Enforcing a high success percentage interferes with reward-based motor learning

Enforcing a high success percentage interferes with reward-based motor learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Enforcing a high success percentage interferes with reward-based motor learning Katinka van der Kooij, Nina M. van Mastrigt, Moira van Leeuwen, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7978191/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Humans can adapt their movements based on binary reward feedback about success and failure. To engage in such ‘reward-based’ motor learning, the learner must encounter at least some failures, but it is unclear what percentage of failures is optimal. For learning, we hypothesize that a success percentage of 50% is optimal, as it provides the most information. For motivation, in contrast, we hypothesize that a success percentage of 80% is optimal, since too many failures can reduce motivation. In this study, we simultaneously test the hypotheses on learning and motivation in participants of a wide age range (7 to 58 years) who performed a brief circle-drawing task. The participant’s goal in this task was to double the size of the baseline circles drawn with the unseen hand. We assigned participants to a reward scheme that targets either 50% success (moderate success group) or 80% success (high success group). In line with our hypothesis on learning, the results show more motor learning in the moderate success group compared to the high success group. In contrast to our hypothesis on motivation, motivation was not higher in the high success group. Physical sciences/Mathematics and computing Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology reward-based motor learning motivation success learning Figures Figure 1 Figure 2 Introduction Skilled painters can instantly paint an eye, with a size appropriate for the human face. Novices will need some practice before they can do this. One type of feedback that can result in learning of movement parameters such as size and direction is ‘reward-based’ feedback about whether a movement is successful or not [ 1 – 3 ]. This ‘reward-based’ motor learning relies on repeating successful movements and exploring following failure by varying the movement plan [ 1 ]. As successes are needed to engage in learning, it is common practice for teachers to adapt the reward criterion to a learner’s performance. For instance, a novice might receive a more lenient criterion compared to an expert. Digital environments have excellent opportunities for tracking performance and adapting the reward criterion. It is unclear, however, what success percentage a reward criterion should target. Moreover, the answer might depend on whether one focuses on learning or motivation [ 4 ]. To engage participants in reward-based learning, a reward criterion should aim for a balance between success and failure. First, failures are needed to evoke exploration, which pushes movement variability beyond the boundaries of motor noise [ 3 , 5 , 6 ]. This allows participants to find the movements that result in reward, even when the rewarded movement is beyond the range of current performance. Second, the reward criterion should lead to feedback that is informative about the proximity to the target performance, helping to direct behavior in the right direction. Hence, many studies have used a reward criterion that is adapted to the participant’s performance to ensure a success percentage of about 50% [ 2 , 6 – 10 ]. To our knowledge, no studies have compared motor learning between conditions using reward criteria that target different success percentages. While failures are needed to evoke exploration, they also influence motivation, which depends on the probability of and value of success [ 11 ]. Failures reduce the probability of success but can also enhance the value of success [ 11 ]. Hence, some failures should be allowed, but not too many. In educational [ 12 ] and sports coaching literature [ 13 ] an 80% success principle has been suggested. Experimental research supports the 80% success principle. In both an interception and a pointing task, an 80% success percentage compared to 30% success percentage resulted in higher self-reported enjoyment for a computer task [ 8 , 14 ]. Furthermore, for the play duration of an online game, we found a curvilinear relationship with the success percentage, which peaked at 80% success [ 15 ]. Finally, older adults performed more repetitions of a stepping game when the success percentage was 80% compared to when it was 100% [ 16 ]. When choosing a success percentage, the choice might thus depend on whether the goal is to learn or to motivate: when the goal is to learn, one should use a lower success percentage compared to when the goal is to motivate [ 4 ]. In this study, we test the influence of success percentage on learning and motivation in a circle-drawing task [ 17 ] in which participants learnt to double the size of a circle they drew based on binary reward feedback. Participants were assigned to either a ‘moderate success’ group, which performs the task with a reward criterion that targets 50% success, or to ‘high success group’, which performs the task with a reward criterion that targets 80% success. We hypothesize that the moderate success group shows the most motor learning while the high success group shows the most motivation. Methods Participants Participants were visitors of the NEMO science museum who were at least seven years old and reported no injury to the dominant hand. These participants were assigned to either the moderate or the high success group. Of the 134 participants in the moderate success group, 102 participants were measured in a previous study [ 17 ]. The remaining 32 participants were measured in the current study. All 133 participants in the high success group were measured in the current study. Participants who spoke Dutch were tested in Dutch, whereas other participants were tested in English. In each success group, one participant was excluded for not finishing the experiment. Four participants in the moderate success group and two participants in the high success group were excluded because they did not draw circles, according to our criteria (see data analysis section). Demographics of the included participants are reported in Table 1 . All methods were carried out in accordance with the Declaration of Helsinki and all experimental protocols (VCWE-2023-100-R2) were approved by the local ethical committee of the Faculty of Behavioural and Movement Sciences, Vaste Commissie Wetenschap en Ethiek . Written informed consent was obtained from alle subjects and/or their legal guardian. Table 1 Participant demographics. Group Age-range (years) Gender Handedness Preferred language Female Male Not specified Left Right Not specified Dutch English Moderate success 7–54 64 58 8 14 97 19 67 63 High success 7–58 64 57 10 18 108 5 72 59 Task Motor learning was assessed in a circle-drawing task, which participants performed with an Intuos Medium Wacom drawing tablet and a laptop (Fig. 1 .a). The participants were seated behind a table and instructed to provide a bear named ‘Ollie’ with a nose by drawing a circle of the right size on the Wacom tablet. The size ( \(\:S\) ) of the drawn circle at a trial \(\:i\) was approximated by calculating the radius as the mean distance ( \(\:r\) ) of a spatially resampled trajectory to the trajectory's center (Fig. 1 .b). To isolate the binary reward feedback, the hand was hidden behind a curtain. A trial started with a display of the bear Ollie, with eyes open and without a nose. Once the participant placed the Wacom pen on the tablet, Ollie closed their eyes and the drawing movement was recorded. The end of the drawing movement was detected once the participant lifted the pen for longer than 500 milliseconds and at least ten samples of pen position had been recorded. After drawing movement ended, Ollie opened their eyes and provided reward feedback by showing a happy or sad mouth (Fig. 1 .a) and playing a ‘bing’ or buzzer sound for 500 ms. After that, the trial ended, and the next trial started. The reward feedback was provided based on the ratio between the size of the drawn circle ( S ) and a target size. For the first five trials (baseline), we used an arbitrarily chosen target size of two Unity units (Fig. 1 .b). For the subsequent trials, the target size was twice the average size of the circles drawn in the first five trials. To handle circle-drawing errors caused by too small (a ratio smaller than one) and too large sizes (a ratio higher than one) in the same way, we calculated a ratio error. This ratio error was computed slightly differently depending on whether the drawn circle was too small or too large. In the case of a smaller drawn circle compared to the target size, we computed the size ratio by dividing the target size by the drawn size. If, however, the drawn circle was too large, the size ratio was computed by dividing the drawn size ( \(\:{S}_{t}\) ) by the target size ( \(\:{T}_{t}\) ). In both cases, the ratio error \(\:{e}_{t}\) was then defined as the size ratio minus 1. This way, the ratio error is positive when the drawn size is either too small or too large, and zero when the drawn size is equal to the target size. $$\:{e}_{t}=Max\left(\frac{{T}_{t}}{{S}_{t}},\:\frac{{S}_{t}}{{T}_{t}}\right)-1$$ For both groups, we compared the ratio error of a drawn circle to the distribution of ratio errors from the previous ten trials (for the first ten trials, we used the distribution of all earlier trials). To create two success groups, we set two different reward criteria (Fig. 1 .c). For the moderate success group, circles with an error smaller than the median were defined as successes. For the high success group, circles with an error smaller than the 80th percentile were defined as successes. Procedure Participants drew 80 circles and were then instructed to call the experimenter, who initiated a ‘free phase’ to assess the participant’s motivation. The experimenter indicated that they had to complete some forms and left the participant the option to either wait or continue the task. If the participant chose to continue, they could draw 20 more circles, after which the task ended automatically. Subsequently, the participant completed a motivation questionnaire, which measured motivation to play the game again and included a modified, shortened version of the Intrinsic Motivation Inventory that we also used in a previous study [ 8 ]. The following items were answered on a five-point Likert scale either in English or in Dutch: I would like to play this game again / Ik wil dit spel nog een keer spelen (motivation to continue) I enjoyed playing this game / Ik vond het leuk om dit spel te spelen (enjoyment) I was good at this game / Ik was goed in dit spel (perceived competence) I tried my best to score as many points as possible / Ik deed mijn best om zoveel mogelijk punten te scoren (effort) I felt nervous while I was playing this game / Ik voelde me nerveus terwijl ik het spel speelde (pressure/tension) In this questionnaire, we also collected data on handedness, age in years, and gender. The preferred language was inferred from the questionnaire used (either a Dutch or English version). Data analysis Data analysis has been pre-registered: https://aspredicted.org/see_one.php . We deviated from the pre-registration in three ways: We used a block size of 5 trials instead of a block size of 10 trials to study learning because this better suits the experimental design; The multilevel regression used a logarithmic relationship between learning and block number instead of a linear relationship because learning cannot increase infinitely; We added an analysis in which we compared the increase in variability following failure between groups using a Mann-Whitney U rank-sum test to better understand differences in learning. We excluded the data from participants who did not finish the entire experiment or who did not draw acceptable circles. A perfect circle has an aspect ratio of 1, meaning that two perpendicular intersections through that circle have equal length, and has zero distance between its start and endpoint. Our definition of an acceptable circle is very lenient and is classified using two criteria. One, the aspect ratio (the largest ratio between two perpendicular intersections of the drawn trajectory) is smaller than 0.1 or larger than 10. Two, the distance between the start and endpoint of the trajectory was larger than half of the trajectory length. Circles were classified as unacceptable when they failed one or both criteria. Unacceptable circles were removed from the analysis of learning, even though the participant received reward feedback on them during the task. The removed data were not replaced. If more than 20% of the trials of a participant were classified as unacceptable, all data of this participant were excluded from the analysis. We measured two key dependent variables: learning and motivation. We analyzed all data from the drawing task in ratios rather than centimeters. Learning was measured based on the ratio between the drawn size (S) and the target size used in the last 75 trials of the task (double the size of the average drawn size in the first five trials). As the participant's task is to double the size, the target ratio is 2.0, and the task is fully learned when the ratio change is 1.0. We will define the amount of learning within a block ( b ) of five trials as the median ratio in the block minus the baseline ratio (1). In line with a previous study [ 17 ], we measure motivation by the average score on the motivation to continue and enjoyment items on the questionnaire. The secondary measure of motivation is whether the participant voluntarily engaged in the free phase. In addition, we report the scores on the perceived competence, pressure/tension, and effort items for the two success groups. To test the hypothesis that the moderate success group shows, greater learning than the high success group we performed a multilevel regression with learning as a function of the logarithm of block number and group, including a random slope for participant and using the moderate success group as the reference group. The block number was log-transformed to approximate exponential learning. We incorporated both the main effects of group and block and the interaction between the two, and used the moderate success group as a reference. A negative coefficient for the interaction of block and high success group would provide support for our hypothesis. To test the hypothesis that motivation is higher in the high success group compared to the moderate success group, we performed a signed Mann-Whitney U test. Explorative analyses An explorative analysis focused on the influence of success group on exploration. We defined exploration as the increase in variability following failure feedback, assuming that variability following success is due to motor noise, and measured variability as trial-to-trial changes in the drawn size [ 18 , 19 ]. To accommodate for signal-dependent noise, the trial-to-trial changes from trial t to trial t + 1 were normalized by the drawn size on trial t . Having calculated the trial-to-trial changes, we could compare these changes between trials on which success feedback was provided and trials on which failure feedback was provided. Before making this comparison, we accounted for differences between groups in which ratio errors received reward feedback. As we used a more lenient reward criterion in the high success group compared to the moderate success group, some successes in the high success group would have been defined as failures in the moderate success group. This difference in sampling successes and failures might bias exploration estimates [ 19 ]. We therefore only selected the trials that would have been defined as either a success or a failure in both groups. For this selection, exploration was measured as the median trial-to-trial change following failure minus the median trial-to-trial change following success. To assess whether the success group affected the exploration, exploration was compared between groups with a two-sided Mann-Whitney U rank-sum test. Results All data are reported as average ± between-participant standard deviation, unless otherwise specified. Of the trials, 2.3 ± 3.5% were excluded based on the criterion for not drawing circles. The success percentage in the moderate success group was 49 ± 7, whereas the success percentage in the high success group was 72 ± 6% (Fig. 2 .a). On average across the experiment, the reward criterion rewarded ratio errors of 0.75 ± 0.48 in the moderate success group and 1.62 ± 3.1 in the high success group (Fig. 2 .b). Although the reward criterion in the high success group on average rewarded errors larger than the baseline error, learning increased with block number for both the moderate and high success group (Fig. 2 .c), evidenced by a positive effect of the logarithm of block on learning ( B = 0.20 ± 0.01, p < 0.001). As we hypothesized, we found an interaction of block and success group on learning with a negative regression coefficient for the high success group ( B = -0.05 ± 0.02, p < 0.001). In contrast to our hypothesis on motivation, motivation (Fig. 2 .d) was not higher in the high success group (z = -0.20, p = 0.84), as it was 3.8 on a scale from 1 to 5 in both groups. Neither did the percentage of participants that continued in the free phase differ (respectively 60% and 59%; X 2 = 0.03, p = 0.86). Mean scores with standard errors per group for each item on the motivation questionnaire are reported in Table 2 . Table 2 Mean scores on the motivation items with standard error. Success group Motivation to continue Enjoyment Perceived competence Effort Pressure/tension Moderate 3.5 ± 0.09 4.2 ± 0.07 3.7 ± 0.07 4.7 ± 0.06 2.7 ± 0.1 High 3.6 ± 0.09 4.1 ± 0.08 3.9 ± 0.07 4.7 ± 0.06 2.2 ± 0.1 An explorative analysis showed that the increase in variability following failure was higher for the moderate success group compared to the high success group (H = 19609, z = 2.62, p = 0.01, Fig. 2 .e), indicating slightly larger exploration in the moderate success group. Discussion In this study, we simultaneously tested the effect of success percentage on motor learning and on motivation. We did so in a circle-drawing task suitable for testing children [ 17 ] and tested a large sample with ages between 7 years old and 58 years old. The moderate success group performed the task with 48% success, whereas the high success group performed the task with 72% success. We hypothesized that, compared to the high success group, the moderate success group would show more motor learning, but less motivation. In line with the hypothesis on learning, we found that the moderate success group learned more compared to the high success group. In contrast to our hypothesis on motivation, we found no difference between groups in self-reported motivation or ‘free phase’ participation. Both groups reported similar moderate to high motivation (3.8 on a scale of one to five). Our finding that the moderate success group learned more than the high success group aligns with the idea that the moderate success group received more opportunities to explore and received more informative success feedback. If participants only explored following failure, the number of explorations was 86% higher in the moderate success group compared to the high success group. Furthermore, lower success percentages evoke higher levels of exploration [ 20 ]. Indeed, following failure, the moderate success group increased variability following failure more than the high success group, which is an indication of exploration [ 19 ]. Lastly, compared to the high success group, the performance in the moderate success group had to be closer to the target performance to be rewarded. On average, the reward criterion in the high success group reward ratio errors of 1.62, whereas the reward criterion in the moderate success group rewarded ratio errors of 0.75. An important question is whether the success percentage influenced both the rate and amount of learning, as we proposed in the introduction. While the logarithmic fits described the data relatively well (Fig. 2 .c), the figure also shows some deviations from true exponential learning, albeit not systematically over time or between groups. This is consistent with learning converging towards a plateau rather than increasing exponentially. Future research, measuring learning on a longer timescale, will have to demonstrate whether the rate of learning, the asymptote of learning, or both are affected by the success percentage. Our finding that motivation did not differ between the two success groups is in contrast with the idea that 80% success is optimally motivating [ 12 , 15 ] and with earlier findings that 80% success resulted in greater motivation compared to 50% success in a pointing task [ 8 ]. The fact that we found no evidence of enhanced motivation in the high success group relative to the moderate success group might be caused by the low sensitivity of the two-item measurement of motivation. Also, in the other studies [ 8 , 15 ] participants received spatial performance feedback in addition to the score rewards, which might have affected the influence of success percentage on motivation. Alternatively, the optimal success percentage might depend on the complexity of the task. In information theory, the success percentage determines information, measured as Shannon entropy, with 50% success resulting in the highest information. The highest information might not always be best though. In the Challenge Point Framework, it has been proposed that the optimal success percentage is the maximum amount of information that can be processed[ 21 ]. Less complex tasks might leave more processing capacity for the successes and failures. The current task was designed to have very low complexity, allowing for reward-based motor learning. The participants were explicitly informed about the task-relevant dimension, rather than having to discover this themselves [ 8 ], and they were rewarded based on a single movement target instead of receiving a different target on each trial [ 8 , 15 ]. Study limitations Study limitations involve the low-precision measurement of motivation with two questionnaire items on a five-point Likert scale and the short duration of the task. A strong point of our study is that we tested a large sample with a wide age range of 7 to 58 years old and showed that the influence of the success group on learning did not depend on age. To be able to test this number of participants in the museum, we had to limit task duration. The brief task duration of about five minutes limits generalization of the results to learning and motivation on longer timescales. While our previous studies on the effect of success percentage on motivation also employed brief tasks and reported similar levels of motivation [ 8 , 15 ], the effect of success percentage on motivation might be stronger for tasks with a longer duration, which allow for a more reliable estimate of the success percentage. Also, the finding of greater learning in the moderate success group might not generalize to tasks that last longer, allowing participants to perform more attempts. Finally, it is unclear, however, how the results would generalize to tasks using non-binary reward feedback, in which the amount of reward decreases with the performance error. This can be realized by providing categorical feedback [ 22 – 25 ] or continuous reward feedback [ 26 – 28 ], the value of which is inversely related to the performance error. Conclusion To conclude, we show that participants show more reward-based motor learning when practicing with a success criterion that aims at a moderate success percentage than when practicing with a success criterion that aims at a high success percentage. There was no evidence that motivation differed between groups. Thus, if a novice painter would practice with a digital painting tool which rewards movements that are close to a target movement, the reward criterion shouldn’t be too lenient, rewarding only a moderate percentage of movements. Declarations Funding This study was funded by a starter grant awarded to Katinka van der Kooij by the Vrije Universiteit Amsterdam. Author Contribution Wrote the manuscript: KK, NMM, ML, MH, JEB, JBJSDesigned the experiment: KK, JBJS, NMM, MLCollected the data: NMM, MH, JEBAnalyzed the data: KK, NMM, MHPrepared the figures: KK, NMMReviewed the manuscript: KK, NMM, ML, MH, JEB, JBJS Acknowledgement We thank the NEMO Science Museum ScienceLive program for providing us with the opportunity to perform research in the museum. We thank Corina Schoorl, Caroline Blom, Ivana Lenardic, Kyara Sannes, Naomi Schriel, Megan Comyns, Danique Turk, Hilde van Doornen, Charlotte Jongenotter and Aesha Sarkar for testing participants in the NEMO Science Museum. Data Availability The datasets generated during and/or analysed during the current study are available on the Open Science Foundation: [https://osf.io/qtn3k/overview](https:/osf.io/qtn3k/overview) References Izawa, J. & Shadmehr, R. 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Cite Share Download PDF Status: Published Journal Publication published 23 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 Dec, 2025 Reviews received at journal 04 Dec, 2025 Reviews received at journal 02 Dec, 2025 Reviews received at journal 24 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers invited by journal 04 Nov, 2025 Editor assigned by journal 04 Nov, 2025 Editor invited by journal 03 Nov, 2025 Submission checks completed at journal 30 Oct, 2025 First submitted to journal 30 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7978191","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":542916052,"identity":"81f5e068-801c-46d9-8ce9-bcd3ac0144cd","order_by":0,"name":"Katinka van der Kooij","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACCTDJBsQ8QJzAYwPiMh4gRUsamE+CFgaGw4S1SDbwGH4uKLPJZ+A5fPDBA5nziRsO8B7Aq0WagcdYesa5NMsG3rZkgwSe20AtfAl4tcgxsCVI87YdNmDg5zGTAGoxNjjAY0BIS/Jv3rb/MC3nCGuRZmA+BrTlgAEDbw9IywE5glokm5mPWfOcSzZg4zkG8kuynORhAlokjjc23+YpszPg50k++PBnjx0P3/Eewwf4tDAwQ2lQ1DAw9iCJEAl+kKZ8FIyCUTAKRgYAALBWPsygun2XAAAAAElFTkSuQmCC","orcid":"","institution":"Vrije Universiteit Amsterdam","correspondingAuthor":true,"prefix":"","firstName":"Katinka","middleName":"van der","lastName":"Kooij","suffix":""},{"id":542916053,"identity":"eacf3d33-8433-45d5-aefb-58cc55996fde","order_by":1,"name":"Nina M. van Mastrigt","email":"","orcid":"","institution":"Justus Liebig Universität Giessen","correspondingAuthor":false,"prefix":"","firstName":"Nina","middleName":"M. van","lastName":"Mastrigt","suffix":""},{"id":542916055,"identity":"d544850e-a133-4f58-960e-1d89e6d44b41","order_by":2,"name":"Moira van Leeuwen","email":"","orcid":"","institution":"Vrije Universiteit Amsterdam","correspondingAuthor":false,"prefix":"","firstName":"Moira","middleName":"van","lastName":"Leeuwen","suffix":""},{"id":542916057,"identity":"a36c1464-e222-4b0a-8003-a9eb796e929b","order_by":3,"name":"Maaike van der Horst","email":"","orcid":"","institution":"Vrije Universiteit Amsterdam","correspondingAuthor":false,"prefix":"","firstName":"Maaike","middleName":"van der","lastName":"Horst","suffix":""},{"id":542916058,"identity":"13059bb8-1a5f-40cd-b533-5d0f04ba523f","order_by":4,"name":"Joyce E. Burger","email":"","orcid":"","institution":"Vrije Universiteit Amsterdam","correspondingAuthor":false,"prefix":"","firstName":"Joyce","middleName":"E.","lastName":"Burger","suffix":""},{"id":542916060,"identity":"806846e7-6fb1-42ab-8caa-7a6051c04002","order_by":5,"name":"Jeroen B. J. Smeets","email":"","orcid":"","institution":"Vrije Universiteit Amsterdam","correspondingAuthor":false,"prefix":"","firstName":"Jeroen","middleName":"B. J.","lastName":"Smeets","suffix":""}],"badges":[],"createdAt":"2025-10-29 09:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7978191/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7978191/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-39639-5","type":"published","date":"2026-02-23T15:59:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96022685,"identity":"1cd67644-5c1f-4da5-b033-a960d36c4eeb","added_by":"auto","created_at":"2025-11-16 16:30:19","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":546827,"visible":true,"origin":"","legend":"","description":"","filename":"vanderKooijetal2025SciRep.docx","url":"https://assets-eu.researchsquare.com/files/rs-7978191/v1/dbba2eb742d2b67a725316fd.docx"},{"id":96022683,"identity":"7580039b-b90b-421d-b9bd-822469231736","added_by":"auto","created_at":"2025-11-16 16:30:19","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7435,"visible":true,"origin":"","legend":"","description":"","filename":"1a87e116f2cb4fc98799ee34c08d002b.json","url":"https://assets-eu.researchsquare.com/files/rs-7978191/v1/0195703e81cfe02ef284275a.json"},{"id":96022689,"identity":"1c33d2e6-4be2-48c0-8a23-4301f91d8f6d","added_by":"auto","created_at":"2025-11-16 16:30:19","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":76620,"visible":true,"origin":"","legend":"","description":"","filename":"1a87e116f2cb4fc98799ee34c08d002b1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7978191/v1/5d64bb6386061aef69a1ac58.xml"},{"id":96022692,"identity":"1e3d1e83-6067-4adb-8a89-96abfdcccf02","added_by":"auto","created_at":"2025-11-16 16:30:19","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":203308,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7978191/v1/6e348e2924978dbcc110d5d1.png"},{"id":96022684,"identity":"3aa5e2c6-4c9c-4159-9198-37094d754370","added_by":"auto","created_at":"2025-11-16 16:30:19","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":48234,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7978191/v1/2bbec1acc30578b79bb69ccf.png"},{"id":96022688,"identity":"4a059e97-e579-4de7-bca1-8e26bd2f7f47","added_by":"auto","created_at":"2025-11-16 16:30:19","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74554,"visible":true,"origin":"","legend":"","description":"","filename":"1a87e116f2cb4fc98799ee34c08d002b1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7978191/v1/563d50ff2cec43b8f3a8864b.xml"},{"id":96245282,"identity":"59f7f96f-6f4e-4af0-bada-c487a5beff58","added_by":"auto","created_at":"2025-11-19 07:20:15","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":85185,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7978191/v1/46fc2f7a8f5d736c19b7fa0e.html"},{"id":96022686,"identity":"0a32caf0-bbf0-44dd-8e07-77fc98451b5a","added_by":"auto","created_at":"2025-11-16 16:30:19","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":341203,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods. a\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e) Experimental set-up with drawing tablet, laptop, and reward feedback. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e) Circle size was calculated as the average distance from points on a resampled trajectory (blue dots) to the center. The target size (red line) was double the average circle size in the first five trials (dotted red line). \u003c/em\u003e\u003cem\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e) Examples of the reward criterion for a participant in the moderate (left panel) and high (right panel) success group. The reward criterion (shaded area) was continuously adapted based on the participant's performance (black dots).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7978191/v1/c0ca2413460ac7f1acf96913.jpeg"},{"id":96022690,"identity":"b5688a49-872c-4fe8-bff1-f0d32f2a5c1c","added_by":"auto","created_at":"2025-11-16 16:30:19","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":136926,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. Data for the moderate success group are shown in light blue, and data for the high success group are shown in dark blue. \u0026nbsp;a) Mean success percentage with standard error of the mean. b) Mean reward criterion (in ratio error) for the two groups. c) Mean learning (the increase in ratio relative to the baseline block) with standard error as a function of the logarithm of block number. d) Mean motivation with standard error. e) Median trial-to-trial change (fraction) following failure and success feedback for the two success groups. Shaded areas show the distribution of the data, and thick lines represent the median. For this analysis, we only used trials that would have been defined as a success or failure by both reward criteria.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7978191/v1/46f25cd23c265d561feb6261.jpeg"},{"id":103765720,"identity":"f2b9871a-2e62-4f11-871e-bb6325e85162","added_by":"auto","created_at":"2026-03-02 16:08:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1051112,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7978191/v1/4a97b612-bb01-432c-9702-24a129bc06d7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enforcing a high success percentage interferes with reward-based motor learning","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSkilled painters can instantly paint an eye, with a size appropriate for the human face. Novices will need some practice before they can do this. One type of feedback that can result in learning of movement parameters such as size and direction is \u0026lsquo;reward-based\u0026rsquo; feedback about whether a movement is successful or not [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This \u0026lsquo;reward-based\u0026rsquo; motor learning relies on repeating successful movements and exploring following failure by varying the movement plan [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As successes are needed to engage in learning, it is common practice for teachers to adapt the reward criterion to a learner\u0026rsquo;s performance. For instance, a novice might receive a more lenient criterion compared to an expert. Digital environments have excellent opportunities for tracking performance and adapting the reward criterion. It is unclear, however, what success percentage a reward criterion should target. Moreover, the answer might depend on whether one focuses on learning or motivation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo engage participants in reward-based learning, a reward criterion should aim for a balance between success and failure. First, failures are needed to evoke exploration, which pushes movement variability beyond the boundaries of motor noise [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This allows participants to find the movements that result in reward, even when the rewarded movement is beyond the range of current performance. Second, the reward criterion should lead to feedback that is informative about the proximity to the target performance, helping to direct behavior in the right direction. Hence, many studies have used a reward criterion that is adapted to the participant\u0026rsquo;s performance to ensure a success percentage of about 50% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. To our knowledge, no studies have compared motor learning between conditions using reward criteria that target different success percentages.\u003c/p\u003e\u003cp\u003eWhile failures are needed to evoke exploration, they also influence motivation, which depends on the probability of and value of success [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Failures reduce the probability of success but can also enhance the value of success [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Hence, some failures should be allowed, but not too many. In educational [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and sports coaching literature [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] an 80% success principle has been suggested. Experimental research supports the 80% success principle. In both an interception and a pointing task, an 80% success percentage compared to 30% success percentage resulted in higher self-reported enjoyment for a computer task [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, for the play duration of an online game, we found a curvilinear relationship with the success percentage, which peaked at 80% success [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Finally, older adults performed more repetitions of a stepping game when the success percentage was 80% compared to when it was 100% [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhen choosing a success percentage, the choice might thus depend on whether the goal is to learn or to motivate: when the goal is to learn, one should use a lower success percentage compared to when the goal is to motivate [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In this study, we test the influence of success percentage on learning and motivation in a circle-drawing task [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] in which participants learnt to double the size of a circle they drew based on binary reward feedback. Participants were assigned to either a \u0026lsquo;moderate success\u0026rsquo; group, which performs the task with a reward criterion that targets 50% success, or to \u0026lsquo;high success group\u0026rsquo;, which performs the task with a reward criterion that targets 80% success. We hypothesize that the moderate success group shows the most motor learning while the high success group shows the most motivation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003eParticipants were visitors of the NEMO science museum who were at least seven years old and reported no injury to the dominant hand. These participants were assigned to either the moderate or the high success group. Of the 134 participants in the moderate success group, 102 participants were measured in a previous study [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The remaining 32 participants were measured in the current study. All 133 participants in the high success group were measured in the current study. Participants who spoke Dutch were tested in Dutch, whereas other participants were tested in English. In each success group, one participant was excluded for not finishing the experiment. Four participants in the moderate success group and two participants in the high success group were excluded because they did not draw circles, according to our criteria (see data analysis section). Demographics of the included participants are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eAll methods were carried out in accordance with the Declaration of Helsinki and all experimental protocols (VCWE-2023-100-R2) were approved by the local ethical committee of the Faculty of Behavioural and Movement Sciences, \u003cem\u003eVaste Commissie Wetenschap en Ethiek\u003c/em\u003e. Written informed consent was obtained from alle subjects and/or their legal guardian.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParticipant demographics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAge-range (years)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eHandedness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003ePreferred language\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot specified\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLeft\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRight\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNot specified\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDutch\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eEnglish\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate success\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u0026ndash;54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh success\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u0026ndash;58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTask\u003c/p\u003e\u003cp\u003eMotor learning was assessed in a circle-drawing task, which participants performed with an Intuos Medium Wacom drawing tablet and a laptop (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.a). The participants were seated behind a table and instructed to provide a bear named \u0026lsquo;Ollie\u0026rsquo; with a nose by drawing a circle of the right size on the Wacom tablet. The size (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:S\\)\u003c/span\u003e\u003c/span\u003e) of the drawn circle at a trial \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e was approximated by calculating the radius as the mean distance (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e) of a spatially resampled trajectory to the trajectory's center (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.b). To isolate the binary reward feedback, the hand was hidden behind a curtain.\u003c/p\u003e\u003cp\u003eA trial started with a display of the bear Ollie, with eyes open and without a nose. Once the participant placed the Wacom pen on the tablet, Ollie closed their eyes and the drawing movement was recorded. The end of the drawing movement was detected once the participant lifted the pen for longer than 500 milliseconds and at least ten samples of pen position had been recorded. After drawing movement ended, Ollie opened their eyes and provided reward feedback by showing a happy or sad mouth (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.a) and playing a \u0026lsquo;bing\u0026rsquo; or buzzer sound for 500 ms. After that, the trial ended, and the next trial started.\u003c/p\u003e\u003cp\u003eThe reward feedback was provided based on the ratio between the size of the drawn circle (\u003cem\u003eS\u003c/em\u003e) and a target size. For the first five trials (baseline), we used an arbitrarily chosen target size of two Unity units (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.b). For the subsequent trials, the target size was twice the average size of the circles drawn in the first five trials. To handle circle-drawing errors caused by too small (a ratio smaller than one) and too large sizes (a ratio higher than one) in the same way, we calculated a ratio error. This ratio error was computed slightly differently depending on whether the drawn circle was too small or too large. In the case of a smaller drawn circle compared to the target size, we computed the size ratio by dividing the target size by the drawn size. If, however, the drawn circle was too large, the size ratio was computed by dividing the drawn size (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{t}\\)\u003c/span\u003e\u003c/span\u003e) by the target size (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{t}\\)\u003c/span\u003e\u003c/span\u003e). In both cases, the ratio error \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{e}_{t}\\)\u003c/span\u003e\u003c/span\u003e was then defined as the size ratio minus 1. This way, the ratio error is positive when the drawn size is either too small or too large, and zero when the drawn size is equal to the target size.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{e}_{t}=Max\\left(\\frac{{T}_{t}}{{S}_{t}},\\:\\frac{{S}_{t}}{{T}_{t}}\\right)-1$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor both groups, we compared the ratio error of a drawn circle to the distribution of ratio errors from the previous ten trials (for the first ten trials, we used the distribution of all earlier trials). To create two success groups, we set two different reward criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.c). For the moderate success group, circles with an error smaller than the median were defined as successes. For the high success group, circles with an error smaller than the 80th percentile were defined as successes.\u003c/p\u003e\u003cp\u003eProcedure\u003c/p\u003e\u003cp\u003e Participants drew 80 circles and were then instructed to call the experimenter, who initiated a \u0026lsquo;free phase\u0026rsquo; to assess the participant\u0026rsquo;s motivation. The experimenter indicated that they had to complete some forms and left the participant the option to either wait or continue the task. If the participant chose to continue, they could draw 20 more circles, after which the task ended automatically. Subsequently, the participant completed a motivation questionnaire, which measured motivation to play the game again and included a modified, shortened version of the Intrinsic Motivation Inventory that we also used in a previous study [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The following items were answered on a five-point Likert scale either in English or in Dutch:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eI would like to play this game again / Ik wil dit spel nog een keer spelen (motivation to continue)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eI enjoyed playing this game / Ik vond het leuk om dit spel te spelen (enjoyment)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eI was good at this game / Ik was goed in dit spel (perceived competence)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eI tried my best to score as many points as possible / Ik deed mijn best om zoveel mogelijk punten te scoren (effort)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eI felt nervous while I was playing this game / Ik voelde me nerveus terwijl ik het spel speelde (pressure/tension)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eIn this questionnaire, we also collected data on handedness, age in years, and gender. The preferred language was inferred from the questionnaire used (either a Dutch or English version).\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eData analysis has been pre-registered: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://aspredicted.org/see_one.php\u003c/span\u003e\u003cspan address=\"https://aspredicted.org/see_one.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. We deviated from the pre-registration in three ways:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWe used a block size of 5 trials instead of a block size of 10 trials to study learning because this better suits the experimental design;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe multilevel regression used a logarithmic relationship between learning and block number instead of a linear relationship because learning cannot increase infinitely;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWe added an analysis in which we compared the increase in variability following failure between groups using a Mann-Whitney U rank-sum test to better understand differences in learning.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eWe excluded the data from participants who did not finish the entire experiment or who did not draw acceptable circles. A perfect circle has an aspect ratio of 1, meaning that two perpendicular intersections through that circle have equal length, and has zero distance between its start and endpoint. Our definition of an acceptable circle is very lenient and is classified using two criteria. One, the aspect ratio (the largest ratio between two perpendicular intersections of the drawn trajectory) is smaller than 0.1 or larger than 10. Two, the distance between the start and endpoint of the trajectory was larger than half of the trajectory length. Circles were classified as unacceptable when they failed one or both criteria. Unacceptable circles were removed from the analysis of learning, even though the participant received reward feedback on them during the task. The removed data were not replaced. If more than 20% of the trials of a participant were classified as unacceptable, all data of this participant were excluded from the analysis. We measured two key dependent variables: learning and motivation. We analyzed all data from the drawing task in ratios rather than centimeters.\u003c/p\u003e\u003cp\u003eLearning was measured based on the ratio between the drawn size (S) and the target size used in the last 75 trials of the task (double the size of the average drawn size in the first five trials). As the participant's task is to double the size, the target ratio is 2.0, and the task is fully learned when the ratio change is 1.0. We will define the amount of learning within a block (\u003cem\u003eb\u003c/em\u003e) of five trials as the median ratio in the block minus the baseline ratio (1).\u003c/p\u003e\u003cp\u003eIn line with a previous study [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], we measure motivation by the average score on the motivation to continue and enjoyment items on the questionnaire. The secondary measure of motivation is whether the participant voluntarily engaged in the free phase. In addition, we report the scores on the perceived competence, pressure/tension, and effort items for the two success groups.\u003c/p\u003e\u003cp\u003eTo test the hypothesis that the moderate success group shows, greater learning than the high success group we performed a multilevel regression with learning as a function of the logarithm of block number and group, including a random slope for participant and using the moderate success group as the reference group. The block number was log-transformed to approximate exponential learning. We incorporated both the main effects of group and block and the interaction between the two, and used the moderate success group as a reference. A negative coefficient for the interaction of block and high success group would provide support for our hypothesis.\u003c/p\u003e\u003cp\u003eTo test the hypothesis that motivation is higher in the high success group compared to the moderate success group, we performed a signed Mann-Whitney U test.\u003c/p\u003e\u003cp\u003eExplorative analyses\u003c/p\u003e\u003cp\u003eAn explorative analysis focused on the influence of success group on exploration.\u003c/p\u003e\u003cp\u003eWe defined exploration as the increase in variability following failure feedback, assuming that variability following success is due to motor noise, and measured variability as trial-to-trial changes in the drawn size [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. To accommodate for signal-dependent noise, the trial-to-trial changes from trial \u003cem\u003et\u003c/em\u003e to trial \u003cem\u003et\u0026thinsp;+\u0026thinsp;1\u003c/em\u003e were normalized by the drawn size on trial \u003cem\u003et\u003c/em\u003e. Having calculated the trial-to-trial changes, we could compare these changes between trials on which success feedback was provided and trials on which failure feedback was provided. Before making this comparison, we accounted for differences between groups in which ratio errors received reward feedback. As we used a more lenient reward criterion in the high success group compared to the moderate success group, some successes in the high success group would have been defined as failures in the moderate success group. This difference in sampling successes and failures might bias exploration estimates [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. We therefore only selected the trials that would have been defined as either a success or a failure in both groups. For this selection, exploration was measured as the median trial-to-trial change following failure minus the median trial-to-trial change following success. To assess whether the success group affected the exploration, exploration was compared between groups with a two-sided Mann-Whitney U rank-sum test.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAll data are reported as average\u0026thinsp;\u0026plusmn;\u0026thinsp;between-participant standard deviation, unless otherwise specified. Of the trials, 2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5% were excluded based on the criterion for not drawing circles. The success percentage in the moderate success group was 49\u0026thinsp;\u0026plusmn;\u0026thinsp;7, whereas the success percentage in the high success group was 72\u0026thinsp;\u0026plusmn;\u0026thinsp;6% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.a). On average across the experiment, the reward criterion rewarded ratio errors of 0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48 in the moderate success group and 1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1 in the high success group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.b).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAlthough the reward criterion in the high success group on average rewarded errors larger than the baseline error, learning increased with block number for both the moderate and high success group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.c), evidenced by a positive effect of the logarithm of block on learning (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). As we hypothesized, we found an interaction of block and success group on learning with a negative regression coefficient for the high success group (\u003cem\u003eB\u003c/em\u003e = -0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eIn contrast to our hypothesis on motivation, motivation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.d) was not higher in the high success group (z = -0.20, p\u0026thinsp;=\u0026thinsp;0.84), as it was 3.8 on a scale from 1 to 5 in both groups. Neither did the percentage of participants that continued in the free phase differ (respectively 60% and 59%; \u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.86). Mean scores with standard errors per group for each item on the motivation questionnaire are reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean scores on the motivation items with standard error.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuccess group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMotivation to continue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEnjoyment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePerceived competence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEffort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePressure/tension\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAn explorative analysis showed that the increase in variability following failure was higher for the moderate success group compared to the high success group (H\u0026thinsp;=\u0026thinsp;19609, z\u0026thinsp;=\u0026thinsp;2.62, p\u0026thinsp;=\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.e), indicating slightly larger exploration in the moderate success group.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we simultaneously tested the effect of success percentage on motor learning and on motivation. We did so in a circle-drawing task suitable for testing children [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and tested a large sample with ages between 7 years old and 58 years old. The moderate success group performed the task with 48% success, whereas the high success group performed the task with 72% success. We hypothesized that, compared to the high success group, the moderate success group would show more motor learning, but less motivation. In line with the hypothesis on learning, we found that the moderate success group learned more compared to the high success group. In contrast to our hypothesis on motivation, we found no difference between groups in self-reported motivation or \u0026lsquo;free phase\u0026rsquo; participation. Both groups reported similar moderate to high motivation (3.8 on a scale of one to five).\u003c/p\u003e\u003cp\u003eOur finding that the moderate success group learned more than the high success group aligns with the idea that the moderate success group received more opportunities to explore and received more informative success feedback. If participants only explored following failure, the number of explorations was 86% higher in the moderate success group compared to the high success group. Furthermore, lower success percentages evoke higher levels of exploration [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Indeed, following failure, the moderate success group increased variability following failure more than the high success group, which is an indication of exploration [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Lastly, compared to the high success group, the performance in the moderate success group had to be closer to the target performance to be rewarded. On average, the reward criterion in the high success group reward ratio errors of 1.62, whereas the reward criterion in the moderate success group rewarded ratio errors of 0.75.\u003c/p\u003e\u003cp\u003eAn important question is whether the success percentage influenced both the rate and amount of learning, as we proposed in the introduction. While the logarithmic fits described the data relatively well (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.c), the figure also shows some deviations from true exponential learning, albeit not systematically over time or between groups. This is consistent with learning converging towards a plateau rather than increasing exponentially. Future research, measuring learning on a longer timescale, will have to demonstrate whether the rate of learning, the asymptote of learning, or both are affected by the success percentage.\u003c/p\u003e\u003cp\u003eOur finding that motivation did not differ between the two success groups is in contrast with the idea that 80% success is optimally motivating [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and with earlier findings that 80% success resulted in greater motivation compared to 50% success in a pointing task [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The fact that we found no evidence of enhanced motivation in the high success group relative to the moderate success group might be caused by the low sensitivity of the two-item measurement of motivation. Also, in the other studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] participants received spatial performance feedback in addition to the score rewards, which might have affected the influence of success percentage on motivation.\u003c/p\u003e\u003cp\u003eAlternatively, the optimal success percentage might depend on the complexity of the task. In information theory, the success percentage determines information, measured as Shannon entropy, with 50% success resulting in the highest information. The highest information might not always be best though. In the Challenge Point Framework, it has been proposed that the optimal success percentage is the maximum amount of information that can be processed[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Less complex tasks might leave more processing capacity for the successes and failures. The current task was designed to have very low complexity, allowing for reward-based motor learning. The participants were explicitly informed about the task-relevant dimension, rather than having to discover this themselves [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and they were rewarded based on a single movement target instead of receiving a different target on each trial [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eStudy limitations\u003c/p\u003e\u003cp\u003eStudy limitations involve the low-precision measurement of motivation with two questionnaire items on a five-point Likert scale and the short duration of the task.\u003c/p\u003e\u003cp\u003eA strong point of our study is that we tested a large sample with a wide age range of 7 to 58 years old and showed that the influence of the success group on learning did not depend on age. To be able to test this number of participants in the museum, we had to limit task duration. The brief task duration of about five minutes limits generalization of the results to learning and motivation on longer timescales. While our previous studies on the effect of success percentage on motivation also employed brief tasks and reported similar levels of motivation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], the effect of success percentage on motivation might be stronger for tasks with a longer duration, which allow for a more reliable estimate of the success percentage. Also, the finding of greater learning in the moderate success group might not generalize to tasks that last longer, allowing participants to perform more attempts.\u003c/p\u003e\u003cp\u003eFinally, it is unclear, however, how the results would generalize to tasks using non-binary reward feedback, in which the amount of reward decreases with the performance error. This can be realized by providing categorical feedback [\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] or continuous reward feedback [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], the value of which is inversely related to the performance error.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo conclude, we show that participants show more reward-based motor learning when practicing with a success criterion that aims at a moderate success percentage than when practicing with a success criterion that aims at a high success percentage. There was no evidence that motivation differed between groups. Thus, if a novice painter would practice with a digital painting tool which rewards movements that are close to a target movement, the reward criterion shouldn\u0026rsquo;t be too lenient, rewarding only a moderate percentage of movements.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was funded by a starter grant awarded to Katinka van der Kooij by the Vrije Universiteit Amsterdam.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWrote the manuscript: KK, NMM, ML, MH, JEB, JBJSDesigned the experiment: KK, JBJS, NMM, MLCollected the data: NMM, MH, JEBAnalyzed the data: KK, NMM, MHPrepared the figures: KK, NMMReviewed the manuscript: KK, NMM, ML, MH, JEB, JBJS\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the NEMO Science Museum ScienceLive program for providing us with the opportunity to perform research in the museum. We thank Corina Schoorl, Caroline Blom, Ivana Lenardic, Kyara Sannes, Naomi Schriel, Megan Comyns, Danique Turk, Hilde van Doornen, Charlotte Jongenotter and Aesha Sarkar for testing participants in the NEMO Science Museum.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available on the Open Science Foundation: [https://osf.io/qtn3k/overview](https:/osf.io/qtn3k/overview)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIzawa, J. \u0026amp; Shadmehr, R. Learning from sensory and reward prediction errors during motor adaptation. \u003cem\u003ePLoS Comput. Biol.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (3), e1002012 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTherrien, A. S., Wolpert, D. M. \u0026amp; Bastian, A. J. 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Neurosci.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e (18), 3604\u0026ndash;3620 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNikooyan, A. A. \u0026amp; Ahmed, A. A. Reward feedback accelerates motor learning. \u003cem\u003eJ. Neurophysiol.\u003c/em\u003e \u003cb\u003e113\u003c/b\u003e, 633\u0026ndash;646 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDam, G., Kording, K. \u0026amp; Wei, K. Credit assignment during movement reinforcement learning. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e (2), e55352 (2013).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"reward-based motor learning, motivation, success, learning","lastPublishedDoi":"10.21203/rs.3.rs-7978191/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7978191/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHumans can adapt their movements based on binary reward feedback about success and failure. To engage in such \u0026lsquo;reward-based\u0026rsquo; motor learning, the learner must encounter at least some failures, but it is unclear what percentage of failures is optimal. For learning, we hypothesize that a success percentage of 50% is optimal, as it provides the most information. For motivation, in contrast, we hypothesize that a success percentage of 80% is optimal, since too many failures can reduce motivation. In this study, we simultaneously test the hypotheses on learning and motivation in participants of a wide age range (7 to 58 years) who performed a brief circle-drawing task. The participant\u0026rsquo;s goal in this task was to double the size of the baseline circles drawn with the unseen hand. We assigned participants to a reward scheme that targets either 50% success (moderate success group) or 80% success (high success group). In line with our hypothesis on learning, the results show more motor learning in the moderate success group compared to the high success group. In contrast to our hypothesis on motivation, motivation was not higher in the high success group.\u003c/p\u003e","manuscriptTitle":"Enforcing a high success percentage interferes with reward-based motor learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-16 16:30:14","doi":"10.21203/rs.3.rs-7978191/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-07T19:05:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-04T19:58:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-02T15:42:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T23:21:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303920555768570224934258779390563245745","date":"2025-11-10T20:26:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323364006608909119700963091964218949317","date":"2025-11-07T17:14:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309784719059894637254906153431521559226","date":"2025-11-07T10:39:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-04T10:16:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-04T09:27:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-03T08:13:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-30T13:56:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-30T13:52:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"16133e93-f2c7-49bd-a7d9-807839ed2d62","owner":[],"postedDate":"November 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57756628,"name":"Physical sciences/Mathematics and computing"},{"id":57756629,"name":"Biological sciences/Neuroscience"},{"id":57756630,"name":"Biological sciences/Psychology"},{"id":57756631,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-03-02T16:04:54+00:00","versionOfRecord":{"articleIdentity":"rs-7978191","link":"https://doi.org/10.1038/s41598-026-39639-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-23 15:59:24","publishedOnDateReadable":"February 23rd, 2026"},"versionCreatedAt":"2025-11-16 16:30:14","video":"","vorDoi":"10.1038/s41598-026-39639-5","vorDoiUrl":"https://doi.org/10.1038/s41598-026-39639-5","workflowStages":[]},"version":"v1","identity":"rs-7978191","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7978191","identity":"rs-7978191","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}