Effort instructions and preparation time in basketball head fakes: A secondary ex-Gaussian analysis of fake-production costs

Effort instructions and preparation time in basketball head fakes: A secondary ex-Gaussian analysis of fake-production costs | 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 Research Article Effort instructions and preparation time in basketball head fakes: A secondary ex-Gaussian analysis of fake-production costs Kofi Nyantakyi Nyantakyi Appiah, Nathanael Adu, Divyanshu Kumar Singh, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9061327/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This secondary analysis examined how try-harder instructions and preparation time influence fake-production costs in basketball head fakes. Using an openly available dataset with 40 male players (22 novices, 18 experienced), ex-Gaussian parameters (mu, tau) of initiation-time distributions were analyzed for passes with and without head fakes across three interstimulus intervals (0, 500, 1000 ms) and two instruction conditions (Standard, Effort). Fake-production costs (Fake - Pass) were computed per condition and analyzed with linear mixed-effects models and within-subject effect sizes. Results showed large fake-production costs in mean RT at 0 ms that were reduced or eliminated at longer ISIs, regardless of instruction. Effort instructions had a limited impact on mean costs but selectively reduced tau-based variability costs, particularly for novices at long ISIs. These findings suggest that preparation time primarily mitigates coordination demands, whereas effort mobilization decreases attentional lapses, especially when deceptive actions are not yet fully automatized. Applied Statistics Psychology Head fake deception effort instructions ex-Gaussian secondary data analysis Figures Figure 1 Figure 2 Figure 3 Introduction Deceptive actions are ubiquitous in competitive sports and serve the strategic purpose of misleading opponents about one's true action intention (Güldenpenning et al., 2017 ). In basketball, one of the most studied deceptive actions is the head fake, in which a player looks in one direction while passing or shooting the ball in the opposite direction (Kunde et al., 2011 ; Sebanz & Shiffrar, 2009 ). From the observer's perspective, head fakes reliably slow down and impair the accuracy of defensive responses, a phenomenon termed the head-fake effect (Kunde et al., 2011 ; Polzien et al., 2020 ). Research has shown that this effect is primarily driven by the automatic processing of head orientation rather than gaze direction (Weigelt et al., 2020 ), and it appears robust across expertise levels, although experts may show some capacity to reduce interference after encountering a fake on the preceding trial (Curby & Gauthier, 2014). Similar deceptive movement phenomena have been documented in other sports, such as the side-step in rugby (Güldenpenning et al., 2017 ), underscoring the generality of deception as a competitive strategy. While most research has focused on how observers are affected by deceptive actions, comparatively little is known about the costs incurred by the producer of a fake. (Güldenpenning et al., 2023 ) introduced the concept of fake-production costs, defined as the increase in reaction time (RT) when a player must generate a pass with a head fake compared to a pass without one. Across two experiments with basketball novices, they demonstrated that producing a head fake significantly slowed initiation times, particularly when little or no preparation time was available (i.e., at short interstimulus intervals [ISIs] of 0–800 ms). Critically, these costs could be eliminated when participants were given sufficient preparation time (ISIs of 1200–1500 ms), suggesting that the temporal coordination of conflicting movement components (passing in one direction while orienting the head in the other) requires cognitive resources that can be allocated in advance given adequate preparation (Böer et al., 2024 ). Subsequent work has extended these findings in important ways. Böer et al. ( 2024 ) showed that practice over multiple sessions reduced fake-production costs, with experienced basketball players exhibiting smaller costs than novices. A further distributional analysis by Böer et al. ( 2025 ) examined how practice modulates the frequency distribution of RTs, using mixture-model and ex-Gaussian decomposition approaches to disentangle changes in typical processing speed from changes in the proportion of slow, lapse-like responses. A separate line of research has investigated whether short-term effort mobilization can enhance performance in speeded tasks. Steinborn et al. ( 2017 ) demonstrated that try-harder instructions cues prompting participants to invest additional cognitive effort on a subset of trials improved RT performance in a simple fore period paradigm. They interpreted these benefits as reflecting reduced attentional lapses (indexed by the tau parameter of the ex-Gaussian distribution) rather than a uniform increase in processing speed (indexed by mu). This distinction is theoretically important because the ex-Gaussian distribution decomposes RTs into a Gaussian component (mu, reflecting modal processing time) and an exponential component (tau, reflecting the rightward tail of the distribution, often associated with attentional lapses or slow responses; Yamashita et al., 2021 ). Building on this framework, Böer et al. ( 2026 ) investigated whether try-harder instructions could reduce fake-production costs in basketball novices and experienced players. Participants performed basketball passes with and without head fakes in a reaction-time paradigm, with try-harder cues appearing on 25% of trials. Results showed that effort instructions improved performance, particularly for novices, whose complex motor actions were less automatized. However, the original analysis primarily examined overall initiation times and movement times, without focusing specifically on condition-wise fake-production cost indices (Fake - Pass) or on how effort instructions differentially affect the mu and tau components of these costs across ISI levels and expertise groups. The present study constitutes a secondary analysis of the publicly available dataset from Böer et al. ( 2026 ), released under a CC BY 4.0 license on the Open Science Framework. Secondary data analysis is a valuable tool for psychological research, enabling new questions to be addressed from existing datasets and contributing to a cumulative, transparent science (Weston et al., 2019 ). We aimed to extend the original findings by (a) computing fake-production cost indices (Fake - Pass) for both the mu and tau parameters of the ex-Gaussian distribution, (b) testing whether effort instructions and preparation time (ISI) interact to modulate these costs using linear mixed-effects models, and (c) examining whether athletic expertise (novice vs. experienced) differentially moderates these patterns. Based on prior findings (Böer et al., 2024 , 2026 ; Güldenpenning et al., 2023 ), we hypothesized the following: Fake-production costs in mu will be largest at the 0 ms ISI and will decrease substantially at longer ISIs (500 ms and 1000 ms), reflecting the role of action preparation in resolving the temporal coordination demands of head fakes, Effort instructions will reduce fake-production costs, with a stronger effect on tau (variability/lapses) than on mu (mean speed), consistent with Böer et al. ( 2026 ) interpretation that effort mobilization primarily reduces attentional lapses and Novices will show larger fake-production costs than experienced players and will benefit more from effort instructions, particularly at challenging ISIs where cognitive demands are highest. Method Study design and data source The present study constitutes a secondary analysis of a publicly available dataset examining the influence of try-harder instructions on fake-production costs during basketball passing movements with and without head fakes (Böer et al., 2026 ). The original data were deposited on the Open Science Framework (OSF) under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits reuse, adaptation, and redistribution provided appropriate credit is given to the original authors (Zimanyi & Ayoun, 2022 ). Because the dataset is fully de-identified and publicly accessible, no additional ethics approval was required for this secondary analysis (see also Dal-Ré, 2016 ), on ethical considerations for secondary data use). The original study employed a mixed factorial design with the within-subjects factors Action (pass without head fake vs. pass with head fake), Interstimulus Interval (ISI: 0 ms, 500 ms, 1000 ms), and Instruction (Standard vs. Effort), and the between-subjects factor Group (basketball novices vs. experienced basketball players). This design builds on earlier work establishing the fake-production cost paradigm (Güldenpenning et al., 2023 ). Participants The dataset comprises 40 male participants, of whom 22 were basketball novices and 18 were experienced basketball players (Böer et al., 2026 ). Participants in the original study were instructed to initiate passing movements as fast and accurately as possible, with try-harder instructions appearing on 25% of trials to prompt additional mobilization of cognitive resources (Böer et al., 2026 ). This proportion is consistent with the effort-mobilization methodology outlined by Kelly et al. ( 2024 ), who recommended embedding try-harder cues in a minority of trials to avoid habituation while ensuring sufficient statistical power. For the present secondary analysis, only male participants were included, as the available data files (Mu_MaleOnly.sav and Tau_MaleOnly.sav) contained ex-Gaussian parameter estimates exclusively for the male subsample. No additional exclusion criteria were applied beyond those implemented by the original authors. Apparatus and procedure The original experimental setup and trial procedure are described in detail by Böer et al. ( 2026 ) and closely followed the paradigm established by Güldenpenning et al. ( 2023 ). In brief, participants stood at approximately 250 cm (about 8.2 ft) in front of a screen wall, positioned at a custom apparatus on which basketball passes with or without head fakes could be executed. On each trial, a visual stimulus indicated the target passing direction (left or right), and a separate cue indicated whether the pass should include a head fake (looking in the direction opposite to the pass) or not. The ISI between a warning signal and the imperative stimulus varied across three levels (0 ms, 500 ms, and 1000 ms), manipulating the time available for action preparation. In the Standard instruction condition, participants received the general instruction to respond as fast and accurately as possible. In the Effort instruction condition, an additional try-harder cue (the German word "Anstrengen") appeared prior to the imperative stimulus on 25% of trials, prompting participants to mobilize all their cognitive resources to perform even faster (Steinborn et al., 2017 ). Initiation times (IT), movement times, and error rates were recorded by the apparatus; the present analyses focused on IT-derived distributional parameters. Ex-Gaussian parameters Reaction time distributions were decomposed into ex-Gaussian parameters by the original authors (Böer et al., 2026 ). The ex-Gaussian distribution is the convolution of a Gaussian (normal) and an exponential distribution and is characterized by three parameters: mu (µ), sigma (σ), and tau (τ). The mu parameter reflects the mode or central tendency of the Gaussian component and is typically interpreted as an index of typical processing speed. The tau parameter reflects the mean of the exponential component (i.e., the rightward tail of the distribution) and is commonly interpreted as an index of the frequency and magnitude of attentional lapses or exceptionally slow responses. These interpretations align with previous research demonstrating that experimental manipulations affecting sustained attention tend to selectively influence tau, whereas manipulations of general processing speed primarily affect mu ((Tse et al., 2010 ; see also Vasquez et al., 2016 ). The present dataset contained a per-participant mu and tau estimate for each cell of the 2 (Action) × 3 (ISI) × 2 (Instruction) design, yielding 12 condition means per participant for each parameter. Additionally, marginal averages collapsed across Instruction (i.e., per Action × ISI) and collapsed across Action (i.e., per ISI) were included in the dataset but were not used in the primary analyses. Data preparation All data processing and statistical analyses were conducted in R (Version 4.5.2) using the following packages: haven (Version 2.5.5) for importing SPSS data files, dplyr (Version 1.2.0) and tidyr for data reshaping and transformation, lme4 (Version 1.1; Bates et al., 2015 ) for fitting linear mixed-effects models, and lmerTest (Version 3.2-0; Bates et al., 2015 ) for obtaining p-values via Satterthwaite's degrees-of-freedom approximation. Effect sizes and visualizations were computed using base R and ggplot2 (Version 4.0.2). The SPSS data files were read into R and reshaped from a wide to long format, yielding one observation per participant per Action × ISI × Instruction cell. Fake-production costs were operationalized as the within-subject difference between the Fake and Pass conditions (Fake - Pass) for each ISI × Instruction cell, separately for mu and tau. This approach follows the convention established in prior fake-production cost studies (Böer et al., 2025 ; Güldenpenning et al., 2023 ). Statistical analysis Mixed-effects models Linear mixed-effects models were used to test the effects of Instruction, ISI, and their interaction on fake-production costs. In the first set of models, Instruction (Standard vs. Effort), and ISI (0 ms vs. 500 ms vs. 1000 ms) were entered as fixed effects with participant as a random intercept. In the second set, Group (Novice vs. Experienced) was added as a between-subjects fixed effect, along with all two-way and three-way interactions with Instruction and ISI. The Effort condition and 0 ms ISI served as reference levels. Models were estimated using restricted maximum likelihood (REML), and p-values for fixed effects were obtained via Satterthwaite's method (Luke, 2017 ). This modeling approach was chosen because it accounts for the repeated-measures structure of the data while accommodating unequal group sizes (22 novices vs. 18 experienced; Shin, 2009 ). Effect sizes Within-subject standardized effect sizes (Cohen's d z ) were computed for the Fake - Pass difference in each ISI × Instruction cell, following the recommendations of Lakens ( 2013 ). Cohen's d z is defined as the mean of the paired differences divided by the standard deviation of the paired differences: $$dz=\frac{{M}_{diff}}{{SD}_{diff}}$$ This metric is appropriate for within-subject designs and provides a standardized index of the magnitude of fake-production costs in each experimental condition.​ Exploratory analyses As an exploratory individual-difference check, Pearson correlations were computed between baseline variability (tau for Pass trials, Standard instruction, 0 ms ISI) and fake-production costs in mu across all ISI × Instruction conditions. These correlations were not preregistered and should be interpreted with caution given the modest sample size and the number of tests conducted.​ Visualization Distributional properties of reaction times were visualized using violin plots with overlaid boxplots and jittered individual data points, faceted by ISI and Instruction. Fake-production costs were plotted as line graphs with standard-error bars to display the pattern of costs across ISI levels for each Instruction and Group condition.​ Results Descriptive statistics and fake-production costs Fake-production costs were computed as the difference in mean reaction time (RT) between passes with head fakes and passes without head fakes (Fake - Pass) for each participant, interstimulus interval (ISI), and instruction condition. This approach follows prior work by Güldenpenning et al. ( 2023 )d er et al. ( 2024 ), who operationalized fake-production costs as the RT disadvantage incurred when generating a deceptive action. Descriptive results for fake-production costs in the ex-Gaussian mu parameter (reflecting typical response speed; Böer et al., 2025 ; see also Steinborn et al., 2017 ) are presented in Table 1 . In general, fake-production costs in mu were largest at the 0 ms ISI and diminished at 500 ms and 1000 ms, consistent with earlier findings on the role of action preparation in eliminating fake-production costs (Güldenpenning et al., 2023 ). Table 1 Descriptive statistics for fake-production costs in mu (mean RT difference, fake - pass) by ISI and instruction condition Instruction ISI Mean Cost (ms) SD n Cohen's d z Effort 0 ms 52.02 67.50 30 0.77 Effort 500 ms 2.32 28.10 30 0.08 Effort 1000 ms −9.23 38.30 30 −0.24 Standard 0 ms 39.74 48.10 30 0.83 Standard 500 ms −6.37 12.70 30 −0.50 Standard 1000 ms 0.74 22.20 30 0.03 Note. Cohen's d z = within-subject standardized mean difference (Lakens, 2013 ). Positive values indicate higher RTs for fake than pass trials. Within-subject effect sizes (Cohen's d z ; Lakens, 2013 ) confirmed large fake-production costs at the 0 ms ISI for both the Effort condition ( d z = 0.77) and the Standard condition ( d z = 0.83), indicating that participants were substantially slower to initiate passes with head fakes when no preparation time was available. At 500 ms and 1000 ms, effect sizes were small or negligible, suggesting that fake-production costs were eliminated with sufficient preparation (Güldenpenning et al., 2023 ). Descriptive results for fake-production costs in the ex-Gaussian tau parameter (reflecting the tail of the RT distribution and often interpreted as attentional lapses or slow responses; (Böer et al., 2025 ) are presented in Table 2 .​ Table 2 Descriptive statistics for fake-production costs in tau (variability difference, fake - pass) by ISI and instruction condition Instruction ISI Mean Cost (ms) SD n Cohen's d z Effort 0 ms 0.02 50.30 30 0.00 Effort 500 ms 16.25 36.10 30 0.45 Effort 1000 ms 15.62 40.20 30 0.39 Standard 0 ms 12.29 40.70 30 0.30 Standard 500 ms 26.37 28.90 30 0.91 Standard 1000 ms 4.34 19.40 30 0.22 Note. Cohen's d z = within-subject standardized mean difference (Cousineau, 2020 ). Positive values show greater variability (more attentional lapses) for fake than pass trials. For tau , the pattern differed from mu. The largest effect size appeared for the Standard/500 ms condition ( d z = 0.91), indicating that participants exhibited more variable (lapse-prone) responses when producing head fakes under standard instructions with moderate preparation time. In the Effort condition at 0 ms , the effect was zero ( d z ≈ 0.00), suggesting that try-harder instructions may have eliminated lapse-related fake-production costs at the shortest ISI. This pattern is consistent with (Steinborn et al., 2017 ) interpretation of effort instructions primarily reducing distributional skewness (i.e., attentional lapses) rather than uniformly speeding responses. Mixed-effects models without expertise group Linear mixed-effects models were fitted using the lmer() function in R (Bates et al., 2015 ) with Satterthwaite degrees of freedom (via lmerTest ). Fake-production costs in mu and tau served as dependent variables, with Instruction (Effort [reference], Standard) and ISI (0 ms [reference], 500 ms, 1000 ms) as fixed effects and participant (VP) as a random intercept. Fake-production costs in mu Results from the linear mixed-effects model predicting fake-production costs in mu (Table 3 ) revealed a significant intercept ( b = 52.02, SE = 7.37, t = 7.06, p < .001), indicating substantial fake-production costs at the 0 ms ISI under Effort instructions. The effects of ISI were highly significant: costs decreased by approximately 49.70 ms at 500 ms ( p < .001) and 61.25 ms at 1000 ms ( p < .001), confirming that longer preparation intervals effectively eliminate fake-production costs in mean RT (Güldenpenning et al., 2023 ). The main effect of Instruction was not significant ( b = − 12.28, p = .221), and the Instruction × ISI interactions were also non-significant ( ps = .117–.800), indicating that try-harder instructions did not reliably reduce fake-production costs in mu beyond the preparation-time effect. Table 3 Fixed effects from linear mixed-effects model predicting fake-production costs in mu Predictor b SE df t p Intercept (Effort, 0 ms) 52.02 7.37 168.42 7.06 < .001 Instruction (Standard) −12.28 9.99 145.00 −1.23 .221 ISI (500 ms) −49.70 9.99 145.00 −4.97 < .001 ISI (1000 ms) −61.25 9.99 145.00 −6.13 < .001 Instruction × ISI (500 ms) 3.59 14.13 145.00 0.25 .800 Instruction × ISI (1000 ms) 22.26 14.13 145.00 1.58 .117 Note. Reference levels: Instruction = Effort; ISI = 0 ms. Random intercept variance for VP = 132.70 ( SD = 11.52); residual variance = 1497.20 ( SD = 38.69). Fake-production costs in Tau For tau (Table 4 ), the intercept was near zero and non-significant ( b = 0.02, p = .998), indicating no systematic fake-related variability costs under Effort instructions at 0 ms ISI. The ISI effects on tau-based costs were marginally significant at 500 ms ( b = 16.25, p = .063) and 1000 ms ( b = 15.62, p = .073). A trending Instruction × ISI (1000 ms) interaction ( b = − 23.58, p = .056) suggested that, at 1000 ms, try-harder instructions may reduce fake-related variability compared to standard instructions. This pattern aligns with the interpretation that effort mobilization primarily targets attentional lapses (tau) rather than mean processing speed (mu) (Tse et al., 2010 ). Table 4 Fixed effects from linear mixed-effects model predicting fake-production costs in tau Predictor b SE df t p Intercept (Effort, 0 ms) 0.02 6.80 147.57 0.00 .998 Instruction (Standard) 12.29 8.66 145.00 1.42 .158 ISI (500 ms) 16.25 8.66 145.00 1.88 .063 ISI (1000 ms) 15.62 8.66 145.00 1.80 .073 Instruction × ISI (500 ms) −2.16 12.25 145.00 −0.18 .860 Instruction × ISI (1000 ms) −23.58 12.25 145.00 −1.93 .056 Note. Reference levels: Instruction = Effort; ISI = 0 ms. Random intercept variance for VP = 262.60 ( SD = 16.20); residual variance = 1125.00 ( SD = 33.54). Mixed-effects models including expertise group To examine whether athletic expertise modulated the pattern of fake-production costs, the models were extended to include Group (Novice [ n = 22] vs. Experienced [ n = 18]) as a between-subjects factor, along with all two- and three-way interactions with Instruction and ISI. This approach is consistent with (Böer et al., 2026 ), who reported that novices benefited more from try-harder instructions than experienced players.​ Fake-production costs in mu with expertise Results (Table 5 ) confirmed strong ISI effects (ISI 500 ms: b = − 64.23, p < .001; ISI 1000 ms: b = − 77.80, p < .001). Additionally, a significant Instruction main effect emerged ( b = − 22.87, p = .037), suggesting that, for novices at 0 ms, standard instructions were associated with lower fake-costs than effort instructions. Importantly, a significant Group × ISI (1000 ms) interaction ( b = 33.89, p = .038) indicated that the reduction in fake-production costs from 0 ms to 1000 ms was less pronounced for experienced players than for novices. This may reflect the fact that experienced players already had lower baseline costs (Böer et al., 2024 ), leaving less room for improvement with extended preparation. The Instruction × ISI (1000 ms) interaction was also significant ( b = 40.69, p = .009), suggesting that the difference between Effort and Standard instructions on fake-costs was modulated by preparation time. No significant three-way interactions emerged ( p s > .14).​ Table 5 Fixed effects from linear mixed-effects model predicting fake-production costs in mu, including group Predictor b SE t p Intercept (Novice, Effort, 0 ms) 63.09 8.29 7.61 < .001 Group (Experienced) −19.95 11.80 −1.69 .092 Instruction (Standard) −22.87 10.88 −2.10 .037 ISI (500 ms) −64.23 10.88 −5.90 < .001 ISI (1000 ms) −77.80 10.88 −7.15 < .001 Group × Instruction 14.17 16.22 0.87 .384 Group × ISI (500 ms) 25.58 16.22 1.58 .116 Group × ISI (1000 ms) 33.89 16.22 2.09 .038 Instruction × ISI (500 ms) 16.66 15.39 1.08 .280 Instruction × ISI (1000 ms) 40.69 15.39 2.64 .009 Group × Instruction × ISI (500 ms) −16.23 22.94 −0.71 .480 Group × Instruction × ISI (1000 ms) −33.93 22.94 −1.48 .141 Note. Reference levels: Group = Novice; Instruction = Effort; ISI = 0 ms. Random intercept variance for VP = 235.30 ( SD = 15.34); residual variance = 1302.80 ( SD = 36.09). Fake-production costs in tau with expertise The tau model with Group (Table 6 ) revealed significant main effects of Instruction ( b = 29.95, p = .002) and ISI (500 ms: b = 29.31, p = .002; 1000 ms: b = 28.46, p = .003), indicating that fake-related variability costs were higher under Standard instructions and at longer ISIs for novices. A significant Group × Instruction interaction ( b = − 29.00, p = .042) showed that the difference in tau-based costs between Standard and Effort instructions was smaller for experienced players. The Instruction × ISI (1000 ms) interaction was significant ( b = − 48.67, p < .001), and critically, a significant three-way Group × Instruction × ISI (1000 ms) interaction ( b = 43.70, p = .031) indicated that the effort-induced reduction in tau-based fake-production costs at 1000 ms was driven more by novices than experienced players. This finding extends Böer et al. ( 2026 ), who reported that effort instructions yielded greater improvement for novices, by showing that this advantage is specifically localized in the variability (tau) component of the RT distribution at longer preparation intervals.​ Table 6 Fixed effects from linear mixed-effects model predicting fake-production costs in tau, including group Predictor b SE t p Intercept (Novice, Effort, 0 ms) −8.68 7.50 −1.16 .248 Group (Experienced) 12.11 10.41 1.16 .246 Instruction (Standard) 29.95 9.52 3.14 .002 ISI (500 ms) 29.31 9.52 3.08 .002 ISI (1000 ms) 28.46 9.52 2.99 .003 Group × Instruction −29.00 14.20 −2.04 .042 Group × ISI (500 ms) −19.67 14.20 −1.39 .168 Group × ISI (1000 ms) −17.45 14.20 −1.23 .220 Instruction × ISI (500 ms) −21.90 13.47 −1.63 .106 Instruction × ISI (1000 ms) −48.67 13.47 −3.61 < .001 Group × Instruction × ISI (500 ms) 29.13 20.08 1.45 .148 Group × Instruction × ISI (1000 ms) 43.70 20.08 2.18 .031 Note. Reference levels: Group = Novice; Instruction = Effort; ISI = 0 ms. Random intercept variance for VP = 277.00 ( SD = 16.64); residual variance = 997.80 ( SD = 31.59). Exploratory individual-difference analyses Exploratory Pearson correlations were computed between baseline variability (tau for Pass trials, Standard instruction, 0 ms ISI) and fake-production costs in mu across all ISI × Instruction conditions. These correlations were small and non-significant (| r | range: .06–.44), with the largest observed in the Effort/500 ms condition ( r = .44). A representative correlation at 0 ms, Standard ( r = .09, 95% CI [− .28, .43], p = .643) confirmed that individual differences in baseline attentional lapses did not reliably predict fake-production costs in mean RT. Given the modest sample size ( N = 30) and the number of tests conducted, these correlations should be interpreted cautiously (Grady et al., 2021 ; Riniolo & Schmidt, 2000 ) ​and are not discussed further. Discussion The present secondary analysis investigated how effort instructions and preparation time modulate fake-production costs in basketball head fakes, using ex-Gaussian parameters derived from initiation-time distributions in a previously collected dataset (Böer et al., 2026 ). Fake-production costs were operationalized as the within-subject difference between passes with and without head fakes and were examined separately for the mean (mu) and tail (tau) components of the RT distribution. The findings extend prior works on the costs of producing deceptive actions in sports and on short-term effort mobilization in speeded tasks by clarifying when and how try-harder instructions influence the different components of fake-production costs. Preparation time and fake-production costs Across analyses, preparation time emerged as the dominant determinant of fake-production costs in mean RT. Fake-production costs in mu were large and positive at the 0 ms ISI and decreased substantially at 500 ms and 1000 ms, with mixed-effects models showing reductions of approximately 50–78 ms relative to the no-preparation baseline. This pattern replicates and extends previous work showing that fake-production costs are pronounced when players must quickly coordinate incompatible movement components but can be eliminated when they have sufficient time to prepare the deceptive action (Böer et al., 2024 ; Güldenpenning et al., 2023 ). The current results therefore reinforce the interpretation that fake-production costs primarily reflect response-response incompatibility during action planning rather than increased perceptual complexity of the stimuli, which are identical across fake and non-fake passes (Güldenpenning et al., 2023 ). Effort instructions and distributional components Contrary to the initial hypothesis, effort instructions did not reliably reduce fake-production costs in mean RT beyond the effect of preparation time. Instruction main effects and Instruction × ISI interactions were small and mostly non-significant in the mu-based models, suggesting that mobilizing additional effort on a subset of trials does not change the average speed difference between fake and non-fake actions. This contrasts with Steinborn et al. ( 2017 ), who found robust mean RT improvements from try-harder instructions in simpler fore period paradigms, and suggests that the complex motor-cognitive demands of producing head fakes may limit the degree to which short-term effort can further accelerate already well-practiced actions. In line with distributional accounts of effort mobilization, more informative effects emerged for tau-based fake-production costs. Descriptively, the largest tau-based fake-production costs occurred under standard instructions at the 500 ms ISI, whereas tau-based costs were near zero at 0 ms in the Effort condition. Mixed-effects models revealed significant main effects of Instruction and ISI, as well as an Instruction × ISI interaction at 1000 ms and a three-way Group × Instruction × ISI interaction, indicating that effort instructions had a more pronounced effect on the variability component of fake-production costs, particularly for novices at longer ISIs. These findings are consistent with the notion that try-harder instructions primarily reduce attentional lapses, reflected in the tail of the RT distribution, rather than uniformly shifting the entire distribution toward faster responses (Steinborn et al., 2017 ; Unsworth et al., 2011 ). Expertise differences Including athletic expertise as a between-subjects factor revealed nuanced group differences that align with earlier work on practice and fake-production costs. For mu-based costs, experienced players tended to show smaller fake-production costs overall, and the Group × ISI interaction indicated that novices benefited more from extending the ISI to 1000 ms. This pattern mirrors evidence that extensive practice reduces fake-production costs and increases the automaticity with which head fakes can be executed (Böer et al., 2024 , 2025 ). For tau, the significant Group × Instruction and Group × Instruction × ISI interactions suggest that effort instructions were especially effective at reducing variability-related fake-production costs in novices at long ISIs, whereas experienced players showed stable variability costs across conditions. Together, these results support the idea that effort mobilization is most beneficial when complex motor actions are not yet fully automatized and still place substantial demands on cognitive capacity (Böer et al., 2026 ). Implications for theories of deceptive actions The present findings contribute to a broader theoretical understanding of deceptive actions in interactive sports. Deception research has traditionally focused on perceptual-cognitive costs for defenders, such as delayed response times and increased error rates when responding to fakes (Kunde et al., 2011 ; Suchotzki et al., 2017 ; Vendemia et al., 2005 ). By examining fake-production costs and their modulation by effort and preparation, the current study highlights that deception also imposes non-trivial costs on the deceiver, which may constrain when and how head fakes are optimally used in competitive play (Böer et al., 2024 ; Güldenpenning et al., 2023 ). From an applied perspective, the results suggest that coaches should consider not only whether head fakes disrupt opponents, but also whether players have sufficient preparation time and automatized skill to execute such fakes without incurring detrimental delays or increased variability in their own performance. Strengths, limitations, and future directions A key strength of this study is the use of an openly shared dataset with rich distributional information, allowing a more fine-grained analysis of fake-production costs than is typically feasible in primary data collections with limited resources (Golbeck et al., 2018 ). Secondary data analysis can deepen understanding of existing datasets, generate new hypotheses, and increase the return on investment for previously collected data when conducted transparently and with clear acknowledgment of the original investigators (J. Wickham, PhD, Rn, Aocn, 2019; Kelly et al., 2024 ). The present work illustrates this potential by revealing distinct effects of effort instructions on mean versus variability components of fake production costs. However, several limitations must be noted. First, because the analyses rely on pre-existing data, no additional measures (e.g., subjective effort, physiological indicators, or defensive performance) could be incorporated, which constrains the interpretation of effort effects and their ecological validity (Halperin & Emanuel, 2020 ). Second, the task was a controlled, single-player reaction-time paradigm with no interacting defender, which may underestimate the cognitive demands, and strategic considerations present in real basketball games (Court Gold et al., 2025 ). Third, the sample comprised only male participants, limiting generalizability to female players or mixed-gender contexts. Future research should address these limitations by implementing interactive attacker-defender paradigms, in virtual reality or small-sided games, to examine fake-production costs and effort mobilization under more ecological conditions (Güldenpenning et al., 2023 ; Müller et al., 2026 ). Combining head fakes with concurrent secondary tasks could further probe how high cognitive load affects the balance between fake-production costs and deceptive benefits, extending recent calls to study deception under realistic multitasking demands in sport (Güldenpenning et al., 2020 ). Additionally, longitudinal studies tracking practice-related changes in both mu and tau under varying effort instructions would help clarify how automatization and effort interact over time to shape the efficiency of deceptive actions (Shao & Lee, 2017 ; Vendemia et al., 2005 ). Conclusion In summary, this secondary analysis shows that preparation time robustly reduces fake-production costs in mean RT, that effort instructions exert their primary influence on variability-related costs, and that novices benefit more than experienced players from effort mobilization at long preparation intervals. These findings refine existing accounts of deceptive action production by demonstrating that short-term effort mobilization and motor expertise differentially shape the central and tail components of RT distributions. By leveraging open data, the study underscores the value of secondary analyses for advancing theory and practice in sport psychology and motor cognition. References Bates D, Mächler M, Bolker B, Walker S (2015) Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw 67(1). https://doi.org/10.18637/jss.v067.i01 Böer NT, Schütz C, Weigelt M, Güldenpenning I (2025) How does practice modulate fake-production costs in a basketball task? Analyses of frequency distributions and mixture effects. Psychol Res 89(2):64. https://doi.org/10.1007/s00426-025-02084-6 Böer NT, Steinborn MB, Weigelt M, Güldenpenning I (2026) Mobilizing effort in complex motor tasks: Try-harder instructions in deceptive actions. Psychol Sport Exerc 84:103083. https://doi.org/10.1016/j.psychsport.2026.103083 Böer NT, Weigelt M, Schütz C, Güldenpenning I (2024) Practice reduces the costs of producing head fakes in basketball. Psychol Res 88(2):523–534. https://doi.org/10.1007/s00426-023-01885-x Court Gold CL, Clark B, Lascu A, Gorman AD, Ball N, Maloney MA (2025) Sampling perception-action couplings from competition create representative basketball shooting tasks: A replication and extension of. Psychol Sport Exerc 78:102828. https://doi.org/10.1016/j.psychsport.2025.102828 Cousineau D (2020) Approximating the distribution of Cohen’s d_p in within-subject designs. Quant Methods Psychol 16(4):418–421. https://doi.org/10.20982/tqmp.16.4.p418 Dal-Ré R (2016) The International Committee of Medical Journal Editors trial data sharing requirement and participants’ consent. Eur J Clin Invest 46(12):971–975. https://doi.org/10.1111/eci.12694 Golbeck J, Mauriello M, Auxier B, Bhanushali KH, Bonk C, Bouzaghrane MA, Buntain C, Chanduka R, Cheakalos P, Everett JB, Falak W, Gieringer C, Graney J, Hoffman KM, Huth L, Ma Z, Jha M, Khan M, Kori V, Visnansky G (2018) Fake News vs Satire: A Dataset and Analysis. Proceedings of the 10th ACM Conference on Web Science , 17–21. https://doi.org/10.1145/3201064.3201100 Grady CL, Rieck JR, Nichol D, Rodrigue KM, Kennedy KM (2021) Influence of sample size and analytic approach on stability and interpretation of brain-behavior correlations in task‐related fMRI data. Hum Brain Mapp 42(1):204–219. https://doi.org/10.1002/hbm.25217 Güldenpenning I, Kunde W, Weigelt M (2017) How to Trick Your Opponent: A Review Article on Deceptive Actions in Interactive Sports. Front Psychol 8:917. https://doi.org/10.3389/fpsyg.2017.00917 Güldenpenning I, Kunde W, Weigelt M (2020) Cognitive load reduces interference by head fakes in basketball. Acta Psychol 203:103013. https://doi.org/10.1016/j.actpsy.2020.103013 Güldenpenning I, Weigelt M, Böer NT, Kunde W (2023) Producing deceptive actions in sports: The costs of generating head fakes in basketball. Hum Mov Sci 87:103045. https://doi.org/10.1016/j.humov.2022.103045 Halperin I, Emanuel A (2020) Rating of Perceived Effort: Methodological Concerns and Future Directions. Sports Med 50(4):679–687. https://doi.org/10.1007/s40279-019-01229-z Wickham J, Rn PD, Aocn R (2019) Secondary Analysis Research. J Adv Practitioner Oncol 10(4). https://doi.org/10.6004/jadpro.2019.10.4.7 Kelly MM, Martin-Peters T, Farber JS (2024) Secondary Data Analysis: Using existing data to answer new questions. J Pediatr Health Care 38(4):615–618. https://doi.org/10.1016/j.pedhc.2024.03.005 Kunde W, Skirde S, Weigelt M (2011) Trust my face: Cognitive factors of head fakes in sports. J Experimental Psychology: Appl 17(2):110–127. https://doi.org/10.1037/a0023756 Lakens D (2013) Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Front Psychol. 4 https://doi.org/10.3389/fpsyg.2013.00863 Luke SG (2017) Evaluating significance in linear mixed-effects models in R. Behav Res Methods 49(4):1494–1502. https://doi.org/10.3758/s13428-016-0809-y Müller D, Höner O, Van Der Veerdonk D, Van Der Meer W, Mann D (2026) Interpersonal interactions improve the representativeness of embodied decision-making behaviour in football. Psychol Sport Exerc 84:103063. https://doi.org/10.1016/j.psychsport.2026.103063 Polzien A, Güldenpenning I, Weigelt M (2020) Examining the Perceptual-Cognitive Mechanism of Deceptive Actions in Sports: Different Processes Contribute to the Head-Fake Effect in Basketball. Exp Psychol 67(6):349–363. https://doi.org/10.1027/1618-3169/a000503 Riniolo TC, Schmidt LA (2000) Searching for Reliable Relationships With Statistics Packages: An Empirical Example of the Potential Problems. J Psychol 134(2):143–151. https://doi.org/10.1080/00223980009600857 Sebanz N, Shiffrar M (2009) Detecting deception in a bluffing body: The role of expertise. Psychon Bull Rev 16(1):170–175. https://doi.org/10.3758/PBR.16.1.170 Shao R, Lee TMC (2017) Are individuals with higher psychopathic traits better learners at lying? Behavioural and neural evidence. Translational Psychiatry 7(7):e1175–e1175. https://doi.org/10.1038/tp.2017.147 Shin JH (2009) Application of Repeated-Measures Analysis of Variance and Hierarchical Linear Model in Nursing Research. Nurs Res 58(3):211–217. https://doi.org/10.1097/NNR.0b013e318199b5ae Steinborn MB, Langner R, Huestegge L (2017) Mobilizing cognition for speeded action: Try-harder instructions promote motivated readiness in the constant-foreperiod paradigm. Psychol Res 81(6):1135–1151. https://doi.org/10.1007/s00426-016-0810-1 Suchotzki K, Verschuere B, Van Bockstaele B, Ben-Shakhar G, Crombez G (2017) Lying takes time: A meta-analysis on reaction time measures of deception. Psychol Bull 143(4):428–453. https://doi.org/10.1037/bul0000087 Tse C-S, Balota DA, Yap MJ, Duchek JM, McCabe DP (2010) Effects of healthy aging and early stage dementia of the Alzheimer’s type on components of response time distributions in three attention tasks. Neuropsychology 24(3):300–315. https://doi.org/10.1037/a0018274 Unsworth N, Spillers GJ, Brewer GA, McMillan B (2011) Attention control and the antisaccade task: A response time distribution analysis. Acta Psychol 137(1):90–100. https://doi.org/10.1016/j.actpsy.2011.03.004 Vasquez BP, Binns MA, Anderson ND (2016) Staying on Task: Age-Related Changes in the Relationship Between Executive Functioning and Response Time Consistency. Journals Gerontol Ser B: Psychol Sci Social Sci 71(2):189–200. https://doi.org/10.1093/geronb/gbu140 Vendemia JMC, Buzan RF, Simon-Dack SL (2005) Reaction Time of Motor Responses in Two-Stimulus Paradigms Involving Deception and Congruity with Varying Levels of Difficulty. Behav Neurol 16(1):25–36. https://doi.org/10.1155/2005/804026 Weigelt M, Güldenpenning I, Steggemann-Weinrich Y (2020) The Head-Fake Effect in Basketball Is Based on the Processing of Head Orientation, but Not on Gaze Direction. Psychology 11(10):1493–1510. https://doi.org/10.4236/psych.2020.1110095 Weston SJ, Ritchie SJ, Rohrer JM, Przybylski AK (2019) Recommendations for Increasing the Transparency of Analysis of Preexisting Data Sets. Adv Methods Practices Psychol Sci 2(3):214–227. https://doi.org/10.1177/2515245919848684 Yamashita A, Rothlein D, Kucyi A, Valera EM, Germine L, Wilmer J, DeGutis J, Esterman M (2021) Variable rather than extreme slow reaction times distinguish brain states during sustained attention. Sci Rep 11(1):14883. https://doi.org/10.1038/s41598-021-94161-0 Zimanyi E, Ayoun EB (2022) Introduction. JCMS: J Cine Media Stud 61(3):154–159. https://doi.org/10.1353/cj.2022.0028 Additional Declarations The authors declare no competing interests. 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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-9061327","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602471888,"identity":"dfffd1bf-b6f3-4819-9b00-08fb5a217d56","order_by":0,"name":"Kofi Nyantakyi Nyantakyi Appiah","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-5770-1006","institution":"Lovely Professional University","correspondingAuthor":true,"prefix":"","firstName":"Kofi","middleName":"Nyantakyi Nyantakyi","lastName":"Appiah","suffix":""},{"id":602471889,"identity":"d66ba4f6-a4f3-428d-a1e1-70268729b8a1","order_by":1,"name":"Nathanael Adu","email":"","orcid":"https://orcid.org/0000-0002-3594-1412","institution":"Mampong Technical College of Education","correspondingAuthor":false,"prefix":"","firstName":"Nathanael","middleName":"","lastName":"Adu","suffix":""},{"id":602471890,"identity":"e605c399-dabe-411e-bdba-1880dafa44ed","order_by":2,"name":"Divyanshu Kumar Singh","email":"","orcid":"https://orcid.org/0009-0002-9388-648X","institution":"Lovely Professional University","correspondingAuthor":false,"prefix":"","firstName":"Divyanshu","middleName":"Kumar","lastName":"Singh","suffix":""},{"id":602471891,"identity":"7e7e967e-94c5-40e4-8f84-8dda093cbb77","order_by":3,"name":"Edward Edem Nartey","email":"","orcid":"https://orcid.org/0009-0004-3675-3351","institution":"University of Cape coast","correspondingAuthor":false,"prefix":"","firstName":"Edward","middleName":"Edem","lastName":"Nartey","suffix":""}],"badges":[],"createdAt":"2026-03-08 01:09:13","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9061327/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9061327/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104288184,"identity":"54fb14b7-ee23-4753-aba3-43e71d35b3ee","added_by":"auto","created_at":"2026-03-10 06:04:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62018,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMean fake‑production cost (Fake - Pass) across preparation intervals as a function of effort instructions.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image.png","url":"https://assets-eu.researchsquare.com/files/rs-9061327/v1/3f267244b31946bf88bdeb9f.png"},{"id":104288183,"identity":"68c83585-111b-4c1f-91d6-aba1cd99bb93","added_by":"auto","created_at":"2026-03-10 06:04:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85617,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMean fake‑production cost (Fake - Pass) across preparation intervals by effort instructions in novices and experienced players.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image.png","url":"https://assets-eu.researchsquare.com/files/rs-9061327/v1/e490b2a8802b8ce2eaa5db06.png"},{"id":104288182,"identity":"d60fbfd9-a633-4645-8eb3-5178dead2142","added_by":"auto","created_at":"2026-03-10 06:04:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":125524,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistributions of mean reaction times for passes with and without head fakes across preparation intervals and instruction conditions.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image.png","url":"https://assets-eu.researchsquare.com/files/rs-9061327/v1/8b72dfd00cdb6f41878700a3.png"},{"id":104405094,"identity":"a47ffe46-f51c-4757-8574-d47dbe848d92","added_by":"auto","created_at":"2026-03-11 12:21:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1275812,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9061327/v1/bbf35356-8f4f-4ebd-bd7f-77820dd9c5f7.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEffort instructions and preparation time in basketball head fakes: A secondary ex-Gaussian analysis of fake-production costs\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDeceptive actions are ubiquitous in competitive sports and serve the strategic purpose of misleading opponents about one's true action intention (G\u0026uuml;ldenpenning et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In basketball, one of the most studied deceptive actions is the head fake, in which a player looks in one direction while passing or shooting the ball in the opposite direction (Kunde et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sebanz \u0026amp; Shiffrar, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). From the observer's perspective, head fakes reliably slow down and impair the accuracy of defensive responses, a phenomenon termed the head-fake effect (Kunde et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Polzien et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Research has shown that this effect is primarily driven by the automatic processing of head orientation rather than gaze direction (Weigelt et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and it appears robust across expertise levels, although experts may show some capacity to reduce interference after encountering a fake on the preceding trial (Curby \u0026amp; Gauthier, 2014). Similar deceptive movement phenomena have been documented in other sports, such as the side-step in rugby (G\u0026uuml;ldenpenning et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), underscoring the generality of deception as a competitive strategy.\u003c/p\u003e \u003cp\u003eWhile most research has focused on how observers are affected by deceptive actions, comparatively little is known about the costs incurred by the producer of a fake. (G\u0026uuml;ldenpenning et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) introduced the concept of fake-production costs, defined as the increase in reaction time (RT) when a player must generate a pass with a head fake compared to a pass without one. Across two experiments with basketball novices, they demonstrated that producing a head fake significantly slowed initiation times, particularly when little or no preparation time was available (i.e., at short interstimulus intervals [ISIs] of 0\u0026ndash;800 ms). Critically, these costs could be eliminated when participants were given sufficient preparation time (ISIs of 1200\u0026ndash;1500 ms), suggesting that the temporal coordination of conflicting movement components (passing in one direction while orienting the head in the other) requires cognitive resources that can be allocated in advance given adequate preparation (B\u0026ouml;er et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSubsequent work has extended these findings in important ways. B\u0026ouml;er et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) showed that practice over multiple sessions reduced fake-production costs, with experienced basketball players exhibiting smaller costs than novices. A further distributional analysis by B\u0026ouml;er et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) examined how practice modulates the frequency distribution of RTs, using mixture-model and ex-Gaussian decomposition approaches to disentangle changes in typical processing speed from changes in the proportion of slow, lapse-like responses.\u003c/p\u003e \u003cp\u003eA separate line of research has investigated whether short-term effort mobilization can enhance performance in speeded tasks. Steinborn et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) demonstrated that try-harder instructions cues prompting participants to invest additional cognitive effort on a subset of trials improved RT performance in a simple fore period paradigm. They interpreted these benefits as reflecting reduced attentional lapses (indexed by the tau parameter of the ex-Gaussian distribution) rather than a uniform increase in processing speed (indexed by mu). This distinction is theoretically important because the ex-Gaussian distribution decomposes RTs into a Gaussian component (mu, reflecting modal processing time) and an exponential component (tau, reflecting the rightward tail of the distribution, often associated with attentional lapses or slow responses; Yamashita et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBuilding on this framework, B\u0026ouml;er et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2026\u003c/span\u003e) investigated whether try-harder instructions could reduce fake-production costs in basketball novices and experienced players. Participants performed basketball passes with and without head fakes in a reaction-time paradigm, with try-harder cues appearing on 25% of trials. Results showed that effort instructions improved performance, particularly for novices, whose complex motor actions were less automatized. However, the original analysis primarily examined overall initiation times and movement times, without focusing specifically on condition-wise fake-production cost indices (Fake - Pass) or on how effort instructions differentially affect the mu and tau components of these costs across ISI levels and expertise groups.\u003c/p\u003e \u003cp\u003eThe present study constitutes a secondary analysis of the publicly available dataset from B\u0026ouml;er et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2026\u003c/span\u003e), released under a CC BY 4.0 license on the Open Science Framework. Secondary data analysis is a valuable tool for psychological research, enabling new questions to be addressed from existing datasets and contributing to a cumulative, transparent science (Weston et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We aimed to extend the original findings by (a) computing fake-production cost indices (Fake - Pass) for both the mu and tau parameters of the ex-Gaussian distribution, (b) testing whether effort instructions and preparation time (ISI) interact to modulate these costs using linear mixed-effects models, and (c) examining whether athletic expertise (novice vs. experienced) differentially moderates these patterns.\u003c/p\u003e \u003cp\u003eBased on prior findings (B\u0026ouml;er et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; G\u0026uuml;ldenpenning et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), we hypothesized the following:\u003c/p\u003e \u003cp\u003eFake-production costs in mu will be largest at the 0 ms ISI and will decrease substantially at longer ISIs (500 ms and 1000 ms), reflecting the role of action preparation in resolving the temporal coordination demands of head fakes, Effort instructions will reduce fake-production costs, with a stronger effect on tau (variability/lapses) than on mu (mean speed), consistent with B\u0026ouml;er et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2026\u003c/span\u003e) interpretation that effort mobilization primarily reduces attentional lapses and Novices will show larger fake-production costs than experienced players and will benefit more from effort instructions, particularly at challenging ISIs where cognitive demands are highest.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and data source\u003c/h2\u003e \u003cp\u003eThe present study constitutes a secondary analysis of a publicly available dataset examining the influence of try-harder instructions on fake-production costs during basketball passing movements with and without head fakes (B\u0026ouml;er et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). The original data were deposited on the Open Science Framework (OSF) under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits reuse, adaptation, and redistribution provided appropriate credit is given to the original authors (Zimanyi \u0026amp; Ayoun, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Because the dataset is fully de-identified and publicly accessible, no additional ethics approval was required for this secondary analysis (see also Dal-R\u0026eacute;, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), on ethical considerations for secondary data use).\u003c/p\u003e \u003cp\u003eThe original study employed a mixed factorial design with the within-subjects factors Action (pass without head fake vs. pass with head fake), Interstimulus Interval (ISI: 0 ms, 500 ms, 1000 ms), and Instruction (Standard vs. Effort), and the between-subjects factor Group (basketball novices vs. experienced basketball players). This design builds on earlier work establishing the fake-production cost paradigm (G\u0026uuml;ldenpenning et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThe dataset comprises 40 male participants, of whom 22 were basketball novices and 18 were experienced basketball players (B\u0026ouml;er et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Participants in the original study were instructed to initiate passing movements as fast and accurately as possible, with try-harder instructions appearing on 25% of trials to prompt additional mobilization of cognitive resources (B\u0026ouml;er et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). This proportion is consistent with the effort-mobilization methodology outlined by Kelly et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who recommended embedding try-harder cues in a minority of trials to avoid habituation while ensuring sufficient statistical power.\u003c/p\u003e \u003cp\u003eFor the present secondary analysis, only male participants were included, as the available data files (Mu_MaleOnly.sav and Tau_MaleOnly.sav) contained ex-Gaussian parameter estimates exclusively for the male subsample. No additional exclusion criteria were applied beyond those implemented by the original authors.\u003c/p\u003e\n\u003ch3\u003eApparatus and procedure\u003c/h3\u003e\n\u003cp\u003eThe original experimental setup and trial procedure are described in detail by B\u0026ouml;er et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2026\u003c/span\u003e) and closely followed the paradigm established by G\u0026uuml;ldenpenning et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In brief, participants stood at approximately 250 cm (about 8.2 ft) in front of a screen wall, positioned at a custom apparatus on which basketball passes with or without head fakes could be executed. On each trial, a visual stimulus indicated the target passing direction (left or right), and a separate cue indicated whether the pass should include a head fake (looking in the direction opposite to the pass) or not.\u003c/p\u003e \u003cp\u003eThe ISI between a warning signal and the imperative stimulus varied across three levels (0 ms, 500 ms, and 1000 ms), manipulating the time available for action preparation. In the Standard instruction condition, participants received the general instruction to respond as fast and accurately as possible. In the Effort instruction condition, an additional try-harder cue (the German word \"Anstrengen\") appeared prior to the imperative stimulus on 25% of trials, prompting participants to mobilize all their cognitive resources to perform even faster (Steinborn et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Initiation times (IT), movement times, and error rates were recorded by the apparatus; the present analyses focused on IT-derived distributional parameters.\u003c/p\u003e\n\u003ch3\u003eEx-Gaussian parameters\u003c/h3\u003e\n\u003cp\u003eReaction time distributions were decomposed into ex-Gaussian parameters by the original authors (B\u0026ouml;er et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). The ex-Gaussian distribution is the convolution of a Gaussian (normal) and an exponential distribution and is characterized by three parameters: mu (\u0026micro;), sigma (σ), and tau (τ). The mu parameter reflects the mode or central tendency of the Gaussian component and is typically interpreted as an index of typical processing speed. The tau parameter reflects the mean of the exponential component (i.e., the rightward tail of the distribution) and is commonly interpreted as an index of the frequency and magnitude of attentional lapses or exceptionally slow responses. These interpretations align with previous research demonstrating that experimental manipulations affecting sustained attention tend to selectively influence tau, whereas manipulations of general processing speed primarily affect mu ((Tse et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; see also Vasquez et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present dataset contained a per-participant mu and tau estimate for each cell of the 2 (Action) \u0026times; 3 (ISI) \u0026times; 2 (Instruction) design, yielding 12 condition means per participant for each parameter. Additionally, marginal averages collapsed across Instruction (i.e., per Action \u0026times; ISI) and collapsed across Action (i.e., per ISI) were included in the dataset but were not used in the primary analyses.\u003c/p\u003e\n\u003ch3\u003eData preparation\u003c/h3\u003e\n\u003cp\u003eAll data processing and statistical analyses were conducted in R (Version 4.5.2) using the following packages: haven (Version 2.5.5) for importing SPSS data files, dplyr (Version 1.2.0) and tidyr for data reshaping and transformation, lme4 (Version 1.1; Bates et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for fitting linear mixed-effects models, and lmerTest (Version 3.2-0; Bates et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for obtaining p-values via Satterthwaite's degrees-of-freedom approximation. Effect sizes and visualizations were computed using base R and ggplot2 (Version 4.0.2).\u003c/p\u003e \u003cp\u003eThe SPSS data files were read into R and reshaped from a wide to long format, yielding one observation per participant per Action \u0026times; ISI \u0026times; Instruction cell. Fake-production costs were operationalized as the within-subject difference between the Fake and Pass conditions (Fake - Pass) for each ISI \u0026times; Instruction cell, separately for mu and tau. This approach follows the convention established in prior fake-production cost studies (B\u0026ouml;er et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; G\u0026uuml;ldenpenning et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eMixed-effects models\u003c/h2\u003e \u003cp\u003eLinear mixed-effects models were used to test the effects of Instruction, ISI, and their interaction on fake-production costs. In the first set of models, Instruction (Standard vs. Effort), and ISI (0 ms vs. 500 ms vs. 1000 ms) were entered as fixed effects with participant as a random intercept. In the second set, Group (Novice vs. Experienced) was added as a between-subjects fixed effect, along with all two-way and three-way interactions with Instruction and ISI. The Effort condition and 0 ms ISI served as reference levels. Models were estimated using restricted maximum likelihood (REML), and p-values for fixed effects were obtained via Satterthwaite's method (Luke, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This modeling approach was chosen because it accounts for the repeated-measures structure of the data while accommodating unequal group sizes (22 novices vs. 18 experienced; Shin, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eEffect sizes\u003c/h3\u003e\n\u003cp\u003eWithin-subject standardized effect sizes (Cohen's d\u003csub\u003ez\u003c/sub\u003e) were computed for the Fake - Pass difference in each ISI \u0026times; Instruction cell, following the recommendations of Lakens (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Cohen's d\u003csub\u003ez\u003c/sub\u003e is defined as the mean of the paired differences divided by the standard deviation of the paired differences:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$dz=\\frac{{M}_{diff}}{{SD}_{diff}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis metric is appropriate for within-subject designs and provides a standardized index of the magnitude of fake-production costs in each experimental condition.​\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eExploratory analyses\u003c/h2\u003e \u003cp\u003eAs an exploratory individual-difference check, Pearson correlations were computed between baseline variability (tau for Pass trials, Standard instruction, 0 ms ISI) and fake-production costs in mu across all ISI \u0026times; Instruction conditions. These correlations were not preregistered and should be interpreted with caution given the modest sample size and the number of tests conducted.​\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eVisualization\u003c/h2\u003e \u003cp\u003eDistributional properties of reaction times were visualized using violin plots with overlaid boxplots and jittered individual data points, faceted by ISI and Instruction. Fake-production costs were plotted as line graphs with standard-error bars to display the pattern of costs across ISI levels for each Instruction and Group condition.​\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics and fake-production costs\u003c/h2\u003e \u003cp\u003eFake-production costs were computed as the difference in mean reaction time (RT) between passes with head fakes and passes without head fakes (Fake - Pass) for each participant, interstimulus interval (ISI), and instruction condition. This approach follows prior work by G\u0026uuml;ldenpenning et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)d er et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who operationalized fake-production costs as the RT disadvantage incurred when generating a deceptive action.\u003c/p\u003e \u003cp\u003eDescriptive results for fake-production costs in the ex-Gaussian mu parameter (reflecting typical response speed; B\u0026ouml;er et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; see also Steinborn et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In general, fake-production costs in mu were largest at the 0 ms ISI and diminished at 500 ms and 1000 ms, consistent with earlier findings on the role of action preparation in eliminating fake-production costs (G\u0026uuml;ldenpenning et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\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\u003e\u003cem\u003eDescriptive statistics for fake-production costs in mu (mean RT difference, fake - pass) by ISI and instruction condition\u003c/em\u003e\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstruction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eISI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean Cost (ms)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCohen's \u003cem\u003ed\u003c/em\u003ez\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;9.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e Cohen's \u003cem\u003ed\u003c/em\u003e\u003csub\u003ez\u003c/sub\u003e = within-subject standardized mean difference (Lakens, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Positive values indicate higher RTs for fake than pass trials.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWithin-subject effect sizes (Cohen's \u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003ez\u003c/em\u003e\u003c/sub\u003e; Lakens, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) confirmed large fake-production costs at the 0 ms ISI for both the Effort condition (\u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003ez\u003c/em\u003e\u003c/sub\u003e = 0.77) and the Standard condition (\u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003ez\u003c/em\u003e\u003c/sub\u003e = 0.83), indicating that participants were substantially slower to initiate passes with head fakes when no preparation time was available. At 500 ms and 1000 ms, effect sizes were small or negligible, suggesting that fake-production costs were eliminated with sufficient preparation (G\u0026uuml;ldenpenning et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDescriptive results for fake-production costs in the ex-Gaussian tau parameter (reflecting the tail of the RT distribution and often interpreted as attentional lapses or slow responses; (B\u0026ouml;er et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) are presented 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\u003e\u003cem\u003eDescriptive statistics for fake-production costs in tau (variability difference, fake - pass) by ISI and instruction condition\u003c/em\u003e\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstruction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eISI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean Cost (ms)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCohen's \u003cem\u003ed\u003c/em\u003e\u003csub\u003ez\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e Cohen's \u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003ez\u003c/em\u003e\u003c/sub\u003e = within-subject standardized mean difference (Cousineau, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Positive values show greater variability (more attentional lapses) for fake than pass trials.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor \u003cem\u003etau\u003c/em\u003e, the pattern differed from mu. The largest effect size appeared for the Standard/500 ms condition (\u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003ez\u003c/em\u003e\u003c/sub\u003e = 0.91), indicating that participants exhibited more variable (lapse-prone) responses when producing head fakes under standard instructions with moderate preparation time. In the Effort condition at 0 \u003cem\u003ems\u003c/em\u003e, the effect was zero (\u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003ez\u003c/em\u003e\u003c/sub\u003e \u0026asymp; 0.00), suggesting that try-harder instructions may have eliminated lapse-related fake-production costs at the shortest ISI. This pattern is consistent with (Steinborn et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) interpretation of effort instructions primarily reducing distributional skewness (i.e., attentional lapses) rather than uniformly speeding responses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMixed-effects models without expertise group\u003c/h2\u003e \u003cp\u003eLinear mixed-effects models were fitted using the \u003cem\u003elmer()\u003c/em\u003e function in R (Bates et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) with Satterthwaite degrees of freedom (via \u003cem\u003elmerTest\u003c/em\u003e). Fake-production costs in mu and tau served as dependent variables, with Instruction (Effort [reference], Standard) and ISI (0 ms [reference], 500 ms, 1000 ms) as fixed effects and participant (VP) as a random intercept.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFake-production costs in mu\u003c/h2\u003e \u003cp\u003eResults from the linear mixed-effects model predicting fake-production costs in mu (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed a significant intercept (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;52.02, SE\u0026thinsp;=\u0026thinsp;7.37, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.06, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), indicating substantial fake-production costs at the 0 \u003cem\u003ems\u003c/em\u003e ISI under Effort instructions. The effects of ISI were highly significant: costs decreased by approximately 49.70 \u003cem\u003ems\u003c/em\u003e at 500 \u003cem\u003ems\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and 61.25 \u003cem\u003ems\u003c/em\u003e at 1000 \u003cem\u003ems\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001), confirming that longer preparation intervals effectively eliminate fake-production costs in mean RT (G\u0026uuml;ldenpenning et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The main effect of Instruction was not significant (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;12.28, \u003cem\u003ep\u003c/em\u003e = .221), and the Instruction \u0026times; ISI interactions were also non-significant (\u003cem\u003eps\u003c/em\u003e = .117\u0026ndash;.800), indicating that try-harder instructions did not reliably reduce fake-production costs in mu beyond the preparation-time effect.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eFixed effects from linear mixed-effects model predicting fake-production costs in mu\u003c/em\u003e\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=\".\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept (Effort, 0 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e168.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstruction (Standard)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;12.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISI (500 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;49.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISI (1000 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;61.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;6.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstruction \u0026times; ISI (500 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstruction \u0026times; ISI (1000 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e Reference levels: Instruction\u0026thinsp;=\u0026thinsp;Effort; ISI\u0026thinsp;=\u0026thinsp;0 ms. Random intercept variance for VP\u0026thinsp;=\u0026thinsp;132.70 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11.52); residual variance\u0026thinsp;=\u0026thinsp;1497.20 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;38.69).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFake-production costs in Tau\u003c/h2\u003e \u003cp\u003eFor tau (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the intercept was near zero and non-significant (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, \u003cem\u003ep\u003c/em\u003e = .998), indicating no systematic fake-related variability costs under Effort instructions at 0 ms ISI. The ISI effects on tau-based costs were marginally significant at 500 ms (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16.25, \u003cem\u003ep\u003c/em\u003e = .063) and 1000 ms (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15.62, \u003cem\u003ep\u003c/em\u003e = .073). A trending Instruction \u0026times; ISI (1000 ms) interaction (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;23.58, \u003cem\u003ep\u003c/em\u003e = .056) suggested that, at 1000 ms, try-harder instructions may reduce fake-related variability compared to standard instructions. This pattern aligns with the interpretation that effort mobilization primarily targets attentional lapses (tau) rather than mean processing speed (mu) (Tse et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eFixed effects from linear mixed-effects model predicting fake-production costs in tau\u003c/em\u003e\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=\".\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept (Effort, 0 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e147.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstruction (Standard)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISI (500 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISI (1000 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstruction \u0026times; ISI (500 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstruction \u0026times; ISI (1000 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;23.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e Reference levels: Instruction\u0026thinsp;=\u0026thinsp;Effort; ISI\u0026thinsp;=\u0026thinsp;0 ms. Random intercept variance for VP\u0026thinsp;=\u0026thinsp;262.60 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16.20); residual variance\u0026thinsp;=\u0026thinsp;1125.00 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;33.54).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMixed-effects models including expertise group\u003c/h2\u003e \u003cp\u003eTo examine whether athletic expertise modulated the pattern of fake-production costs, the models were extended to include Group (Novice [\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22] vs. Experienced [\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18]) as a between-subjects factor, along with all two- and three-way interactions with Instruction and ISI. This approach is consistent with (B\u0026ouml;er et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2026\u003c/span\u003e), who reported that novices benefited more from try-harder instructions than experienced players.​\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFake-production costs in mu with expertise\u003c/h2\u003e \u003cp\u003eResults (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) confirmed strong ISI effects (ISI 500 ms: \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;64.23, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; ISI 1000 ms: \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;77.80, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Additionally, a significant Instruction main effect emerged (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;22.87, \u003cem\u003ep\u003c/em\u003e = .037), suggesting that, for novices at 0 ms, standard instructions were associated with lower fake-costs than effort instructions. Importantly, a significant Group \u0026times; ISI (1000 ms) interaction (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;33.89, \u003cem\u003ep\u003c/em\u003e = .038) indicated that the reduction in fake-production costs from 0 ms to 1000 ms was less pronounced for experienced players than for novices. This may reflect the fact that experienced players already had lower baseline costs (B\u0026ouml;er et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), leaving less room for improvement with extended preparation. The Instruction \u0026times; ISI (1000 ms) interaction was also significant (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;40.69, \u003cem\u003ep\u003c/em\u003e = .009), suggesting that the difference between Effort and Standard instructions on fake-costs was modulated by preparation time. No significant three-way interactions emerged (\u003cem\u003ep\u003c/em\u003es \u0026gt; .14).​\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eFixed effects from linear mixed-effects model predicting fake-production costs in mu, including group\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept (Novice, Effort, 0 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup (Experienced)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;19.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstruction (Standard)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;22.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISI (500 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;64.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;5.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISI (1000 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;77.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;7.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup \u0026times; Instruction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup \u0026times; ISI (500 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup \u0026times; ISI (1000 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstruction \u0026times; ISI (500 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstruction \u0026times; ISI (1000 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup \u0026times; Instruction \u0026times; ISI (500 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;16.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.480\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup \u0026times; Instruction \u0026times; ISI (1000 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;33.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e Reference levels: Group\u0026thinsp;=\u0026thinsp;Novice; Instruction\u0026thinsp;=\u0026thinsp;Effort; ISI\u0026thinsp;=\u0026thinsp;0 ms. Random intercept variance for VP\u0026thinsp;=\u0026thinsp;235.30 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15.34); residual variance\u0026thinsp;=\u0026thinsp;1302.80 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;36.09).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eFake-production costs in tau with expertise\u003c/h2\u003e \u003cp\u003eThe tau model with Group (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) revealed significant main effects of Instruction (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;29.95, \u003cem\u003ep\u003c/em\u003e = .002) and ISI (500 ms: \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;29.31, \u003cem\u003ep\u003c/em\u003e = .002; 1000 ms: \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;28.46, \u003cem\u003ep\u003c/em\u003e = .003), indicating that fake-related variability costs were higher under Standard instructions and at longer ISIs for novices. A significant Group \u0026times; Instruction interaction (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;29.00, \u003cem\u003ep\u003c/em\u003e = .042) showed that the difference in tau-based costs between Standard and Effort instructions was smaller for experienced players. The Instruction \u0026times; ISI (1000 ms) interaction was significant (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;48.67, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and critically, a significant three-way Group \u0026times; Instruction \u0026times; ISI (1000 ms) interaction (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;43.70, \u003cem\u003ep\u003c/em\u003e = .031) indicated that the effort-induced reduction in tau-based fake-production costs at 1000 ms was driven more by novices than experienced players. This finding extends B\u0026ouml;er et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2026\u003c/span\u003e), who reported that effort instructions yielded greater improvement for novices, by showing that this advantage is specifically localized in the variability (tau) component of the RT distribution at longer preparation intervals.​\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eFixed effects from linear mixed-effects model predicting fake-production costs in tau, including group\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept (Novice, Effort, 0 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;8.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup (Experienced)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstruction (Standard)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISI (500 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISI (1000 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup \u0026times; Instruction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;29.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup \u0026times; ISI (500 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;19.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup \u0026times; ISI (1000 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;17.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstruction \u0026times; ISI (500 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;21.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstruction \u0026times; ISI (1000 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;48.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup \u0026times; Instruction \u0026times; ISI (500 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup \u0026times; Instruction \u0026times; ISI (1000 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e Reference levels: Group\u0026thinsp;=\u0026thinsp;Novice; Instruction\u0026thinsp;=\u0026thinsp;Effort; ISI\u0026thinsp;=\u0026thinsp;0 ms. Random intercept variance for VP\u0026thinsp;=\u0026thinsp;277.00 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16.64); residual variance\u0026thinsp;=\u0026thinsp;997.80 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31.59).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eExploratory individual-difference analyses\u003c/h2\u003e \u003cp\u003eExploratory Pearson correlations were computed between baseline variability (tau for Pass trials, Standard instruction, 0 ms ISI) and fake-production costs in mu across all ISI \u0026times; Instruction conditions. These correlations were small and non-significant (|\u003cem\u003er\u003c/em\u003e| range: .06\u0026ndash;.44), with the largest observed in the Effort/500 ms condition (\u003cem\u003er\u003c/em\u003e = .44). A representative correlation at 0 ms, Standard (\u003cem\u003er\u003c/em\u003e = .09, 95% CI [\u0026minus;\u0026thinsp;.28, .43], \u003cem\u003ep\u003c/em\u003e = .643) confirmed that individual differences in baseline attentional lapses did not reliably predict fake-production costs in mean RT. Given the modest sample size (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30) and the number of tests conducted, these correlations should be interpreted cautiously (Grady et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Riniolo \u0026amp; Schmidt, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) ​and are not discussed further.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present secondary analysis investigated how effort instructions and preparation time modulate fake-production costs in basketball head fakes, using ex-Gaussian parameters derived from initiation-time distributions in a previously collected dataset (B\u0026ouml;er et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Fake-production costs were operationalized as the within-subject difference between passes with and without head fakes and were examined separately for the mean (mu) and tail (tau) components of the RT distribution. The findings extend prior works on the costs of producing deceptive actions in sports and on short-term effort mobilization in speeded tasks by clarifying when and how try-harder instructions influence the different components of fake-production costs.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003ePreparation time and fake-production costs\u003c/h2\u003e \u003cp\u003eAcross analyses, preparation time emerged as the dominant determinant of fake-production costs in mean RT. Fake-production costs in mu were large and positive at the 0 ms ISI and decreased substantially at 500 ms and 1000 ms, with mixed-effects models showing reductions of approximately 50\u0026ndash;78 ms relative to the no-preparation baseline. This pattern replicates and extends previous work showing that fake-production costs are pronounced when players must quickly coordinate incompatible movement components but can be eliminated when they have sufficient time to prepare the deceptive action (B\u0026ouml;er et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; G\u0026uuml;ldenpenning et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The current results therefore reinforce the interpretation that fake-production costs primarily reflect response-response incompatibility during action planning rather than increased perceptual complexity of the stimuli, which are identical across fake and non-fake passes (G\u0026uuml;ldenpenning et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eEffort instructions and distributional components\u003c/h2\u003e \u003cp\u003eContrary to the initial hypothesis, effort instructions did not reliably reduce fake-production costs in mean RT beyond the effect of preparation time. Instruction main effects and Instruction \u0026times; ISI interactions were small and mostly non-significant in the mu-based models, suggesting that mobilizing additional effort on a subset of trials does not change the average speed difference between fake and non-fake actions. This contrasts with Steinborn et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who found robust mean RT improvements from try-harder instructions in simpler fore period paradigms, and suggests that the complex motor-cognitive demands of producing head fakes may limit the degree to which short-term effort can further accelerate already well-practiced actions.\u003c/p\u003e \u003cp\u003eIn line with distributional accounts of effort mobilization, more informative effects emerged for tau-based fake-production costs. Descriptively, the largest tau-based fake-production costs occurred under standard instructions at the 500 ms ISI, whereas tau-based costs were near zero at 0 ms in the Effort condition. Mixed-effects models revealed significant main effects of Instruction and ISI, as well as an Instruction \u0026times; ISI interaction at 1000 ms and a three-way Group \u0026times; Instruction \u0026times; ISI interaction, indicating that effort instructions had a more pronounced effect on the variability component of fake-production costs, particularly for novices at longer ISIs. These findings are consistent with the notion that try-harder instructions primarily reduce attentional lapses, reflected in the tail of the RT distribution, rather than uniformly shifting the entire distribution toward faster responses (Steinborn et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Unsworth et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eExpertise differences\u003c/h2\u003e \u003cp\u003eIncluding athletic expertise as a between-subjects factor revealed nuanced group differences that align with earlier work on practice and fake-production costs. For mu-based costs, experienced players tended to show smaller fake-production costs overall, and the Group \u0026times; ISI interaction indicated that novices benefited more from extending the ISI to 1000 ms. This pattern mirrors evidence that extensive practice reduces fake-production costs and increases the automaticity with which head fakes can be executed (B\u0026ouml;er et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For tau, the significant Group \u0026times; Instruction and Group \u0026times; Instruction \u0026times; ISI interactions suggest that effort instructions were especially effective at reducing variability-related fake-production costs in novices at long ISIs, whereas experienced players showed stable variability costs across conditions. Together, these results support the idea that effort mobilization is most beneficial when complex motor actions are not yet fully automatized and still place substantial demands on cognitive capacity (B\u0026ouml;er et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eImplications for theories of deceptive actions\u003c/h2\u003e \u003cp\u003eThe present findings contribute to a broader theoretical understanding of deceptive actions in interactive sports. Deception research has traditionally focused on perceptual-cognitive costs for defenders, such as delayed response times and increased error rates when responding to fakes (Kunde et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Suchotzki et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vendemia et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). By examining fake-production costs and their modulation by effort and preparation, the current study highlights that deception also imposes non-trivial costs on the deceiver, which may constrain when and how head fakes are optimally used in competitive play (B\u0026ouml;er et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; G\u0026uuml;ldenpenning et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). From an applied perspective, the results suggest that coaches should consider not only whether head fakes disrupt opponents, but also whether players have sufficient preparation time and automatized skill to execute such fakes without incurring detrimental delays or increased variability in their own performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eStrengths, limitations, and future directions\u003c/h2\u003e \u003cp\u003eA key strength of this study is the use of an openly shared dataset with rich distributional information, allowing a more fine-grained analysis of fake-production costs than is typically feasible in primary data collections with limited resources (Golbeck et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Secondary data analysis can deepen understanding of existing datasets, generate new hypotheses, and increase the return on investment for previously collected data when conducted transparently and with clear acknowledgment of the original investigators (J. Wickham, PhD, Rn, Aocn, 2019; Kelly et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The present work illustrates this potential by revealing distinct effects of effort instructions on mean versus variability components of fake production costs.\u003c/p\u003e \u003cp\u003eHowever, several limitations must be noted. First, because the analyses rely on pre-existing data, no additional measures (e.g., subjective effort, physiological indicators, or defensive performance) could be incorporated, which constrains the interpretation of effort effects and their ecological validity (Halperin \u0026amp; Emanuel, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Second, the task was a controlled, single-player reaction-time paradigm with no interacting defender, which may underestimate the cognitive demands, and strategic considerations present in real basketball games (Court Gold et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Third, the sample comprised only male participants, limiting generalizability to female players or mixed-gender contexts.\u003c/p\u003e \u003cp\u003eFuture research should address these limitations by implementing interactive attacker-defender paradigms, in virtual reality or small-sided games, to examine fake-production costs and effort mobilization under more ecological conditions (G\u0026uuml;ldenpenning et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; M\u0026uuml;ller et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Combining head fakes with concurrent secondary tasks could further probe how high cognitive load affects the balance between fake-production costs and deceptive benefits, extending recent calls to study deception under realistic multitasking demands in sport (G\u0026uuml;ldenpenning et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, longitudinal studies tracking practice-related changes in both mu and tau under varying effort instructions would help clarify how automatization and effort interact over time to shape the efficiency of deceptive actions (Shao \u0026amp; Lee, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vendemia et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this secondary analysis shows that preparation time robustly reduces fake-production costs in mean RT, that effort instructions exert their primary influence on variability-related costs, and that novices benefit more than experienced players from effort mobilization at long preparation intervals. These findings refine existing accounts of deceptive action production by demonstrating that short-term effort mobilization and motor expertise differentially shape the central and tail components of RT distributions. By leveraging open data, the study underscores the value of secondary analyses for advancing theory and practice in sport psychology and motor cognition.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBates D, M\u0026auml;chler M, Bolker B, Walker S (2015) Fitting Linear Mixed-Effects Models Using lme4. 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JCMS: J Cine Media Stud 61(3):154\u0026ndash;159. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1353/cj.2022.0028\u003c/span\u003e\u003cspan address=\"10.1353/cj.2022.0028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Lovely Professional University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Head fake, deception, effort instructions, ex-Gaussian, secondary data analysis","lastPublishedDoi":"10.21203/rs.3.rs-9061327/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9061327/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis secondary analysis examined how try-harder instructions and preparation time influence fake-production costs in basketball head fakes. Using an openly available dataset with 40 male players (22 novices, 18 experienced), ex-Gaussian parameters (mu, tau) of initiation-time distributions were analyzed for passes with and without head fakes across three interstimulus intervals (0, 500, 1000 ms) and two instruction conditions (Standard, Effort). Fake-production costs (Fake - Pass) were computed per condition and analyzed with linear mixed-effects models and within-subject effect sizes. Results showed large fake-production costs in mean RT at 0 ms that were reduced or eliminated at longer ISIs, regardless of instruction. Effort instructions had a limited impact on mean costs but selectively reduced tau-based variability costs, particularly for novices at long ISIs. These findings suggest that preparation time primarily mitigates coordination demands, whereas effort mobilization decreases attentional lapses, especially when deceptive actions are not yet fully automatized.\u003c/p\u003e","manuscriptTitle":"Effort instructions and preparation time in basketball head fakes: A secondary ex-Gaussian analysis of fake-production costs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 06:04:50","doi":"10.21203/rs.3.rs-9061327/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d991011-7aa9-4057-a84e-5fbb958789ff","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64112974,"name":"Applied Statistics"},{"id":64112975,"name":"Psychology"}],"tags":[],"updatedAt":"2026-03-10T06:04:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 06:04:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9061327","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9061327","identity":"rs-9061327","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}