Dopamine, Motor Imagery, and Proprioception: A Neurochemical Probe into the Perception-Imagery Debate

Dopamine, Motor Imagery, and Proprioception: A Neurochemical Probe into the Perception-Imagery Debate | 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 Dopamine, Motor Imagery, and Proprioception: A Neurochemical Probe into the Perception-Imagery Debate Parisa Hejazi Dinan, Moslem Bahmani, Usef Garmanjani, Gholam hossein Nazemzadegan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6828758/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Dec, 2025 Read the published version in Psychological Research → Version 1 posted 4 You are reading this latest preprint version Abstract The relationship between mental imagery and perception is a key debate in cognitive neuroscience. This study experimentally investigated how acute dopaminergic modulation influences motor imagery and proprioceptive perception. We conducted a double-blind, placebo-controlled, pre-post study with 42 healthy young adults, using branched-chain amino acids (BCAAs) to indirectly manipulate dopaminergic tone. We assessed serum prolactin levels, motor imagery ability (MIQ-3), and passive limb positioning performance. Results confirmed successful dopaminergic modulation: the BCAA group's prolactin levels remained stable, while controls showed a typical diurnal decrease. This modulation selectively impaired internal visual motor imagery, suppressing the natural improvement observed in the placebo group. For proprioception, overall mean Constant Error (CE) and Absolute Error (AE) were unaffected; however, motor consistency (Variable Error, VE) significantly worsened in the BCAA group and improved in controls. Exploratory analyses also revealed complex, angle-dependent changes in CE and AE. This nuanced dissociation suggests dopamine differentially affects motor simulation and perception. Findings challenge strong perceptualist theories, supporting partial functional dissociation and highlighting the sensitivity of specific motor imagery and angle-specific proprioceptive processing to neurochemical fluctuations. Dopaminergic Imagery Neuromodulation Perception Brain dysfunction Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The fundamental question of whether mental imagery is merely a faint echo of perception or emerges from fundamentally different processes has long fueled debates in cognitive neuroscience (Cavedon-Taylor, 2021b; Nanay, 2021; Pearson, 2019; Pearson, Naselaris, Holmes, & Kosslyn, 2015). While perceptualist accounts, which suggest shared underlying mechanisms, have traditionally held a slightly more dominant position, novel clinical dissociations strongly challenge this view. For instance, individuals retaining vivid mental imagery despite cortical blindness, or conversely, experiencing aphantasia—the striking inability to form mental images despite intact perception (de Gelder, Tamietto, Pegna, & Van den Stock, 2015; Lambert, Sampaio, Mauss, & Scheiber, 2004; Liu & Bartolomeo, 2023)—intensify interest in whether imagery and perception truly rely on the same neural architecture or merely converge due to task demands (Cavedon-Taylor, 2021b; Pearson, 2019). Perceptualist accounts posit that imagery and perception exist on a continuum within common neural systems, arguing that imagery arises from reactivating perceptual representations in sensory cortices with weaker bottom-up input (Nanay, 2021; Pearson, 2019). In contrast, non-perceptualist perspectives contend that mental imagery might be grounded in symbolic or conceptual processes functionally distinct from perception (Cavedon-Taylor, 2021a, 2021b). While this debate has seen substantial evolution and extensive study in the visual domain, it has received comparatively less attention in the motor system, particularly regarding its neurochemical basis in healthy populations and its direct comparison with proprioceptive perception. Motor imagery, unlike its visual counterpart, inherently involves multiple modalities—kinesthetic, proprioceptive, and visual—all woven into the internal simulation of movement. These simulations recruit neural circuits that partially overlap with action execution, including premotor, parietal, and cerebellar regions (Hardwick, Caspers, Eickhoff, & Swinnen, 2018; Hétu et al., 2013). Neuroimaging and lesion studies further link motor imagery and proprioception to shared internal models, often implicating the cerebellum and somatosensory cortices in the integration of predictive and sensory information (Kilteni, Andersson, Houborg, & Ehrsson, 2018; Synofzik, Lindner, & Thier, 2008). According to Motor Simulation Theory (MST), a perceptualist account, motor imagery relies on the neural mechanisms normally involved in actual movement execution. From this perspective, motor imagery is expected to be influenced by the same neuromodulatory factors that govern motor control (Jeannerod, 2001). Yet not all accounts agree. The Motor-Cognition model, for example, casts motor imagery as an abstract, cognitive-level planning process that can proceed independently of online sensorimotor feedback (Glover & Baran, 2017). This view resonates with non-perceptualist frameworks, suggesting that (motor) imagery and (motor) perception may rely on dissociable mechanisms. In line with this argument, clinical patterns underscore dopamine’s role in motor imagery. In Parkinson’s disease, dopamine loss is linked to impaired motor imagery despite relatively intact movement early on (Helmich, de Lange, Bloem, & Toni, 2007; Heremans et al., 2011). Schizophrenia, which involves dopaminergic dysregulation, also shows disrupted motor imagery—even as visual hallucinations and vivid visual imagery may persist (Chen et al., 2015; Lallart, Jouvent, Herrmann, Beauchet, & Allali, 2012; Michely et al., 2015; Shine et al., 2015). These contrasting profiles suggest that dopamine is crucial for motor imagery and raise the question of whether it influences motor imagery and proprioception together, as perceptualist theories propose, or if these functions can be experimentally dissociated. These clinical insights point to the critical role of the dopaminergic system, which is central to motor control by modulating forward models—predictive mechanisms that estimate the sensory consequences of action based on efference copies (Lallart et al., 2012). Specifically, we targeted dopaminergic modulation within the nigrostriatal pathway, given its established role in motor control and its degeneration in Parkinson's disease (Dauer & Przedborski, 2003), which we highlighted earlier. These internal models are not only crucial for movement execution but are also believed to support motor imagery (Jeannerod, 2001; Kilner & Friston, 2010). From a perceptualist standpoint, motor imagery is an internal emulation of movement that recruits these same predictive mechanisms (Grush, 2004). Thus, disrupting dopaminergic tone should compromise both imagery and the movement execution (K. J. Friston et al., 2012). One method for reducing central dopamine availability, as indexed by a rise in peripheral prolactin levels, involves branched-chain amino acid (BCAA) supplementation. While an indirect method, this approach has been validated in previous research as a means to achieve modest, physiologically grounded changes in dopaminergic tone (Gijsman et al., 2002; Neuhaus et al., 2009; Scarna, McTavish, Cowen, Goodwin, & Rogers, 2005). This neurochemical approach allowed us to experimentally probe how transient reductions in dopaminergic tone impact both motor imagery and proprioceptive perception in healthy adults, thereby offering a direct neurochemical test of the perceptualist claim that these processes share underlying mechanisms. Previous research often relies on correlational studies or investigations solely within clinical populations (Hardwick et al., 2018; Lallart et al., 2012), making it challenging to draw causal inferences about how neurochemical factors influence these processes. A key gap is the lack of direct experimental manipulation of neuromodulators, like dopamine, to observe their impact on both motor imagery and proprioception. Accordingly, this study directly addresses these limitations by providing a novel neurochemical test of the perceptualist account in the motor domain. We aimed to investigate if and how proprioceptive errors and motor imagery performance are related to dopamine availability. To achieve this, we employed BCAA and tryptophan supplementation to precisely and transiently reduce central dopaminergic tone (Gijsman et al., 2002). By simultaneously measuring motor imagery performance and limb positioning accuracy—a critical probe of internal model function that provides a purely perceptual readout devoid of overt motor commands—we directly evaluate whether these processes are supported by a common predictive system modulated by dopamine. If performance on both tasks declines in parallel under dopaminergic challenge, this would bolster the perceptualist view of shared neural mechanisms. Conversely, if one process is selectively disrupted while the other remains intact, it would suggest a functional dissociation, consistent with non-perceptualist theories. Method Participants Forty-two healthy college students initially participated in the study (21 in the BCAA group, 21 in the placebo group; 10 females total; overall mean age = 21.09 ± 2.09 years; BCAA group: 20.68 ± 1.52 years, placebo group: 21.55 ± 2.69 years). All participants were healthy individuals with no history of neurological or motor impairments. One participant was left-handed. Participants were randomly assigned to either the experimental (BCAA) or control (placebo) group. Written informed consent was obtained from all participants prior to participation. The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (Approval Code: IR.LUMS.REC.1403.292). Power analysis using G*Power (version 3.1.9.2, Heinrich Heine Universität, Düsseldorf, Germany) was conducted to determine the sufficient sample size for rejecting the null hypothesis. For measures with two repetitions (including serum prolactin, MIQ-3, and proprioception error), an effect size of f = 0.25 and a desired power of 1 − β = 0.80 (with α = 0.05), the analysis revealed that 34 participants would be sufficient to correctly reject the null hypothesis. While 42 participants initially volunteered, some did not complete all measures. For the prolactin test, 19 participants in the BCAA group and 18 in the placebo group completed the measures. A few participants also didn't complete the MIQ and proprioception tests, either due to giving up or other reasons. Additionally, data from one participant in the proprioception analysis was identified as an outlier and removed. Despite these reductions from the initial sample size, the final number of participants for each analysis remained sufficient, meeting or exceeding the sample sizes suggested by our a priori G*Power analyses. Design and Procedure The study employed a double-blind, placebo-controlled, between-subjects design. All testing took place in the morning, at least two hours after participants’ habitual wake-up time, to control for natural variations in prolactin levels. Each participant underwent both a pretest and a posttest session, separated by approximately three hours. At baseline (pretest), participants provided a venous blood sample for serum prolactin analysis. Before the main experimental trials, each participant performed one practice trial of the limb positioning task in the presence of an experimenter to ensure full understanding of the task goal. Following this, they completed the limb positioning task, which utilized a robotic isokinetic dynamometer. Motor imagery ability was also assessed using the Movement Imagery Questionnaire-3 (MIQ-3) (Williams et al., 2012). Following the pretest, participants consumed either the BCAA supplement (experimental group) or the placebo (control group). The BCAA supplement consisted of 60 grams total: 30 grams of branched-chain amino acids (BCAAs) per participant, with a L-leucine:L-isoleucine:L-valine ratio of 2:1:1, 30 grams of carbohydrate (e.g., maltodextrin), and an additional 2 grams of tryptophan to counteract potential serotonin depletion associated with BCAA intake. The placebo group received 30 grams of carbohydrate (e.g., maltodextrin) matched in taste, texture, and appearance to the BCAA mixture, ensuring blinding of both participants and experimenters. Three hours after ingestion—based on prior evidence indicating peak effects of BCAA on prolactin within this window (Gijsman et al., 2002)—participants returned for the posttest. During this session, serum prolactin levels were again measured, and participants repeated the blindfolded limb positioning task and the MIQ-3. Materials and Measures Serum Prolactin Venous blood samples (5 mL) were collected from an antecubital vein by a trained phlebotomist at both pre- and post-test sessions. For consistent processing, blood was drawn directly into BD Vacutainer SST II Advance clot activator tubes (Becton, Dickinson and Company), which had been obtained in advance from the certified governmental hospital laboratory. Following collection, samples were allowed to clot undisturbed at room temperature for 30 minutes. The governmental hospital laboratory, where technical analyses were performed, was located in close proximity to the data collection site. This allowed for the clotted blood samples to be immediately transported, maintained under cooling conditions (in a cooler with ice packs), to the laboratory. Upon receipt, blood samples were promptly centrifuged at 3000 rpm for 10 minutes at 4°C. The resulting serum was carefully separated from cellular components, aliquoted into cryovials, and immediately stored at − 80°C to preserve sample integrity until biochemical analysis. Serum prolactin concentrations were subsequently quantified at a certified governmental hospital laboratory using a human prolactin ELISA kit (ABC Diagnostics, Catalog No. 12345). Motor Imagery: Movement Imagery Questionnaire-3 (MIQ-3) : The Movement Imagery Questionnaire-3 (MIQ-3; (Williams et al., 2012) was used to assess participants' self-reported ability to vividly and accurately imagine movements. This validated questionnaire consists of 12 items, with each item requiring participants to imagine a specific movement (e.g., a knee lift or bending at the waist) from three distinct perspectives: internal visual imagery (seeing the movement from within one's own body), external visual imagery (seeing oneself perform the movement from an external viewpoint, as if watching a video), and kinesthetic imagery (feeling the movement kinesthetically, e.g., muscle sensations). For each item and perspective, participants rated the ease or clarity of their imagery on a 7-point Likert-type scale, ranging from 1 (very hard to see/feel) to 7 (very easy to see/feel). Scores for each imagery subscale (internal visual, external visual, kinesthetic) were summed, yielding a range from 4 to 28 for each. A total MIQ-3 score (sum of all 12 items across all three perspectives) was also calculated. Limb Positioning Task : Proprioceptive motor perception was evaluated using a passive limb positioning task performed on a Biodex System 3 Pro™ Isokinetic Dynamometer. Participants were seated with their dominant arm (for all but one left-handed participant, this was the right arm) secured to the dynamometer's arm. With eyes closed (blindfolded), their arm was passively moved from a standardized starting position (e.g., full elbow extension) to target joint angles of 30° and 50° of elbow flexion. The robotic arm moved at a constant speed of 10 degrees per second. Participants were instructed to stop the device by pressing a handheld lever with their non-dominant hand as soon as they perceived their arm reaching the target angle. Each target angle was presented 3 times in a randomized order. The dynamometer's internal goniometer (precision: 0.1°) recorded the actual angle at which the participant stopped the movement. Three limb-positioning performance metrics were calculated for each participant at pre- and post-test: Constant Error (CE) : This metric quantified the average directional bias (undershoot or overshoot) for each target angle. For each of the three trials at a given angle, CE was calculated as the difference between the actual stopped angle and the target angle (Actual Angle − Target Angle). These three values were then averaged for each angle (30° and 50°). For example, the average CE for the three 30° trials at pre-test was compared to the average CE for the three 30° trials at post-test. Absolute Error (AE) : This metric quantified the average magnitude of error, regardless of direction, for each target angle. For each of the three trials at a given angle, AE was calculated as the absolute difference between the actual stopped angle and the target angle (∣Actual Angle − Target Angle∣). These three values were then averaged for each angle (30° and 50°). Variable Error (VE) : This metric quantified the consistency of performance. To increase the number of trials for a more robust measure of consistency, VE was calculated using the combined data from all six trials (three trials for 30° and three trials for 50°) at each time point (pre- and post-test). VE was determined by computing the standard deviation of the constant errors across these six combined trials, using the following formula: $$\:VE=\frac{\sqrt{{{\sum\:}_{i=1}^{n}(X}_{i}-\:\stackrel{-}{X}}{)}^{2}}{n}$$ Where: \(\:{X\:}_{i}\) = Individual trial response (e.g., reproduced joint angle) \(\:\stackrel{-}{X}\) = Mean response across all trials n = Number of trials Statistical Analyses A 2 (Group: BCAA vs. Placebo) × 2 (Time: Pre-test vs. Post-test) mixed-design ANOVA was conducted for each dependent variable: serum prolactin levels, motor imagery ability (MIQ-3 total score, and separate analyses for internal visual, external visual, and kinesthetic subscale scores), and overall limb positioning accuracy at 30° and 50° of elbow flexion. Additionally, to specifically examine constant and absolute error, separate 2 (Group: BCAA vs. Placebo) × 2 (Time: Pre-test vs. Post-test) × 2 (Condition: 30° vs. 50° elbow flexion) mixed-design ANOVAs were conducted for Constant Error (CE) and Absolute Error (AE). In all analyses, Group was treated as a between-subjects factor, and Time (and Condition, where applicable for CE and AE) as within-subjects factors. Where significant main effects or interactions were observed, Bonferroni-adjusted pairwise comparisons were used to further examine the differences. Effect sizes were reported using partial eta squared ( \(\:{\eta\:}_{p}^{2}\) ​) for ANOVA results. All assumptions of normality and homogeneity of variance were checked and met. Statistical significance was determined at p .5). However, a significant Group × Time interaction was observed, F (1, 35) = 5.99, p = .020, \(\:{\eta\:}_{p}^{2}\) ​=.146 (Fig. 1 ). Follow-up Bonferroni-adjusted pairwise comparisons indicated that prolactin levels significantly decreased in the Placebo group from pre- ( M = 20.68, SD = 6.70) to post-test ( M = 16.28, SD = 6.79; p = .013). In contrast, prolactin levels in the BCAA group did not significantly change from pre- ( M = 18.46, SD = 8.86) to post-test ( M = 19.81, SD = 8.30; p = .521). There were no significant between-group differences at either time point ( p pre ​=.512, p post ​=.167). To further confirm the differential effect, a delta score (post- minus pre-test change in prolactin) was calculated for each participant. An independent samples t-test on these difference scores revealed a significant group difference, t (35) = 2.47, p = .020, with positive changes in prolactin level for the BCAA group ( M = 1.34, SD = 8.95), and negative changes for the Placebo group ( M = − 4.16, SD = 4.50). These findings confirm successful dopaminergic modulation, indicating that BCAA supplementation successfully prevented the expected diurnal decline in prolactin, thereby dampening central dopaminergic activity. Motor imagery (MIQ-3) MIQ-3 Total Score For MIQ-3 total scores, a significant main effect of Time was observed, F (1, 37) = 8.64, p = .006, \(\:{\eta\:}_{p}^{2}\) = .189. A significant Group × Time interaction was also found, F (1, 37) = 6.46, p = .015, \(\:{\eta\:}_{p}^{2}\) = .149. Pairwise comparisons showed no significant between-group difference at pre-test ( p > .05). At post-test, the BCAA group exhibited significantly lower MIQ-3 total scores ( M = 24.15, SD = 2.31, N = 20) compared to the Control group ( M = 25.63, SD = 2.07, N = 19; p = .042). Within-group changes indicated that the BCAA group showed no significant change in MIQ-3 total scores from pre- ( M = 24.03, SD = 2.51) to post-test ( p > .05). In contrast, the Control group demonstrated a significant improvement in their MIQ-3 total scores from pre- ( M = 24.02, SD = 2.77) to post-test ( p .05). Kinesthetic Motor Imagery For kinesthetic motor imagery scores, a significant main effect of Time was observed, F (1, 37) = 6.03, p = .019, η p ² = .140. Kinesthetic imagery scores improved from pre-test ( M = 23.77, SD = 3.25) to post-test ( M = 24.64, SD = 2.46) across both groups. There were no significant main effects of group ( p > .05) or a Group × Time interaction ( p > .05). Internal Visual Motor Imagery For internal visual imagery scores, no significant main effects of group ( F (1, 37) = 0.05, p = .830, \(\:{\eta\:}_{p}^{2}\) = .001) or time ( F (1, 37) = 0.54, p = .468, \(\:{\eta\:}_{p}^{2}\) = .014) were found. These effects, however, were superseded by a significant Group × Time interaction, F (1, 37) = 8.60, p = .006, \(\:{\eta\:}_{p}^{2}\) = .189. Pairwise comparisons using Bonferroni adjustments indicated a significant between-group difference at post-test ( p = .011), with the Control group ( M = 25.58, SD = 2.06) showing significantly higher scores than the BCAA group ( M = 23.55, SD = 2.69). Regarding within-group changes, the Control group showed a significant increase from pre- ( M = 23.89, SD = 2.58) to post-test ( M = 25.58, SD = 2.06; p = .03). In contrast, the BCAA group showed a slight, non-significant decrease from pre- ( M = 24.00, SD = 2.90) to post-test ( M = 23.55, SD = 2.69; p = .713). External Visual Imagery For external visual imagery scores, no significant main effect of Group ( p > .05), no significant main effect of Time ( p > .05), and no significant Group × Time interaction ( p > .05) were found (Fig. 2 ). Proprioceptive Accuracy (Limb Positioning) Primary Analyses For CE and AE at both 30-degree and 50-degree limb positioning, and for total CE and total AE, no significant main effects of group or time, nor a significant group × time interaction, were observed ( ps > .05), except as detailed in the following exploratory secondary analyses by Condition. Secondary Analyses (CE) : A 2 (Group: BCAA vs. Placebo) × 2 (Time: Pre-test vs. Post-test) × 2 (Condition: 30° vs. 50° elbow flexion) mixed-design ANOVA was conducted on proprioception CE. This analysis revealed a significant main effect of condition, F (1, 32) = 20.685, p < .001, \(\:{\eta\:}_{p}^{2}\) ​=.393, indicating higher degrees of CE in 50° ( M = − 3.80, SD = 5.45) relative to 30° ( M = − 1.54, SD = 4.51). More importantly, a significant three-way Group × Time × Condition interaction was observed, F (1, 32) = 8.68, p = .006, \(\:{\eta\:}_{p}^{2}\) ​=.21. No other main effects or interactions were significant (ps > .05). To unpack the significant three-way interaction for CE, Bonferroni-adjusted pairwise comparisons were performed. For the BCAA group, at the 50° condition, proprioception errors significantly decreased from pre-test ( M = − 5.82, SD = 6.07) to post-test ( M = − 2.32, SD = 3.21; MD = − 3.50, p = .026). This indicates an improvement, i.e., reduced undershooting. Also for the BCAA group, at pre-test, errors in the 50° condition ( M = − 5.82, SD = 6.07) were significantly higher (more negative, indicating greater undershooting) compared to the 30° condition ( M = − 0.94, SD = 5.02; MD = 4.88, p < .001). For the BCAA group, at post-test, errors in the 50° condition ( M = − 2.32, SD = 3.21) were significantly higher (more negative, indicating greater undershooting) compared to the 30° condition ( M = − 0.44, SD = 5.01; MD = − 1.88, p = .026). Furthermore, for the Placebo group, at post-test, errors in the 50° condition ( M = − 3.86, SD = 5.16) were significantly higher (more negative, indicating greater undershooting) compared to the 30° condition ( M = 0.16, SD = 3.30; MD = 4.019, p = .001). Secondary analyses (AE) : An additional exploratory 2 (Group: BCAA vs. Placebo) × 2 (Time: Pre-test vs. Post-test) × 2 (Condition: 30° vs. 50° elbow flexion) mixed-design ANOVA was conducted on proprioception AE. This analysis revealed a significant main effect of Time, F (1, 32) = 7.696, p = .009, \(\:{\eta\:}_{p}^{2}\) ​=.194, and a significant main effect of Condition, F (1, 32) = 17.286, p < .001, ηp2​=.351. More importantly, a significant three-way Group × Time × Condition interaction was observed, F (1, 32) = 4.66, p = .038, \(\:{\eta\:}_{p}^{2}\) ​=.127. The Group × Condition interaction approached statistical significance, F (1, 32) = 4.125, p = .051, \(\:{\eta\:}_{p}^{2}\) ​=.114. No other main effects or interactions were significant (ps > .05). To unpack the significant three-way interaction for AE, Bonferroni-adjusted pairwise comparisons were performed. Between-group comparisons revealed that at Post-test, at the 50° condition, the BCAA group ( M = 2.93, SD = 2.63) exhibited significantly lower AE compared to the Placebo group ( M = 5.59, SD = 3.05; MD = − 2.657, p = .010). Within the BCAA group, AE at the 50° condition significantly decreased from Pre-test ( M = 6.73, SD = 4.99) to Post-test ( M = 2.93, SD = 2.63; MD = 3.794, p = .005). Additionally, At Pre-test, for the BCAA group, AE at the 50° condition ( M = 6.73, SD = 4.99) was significantly higher than at the 30° condition ( M = 4.08, SD = 2.91; MD = − 2.647, p = .009). This difference was not significant at Post-test ( p = .28). For the Placebo group, AE at the 50° condition ( M Pre ​=6.53, SD Pre ​=3.88; M Post ​ =5.59, SD Post ​=3.05) was consistently and significantly higher than at the 30° condition ( M Pre ​=4.25, SD Pre ​=2.68; M Post ​ =2.78, SD Post ​= 1.63) at both Pre-test ( MD = − 2.275, p = .022) and Post-test ( MD = − 2.804, p = .002). Variable Error (VE) For variable error (VE), a significant Group × Time interaction was observed, F (1, 36) = 10.72, p = .002, \(\:{\eta\:}_{p}^{2}\) = .245. Pairwise comparisons using Bonferroni adjustments revealed a marginally significant between-group difference at pre-test ( p= .050) with higher VE for the control ( M = 9.73, SD = 2.53), compared to BCAA ( M = 8.20, SD = 1.82) group. A significant between-group difference was also found at post-test ( p = .027), where the BCAA group ( M = 10.00, SD = 2.87) exhibited significantly higher VE scores than the Control group ( M = 8.11, SD = 1.89). Regarding within-group changes, the BCAA group’s VE scores significantly increased from pre- ( M = 8.20, SD = 1.82) to post-test ( M = 10.00, SD = 2.87; p = .022). Conversely, the Control group’s VE scores significantly decreased from pre- ( M = 9.73, SD = 2.53) to post-test ( M = 8.11, SD = 1.89; p = .033) (Fig. 4 ). Discussion This study investigated the neurochemical underpinnings of motor imagery and proprioceptive perception, specifically exploring the causal role of dopamine in modulating these processes. Our findings offer a nuanced perspective, revealing both shared vulnerabilities (e.g., dopamine's consistent impact on internal visual imagery and proprioceptive consistency) and intriguing dissociations (e.g., differential effects across imagery modalities or specific proprioceptive angles) that refine our understanding of how the brain simulates and perceives movement (Pearson et al., 2015). To start with, our manipulation check confirmed that BCAA supplementation effectively dampened central dopaminergic activity. This was evidenced by a significant Group × Time interaction on prolactin levels: while the Placebo group exhibited a typical diurnal decline in prolactin, the BCAA group's prolactin levels remained stable, effectively preventing this natural decrease (Gijsman et al., 2002; Neuhaus et al., 2009; Scarna et al., 2005). While peripheral prolactin is an indirect marker, the selective behavioral effects observed strongly point to the involvement of the nigrostriatal pathway, a system crucial for motor control and known to be compromised in conditions like Parkinson's disease (Dauer & Przedborski, 2003). This precise neurochemical modulation allowed us to move beyond correlational studies and directly investigate the causal role of dopamine in these domains. A key finding was the selective impairment of internal visual motor imagery under reduced dopaminergic tone. The Placebo group showed significant improvements in their ability to vividly imagine movements from a first-person perspective, reflecting a natural practice effect (Brietzke, Cesario, Hettinga, & Pires, 2022). In contrast, the BCAA group exhibited no improvement in internal visual imagery; their scores remained stable or even slightly decreased, resulting in significantly lower scores at post-test compared to the Placebo group. This finding suggests that our experimentally reduced dopaminergic tone specifically hindered the optimization or refinement of these internally generated, first-person motor representations. This observation aligns with aspects of Motor Simulation Theory (MST), a perceptualist account which posits that internal motor imagery recruits neural machinery also involved in actual movement. Our results indicate that central dopamine availability is crucial for the efficient generation or refinement of such vivid, first-person motor imagery. Dopamine's role in modulating the precision and plasticity of these forward models (Friston et al., 2012; Kilner & Friston, 2010) directly implicates it in the fidelity and refinement of internal simulation. Crucially, this effect was selective within motor imagery modalities. Kinesthetic and external visual imagery remained largely unaffected by the dopaminergic challenge. This dissociation challenges a strong, unitary perceptualist view and supports the idea that motor imagery is a multi-component process (Glover & Baran, 2017). Kinesthetic imagery, focused on the feeling of movement, may rely more on efference copy and proprioceptive feedback, likely modulated by distinct or less acutely dopamine-sensitive systems (Kilteni et al., 2018). External visual imagery, involving a third-person perspective, may engage higher-level conceptual representations and distinct neural substrates such as the temporoparietal junction and precuneus (Blanke, Slater, & Serino, 2015; Ionta et al., 2011). These areas are associated with perspective-taking (Cavanna & Trimble, 2006; Saxe & Kanwisher, 2013) and are not traditionally implicated in dopamine-rich motor loops (Obeso et al., 2008). Thus, the sparing of external visual imagery is consistent with non-perceptualist frameworks that emphasize abstract planning (Cavedon-Taylor, 2021). Regarding proprioceptive accuracy, our findings revealed a nuanced deficit. While the average accuracy of passive limb positioning (constant error, CE, and absolute error, AE) remained largely preserved when analyzed as a whole, variable error (VE), an indicator of movement consistency, significantly increased in the BCAA group, contrasting with a decrease in VE observed in the Placebo group. Consequently, at post-test, the BCAA group exhibited significantly higher VE compared to controls. This suggests that while participants could still identify target positions on average, their perceptual judgments became less consistent—a pattern linked to instability in internal models (Synofzik, Thier, Leube, Schlotterbeck, & Lindner, 2010). Dopamine is thought to calibrate the precision of these models, and the increased variability under dopaminergic depletion fits with computational accounts where dopamine modulates the confidence in predictions (K. Friston, Kilner, & Harrison, 2006; K. J. Friston et al., 2012). These results are consistent with motor control models in Parkinson’s disease, where patients often exhibit increased variability in perceptual-motor tasks (Davis, Sivaramakrishnan, Rolin, & Subramanian, 2025; Helmich et al., 2007; Jones et al., 2011). The exploratory analyses on proprioception Constant Error (CE) and Absolute Error (AE), broken down by specific joint angles, further illuminate the intricate role of dopamine and its impact on proprioception. The significant three-way interaction of Group × Time × Condition for both CE and AE demonstrates that the changes in proprioceptive errors over time differ between groups and across the 30° and 50° conditions in complex ways. Notably, for the BCAA group, we observed a significant improvement in proprioceptive accuracy at the 50° condition from pre- to post-test, as evidenced by a reduction in both constant error (CE, i.e., reduced undershooting) and absolute error (AE). This suggests that despite dampened dopaminergic tone, participants in the BCAA group were able to refine their accuracy in the more kinesthetically demanding 50° elbow flexion. This seemingly paradoxical finding could indicate a task-dependent adaptation, where, despite a general dopaminergic modulation, specific mechanisms for error correction in a challenging range of motion remain functional or even show improvement. However, this improvement did not eliminate a persistent relative deficit at the 50° angle. Even before the intervention, at pre-test, the BCAA group's errors in the 50° condition were significantly higher (both more undershooting in CE and higher in AE) compared to the 30° condition, indicating that the 50° angle may be inherently more challenging or less accurately perceived for this group. Intriguingly, this pattern persisted at post-test for the BCAA group in terms of CE, where errors in the 50° condition remained significantly higher compared to the 30° condition. While AE for the BCAA group at 50° was no longer significantly higher than 30° at post-test, suggesting a notable reduction in overall error magnitude at this challenging angle, the persistent CE difference points to a remaining directional bias. This suggests that while some learning or adaptation occurred at 50°, the underlying dopaminergic modulation might limit the overall precision or ability to reach the same level of accuracy as in the less demanding 30° condition. These angle-specific challenges were also evident in the Placebo group. At post-test, the Placebo group also exhibited significantly higher absolute errors at the 50° condition compared to the BCAA group, indicating that the BCAA intervention conferred some benefit in reducing overall error magnitude at this challenging angle. Furthermore, for the Placebo group, absolute errors were consistently higher at the 50° condition relative to the 30° condition in both pre- and post-tests, reinforcing the notion that the 50° angle presents a greater proprioceptive challenge irrespective of dopaminergic manipulation. Similarly, for the Placebo group, constant errors at post-test in the 50° condition were significantly higher compared to the 30° condition. These comprehensive findings suggest that dopaminergic modulation might influence the learning and consolidation of positional accuracy in a non-uniform way, impacting the processing of more challenging or less frequently encountered joint angles differently. The consistent observation of higher errors at 50° compared to 30° across groups and time points (especially in the BCAA group and Placebo post-test) suggests a general biomechanical or perceptual difficulty at that angle, which is then differentially influenced by dopaminergic tone. This detailed understanding of proprioceptive alterations under dopaminergic manipulation moves beyond a simple global accuracy deficit to highlight a more complex, context-dependent influence. The parallel—but qualitatively distinct—disruptions in internal visual imagery and proprioceptive variability, alongside the nuanced angle-specific effects on constant error and absolute error in proprioception, under dopaminergic challenge support a refined perceptualist account. Both motor imagery (specifically internal visual) and proprioception (particularly consistency and angle-specific adaptation) depend on dopamine-sensitive internal modeling mechanisms, but the sparing of other imagery types underscores the modularity of motor simulation (Hardwick et al., 2018). This may move the debate beyond a binary perceptualist vs. non-perceptualist framing, toward a more biologically realistic hybrid model. Our findings carry important clinical implications. The selective disruption of internal visual motor imagery, coupled with increased proprioceptive variability, and the differential impact on proprioception at specific joint angles, mirror patterns observed in early Parkinson’s disease (Heremans et al., 2011; Helmich et al., 2007). Dopamine depletion is also linked to disrupted motor simulation in schizophrenia (Chen et al., 2015) and may contribute to altered bodily self-awareness in depersonalization and psychosis (Büetiger et al., 2020; Mograbi, Rodrigues, Bienemann, & Huntley, 2024). These insights may inform new strategies for neurorehabilitation, such as pairing dopaminergic medication with first-person motor imagery training, particularly for specific motor tasks or joint ranges that are more sensitive to dopaminergic modulation. More broadly, dopaminergic influences on simulation may extend to performance domains such as sports and neuroprosthetics, where internal models play a critical role in real-time control and motor learning (Cano-De-La-Cuerda et al., 2015; Kawato & Wolpert, 2007). However, certain limitations should be acknowledged. Prolactin is an indirect index of dopamine, and future studies should include brain imaging for more direct mapping of dopaminergic modulation (Nagano-Saito, Martinu, & Monchi, 2014; Oldehinkel et al., 2022). Our healthy adult sample allowed for tight experimental control, but extending this paradigm to clinical populations with chronic dopamine dysregulation (e.g., PD, schizophrenia) will provide critical ecological validity. Additionally, supplementing the MIQ-3 with objective measures (e.g., chronometry, fMRI decoding, or EEG ERD/ERS) could bolster future assessments of imagery performance (Pilgramm et al., 2016; Zich et al., 2015). Future research could also further explore the biomechanical or neurological reasons why the 50° elbow flexion might show different sensitivity to dopaminergic modulation compared to the 30° flexion, potentially using more detailed kinematic or neural measures. In conclusion, our study provides compelling evidence for the causal role of dopamine in shaping both motor imagery and proprioceptive perception. By experimentally modulating central dopaminergic tone, we unveiled a nuanced dissociation: a clear impairment in internal visual motor imagery alongside a subtle but significant impact on proprioceptive consistency, and specific, angle-dependent changes in proprioceptive constant error and absolute error. These findings critically challenge strong versions of perceptualist theories that posit fully shared mechanisms between imagery and perception. Instead, they support a refined model that acknowledges shared dopamine-sensitive internal modeling processes alongside distinct, modality-specific neural substrates and potentially angle-specific processing within a single modality. Our work highlights that specific forms of motor simulation, particularly first-person visual imagery, are exquisitely sensitive to neurochemical fluctuations. This offers valuable insights into the complex neurochemical architecture of embodied cognition, with direct implications for understanding and treating motor and perceptual deficits in neurological and psychiatric conditions, as well as for optimizing performance in fields like sports and neuroprosthetics. Future research employing more direct measures of brain dopamine and expanding to clinical populations will further illuminate these intricate relationships. Declarations Acknowledgments: We would like to thank participants who participated in this study. Declaration of competing interests: The authors declare that there is no any conflict of interest in the present work. Declaration of generative AI in scientific writing: During the preparation of this work the author(s) used generative AI tools including ChatGPT and Google Gemini in order to improve the readability and language of the manuscript. After using these tools, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article. Data availability statement: The data that support the findings of this study are available from the corresponding author, upon reasonable request. Author Contribution Parisa Hejazi Dinan and Moslem Bahmani designed and conceptualized the study. Moslem bahmani wrote the initial draft of the manuscript. Davoud Fazeli, and Gholamhossain Nazemzadegan edited the drafted manuscript. Usef Garmanjani contributed to the design, data collection and processing. 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Proceedings of the Royal Society B: Biological Sciences, 282 (1798), 20142047. Synofzik, M., Lindner, A., & Thier, P. (2008). The cerebellum updates predictions about the visual consequences of one's behavior. Current Biology, 18 (11), 814-818. Synofzik, M., Thier, P., Leube, D. T., Schlotterbeck, P., & Lindner, A. (2010). Misattributions of agency in schizophrenia are based on imprecise predictions about the sensory consequences of one's actions. Brain, 133 (1), 262-271. Williams, S. E., Cumming, J., Ntoumanis, N., Nordin-Bates, S. M., Ramsey, R., & Hall, C. (2012). Further validation and development of the movement imagery questionnaire. Journal of Sport and Exercise Psychology, 34 (5), 621-646. Zich, C., Debener, S., Kranczioch, C., Bleichner, M. G., Gutberlet, I., & De Vos, M. (2015). Real-time EEG feedback during simultaneous EEG–fMRI identifies the cortical signature of motor imagery. Neuroimage, 114 , 438-447. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Dec, 2025 Read the published version in Psychological Research → Version 1 posted Editorial decision: Revision requested 11 Jun, 2025 Editor assigned by journal 06 Jun, 2025 Submission checks completed at journal 05 Jun, 2025 First submitted to journal 05 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6828758","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469694092,"identity":"5fa12491-c57f-4b66-8305-26886b127523","order_by":0,"name":"Parisa Hejazi Dinan","email":"","orcid":"","institution":"Alzahra University","correspondingAuthor":false,"prefix":"","firstName":"Parisa","middleName":"Hejazi","lastName":"Dinan","suffix":""},{"id":469694093,"identity":"e91b50b8-1272-47e4-a577-63923174ce8c","order_by":1,"name":"Moslem Bahmani","email":"data:image/png;base64,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","orcid":"","institution":"Alzahra University","correspondingAuthor":true,"prefix":"","firstName":"Moslem","middleName":"","lastName":"Bahmani","suffix":""},{"id":469694094,"identity":"c140d7fd-47cb-4a79-9ef0-4b93c5c4e772","order_by":2,"name":"Usef Garmanjani","email":"","orcid":"","institution":"Shiraz University","correspondingAuthor":false,"prefix":"","firstName":"Usef","middleName":"","lastName":"Garmanjani","suffix":""},{"id":469694095,"identity":"da60b033-c444-4be9-8108-2b2d64f19d6f","order_by":3,"name":"Gholam hossein Nazemzadegan","email":"","orcid":"","institution":"Shiraz University","correspondingAuthor":false,"prefix":"","firstName":"Gholam","middleName":"hossein","lastName":"Nazemzadegan","suffix":""},{"id":469694096,"identity":"01e6bcd4-7e12-4448-b71c-c011ff4e2070","order_by":4,"name":"Davoud Fazeli","email":"","orcid":"","institution":"Shiraz University","correspondingAuthor":false,"prefix":"","firstName":"Davoud","middleName":"","lastName":"Fazeli","suffix":""}],"badges":[],"createdAt":"2025-06-05 11:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6828758/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6828758/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00426-025-02225-x","type":"published","date":"2025-12-26T15:57:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85048154,"identity":"6a0879fc-609b-49c4-8c10-39a4ba17991b","added_by":"auto","created_at":"2025-06-20 10:53:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41627,"visible":true,"origin":"","legend":"\u003cp\u003eSerum Prolactin Levels Before and After Intervention in BCAA and Placebo Groups. Bars represent mean serum prolactin concentrations (ng/mL) at Pre-Test and Post-Test for both the Branched-Chain Amino Acid (BCAA) and Placebo groups. Error bars denote the standard deviation (SD). Significance Levels: ∗p\u0026lt;0.05, ∗∗p\u0026lt;0.01, ∗∗∗p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6828758/v1/3bca7e2e1160dd311a4b8516.png"},{"id":85047767,"identity":"ea63c0f6-5979-4fe0-90a5-3eaa038cf5ec","added_by":"auto","created_at":"2025-06-20 10:45:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77945,"visible":true,"origin":"","legend":"\u003cp\u003eMovement Imagery Questionnaire-3 (MIQ-3) Scores Across Groups and Timepoints. Panel (a) displays mean and standard deviation (SD) of total MIQ-3 scores, while panels (b), (c), and (d) show scores for the Kinesthetic, Internal Visual, and External Visual subscales, respectively. Significance Levels: ∗p\u0026lt;0.05, ∗∗p\u0026lt;0.01, ∗∗∗p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6828758/v1/52f893299909e91a0bf7384c.png"},{"id":85047765,"identity":"51d85e85-9cb7-478f-b3b2-f7822a30f6e4","added_by":"auto","created_at":"2025-06-20 10:45:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":188358,"visible":true,"origin":"","legend":"\u003cp\u003eConstant Error and Absolute Error in Passive Limb Positioning Across Groups and Timepoints. Panels (a), (b), and (c) display Constant Error (CE) at 30%, 50%, and total range of motion, respectively. Panels (d), (e), and (f) display Absolute Error (AE) at 30%, 50%, and total range of motion, respectively. Bars represent mean error scores for the BCAA and Placebo groups at Pre-Test and Post-Test, with error bars indicating the standard deviation (SD). Significance Levels: ∗p\u0026lt;0.05, ∗∗p\u0026lt;0.01, ∗∗∗p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6828758/v1/e8f78b63e78c84cd3a5baa18.png"},{"id":85047766,"identity":"c085ee3c-b6fb-4f43-8de1-1bf419ca7200","added_by":"auto","created_at":"2025-06-20 10:45:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":88678,"visible":true,"origin":"","legend":"\u003cp\u003eVariable Error (VE) in Passive Limb Positioning Across Groups and Timepoints. Bars represent mean Variable Error (VE) for the BCAA and Placebo groups at Pre-Test and Post-Test. Error bars indicate the standard deviation (SD).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6828758/v1/82ca009bd0b72a1d626fcde8.png"},{"id":99173200,"identity":"14c00636-055e-4aa4-9d02-fc7f57cd4da6","added_by":"auto","created_at":"2025-12-29 16:12:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1198005,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6828758/v1/9be65898-da17-414e-8719-dc80694c0f21.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dopamine, Motor Imagery, and Proprioception: A Neurochemical Probe into the Perception-Imagery Debate","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe fundamental question of whether mental imagery is merely a faint echo of perception or emerges from fundamentally different processes has long fueled debates in cognitive neuroscience (Cavedon-Taylor, 2021b; Nanay, 2021; Pearson, 2019; Pearson, Naselaris, Holmes, \u0026amp; Kosslyn, 2015). While perceptualist accounts, which suggest shared underlying mechanisms, have traditionally held a slightly more dominant position, novel clinical dissociations strongly challenge this view. For instance, individuals retaining vivid mental imagery despite cortical blindness, or conversely, experiencing aphantasia\u0026mdash;the striking inability to form mental images despite intact perception (de Gelder, Tamietto, Pegna, \u0026amp; Van den Stock, 2015; Lambert, Sampaio, Mauss, \u0026amp; Scheiber, 2004; Liu \u0026amp; Bartolomeo, 2023)\u0026mdash;intensify interest in whether imagery and perception truly rely on the same neural architecture or merely converge due to task demands (Cavedon-Taylor, 2021b; Pearson, 2019). Perceptualist accounts posit that imagery and perception exist on a continuum within common neural systems, arguing that imagery arises from reactivating perceptual representations in sensory cortices with weaker bottom-up input (Nanay, 2021; Pearson, 2019). In contrast, non-perceptualist perspectives contend that mental imagery might be grounded in symbolic or conceptual processes functionally distinct from perception (Cavedon-Taylor, 2021a, 2021b). While this debate has seen substantial evolution and extensive study in the visual domain, it has received comparatively less attention in the motor system, particularly regarding its neurochemical basis in healthy populations and its direct comparison with proprioceptive perception.\u003c/p\u003e \u003cp\u003eMotor imagery, unlike its visual counterpart, inherently involves multiple modalities\u0026mdash;kinesthetic, proprioceptive, and visual\u0026mdash;all woven into the internal simulation of movement. These simulations recruit neural circuits that partially overlap with action execution, including premotor, parietal, and cerebellar regions (Hardwick, Caspers, Eickhoff, \u0026amp; Swinnen, 2018; H\u0026eacute;tu et al., 2013). Neuroimaging and lesion studies further link motor imagery and proprioception to shared internal models, often implicating the cerebellum and somatosensory cortices in the integration of predictive and sensory information (Kilteni, Andersson, Houborg, \u0026amp; Ehrsson, 2018; Synofzik, Lindner, \u0026amp; Thier, 2008). According to Motor Simulation Theory (MST), a perceptualist account, motor imagery relies on the neural mechanisms normally involved in actual movement execution. From this perspective, motor imagery is expected to be influenced by the same neuromodulatory factors that govern motor control (Jeannerod, 2001).\u003c/p\u003e \u003cp\u003eYet not all accounts agree. The Motor-Cognition model, for example, casts motor imagery as an abstract, cognitive-level planning process that can proceed independently of online sensorimotor feedback (Glover \u0026amp; Baran, 2017). This view resonates with non-perceptualist frameworks, suggesting that (motor) imagery and (motor) perception may rely on dissociable mechanisms. In line with this argument, clinical patterns underscore dopamine\u0026rsquo;s role in motor imagery. In Parkinson\u0026rsquo;s disease, dopamine loss is linked to impaired motor imagery despite relatively intact movement early on (Helmich, de Lange, Bloem, \u0026amp; Toni, 2007; Heremans et al., 2011). Schizophrenia, which involves dopaminergic dysregulation, also shows disrupted motor imagery\u0026mdash;even as visual hallucinations and vivid visual imagery may persist (Chen et al., 2015; Lallart, Jouvent, Herrmann, Beauchet, \u0026amp; Allali, 2012; Michely et al., 2015; Shine et al., 2015). These contrasting profiles suggest that dopamine is crucial for motor imagery and raise the question of whether it influences motor imagery and proprioception together, as perceptualist theories propose, or if these functions can be experimentally dissociated.\u003c/p\u003e \u003cp\u003eThese clinical insights point to the critical role of the dopaminergic system, which is central to motor control by modulating forward models\u0026mdash;predictive mechanisms that estimate the sensory consequences of action based on efference copies (Lallart et al., 2012). Specifically, we targeted dopaminergic modulation within the nigrostriatal pathway, given its established role in motor control and its degeneration in Parkinson's disease (Dauer \u0026amp; Przedborski, 2003), which we highlighted earlier. These internal models are not only crucial for movement execution but are also believed to support motor imagery (Jeannerod, 2001; Kilner \u0026amp; Friston, 2010). From a perceptualist standpoint, motor imagery is an internal emulation of movement that recruits these same predictive mechanisms (Grush, 2004). Thus, disrupting dopaminergic tone should compromise both imagery and the movement execution (K. J. Friston et al., 2012).\u003c/p\u003e \u003cp\u003eOne method for reducing central dopamine availability, as indexed by a rise in peripheral prolactin levels, involves branched-chain amino acid (BCAA) supplementation. While an indirect method, this approach has been validated in previous research as a means to achieve modest, physiologically grounded changes in dopaminergic tone (Gijsman et al., 2002; Neuhaus et al., 2009; Scarna, McTavish, Cowen, Goodwin, \u0026amp; Rogers, 2005). This neurochemical approach allowed us to experimentally probe how transient reductions in dopaminergic tone impact both motor imagery and proprioceptive perception in healthy adults, thereby offering a direct neurochemical test of the perceptualist claim that these processes share underlying mechanisms.\u003c/p\u003e \u003cp\u003ePrevious research often relies on correlational studies or investigations solely within clinical populations (Hardwick et al., 2018; Lallart et al., 2012), making it challenging to draw causal inferences about how neurochemical factors influence these processes. A key gap is the lack of direct experimental manipulation of neuromodulators, like dopamine, to observe their impact on both motor imagery and proprioception. Accordingly, this study directly addresses these limitations by providing a novel neurochemical test of the perceptualist account in the motor domain. We aimed to investigate if and how proprioceptive errors and motor imagery performance are related to dopamine availability. To achieve this, we employed BCAA and tryptophan supplementation to precisely and transiently reduce central dopaminergic tone (Gijsman et al., 2002). By simultaneously measuring motor imagery performance and limb positioning accuracy\u0026mdash;a critical probe of internal model function that provides a purely perceptual readout devoid of overt motor commands\u0026mdash;we directly evaluate whether these processes are supported by a common predictive system modulated by dopamine. If performance on both tasks declines in parallel under dopaminergic challenge, this would bolster the perceptualist view of shared neural mechanisms. Conversely, if one process is selectively disrupted while the other remains intact, it would suggest a functional dissociation, consistent with non-perceptualist theories.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eForty-two healthy college students initially participated in the study (21 in the BCAA group, 21 in the placebo group; 10 females total; overall mean age\u0026thinsp;=\u0026thinsp;21.09\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09 years; BCAA group: 20.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52 years, placebo group: 21.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2.69 years). All participants were healthy individuals with no history of neurological or motor impairments. One participant was left-handed. Participants were randomly assigned to either the experimental (BCAA) or control (placebo) group. Written informed consent was obtained from all participants prior to participation. The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (Approval Code: IR.LUMS.REC.1403.292).\u003c/p\u003e \u003cp\u003ePower analysis using G*Power (version 3.1.9.2, Heinrich Heine Universit\u0026auml;t, D\u0026uuml;sseldorf, Germany) was conducted to determine the sufficient sample size for rejecting the null hypothesis. For measures with two repetitions (including serum prolactin, MIQ-3, and proprioception error), an effect size of \u003cem\u003ef\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.25 and a desired power of 1\u0026thinsp;\u0026minus;\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.80 (with \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05), the analysis revealed that 34 participants would be sufficient to correctly reject the null hypothesis. While 42 participants initially volunteered, some did not complete all measures. For the prolactin test, 19 participants in the BCAA group and 18 in the placebo group completed the measures. A few participants also didn't complete the MIQ and proprioception tests, either due to giving up or other reasons. Additionally, data from one participant in the proprioception analysis was identified as an outlier and removed. Despite these reductions from the initial sample size, the final number of participants for each analysis remained sufficient, meeting or exceeding the sample sizes suggested by our a priori G*Power analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDesign and Procedure\u003c/h3\u003e\n\u003cp\u003eThe study employed a double-blind, placebo-controlled, between-subjects design. All testing took place in the morning, at least two hours after participants\u0026rsquo; habitual wake-up time, to control for natural variations in prolactin levels. Each participant underwent both a pretest and a posttest session, separated by approximately three hours. At baseline (pretest), participants provided a venous blood sample for serum prolactin analysis. Before the main experimental trials, each participant performed one practice trial of the limb positioning task in the presence of an experimenter to ensure full understanding of the task goal. Following this, they completed the limb positioning task, which utilized a robotic isokinetic dynamometer. Motor imagery ability was also assessed using the Movement Imagery Questionnaire-3 (MIQ-3) (Williams et al., 2012).\u003c/p\u003e \u003cp\u003eFollowing the pretest, participants consumed either the BCAA supplement (experimental group) or the placebo (control group). The BCAA supplement consisted of 60 grams total: 30 grams of branched-chain amino acids (BCAAs) per participant, with a L-leucine:L-isoleucine:L-valine ratio of 2:1:1, 30 grams of carbohydrate (e.g., maltodextrin), and an additional 2 grams of tryptophan to counteract potential serotonin depletion associated with BCAA intake. The placebo group received 30 grams of carbohydrate (e.g., maltodextrin) matched in taste, texture, and appearance to the BCAA mixture, ensuring blinding of both participants and experimenters.\u003c/p\u003e \u003cp\u003eThree hours after ingestion\u0026mdash;based on prior evidence indicating peak effects of BCAA on prolactin within this window (Gijsman et al., 2002)\u0026mdash;participants returned for the posttest. During this session, serum prolactin levels were again measured, and participants repeated the blindfolded limb positioning task and the MIQ-3.\u003c/p\u003e\n\u003ch3\u003eMaterials and Measures\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eSerum Prolactin\u003c/strong\u003e \u003cp\u003eVenous blood samples (5 mL) were collected from an antecubital vein by a trained phlebotomist at both pre- and post-test sessions. For consistent processing, blood was drawn directly into BD Vacutainer SST II Advance clot activator tubes (Becton, Dickinson and Company), which had been obtained in advance from the certified governmental hospital laboratory. Following collection, samples were allowed to clot undisturbed at room temperature for 30 minutes. The governmental hospital laboratory, where technical analyses were performed, was located in close proximity to the data collection site. This allowed for the clotted blood samples to be immediately transported, maintained under cooling conditions (in a cooler with ice packs), to the laboratory. Upon receipt, blood samples were promptly centrifuged at 3000 rpm for 10 minutes at 4\u0026deg;C. The resulting serum was carefully separated from cellular components, aliquoted into cryovials, and immediately stored at \u0026minus;\u0026thinsp;80\u0026deg;C to preserve sample integrity until biochemical analysis. Serum prolactin concentrations were subsequently quantified at a certified governmental hospital laboratory using a human prolactin ELISA kit (ABC Diagnostics, Catalog No. 12345).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMotor Imagery: Movement Imagery Questionnaire-3 (MIQ-3)\u003c/b\u003e: The Movement Imagery Questionnaire-3 (MIQ-3; (Williams et al., 2012) was used to assess participants' self-reported ability to vividly and accurately imagine movements. This validated questionnaire consists of 12 items, with each item requiring participants to imagine a specific movement (e.g., a knee lift or bending at the waist) from three distinct perspectives: internal visual imagery (seeing the movement from within one's own body), external visual imagery (seeing oneself perform the movement from an external viewpoint, as if watching a video), and kinesthetic imagery (feeling the movement kinesthetically, e.g., muscle sensations). For each item and perspective, participants rated the ease or clarity of their imagery on a 7-point Likert-type scale, ranging from 1 (very hard to see/feel) to 7 (very easy to see/feel). Scores for each imagery subscale (internal visual, external visual, kinesthetic) were summed, yielding a range from 4 to 28 for each. A total MIQ-3 score (sum of all 12 items across all three perspectives) was also calculated.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimb Positioning Task\u003c/b\u003e: Proprioceptive motor perception was evaluated using a passive limb positioning task performed on a Biodex System 3 Pro\u0026trade; Isokinetic Dynamometer. Participants were seated with their dominant arm (for all but one left-handed participant, this was the right arm) secured to the dynamometer's arm. With eyes closed (blindfolded), their arm was passively moved from a standardized starting position (e.g., full elbow extension) to target joint angles of 30\u0026deg; and 50\u0026deg; of elbow flexion. The robotic arm moved at a constant speed of 10 degrees per second. Participants were instructed to stop the device by pressing a handheld lever with their non-dominant hand as soon as they perceived their arm reaching the target angle. Each target angle was presented 3 times in a randomized order. The dynamometer's internal goniometer (precision: 0.1\u0026deg;) recorded the actual angle at which the participant stopped the movement.\u003c/p\u003e \u003cp\u003eThree limb-positioning performance metrics were calculated for each participant at pre- and post-test:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eConstant Error (CE)\u003c/b\u003e: This metric quantified the average \u003cem\u003edirectional bias\u003c/em\u003e (undershoot or overshoot) for each target angle. For each of the three trials at a given angle, CE was calculated as the difference between the actual stopped angle and the target angle (Actual Angle\u0026thinsp;\u0026minus;\u0026thinsp;Target Angle). These three values were then averaged for each angle (30\u0026deg; and 50\u0026deg;). For example, the average CE for the three 30\u0026deg; trials at pre-test was compared to the average CE for the three 30\u0026deg; trials at post-test.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAbsolute Error (AE)\u003c/b\u003e: This metric quantified the average \u003cem\u003emagnitude\u003c/em\u003e of error, regardless of direction, for each target angle. For each of the three trials at a given angle, AE was calculated as the absolute difference between the actual stopped angle and the target angle (∣Actual Angle\u0026thinsp;\u0026minus;\u0026thinsp;Target Angle∣). These three values were then averaged for each angle (30\u0026deg; and 50\u0026deg;).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eVariable Error (VE)\u003c/b\u003e: This metric quantified the \u003cem\u003econsistency\u003c/em\u003e of performance. To increase the number of trials for a more robust measure of consistency, VE was calculated using the combined data from all six trials (three trials for 30\u0026deg; and three trials for 50\u0026deg;) at each time point (pre- and post-test). VE was determined by computing the standard deviation of the constant errors across these six combined trials, using the following formula:\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:VE=\\frac{\\sqrt{{{\\sum\\:}_{i=1}^{n}(X}_{i}-\\:\\stackrel{-}{X}}{)}^{2}}{n}$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{X\\:}_{i}\\)\u003c/span\u003e \u003c/span\u003e = Individual trial response (e.g., reproduced joint angle)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{X}\\)\u003c/span\u003e \u003c/span\u003e = Mean response across all trials\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Number of trials\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eA 2 (Group: BCAA vs. Placebo) \u0026times; 2 (Time: Pre-test vs. Post-test) mixed-design ANOVA was conducted for each dependent variable: serum prolactin levels, motor imagery ability (MIQ-3 total score, and separate analyses for internal visual, external visual, and kinesthetic subscale scores), and overall limb positioning accuracy at 30\u0026deg; and 50\u0026deg; of elbow flexion. Additionally, to specifically examine constant and absolute error, separate 2 (Group: BCAA vs. Placebo) \u0026times; 2 (Time: Pre-test vs. Post-test) \u0026times; 2 (Condition: 30\u0026deg; vs. 50\u0026deg; elbow flexion) mixed-design ANOVAs were conducted for Constant Error (CE) and Absolute Error (AE). In all analyses, Group was treated as a between-subjects factor, and Time (and Condition, where applicable for CE and AE) as within-subjects factors. Where significant main effects or interactions were observed, Bonferroni-adjusted pairwise comparisons were used to further examine the differences. Effect sizes were reported using partial eta squared (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e​) for ANOVA results. All assumptions of normality and homogeneity of variance were checked and met. Statistical significance was determined at p\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eProlactin Manipulation Check\u003c/h2\u003e \u003cp\u003eFor the serum prolactin levels, the analysis revealed no significant main effect of group and main effect of time (\u003cem\u003eps\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.5). However, a significant Group \u0026times; Time interaction was observed, \u003cem\u003eF\u003c/em\u003e(1, 35)\u0026thinsp;=\u0026thinsp;5.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.020, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e​=.146 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Follow-up Bonferroni-adjusted pairwise comparisons indicated that prolactin levels significantly decreased in the Placebo group from pre- (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20.68, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.70) to post-test (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16.28, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.79; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.013). In contrast, prolactin levels in the BCAA group did not significantly change from pre- (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18.46, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.86) to post-test (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19.81, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.30; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.521). There were no significant between-group differences at either time point (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003epre\u003c/em\u003e\u003c/sub\u003e​=.512, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003epost\u003c/em\u003e\u003c/sub\u003e​=.167). To further confirm the differential effect, a delta score (post- minus pre-test change in prolactin) was calculated for each participant. An independent samples t-test on these difference scores revealed a significant group difference, \u003cem\u003et\u003c/em\u003e(35)\u0026thinsp;=\u0026thinsp;2.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.020, with positive changes in prolactin level for the BCAA group (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.34, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.95), and negative changes for the Placebo group (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.16, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.50). These findings confirm successful dopaminergic modulation, indicating that BCAA supplementation successfully prevented the expected diurnal decline in prolactin, thereby dampening central dopaminergic activity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMotor imagery (MIQ-3)\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eMIQ-3 Total Score\u003c/strong\u003e \u003cp\u003eFor MIQ-3 total scores, a significant main effect of Time was observed, \u003cem\u003eF\u003c/em\u003e(1, 37)\u0026thinsp;=\u0026thinsp;8.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.006, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e = .189. A significant Group \u0026times; Time interaction was also found, \u003cem\u003eF\u003c/em\u003e(1, 37) = 6.46, \u003cem\u003ep\u003c/em\u003e = .015, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e = .149. Pairwise comparisons showed no significant between-group difference at pre-test (\u003cem\u003ep\u003c/em\u003e \u0026gt; .05). At post-test, the BCAA group exhibited significantly lower MIQ-3 total scores (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24.15, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.31, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20) compared to the Control group (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;25.63, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.07, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19; \u003cem\u003ep\u003c/em\u003e = .042). Within-group changes indicated that the BCAA group showed no significant change in MIQ-3 total scores from pre- (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24.03, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.51) to post-test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05). In contrast, the Control group demonstrated a significant improvement in their MIQ-3 total scores from pre- (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24.02, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.77) to post-test (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001). The main effect of group, however, was not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eKinesthetic Motor Imagery\u003c/strong\u003e \u003cp\u003eFor kinesthetic motor imagery scores, a significant main effect of Time was observed, \u003cem\u003eF\u003c/em\u003e(1, 37)\u0026thinsp;=\u0026thinsp;6.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.019, \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u0026sup2;\u003c/em\u003e = .140. Kinesthetic imagery scores improved from pre-test (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23.77, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.25) to post-test (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24.64, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.46) across both groups. There were no significant main effects of group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05) or a Group \u0026times; Time interaction (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInternal Visual Motor Imagery\u003c/strong\u003e \u003cp\u003eFor internal visual imagery scores, no significant main effects of group (\u003cem\u003eF\u003c/em\u003e(1, 37)\u0026thinsp;=\u0026thinsp;0.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.830, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e= .001) or time (\u003cem\u003eF\u003c/em\u003e(1, 37) = 0.54, \u003cem\u003ep\u003c/em\u003e = .468, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e = .014) were found. These effects, however, were superseded by a significant Group \u0026times; Time interaction, \u003cem\u003eF\u003c/em\u003e(1, 37) = 8.60, \u003cem\u003ep\u003c/em\u003e = .006, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e = .189. Pairwise comparisons using Bonferroni adjustments indicated a significant between-group difference at post-test (\u003cem\u003ep\u003c/em\u003e = .011), with the Control group (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;25.58, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.06) showing significantly higher scores than the BCAA group (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23.55, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.69). Regarding within-group changes, the Control group showed a significant increase from pre- (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23.89, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.58) to post-test (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;25.58, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.06; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.03). In contrast, the BCAA group showed a slight, non-significant decrease from pre- (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24.00, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.90) to post-test (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23.55, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.69; \u003cem\u003ep\u003c/em\u003e = .713).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExternal Visual Imagery\u003c/strong\u003e \u003cp\u003eFor external visual imagery scores, no significant main effect of Group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05), no significant main effect of Time (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05), and no significant Group \u0026times; Time interaction (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05) were found (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eProprioceptive Accuracy (Limb Positioning)\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003ePrimary Analyses\u003c/strong\u003e \u003cp\u003eFor CE and AE at both 30-degree and 50-degree limb positioning, and for total CE and total AE, no significant main effects of group or time, nor a significant group \u0026times; time interaction, were observed (\u003cem\u003eps\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05), except as detailed in the following \u003cem\u003eexploratory secondary analyses by Condition.\u003c/em\u003e\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSecondary Analyses (CE)\u003c/b\u003e: A 2 (Group: BCAA vs. Placebo) \u0026times; 2 (Time: Pre-test vs. Post-test) \u0026times; 2 (Condition: 30\u0026deg; vs. 50\u0026deg; elbow flexion) mixed-design ANOVA was conducted on proprioception CE. This analysis revealed a significant main effect of condition, \u003cem\u003eF\u003c/em\u003e(1, 32)\u0026thinsp;=\u0026thinsp;20.685, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e​=.393, indicating higher degrees of CE in 50\u0026deg; (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.80, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.45) relative to 30\u0026deg; (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.54, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.51). More importantly, a significant three-way Group \u0026times; Time \u0026times; Condition interaction was observed, \u003cem\u003eF\u003c/em\u003e(1, 32)\u0026thinsp;=\u0026thinsp;8.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.006, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e​=.21. No other main effects or interactions were significant (ps\u0026thinsp;\u0026gt;\u0026thinsp;.05).\u003c/p\u003e \u003cp\u003eTo unpack the significant three-way interaction for CE, Bonferroni-adjusted pairwise comparisons were performed. For the BCAA group, at the 50\u0026deg; condition, proprioception errors significantly decreased from pre-test (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;5.82, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.07) to post-test (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.32, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.21; \u003cem\u003eMD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.50, p\u0026thinsp;=\u0026thinsp;.026). This indicates an improvement, i.e., reduced undershooting. Also for the BCAA group, at pre-test, errors in the 50\u0026deg; condition (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;5.82, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.07) were significantly higher (more negative, indicating greater undershooting) compared to the 30\u0026deg; condition (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.94, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.02; \u003cem\u003eMD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). For the BCAA group, at post-test, errors in the 50\u0026deg; condition (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.32, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.21) were significantly higher (more negative, indicating greater undershooting) compared to the 30\u0026deg; condition (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.44, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.01; \u003cem\u003eMD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.026). Furthermore, for the Placebo group, at post-test, errors in the 50\u0026deg; condition (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.86, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.16) were significantly higher (more negative, indicating greater undershooting) compared to the 30\u0026deg; condition (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.16, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.30; \u003cem\u003eMD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.019, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSecondary analyses (AE)\u003c/b\u003e: An additional exploratory 2 (Group: BCAA vs. Placebo) \u0026times; 2 (Time: Pre-test vs. Post-test) \u0026times; 2 (Condition: 30\u0026deg; vs. 50\u0026deg; elbow flexion) mixed-design ANOVA was conducted on proprioception AE. This analysis revealed a significant main effect of Time, \u003cem\u003eF\u003c/em\u003e(1, 32)\u0026thinsp;=\u0026thinsp;7.696, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.009, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e​=.194, and a significant main effect of Condition, \u003cem\u003eF\u003c/em\u003e(1, 32)\u0026thinsp;=\u0026thinsp;17.286, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, ηp2​=.351. More importantly, a significant three-way Group \u0026times; Time \u0026times; Condition interaction was observed, \u003cem\u003eF\u003c/em\u003e(1, 32)\u0026thinsp;=\u0026thinsp;4.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.038, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e​=.127. The Group \u0026times; Condition interaction approached statistical significance, \u003cem\u003eF\u003c/em\u003e(1, 32)\u0026thinsp;=\u0026thinsp;4.125, p\u0026thinsp;=\u0026thinsp;.051, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e​=.114. No other main effects or interactions were significant (ps\u0026thinsp;\u0026gt;\u0026thinsp;.05). To unpack the significant three-way interaction for AE, Bonferroni-adjusted pairwise comparisons were performed. Between-group comparisons revealed that at Post-test, at the 50\u0026deg; condition, the BCAA group (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.93, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.63) exhibited significantly lower AE compared to the Placebo group (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.59, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.05; \u003cem\u003eMD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.657, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.010). Within the BCAA group, AE at the 50\u0026deg; condition significantly decreased from Pre-test (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.73, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.99) to Post-test (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.93, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.63; \u003cem\u003eMD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.794, p\u0026thinsp;=\u0026thinsp;.005). Additionally, At Pre-test, for the BCAA group, AE at the 50\u0026deg; condition (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.73, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.99) was significantly higher than at the 30\u0026deg; condition (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.08, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.91; \u003cem\u003eMD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.647, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.009). This difference was not significant at Post-test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.28).\u003c/p\u003e \u003cp\u003eFor the Placebo group, AE at the 50\u0026deg; condition (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003ePre\u003c/em\u003e\u003c/sub\u003e​=6.53, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003e\u003cem\u003ePre\u003c/em\u003e\u003c/sub\u003e​=3.88; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003ePost\u003c/em\u003e\u003c/sub\u003e​ =5.59, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003e\u003cem\u003ePost\u003c/em\u003e\u003c/sub\u003e​=3.05) was consistently and significantly higher than at the 30\u0026deg; condition (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003ePre\u003c/em\u003e\u003c/sub\u003e​=4.25, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003e\u003cem\u003ePre\u003c/em\u003e\u003c/sub\u003e​=2.68; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003ePost\u003c/em\u003e​\u003c/sub\u003e=2.78, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003e\u003cem\u003ePost\u003c/em\u003e​=\u003c/sub\u003e1.63) at both Pre-test (\u003cem\u003eMD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.275, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.022) and Post-test (\u003cem\u003eMD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.804, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eVariable Error (VE)\u003c/strong\u003e \u003cp\u003eFor variable error (VE), a significant Group \u0026times; Time interaction was observed, \u003cem\u003eF\u003c/em\u003e(1, 36)\u0026thinsp;=\u0026thinsp;10.72, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e = .245. Pairwise comparisons using Bonferroni adjustments revealed a marginally significant between-group difference at pre-test (\u003cem\u003ep=\u003c/em\u003e .050) with higher VE for the control (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.73, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.53), compared to BCAA (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.20, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.82) group. A significant between-group difference was also found at post-test (\u003cem\u003ep\u003c/em\u003e = .027), where the BCAA group (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10.00, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.87) exhibited significantly higher VE scores than the Control group (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.11, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.89). Regarding within-group changes, the BCAA group\u0026rsquo;s VE scores significantly increased from pre- (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.20, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.82) to post-test (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10.00, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.87; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.022). Conversely, the Control group\u0026rsquo;s VE scores significantly decreased from pre- (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.73, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.53) to post-test (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.11, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.89; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.033) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the neurochemical underpinnings of motor imagery and proprioceptive perception, specifically exploring the causal role of dopamine in modulating these processes. Our findings offer a nuanced perspective, revealing both shared vulnerabilities (e.g., dopamine's consistent impact on internal visual imagery and proprioceptive consistency) and intriguing dissociations (e.g., differential effects across imagery modalities or specific proprioceptive angles) that refine our understanding of how the brain simulates and perceives movement (Pearson et al., 2015).\u003c/p\u003e \u003cp\u003eTo start with, our manipulation check confirmed that BCAA supplementation effectively dampened central dopaminergic activity. This was evidenced by a significant Group \u0026times; Time interaction on prolactin levels: while the Placebo group exhibited a typical diurnal decline in prolactin, the BCAA group's prolactin levels remained stable, effectively preventing this natural decrease (Gijsman et al., 2002; Neuhaus et al., 2009; Scarna et al., 2005). While peripheral prolactin is an indirect marker, the selective behavioral effects observed strongly point to the involvement of the nigrostriatal pathway, a system crucial for motor control and known to be compromised in conditions like Parkinson's disease (Dauer \u0026amp; Przedborski, 2003). This precise neurochemical modulation allowed us to move beyond correlational studies and directly investigate the causal role of dopamine in these domains.\u003c/p\u003e \u003cp\u003eA key finding was the selective impairment of internal visual motor imagery under reduced dopaminergic tone. The Placebo group showed significant improvements in their ability to vividly imagine movements from a first-person perspective, reflecting a natural practice effect (Brietzke, Cesario, Hettinga, \u0026amp; Pires, 2022). In contrast, the BCAA group exhibited no improvement in internal visual imagery; their scores remained stable or even slightly decreased, resulting in significantly lower scores at post-test compared to the Placebo group. This finding suggests that our experimentally reduced dopaminergic tone specifically hindered the optimization or refinement of these internally generated, first-person motor representations. This observation aligns with aspects of Motor Simulation Theory (MST), a perceptualist account which posits that internal motor imagery recruits neural machinery also involved in actual movement. Our results indicate that central dopamine availability is crucial for the efficient generation or refinement of such vivid, first-person motor imagery. Dopamine's role in modulating the precision and plasticity of these forward models (Friston et al., 2012; Kilner \u0026amp; Friston, 2010) directly implicates it in the fidelity and refinement of internal simulation.\u003c/p\u003e \u003cp\u003eCrucially, this effect was selective within motor imagery modalities. Kinesthetic and external visual imagery remained largely unaffected by the dopaminergic challenge. This dissociation challenges a strong, unitary perceptualist view and supports the idea that motor imagery is a multi-component process (Glover \u0026amp; Baran, 2017). Kinesthetic imagery, focused on the feeling of movement, may rely more on efference copy and proprioceptive feedback, likely modulated by distinct or less acutely dopamine-sensitive systems (Kilteni et al., 2018). External visual imagery, involving a third-person perspective, may engage higher-level conceptual representations and distinct neural substrates such as the temporoparietal junction and precuneus (Blanke, Slater, \u0026amp; Serino, 2015; Ionta et al., 2011). These areas are associated with perspective-taking (Cavanna \u0026amp; Trimble, 2006; Saxe \u0026amp; Kanwisher, 2013) and are not traditionally implicated in dopamine-rich motor loops (Obeso et al., 2008). Thus, the sparing of external visual imagery is consistent with non-perceptualist frameworks that emphasize abstract planning (Cavedon-Taylor, 2021).\u003c/p\u003e \u003cp\u003eRegarding proprioceptive accuracy, our findings revealed a nuanced deficit. While the average accuracy of passive limb positioning (constant error, CE, and absolute error, AE) remained largely preserved when analyzed as a whole, variable error (VE), an indicator of movement consistency, significantly increased in the BCAA group, contrasting with a decrease in VE observed in the Placebo group. Consequently, at post-test, the BCAA group exhibited significantly higher VE compared to controls. This suggests that while participants could still identify target positions on average, their perceptual judgments became less consistent\u0026mdash;a pattern linked to instability in internal models (Synofzik, Thier, Leube, Schlotterbeck, \u0026amp; Lindner, 2010). Dopamine is thought to calibrate the precision of these models, and the increased variability under dopaminergic depletion fits with computational accounts where dopamine modulates the confidence in predictions (K. Friston, Kilner, \u0026amp; Harrison, 2006; K. J. Friston et al., 2012). These results are consistent with motor control models in Parkinson\u0026rsquo;s disease, where patients often exhibit increased variability in perceptual-motor tasks (Davis, Sivaramakrishnan, Rolin, \u0026amp; Subramanian, 2025; Helmich et al., 2007; Jones et al., 2011).\u003c/p\u003e \u003cp\u003eThe exploratory analyses on proprioception Constant Error (CE) and Absolute Error (AE), broken down by specific joint angles, further illuminate the intricate role of dopamine and its impact on proprioception. The significant three-way interaction of Group \u0026times; Time \u0026times; Condition for both CE and AE demonstrates that the changes in proprioceptive errors over time differ between groups and across the 30\u0026deg; and 50\u0026deg; conditions in complex ways. Notably, for the BCAA group, we observed a significant improvement in proprioceptive accuracy at the 50\u0026deg; condition from pre- to post-test, as evidenced by a reduction in both constant error (CE, i.e., reduced undershooting) and absolute error (AE). This suggests that despite dampened dopaminergic tone, participants in the BCAA group were able to refine their accuracy in the more kinesthetically demanding 50\u0026deg; elbow flexion. This seemingly paradoxical finding could indicate a task-dependent adaptation, where, despite a general dopaminergic modulation, specific mechanisms for error correction in a challenging range of motion remain functional or even show improvement.\u003c/p\u003e \u003cp\u003e However, this improvement did not eliminate a persistent relative deficit at the 50\u0026deg; angle. Even before the intervention, at pre-test, the BCAA group's errors in the 50\u0026deg; condition were significantly higher (both more undershooting in CE and higher in AE) compared to the 30\u0026deg; condition, indicating that the 50\u0026deg; angle may be inherently more challenging or less accurately perceived for this group. Intriguingly, this pattern persisted at post-test for the BCAA group in terms of CE, where errors in the 50\u0026deg; condition remained significantly higher compared to the 30\u0026deg; condition. While AE for the BCAA group at 50\u0026deg; was no longer significantly higher than 30\u0026deg; at post-test, suggesting a notable reduction in overall error magnitude at this challenging angle, the persistent CE difference points to a remaining directional bias. This suggests that while some learning or adaptation occurred at 50\u0026deg;, the underlying dopaminergic modulation might limit the overall precision or ability to reach the same level of accuracy as in the less demanding 30\u0026deg; condition.\u003c/p\u003e \u003cp\u003eThese angle-specific challenges were also evident in the Placebo group. At post-test, the Placebo group also exhibited significantly higher absolute errors at the 50\u0026deg; condition compared to the BCAA group, indicating that the BCAA intervention conferred some benefit in reducing overall error magnitude at this challenging angle. Furthermore, for the Placebo group, absolute errors were consistently higher at the 50\u0026deg; condition relative to the 30\u0026deg; condition in both pre- and post-tests, reinforcing the notion that the 50\u0026deg; angle presents a greater proprioceptive challenge irrespective of dopaminergic manipulation. Similarly, for the Placebo group, constant errors at post-test in the 50\u0026deg; condition were significantly higher compared to the 30\u0026deg; condition.\u003c/p\u003e \u003cp\u003eThese comprehensive findings suggest that dopaminergic modulation might influence the learning and consolidation of positional accuracy in a non-uniform way, impacting the processing of more challenging or less frequently encountered joint angles differently. The consistent observation of higher errors at 50\u0026deg; compared to 30\u0026deg; across groups and time points (especially in the BCAA group and Placebo post-test) suggests a general biomechanical or perceptual difficulty at that angle, which is then differentially influenced by dopaminergic tone. This detailed understanding of proprioceptive alterations under dopaminergic manipulation moves beyond a simple global accuracy deficit to highlight a more complex, context-dependent influence.\u003c/p\u003e \u003cp\u003eThe parallel\u0026mdash;but qualitatively distinct\u0026mdash;disruptions in internal visual imagery and proprioceptive variability, alongside the nuanced angle-specific effects on constant error and absolute error in proprioception, under dopaminergic challenge support a refined perceptualist account. Both motor imagery (specifically internal visual) and proprioception (particularly consistency and angle-specific adaptation) depend on dopamine-sensitive internal modeling mechanisms, but the sparing of other imagery types underscores the modularity of motor simulation (Hardwick et al., 2018). This may move the debate beyond a binary perceptualist vs. non-perceptualist framing, toward a more biologically realistic hybrid model.\u003c/p\u003e \u003cp\u003eOur findings carry important clinical implications. The selective disruption of internal visual motor imagery, coupled with increased proprioceptive variability, and the differential impact on proprioception at specific joint angles, mirror patterns observed in early Parkinson\u0026rsquo;s disease (Heremans et al., 2011; Helmich et al., 2007). Dopamine depletion is also linked to disrupted motor simulation in schizophrenia (Chen et al., 2015) and may contribute to altered bodily self-awareness in depersonalization and psychosis (B\u0026uuml;etiger et al., 2020; Mograbi, Rodrigues, Bienemann, \u0026amp; Huntley, 2024). These insights may inform new strategies for neurorehabilitation, such as pairing dopaminergic medication with first-person motor imagery training, particularly for specific motor tasks or joint ranges that are more sensitive to dopaminergic modulation. More broadly, dopaminergic influences on simulation may extend to performance domains such as sports and neuroprosthetics, where internal models play a critical role in real-time control and motor learning (Cano-De-La-Cuerda et al., 2015; Kawato \u0026amp; Wolpert, 2007).\u003c/p\u003e \u003cp\u003eHowever, certain limitations should be acknowledged. Prolactin is an indirect index of dopamine, and future studies should include brain imaging for more direct mapping of dopaminergic modulation (Nagano-Saito, Martinu, \u0026amp; Monchi, 2014; Oldehinkel et al., 2022). Our healthy adult sample allowed for tight experimental control, but extending this paradigm to clinical populations with chronic dopamine dysregulation (e.g., PD, schizophrenia) will provide critical ecological validity. Additionally, supplementing the MIQ-3 with objective measures (e.g., chronometry, fMRI decoding, or EEG ERD/ERS) could bolster future assessments of imagery performance (Pilgramm et al., 2016; Zich et al., 2015). Future research could also further explore the biomechanical or neurological reasons why the 50\u0026deg; elbow flexion might show different sensitivity to dopaminergic modulation compared to the 30\u0026deg; flexion, potentially using more detailed kinematic or neural measures.\u003c/p\u003e \u003cp\u003eIn conclusion, our study provides compelling evidence for the causal role of dopamine in shaping both motor imagery and proprioceptive perception. By experimentally modulating central dopaminergic tone, we unveiled a nuanced dissociation: a clear impairment in internal visual motor imagery alongside a subtle but significant impact on proprioceptive consistency, and specific, angle-dependent changes in proprioceptive constant error and absolute error. These findings critically challenge strong versions of perceptualist theories that posit fully shared mechanisms between imagery and perception. Instead, they support a refined model that acknowledges shared dopamine-sensitive internal modeling processes alongside distinct, modality-specific neural substrates and potentially angle-specific processing within a single modality. Our work highlights that specific forms of motor simulation, particularly first-person visual imagery, are exquisitely sensitive to neurochemical fluctuations. This offers valuable insights into the complex neurochemical architecture of embodied cognition, with direct implications for understanding and treating motor and perceptual deficits in neurological and psychiatric conditions, as well as for optimizing performance in fields like sports and neuroprosthetics. Future research employing more direct measures of brain dopamine and expanding to clinical populations will further illuminate these intricate relationships.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank participants who participated in this study.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Declaration of competing interests:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no any conflict of interest in the present work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI in scientific writing:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the author(s) used generative AI tools including ChatGPT and Google Gemini in order to improve the readability and language of the manuscript. After using these tools, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author, upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParisa Hejazi Dinan and Moslem Bahmani designed and conceptualized the study. Moslem bahmani wrote the initial draft of the manuscript. Davoud Fazeli, and Gholamhossain Nazemzadegan edited the drafted manuscript. Usef Garmanjani contributed to the design, data collection and processing. All participants read and approved the final submission.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBlanke, O., Slater, M., \u0026amp; Serino, A. (2015). 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Real-time EEG feedback during simultaneous EEG\u0026ndash;fMRI identifies the cortical signature of motor imagery. \u003cem\u003eNeuroimage, 114\u003c/em\u003e, 438-447. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"psychological-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prpf","sideBox":"Learn more about [Psychological Research](http://link.springer.com/journal/426)","snPcode":"426","submissionUrl":"https://submission.nature.com/new-submission/426/3","title":"Psychological Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Dopaminergic, Imagery, Neuromodulation, Perception, Brain dysfunction","lastPublishedDoi":"10.21203/rs.3.rs-6828758/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6828758/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe relationship between mental imagery and perception is a key debate in cognitive neuroscience. This study experimentally investigated how acute dopaminergic modulation influences motor imagery and proprioceptive perception. We conducted a double-blind, placebo-controlled, pre-post study with 42 healthy young adults, using branched-chain amino acids (BCAAs) to indirectly manipulate dopaminergic tone. We assessed serum prolactin levels, motor imagery ability (MIQ-3), and passive limb positioning performance. Results confirmed successful dopaminergic modulation: the BCAA group's prolactin levels remained stable, while controls showed a typical diurnal decrease. This modulation selectively impaired internal visual motor imagery, suppressing the natural improvement observed in the placebo group. For proprioception, overall mean Constant Error (CE) and Absolute Error (AE) were unaffected; however, motor consistency (Variable Error, VE) significantly worsened in the BCAA group and improved in controls. Exploratory analyses also revealed complex, angle-dependent changes in CE and AE. This nuanced dissociation suggests dopamine differentially affects motor simulation and perception. Findings challenge strong perceptualist theories, supporting partial functional dissociation and highlighting the sensitivity of specific motor imagery and angle-specific proprioceptive processing to neurochemical fluctuations.\u003c/p\u003e","manuscriptTitle":"Dopamine, Motor Imagery, and Proprioception: A Neurochemical Probe into the Perception-Imagery Debate","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-20 10:45:12","doi":"10.21203/rs.3.rs-6828758/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-11T09:04:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-06T08:07:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-06T03:25:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Psychological Research","date":"2025-06-05T11:27:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"psychological-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prpf","sideBox":"Learn more about [Psychological Research](http://link.springer.com/journal/426)","snPcode":"426","submissionUrl":"https://submission.nature.com/new-submission/426/3","title":"Psychological Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a174d964-07de-47ab-8f1f-6a67a8749b2a","owner":[],"postedDate":"June 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T16:12:14+00:00","versionOfRecord":{"articleIdentity":"rs-6828758","link":"https://doi.org/10.1007/s00426-025-02225-x","journal":{"identity":"psychological-research","isVorOnly":false,"title":"Psychological Research"},"publishedOn":"2025-12-26 15:57:14","publishedOnDateReadable":"December 26th, 2025"},"versionCreatedAt":"2025-06-20 10:45:12","video":"","vorDoi":"10.1007/s00426-025-02225-x","vorDoiUrl":"https://doi.org/10.1007/s00426-025-02225-x","workflowStages":[]},"version":"v1","identity":"rs-6828758","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6828758","identity":"rs-6828758","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}