Three-dimensional (3D) representations of the environment are often critical for selecting actions that achieve desired goals. The success of these goal-directed actions relies on 3D sensorimotor transformations that are experience-dependent. Here we investigated the relationships between the robustness of 3D visual representations, choice-related activity, and motor-related activity in parietal cortex. Macaque monkeys performed an eight-alternative 3D orientation discrimination task and a visually guided saccade task while we recorded from the caudal intraparietal area using laminar probes. We found that neurons with more robust 3D visual representations preferentially carried choice-related activity. Following the onset of choice-related activity, the robustness of the 3D representations further increased for those neurons. We additionally found that 3D orientation and saccade direction preferences aligned, particularly for neurons with choice-related activity, reflecting an experience-dependent sensorimotor association. These findings reveal previously unrecognized links between the fidelity of ecologically relevant object representations, choice-related activity, and motor-related activity. Interactions with the environment require the mapping of sensory information to motor responses. The parietal cortex is an important site of sensorimotor transformations . ParietaWithin parietal cortex, a major site of 3D visual processing is the caudal intraparietal (CIP) area. Neurons in CIP are tuned for 3D orientation  signaledThe anatomical projections and effective connectivity of CIP suggest that it may contribute to goal-directed sensorimotor transformations . In partHere we investigated the relationships between sensory representations, choice-related activity, and motor-related activity in CIP. Neuronal activity was recorded while macaque monkeys performed an eight-alternative forced choice (8AFC) tilt discrimination task with planar surfaces at different slants and distances . The neuThese results reveal systematic relationships between sensory representations, choice-related activity, and motor-related activity in CIP. Neurons with more robust 3D representations (i.e. those whose 3D orientation selectivity was more tolerant to distance) were most strongly coupled to behavior. Beyond traditional associations between neuronal choice-related activity and decision processes, our findings identify links to the fidelity of high-level sensory representations and the strength of sensorimotor associations. The coupling of sensory and motor functions at the level of single CIP neurons may facilitate sensorimotor transformations over short timescales that enable successful interactions with the 3D world.We tested three hypotheses about the relationships between sensory representations, choice-related activity, and motor-related activity. First, that neurons with more robust 3D representations would preferentially carry choice-related activity. Second, that choice-related activity would be associated with further increases in the robustness of 3D selectivity, not the attenuation of information not directly relevant to the task at hand (i.e. nuisance variables). Third, that sensorimotor associations would be experience dependent and stronger for neurons with choice-related activity.Testing our hypotheses required a protocol that could be used to simultaneously measure the 3D visual sensitivity of the monkeys as well as quantify the 3D selectivity of individual neurons. To this end, we developed an 8AFC tilt discrimination task in which the side of a viewed plane that was nearest was reported through a saccade to one of eight choice targets . We thenThe 3D orientation of a planar surface is described by tilt and slant . Tilt 0. Thus, w−11) and distance (both p≤1.3x10−34) but not tilt  . The senribution  and was We next needed to quantify the representation of 3D surface pose in CIP. Although previous studies examined the 3D orientation selectivity of CIP neurons , it is cSensory and choice-related signals co-exist in CIP but follow distinct time courses . In FiguIn an idealized 3D representation, the orientation tuning curve shape  is independent of distance, indicating that the 3D pose tuning curve is separable over orientation and distance. To quantify how well CIP activity conformed to this ideal, we fit each neuron’s responses with a separable model . The mod−3 and Bonferroni-Holm corrected). At each of these distances, the mean slant preference across the population was larger than at 57 cm. We therefore wanted to identify the neuronal factors underlying this dependency.The 3D orientation preferences of CIP neurons were previously found to be uniformly distributed when measured at the fixation distance . To test−3, N = 144 tuned at both distances) as well as between 97 and 137 cm . These results indicate an inseparability in the 3D pose tuning such that the slant preferences tended to increase with distance behind fixation. The slant preferences of individual neurons were also larger at 37 than 57 cm, but the difference was not significant . This finding suggests a second  possibility that neurons tuned for distances away from fixation tended to prefer larger slants. To test this, we compared the slant preferences of neurons tuned at 57 cm only (N = 75) to the slant preferences of the combined set of neurons tuned at 37, 97, or 137 only (N = 31). Consistent with this possibility, the mean slant preference of neurons tuned at a single distance away from fixation was 7.6° greater than for neurons tuned only at fixation . Thus, the larger mean slant preferences at distances away from fixation reflected an inseparability in the 3D pose tuning of individual neurons as well as a tendency for neurons that represented distances away from fixation to prefer larger slants.One possibility was that the slant preferences of individual neurons increased with distance from fixation. To test this, we performed pairwise comparisons of the slant preferences of individual neurons with significant orientation tuning at adjacent distances . Indeed, the preferences tended to increase between 57 and 97 cm . Thus, the 3D tilt sensitivity of the monkeys was closely related to the tilt discriminability of CIP neurons over a wide range of viewing conditions.We next wanted to compare the behavioral and neuronal findings. Two prominent features of the behavioral data were increasing tilt sensitivity as a function of slant and an inverted U-shape relationship between tilt sensitivity and distance that peaked at the fixation distance . These f−308; Monkey F: r = 0.87, p=2.2×10−308). This result parallels the strong correlation between tilt sensitivity and TDI value (which depends on response modulation and variability). The finding that the neuronal responses predicted behavioral sensitivity across performance levels ranging from near-chance to near-perfect suggests that CIP activity may constrain the precision of tilt perception.We previously found that behavioral tilt sensitivity was consistent with a neuronal probabilistic population code model that integrated perspective and stereoscopic cues to 3D orientation . That moTo test our hypotheses regarding the fidelity of 3D visual representations and choice-related activity, we needed to classify the neurons according to whether or not they carried choice-related activity. To ensure that we distinguished choice-related activity from tilt-selective responses, we performed the classification using responses to frontoparallel planes (S = 0°) only. Because tilt is undefined at that orientation, the stimuli were task-ambiguous. To increase the statistical power, we combined responses across all distances after separately z-scoring them at each distance. Responses were then grouped according to the choice made on each trial.An advantage of the 8AFC task was that it could reveal parametric choice tuning, which is not possible with typical binary choice tasks. We therefore computed eight population-level time courses relative to the choice that elicited the maximum response for each neuron . The resAcross the population, 201 neurons (46%) had significant choice-related activity . Choice tuning curves with von Mises fits are shown for the example neurons in For comparison, surface tilt tuning curves measured during the SD window and marginalized over slant and distance are also shown in Because the SD and SPC windows were defined using a population-level measure, a potential concern was that they were not sensitive to individual neuron differences in the onset of choice-related activity, which could inadvertently introduce choice-related activity into the SD window. We therefore tested each neuron for choice-related activity in the SD window . Consistent with the expected rate of false positives, the result of this test was significant for only 5% of neurons  and not associated with the classification of having choice-related activity since only 13 had significant choice tuning in the SPC window. Moreover, excluding those 22 neurons did not significantly affect the mean Tolerances of neurons with or without choice-related activity in either time window . These findings suggest that the SD and SPC windows defined reliable, functionally distinct response epochs at the individual neuron level. Another potential concern was that the SPC window was longer than the SD window. We therefore tested a short SPC window (850 ms – 1 s) that matched the duration of the SD window (150 ms) and found similar results . Thus, tWe wanted to further evaluate if the alignment of preferred surface tilts and choice report directions reflected a naturally occurring correspondence or a learned association. The current experiment was designed to distinguish between these possibilities through a comparison with results from another recent CIP study . The twoThe above analyses identified response epochs that were either sensory dominant (SD) or contained sensory plus choice (SPC) activity, and two neuronal subpopulations based on whether or not they had choice-related activity. These distinctions allowed us to next test our hypotheses regarding the fidelity of 3D visual representations and choice-related activity by comparing the 3D pose tuning curves across the time windows and subpopulations. To illustrate how the tuning curves changed across the time windows, the example neurons’ 3D pose tuning curves measured during the SPC window are shown in −18; The changes in the example neurons’ 3D pose tuning curves across the time windows were representative of the population. In particular, more neurons showed significant orientation tuning at each distance in the SPC window than the SD window  to compare the 3D visual representations of neurons with versus without choice-related activity . ConsistWe next wanted to test our hypothesis that choice-related activity would be associated with further increases in the robustness of 3D selectivity (i.e. that 3D orientation selectivity would become more tolerant to distance). To start, we compared each neuron’s 3D pose tuning in the SD and SPC windows by calculating the correlation over all 132 poses. The mean correlation was r = 0.49 ± 0.27 Std Dev (N = 437), indicating that the tuning curves were generally similar but not identical. Besides our hypothesis, an alternative explanation for this relatively modest average correlation could be the attenuation of selectivity for slant and distance (nuisance variables in the task), leaving only selectivity for the task-relevant variable (tilt). Contrary to this possibility, slant and distance tuning persisted in the SPC window for the example neurons . To quan−9; −9; −11. Thus, more robust 3D selectivity was associated with stronger choice-related activity.Having ruled out that choice-related activity was associated with the attenuation of selectivity for slant and distance, we tested our hypothesis that it would be associated with an increase in the robustness of 3D selectivity. Specifically, that 3D orientation selectivity would be more tolerant to distance in the SPC than the SD window. First, we compared the mean Tolerance values between neurons with (0.72) and without (0.60) choice-related activity in the SPC window and found that the difference was statistically significant . Thus, the orientation preferences of individual neurons were more similar across distance after the onset of choice-related activity than before. Importantly, we repeated this analysis for neurons without choice-related activity and found that the cumulative density functions were not significantly different across the time windows (p=0.91). These findings indicate that choice-related activity was associated with an increase in the similarity of 3D orientation preferences across distance.The analysis of Tolerance values indicated that 3D orientation tuning became more similar across distance after the onset of choice-related activity but could not reveal which aspects of the tuning curves  specifically changed. We therefore quantified how much the orientation preferences of individual neurons differed across distance in each time window. For each neuron with choice-related activity, we used the orientation preferences from the Bingham function fits in both time windows to calculate a principal orientation preference  and SPC λ1 = −0.05 . Thus, regardless of choice-related activity, tilt and slant tuning widths were more similar later in the response than earlier. Although the isotropy of the tuning curves was similar for the two subpopulations in the SD window (p=0.89), neurons with choice-related activity had more isotropic tuning than neurons without choice-related activity in the SPC window (p=1.4×10−6). Third, we found that the orientation of the major/minor axes of the tuning curves generally aligned with the slant and tilt axes of the coordinate space, and did not differ between the subpopulations in either time window or across time windows . These findings indicate that choice-related activity was associated with a greater change in the aspect ratio of the tuning curves, such that the slant and tilt tuning widths became more similar. One potential consequence of this change is that the Fisher information along the two orientation dimensions would become more similar. This could theoretically make the monkeys’ sensitivities to small differences in slant or tilt more equivalent  input from V3A and projects to LIP , two areImportantly, the visually guided saccade task revealed that CIP carries motor-related activity. Following our analysis of choice-related activity, we computed eight population-level time courses of saccade-related activity relative to the saccade direction that elicited the maximum response for each neuron . SaccadeAcross the population, 274 neurons (63%) had significant saccade direction tuning . Saccade direction tuning curves with von Mises fits are shown for the example neurons in n tuning  and 6A,Bn tuning , and sacn tuning . The surn tuning  and 6C an tuning  but not n tuning . The foun tuning  and 6D bn tuning , had sacn tuning . The surn tuning  and 6E hn tuning  and saccn tuning . The sac−5; Having discovered saccade direction selectivity in CIP, we were able to test for a sensorimotor association between the surface tilt and saccade direction preferences. For each neuron with significant saccade direction tuning, we compared the principal surface tilt (SD and SPC windows) and saccade direction preference from the von Mises fit. Across the population, the preferences aligned both for neurons with  and with−6). We also found that many neurons had saccade-related activity only  or choice-related activity only , indicating that these properties were dissociable across the population  had both choice- and saccade-related activity. For these neurons, the choice and saccade direction preferences generally aligned . The medpulation . Togethe−5) and similar reductions in the angular deviations in orientation preferences across distance (both p≤0.014). Thus, choice-related activity was sufficient for the robustness of 3D selectivity to increase across the time windows and saccade-related activity was not necessary. Second, we compared the two subpopulations without choice-related activity (C-S+ and C-S-). The Tolerance values of these subpopulations were not significantly different in either time window  but not the SPC window  choice- and saccade-related activity , (2) choice- but not saccade-related activity , (3) no choice- but saccade-related activity , and (4) neither choice- nor saccade-related activity . First, we compared the two subpopulations with choice-related activity (C+S+ and C+S-). The Tolerance values of these subpopulations were not significantly different in either time window  preferentially carried choice-related activity. Moreover, greater tolerance was correlated with stronger choice-related activity. Thus, the activity of neurons with more robust 3D selectivity was more tightly coupled to the monkeys’ behavior. The extent to which subcortical vestibular neurons resolve the gravito-inertial acceleration ambiguity necessary to represent translation independently of head tilt similarly correlates with the strength of choice-related activity . Across Our behavioral task required the monkeys to report the tilt of a plane regardless of its slant or distance. As such, tilt was the directly relevant stimulus feature while slant and distance were nuisance variables that modulated performance . This diChoice-related activity is traditionally associated with feedforward contributions to perception . HoweverFor the current study, we trained monkeys to report the near side of a viewed plane, whereas a previous CIP study had monkeys report the far side . Thus, tWe discovered that the functional properties of CIP neurons extend beyond sensory and perceptual processing. Specifically, CIP activity correlated with the direction and timing of visually guided saccades, implicating the area in visuomotor control for the first time. Moreover, there was a sensorimotor association between the surface tilt and saccade direction preferences, indicating that visual and oculomotor activities were not simply multiplexed. Correspondingly, for neurons with both choice- and saccade-related activity, the choice report and saccade direction preferences also generally aligned. However, it is important to emphasize that the choice- and saccade-related activities were distinct from each other. First, the choice and saccade tuning curves of individual neurons systematically differed in bandwidth. Second, these functional properties were dissociable since many neurons were only tuned for choice report or saccade direction, not both. These findings suggest that the choice-related activity did not simply reflect saccade preparation signals. Moreover, the dissociation of these properties across the population allowed us to test if the robustness of 3D selectivity was distinctly associated with choice-related activity or if it was also linked to saccade-related activity. For example, it is conceivable that the addition of saccade preparation signals to the sensory responses might appear like an increase in the robustness of 3D selectivity since the saccade signals in our task were likely independent of the surface’s distance. However, that was not the case. Across the population, we found that choice-related activity was both necessary and sufficient for the robustness of 3D selectivity to increase across the time windows. In contrast, saccade-related activity was neither necessary nor sufficient. If choice- and saccade-related activities simply reflected a common signal, then saccade-related activity would have also likely been associated with an increase in the fidelity of the 3D visual representations, which was not the case. Thus, these findings further support that choice- and saccade-related activities were functionally distinct, and suggest that only choice-related activity was linked to the robustness of the 3D visual representations.We further found that sensorimotor associations were strongest for neurons with choice-related activity (and therefore the most robust 3D selectivity). Thus, a potential implication of the current findings is that neurons with robust 3D selectivity and strong sensorimotor associations have the greatest weight in determining motor responses. Indeed, associating robust sensory and motor-related activity at the single neuron level may help ensure successful and timely interactions with the environment since the same neurons represent the relevant sensory information and signal the appropriate motor response. Building on these findings, future studies can test this possibility using reaction time tasks to evaluate the time course of 3D sensory and sensorimotor processing. To date, relatively few studies have used reaction time tasks to assay 3D visuomotor processing . Such taIn addition to oculomotor processing, connections between CIP and prehensile areas suggest that the area’s sensorimotor functions may be broader. This possibility is consistent with recent work implicating human parietal regions that integrate visual orientation and saccade signals in the updating of grasp plans , and wilMacaca mulatta; Monkey L: 6 years of age, ~9.5 kg; Monkey F: 5 years, ~6.4 kg) were surgically implanted with a Delrin ring for head restraint and a removable recording grid for guiding electrodes  at the University of Wisconsin–Madison (Protocol G005229). Two male rhesus monkeys  and the dots were bright (35.1 cd/m2), measured through the glasses . Planes were presented at all combinations of eight tilts , five slants , and four distances . Fixation was always at screen distance (57 cm). At 37 cm, all dots were in front of the fixation target. At 57 cm, the dots were distributed about the fixation target. At 97 and 137 cm, all dots were behind fixation. Presenting the stimuli in front of, distributed about, and behind fixation prevented the monkeys from relying on local absolute disparity cues to perform the task. The dots were rendered with stereoscopic and perspective cues and scaled according to the distance so that their screen size only depended on slant. At S = 0°, the dot size was 0.35° isotropic.The stimuli were the same as the combined-cue stimuli in our previous work . They coThe monkeys were trained to perform an 8AFC tilt discrimination task . Each trEach trial began with fixation of a target at the center of the screen for 1.3 s to match the total fixation duration in the 8AFC tilt discrimination task. The fixation target then disappeared and a single saccade target appeared at one of the eight choice target locations. A saccade to the target was made for a liquid reward.i) planes:  × four distances (N = 160), and (ii) saccades: 8 directions × four repeats (N = 32). A trial was aborted and data discarded if fixation was broken before the choice or saccade targets appeared, or if the trial was not completed within 500 ms of their appearance. At least five completed blocks were required to include a neuron for analysis.Stimuli were presented in a pseudorandom order within a block design. A block included one completed trial for each of the following:  followed by manual refinement using Offline Sorter . Only well-isolated single neurons verified by at least two authors were included for analysis: 218 neurons from the left hemisphere of Monkey L (26 sessions) and 219 neurons from the right hemisphere of Monkey F (27 sessions).Area CIP was identified based on anatomical and functional properties . BrieflyTilt discrimination performance was quantified by fitting a von Mises probability density function to the distribution of reported tilt errors , ΔTilt =The mean (µ) and concentration  was created for each trial that a plane was presented by convolving the spike train  with the function :(2)α(t)=Here, We computed a TDI to quantify how well preferred and non-preferred tilts could be discriminated using individual neuron responses :(3)TDI=RmaxR and minR are the maximum and minimum mean responses across a tilt tuning curve, SSE is the sum squared error around the mean responses, N is the total number of trials, and M is the number of tilts (M = 8). A TDI was calculated for each combination of slant and distance. Neurons with strong (weak) response modulation relative to their variability have TDIs closer to 1 (0).Here, We tested for choice-related activity using the frontoparallel plane data. To combine data across distances, we z-scored all baseline subtracted frontoparallel plane responses at each distance. We then grouped the z-scored firing rates according to the choice. A neuron was classified as having choice-related activity if the z-scored firing rates significantly depended on the choice . For each of these neurons, an average SDF was calculated for each choice , and labeled relative to the choice that elicited the maximum z-scored firing rate for the neuron: preferred choice, ±45°, ±90°, ±135°, and 180°. The SDFs were then averaged across neurons to create eight population-level time courses. The onset of choice-related activity was defined as the first time point that the time courses significantly differed . To refine the estimate, we iteratively repeated this process with all neurons, each time calculating the firing rates starting from the previous estimate of the onset of choice-related activity to the end of the 1 s stimulus presentation . This process was repeated until the onset no longer changed.For each distance and time window, we tested for orientation tuning . The preferred tilt and slant was estimated for each significant case by fitting a Bingham function . DiffereThe normal vectors were then arranged in a matrix and the eigenvectors were calculated. The principal orientation was defined as the eigenvector with the greatest eigenvalue. Principal orientations were calculated using the SD window only and both (SD and SPC) windows. Second, we rotated the normal vectors such that the principal orientation aligned with the north pole  matrix of responses with a multiplicatively separable model:Here, We also tested an additively separable model, he error , giving A saccade onset was defined as the first time point that the velocity of either eye was ≥ 150°/s. A neuron was classified as having saccade direction selectivity if the baseline subtracted firing rates significantly depended on the saccade direction . For each of these neurons, an average SDF was calculated for each saccade direction  and labeled relative to the saccade direction that elicited the maximum firing rate for the neuron. The SDFs were then averaged across neurons to create eight time courses. The start of saccade-related activity was defined as the earliest time point before saccade onset that the time courses significantly differed . To refine the estimate, we iteratively repeated this process with all neurons, each time calculating the firing rates from the previous estimate of the start of saccade-related activity to the saccade onset. This process was repeated until the onset no longer changed.Vergence eye movements can potentially contribute to neuronal responses. We therefore tested if our 3D pose tuning measurements were significantly affected by small vergence eye movements that did not violate the vergence window. For each neuron, we performed an ANCOVA to test for main effects of stimulus tuning with vergence included as a covariate . Tilt l, and ind In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:This study advances our understanding of 3D visual representations in the primate posterior parietal cortex, and how encoding of sensory, choice, and motor factors interact in the caudal intraparietal area during perceptual decisions based on visual depth cues.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Joshua Gold as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Choice activity stabilizes sensory representations and mediates sensorimotor associations in parietal cortex" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife". Please let us know if you would like to pursue this option. As the editors have judged that your manuscript is of interest, but as described below that additional analysis, substantial revisions to the manuscript, and possibly new data are needed to address a number of major concerns and points of confusion. The reviewers were unanimous in wanting to give you the opportunity to respond to their concerns but also emphasized that substantial improvements are needed to merit acceptance in our journal. We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at Summary:This is an interesting study which examines the role of parietal cortex area CIP in judgments and choices with 3D stimuli. The stimuli consist of a parametric set of patterns at a range of tilts, slants and distances in depth. The monkeys' task is to report the perceived tilt by making a saccade to a target that is closest to the viewer in depth. Neuronal mechanisms underlying 3D perception and particularly choices/decision are relatively understudied, so this study is novel in that regard. The mapping of the tilt to saccade is fixed so that the choice (saccade direction) is known as soon as the stimulus is viewed. The main result of the study is that there is substantial encoding correlated with 3D tilt as well as with choice (saccade direction). Interestingly there is a strong correspondence observed between tilt and saccade direction, which the authors interpret as associative encoding which combined stimulus tilt encoding with encoding of the appropriate saccade direction, suggestive of a role for CIP in mediating decisions during this task.Essential revisions:1) All reviewers raised serious concerns about the ability to dissociate stimulus from choice/saccade encoding. This problem is in part due to the experimental design, which uses a rigid link between the monkeys' decisions and the direction of the saccades. Further, there is concern about the attempt to dissect sensory and choice information using different time windows. Sensory only (SO) window and sensory plus choice (SPC) windows are used for analysis. Since the animal can plan his response as soon as he determines the tilt, how can you be sure that the SO window doesn't contain any choice information? More description and rationale for this is needed, and likely a revised analysis approach is needed. How confident are the authors in the determination of the onset of choice information? This could vary from session to session or from neuron to neuron in principle, yet the timings were determined from an across session average. The SO vs SPC time windows are described in a confusing way-as if they truly dissociate sensory and choice information in a clean way. Instead it is an arbitrary temporal cut-off and the windows each likely include a mixture of sensory and choice information.2) It is potentially of strong interest that there is a correspondence between tilt and choice selectivity as shown in 5G. A key question is whether this correspondence arose because of training, or because this could be a naturally occurring correspondence. It is possible that eye movements are naturally drawn to the closest part of a tilted 3D object. It would be very interesting to test in naïve animals whether this is indeed the case (perhaps during passive presentation of tilted objects and free viewing). If there is no such effect in naïve animals, but it is observed in the trained animals, this would support the idea that the correspondence arose because of training. Was the tuning assessed for each neuron during passive viewing outside the context of the task? If so, this might be a way to get at the question by comparing task vs passive viewing.3) The reviewers agreed with one another that the results are described throughout the manuscript in a way which implies a causal relationship between CIP activity/encoding and choice. But given the correlational approaches used here, this message should be revised and clarified in order to avoid confusion and to better bring the discussion of the paper in line with the observed results and experimental design.4) All reviewers were in agreement that the writing of the manuscript needs to be improved substantially in order to help readers follow the logic of the analyses, experimental design, and results.5) Analysis related to Figure 4: authors should clarify which period of the task they are describing here, is it SO or SPC? They separate sensory (SO) and choice response (SPC) periods. The distinction between SO and SPC seems pretty arbitrary. The only neuronal activity they show up to Figure 5 are heat map. It would be helpful to see the actual time course of the neuronal response for example neurons.6) Although they show that 5 example neurons, they do not provide clear population analyzes of 3D orientation selectivity. At least, they should show the distribution of tolerance index for all the neurons. Given the response shown in Figure 5A, I wonder how neurons could be tilt selective during the SO period. I'd like to see the actual response of these neurons.7) There is confusion regarding the discussion of vergence, raised by reviewer 3, which needs to be addressed. Also, the correlation of tilt selectivity between SO and SPC shown in Figure 7 needs to be clarified and explained in more detail.eLife. Your revised article has been evaluated by Joshua Gold (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Functional links between sensory representations, choice activity, and sensorimotor associations in parietal cortex" for further consideration by The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:1) The new analysis with previous data required additional clarification and explanation, and perhaps additional analysis. The reviewers agreed that there is concern about whether the observed results can be explained by saccade preparation. Specifically one reviewer states that "I don't understand how 3D selectivity and choice can be aligned as shown in Figure 5 considering that choice changes depending on the training . If a neuron is tuned to right forward, and monkey is trained to choose forward, the neuron will fire strongly during SPC when stim is right forward and therefore when monkey is planning a saccade to the right. When trained to report away, the same neuron should, according to Figure 5—figure supplement 1, responds strongly during SPC when planning a saccade to the left. How is that possible given that 3D selectivity is getting stronger during SPC and that 3D selectivity and saccade spatial selective strongly overlap ? I understand that they are not analyzing the same neurons, but then, does it only mean that choice selectivity in CIP reflects saccade preparation? I'm genuinely afraid I am missing something important here."Reviewer #1:In reviewing this revised manuscript, the authors have done a thorough job in responding to the previous round of review. They have clarified key points, and in particular largely resolved major concerns about whether the observed association like effects were indeed due to training by including an additional supplementary figure, and by more thoroughly referring to previous results from their group which help address this concern.Reviewer #2:The authors did an excellent job in revising the manuscript. Specifically, the comparison with the task where the monkeys had to indicate the opposite features of the stimulus is crucial to demonstrate that the association between stimulus orientation and choice direction was indeed learned. I have no further major comments.Reviewer #3:I recognize authors spend a lot of energy trying to address our comments. I am not sure they fully manage to address them though.Here are the main claims (Abstract):Choice related activity (saccade) is carried by neurons with robust 3D selectivity.Following onset of choice activity 3D robustness increased3D selectivity and saccade selectivity alignedHere are my main concerns:1) Specifically, their analysis consisting of testing for choice related activity in the SD window is not convincing. Our point was that some choice related activity could start before the end of SD window and before the start of the SPC window, not that the activity during the entire SD window was related to choice. In this regard, they failed to address our concerns.2) I am still very confused by Figure 5—figure supplement 2 and the claim that 3D selectivity and saccade/choice selectivity are aligned:Let's consider a neuron tuned to top near. We have 4 conditions based on task design (near-CN-or far-CF) and stimulus slant (Top Near-TN-or Bottom near-BN): a) CN/TN, b) CN/BN, c) CF/TN, d) CF/BN.Condition a: choose near with stimulus top near: this neuron will have a strong visual response which will stay sustained since it is aligned to motor plan.Condition b: Choose near with stimulus bottom near: this neuron will have a weak visual response, which will stay weak during motor planning.Condition c: choose far with stimulus top near: there will be a strong visual response  which should decrease during motor planning.Condition d: choose far with stimulus bottom near (saccade to the top): there will be a weak visual response  which should increase during motor planning.I am confused how this is translated to Figure 5 sp2 and how does that match authors' claim that "Following onset of choice activity 3D robustness increased" and that choice and "3D selectivity and saccade selectivity are aligned". Separating between SC and SPC window does not help at all to understand the neuronal response properties.3) Their attempt to dissociate choice and motor planning is not successful. Stimuli and motor action are still strongly linked whether monkeys have to report near or far side. I don't see how it breaks the link.4) Despite their effort to improve the flow of argument in the article, I still find it very difficult to follow. Essential revisions:1) All reviewers raised serious concerns about the ability to dissociate stimulus from choice/saccade encoding. This problem is in part due to the experimental design, which uses a rigid link between the monkeys' decisions and the direction of the saccades.To dissociate stimulus from choice/saccade encoding, we now compare results when the monkeys were trained to report opposite features of the same stimuli . This task difference allows us to dissociate stimulus orientation from choice/saccade directions since opposite choice report directions were made for the same surface orientation. Importantly, the association between stimulus orientation and choice report direction was reversed across the two data sets, indicating that the association reflected task training. For instance, consider a typical neuron preferring bottom-near planes. If the monkey were trained to report the near side, the neuronal responses were larger if the monkey selected the lower choice target. However, if the monkey were trained to report the far side, the responses were larger if the monkey selected the upper choice target. This indicates that the stimulus and choice/saccade encodings were dissociable. These findings are presented in subsection “Choice-related activity was parametrically tuned and aligned with the tilt preferences”, in new Figure 5—figure supplement 2, and further discussed in subsection “Associations between 3D selectivity and choice-related activity”. We provide greater detail about these additions in Essential Revision 2.Further, there is concern about the attempt to dissect sensory and choice information using different time windows. Sensory only (SO) window and sensory plus choice (SPC) windows are used for analysis. Since the animal can plan his response as soon as he determines the tilt, how can you be sure that the SO window doesn't contain any choice information? More description and rationale for this is needed, and likely a revised analysis approach is needed. How confident are the authors in the determination of the onset of choice information? This could vary from session to session or from neuron to neuron in principle, yet the timings were determined from an across session average. The SO vs SPC time windows are described in a confusing way-as if they truly dissociate sensory and choice information in a clean way. Instead it is an arbitrary temporal cut-off and the windows each likely include a mixture of sensory and choice information.One of our primary goals was to assess if the robustness of 3D sensory representations was associated with choice-related activity. To test this, we identified two subpopulations of neurons based on whether or not they carried choice-related activity and two time windows based on the onset of choice-related activity. By comparing the 3D tuning properties of neurons between the subpopulations as well as within the subpopulations across the time windows, we were able to identify relationships between the robustness of 3D selectivity and choice-related activity. To help clarify this, we expanded the description and rationale behind the analyses and made it more explicit that the windows were operationally defined based on the population activity .The reviewers raise a great point about confidence in the onset of choice-related activity. To address this question, we incorporated several new analyses which verified that the two windows defined functionally distinct response epochs at the individual neuron level. We provide greater details in Essential Revision 5, but briefly we: (1) verified the absence of significant choice-related activity in the SO window for individual neurons, (2) showed that the results were highly similar if choice tuning was assessed using full and short SPC windows, thus indicating that the two-window analysis was sufficient to characterize the findings, and (3) verified the time course of choice-related activity using a state-space analysis that provided a second estimate of the onset of choice-related activity. The second estimate of choice onset was similar to the first and corroborated the absence of significant choice-related activity in the SO window. These results are presented in subsection “Choice-related activity was parametrically tuned and aligned with the tilt preferences” and in new Figure 5—figure supplement 1.While the new analyses confirmed that choice-related activity was not detectable before ~202– 232 ms, we changed the name of the “sensory only (SO)” window to the “sensory dominant (SD)” window (52 to 202 ms) to leave room for the possibility that some choice-related activity may exist in this time window (though it was not detectable). From this point forward in the responses, we refer to the old SO window by its new name (SD window).We found that the two-window analysis facilitated our goal of testing whether high-level sensory representations were associated with choice-related activity. These findings motivate and will be essential for interpreting the results of future experiments designed to evaluate the time course of 3D sensory and sensorimotor processing and the potential consequences of neuron, session, and/or condition variability in the timing of choice-related activity. Surprisingly, few studies have used reaction time tasks to assay 3D vision. The potential for future studies using reaction time tasks to investigate these important questions are now discussed in subsection “Sensorimotor and choice-motor associations”.2) It is potentially of strong interest that there is a correspondence between tilt and choice selectivity as shown in 5G. A key question is whether this correspondence arose because of training, or because this could be a naturally occurring correspondence. It is possible that eye movements are naturally drawn to the closest part of a tilted 3D object. It would be very interesting to test in naïve animals whether this is indeed the case (perhaps during passive presentation of tilted objects and free viewing). If there is no such effect in naïve animals, but it is observed in the trained animals, this would support the idea that the correspondence arose because of training. Was the tuning assessed for each neuron during passive viewing outside the context of the task? If so, this might be a way to get at the question by comparing task vs passive viewing.The reviewers raise a great question regarding whether the correspondence between sensory and choice selectivity is naturally occurring or due to training. We buried too deeply  that the study was designed to distinguish between these possibilities through a comparison with our recent CIP work . Specifically, the current task was to report the near side of the plane whereas the previous task was to report the far side. By having monkeys report opposite features of the same surface orientation, we were able to dissociate the stimulus from the choice report/saccade direction. For example, for a bottom-near plane, the correct response was the lower choice target in this study but the upper choice target in the previous study. This difference allowed us to evaluate if the association reflected the task training. Importantly, the choice and surface orientation preferences aligned in both cases according to the training. For example, consider a neuron that preferred bottom-near planes. If trained to report the near side, the preferred choice report direction was typically down. In contrast, if trained to report the far side, the preferred choice report direction was typically up. Thus, the correspondence was flexible and experience dependent, and did not simply reflect a tendency for the eyes to be drawn to the closest part of the surface.To give this finding a more prevalent place in the manuscript, we now present choice-conditioned tuning curves  which show that the same choice report direction was associated with opposite effects on neuronal activity depending on the training. These findings are presented in subsection “Choice-related activity was parametrically tuned and aligned with the tilt preferences”, in new Figure 5—figure supplement 2, and further discussed in subsection “Associations between 3D selectivity and choice-related activity”. We also note that a full comparison of responses measured during the 8AFC task versus passive viewing would be impractical due to the large number of 3D pose conditions (N = 132) and would likely yield ambiguous results due to the training .3) The reviewers agreed with one another that the results are described throughout the manuscript in a way which implies a causal relationship between CIP activity/encoding and choice. But given the correlational approaches used here, this message should be revised and clarified in order to avoid confusion and to better bring the discussion of the paper in line with the observed results and experimental design.associations between various sensory, choice-, and motor-related properties. We correspondingly changed the manuscript title to “Functional links between sensory representations, choice activity, and sensorimotor associations in parietal cortex”.We agree and note that it was not our intention to imply a causal relationship. Thank you for bringing this to our attention. We made revisions throughout the text to assiduously avoid any language that might lead to this interpretation. For example, we no longer refer to any process or function as potentially mediating any other. Instead, we refer to discovered 4) All reviewers were in agreement that the writing of the manuscript needs to be improved substantially in order to help readers follow the logic of the analyses, experimental design, and results.Thank you for bringing this to our attention. As suggested, we made substantial revisions throughout the manuscript to help readers more easily follow the logic of the analyses, experimental design, and results. For example, we expanded the opening statements in each section of the Results to clarify the question(s) driving the upcoming analyses. We also made organizational changes to improve the overall flow. We believe that these revisions substantially improved the manuscript.5) Analysis related to Figure 4: authors should clarify which period of the task they are describing here, is it SO or SPC?We now clarify in both the text  and figure legend that neuronal data analyzed in Figure 4 is from the SD window.They separate sensory (SO) and choice response (SPC) periods. The distinction between SO and SPC seems pretty arbitrary.As discussed in Essential Revision 1, we revised the text to clarify the rationale behind this distinction. Here we go into greater detail about the analyses we incorporated to confirm that the SD and SPC windows defined reliable, functionally distinct response epochs. Some of these points are discussed further in Essential Revisions 1 and 6 relating to the SD/SPC windows, state-space analysis, and dissociating choice- from saccade-related activities.1) We confirmed at the individual neuron level that significant choice-related activity was rare in the SD window  and not linked to a neuron being classified as having choice-related activity since only 13 had choice-related activity in the SPC window. Excluding these neurons did not affect the mean Tolerances of neurons with/without choice-related activity in either time window. These results are presented in subsection “Choice-related activity was parametrically tuned and aligned with the tilt preferences”.2) We confirmed that choice tuning curves calculated over the full SPC window (from the onset of choice-related activity to the end of the stimulus presentation) were highly similar to those calculated over a short SPC window matching the duration of the SD window. This analysis indicates that the full SPC window was sufficient to characterize choice tuning and its relationship to surface tilt selectivity. These results are presented in subsection “Choice-related activity was parametrically tuned and aligned with the tilt preferences” and in new Figure 5—figure supplement 1A-D.3) We performed a state-space analysis  which provided a second estimate of the time course of choice-related activity. Based on this analysis, choice-related activity was not detectable until 232 ms . Importantly, this corroborates that choice-related activity was not detectable in the SD window. These results are presented in subsection “Choice-related activity was parametrically tuned and aligned with the tilt preferences” and in new Figure 5—figure supplement 1E,F.4) We performed a parametric analysis of the shape of the 3D orientation tuning curves . These results showed that the orientation tuning curves of neurons with/without choice-related activity were similar during the SD window. Changes in the shape of the tuning curves across the SD and SPC windows were greater for neurons with than without choice-related activity, supporting the distinction between the two neuronal subpopulations and the two time windows. These findings and their interpretation are presented in subsection “Choice-related activity was associated with more robust 3D selectivity” and in new Figure 7—figure supplement 1.5) We found that increases in the robustness of 3D selectivity  across the time windows was associated with choice- but not saccade-related activity. These findings are presented in subsection “Distinguishing functional links between the robustness of 3D selectivity and choice versus saccade-related activity”, in new Figure 10, and further discussed in subsection “Sensorimotor and choice-motor associations”.The only neuronal activity they show up to Figure 5 are heat map. It would be helpful to see the actual time course of the neuronal response for example neurons.Thank you for the suggestion. As we discuss in Essential Revision 6, we now show response time courses for preferred and non-preferred stimuli in new Figure 3—figure supplement 1A.6) Although they show that 5 example neurons, they do not provide clear population analyzes of 3D orientation selectivity. At least, they should show the distribution of tolerance index for all the neurons.We agree that it would be beneficial to include more details regarding 3D orientation selectivity across the population. In particular, how that selectivity differed for neurons with/without choice-related activity and how it differed across the time windows. We therefore incorporated three new analyses. Note that all of the population analyses included in the original manuscript remain, including: (1) distributions of 3D orientation preferences at each distance in Figure 3F, (2) histograms showing the proportion of neurons with orientation tuning as a function of distance and the number of distances in Figure 6F,G, and (3) Tolerance distributions comparing neurons with/without choice-related activity in Figure 7A-C.1) As suggested, we added Tolerance distributions for all neurons in each time window in new Figure 3—figure supplement 1B,C. These are the same data as in Figure 7A-C but presented agnostically to whether or not the neurons had choice-related activity. Pointers to these plots are found in subsection “The representation of 3D surface pose by CIP neurons” and subsection “Choice-related activity was associated with more robust 3D selectivity”.2) We computed a tilt discrimination index (TDI) to assess how well preferred and nonpreferred tilts could be discriminated using single neuron responses. The TDI values were compared to the behavioral tilt sensitivities as a function of slant and distance. This analysis shows that neuronal tilt discriminability and behavioral tilt sensitivity closely mirror each other. The findings are presented in subsection “Neuronal correlates of 3D tilt sensitivity” and in Figure 4A,B.3) We conducted a parametric analysis of the orientation tuning curve shapes. This analysis tested for differences in the orientation tuning of neurons with/without choice-related activity and differences in tuning across the SD/SPC windows. In brief, bandwidths did not differ between the two neuronal subpopulations or across the time windows. The tuning curves of both subpopulations were more isotropic (less elongated) in the SPC than the SD window, indicating that tilt and slant tuning widths became more similar later in the response. Neurons with choice-related activity showed greater changes in tuning isotropy than neurons without choice-related activity. These findings and their interpretation are presented in subsection “Choice-related activity was associated with more robust 3D selectivity” and in new Figure 7—figure supplement 1.Given the response shown in Figure 5A, I wonder how neurons could be tilt selective during the SO period. I'd like to see the actual response of these neurons.We believe that there was some confusion here, and therefore expanded the text describing the quantification of choice-related activity in subsection “Choice-related activity was parametrically tuned and aligned with the tilt preferences”. Specifically, the assessment of choice-related activity was performed using frontoparallel planes only because tilt is undefined at that orientation. Because Figure 5A shows responses to frontoparallel planes, there is no tilt selectivity in either the SD or SPC window. Differences between the curves during the SPC window reflect choice-related activity. To further clarify this point, we now show response time courses for preferred and non-preferred stimuli  in new Figure 3—figure supplement 1A. We also report that the population responses to preferred and non-preferred stimuli became significantly different 58 ms after stimulus onset. This is only 6 ms after the median visual response latency, indicating that 3D pose selectivity started early in the responses (subsection “The representation of 3D surface pose by CIP neurons”).7) There is confusion regarding the discussion of vergence, raised by reviewer 3, which needs to be addressed.Thank you for drawing this confusion to our attention. In the revised manuscript we clarify that this test evaluated if the neuronal responses were significantly affected by small vergence eye movements that did not violate the vergence window. As suggested, we moved the analysis so that it would not distract from the 3D pose tuning results. It is now located at the end of the Materials and methods.Also, the correlation of tilt selectivity between SO and SPC shown in Figure 7 needs to be clarified and explained in more detail.We completely agree. To clarify how we quantified differences in tilt preferences across distance in the SD and SPC windows, we added a schematic which illustrates each step of the analysis . We also expanded the corresponding text in subsection “Choice-related activity was associated with more robust 3D selectivity”. Pointers to the Materials and methods direct readers to the mathematical details.The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:1) The new analysis with previous data required additional clarification and explanation, and perhaps additional analysis. The reviewers agreed that there is concern about whether the observed results can be explained by saccade preparation. Specifically one reviewer states that "I don't understand how 3D selectivity and choice can be aligned as shown in Figure 5 considering that choice changes depending on the training . If a neuron is tuned to right forward, and monkey is trained to choose forward, the neuron will fire strongly during SPC when stim is right forward and therefore when monkey is planning a saccade to the right. When trained to report away, the same neuron should, according to Figure 5—figure supplement 1, responds strongly during SPC when planning a saccade to the left. How is that possible given that 3D selectivity is getting stronger during SPC and that 3D selectivity and saccade spatial selective strongly overlap ? I understand that they are not analyzing the same neurons, but then, does it only mean that choice selectivity in CIP reflects saccade preparation? I'm genuinely afraid I am missing something important here."The four conditions outlined in Reviewer 3’s second comment below were helpful for understanding where the confusion arose regarding the alignment of tilt and choice report/saccade signals. Please see our response to that comment for further clarification and pointers to text edits. Briefly, choice report/saccade direction and planar surface tilt are 360° periodic variables. As matters of convention, 0° corresponds to a rightward choice report/saccade direction as well as right-side near planes. Given these conventions and a learned association between surface tilt and choice report/saccade directions, we found the following results: (1) if the monkey was trained to report near, then the surface tilt and choice report/saccade direction preferences had the same value, and (2) if the monkey was trained to report far, then the surface tilt and choice report/saccade direction preferences were 180° apart. That is, the alignment (being either “in phase” or “out of phase”) depended on the specific training, as shown in Figure 5—figure supplement 2.Importantly, several lines of evidence supported that the choice- and saccade-related activities were functionally distinct. First, we found systematic differences in the choice and saccade tuning curve bandwidths of individual neurons. Second, the choice- and saccade-related activities were dissociable across the population. Specifically, many neurons exhibited significant saccade direction tuning only or choice tuning only (not both). We incorporated additional text emphasizing these findings in the Results and Discussion. Third, we found that the robustness of 3D selectivity was associated with choice-related activity but not saccade-related activity. For that analysis, we divided neurons into four subpopulations based on whether or not they had choice-related activity  and whether or not they had saccade-related activity . Neurons with saccade-related activity but not choice-related activity did not show an increase in the robustness of 3D selectivity across time windows. Thus, a change in 3D selectivity could not be explained by saccade preparation signals alone. In contrast, neurons with choice-related activity showed an increase in the robustness of their 3D selectivity across the time windows. Importantly, the change in 3D selectivity did not depend on whether or not the neurons had saccade-related activity. Thus, we found no association between saccade preparation signals and improvements in 3D selectivity. These results further support that choice- and saccade related activities did not simply reflect a common signal. We incorporated additional text to further clarify these points in the Results and Discussion.Reviewer #1:In reviewing this revised manuscript, the authors have done a thorough job in responding to the previous round of review. They have clarified key points, and in particular largely resolved major concerns about whether the observed association like effects were indeed due to training by including an additional supplementary figure, and by more thoroughly referring to previous results from their group which help address this concern.We thank the reviewer again for their feedback. The comments helped us substantially improve the manuscript.Reviewer #2:The authors did an excellent job in revising the manuscript. Specifically, the comparison with the task where the monkeys had to indicate the opposite features of the stimulus is crucial to demonstrate that the association between stimulus orientation and choice direction was indeed learned. I have no further major comments.Reviewer #3:I recognize authors spend a lot of energy trying to address our comments. I am not sure they fully manage to address them though.Here are the main claims (Abstract):• Choice related activity (saccade) is carried by neurons with robust 3D selectivity.• Following onset of choice activity 3D robustness increased• 3D selectivity and saccade selectivity alignedHere are my main concerns:1) Specifically, their analysis consisting of testing for choice related activity in the SD window is not convincing. Our point was that some choice related activity could start before the end of SD window and before the start of the SPC window, not that the activity during the entire SD window was related to choice. In this regard, they failed to address our concerns.In addition to the ANOVA-based analysis found at line 382, which confirmed that choice-related activity was not detectable during the sensory dominant (SD) window for individual neurons, we verified that choice-related activity was not detectable in the SD window at the population level using: (1) an ANOVA-based analysis and (2) a state-space analysis combined with a choice classification procedure. We still used “dominant” when defining the window to reflect the possibility that a small amount of choice-related activity went undetected, perhaps starting just before the end of the SD window. We further clarified this point in the Results. Importantly, our main analyses focused on changes in 3D selectivity between the two time windows which differed in the amount of choice-related activity present. As such, any choice-related activity in the SD window would only reduce the observed differences. The fact that we found significant changes in 3D selectivity across the two time windows suggests that if any choice-related activity were in the SD window then it did not affect our conclusions.2) I am still very confused by Figure 5—figure supplement 2 and the claim that 3D selectivity and saccade/choice selectivity are aligned:Let's consider a neuron tuned to top near. We have 4 conditions based on task design (near-CN-or far-CF) and stimulus slant (Top Near-TN-or Bottom near-BN): a) CN/TN, b) CN/BN, c) CF/TN, d) CF/BN.Condition a: choose near with stimulus top near: this neuron will have a strong visual response which will stay sustained since it is aligned to motor plan.Condition b: Choose near with stimulus bottom near: this neuron will have a weak visual response, which will stay weak during motor planning.Condition c: choose far with stimulus top near: there will be a strong visual response  which should decrease during motor planning.Condition d: choose far with stimulus bottom near (saccade to the top): there will be a weak visual response  which should increase during motor planning.I am confused how this is translated to Figure 5 sp2 and how does that match authors' claim that "Following onset of choice activity 3D robustness increased" and that choice and "3D selectivity and saccade selectivity are aligned". Separating between SC and SPC window does not help at all to understand the neuronal response properties.Thank you for laying out these four conditions. They helped us to understand where the confusion arose. We made corresponding text edits to clarify the relevant points in the Results and Discussion. As we discuss in our response preceding the reviewer comments, the confusion seems to revolve around the conventions for describing choice report/saccade directions and surface tilt.We found that the alignment was reversed if the monkey was trained to report the near versus far side of the plane, indicating that the association was experience dependent. In the following description, note that 90° indicates top-near planes as well as upward choice reports/saccades. If the monkey was trained to report near, a preference for top-near planes (tilt = 90°) aligned with a preference for upward choice reports . In contrast, if the monkey was trained to report far, a preference for top-near planes (tilt = 90°) aligned with a preference for downward choice reports . As such, conditions c and d outlined above are incorrect. For condition c, the stimulus response would be strong as indicated but so would the choice component since downward choices indicated that the bottom was far. For condition d, both the stimulus response and choice component would be weak since they correspond to the non-preferred surface tilt and non-preferred choice report direction.3) Their attempt to dissociate choice and motor planning is not successful. Stimuli and motor action are still strongly linked whether monkeys have to report near or far side. I don't see how it breaks the link.In the revised manuscript, we elaborated on findings which support that choice- and saccade-related activities are dissociable. Briefly, the dissociation was supported by three findings: (1) systematic differences in the choice and saccade tuning bandwidths of individual neurons, (2) dissociation of choice- and saccade-related activities across the population , and (3) an increase in the robustness of 3D selectivity across the time windows was only associated with choice-related activity, not saccade-related activity. If choice- and saccade-related activities simply reflected a common signal, saccade-related activity would likely have also been associated with the robustness of 3D selectivity, which was not the case. Please see our response preceding the reviewer comments for further details and pointers to text edits.A main finding of the study is that there is indeed a sensorimotor association linking the stimuli and motor response. The experiment referenced suggests that the association is experience dependent. We made additional text edits to further clarify that the comparison of results obtained with tasks in which the monkeys reported opposite stimulus features demonstrates that the association between surface tilt and the monkey’s response direction was experience dependent. Please see our responses above related to Figure 5—figure supplement 2 for further details and pointers to text edits.4) Despite their effort to improve the flow of argument in the article, I still find it very difficult to follow.We thank the reviewer for the additional feedback, and hope that the additions summarized above have resolved the remaining points of confusion.


Rhinoviruses cause the common cold. They are icosahedral, built from sixty copies each of the capsid proteins VP1 through VP4 arranged in a pseudo T = 3 lattice. This shell encases a ss(+) RNA genome. Three-D classification of single and oligomeric asymmetric units computationally excised from a 2.9 Å cryo-EM density map of rhinovirus A89, showed that VP4 and the N-terminal extension of VP1 adopt different conformations within the otherwise identical 3D-structures. Analysis of up to sixty classes of single subunits and of six classes of subunit dimers, trimers, and pentamers revealed different orientations of the amino acid residues at the interface with the RNA suggesting that local asymmetry is dictated by disparities of the interacting nucleotide sequences. The different conformations escape detection by 3-D structure determination of entire virions with the conformational heterogeneity being only indicated by low density. My results do not exclude that the RNA follows a conserved assembly mechanism, contacting most or all asymmetric units in a specific way. However, as suggested by the gradual loss of asymmetry with increasing oligomerization and the 3D-structure of entire virions reconstructed by using Euler angles selected in the classification of single subunits, RNA path and/or folding likely differ from virion to virion. From analysis of up to sixty classes of single subunits and of six classes of subunit dimers, trimers, and pentamers, Dieter Blaas demonstrates different orientations of the amino acid residues at the interface with the RNA. This study suggests that RNA path and/or folding is likely to differ from virion to virion. Both models suggest that charge complementarity and/or stacking of nucleobases against aromatic amino acid residues might play a role in attachment of the RNA to the inner wall of the proteinaceous viral shell. Indeed, in X-ray structures of several picornaviruses unassigned electron density has been seen close to a conserved tryptophan in viral capsid protein 2 (VP2). It presumably originates from a stacked nucleotide8.The packing of viral RNA genomes inside a protein shell is poorly understood; genome encapsulation might either involve active vectorial transport into the immature protein capsid by an ATP-driven motor, starting from the 5′-end that is first synthesised and thus first egresses from the replication complexENTEROVIRUSES with rhinovirus (RV) species A–C among many other animal and human pathogens so far classified into 47 genera (https://www.picornaviridae.com/). Although only occasionally giving rise to severe disease, RVs are economically important as they cause the common cold with its annoying and difficult-to-treat symptoms; currently no causal medication or vaccine is available9. In ENTEROVIRUSES, encapsidation depends on ongoing protein and RNA synthesis10. Since replication occurs on de novo formed membrane structures, the viral nucleic acid might be discriminated from cellular RNAs by local seclusion thus avoiding packaging of cellular RNA11. For the closely related Coxsackie and Polioviruses, the putative viral helicase 2C, a component of the replication complex, was shown to be involved in encapsulation by interacting with VP3, although the exact role of this association is unclear14. Regardless of the mechanism of the assembly process, in the mature virion, the RNA is presumably attached to the inner wall of the protein shell via multiple but weak interactions. Indeed, in Parechoviruses, another picornavirus genus, multiple, highly degenerate sequence stretches were found to interact with patches of the capsid proteins15. Because of the intrinsic asymmetry of the RNA, these contact sites might adopt unequal conformations and convey regionally limited asymmetric features to the otherwise strictly icosahedral particle16. Local asymmetry is also manifest during RNA exit occurring at one of the twofold or threefold axes of symmetry, as shown for Poliovirus18 and suggested for RV-A2 by the geometry of attachment of subviral A-particles, intermediates of uncoating, to lipid membranes19. At least in RV-A2, the RNA leaves the virion with its 3′-end first, although it is unknown whether this can occur at any of the symmetry-equivalent positions or just at a predetermined site slightly deviating from perfect icosahedral symmetry21. One might speculate that the low numbers of VP0 that have not undergone maturation cleavage into VP4 and VP2 and are found in mature virions, play a directive role in assembly and/or directional RNA egress22. On the other hand, RNA exit in bulk through large holes opening via (reversible) dissociation of one or more pentamers from the virus shell has been proposed for the insect pathogen Triatoma virus23 and the picornavirus Echovirus 1824. Such a dissociation/reassociation of subunits has also been purported for hepatitis B virus, where storage of two differently labelled virus populations for extended times showed the exchange of subunits suggesting that disassembly/reassembly was occurring25. The above asymmetric features of icosahedral viruses cannot be observed by X-ray crystallography and cryogenic electron microscopy (cryo-EM) employing icosahedral averaging. Even in non-averaged cryo-EM image reconstructions minor asymmetric features escape detection because the large symmetric parts might overwhelm the small differences during image alignment.The large family of picornaviruses includes the genus 26, such local asymmetries became clearly visible. However, they gradually vanished upon extending the analysis onto dimers, trimers, and pentamers, suggesting that this local asymmetry does not go far beyond single subunits. This implies that the interaction of different sections of the viral RNA genome—adopting different secondary structures—with the N-terminal extensions of VP1, parts of VP4, and other amino acid residues at the protein interface, impart onto them different conformations. Assuming that the RNA establishes at least one contact with each asymmetric unit, my data indicate that it most probably does not follow the same assembly path in all virions but adopts different secondary structures within individual viral particles. A Hamiltonian path has been proposed to be followed by the genomic RNA in MS2 phage27.Avoiding the above pitfalls by 3D-classification of single asymmetric units of RV-A89 previously solved to 2.9 Å26, Fig. ENTEROVIRUSES28. Taken together, this led me to consider this RV-A89 3D-structure as native and use it for the present investigation.In a previous study of the 3D-structure of a complex between RV-B5 and the capsid-binding pyrazolopyrimidine antiviral OBR-5-340, the non-binding RV-A89 was used as a control; its 3D-structure in the presence of OBR-5-340 was determined to 2.9 Å by imposing icosahedral symmetry30 I icosahedrally expanded the Euler angles of the aligned particle images obtained from the above analysis26, resulting in a dataset of 5,851,680 particle orientations with sixty sets of Euler angles for each individual particle image compatible with the recorded projection image. By using the previously fitted atomic coordinates (pdb-6SK7), in which gaps were filled with the corresponding backbone coordinates of RV-A16 (1AYN), whose X-ray structure is more complete as it includes density for almost all amino acid residues4 Fig. 31. The resulting map was then used as reference volume and for computing a soft mask sufficiently extended to include some density from residues contributed by the adjacent symmetry-related asymmetric units  reported by Relion-3.0 was ~4.3 Å. The upper panel of Supplementary Fig. The above two 3D classes presumably reflect the most dissimilar conformations in single subunits of RV-A89. However, each of the maps is necessarily an average of similar and less populated structural variants because of the limited number of classes included in the analysis. For a better differentiation I thus arbitrarily extended the analysis onto 6, 12, and finally twenty classes. At iteration 100, the nominal resolution for 6 classes , essentially the same results were obtained for up to 20 classes. Furthermore, in combination with a reduction of the box size from 450 px to 150 px, analysis of even 60 classes became possible albeit the apparent resolution decreased to below 6 Å for 12 of the classes . This extensive classification also revealed differences in the main part of the asymmetric units  and the final unsharpened density map at 2.9 Å resolution were used in the present analyses.All methods including RV-A89 preparation, purification, incubation with OBR-5-340, freezing, cryo-EM data collection, selection of full particle images, and image reconstruction of RV-A89 were detailed previously and can be found in the Supporting Information associated with ref. d-maltoside was added to a final concentration of 0.02%, and aliquots of 4 μl were applied to glow-discharged  grids (Agar Scientific) with an additional layer of 4 nm continuous carbon. The grids were blotted for 3 s at 100% humidity and plunge-frozen in liquid ethane cooled with liquid nitrogen, by using a Vitrobot Mark III. Grids were imaged on a FEI Polara EM operating at 300 kV equipped with Gatan K2 Summit direct electron detector. Movies were recorded with a K2 camera in dose-fractionation mode at a calibrated magnification of 37,000×, corresponding to 0.97 Å per physical pixel. The dose rate on the specimen was set to 8 e/Å2s and total exposure time was 5 s, resulting in a total dose of 40 e/Å2. Picking, image reconstruction and refinement were carried out with Relion-3.0-beta230 by using a previously determined map of RV-A2, Fourier-filtered to 60 Å, as starting volume. “Bad particles” were removed in repeated steps of 2D- and 3D-classifications and on the basis of excessive values of the relion parameter _rlnLogLikeliContribution. A model was built with the beta version of SwissModel https://swissmodel.expasy.org/interactive# (as of April 2018) allowing simultaneous modelling of hetero- oligomers using the Uniprot RV-A89 sequence B9V4A5_HRV89 and the automatically selected RV-A16 structure (1AYN) as template (the P1 region including the four capsid proteins has 74% identity and 84% positives). Coordinates were fitted and refined via Phenix35 and Rosetta36, and validated with Coot37 and Molprobity38; the density map and the fitted atomic coordinates are available under EMD-10222 and PDB 6SK7, respectively. Note that parts of the VPs, in particular of VP4, could not be modelled without ambiguity and are thus omitted in 6SK7. However, for making the reference volumes and masks, missing parts were filled in with the more complete coordinates of RV-A16 , were mixed with 1 μl of OBR-5-340 in DMSO to give final concentrations of 1 mM and 10% respectively, incubated at 37 °C for 1 h, n-dodecyl-ß-see Fig. .31, a zone extending by 5 Å over the above atomic coordinates was cut out from the refined, but not sharpened RV-A89 volume (EMD-10222) and used as starting map in the classifications and for computing the masks . Where explicitly mentioned, particle subtraction was performed with relion-3.1-beta prior to classification with essentially the same results; since the box size was reduced from 450 px to 150 px, classification into 60 classes became computationally possible (Supplementary Fig. 39. This “fitness” has a negative value, i.e., −1 for identity and 0 for no similarity. Where indicated, controls were obtained by a 3D-classification run with one 3D-class only.By using ChimeraParticle images with the associated alignment parameters taken from class 1, class 3, and class 9 from the run with 20 classes Fig.  and chosFurther information on research design is available in the Supplementary InformationReporting SummaryPeer Review File


O-fatty acylated anti-HIV nucleoside conjugates. The anti-HIV nucleosides interfere with HIV RNA-dependent DNA polymerase and/or act as chain terminators. Normal human fibroblast lung cells (MRC-5) were used to determine the cytotoxicity of the compounds. The study revealed that remdesivir exhibited an EC50 value of 0.07 µM against HCoV-229E with TC50 of > 2.00 µM against MRC-5 cells. Parent NRTIs were found to be inactive against (HCoV-229E) at tested concentrations. Among all the NRTIs and 5′-O-fatty acyl conjugates of NRTIs, 5′-O-tetradecanoyl ester conjugate of FTC showed modest activity with EC50 and TC50 values of 72.8 µM and 87.5 µM, respectively. These data can be used for the design of potential compounds against other coronaviruses.Remdesivir is a nucleotide prodrug that is currently undergoing extensive clinical trials for the treatment of COVID-19. The prodrug is metabolized to its active triphosphate form and interferes with the action of RNA-dependent RNA polymerase of SARS-COV-2. Herein, we report the antiviral activity of remdesivir against human coronavirus 229E (HCoV-229E) compared to known anti-HIV agents. These agents included tenofovir (TFV), 4′-ethynyl-2-fluoro-2′-deoxyadenosine (EFdA), alovudine (FLT), lamivudine (3TC), and emtricitabine (FTC), known as nucleoside reverse-transcriptase inhibitors (NRTIs), and a number of 5′- Human coronavirus 229E (HCoV-229E) is one of the seven known human coronaviruses, which include HCoV-NL63, HCoV-OC43, HCoV-HKU1, MERS-CoV, SARS-CoV-1, and SARS-CoV-2. HCoV-229E is a member of the genus Alphacoronavirus, which infects humans and bats . The infAs of May 16, 2020, according to the Johns Hopkins Coronavirus Resource Center, the human mortality of the COVID-19 pandemic infection is 313,220 people, while 4,720,197 people have been infected globally. Currently, there are no approved drugs, monoclonal antibodies, or vaccines to treat or prevent human infections caused by SARS-CoV-2. The discovery and approval of new compounds take several years. Therefore, several existing drug and potential drug candidates such as remdesivir and other antiviral agents have been considered to be repurposed as COVID-19 treatments.Remdesivir GS-5734,  was deveOn May 1, 2020, the US Food and Drug Administration  and is used in the IV dosage form. The compound is a broad-spectrum antiviral nucleotide prodrug that is metabolized first to the active triphosphate analog that inhibits RNA-dependent RNA polymerase (RdRp), resulting in diminished viral RNA replication  h and is. RemdesiReverse transcriptase is an enzyme in the human immunodeficiency virus (HIV) and many retroviruses that convert the RNA template to DNA. The enzyme has three enzymatic functions at the same time: (1) RNA-dependent DNA polymerase (RdRp), (2) RNase H, and (3) DNA-dependent DNA polymerase. The RNA-dependent DNA polymerase is used to synthesize a complementary DNA strand to the RNA template. After the removal of the RNA strand from the RNA–DNA hybrid double helix by RNase H, the DNA-dependent DNA polymerase completes double-stranded DNA synthesis . SeveralHere, we selected five NRTIs to be evaluated versue remdesivir against HCoV-229E. Tenofovir   is a nucThus, the antiviral activity of remdesivir against HCoV-229E was compared with selected NRTIs and selected fatty acyl conjugates. The goal was to determine the antiviral activity and toxicity against HCoV-229E in an anti-coronavirus cytoprotection assay. The method could be used to select potential compounds rationally for evaluation against SARS-CoV-2.Nine compounds were evaluated against HCoV-229E in normal human fibroblast lung cells (MRC-5) at six test concentrations. The compounds included five NRTIs (TFV (2), EFdA (3), FLT (4), 3TC (6), and FTC (8)) and three fatty acyl conjugates of FLT (5), 3TC (7), and FTC (9). We have previously reported the synthesis and evaluation of fatty acyl conjugates ,25,26,2750 and TC50 values of 0.07 µM and > 2.0, respectively, against HCoV-229E. Meanwhile, 5′-O-tetradecanoyl ester conjugate of FTC (9) demonstrated an EC50 value of 72.8 µM, but showed a very low calculated therapeutic index of 1.2 due to its cytotoxicity to MRC-5 cells. Compounds 3, 5, and 7 were cytotoxic to MRC-5 cells at TC50, ranging from 45.4–55.3 µM. The parent NRTIs, 4, 6, and 8, were not active against HCoV-229E at the tested concentrations. As representative examples, dose-response curves for remdesivir (1), FTC (8), 5′-O-(tetradecanoyl)FTC (9) are shown in The antiviral efficacy and cellular toxicity data are summarized in These data indicate that remdesivir acts as an antiviral agent against HCoV-229E, while anti-NRTIs agents were found to be ineffective. This could be due to the unique interaction of remdesivir with RNA-dependent RNA polymerase in coronaviruses such as HCoV-229E, while NRTIs inhibit reverse transcriptase. This enzyme has RNA-dependent DNA polymerase function. NRTIs also act as DNA synthesis chain terminators. The mode of interaction of remdesivir with RNA polymerase and the crystal structure of protein-nucleotide have not been published yet. The structure of remdesivir is unique as a nucleotide prodrug, with the presence of a nitrile group at the 1′ position and both 3′ and 4′-hydroxyl groups, leading to strong binding to RNA polymerases that differentiates this compound from the other nucleoside analogs represented here. The structure of RNA-dependent RNA polymerase of SARS-COV-2 was recently published . Further50 value of 0.07 µM and a therapeutic index of more than 28.6 µM. The 5′-O-(tetradecanoul) ester derivative of FTC showed modest activity, with an EC50 value of 82 µM. In general, NRTIs did not show comparable activity against HCoV-229E, compared to remdesivir. This work advances scientific knowledge in the area of the testing of antiviral compounds and the activity of anti-HIV drugs against coronaviruses. This information could also be used to design compounds that are potentially effective against other coronaviruses, such as SARS-COV-2.A series of anti-HIV nucleosides and their fatty acyl derivatives were compared with remdesivir for antiviral activity against HCoV-229E in MRC-5 cells. Among all the compounds, remdesivir was found to be potent, with an ECThe anti-HIV nucleosides were purchased from Euro Asia Trans Continental . The synthesis and evaluation of fatty acyl conjugates were conducted according to the previously reported procedures in our laboratory ,25,26,27l-glutamine (2 mM), streptomycin (100 µg/mL), and NEAA (0.1 mM) using T-75 flasks before use in the antiviral assay. Preceding the assay, the cells were divided into 1:2 to make sure they were in an exponential growth phase at the time of infection. The quantification of total cells and viability were conducted using a hemocytometer and Trypan Blue dye exclusion.The viral assay protocols were approved by the Institutional Biosafety Committee (IBC) at Imquest Biosciences. MRC-5 cells were obtained from ATCC (CCL-171) and passaged in the DMEM medium supplemented with FBS (10%), penicillin (100 U/mL), sodium pyruvate (1 mM), Coronavirus 229E (HCoV-229E) was obtained from ATCC (VR-740) and grown in MRC-5 cells (ATCC# CCL-171) for the production of a stock virus pool. A pretitered aliquot of the virus was removed from the freezer (−80 °C). The aliquot was allowed to thaw slowly to room temperature in a biological safety cabinet. The virus was resuspended and diluted into assay medium , l-glutamine (2 mM), streptomycin (100 µg/mL)), in such a way that 100 µL of virus was added to each well. This amount was determined to result in 85 to 95% cell death at 6 days postinfection. Each plate contained virus control wells (cells plus virus), triplicate cell control wells (cells only), drug toxicity wells according to the compound (cells plus drug only), as well as triplicate experimental wells (drug plus cells plus virus).3 cells per well in the tissue culture medium. Then, the cells (a volume of 100 µL) were added to flat-bottom microtiter plates. The microtiter plates were incubated at 37 °C/5% CO2 overnight to allow cell adherence to occur. After incubation of the test compounds at 37 °C in a 5% CO2 incubator for six days, the plates were stained with the tetrazolium dye 2,3-bis(2-methoxy-4- nitro-5-sulfophenyl)-5-[(phenylamino)carbonyl]-2H-tetrazolium hydroxide (XTT). XTT tetrazolium is known to be metabolized by the mitochondrial enzymes of metabolically active cells into a soluble formazan product. This reaction makes it possible to promptly perform quantitative analyses of the inhibition of virus-induced cell killing by antiviral test substances. XTT was prepared daily as a stock solution of 1 mg/mL in RPMl1640. Phenazine methosulfate (PMS) solution was prepared in PBS (0.15 mg/mL) and stored in the dark at −20 °C. XTT/PMS stock was prepared immediately before each use by adding a volume of 40 µl of PMS per ml of XTT solution. XTT/PMS (50 µL) was added to each well of the plate, and the plate was reincubated for 4 h at 37 °C. Plates were sealed with adhesive plate sealers. Then, they were shaken gently or inverted several times to mix the soluble formazan product. The plates were read spectrophotometrically at 450/650 nm with a Molecular Devices Vmax plate reader . A cell viability of more than 95% for the cells was utilized in the assay. The cells were resuspended at 3 × 1050, EC50, and Tl50 values. The graphical presentation shows the percentages of cell viability and of reduced viral Viral cytopathic effect (CPE) at each test concentration.The raw data were collected from Softmax Pro  and imported into a Microsoft Excel Xlfit4  spreadsheet for analysis using four-parameter curve fit calculations. The antiviral activity and toxicity with graphic representation of the data were provided in a Plate Analysis Report (PAR) summarizing the individual compound activity and a summary table of calculated TC