On the evolution of neural decisions from uncertain visual input to uncertain actions

  1. Alessandro Tomassini
  2. Darren Price
  3. Jiaxiang Zhang
  4. James B Rowe

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Abstract

Behavior can be conceived as the result of a sequence in which the outcomes of perceptual decisions inform decisions on which action to take. However, the relationship between these processes, and spatiotemporal dynamics of the visual-to-motor transformation remains unclear. Here, we combined accumulation-to-threshold models and electro-magnetoencephalography, to trace neural correlates of sensorimotor decisions in space, time and frequency. We challenge the assumption of sequential decisions, with evidence that visuomotor processing unfolds through a continuous flow of information from sensory to motor regions. Action selection is initiated before regional visual decisions are completed. By linking behavior and physiology through theoretical decision models, we identify simultaneous forward and backward flow of information for visuomotor decisions between sensory and motor regions, in beta and gamma ranges. The model of integrated visuomotor decisions provides a powerful approach to investigate behavioral disorders that impair the ability to use sensory inputs to guide appropriate actions.

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  1. eLife

    ###Reviewer #2:

    The authors investigated the joint influences of visual evidence strength and action (un)certainty on the formation of perceptual decisions, and used MEEG to track the associated cascade of visual-motor processing using a relatively complex set of analyses. This manuscript addresses a general question that has already attracted (but also continues to attract) considerable interest. One of the main advances of this specific work (in addition to the advanced MEEG analyses) is the explicit manipulation of action certainty in addition to evidence strength. My enthusiasm for this work, however, remains somewhat limited in light of the following aspects.

    1. The article is set-up from a perspective of adjudicating between strictly "serial models" of perceptual decisions in which decisions are reached about what is viewed before turning to the appropriate action, versus more "continuous models" in which potential action plans are formed while evidence accumulation is still taking place. Is there not already ample evidence for the latter scenario (e.g., the work of Tobias Donner, Floris de Lange, Ian Could, and others)? Moreover, the authors currently provide only a single reference for the serial model, which dates back to 1966. Thus, the temporal overlap between visual evidence accumulation and action planning is, in itself, not very surprising, nor new; and yet it appears a central component of the article's pitch.

    2. While the manipulation of action (un)certainly provides an interesting extension of the popular random-dot-motion task, the nature and rationale of this manipulation remain insufficiently unclear. Do participants view multiple patches of equal coherent motion and arbitrarily decide which to respond to? If so, does this not confound action uncertainty with evidence (i.e., more patches with motion may give more evidence)? And should this not make participants faster, rather than slower? Are they slower simply because they are asked to make a "fresh" response? At a minimum the authors should more clearly explain this manipulation, starting in the Results section. In this, the authors should clarify exactly how visual signals and action certainly are independent in their design, or (as I suspect) acknowledge that the current manipulation confounds action certainty with the availability, collective strength, and/or spatial region of the visual evidence (which may each in turn affect neural signals throughout the brain).

    3. It would help to first show the (basic) effects of sensory and action certainty on time-frequency activity in several brain areas (at least visual and motor), for example by showing power modulations for each of the certainty levels, together with a contrast plot of high vs low certainty. This would help understand the data, before turning to the more complex analyses. Such a plot may reveal, for example, decreased alpha activity in posterior sites with higher action uncertainty, simply as a result of more visual stimulation. If so, this may be problematic for the more complex analyses of transfer entropy. It could also help justify the current focus on beta and gamma (but not, for example, alpha) and to help understand the distinction between modulations in beta and gamma.

    4. I am surprised the authors find a gamma decrease rather than an increase. Does gamma not usually increase with motor preparation (e.g., Donner et al. Current Biology 2009) and visual attention (e.g., Fries et al., Science, 2001; Siegel et al., Neuron, 2008)?

    5. Given that both certainty manipulations affected RT, are all neural correlates of these certainty manipulations not "confounded" with differences in RT?

    6. Do the two uncertainty factors (sensory and action certainty) interact? This information appears missing from the analysis of the behavioural data. Also, if these two factors interact, it would be sensible to also explore this in the modelling and MEEG analyses.

  2. eLife

    ###Reviewer #1:

    This study uses combined EEG/MEG to characterise the neural dynamics of the visuomotor decision process by separately manipulating its perceptual- and action-related components. Subjects monitored 4 simultaneous random dot stimuli to detect changes from incoherent to coherent motion, and indicated detection with a finger press. Perceptual and action uncertainty were manipulated by varying the motion coherence of the stimuli, and number of motor response options (1 vs. 3), respectively.

    Authors identify activity in the beta and gamma bands correlating with decision-related trajectories predicted by an accumulation-to-bound model. They reveal distributed networks in both frequency bands that show a negative relationship with the predicted patterns (i.e., desynchronization after onset of coherent motion). Several interesting findings stand out: 1) beta activity follows a gradual progression from posterior to anterior regions, a finding further supported by a connectivity analysis assessing the direction of information flow. 2) The accumulating signals across the identified regions overlap in time, which is taken as evidence for a continuous flow of information along the visual-to-motor pathway. 3) regions where (beta) activity flow is modulated by perceptual (as opposed to action) uncertainty show earlier responses to perceptual evidence, and are more likely to drive the information flow to downstream areas.

    This is overall a well-written, clearly structured paper on an ever-relevant topic. Authors use elegant, rigorous statistical methodology, and their characterisation of beta activity provides some important insight into the global neural dynamics of decision making, in particular the temporal properties of decision-related signals across the perception-to-action processing pipeline. I do however have a couple of points of concern regarding parts of the results (in particular those involving gamma activity) and their interpretation:

    1. Gamma band activity is seen to exhibit a negative relationship with the predicted accumulating signal, with a gradual desynchronisation upon the onset of perceptual evidence (coherent motion). I found this surprising, as several previous studies looking at decision-related activity have shown increases in gamma activity with perceptual evidence (Polania et al. 2014 Neuron, Donner et al 2009 Curr. Biol., Wilming et al. 2020 biorxiv). Is it possible that with the broad gamma range investigated here (31-90Hz) and the spectral smoothing involved, the negative relationship might be at least partly driven by activity in the lower ranges, i.e., qualitatively closer to task/motor-related beta desynchronisation? It would be interesting to see if the significant negative correlation is maintained with a slightly narrower gamma range (e.g., >35Hz or >40Hz). Either way, I think it's important for these results to be discussed in relation to the literature mentioned above.

    2. Regarding the interpretation of the beta-gamma relationship, authors seem to place the results in the context of feedforward/feedback information dynamics (or at least they make several references to the literature throughout the manuscript). I am not sure if I understand or agree with this interpretation - if anything, doesn't the temporal progression of decision-related information for gamma and beta observed here (e.g., Fig. 5b) go against the current understanding of their roles in feedforward and feedback information flow, respectively? Some clarification on this point would be very useful.

    3. While the timing of beta/gamma decision-related accumulation is summarised in Figs. 4/5, I think it would be informative to also include (either in the main figures or as supplement) the actual trial-averaged traces, highlighting the overall timing differences between activity in the two bands (from Fig. 4), as well as the progression across the anterior-posterior axis (shown in Fig. 5).

  3. eLife

    ##Preprint Review

    This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 2 of the manuscript.

    ###Summary:

    While we found the topic very relevant - especially the role of large-scale beta dynamics in visuomotor processing - and the approach used interesting, our overall enthusiasm was limited by concerns regarding novelty, design and interpretation. Critically, it remains unclear whether we are dealing with narrowband oscillations here, especially regarding the reported gamma band results, but also in terms of separating different oscillatory contributions in the alpha/beta frequency ranges. Since everything that follows hinges on this assertion, one would have to first establish a separation of these different spectral contributions in order to attribute particular dynamics to particular bands.

  4. Version published to 10.1101/803049 on bioRxiv