection Period. AER stands for Arithmetic Estimation Round and represents baseline arithmetic skills. * = Significant when p < .05, ** = Significant when p < .001." /> </p> <p></p> <p>We also investigated whether students chose higher or lower difficulty levels in the arithmetic task compared to their measured math ability. We compared baseline arithmetic skills with the average chosen difficulty level during the choice phase. A (RM)ANOVA yielded a main effect of Experimental Phase (<emph>F</emph>(<reflink idref="bib1" id="ref52">1</reflink>,<reflink idref="bib113" id="ref53">113</reflink>) = 38.64, <emph>p</emph> < .001) with large effect size (η<sups>2</sups> = 0.26): students tended to choose on average lower difficulty levels (<emph>M</emph> = 2.85, <emph>SD</emph> = 0.80) than their measured math ability (<emph>M</emph> = 3.28, <emph>SD</emph> = 0.58).</p> <p>A linear mixed model analysis was performed to investigate the effects of making mistakes (level 1), PEP reactivity (level 1) and growth mindset (level 2), while controlling for round and baseline arithmetic skills (AER score) on the chosen difficulty level in each individual round. First, there were effects of both the round (b = 0.06, <emph>SE</emph> = 0.03, p < .001) and the baseline arithmetic skills covariates (<emph>b</emph> = 0.601, <emph>SE</emph> = 0.120, <emph>p</emph> < .001): The chosen difficulty level was higher for higher baseline skills, and for later rounds. We also found an effect of making mistakes (<emph>b</emph> = −0.012, <emph>SE</emph> = 0.002, <emph>p < </emph>.001): Making mistakes in one round was related to choosing lower difficulty levels in the subsequent round. PEP reactivity was not related to choice in the subsequent round (<emph>b</emph> = 0.021, <emph>SE</emph> = 0.015, <emph>p</emph> = .169). In addition, there was no effect of growth mindset on choice (<emph>b</emph> = 0.011, <emph>SE</emph> = 0.009, <emph>p</emph> = .259). Furthermore, both the mistakes*growth mindset (<emph>b</emph> = 0.00, <emph>SE</emph> = 0.00, <emph>p</emph> = .823) and PEP reactivity*growth mindset interactions were not significant (<emph>b</emph> = −.001, <emph>SE</emph> = 0.002, <emph>p</emph> = .462).</p> <p>Because we could not confirm our hypotheses regarding mindset and PEP reactivity, we conducted additional Bayesian LMM testing, to assist with interpreting our null findings We found anecdotal evidence in favor of the null hypotheses that there are no main effects of mindset (<emph>Bayes Factor</emph> (<emph>BF</emph>) = 0.84) and PEP reactivity (<emph>BF</emph> = 0.59) on difficulty level choice. We found extreme evidence in favor of the null hypotheses that there are no interaction effects between mindset and mistakes (<emph>BF</emph> = 0.00), and between mindset and PEP reactivity (<emph>BF</emph> = 0.01), on difficulty level choice (see Appendix S1 for further results and corresponding bayes factors).</p> <hd id="AN0163976527-18">DISCUSSION</hd> <p>This research aimed to investigate whether mindset, making mistakes and PEP reactivity impact effort‐related choices, and whether these factors interact. We hypothesized that a stronger growth mindset would predict choosing higher difficulty levels, and that both making mistakes and a higher PEP reactivity would predict choosing lower difficulty levels in the subsequent round. We predicted that the latter effects would be stronger in students with a stronger fixed mindset, because they experience failure as a lack of ability (Dweck & Leggett, [<reflink idref="bib17" id="ref54">17</reflink>]).</p> <p>In contrast to this prediction, we did not find that students with a stronger growth mindset chose higher difficulty levels. The null result could mean that the relationship between the mindset and the choice to invest effort is more complex than previously thought. We added a grading manipulation to avoid students consistently choosing low difficulty levels; however, this may have affected our results. We initially predicted that students with stronger growth mindsets would choose higher difficulty levels because according to the literature these students tend to challenge themselves and adopt learning goals (Burnette et al., [<reflink idref="bib10" id="ref55">10</reflink>]). However, students with a stronger fixed mindset may also have chosen higher difficulty levels because they often adopt performance goals and strive to outperform others. To make it even more complex, individuals with a stronger growth mindset can also adopt performance goals or even a mix of both (e.g., Molden & Dweck, [<reflink idref="bib30" id="ref56">30</reflink>]; Yu & McLellan, [<reflink idref="bib43" id="ref57">43</reflink>]). For theory, this means that only considering growth vs. a fixed mindset could lead to misleading results because mindset is embedded within a broader meaning system. This means that the influence of mindset on a certain outcome might differ depending on other individual tendencies within this meaning system such as goal orientation and effort beliefs. For future research it is suggested to use a person‐oriented approach to be able to investigate the effects on motivated behaviors from a broader meaning system perspective, that is, based on subgroups that contain more than only mindset (e.g., Yu & McLellan, [<reflink idref="bib43" id="ref58">43</reflink>]).</p> <p>In line with our hypotheses, we did find that when students made more mistakes, they subsequently chose lower difficulty levels. However, the predicted interaction between mistakes and the growth mindset was not significant. This is surprising, considering that previous research suggests that individuals with a growth mindset tend to challenge themselves more in response to making mistakes (Dweck, [<reflink idref="bib15" id="ref59">15</reflink>], [<reflink idref="bib16" id="ref60">16</reflink>]; Blackwell et al., [<reflink idref="bib6" id="ref61">6</reflink>]). A study of Bempechat, London, and Dweck ([<reflink idref="bib5" id="ref62">5</reflink>]) showed, for example, that after initial failure, fixed mindset‐primed children preferred easier problems that would validate their ability, whereas growth mindset‐primed children preferred more challenging problems that would develop their ability.</p> <p>Furthermore, we hypothesized that choice was also influenced by PEP reactivity in the previous round, where more effort mobilization (reflected as more negative PEP reactivity) would predict choosing lower difficulty levels in the following round in students with a stronger fixed mindset. We did not find a direct effect of PEP reactivity on subsequent choices, nor could we find an interaction between PEP reactivity and mindset. Although the validation analysis showed that the task elicited effort mobilization, higher physiological effort mobilization did not predict whether students choose higher or lower difficulty levels during the arithmetic task. It could be that PEP reactivity is a too general measure of SNS within the context of our arithmetic task, making it difficult to understand its meaning and behavioral consequences (Vu et al., [<reflink idref="bib39" id="ref63">39</reflink>]; Mendes, [<reflink idref="bib29" id="ref64">29</reflink>]). The heterogeneity in the mindset meaning system might also have resulted in elevated levels of SNS activity in students at both end of the mindset continuum, which made it difficult to predict whether high or low difficulty levels were chosen based on physiological state. Therefore, PEP might not be an optimal measure to distinguish between different underlying physiological states resulting in choosing lower or higher difficulty levels within this complex mindset meaning system.</p> <p>It could be that students with a fixed mindset experience high physiological effort mobilization as 'threat' whereas students with a growth mindset experience it as 'challenge'. Therefore, another interesting approach to investigate individual differences in physiological states and underlying motivations of effort‐related choices could be the biopsychosocial model of threat and challenge (Blascovich, [<reflink idref="bib7" id="ref65">7</reflink>]; Blascovich & Tomaka, [<reflink idref="bib8" id="ref66">8</reflink>]). As suggested by Uphill, Rossato, Swain, and O'Driscoll ([<reflink idref="bib38" id="ref67">38</reflink>]), during threat, the situation is appraised as self‐relevant, and the individual perceives insufficient personal resources to meet the demands of the task. During challenge the situation is also appraised as self‐relevant, but the individual perceives enough personal resources to meet the demands of the task (Blascovich & Tomaka, [<reflink idref="bib8" id="ref68">8</reflink>]; Tomaka, Blascovich, Kibler, & Ernst, [<reflink idref="bib37" id="ref69">37</reflink>]; Seery, [<reflink idref="bib35" id="ref70">35</reflink>]). Even though both states result in heightened arousal, the biopsychosocial model suggests that two different appraisals underlie them. This can be investigated using other more refined cardiac measures alongside PEP, such as cardiac output, stroke volume, and total peripheral resistance. This approach has already been used in the study of Yeager et al. ([<reflink idref="bib42" id="ref71">42</reflink>]), where they found that when individuals were taught a growth mindset (about personality) during an intervention, they showed reduced threat‐type physiological reactions (elevated cardiovascular responses in combination with lower total peripheral resistance) to (academic) stressors.</p> <p>A limitation of the arithmetic task in the current study is that we factored difficulty level into grading rules adapted from the study of Engle‐Friedman et al. ([<reflink idref="bib18" id="ref72">18</reflink>]). We included this to avoid students would choose low difficulty levels to gain a higher performance score, in line with the idea that information about incentives is used to decide how much effort will be exerted (Davidow, Insel, & Somerville, [<reflink idref="bib12" id="ref73">12</reflink>]). However, the possibility of conflicting goals within the mindset meaning system in response to these incentives might have complicated the interpretation of our results. For example, in another version of the task in which there were no incentives, we did find an effect of mindset on choice to invest effort in adolescents (Janssen et al., [<reflink idref="bib23" id="ref74">23</reflink>]). In future studies, it would be interesting to manipulate the incentive system in different ways to identify the circumstances under which an incentive can increase investment effort. A recent study for example showed that incentivizing effort mobilization was related to choosing higher difficulty levels during the MET (Clay, Mlynski, Korb, Goschke, & Job, [<reflink idref="bib11" id="ref75">11</reflink>]).</p> <p>In conclusion, this research aimed to investigate whether mindset, making mistakes, and PEP reactivity impact effort‐related choices and whether these factors interact. The main findings demonstrated no effect of mindset and PEP reactivity on effort‐related choices. In addition, no interaction was found between the mindset and either making mistakes or PEP reactivity. However, while controlling for baseline math ability, we found that making mistakes in one round resulted in choosing lower difficulty levels in the subsequent round. Future studies may investigate more specific physiological patterns that can distinguish between threat and challenge states during a similarly realistic school‐related task with self‐adjusted difficulty, which can contribute to a better specification of the underlying mechanisms. Although we could not find any effects of the mindset, this study provides a novel approach for integrating different dynamic levels of influence on effort‐related choices during an educationally relevant task.</p> <hd id="AN0163976527-19">ACKNOWLEDGMENT</hd> <p>This research was supported by European Research Council Starting Grant 716736 (BRAINBELIEFS) to N. v. A.</p> <hd id="AN0163976527-20">CONFLICT OF INTEREST</hd> <p>The authors have stated explicitly that there are no conflicts of interest in connection with this article.</p> <p>GRAPH: Appendix S1. Supporting Information.</p> <ref id="AN0163976527-21"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref11" type="bt">1</bibl> <bibtext> Bandura, A. (1986) Social foundations of thought and action. Englewood Cliffs, NJ : Prentice Hall.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref13" type="bt">2</bibl> <bibtext> Bandura, A. (2001). Social cognitive theory: An agentic perspective. 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  Data: A Novel Approach to Investigate the Impact of Mindset and Physiology on the Choice to Invest Effort during an Arithmetic Task
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  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Nieuwenhuis%2C+Smiddy%22">Nieuwenhuis, Smiddy</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7930-2866">0000-0002-7930-2866</externalLink>)<br /><searchLink fieldCode="AR" term="%22Janssen%2C+Tieme+W%2E+P%2E%22">Janssen, Tieme W. P.</searchLink><br /><searchLink fieldCode="AR" term="%22van+der+Mee%2C+Denise+J%2E%22">van der Mee, Denise J.</searchLink><br /><searchLink fieldCode="AR" term="%22Rahman%2C+Farah+A%2E%22">Rahman, Farah A.</searchLink><br /><searchLink fieldCode="AR" term="%22Meeter%2C+Martijn%22">Meeter, Martijn</searchLink><br /><searchLink fieldCode="AR" term="%22van+Atteveldt%2C+Nienke+M%2E%22">van Atteveldt, Nienke M.</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-3387-6151">0000-0002-3387-6151</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Mind%2C+Brain%2C+and+Education%22"><i>Mind, Brain, and Education</i></searchLink>. May 2023 17(2):123-131.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 9
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2023
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Physiology%22">Physiology</searchLink><br /><searchLink fieldCode="DE" term="%22World+Views%22">World Views</searchLink><br /><searchLink fieldCode="DE" term="%22Individual+Development%22">Individual Development</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+Making%22">Decision Making</searchLink><br /><searchLink fieldCode="DE" term="%22Intention%22">Intention</searchLink><br /><searchLink fieldCode="DE" term="%22Motivation%22">Motivation</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Activities%22">Learning Activities</searchLink><br /><searchLink fieldCode="DE" term="%22Failure%22">Failure</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/mbe.12356
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1751-2271<br />1751-228X
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Growth mindset, the belief that personal attributes such as intelligence are malleable, has previously been related to more effort investment. Here, we investigated how undergraduates' mindset (N = 114) relates to the choice to invest effort during an arithmetic task, indexed by whether they make low vs. high effort-related choices. Social cognitive theory suggests that past performance experiences (mastery vs. failure) and physiological state are important sources for competence self-evaluations. Therefore, in addition to mindset, we also investigated how effort-related choices are influenced more dynamically, by failures and physiological responses during the task. Growth mindset and physiological effort mobilization did not predict effort-related choices but making mistakes did predict lower effort choices in the subsequent round. This study further supports the importance of mastery experiences for effort investment and provides a novel approach for integrating different levels of influence on effort-related choices during an educationally-relevant task.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2023
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1379040
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1379040
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1111/mbe.12356
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
        StartPage: 123
    Subjects:
      – SubjectFull: Undergraduate Students
        Type: general
      – SubjectFull: Physiology
        Type: general
      – SubjectFull: World Views
        Type: general
      – SubjectFull: Individual Development
        Type: general
      – SubjectFull: Decision Making
        Type: general
      – SubjectFull: Intention
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      – SubjectFull: Motivation
        Type: general
      – SubjectFull: Learning Activities
        Type: general
      – SubjectFull: Failure
        Type: general
    Titles:
      – TitleFull: A Novel Approach to Investigate the Impact of Mindset and Physiology on the Choice to Invest Effort during an Arithmetic Task
        Type: main
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            – D: 01
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              Type: published
              Y: 2023
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            – Type: issn-print
              Value: 1751-2271
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              Value: 1751-228X
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            – Type: volume
              Value: 17
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            – TitleFull: Mind, Brain, and Education
              Type: main
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