Neural dynamics of causal inference in the macaque frontoparietal circuit
Curation statements for this article:-
Curated by eLife
Evaluation summary:
This study investigates the neural basis of the hidden causal structure between visual and proprioceptive signals in the primate premotor and parietal circuit during reaching tasks executed in a virtual reality environment, where information between the two modalities can be dissociated. Modelling is used to characterize the proprioceptive drift of the monkeys when integrating bimodal information. The key novel result is that premotor neurons represent the integration of bimodal information for small disparities and the segregation for large disparities between the proprioceptive and visual information, while parietal cells show reaching tuning changes that support the updating sensory uncertainty between tasks. Overall, the experiments are technically sound, and the conclusions are mostly well supported. However, a simpler framing of the paper could make the main message easier to grasp, the analysis of Bayesian models seems to lack major details, the statistical reporting is below standard, and a large part of the extensive literature on the role of premotor and parietal cortex in visuomotor behavior is lacking.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (eLife)
- Neuroscience (eLife)
Abstract
Natural perception relies inherently on inferring causal structure in the environment. However, the neural mechanisms and functional circuits essential for representing and updating the hidden causal structure and corresponding sensory representations during multisensory processing are unknown. To address this, monkeys were trained to infer the probability of a potential common source from visual and proprioceptive signals based on their spatial disparity in a virtual reality system. The proprioceptive drift reported by monkeys demonstrated that they combined previous experience and current multisensory signals to estimate the hidden common source and subsequently updated the causal structure and sensory representation. Single-unit recordings in premotor and parietal cortices revealed that neural activity in the premotor cortex represents the core computation of causal inference, characterizing the estimation and update of the likelihood of integrating multiple sensory inputs at a trial-by-trial level. In response to signals from the premotor cortex, neural activity in the parietal cortex also represents the causal structure and further dynamically updates the sensory representation to maintain consistency with the causal inference structure. Thus, our results indicate how the premotor cortex integrates previous experience and sensory inputs to infer hidden variables and selectively updates sensory representations in the parietal cortex to support behavior. This dynamic loop of frontal-parietal interactions in the causal inference framework may provide the neural mechanism to answer long-standing questions regarding how neural circuits represent hidden structures for body awareness and agency.
Article activity feed
-
-
Evaluation summary:
This study investigates the neural basis of the hidden causal structure between visual and proprioceptive signals in the primate premotor and parietal circuit during reaching tasks executed in a virtual reality environment, where information between the two modalities can be dissociated. Modelling is used to characterize the proprioceptive drift of the monkeys when integrating bimodal information. The key novel result is that premotor neurons represent the integration of bimodal information for small disparities and the segregation for large disparities between the proprioceptive and visual information, while parietal cells show reaching tuning changes that support the updating sensory uncertainty between tasks. Overall, the experiments are technically sound, and the conclusions are mostly well supported. However, a …
Evaluation summary:
This study investigates the neural basis of the hidden causal structure between visual and proprioceptive signals in the primate premotor and parietal circuit during reaching tasks executed in a virtual reality environment, where information between the two modalities can be dissociated. Modelling is used to characterize the proprioceptive drift of the monkeys when integrating bimodal information. The key novel result is that premotor neurons represent the integration of bimodal information for small disparities and the segregation for large disparities between the proprioceptive and visual information, while parietal cells show reaching tuning changes that support the updating sensory uncertainty between tasks. Overall, the experiments are technically sound, and the conclusions are mostly well supported. However, a simpler framing of the paper could make the main message easier to grasp, the analysis of Bayesian models seems to lack major details, the statistical reporting is below standard, and a large part of the extensive literature on the role of premotor and parietal cortex in visuomotor behavior is lacking.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)
-
Joint Public Review:
This manuscript focuses on the neural basis of the hidden causal structure between visual and proprioceptive signals in the primate premotor and parietal circuit during reaching tasks executed in a virtual reality environment, where information between the two modalities can be dissociated. In the visual-proprioceptive conflict condition, there was a proprioceptive drift in reaching due to the angle disparity between the nonvisible monkey arm (proprioceptive) and a virtual visual arm. The drift showed large values for small levels of disparity, suggesting integration between modalities, and small values for large disparities, suggesting modality segregation. This was captured by a BPI model that provides the posterior probability of a common hidden sensory source and integrates the effect of previous trials on …
Joint Public Review:
This manuscript focuses on the neural basis of the hidden causal structure between visual and proprioceptive signals in the primate premotor and parietal circuit during reaching tasks executed in a virtual reality environment, where information between the two modalities can be dissociated. In the visual-proprioceptive conflict condition, there was a proprioceptive drift in reaching due to the angle disparity between the nonvisible monkey arm (proprioceptive) and a virtual visual arm. The drift showed large values for small levels of disparity, suggesting integration between modalities, and small values for large disparities, suggesting modality segregation. This was captured by a BPI model that provides the posterior probability of a common hidden sensory source and integrates the effect of previous trials on updating the causal structure and the sensory representation. In the bimodal conflict task, neurons in premotor cortex showed stronger visual arm information, whereas parietal cortex showed stronger proprioceptive signals. Notably, single cell and population activity carries information about both the integration of bimodal information for small disparities and the segregation for large disparities, especially in premotor cortex. In addition, some information about the hidden causal structure of the previous trials is present in premotor cortex. Finally, the comparison in neural activity in the visual proprioceptive aligned task and the visual proprioceptive conflict condition revealed that parietal cortex shows smaller accuracy to represent arm location in the later condition, supporting the notion that this area is involved in updating sensory uncertainty. These results support the notion that premotor cortex encodes the causal hidden structure of bimodal integration/segregation, while parietal cortex is more focused on weighting the sensory input from both modalities. In general, the experiments are technically sound, and the conclusions are mostly well supported. However, a simpler framing of the paper could make the main message easier to grasp, the analysis of Bayesian models seems to lack major details, and the statistical reporting is below standard.
On the upside, this is one of the few studies that have investigated, at single-cell level, the neural representation of body ownership and sense of agency in monkeys, through a well-designed experimental paradigm. By using a Bayesian causal inference model, the author explored in a quantitative fashion these neural constructs, by inferring hidden variable such as the posterior probability of a common source of inputs and the relative neural representations. Through advanced data analysis, the findings offer an interesting view of neural dynamics associated to causal inference in frontal and parietal cortex. Distinct roles of premotor and parietal neurons are highlighted, particularly in the temporal domain of these processes. Based on the results, the authors suggest that premotor cortex is where causal inference is computed and then the outcome of this computation is addressed to parietal cortex.
On the downside, to be appreciated by a wide audience the manuscript would benefit from a significant re-writing, aimed at simplifying the way the overall aims are introduced, as well as the results and data analysis reported. The latter is mostly based on rigorous, but niche statistical tools, that beyond their inherent complexity, do not allow the reader to easily follow and appreciate the quality of the neural data. The experimental paradigm is well designed to dissociate visual and proprioceptive inputs during arm reaching. However, the data set do not include eye movement control, necessary when studying visuomotor behavior associated to neural activity of cortical areas influenced, although with different strength, by eye position and movement signals, such as premotor and parietal cortex. Importantly, the analysis of Bayesian models seems to lack major details, the statistical reporting is below standard (missing effect sizes, degrees of freedom, lack of individual data in figures), the study is features many unjustified parameter choices and key results seem to lack statistical support: not all statements about differences between parietal and premotor cortex seem supported by a direct statistical comparison. Further, while three monkeys contributed data, for only one does the study report data from both brain regions; this makes the claim of a difference between brain regions rather weak and this shortcoming needs to be clearly acknowledged. The actual underlying data (e.g. how single neuron responses are converted to tuning curves; how decoding accuracies vary across neurons) is not shown, which makes it difficult to interpret the robustness of the results. In particular, the units of analyses vary tremendously between Figures (experimental blocks, neurons, pseudo-epochs, etc). Finally, the associated literature, consisting in many studies, has been totally ignored.
-
