Contribution of displacement, duration, and velocity on auditory motion direction perception in macaque monkeys

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Schoenhaut, Ramnarayan Ramachandran, Mark T. Wallace This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6254702/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Motion perception is a key aspect of sensory processing that enables successful interaction with the environment. While visual motion perception has been extensively studied, little is known about the determinants of auditory motion perception. Our study explores how the perception of auditory motion direction changes with manipulations of low-level stimulus parameters in nonhuman primates (NHPs). Macaque monkeys were trained to perform a 2-AFC task in which they judged the direction of noisy auditory motion stimuli. We systematically manipulated stimulus duration, velocity, and displacement to evaluate their respective influence on motion sensitivity. Displacement had the greatest impact, while the relative influence of duration versus velocity depended upon the duration of the stimulus. These findings suggest that auditory motion direction is most likely processed by a snapshot mechanism, in which stimulus velocity is inferred by sequential snapshots of auditory stimulus location, rather than by velocity-selective motion detectors similar to those found in the visual system. To our knowledge, this study is the first to characterize the influence of low-level stimulus parameters on auditory motion perception in awake, behaving NHPs, and forms the basis for future neurophysiological investigations. Biological sciences/Neuroscience/Auditory system Biological sciences/Neuroscience/Sensory processing Biological sciences/Neuroscience/Cognitive neuroscience/Perception Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction In typical listening scenarios, it is uncommon for sound location cues to stay fixed over time because the head, body, and external sound sources are frequently in motion. As a result, effective auditory scene analysis relies on the capacity to encode movement - a change in location over time. The resulting property of velocity is derived from the stimulus’ duration and displacement of motion. However, it is unclear how the stimulus factors of velocity, displacement, and duration are weighted during auditory motion perception. Sensitivity to these motion parameters must be understood in a psychophysically meaningful way in order to manage their covariation in the experimental design and the interpretation of the effective stimulus features underlying the perception of auditory motion. While there are some examples of cortical and subcortical structures with demonstrated sensitivity to auditory motion[1–7], the nature of the representations in these areas remains debated[8,9]. Sound location is primarily determined by the difference in time (interaural time difference, ITD) and sound level (interaural level difference, ILD) reaching the two ears[10,11], as well as by monaural spectral cues[12,13]. Neurons in the superior olivary complex are particularly sensitive to these interaural spatial cues, which allow the auditory system to estimate the azimuthal location of a sound source[14,15]. However, how motion is derived from these spatial cues remains unresolved, with two primary theories proposed. The first, often called the snapshot theory, posits that auditory motion velocity is not directly represented but instead inferred by successively sampling sound location and calculating the change in position over time[16–18]. Since this theory depends on at least two “snapshots” of stimulus location taken over time, the displacement (i.e., change in position) and duration of a moving sound would be the only information needed in order to compute auditory motion[19]. The second theory posits that the auditory system contains specialized motion detectors similar to those seen in the visual system[20–24], with neurons demonstrating selectivity for auditory motion velocity[25]. For such a mechanism to exist, listeners’ behavior would be sensitive to velocity without necessarily being sensitive to the sound’s displacement and duration. Testing between these theories is complicated by the challenges of designing an experiment in which these naturally covarying components of motion can be separated. This complication highlights the need to understand the relative contribution of motion characteristics in a psychophysically meaningful way. Some previous psychophysical studies attempting to differentiate between these two theories have implemented velocity discrimination tasks, therefore biasing responses by directing subjects towards a particular auditory feature (i.e., velocity[26]). Alternatively, a human psychophysics study using an oddball paradigm has suggested that displacement and duration are the primary cues for auditory motion perception, while velocity–unlike in vision[27,28]–is only used when distance and duration cues are unreliable[29]. Evidence regarding these two theories in behaving non-human primates, however, has yet to be gathered. This gap is surprising given that non-human primates have long served as an important model for understanding the neural mechanisms underlying spatial processing, offering critical insights into how sensory information is integrated to guide goal-directed behavior. Non-human primates are uniquely suited for such investigations due to their well-characterized neural circuits and sophisticated behavioral capabilities, which have been shown to closely parallel those of humans in many domains including visual motion processing. In the present study, we aim to determine the degree to which motion velocity, displacement, and/or duration contribute towards perception of auditory motion direction. We trained two macaque monkeys to respond to the direction of a simulated motion stimulus presented at various signal-to-noise ratios, velocities, durations, and displacements, and compared the sensitivity to motion direction across conditions. This study represents the first concerted effort to understand the factors governing primate auditory motion perception for stimuli moving in the azimuthal plane. Our findings indicate that auditory motion direction perception is predominantly driven by displacement cues, with lesser contributions from duration and velocity. Methods Subjects This study contains data from two adult male rhesus macaques ( Macaca mulatta ) obtained from the California National Primate Research Center (Davis, CA). The ages of the macaques, referred to as Monkey “A” and Monkey “B,” were 8 and 6 years old, respectively, at the beginning of the study. Their weights were between 11-13 kg when the study began. The diet of these macaques included a standard feed (LabDiet Monkey Diet 5037 and 5050, Purina, St Louis, MO) with additional fresh produce and items for foraging. Various types of enrichment such as manipulative tools and sensory stimuli (auditory, visual, and olfactory) were provided on a rotating schedule. The macaques underwent fluid restriction and were given municipal water that was filtered. Weekly weight monitoring was conducted 4 to 5 times a week, ensuring the weights remained within the veterinarian-approved reference range for healthy study conditions. The animals were kept on a consistent 12-hour light/dark cycle, with all experimental activities conducted between 8 AM and 6 PM during the light period. Despite efforts through behavioral assessments to find suitable social companions, the macaques were individually housed due to social incompatibility; however, they maintained visual, auditory, and olfactory contact with other macaques within the same room. This housing environment was part of an AAALAC-accredited facility and adhered to the Guide for the Care and Use of Laboratory Animals , the Public Health Service Policy on Humane Care and Use of Laboratory Animals, as well as the Animal Welfare Act and Regulations. The macaques also received regular health checks, including biannual physical examinations and tuberculosis screenings and adjustments to their reference weights as needed. All experimental protocols involving these animals were approved by the Animal Care and Use Committee at Vanderbilt University Medical Center (VUMC) and carried out in accordance with ARRIVE guidelines. Surgical Procedures The preparation of monkeys for behavioral experiments adhered to established methods in nonhuman primate research[30–33]. To minimize head movement and reduce variation in sound pressure level at the ear, a headpost made of stainless steel was affixed to the monkeys’ skulls. This device helped stabilize head position across sessions. First, magnetic resonance imaging (MRI) was conducted in a Philips Intera Achieva 3T scanner, equipped with SENSE Flex-S surface coils positioned either above or below the animal’s head. The purpose of the resulting T1-weighted gradient-echo structural images was to determine the best placement location of the headpost. For surgical procedures, anesthesia was induced using ketamine and midazolam, followed by maintenance with isoflurane. While anesthetized, the headpost was anchored to the skull with 7 mm titanium screws (Gray Matter Research LLC, Bozeman, MT) and was secured further with bone cement (Heraeus Incorporated, Yardley, PA). Throughout all surgical procedures, fluids and antibiotics were administered intra-procedurally, with pre- and post-operative analgesics provided under the supervision of veterinary staff. The monkeys were continuously monitored under veterinary oversight until the monkeys fully recovered from surgery. Apparatus and Stimuli Monkeys were seated and head restrained in a primate chair that was designed and constructed in-house and secured to a fixed pedestal, located 21.65 inches from the screen and speakers that delivered visual and auditory stimuli. Stimulus generation, event timing, task control, and fluid reward delivery were done with MatLab (The MathWorks, Inc., Natick, MA) and OpenEx software (System 3, TDT Inc., Alachua, FL). Stimuli were presented using PsychToolbox version 3[34]. Auditory stimuli were presented in the free field via two speakers (3W 8 ohm 2x3 inch loudspeaker, Allied Electronics, Inc) located 34 inches (78°) apart in the frontal field. Each speaker was calibrated with a ¼ inch microphone (378C01, PCB Piezotronics) positioned at the location where the entry to the monkey’s ear canal would be during experiments. The speaker was calibrated to ensure that outputs were within 3 dB up to 20 kHz. Auditory stimuli (Figure 1A) were adapted from a previous study[16] and consisted of broad-band (0.005-24.4 kHz) white noise signals (10ms rise and fall) sampled at a rate of 48828 samples per second. The motion signal was either leftward or rightward, embedded in partially correlated noise, and played through the speakers. Each condition was presented at 12 different log-spaced noise levels (i.e. coherences) ranging from fully noise to fully signal. The auditory stimuli consisted of four different components. First, individual white noise streams were presented through each speaker (100% amplitude; inter-signal correlation=0; N1 and N2). Second, a combined white noise signal was presented through both speakers (100% amplitude, inter-signal correlation = 1; N3). The fourth signal stream contained the apparent motion cue, in which the sound’s amplitude faded between the two speakers from 100% to 0% over the course of one trial (inter-signal-correlation = 0.5; N4) to create binaural motion cues. The velocity, duration, and displacement (centered at midline) varied between conditions (Table 1). Visual task components (fixation point and targets) were presented on a 1280x1024 cathode ray tube (CRT) monitor screen (HP p1230 22”) located in the frontal field 21.65 inches from the monkey’s head at eye level and centered horizontally between the two speakers. The monitor had a refresh rate of 75.025 Hz, and the screen subtended 40.56° × 30.98° of the visual angle. Eye position was monitored and recorded continuously at 120 Hz using an eye tracker (Applied Science Laboratories Eye-Trac 6). Eye position was calibrated daily prior to each monkey beginning the task. A Minolta Chroma Meter CS-100 was used to verify the luminance of the visual stimuli. Behavioral Training and Task Design The sequence of each trial is depicted in Figure 1B. At the beginning of each trial, a 0.4° diameter white fixation point (111 cd/m²) appeared on a black background (0.7 cd/m²). The monkey had one second to initiate fixation on the fixation point within an 8° diameter circular window and maintain it for 0.2 seconds. If fixation was maintained, then the auditory stimulus (simulated leftward or rightward motion) was presented for the duration specified by the condition. If fixation was maintained during stimulus presentation, the fixation point was extinguished at the end of the stimulus duration and a white, 0.4° diameter target point appeared on the left and right sides of the screen. Once the targets appeared, the monkey had 1.5 seconds to initiate and maintain fixation for 0.15 seconds on the target corresponding to the direction of motion of the stimulus. If the monkey responded correctly, a fluid reward (water or juice) was dispensed from a spout. The subsequent trial began after a 0.8 second inter-trial interval. If the monkey failed to initiate fixation within the waiting period or broke fixation early once initiated at any point during the trial, they were given a 2.5 second time out with a black, blank screen. Trials for each coherence were ordered randomly across the session. At least 300 trials for each coherence were collected per condition per monkey across several sessions. Analyses For each condition (i.e. each unique combination of stimulus velocity, duration, and displacement; Table 1) for each monkey, the probability of rightward response across coherences for leftward (negative coherences) and rightward (positive coherences) trials were plotted then fit to a modified cumulative gaussian that takes into account lapse rate. The slope of the dynamic range of this function was then calculated and taken to represent behavioral sensitivity to auditory motion direction. Mean slopes and standard deviations were calculated using a Monte Carlo resampling procedure (also known as Repeated Random Subsampling Cross-Validation), where in each iteration, a random 10% of the data was removed, the slope was calculated on the remaining 90%, and this process was repeated for 1000 permutations. For each monkey, slopes across conditions were then fit with all possible unique linear regression models that could be created with slope as the dependent variable and velocity, duration, and/or displacement (and their two- and three-way interactions) as the independent variables. This was also done for each of the 1000 different data sets created in the aforementioned permutation testing, each with one slope per unique condition. The adjusted R 2 values for each model was calculated by modifying the R 2 values to account for the number of predictors and the sample size. This adjustment involved subtracting the proportion of unexplained variance, scaled by the ratio of the total sample size minus one to the sample size minus the number of predictors minus one. This scaling penalized the mean R 2 value for models with a higher number of predictors, ensuring that the metric reflects the model's true explanatory power rather than the mere inclusion of additional variables. Adjusted R 2 values for each model were then averaged across the 1000 permutations for each monkey, and 95% confidence intervals were calculated. An additional set of linear regression models and mean adjusted R 2 values was also created in the same manner, but with slopes from conditions with durations below 0.367s excluded from the data set. Results Psychophysical performance First, we analyzed psychometric performance for each monkey on the motion direction task to compare their performance across conditions with the same duration, displacement, and velocity as expemplified in Figure 1C. These functions plot the proportion of rightward responses as a function of stimulus coherence, with random motion (coherence 0) at the x-axis center and fully coherent motion (coherence -1 and 1) at either extreme. Since performance improved with increased stimulus coherence (higher proportion rightward response on rightward trials, lower proportion rightward response on leftward trials), comparing the slopes of these psychometric functions across conditions allowed us to assess the monkeys' sensitivity to auditory motion. Figures 2, 3, and 4 show these psychometric functions for both monkeys under three sets of conditions, respectively: those with the same duration but different velocities and displacements, those with the same velocity but different displacements and durations, and those with the same displacement but different durations and velocities. Figure 5 summarizes these results by plotting the slopes of each psychometric function for the various conditions and combinations. Figure 2 shows the psychometric functions for the two monkeys under conditions with the same duration (0.834 seconds) stimulus. This duration was chosen because it fell in the middle of the range of durations included in our condition set, and allowed for the displacements in the constant duration and constant velocity condition sets to be the same. Note the similarity in the general shape of the psychometric curves for the two monkeys. When duration was held constant at 0.834s, psychometric function slopes increased with increases in velocity and displacement for both monkeys. Thus, monkeys were more sensitive to auditory motion direction at higher displacements and velocities. To determine whether this effect is predominantly driven by displacement versus velocity and thus to elucidate the contribution of duration, two additional sets of experiments were conducted. In the second set of experiments, velocity was held constant at 59.95°/s (Figure 3). This velocity was chosen because it fell in the middle of the range of velocities possible in our experimental setup. It also allowed for the stimulus displacements to match those in the constant duration set of conditions, and the durations to match those in the constant displacement set of conditions. Again, note the similarities in the psychometric curves between the two animals. When velocity was held constant, the slope of the psychometric functions increased with increases in auditory motion duration and displacement. Note that the slopes for both monkeys were lowest (i.e., shallowest) for the 8°, 0.133s condition. To round out the analyses, a final set of experiments was needed to elucidate whether displacement or duration is responsible for this reduced sensitivity to auditory motion direction. In the third set of experiments, displacement was held constant at 22°, while velocity and duration varied in each condition. This displacement was chosen because it fell in the middle of the range of displacements possible in our experimental setup, and allowed for the stimulus durations in these conditions to match those in the constant velocity set of conditions. In the psychometric functions generated in these experiments, there was little increase in slope with increasing duration for durations longer than 0.367 seconds (Figure 4; 5C). This suggests that sensitivity to auditory motion direction was most influenced by stimulus duration under our set of velocities and displacements – at least for stimuli less than 0.367 seconds in duration. Therefore, the minimum time needed for temporal integration of auditory motion cues likely lies somewhere between 0.133 and 0.367 seconds. At this point, the subjects had likely accumulated enough sensory evidence to make a decision regarding the direction of the auditory motion stimulus, so the additional evidence did little to improve sensitivity. Further support for this idea is detailed below. Figure 5 summarizes the results of the psychometric functions shown in Figures 2 - 4. Figure 5 shows the changes in sensitivity (mean slope of the psychometric function calculated using a Monte Carlo resampling procedure with 1000 permutations) as a function of one of the parameters for changes in the other two parameters. Thus, in Figure 5A, the mean slopes from the constant duration (varying velocity, solid lines) and constant velocity (varying duration, dotted lines) condition sets were plotted against displacement. Overall, increasing displacement resulted in increased mean slopes in both sets of conditions. This same general pattern can be seen for both monkeys (red–Monkey A, black–Monkey B). In Figure 5B, the mean slopes from the constant displacement (dashed lines) and constant duration (solid lines) condition sets were plotted against velocity. In the constant displacement conditions, higher velocities had lower durations, while in the constant duration conditions, higher velocities had higher displacements. As velocity increases, both monkeys show an overall increase in auditory motion direction sensitivity in the constant duration (varying displacement) condition set, peaking at a slope of 1.75 (standard deviation=0.021) for Monkey A and 2.36 (standard deviation=0.047) for Monkey B, while for the constant displacement (varying duration) condition set, mean slopes peak at 1.29 for Monkey A (standard deviation=0.061) and 1.23 for Monkey B (standard deviation=0.026). Again, the overall pattern is similar in both monkeys. The overall differences in how sensitivity changes with velocity between the constant duration (varying displacement) and constant displacement (varying duration) sets for both monkeys suggests a greater impact of changing displacement than changing duration on auditory motion sensitivity. This begs the question of whether the increase in mean slope with increasing displacement in the constant velocity (varying duration) condition set seen in Figure 5A is being driven by the changes in displacement rather than changes in duration. To test this, we plotted the constant displacement (varying velocity, dashed lines) and constant velocity (varying displacement, dotted lines) condition sets against duration in Figure 5C. Both monkeys performed similarly for both condition sets for durations of 0.133 and 0.367 seconds. For durations above 0.367 seconds, results for each condition set begin to deviate. For the constant velocity (varying displacement) condition set, both monkeys show a progressive increase in mean slope with increasing duration, peaking at a mean slope of 1.86 (standard deviation=0.029) for Monkey A and 2.36 (standard deviation=0.051) for Monkey B; however, for the constant displacement (varying velocity) condition set, overall sensitivity to auditory motion seems to plateau, peaking at 1.29 (standard deviation=0.061) for Monkey A and standard deviation=0.026) for Monkey B. Modeling Although it is not possible to fully disentangle the effects of duration, displacement, and velocity due to their inherent collinearity, the design of our condition set allowed us to assess the relative contribution of each motion attribute and their combinations to behavioral outcomes. To achieve this, we generated all possible unique linear regression models using slope as the dependent variable, with velocity, duration, and/or displacement (along with their two- and three-way interactions) as independent variables. We then compared the mean adjusted R² values of each model—calculated using Monte Carlo resampling with 1000 permutations—to determine which motion parameters or combinations best explained the data while accounting for model complexity. A higher mean adjusted R² indicated a better model fit. Figure 6A and B display the mean adjusted R² values for models run on all conditions for each monkey separately. While some differences were observed between the two animals, the overall pattern of results was highly similar. The model that included only velocity as a predictor (Model 1) did not significantly explain variance in slope (Monkey A: n = 15, coefficient estimate = 0.001, standard error = 0.003, t = 0.150, p = 0.884; Monkey B: n = 15, coefficient estimate = 0.002, standard error = 0.005, t = 0.455, p = 0.657). Models that included only duration (Model 2) or both duration and velocity (Model 4) as main effects had significantly lower mean adjusted R² values than all other models, except Model 1 (Mann-Whitney U tests with Bonferroni correction, n = 1000, α = 0.0029; Monkey A: p < 1.0e-150; Monkey B: p < 1.0e-300). However, Model 4 (which included both duration and velocity) had a significantly higher mean adjusted R² than Model 2 (which included only duration), indicating that velocity provided additional explanatory power when combined with duration (Mann-Whitney U test with Bonferroni correction, n = 1000, α = 0.0029; Monkey A: p = 3.313e-192; Monkey B: p = 1.067e-317). All subsequent models shown in Figure 6A and B (except for Model 9: slope ~ 1 + velocity * duration) included displacement as an independent variable, either on its own or as part of a two- or three-way interaction term. These models produced significantly higher mean adjusted R² values than models without displacement for both monkeys (Mann-Whitney U tests with Bonferroni correction, n = 1000, α = 0.0029; Monkey A: p < 1.0e-235; Monkey B: p < 1.0e-300). Notably, the models slope ~ 1 + velocity + duration (Model 4; Mann-Whitney U tests with Bonferroni correction, n = 1000, α = 0.0029; Monkey A: p = 6.544e-238; Monkey B: p = 1.020e-317) and slope ~ 1 + duration (Model 2; Mann-Whitney U tests with Bonferroni correction, n = 1000, α = 0.0029; Monkey A: p = 1.228e-245; Monkey B: p = 8.046e-320) had significantly lower mean adjusted R² values than the model with displacement as the sole predictor (Model 3), further highlighting the dominant role of displacement in explaining variations in psychometric function slope. Additionally, we examined the linear regression models (n=13) that included duration, displacement, and velocity as main effects (Model 7: slope ~ 1 + velocity + duration + displacement). For Monkey A, neither duration (coefficient estimate = 0.030, standard error = 0.32, t = 0.094, p = 0.927) nor velocity (coefficient estimate = -0.003, standard error = 0.003, t = -1.219, p = 0.251) had a significant effect on slope. Similarly, for Monkey B, duration (coefficient estimate = -0.214, standard error = 0.219, t = -0.976, p = 0.350) did not significantly predict slope, though velocity showed a marginally significant effect (coefficient estimate = -0.005, standard error = 0.002, t = -2.370, p = 0.037). In contrast, displacement had the strongest influence on slope for both monkeys, with significant effects observed in Monkey A (coefficient estimate = 0.019, standard error = 0.004, t = 5.238, p = 3.798e-4) and Monkey B (coefficient estimate = 0.030, standard error = 0.003, t = 10.226, p = 5.911e-7). Since increasing stimulus duration beyond 0.367 seconds resulted in only minimal improvements in sensitivity to auditory motion direction (Figures 3, 4, 5B, and 5C), we reanalyzed the data using only trials with durations of 0.367 seconds or longer (Figures 6C and 6D). This allowed us to assess whether the influence of duration observed in earlier models was primarily driven by shorter-duration trials. When focusing on longer-duration conditions, the effect of duration weakened. The duration-only model (Model 2) was not significant for either monkey (Monkey A: coefficient estimate = 0.594, standard error = 0.456, t = 1.301, p = 0.22; Monkey B: coefficient estimate = 0.517, standard error = 0.641, t = 0.806, p = 0.437). In contrast, velocity became a stronger predictor, as the velocity-only model (Model 1) was significant under these conditions (Monkey A: coefficient estimate = 0.013, standard error = 0.004, t = 3.75, p = 0.003; Monkey B: coefficient estimate = 0.020, standard error = 0.004, t = 5.584, p = 1.64e-4). Despite this shift, models that included displacement continued to explain significantly more variance than those that excluded it (Mann-Whitney U tests with Bonferroni correction, n = 1000, α = 0.0029; Monkey A: p = 0; Monkey B: p < 2.9e-320). These findings suggest that the greater influence of duration relative to velocity observed in the full dataset (Figures 6A and 6B) was primarily driven by trials with shorter durations. Discussion Although some cortical and subcortical structures are responsive to auditory motion, how they represent and process motion remains debated. This study provides evidence as to whether auditory motion perception relies on specialized motion detectors or infers motion through sequential sound location processing. While velocity, duration, and displacement play distinct roles in each mechanism, their relative contributions to motion perception were previously unexplored. Overall, our results suggest that the duration, displacement, and velocity of an auditory motion stimulus all have the capacity to influence sensitivity to motion direction. However, out of the three tested motion parameters, displacement was found to have the greatest effect on lateral (leftward vs. rightward) direction perception. Additionally, the impact of duration or velocity also depended on the duration of the stimulus. For short duration (i.e., less than 0.367 seconds) stimuli, duration was more influential than velocity. For longer-duration stimuli, velocity was the more impactful motion parameter. These results suggest that there is a duration threshold between 0.133 and 0.367 seconds, beyond which longer stimulus durations do not facilitate accurate auditory motion direction perception. These results provide supporting evidence for a so-called snapshot mechanism for auditory motion perception, in which displacement cues are weighted more heavily than velocity cues. With such a mechanism, “snapshots” of auditory stimulus location are sampled at different points in time over the duration of the stimulus using sound localization cues such as ITDs and ILDs, and velocity is then inferred from the displacement of the sound source between snapshots. In such a model, duration and displacement information alone would be sufficient to support auditory motion processing. Our findings show that displacement alone accounted for the most variance across both model sets, while duration alone explained the most variance when including stimuli of 0.367 seconds or less, supporting this theory. When modeling data from all conditions, the model with velocity as the only independent variable was not significant. This indicates that for the range of velocities, durations, and displacements included in our experiment, velocity information alone is not sufficient to account for monkeys’ sensitivity to auditory motion direction. It is therefore unlikely that a velocity detector mechanism for auditory motion processing exists. Interestingly, our modeling results for longer duration stimuli (> 0.367 s) show pairing of velocity information with displacement and/or duration information yielded mean adjusted R 2 values as high or higher than those of displacement information alone. Nevertheless, if a velocity detector mechanism were to dominate motion processing, listeners’ behavior would have to be highly sensitive to velocity without necessarily being sensitive to displacement and duration. At least for neocortical structures, it appears highly likely that sound-source locations are encoded through a distributed representation, rather than via a direct mapping of auditory space onto individual neurons with discrete receptive fields[35]. Indeed, studies of sound localization in cats show localization to be more accurate when based on spike patterns that consistently preserved detailed spike timing, compared to relying solely on spike counts[36,37]. In such a scenario, individual neurons have been labeled as “panoramic localizers,” containing spatial information within the dynamics of their firing patterns, which are part of a network that builds the distributed code of space. Such neurons may also play an integral role in the computation of auditory motion, with the spiking patterns not only signifying spatial location but also the change in location over time. The current results argue for displacement being a key parameter in this distributed computation. Our findings align with a previous psychophysical study which found that the speed of an auditory stimulus is a secondary cue, used only when distance and duration information are unreliable[29]. The results in their study, however, suggest that listeners were most sensitive to duration rather than displacement. This may be due to differences in stimulus and task design between the two studies. The stimulus in the aforementioned study used auditory motion stimuli based on head-related transfer functions (HRTFs), so the motion contained only interaural time differences as a cue to stimulus location, unlike the present experiment, which predominantly manipulated interaural level differences (and spectral cues) between two displaced speakers. Moreover, their use of a different task, one in which listeners are presented with three stimuli on each trial and asked to choose which is unique, introduces an additional temporal component to the task due to the sequential presentation of stimuli on each trial. The prominence of timing information within their design likely accounts for the subjects' heightened sensitivity to duration over displacement. In contrast, in the present study, monkeys were required to respond to an inherently spatial feature of the stimulus–its direction. Because displacement is a spatial stimulus attribute, it is likely more directly tied to the perception of direction compared to duration–a temporal stimulus attribute. Therefore, in the current work, displacement more strongly influenced the monkeys' decisions as opposed to duration. Hence, the task the listener is performing is an important factor in whether they are more sensitive to duration versus displacement information. Our behavioral results alone can’t adjudicate between competing theories of how auditory motion is processed in the brain. Nonetheless, there are valuable insights to be gained from previous neurophysiological studies of auditory motion. A number of those using animal models have evaluated the claim that there are specialized auditory motion detector neurons at subcortical and cortical auditory structures[1–7,38]. Numerous studies have used dynamic motion stimuli, in particular, binaural beats, to suggest sensitivity to auditory motion direction and velocity beginning at the earliest binaural center in the brainstem–the superior olivary complex (SOC)[39–41]. Binaural beats occur when two tones (or amplitude modulations) of slightly different frequencies are presented separately to each ear through headphones. Manipulating the difference between the frequencies over time can simulate auditory motion because the beat–an illusory tone that results from the summation of the diotic stimuli–seems to move through virtual auditory space. Some have argued that SOC neurons’ sensitivity to binaural beats does not necessarily mean they encode motion as a distinct feature and may simply reflect moment-to-moment changes in static spatial cues (ITDs/ILDs). However, since the results of our study suggest that the sequential sampling of static spatial cues is sufficient for auditory motion processing under a large range of stimulus velocities, durations, and displacements, it is possible that the SOC is the first structure in the ascending auditory pathway to contribute to the perception of auditory motion along the azimuthal plane. Since binaural beats differ greatly from true auditory motion, future studies of SOC activation in the presence of more ecologically valid auditory motion stimuli such as those used in the present experiment are needed to further elucidate its role in auditory motion processing. Studies have addressed the possibility of auditory motion encoding in the inferior colliculus (IC)[1,2,8] and in the optic tectum of the barn owl[3]. These studies complement the rich literature on auditory spatial maps in the barn owl[42,43] and the great degree of convergence from brainstem nuclei on the IC[42,43]. Differences in stimulus configurations complicate generalization across studies. Moreover, studies are equivocal with regard to the presence or absence of motion-selective responses in the IC, with some suggesting apparent motion responses are simply the result of spatial masking (i.e. that the preceding stimulus elicits adaptation or suppression)[1,44] and other studies suggesting that the presence of directional selectivity is sufficient to underpin auditory motion perception[1,45]. However, even the presence of spatial direction selectivity in the IC has been questioned[46], and was qualified with the term “directional sensitivity” by Ingham et al.[1]. Thus, future neurophysiological studies are needed to conclusively define the role of the inferior colliculus in auditory spatial and motion processing. While auditory motion processing beyond primary auditory cortex has not been thoroughly investigated in animal models, studies in macaques have shown that sensitivity to static spatial information increases from A1 to the caudomedial (CM) and caudolateral (CL) belt areas[47–49] which suggests that motion sensitivity could exist along such a gradient. Moreover, human fMRI studies have implicated the planum temporale–which contains areas homologous to macaque areas CM and CL–in auditory motion processing[50,51]. Studies have also characterized how auditory cortical neurons are sensitive to dynamic sound localization cues[4,5,52]. As described previously, some of these results can be explained by spatial masking, which Poirier et al.[53] addressed in their neuroimaging study by creating auditory motion stimuli and collecting primary auditory cortex responses to spectrotemporal and stationary control stimuli to regress these sound features out of the putative motion response. Their results suggest that BOLD responses in the primary auditory cortex do not exhibit true motion direction selectivity, but can instead be accounted for by simpler spectral and temporal sound features. It is possible that if a range of durations, velocities, and displacements that were not possible to include in our present experimental setup were used, that the relative role of each parameter would differ. While we were able to identify a threshold in which increasing duration has little effect on auditory motion perception, it is possible that similar thresholds exist for velocity and displacement with an expanded set of stimulus parameter values. Moreover, we only tested auditory stimuli moving in the horizontal plane. Results may differ for motion with a vertical component given the different auditory localization cues used for elevation processing, such as monaural and spectral cues. They may also differ if looming, receding, or radial motion were to be used due to the difference in functional relevance of these types of motion; while they can occur with object motion, they’re common with translational self-motion or the rotation of the head in the world. While the lateral motion studies here can be a result of both self and object motion, the range of stimulus values common for these types of motion may vary. Therefore, the applicability of our findings to other stimulus parameters and forms of motion should be considered critically. Additionally, it should be noted that while the present study’s design and analysis were designed to isolate the effects of displacement, duration, and velocity as best as possible, these factors are inextricable. For comparable visual stimuli, like random dot kinematograms (RDK), moving dots across a window with a certain displacement can be randomly replotted at the beginning of the stimulus window once they reach the other side, allowing for displacement to vary separately from motion velocity. This is not a feasible design for an auditory motion stimulus. Therefore, while the confounds of the other motion parameters have been dramatically reduced in our design, they are not non-existent. Our results suggest that auditory motion processing primarily relies on the displacement of the sound source when monkeys were asked to judge the direction of auditory motion. This finding supports the snapshot model of auditory motion perception, in which auditory motion direction is inferred through sequential processing of sound location. Future studies implementing a similar task design during simultaneous neural recordings would be beneficial to further elucidate the mechanisms used to process auditory motion. Declarations Author contributions A.M.S., M.T.W., and R.R. designed and planned the experiments. A.M.S. collected the data, performed the analysis and modeling, and wrote the manuscript. M.T.W. and R.R. edited the manuscript. All authors revised and approved the final version of the manuscript. Acknowledgments The authors would like to acknowledge Mary Feurtado for assistance with procedures involving anesthesia, Jackson Mayfield for technical assistance, Wesley Williams for assistance with data collection, and Bruce Williams and Roger Williams for building experimental hardware. The authors thank Dr. Andrew Tomarken for his advice on statistical analyses and modeling, as well as Dr. Gregory DeAngelis for helpful guidance throughout the experiment. Finally, the authors would like to acknowledge the National Institutes of Health, the National Institute on Deafness and Other Communication Disorders, the National Eye Institute, and the National Science Foundation for funding this research through the following grant support: NIH F31EY035167 and NSF DGE-1922697 to A.M.S., and R01DC015988-05 and R01DC020888-02 to R.R.. Data availability The datasets generated during and/or analysed during this study are available from the corresponding author on reasonable request. Additional Information The authors have no conflicts of interest to disclose. References Ingham, N. J., Hart, H. C. & McAlpine, D. Spatial receptive fields of inferior colliculus neurons to auditory apparent motion in free field. J. Neurophysiol. 85 , 23–33 (2001). Spitzer, M. W. & Semple, M. N. Responses of inferior colliculus neurons to time-varying interaural phase disparity: effects of shifting the locus of virtual motion. J. Neurophysiol. 69 , 1245–1263 (1993). 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Responses of neurons in the inferior colliculus to dynamic interaural phase cues: evidence for a mechanism of binaural adaptation. J. Neurophysiol. 83 , 1356–1365 (2000). Carlile, S. & Leung, J. The Perception of Auditory Motion. Trends Hear 20 , (2016). Shaw, E. A. Transformation of sound pressure level from the free field to the eardrum in the horizontal plane. J Acoust Soc Am 56 , 1848–1861 (1974). Middlebrooks, J. C. & Green, D. M. Sound localization by human listeners. Annu Rev Psychol 42 , 135–159 (1991). Grothe, B., Pecka, M. & McAlpine, D. Mechanisms of sound localization in mammals. Physiol Rev 90 , 983–1012 (2010). Middlebrooks, J. C. Narrow-band sound localization related to external ear acoustics. J Acoust Soc Am 92 , 2607–2624 (1992). Tollin, D. J. The lateral superior olive: a functional role in sound source localization. Neuroscientist 9 , 127–143 (2003). Carr, C. E. & Konishi, M. A circuit for detection of interaural time differences in the brain stem of the barn owl. 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Coding of horizontal disparity and velocity by MT neurons in the alert macaque. J Neurophysiol 89 , 1094–1111 (2003). Duffy, C. J. & Wurtz, R. H. Sensitivity of MST neurons to optic flow stimuli. I. A continuum of response selectivity to large-field stimuli. J Neurophysiol 65 , 1329–1345 (1991). Duffy, C. J. MST neurons respond to optic flow and translational movement. J Neurophysiol 80 , 1816–1827 (1998). Griffiths, T. D. et al. Evidence for a sound movement area in the human cerebral cortex. Nature 383 , 425–427 (1996). Carlile, S. & Best, V. Discrimination of sound source velocity in human listeners. J. Acoust. Soc. Am. 111 , 1026–1035 (2002). Reisbeck, T. E. & Gegenfurtner, K. R. Velocity tuned mechanisms in human motion processing. Vision Res 39 , 3267–3285 (1999). Lappin, J. S., Bell, H. H., Harm, O. J. & Kottas, B. On the relation between time and space in the visual discrimination of velocity. J Exp Psychol Hum Percept Perform 1 , 383–394 (1975). Freeman, T. C. A. et al. Discrimination contours for moving sounds reveal duration and distance cues dominate auditory speed perception. PLoS One 9 , e102864 (2014). Mackey, C. A., Hauser, S., Schoenhaut, A. M., Temghare, N. & Ramachandran, R. Hierarchical differences in the encoding of amplitude modulation in the subcortical auditory system of awake nonhuman primates. J Neurophysiol 132 , 1098–1114 (2024). Rocchi, F. & Ramachandran, R. Foreground stimuli and task engagement enhance neuronal adaptation to background noise in the inferior colliculus of macaques. J Neurophysiol 124 , 1315–1326 (2020). Mackey, C. A. et al. Hierarchical differences in the encoding of sound and choice in the subcortical auditory system. J Neurophysiol 129 , 591–608 (2023). Dylla, M., Hrnicek, A., Rice, C. & Ramachandran, R. Detection of tones and their modification by noise in nonhuman primates. J Assoc Res Otolaryngol 14 , 547–560 (2013). Brainard, D. H. The Psychophysics Toolbox. Spat. Vis. 10 , 433–436 (1997). Middlebrooks, J. C. A Search for a Cortical Map of Auditory Space. J Neurosci 41 , 5772–5778 (2021). Middlebrooks, J. C., Clock, A. E., Xu, L. & Green, D. M. A panoramic code for sound location by cortical neurons. Science 264 , 842–844 (1994). Middlebrooks, J. C., Xu, L., Eddins, A. C. & Green, D. M. Codes for sound-source location in nontonotopic auditory cortex. J Neurophysiol 80 , 863–881 (1998). Rauschecker, J. P. & Tian, B. Mechanisms and streams for processing of ‘what’ and ‘where’ in auditory cortex. Proc. Natl. Acad. Sci. U. S. A. 97 , 11800–11806 (2000). Wernick, J. S. Electrical Activity of the Superior Olivary Complex of the Cat Evoked by Stimuli with Constantly Changing Interaural Phase Relations . (1967). Spitzer, M. W. & Semple, M. N. Transformation of binaural response properties in the ascending auditory pathway: influence of time-varying interaural phase disparity. J Neurophysiol 80 , 3062–3076 (1998). Zhou, Y., Carney, L. H. & Colburn, H. S. A model for interaural time difference sensitivity in the medial superior olive: interaction of excitatory and inhibitory synaptic inputs, channel dynamics, and cellular morphology. J Neurosci 25 , 3046–3058 (2005). Knudsen, E. I. & Konishi, M. A neural map of auditory space in the owl. Science 200 , 795–797 (1978). Winer, J. A. & Schreiner, C. E. The Inferior Colliculus . (Springer Science & Business Media, 2005). Wilson, W. W. & O’Neill, W. E. Auditory motion induces directionally dependent receptive field shifts in inferior colliculus neurons. J Neurophysiol 79 , 2040–2062 (1998). Spitzer, M. W. & Semple, M. N. Interaural phase coding in auditory midbrain: influence of dynamic stimulus features. Science 254 , 721–724 (1991). Zuk, N. J. & Delgutte, B. Neural coding and perception of auditory motion direction based on interaural time differences. J Neurophysiol 122 , 1821–1842 (2019). Recanzone, G. H., Guard, D. C., Phan, M. L. & Su, T. K. Correlation between the activity of single auditory cortical neurons and sound-localization behavior in the macaque monkey. J. Neurophysiol. 83 , 2723–2739 (2000). Kusmierek, P. & Rauschecker, J. P. Selectivity for space and time in early areas of the auditory dorsal stream in the rhesus monkey. J. Neurophysiol. 111 , 1671–1685 (2014). Miller, L. M. & Recanzone, G. H. Populations of auditory cortical neurons can accurately encode acoustic space across stimulus intensity. Proc. Natl. Acad. Sci. U. S. A. 106 , 5931–5935 (2009). Baumgart, F., Gaschler-Markefski, B., Woldorff, M. G., Heinze, H. J. & Scheich, H. A movement-sensitive area in auditory cortex. Nature 400 , 724–726 (1999). Warren, J. D., Zielinski, B. A., Green, G. G. R., Rauschecker, J. P. & Griffiths, T. D. Perception of sound-source motion by the human brain. Neuron 34 , 139–148 (2002). Malone, B. J., Scott, B. H. & Semple, M. N. Context-dependent adaptive coding of interaural phase disparity in the auditory cortex of awake macaques. J Neurosci 22 , 4625–4638 (2002). Poirier, C. et al. Auditory motion-specific mechanisms in the primate brain. PLoS Biol. 15 , e2001379 (2017). Tables Table 1. Table of task conditions organized into sets according to the motion parameter that remains constant in each set. Motion Parameter Constant Duration Conditions Constant Velocity Conditions Constant Displacement Conditions Duration (s) 0.834 0.133 0.367 0.601 0.834 1.10 1.30 0.133 0.367 0.601 0.834 1.10 1.30 Displacement (°) 8 22 36 50 66 78 8 22 36 50 66 78 22 Velocity (°/s) 9.59 26.38 43 59.95 76.74 93.5 59.95 165.4 59.95 36.61 26.38 20 16.9 Table 2. Table of regression model formulas and their model numbers that correspond to the x-axis labels in Figure 6. Regression Model Number Regression Model Formula 1 slope ~ 1 + velocity 2 slope ~ 1 + duration 3 slope ~ 1 + displacement 4 slope ~ 1 + velocity + duration 5 slope ~ 1 + velocity + displacement 6 slope ~ 1 + duration + displacement 7 slope ~ 1 + velocity + duration + displacement 8 slope ~ 1 + velocity*displacement 9 slope ~ 1 + velocity*duration 10 slope ~ 1 + duration*displacement 11 slope ~ 1 + displacement + velocity*duration 12 slope ~ 1 + duration + velocity*displacement 13 slope ~ 1 + velocity + duration*displacement 14 slope ~ 1 + velocity*duration + velocity*displacement 15 slope ~ 1 + velocity*duration + duration*displacement 16 slope ~ 1 + velocity*displacement + duration*displacement 17 slope ~ 1 + velocity*duration + velocity*displacement + duration*displacement 18 slope ~ 1 + velocity*duration*displacement Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 May, 2025 Reviews received at journal 20 May, 2025 Reviewers agreed at journal 08 May, 2025 Reviews received at journal 29 Apr, 2025 Reviewers agreed at journal 07 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviewers invited by journal 02 Apr, 2025 Editor assigned by journal 02 Apr, 2025 Editor invited by journal 27 Mar, 2025 Submission checks completed at journal 27 Mar, 2025 First submitted to journal 18 Mar, 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-6254702","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":439735898,"identity":"fb3d3484-ff73-41a8-a903-f284fd8b8360","order_by":0,"name":"Adriana M. Schoenhaut","email":"","orcid":"","institution":"Vanderbilt University","correspondingAuthor":false,"prefix":"","firstName":"Adriana","middleName":"M.","lastName":"Schoenhaut","suffix":""},{"id":439735902,"identity":"5f04ddb1-d932-4417-9af1-067711fad93f","order_by":1,"name":"Ramnarayan Ramachandran","email":"","orcid":"","institution":"Vanderbilt Brain Institute","correspondingAuthor":false,"prefix":"","firstName":"Ramnarayan","middleName":"","lastName":"Ramachandran","suffix":""},{"id":439735906,"identity":"4c71779e-9c11-41c3-8aa8-8b2a628f489d","order_by":2,"name":"Mark T. Wallace","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIie2QvWrDMBRGJQTXi4hWmRa/QUFgSLs0fRUbg7q4JdDFkCGbs6Sd27fwlNlgqBeVrt4aE9CUwWOgpcSiP5OcZuygM3wIocP9rhByOP4nxMTpyGRnokSAoqk5RQcVCn3gx19FiOMUQn8UdEA5W7xsBM4nFLyq3Vzmk9tRfa/Xa/GBmJcKmzJW12GE84QClWF4kyd3vqrPhSnmL7d2pZSkxCvSF0vhJFUkLhoJ3CiiGZjyqo0yp8C23vuFmsfFm/5SroaURpIIryoKPAWCsqqfAt9T+JCiiYg/617Rob/M6vhJSa/fJaRc6am9mCS8U7OAsaTtdmIWP9TP0O6yIGCLpLD+siGyXdLB5w6Hw+H4kz2s6lneqBJwhAAAAABJRU5ErkJggg==","orcid":"","institution":"Vanderbilt Brain Institute","correspondingAuthor":true,"prefix":"","firstName":"Mark","middleName":"T.","lastName":"Wallace","suffix":""}],"badges":[],"createdAt":"2025-03-18 15:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6254702/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6254702/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-12642-y","type":"published","date":"2025-08-01T16:13:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81286744,"identity":"13a71426-d363-4491-9a01-bd33c0f84a1b","added_by":"auto","created_at":"2025-04-24 10:56:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2735048,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStimulus and behavioral task design.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Auditory stimulus design. Sounds were presented in the free field via two speakers located 34 inches apart in the frontal field (left speaker (green); right speaker (blue)). The auditory stimuli consisted of four different components: uncorrelated white noise streams presented through each speaker (100% amplitude; N1 and N2); a correlated white noise signal presented through both speakers (100% amplitude, inter-signal correlation = 1; N3); the apparent motion cue, in which the sound’s amplitude faded between the two speakers from 100% to 0% over the course of one trial (inter-signal-correlation = 0.5; N4) to create binaural motion cues at 11 different coherences. Stimulus coherence was changed by increasing or decreasing the slope of the stimulus envelope coming from each speaker.\u003c/p\u003e\n\u003cp\u003e(b) The two-alternative forced choice (2-AFC) task design. The subject fixated on a fixation point for 0.20s then maintained fixation as the stimulus was presented. After stimulus presentation, the fixation point was extinguished and two target points appeared on either side of the screen. The subject was required to saccade to the target corresponding to the direction of the stimulus and fixate on that target for 0.15s. Successful fixation on the correct target resulted in a fluid reward. A saccade to the incorrect target resulted in no reward. Both outcomes resulted in a 0.80s inter-trial interval followed by the next trial. A failure to fixate on the fixation point or target for the necessary amount of time resulted in a 2.5s time out.\u003c/p\u003e\n\u003cp\u003e(c) Example psychometric functions. The slope of the dynamic range is a measure of the perceptual sensitivity to auditory motion direction.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6254702/v1/efa174b9ec9da411970cb064.png"},{"id":81287053,"identity":"4dd0a453-1c41-4a30-9e00-01f9fcfbc2c6","added_by":"auto","created_at":"2025-04-24 11:04:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2197750,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePsychometric functions indicating motion direction discrimination performance across the constant duration condition set.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Psychometric functions for Monkey ‘A’ for a constant stimulus duration of 0.834s. Symbols with different colors indicate the different combinations of displacement and velocities. The colored curves represent the modified cumulative gaussian fits to the psychometric functions. Positive (negative) coherences indicate rightward (leftward) motion stimuli.\u003c/p\u003e\n\u003cp\u003e(b) Psychometric functions for Monkey ‘B’. Format is similar to (a).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6254702/v1/841e9e1f161332b2b087b2ba.png"},{"id":81287054,"identity":"a41bdbe3-2f63-483a-b337-fa35c7818fb3","added_by":"auto","created_at":"2025-04-24 11:04:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2200868,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePsychometric functions indicating motion direction discrimination performance across the constant velocity condition set.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Psychometric functions for Monkey ‘A’ for a constant velocity of 59.95°/s. Symbols with different colors indicate the different combinations of durations and displacements. The colored curves represent the modified cumulative gaussian fits to the psychometric functions. Positive (negative) coherences indicate rightward (leftward) motion stimuli.\u003c/p\u003e\n\u003cp\u003e(b) Psychometric functions for Monkey ‘B’. Format is similar to (a).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6254702/v1/2027dd5225b06659302f47a4.png"},{"id":81287055,"identity":"5ee62399-991b-434c-a5aa-c830565e816f","added_by":"auto","created_at":"2025-04-24 11:04:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2159138,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePsychometric functions indicating motion direction discrimination performance across the constant displacement condition set.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Psychometric functions for Monkey ‘A’ for a constant displacement of 22°. The colored curves represent the modified cumulative gaussian fits to the psychometric functions. Positive (negative) coherences indicate rightward (leftward) motion stimuli.\u003c/p\u003e\n\u003cp\u003e(b) Psychometric functions for Monkey ‘B’. Format is similar to (a).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6254702/v1/347c45798bdab21440274855.png"},{"id":81287058,"identity":"f4ff3311-7f97-494f-bd0e-7313e6e04f12","added_by":"auto","created_at":"2025-04-24 11:04:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2470470,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMotion direction sensitivity summaries.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Mean auditory motion direction sensitivity is shown as a function of displacement with a duration of 0.834s (varying velocity), circles and solid lines; and with a velocity of 59.95°/s (varying duration), triangles and dotted lines. Red and black symbols and lines represent data from monkey ‘A’ and ‘B’, respectively. Error bars show the standard deviation of the mean slopes.\u003c/p\u003e\n\u003cp\u003e(b) Mean auditory motion direction sensitivity is shown as a function of velocity with a displacement of 22° (varying duration), diamonds and dashed lines; with a duration of 0.834s (varying displacement), circles and solid lines. Format is similar to (a).\u003c/p\u003e\n\u003cp\u003e(c) Mean auditory motion direction sensitivity as a function of duration with a displacement of 22° (varying velocity), diamonds and dashed lines; with a velocity of 59.95°/s (varying displacement), triangles and dotted lines). Format is similar to (a).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6254702/v1/8ec3fbda21bd684236ae3218.png"},{"id":81287061,"identity":"3dbce792-fc04-44d7-a64a-c54c3b9a367d","added_by":"auto","created_at":"2025-04-24 11:04:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5175475,"visible":true,"origin":"","legend":"\u003cp\u003e(d) Mean adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u0026nbsp; \u003c/em\u003e\u003c/sup\u003evalues of the linear regression models run on slopes from conditions with durations \u0026gt; 0.367s for Monkey ‘B’.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModeling motion sensitivity.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigures show\u003cstrong\u003e \u003c/strong\u003emean adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u0026nbsp; \u003c/em\u003e\u003c/sup\u003evalues of all unique linear regression models that could be to model perceptual sensitivity as a function of velocity, duration, and/or displacement (and their two- and three-way interactions) for monkey ‘A’ (circles) and monkey ‘B’ (triangles). Error bars represent 95% confidence intervals across 1000 permutations of Monte Carlo resampling. Model formulas corresponding to the model numbers on the x-axis are provided in Table 2. Red markers indicate mean adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u0026nbsp; \u003c/em\u003e\u003c/sup\u003evalues for models without interaction terms included in the independent variables, blue markers indicate mean adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u0026nbsp; \u003c/em\u003e\u003c/sup\u003evalues for models that have a two-way interaction as at least one of its independent variable(s), and green markers indicate mean adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u0026nbsp; \u003c/em\u003e\u003c/sup\u003evalues for models that have a three-way interaction term as its independent variable.\u003c/p\u003e\n\u003cp\u003e(a) Mean adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u0026nbsp; \u003c/em\u003e\u003c/sup\u003evalues of the linear regression models run on slopes from all conditions for Monkey ‘A’.\u003c/p\u003e\n\u003cp\u003e(b) Mean adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u0026nbsp; \u003c/em\u003e\u003c/sup\u003evalues of the linear regression models run on slopes from all conditions for Monkey ‘B’.\u003c/p\u003e\n\u003cp\u003e(c) Mean adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u0026nbsp; \u003c/em\u003e\u003c/sup\u003evalues of the linear regression models run on slopes from conditions with durations \u0026gt; 0.367s for Monkey ‘A’.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6254702/v1/c416875a66bb48abc849ee46.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Contribution of displacement, duration, and velocity on auditory motion direction perception in macaque monkeys","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn typical listening scenarios, it is uncommon for sound location cues to stay fixed over time because the head, body, and external sound sources are frequently in motion. As a result, effective auditory scene analysis relies on the capacity to encode movement - a change in location over time. The resulting property of velocity is derived from the stimulus\u0026rsquo; duration and displacement of motion. However, it is unclear how the stimulus factors of velocity, displacement, and duration are weighted during auditory motion perception. Sensitivity to these motion parameters must be understood in a psychophysically meaningful way in order to manage their covariation in the experimental design and the interpretation of the effective stimulus features underlying the perception of auditory motion.\u003c/p\u003e \u003cp\u003eWhile there are some examples of cortical and subcortical structures with demonstrated sensitivity to auditory motion[1\u0026ndash;7], the nature of the representations in these areas remains debated[8,9]. Sound location is primarily determined by the difference in time (interaural time difference, ITD) and sound level (interaural level difference, ILD) reaching the two ears[10,11], as well as by monaural spectral cues[12,13]. Neurons in the superior olivary complex are particularly sensitive to these interaural spatial cues, which allow the auditory system to estimate the azimuthal location of a sound source[14,15]. However, how motion is derived from these spatial cues remains unresolved, with two primary theories proposed. The first, often called the snapshot theory, posits that auditory motion velocity is not directly represented but instead inferred by successively sampling sound location and calculating the change in position over time[16\u0026ndash;18]. Since this theory depends on at least two \u0026ldquo;snapshots\u0026rdquo; of stimulus location taken over time, the displacement (i.e., change in position) and duration of a moving sound would be the only information needed in order to compute auditory motion[19]. The second theory posits that the auditory system contains specialized motion detectors similar to those seen in the visual system[20\u0026ndash;24], with neurons demonstrating selectivity for auditory motion velocity[25]. For such a mechanism to exist, listeners\u0026rsquo; behavior would be sensitive to velocity without necessarily being sensitive to the sound\u0026rsquo;s displacement and duration. Testing between these theories is complicated by the challenges of designing an experiment in which these naturally covarying components of motion can be separated. This complication highlights the need to understand the relative contribution of motion characteristics in a psychophysically meaningful way.\u003c/p\u003e \u003cp\u003eSome previous psychophysical studies attempting to differentiate between these two theories have implemented velocity discrimination tasks, therefore biasing responses by directing subjects towards a particular auditory feature (i.e., velocity[26]). Alternatively, a human psychophysics study using an oddball paradigm has suggested that displacement and duration are the primary cues for auditory motion perception, while velocity\u0026ndash;unlike in vision[27,28]\u0026ndash;is only used when distance and duration cues are unreliable[29]. Evidence regarding these two theories in behaving non-human primates, however, has yet to be gathered. This gap is surprising given that non-human primates have long served as an important model for understanding the neural mechanisms underlying spatial processing, offering critical insights into how sensory information is integrated to guide goal-directed behavior. Non-human primates are uniquely suited for such investigations due to their well-characterized neural circuits and sophisticated behavioral capabilities, which have been shown to closely parallel those of humans in many domains including visual motion processing.\u003c/p\u003e \u003cp\u003eIn the present study, we aim to determine the degree to which motion velocity, displacement, and/or duration contribute towards perception of auditory motion direction. We trained two macaque monkeys to respond to the direction of a simulated motion stimulus presented at various signal-to-noise ratios, velocities, durations, and displacements, and compared the sensitivity to motion direction across conditions. This study represents the first concerted effort to understand the factors governing primate auditory motion perception for stimuli moving in the azimuthal plane. Our findings indicate that auditory motion direction perception is predominantly driven by displacement cues, with lesser contributions from duration and velocity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eSubjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study contains data from two adult male rhesus macaques (\u003cem\u003eMacaca mulatta\u003c/em\u003e) obtained from the California National Primate Research Center (Davis, CA). The ages of the macaques, referred to as Monkey “A” and Monkey “B,” were 8 and 6 years old, respectively, at the beginning of the study. Their weights were between 11-13 kg when the study began. The diet of these macaques included a standard feed (LabDiet Monkey Diet 5037 and 5050, Purina, St Louis, MO) with additional fresh produce and items for foraging. Various types of enrichment such as manipulative tools and sensory stimuli (auditory, visual, and olfactory) were provided on a rotating schedule. The macaques underwent fluid restriction and were given municipal water that was filtered. Weekly weight monitoring was conducted 4 to 5 times a week, ensuring the weights remained within the veterinarian-approved reference range for healthy study conditions.\u003c/p\u003e\n\u003cp\u003eThe animals were kept on a consistent 12-hour light/dark cycle, with all experimental activities conducted between 8 AM and 6 PM during the light period. Despite efforts through behavioral assessments to find suitable social companions, the macaques were individually housed due to social incompatibility; however, they maintained visual, auditory, and olfactory contact with other macaques within the same room. This housing environment was part of an AAALAC-accredited facility and adhered to the \u003cem\u003eGuide for the Care and Use of Laboratory Animals\u003c/em\u003e, the Public Health Service Policy on Humane Care and Use of Laboratory Animals, as well as the Animal Welfare Act and Regulations. The macaques also received regular health checks, including biannual physical examinations and tuberculosis screenings and adjustments to their reference weights as needed. All experimental protocols involving these animals were approved by the Animal Care and Use Committee at Vanderbilt University Medical Center (VUMC) and carried out in accordance with ARRIVE guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurgical Procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe preparation of monkeys for behavioral experiments adhered to established methods in nonhuman primate research[30–33]. To minimize head movement and reduce variation in sound pressure level at the ear, a headpost made of stainless steel was affixed to the monkeys’ skulls. This device helped stabilize head position across sessions. First, magnetic resonance imaging (MRI) was conducted in a Philips Intera Achieva 3T scanner, equipped with SENSE Flex-S surface coils positioned either above or below the animal’s head. The purpose of the resulting T1-weighted gradient-echo structural images was to determine the best placement location of the headpost. For surgical procedures, anesthesia was induced using ketamine and midazolam, followed by maintenance with isoflurane. While anesthetized, the headpost was anchored to the skull with 7 mm titanium screws (Gray Matter Research LLC, Bozeman, MT) and was secured further with bone cement (Heraeus Incorporated, Yardley, PA).\u003c/p\u003e\n\u003cp\u003eThroughout all surgical procedures, fluids and antibiotics were administered intra-procedurally, with pre- and post-operative analgesics provided under the supervision of veterinary staff. The monkeys were continuously monitored under veterinary oversight until the monkeys fully recovered from surgery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApparatus and Stimuli\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMonkeys were seated and head restrained in a primate chair that was designed and constructed in-house and secured to a fixed pedestal, located 21.65 inches from the screen and speakers that delivered visual and auditory stimuli. Stimulus generation, event timing, task control, and fluid reward delivery were done with MatLab (The MathWorks, Inc., Natick, MA) and OpenEx software (System 3, TDT Inc., Alachua, FL). Stimuli were presented using PsychToolbox version 3[34]. \u003c/p\u003e\n\u003cp\u003eAuditory stimuli were presented in the free field via two speakers (3W 8 ohm 2x3 inch loudspeaker, Allied Electronics, Inc) located 34 inches (78°) apart in the frontal field. Each speaker was calibrated with a ¼ inch microphone (378C01, PCB Piezotronics) positioned at the location where the entry to the monkey’s ear canal would be during experiments. The speaker was calibrated to ensure that outputs were within 3 dB up to 20 kHz.\u003c/p\u003e\n\u003cp\u003eAuditory stimuli (Figure 1A) were adapted from a previous study[16] and consisted of broad-band (0.005-24.4 kHz) white noise signals (10ms rise and fall) sampled at a rate of 48828 samples per second. The motion signal was either leftward or rightward, embedded in partially correlated noise, and played through the speakers. Each condition was presented at 12 different log-spaced noise levels (i.e. coherences) ranging from fully noise to fully signal. The auditory stimuli consisted of four different components. First, individual white noise streams were presented through each speaker (100% amplitude; inter-signal correlation=0; N1 and N2). Second, a combined white noise signal was presented through both speakers (100% amplitude, inter-signal correlation = 1; N3). The fourth signal stream contained the apparent motion cue, in which the sound’s amplitude faded between the two speakers from 100% to 0% over the course of one trial (inter-signal-correlation = 0.5; N4) to create binaural motion cues. The velocity, duration, and displacement (centered at midline) varied between conditions (Table 1). \u003c/p\u003e\n\u003cp\u003eVisual task components (fixation point and targets) were presented on a 1280x1024 cathode ray tube (CRT) monitor screen (HP p1230 22”) located in the frontal field 21.65 inches from the monkey’s head at eye level and centered horizontally between the two speakers. The monitor had a refresh rate of 75.025 Hz, and the screen subtended 40.56° × 30.98° of the visual angle. \u003c/p\u003e\n\u003cp\u003eEye position was monitored and recorded continuously at 120 Hz using an eye tracker (Applied Science Laboratories Eye-Trac 6). Eye position was calibrated daily prior to each monkey beginning the task. A Minolta Chroma Meter CS-100 was used to verify the luminance of the visual stimuli.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBehavioral Training and Task Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sequence of each trial is depicted in Figure 1B. At the beginning of each trial, a 0.4° diameter white fixation point (111 cd/m²) appeared on a black background (0.7 cd/m²). The monkey had one second to initiate fixation on the fixation point within an 8° diameter circular window and maintain it for 0.2 seconds. If fixation was maintained, then the auditory stimulus (simulated leftward or rightward motion) was presented for the duration specified by the condition. If fixation was maintained during stimulus presentation, the fixation point was extinguished at the end of the stimulus duration and a white, 0.4° diameter target point appeared on the left and right sides of the screen. Once the targets appeared, the monkey had 1.5 seconds to initiate and maintain fixation for 0.15 seconds on the target corresponding to the direction of motion of the stimulus. If the monkey responded correctly, a fluid reward (water or juice) was dispensed from a spout. The subsequent trial began after a 0.8 second inter-trial interval. If the monkey failed to initiate fixation within the waiting period or broke fixation early once initiated at any point during the trial, they were given a 2.5 second time out with a black, blank screen. Trials for each coherence were ordered randomly across the session. At least 300 trials for each coherence were collected per condition per monkey across several sessions. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each condition (i.e. each unique combination of stimulus velocity, duration, and displacement; Table 1) for each monkey, the probability of rightward response across coherences for leftward (negative coherences) and rightward (positive coherences) trials were plotted then fit to a modified cumulative gaussian that takes into account lapse rate. The slope of the dynamic range of this function was then calculated and taken to represent behavioral sensitivity to auditory motion direction. Mean slopes and standard deviations were calculated using a Monte Carlo resampling procedure (also known as Repeated Random Subsampling Cross-Validation), where in each iteration, a random 10% of the data was removed, the slope was calculated on the remaining 90%, and this process was repeated for 1000 permutations. \u003c/p\u003e\n\u003cp\u003eFor each monkey, slopes across conditions were then fit with all possible unique linear regression models that could be created with slope as the dependent variable and velocity, duration, and/or displacement (and their two- and three-way interactions) as the independent variables. This was also done for each of the 1000 different data sets created in the aforementioned permutation testing, each with one slope per unique condition. The adjusted \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e \u003c/em\u003evalues for each model was calculated by modifying the \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e \u003c/em\u003evalues to account for the number of predictors and the sample size. This adjustment involved subtracting the proportion of unexplained variance, scaled by the ratio of the total sample size minus one to the sample size minus the number of predictors minus one. This scaling penalized the mean \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e value for models with a higher number of predictors, ensuring that the metric reflects the model's true explanatory power rather than the mere inclusion of additional variables. Adjusted \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e values for each model were then averaged across the 1000 permutations for each monkey, and 95% confidence intervals were calculated. An additional set of linear regression models and mean adjusted \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e values was also created in the same manner, but with slopes from conditions with durations below 0.367s excluded from the data set. \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003ePsychophysical performance\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFirst, we analyzed psychometric performance for each monkey on the motion direction task to compare their performance across conditions with the same duration, displacement, and velocity as expemplified in Figure 1C. These functions plot the proportion of rightward responses as a function of stimulus coherence, with random motion (coherence 0) at the x-axis center and fully coherent motion (coherence -1 and 1) at either extreme. Since performance improved with increased stimulus coherence (higher proportion rightward response on rightward trials, lower proportion rightward response on leftward trials), comparing the slopes of these psychometric functions across conditions allowed us to assess the monkeys' sensitivity to auditory motion. Figures 2, 3, and 4 show these psychometric functions for both monkeys under three sets of conditions, respectively: those with the same duration but different velocities and displacements, those with the same velocity but different displacements and durations, and those with the same displacement but different durations and velocities. Figure 5 summarizes these results by plotting the slopes of each psychometric function for the various conditions and combinations.\u003c/p\u003e\n\u003cp\u003eFigure 2 shows the psychometric functions for the two monkeys under conditions with the same duration (0.834 seconds) stimulus. This duration was chosen because it fell in the middle of the range of durations included in our condition set, and allowed for the displacements in the constant duration and constant velocity condition sets to be the same. Note the similarity in the general shape of the psychometric curves for the two monkeys. When duration was held constant at 0.834s, psychometric function slopes increased with increases in velocity and displacement for both monkeys. Thus, monkeys were more sensitive to auditory motion direction at higher displacements and velocities. To determine whether this effect is predominantly driven by displacement versus velocity and thus to elucidate the contribution of duration, two additional sets of experiments were conducted.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the second set of experiments, velocity was held constant at 59.95°/s (Figure 3). This velocity was chosen because it fell in the middle of the range of velocities possible in our experimental setup. It also allowed for the stimulus displacements to match those in the constant duration set of conditions, and the durations to match those in the constant displacement set of conditions. Again, note the similarities in the psychometric curves between the two animals. When velocity was held constant, the slope of the psychometric functions increased with increases in auditory motion duration and displacement. Note that the slopes for both monkeys were lowest\u0026nbsp;(i.e., shallowest) for the 8°, 0.133s condition. To round out the analyses, a final set of experiments was needed to elucidate whether displacement or duration is responsible for this reduced sensitivity to auditory motion direction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the third set of experiments, displacement was held constant at 22°, while velocity and duration varied in each condition. This displacement was chosen because it fell in the middle of the range of displacements possible in our experimental setup, and allowed for the stimulus durations in these conditions to match those in the constant velocity set of conditions. In the psychometric functions generated in these experiments, there was little increase in slope with increasing duration for durations longer than 0.367 seconds (Figure 4; 5C). This suggests that sensitivity to auditory motion direction was most influenced by stimulus duration under our set of velocities and displacements – at least for stimuli less than 0.367 seconds in duration. Therefore, the minimum time needed for temporal integration of auditory motion cues likely lies somewhere between 0.133 and 0.367 seconds. At this point, the subjects had likely accumulated enough sensory evidence to make a decision regarding the direction of the auditory motion stimulus, so the additional evidence did little to improve sensitivity. Further support for this idea is detailed below.\u003c/p\u003e\n\u003cp\u003eFigure 5 summarizes the results of the psychometric functions shown in Figures 2 - 4. Figure 5 shows the changes in sensitivity (mean slope of the psychometric function calculated using a Monte Carlo resampling procedure with 1000 permutations) as a function of one of the parameters for changes in the other two parameters. Thus, in Figure 5A, the mean slopes from the constant duration (varying velocity, solid lines) and constant velocity (varying duration, dotted lines) condition sets were plotted against displacement. Overall, increasing displacement resulted in increased mean slopes in both sets of conditions. This same general pattern can be seen for both monkeys (red–Monkey A, black–Monkey B). In Figure 5B, the mean slopes from the constant displacement (dashed lines) and constant duration (solid lines) condition sets were plotted against velocity. In the constant displacement conditions, higher velocities had lower durations, while in the constant duration conditions, higher velocities had higher displacements. As velocity increases, both monkeys show an overall increase in auditory motion direction sensitivity in the constant duration (varying displacement) condition set, peaking at a slope of 1.75 (standard deviation=0.021) for Monkey A and 2.36 (standard deviation=0.047) for Monkey B, while for the constant displacement (varying duration) condition set, mean slopes peak at 1.29 for Monkey A (standard deviation=0.061) and 1.23 for Monkey B (standard deviation=0.026). Again, the overall pattern is similar in both monkeys.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe overall differences in how sensitivity changes with velocity between the constant duration (varying displacement) and constant displacement (varying duration) sets for both monkeys suggests a greater impact of changing displacement than changing duration on auditory motion sensitivity. This begs the question of whether the increase in mean slope with increasing displacement in the constant velocity (varying duration) condition set seen in Figure 5A is being driven by the changes in displacement rather than changes in duration. To test this, we plotted the constant displacement (varying velocity, dashed lines) and constant velocity (varying displacement, dotted lines) condition sets against duration in Figure 5C. Both monkeys performed similarly for both condition sets for durations of 0.133 and 0.367 seconds. For durations above 0.367 seconds, results for each condition set begin to deviate. For the constant velocity (varying displacement) condition set, both monkeys show a progressive increase in mean slope with increasing duration, peaking at a mean slope of 1.86 (standard deviation=0.029) for Monkey A and 2.36 (standard deviation=0.051) for Monkey B; however, for the constant displacement (varying velocity) condition set, overall sensitivity to auditory motion seems to plateau, peaking at 1.29 (standard deviation=0.061) for Monkey A and standard deviation=0.026) for Monkey B.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eModeling\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAlthough it is not possible to fully disentangle the effects of duration, displacement, and velocity due to their inherent collinearity, the design of our condition set allowed us to assess the relative contribution of each motion attribute and their combinations to behavioral outcomes. To achieve this, we generated all possible unique linear regression models using slope as the dependent variable, with velocity, duration, and/or displacement (along with their two- and three-way interactions) as independent variables. We then compared the mean adjusted \u003cem\u003eR²\u003c/em\u003e values of each model—calculated using Monte Carlo resampling with 1000 permutations—to determine which motion parameters or combinations best explained the data while accounting for model complexity. A higher mean adjusted\u003cem\u003e\u0026nbsp;R²\u003c/em\u003e indicated a better model fit.\u003c/p\u003e\n\u003cp\u003eFigure 6A and B display the mean adjusted\u003cem\u003e\u0026nbsp;R²\u003c/em\u003e values for models run on all conditions for each monkey separately. While some differences were observed between the two animals, the overall pattern of results was highly similar. The model that included only velocity as a predictor (Model 1) did not significantly explain variance in slope (Monkey A: n = 15, coefficient estimate = 0.001, standard error = 0.003, t = 0.150, p = 0.884; Monkey B: n = 15, coefficient estimate = 0.002, standard error = 0.005, t = 0.455, p = 0.657). Models that included only duration (Model 2) or both duration and velocity (Model 4) as main effects had significantly lower mean adjusted \u003cem\u003eR²\u0026nbsp;\u003c/em\u003evalues than all other models, except Model 1 (Mann-Whitney U tests with Bonferroni correction, n = 1000, α = 0.0029; Monkey A: p \u0026lt; 1.0e-150; Monkey B: p \u0026lt; 1.0e-300).\u003c/p\u003e\n\u003cp\u003eHowever, Model 4 (which included both duration and velocity) had a significantly higher mean adjusted \u003cem\u003eR²\u003c/em\u003e than Model 2 (which included only duration), indicating that velocity provided additional explanatory power when combined with duration (Mann-Whitney U test with Bonferroni correction, n = 1000, α = 0.0029; Monkey A: p = 3.313e-192; Monkey B: p = 1.067e-317). All subsequent models shown in Figure 6A and B (except for Model 9: slope ~ 1 + velocity * duration) included displacement as an independent variable, either on its own or as part of a two- or three-way interaction term. These models produced significantly higher mean adjusted \u003cem\u003eR²\u0026nbsp;\u003c/em\u003evalues than models without displacement for both monkeys (Mann-Whitney U tests with Bonferroni correction, n = 1000, α = 0.0029; Monkey A: p \u0026lt; 1.0e-235; Monkey B: p \u0026lt; 1.0e-300).\u003c/p\u003e\n\u003cp\u003eNotably, the models slope ~ 1 + velocity + duration (Model 4; Mann-Whitney U tests with Bonferroni correction, n = 1000, α = 0.0029; Monkey A: p = 6.544e-238; Monkey B: p = 1.020e-317) and slope ~ 1 + duration (Model 2; Mann-Whitney U tests with Bonferroni correction, n = 1000, α = 0.0029; Monkey A: p = 1.228e-245; Monkey B: p = 8.046e-320) had significantly lower mean adjusted \u003cem\u003eR²\u003c/em\u003e values than the model with displacement as the sole predictor (Model 3), further highlighting the dominant role of displacement in explaining variations in psychometric function slope.\u003c/p\u003e\n\u003cp\u003eAdditionally, we examined the linear regression models (n=13) that included duration, displacement, and velocity as main effects (Model 7: slope ~ 1 + velocity + duration + displacement). For Monkey A, neither duration (coefficient estimate = 0.030, standard error = 0.32, t = 0.094, p = 0.927) nor velocity (coefficient estimate = -0.003, standard error = 0.003, t = -1.219, p = 0.251) had a significant effect on slope. Similarly, for Monkey B, duration (coefficient estimate = -0.214, standard error = 0.219, t = -0.976, p = 0.350) did not significantly predict slope, though velocity showed a marginally significant effect (coefficient estimate = -0.005, standard error = 0.002, t = -2.370, p = 0.037). In contrast, displacement had the strongest influence on slope for both monkeys, with significant effects observed in Monkey A (coefficient estimate = 0.019, standard error = 0.004, t = 5.238, p = 3.798e-4) and Monkey B (coefficient estimate = 0.030, standard error = 0.003, t = 10.226, p = 5.911e-7). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince increasing stimulus duration beyond 0.367 seconds resulted in only minimal improvements in sensitivity to auditory motion direction (Figures 3, 4, 5B, and 5C), we reanalyzed the data using only trials with durations of 0.367 seconds or longer (Figures 6C and 6D). This allowed us to assess whether the influence of duration observed in earlier models was primarily driven by shorter-duration trials.\u003c/p\u003e\n\u003cp\u003eWhen focusing on longer-duration conditions, the effect of duration weakened. The duration-only model (Model 2) was not significant for either monkey (Monkey A: coefficient estimate = 0.594, standard error = 0.456, t = 1.301, p = 0.22; Monkey B: coefficient estimate = 0.517, standard error = 0.641, t = 0.806, p = 0.437). In contrast, velocity became a stronger predictor, as the velocity-only model (Model 1) was significant under these conditions (Monkey A: coefficient estimate = 0.013, standard error = 0.004, t = 3.75, p = 0.003; Monkey B: coefficient estimate = 0.020, standard error = 0.004, t = 5.584, p = 1.64e-4).\u003c/p\u003e\n\u003cp\u003eDespite this shift, models that included displacement continued to explain significantly more variance than those that excluded it (Mann-Whitney U tests with Bonferroni correction, n = 1000, α = 0.0029; Monkey A: p = 0; Monkey B: p \u0026lt; 2.9e-320). These findings suggest that the greater influence of duration relative to velocity observed in the full dataset (Figures 6A and 6B) was primarily driven by trials with shorter durations.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough some cortical and subcortical structures are responsive to auditory motion, how they represent and process motion remains debated. This study provides evidence as to whether auditory motion perception relies on specialized motion detectors or infers motion through sequential sound location processing. While velocity, duration, and displacement play distinct roles in each mechanism, their relative contributions to motion perception were previously unexplored. Overall, our results suggest that the duration, displacement, and velocity of an auditory motion stimulus all have the capacity to influence sensitivity to motion direction. However, out of the three tested motion parameters, displacement was found to have the greatest effect on lateral (leftward vs. rightward) direction perception. Additionally, the impact of duration or velocity also depended on the duration of the stimulus. For short duration (i.e., less than 0.367 seconds) stimuli, duration was more influential than velocity. For longer-duration stimuli, velocity was the more impactful motion parameter. These results suggest that there is a duration threshold between 0.133 and 0.367 seconds, beyond which longer stimulus durations do not facilitate accurate auditory motion direction perception. \u003c/p\u003e\n\u003cp\u003eThese results provide supporting evidence for a so-called snapshot mechanism for auditory motion perception, in which displacement cues are weighted more heavily than velocity cues. With such a mechanism, \u0026ldquo;snapshots\u0026rdquo; of auditory stimulus location are sampled at different points in time over the duration of the stimulus using sound localization cues such as ITDs and ILDs, and velocity is then inferred from the displacement of the sound source between snapshots. In such a model, duration and displacement information alone would be sufficient to support auditory motion processing. Our findings show that displacement alone accounted for the most variance across both model sets, while duration alone explained the most variance when including stimuli of 0.367 seconds or less, supporting this theory.\u003c/p\u003e\n\u003cp\u003eWhen modeling data from all conditions, the model with velocity as the only independent variable was not significant. This indicates that for the range of velocities, durations, and displacements included in our experiment, velocity information alone is not sufficient to account for monkeys\u0026rsquo; sensitivity to auditory motion direction. It is therefore unlikely that a velocity detector mechanism for auditory motion processing exists. Interestingly, our modeling results for longer duration stimuli (\u0026gt; 0.367 s) show pairing of velocity information with displacement and/or duration information yielded mean adjusted \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e values as high or higher than those of displacement information alone. Nevertheless, if a velocity detector mechanism were to dominate motion processing, listeners\u0026rsquo; behavior would have to be highly sensitive to velocity without necessarily being sensitive to displacement and duration. \u003c/p\u003e\n\u003cp\u003eAt least for neocortical structures, it appears highly likely that sound-source locations are encoded through a distributed representation, rather than via a direct mapping of auditory space onto individual neurons with discrete receptive fields[35]. Indeed, studies of sound localization in cats show localization to be more accurate when based on spike patterns that consistently preserved detailed spike timing, compared to relying solely on spike counts[36,37]. In such a scenario, individual neurons have been labeled as \u0026ldquo;panoramic localizers,\u0026rdquo; containing spatial information within the dynamics of their firing patterns, which are part of a network that builds the distributed code of space. Such neurons may also play an integral role in the computation of auditory motion, with the spiking patterns not only signifying spatial location but also the change in location over time. The current results argue for displacement being a key parameter in this distributed computation.\u003c/p\u003e\n\u003cp\u003eOur findings align with a previous psychophysical study which found that the speed of an auditory stimulus is a secondary cue, used only when distance and duration information are unreliable[29]. The results in their study, however, suggest that listeners were most sensitive to duration rather than displacement. This may be due to differences in stimulus and task design between the two studies. The stimulus in the aforementioned study used auditory motion stimuli based on head-related transfer functions (HRTFs), so the motion contained only interaural time differences as a cue to stimulus location, unlike the present experiment, which predominantly manipulated interaural level differences (and spectral cues) between two displaced speakers. Moreover, their use of a different task, one in which listeners are presented with three stimuli on each trial and asked to choose which is unique, introduces an additional temporal component to the task due to the sequential presentation of stimuli on each trial. The prominence of timing information within their design likely accounts for the subjects\u0026apos; heightened sensitivity to duration over displacement. In contrast, in the present study, monkeys were required to respond to an inherently spatial feature of the stimulus\u0026ndash;its direction. Because displacement is a spatial stimulus attribute, it is likely more directly tied to the perception of direction compared to duration\u0026ndash;a temporal stimulus attribute. Therefore, in the current work, displacement more strongly influenced the monkeys\u0026apos; decisions as opposed to duration. Hence, the task the listener is performing is an important factor in whether they are more sensitive to duration versus displacement information. \u003c/p\u003e\n\n\u003cp\u003eOur behavioral results alone can\u0026rsquo;t adjudicate between competing theories of how auditory motion is processed in the brain. Nonetheless, there are valuable insights to be gained from previous neurophysiological studies of auditory motion. A number of those using animal models have evaluated the claim that there are specialized auditory motion detector neurons at subcortical and cortical auditory structures[1\u0026ndash;7,38]. Numerous studies have used dynamic motion stimuli, in particular, binaural beats, to suggest sensitivity to auditory motion direction and velocity beginning at the earliest binaural center in the brainstem\u0026ndash;the superior olivary complex (SOC)[39\u0026ndash;41]. Binaural beats occur when two tones (or amplitude modulations) of slightly different frequencies are presented separately to each ear through headphones. Manipulating the difference between the frequencies over time can simulate auditory motion because the beat\u0026ndash;an illusory tone that results from the summation of the diotic stimuli\u0026ndash;seems to move through virtual auditory space. Some have argued that SOC neurons\u0026rsquo; sensitivity to binaural beats does not necessarily mean they encode motion as a distinct feature and may simply reflect moment-to-moment changes in static spatial cues (ITDs/ILDs). However, since the results of our study suggest that the sequential sampling of static spatial cues is sufficient for auditory motion processing under a large range of stimulus velocities, durations, and displacements, it is possible that the SOC is the first structure in the ascending auditory pathway to contribute to the perception of auditory motion along the azimuthal plane. Since binaural beats differ greatly from true auditory motion, future studies of SOC activation in the presence of more ecologically valid auditory motion stimuli such as those used in the present experiment are needed to further elucidate its role in auditory motion processing. \u003c/p\u003e\n\u003cp\u003eStudies have addressed the possibility of auditory motion encoding in the inferior colliculus (IC)[1,2,8] and in the optic tectum of the barn owl[3]. These studies complement the rich literature on auditory spatial maps in the barn owl[42,43] and the great degree of convergence from brainstem nuclei on the IC[42,43]. Differences in stimulus configurations complicate generalization across studies. Moreover, studies are equivocal with regard to the presence or absence of motion-selective responses in the IC, with some suggesting apparent motion responses are simply the result of spatial masking (i.e. that the preceding stimulus elicits adaptation or suppression)[1,44] and other studies suggesting that the presence of directional selectivity is sufficient to underpin auditory motion perception[1,45]. However, even the presence of spatial direction selectivity in the IC has been questioned[46], and was qualified with the term \u0026ldquo;directional sensitivity\u0026rdquo; by Ingham et al.[1]. Thus, future neurophysiological studies are needed to conclusively define the role of the inferior colliculus in auditory spatial and motion processing. \u003c/p\u003e\n\u003cp\u003eWhile auditory motion processing beyond primary auditory cortex has not been thoroughly investigated in animal models, studies in macaques have shown that sensitivity to static spatial information increases from A1 to the caudomedial (CM) and caudolateral (CL) belt areas[47\u0026ndash;49] which suggests that motion sensitivity could exist along such a gradient. Moreover, human fMRI studies have implicated the planum temporale\u0026ndash;which contains areas homologous to macaque areas CM and CL\u0026ndash;in auditory motion processing[50,51]. Studies have also characterized how auditory cortical neurons are sensitive to dynamic sound localization cues[4,5,52]. As described previously, some of these results can be explained by spatial masking, which Poirier et al.[53] addressed in their neuroimaging study by creating auditory motion stimuli and collecting primary auditory cortex responses to spectrotemporal and stationary control stimuli to regress these sound features out of the putative motion response. Their results suggest that BOLD responses in the primary auditory cortex do not exhibit true motion direction selectivity, but can instead be accounted for by simpler spectral and temporal sound features. \u003c/p\u003e\n\u003cp\u003eIt is possible that if a range of durations, velocities, and displacements that were not possible to include in our present experimental setup were used, that the relative role of each parameter would differ. While we were able to identify a threshold in which increasing duration has little effect on auditory motion perception, it is possible that similar thresholds exist for velocity and displacement with an expanded set of stimulus parameter values. Moreover, we only tested auditory stimuli moving in the horizontal plane. Results may differ for motion with a vertical component given the different auditory localization cues used for elevation processing, such as monaural and spectral cues. They may also differ if looming, receding, or radial motion were to be used due to the difference in functional relevance of these types of motion; while they can occur with object motion, they\u0026rsquo;re common with translational self-motion or the rotation of the head in the world. While the lateral motion studies here can be a result of both self and object motion, the range of stimulus values common for these types of motion may vary. Therefore, the applicability of our findings to other stimulus parameters and forms of motion should be considered critically.\u003c/p\u003e\n\u003cp\u003eAdditionally, it should be noted that while the present study\u0026rsquo;s design and analysis were designed to isolate the effects of displacement, duration, and velocity as best as possible, these factors are inextricable. For comparable visual stimuli, like random dot kinematograms (RDK), moving dots across a window with a certain displacement can be randomly replotted at the beginning of the stimulus window once they reach the other side, allowing for displacement to vary separately from motion velocity. This is not a feasible design for an auditory motion stimulus. Therefore, while the confounds of the other motion parameters have been dramatically reduced in our design, they are not non-existent. \u003c/p\u003e\n\u003cp\u003eOur results suggest that auditory motion processing primarily relies on the displacement of the sound source when monkeys were asked to judge the direction of auditory motion. This finding supports the snapshot model of auditory motion perception, in which auditory motion direction is inferred through sequential processing of sound location. Future studies implementing a similar task design during simultaneous neural recordings would be beneficial to further elucidate the mechanisms used to process auditory motion. \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.M.S., M.T.W., and R.R. designed and planned the experiments. A.M.S. collected the data, performed the analysis and modeling, and wrote the manuscript. M.T.W. and R.R. edited the manuscript. All authors revised and approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge Mary Feurtado for assistance with procedures involving anesthesia, Jackson Mayfield for technical assistance, Wesley Williams for assistance with data collection, and Bruce Williams and Roger Williams for building experimental hardware. The authors thank Dr. Andrew Tomarken for his advice on statistical analyses and modeling, as well as Dr. Gregory DeAngelis for helpful guidance throughout the experiment. Finally, the authors would like to acknowledge the National Institutes of Health, the National Institute on Deafness and Other Communication Disorders, the National Eye Institute, and the National Science Foundation for funding this research through the following grant support: NIH F31EY035167 and NSF DGE-1922697 to A.M.S., and R01DC015988-05 and R01DC020888-02 to R.R..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eIngham, N. J., Hart, H. C. \u0026amp; McAlpine, D. 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Mechanisms and streams for processing of \u0026lsquo;what\u0026rsquo; and \u0026lsquo;where\u0026rsquo; in auditory cortex. \u003cem\u003eProc. Natl. Acad. Sci. U. S. A.\u003c/em\u003e\u003cstrong\u003e97\u003c/strong\u003e, 11800\u0026ndash;11806 (2000).\u003c/li\u003e\n \u003cli\u003eWernick, J. S. \u003cem\u003eElectrical Activity of the Superior Olivary Complex of the Cat Evoked by Stimuli with Constantly Changing Interaural Phase Relations\u003c/em\u003e. (1967).\u003c/li\u003e\n \u003cli\u003eSpitzer, M. W. \u0026amp; Semple, M. N. Transformation of binaural response properties in the ascending auditory pathway: influence of time-varying interaural phase disparity. \u003cem\u003eJ Neurophysiol\u003c/em\u003e\u003cstrong\u003e80\u003c/strong\u003e, 3062\u0026ndash;3076 (1998).\u003c/li\u003e\n \u003cli\u003eZhou, Y., Carney, L. H. \u0026amp; Colburn, H. S. A model for interaural time difference sensitivity in the medial superior olive: interaction of excitatory and inhibitory synaptic inputs, channel dynamics, and cellular morphology. \u003cem\u003eJ Neurosci\u003c/em\u003e\u003cstrong\u003e25\u003c/strong\u003e, 3046\u0026ndash;3058 (2005).\u003c/li\u003e\n \u003cli\u003eKnudsen, E. I. \u0026amp; Konishi, M. A neural map of auditory space in the owl. \u003cem\u003eScience\u003c/em\u003e\u003cstrong\u003e200\u003c/strong\u003e, 795\u0026ndash;797 (1978).\u003c/li\u003e\n \u003cli\u003eWiner, J. A. \u0026amp; Schreiner, C. E. \u003cem\u003eThe Inferior Colliculus\u003c/em\u003e. (Springer Science \u0026amp; Business Media, 2005).\u003c/li\u003e\n \u003cli\u003eWilson, W. W. \u0026amp; O\u0026rsquo;Neill, W. E. Auditory motion induces directionally dependent receptive field shifts in inferior colliculus neurons. \u003cem\u003eJ Neurophysiol\u003c/em\u003e\u003cstrong\u003e79\u003c/strong\u003e, 2040\u0026ndash;2062 (1998).\u003c/li\u003e\n \u003cli\u003eSpitzer, M. W. \u0026amp; Semple, M. N. Interaural phase coding in auditory midbrain: influence of dynamic stimulus features. \u003cem\u003eScience\u003c/em\u003e\u003cstrong\u003e254\u003c/strong\u003e, 721\u0026ndash;724 (1991).\u003c/li\u003e\n \u003cli\u003eZuk, N. J. \u0026amp; Delgutte, B. Neural coding and perception of auditory motion direction based on interaural time differences. \u003cem\u003eJ Neurophysiol\u003c/em\u003e\u003cstrong\u003e122\u003c/strong\u003e, 1821\u0026ndash;1842 (2019).\u003c/li\u003e\n \u003cli\u003eRecanzone, G. H., Guard, D. C., Phan, M. L. \u0026amp; Su, T. K. Correlation between the activity of single auditory cortical neurons and sound-localization behavior in the macaque monkey. \u003cem\u003eJ. Neurophysiol.\u003c/em\u003e\u003cstrong\u003e83\u003c/strong\u003e, 2723\u0026ndash;2739 (2000).\u003c/li\u003e\n \u003cli\u003eKusmierek, P. \u0026amp; Rauschecker, J. P. Selectivity for space and time in early areas of the auditory dorsal stream in the rhesus monkey. \u003cem\u003eJ. Neurophysiol.\u003c/em\u003e\u003cstrong\u003e111\u003c/strong\u003e, 1671\u0026ndash;1685 (2014).\u003c/li\u003e\n \u003cli\u003eMiller, L. M. \u0026amp; Recanzone, G. H. Populations of auditory cortical neurons can accurately encode acoustic space across stimulus intensity. \u003cem\u003eProc. Natl. Acad. Sci. U. S. A.\u003c/em\u003e\u003cstrong\u003e106\u003c/strong\u003e, 5931\u0026ndash;5935 (2009).\u003c/li\u003e\n \u003cli\u003eBaumgart, F., Gaschler-Markefski, B., Woldorff, M. G., Heinze, H. J. \u0026amp; Scheich, H. A movement-sensitive area in auditory cortex. \u003cem\u003eNature\u003c/em\u003e\u003cstrong\u003e400\u003c/strong\u003e, 724\u0026ndash;726 (1999).\u003c/li\u003e\n \u003cli\u003eWarren, J. D., Zielinski, B. A., Green, G. G. R., Rauschecker, J. P. \u0026amp; Griffiths, T. D. Perception of sound-source motion by the human brain. \u003cem\u003eNeuron\u003c/em\u003e\u003cstrong\u003e34\u003c/strong\u003e, 139\u0026ndash;148 (2002).\u003c/li\u003e\n \u003cli\u003eMalone, B. J., Scott, B. H. \u0026amp; Semple, M. N. Context-dependent adaptive coding of interaural phase disparity in the auditory cortex of awake macaques. \u003cem\u003eJ Neurosci\u003c/em\u003e\u003cstrong\u003e22\u003c/strong\u003e, 4625\u0026ndash;4638 (2002).\u003c/li\u003e\n \u003cli\u003ePoirier, C. \u003cem\u003eet al.\u003c/em\u003e Auditory motion-specific mechanisms in the primate brain. \u003cem\u003ePLoS Biol.\u003c/em\u003e\u003cstrong\u003e15\u003c/strong\u003e, e2001379 (2017).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Table of task conditions organized into sets according to the motion parameter that remains constant in each set. \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"719\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMotion Parameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 207px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant Duration Conditions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 220px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant Velocity Conditions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 220px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant Displacement Conditions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eDuration\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 207px;\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eDisplacement\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 220px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eVelocity (\u0026deg;/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e9.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e26.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e59.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e76.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e93.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 220px;\"\u003e\n \u003cp\u003e59.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e165.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e59.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e36.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e26.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Table of regression model formulas and their model numbers that correspond to the x-axis labels in Figure 6. \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"719\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegression Model Number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 545px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegression Model Formula\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eslope ~ 1 + velocity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eslope ~ 1 + duration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eslope ~ 1 + displacement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eslope ~ 1 + velocity + duration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eslope ~ 1 + velocity + displacement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eslope ~ 1 + duration + displacement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eslope ~ 1 + velocity + duration + displacement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eslope ~ 1 + velocity*displacement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eslope ~ 1 + velocity*duration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eslope ~ 1 + duration*displacement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eslope ~ 1 + displacement + velocity*duration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eslope ~ 1 + duration + velocity*displacement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eslope ~ 1 + velocity + duration*displacement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eslope ~ 1 + velocity*duration + velocity*displacement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eslope ~ 1 + velocity*duration + duration*displacement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eslope ~ 1 + velocity*displacement + duration*displacement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eslope ~ 1 + velocity*duration + velocity*displacement + duration*displacement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eslope ~ 1 + velocity*duration*displacement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6254702/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6254702/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMotion perception is a key aspect of sensory processing that enables successful interaction with the environment. While visual motion perception has been extensively studied, little is known about the determinants of auditory motion perception. Our study explores how the perception of auditory motion direction changes with manipulations of low-level stimulus parameters in nonhuman primates (NHPs). Macaque monkeys were trained to perform a 2-AFC task in which they judged the direction of noisy auditory motion stimuli. We systematically manipulated stimulus duration, velocity, and displacement to evaluate their respective influence on motion sensitivity. Displacement had the greatest impact, while the relative influence of duration versus velocity depended upon the duration of the stimulus. These findings suggest that auditory motion direction is most likely processed by a snapshot mechanism, in which stimulus velocity is inferred by sequential snapshots of auditory stimulus location, rather than by velocity-selective motion detectors similar to those found in the visual system. To our knowledge, this study is the first to characterize the influence of low-level stimulus parameters on auditory motion perception in awake, behaving NHPs, and forms the basis for future neurophysiological investigations.\u003c/p\u003e","manuscriptTitle":"Contribution of displacement, duration, and velocity on auditory motion direction perception in macaque monkeys","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-24 10:56:44","doi":"10.21203/rs.3.rs-6254702/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-21T06:39:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-20T22:26:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82718890885904929644869246475645979680","date":"2025-05-08T18:23:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-29T10:13:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221538082948196557388217560176812306406","date":"2025-04-07T20:26:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149473164270365141744005729012658350546","date":"2025-04-06T10:32:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-02T18:46:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-02T18:27:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-27T19:40:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-27T04:18:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-18T15:31:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e725912f-489b-461c-9af0-d5590eb58706","owner":[],"postedDate":"April 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46814507,"name":"Biological sciences/Neuroscience/Auditory system"},{"id":46814508,"name":"Biological sciences/Neuroscience/Sensory processing"},{"id":46814509,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Perception"}],"tags":[],"updatedAt":"2025-08-04T16:43:22+00:00","versionOfRecord":{"articleIdentity":"rs-6254702","link":"https://doi.org/10.1038/s41598-025-12642-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-08-01 16:13:21","publishedOnDateReadable":"August 1st, 2025"},"versionCreatedAt":"2025-04-24 10:56:44","video":"","vorDoi":"10.1038/s41598-025-12642-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-12642-y","workflowStages":[]},"version":"v1","identity":"rs-6254702","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6254702","identity":"rs-6254702","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}