Center-surround inhibition by expectation: a neuro-computational account
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eLife Assessment
This is a methodologically rich manuscript that is important for elucidating the neural mechanisms of expectation in perception. The analyses are convincing in extending analogous findings in attention and working memory. With further clarification, the findings will be of broad interest to vision researchers.
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Abstract
Abstract
Expectation is beneficial for adaptive behavior through quickly deducing plausible interpretations of information. The profile and underlying neural computations of this process, however, remain unclear. When participants expected a grating with a specific orientation, we found a center-surround inhibition profile in orientation space, which was independent from attentional modulations by task-relevance. Using computational modeling, we showed that this center-surround inhibition could be reproduced by either a sharpening of tuning curves of expected orientation or a shift of tuning curves of unexpected orientations. Intriguingly, these two computations were further supported by orientation-adjustment and orientation-discrimination experiments. Finally, the ablation studies in convolutional neural networks revealed that predictive coding feedback played a critical role in the center-surround inhibition in expectation. Altogether, our study reveals for the first time that expectation results in both enhancement and suppression, optimizing plausible interpretations during perception by enhancing expected and attenuating similar but irrelevant and potentially interfering representations.
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eLife Assessment
This is a methodologically rich manuscript that is important for elucidating the neural mechanisms of expectation in perception. The analyses are convincing in extending analogous findings in attention and working memory. With further clarification, the findings will be of broad interest to vision researchers.
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Reviewer #1 (Public review):
Summary:
The authors tested two competing mechanisms of expectation: (1) a sharpening model that suppresses unexpected information via center-surround inhibition; (2) a cancelation model that predicts a monotonic gradient response profile. Using two psychophysical experiments manipulating feature space distance between expected and unexpected stimuli, the results consistently supported the sharpening model. Computational modeling further showed that expectation effects were explained by either sharpened tuning curves or tuning shifts. Finally, convolutional neural network simulations revealed that feedback connections critically mediate the observed center-surround inhibition.
Strengths:
The manuscript provides compelling and convergent evidence from both psychophysical experiments and computational modeling …
Reviewer #1 (Public review):
Summary:
The authors tested two competing mechanisms of expectation: (1) a sharpening model that suppresses unexpected information via center-surround inhibition; (2) a cancelation model that predicts a monotonic gradient response profile. Using two psychophysical experiments manipulating feature space distance between expected and unexpected stimuli, the results consistently supported the sharpening model. Computational modeling further showed that expectation effects were explained by either sharpened tuning curves or tuning shifts. Finally, convolutional neural network simulations revealed that feedback connections critically mediate the observed center-surround inhibition.
Strengths:
The manuscript provides compelling and convergent evidence from both psychophysical experiments and computational modeling to robustly support the sharpening model of expectation, demonstrating clear center-surround inhibition of unexpected information.
Weaknesses:
The manuscript could directly validate the experimental manipulations and address how these results reconcile with existing literature on expectation effects.
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Reviewer #2 (Public review):
Summary:
This is a compelling and methodologically rich manuscript. The authors used a variety of methods, including psychophysics, computational modeling, and artificial neural networks, to reveal a non-monotonic, center-surround "Mexican-hat" profile of expectation in orientation space. Their data convincingly extend analogous findings in attention and working memory, and the modeling nicely teases apart sharpening vs. shift mechanisms.
Strengths:
The findings are novel and important in elucidating the potential neural mechanisms by which expectation shapes perception. The authors conducted a series of well-designed psychophysical experiments to careful examination of the profile of expectation's modulation. Computational modeling also provides further insights, linking the neural mechanisms of expectation …
Reviewer #2 (Public review):
Summary:
This is a compelling and methodologically rich manuscript. The authors used a variety of methods, including psychophysics, computational modeling, and artificial neural networks, to reveal a non-monotonic, center-surround "Mexican-hat" profile of expectation in orientation space. Their data convincingly extend analogous findings in attention and working memory, and the modeling nicely teases apart sharpening vs. shift mechanisms.
Strengths:
The findings are novel and important in elucidating the potential neural mechanisms by which expectation shapes perception. The authors conducted a series of well-designed psychophysical experiments to careful examination of the profile of expectation's modulation. Computational modeling also provides further insights, linking the neural mechanisms of expectation to behavioral results.
Weaknesses:
There are several aspects that could be strengthened or clarified.
(1) The sharpening model of expectation can predict surround suppression. The authors could further clarify how the cancellation model predicts a monotonic profile of expectation (Figure 1C) with the highest response at the expected orientation, while the cancellation model suggests a suppression of neurons tuned toward the expected stimulus.
(2) I'm a bit concerned about whether the profile solely arises from modulation of expectation. The two auditory cues are each associated with a fixed orientation, which may be confounded by other cognitive processes like visual working memory or attention (which I think the authors also discussed). Although the authors tried to use SFD task to render orientation task-irrelevant, luminance edges (i.e., orientation) and spatial frequency in gratings are highly intertwined and orientation of the gratings may help recall the first grating's SF (fixed at 0.9 c/{degree sign}), especially given the first and second grating's orientations are not very different (4.8{degree sign}).
(3) For each of the expected orientations (20{degree sign} or 70{degree sign}), the unexpected ones are linearly separable (i.e., all unexpected ones lie on one side of the expected angle). This might further encourage people to shift their attended or expected orientation, according to the optimal tuning hypothesis. Would this provide an alternative explanation to the tuning shift that the authors found?
(4) It is great that the authors conducted computational modeling to elucidate the potential neuronal mechanisms of expectation. But I think the sharpening hypothesis (e.g., reviewed in de Lange, Heilbron & Kok, 2018) focuses on the neural population level, i.e., narrowing of population tuning profile, while the authors conducted the sharpening at the neuronal tuning level. However, the sharpening of population does not necessarily rely on the sharpening of individual neuronal tuning. For example, neuronal gain modulation can also account for such population sharpening. I think similar logic applies to the orientation adjustment experiment. The behavioral level shift does not necessarily suggest a similar shift at the neuronal level. I would recommend that the authors comment on this.
(5) If the orientation adjustment experiment suggests that both sharpening and shifting are present at the same time, have the authors tried combining both in their computational model?
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