Overt visual attention modulates decision-related signals in ventral and dorsal medial prefrontal cortex
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eLife Assessment
This important study uses an innovative task design combined with eye tracking and fMRI to distinguish brain regions that encode the value of individual items from those that accumulate those values for value-based choices. It shows that distinct brain regions carry signals for currently evaluated and previously accumulated evidence. The study provides solid evidence in support of most of its claims, albeit with current minor weaknesses concerning the evidence in favour of gaze-modulation of the fMRI signal. The work will be of interest to neuroscientists working on attention and decision-making.
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Abstract
When indicating a preference between two options, decision makers are thought to compare and accumulate evidence in an attention-guided process. Little is known about this process’s neural substrates or how visual attention affects the representations of accumulated evidence. We conducted a simultaneous eye-tracking and fMRI experiment in which human subjects gradually learned about the value of two food-lotteries. With this design we were able to extend decisions over a prolonged time-course, manipulate the temporal onset of evidence, and therefore dissociate sampled and accumulated evidence. Consistent with past work, we found correlates of sampled evidence in ventromedial prefrontal cortex (vmPFC), and correlates of accumulated evidence in the prefrontal and parietal cortex. We also found that more gaze at an option increased its choice probability and that gaze amplified sampled-value signals in the vmPFC and ventral striatum. Most importantly, we found that gaze modulated accumulated-value signals in the pre-supplementary motor area (pre-SMA), providing novel evidence that visual attention has lasting effects on decision variables and suggesting that activity in the pre-SMA reflects accumulated evidence and not decision conflict. These results shed new light on the neural mechanisms underlying gaze-driven decision processes.
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eLife Assessment
This important study uses an innovative task design combined with eye tracking and fMRI to distinguish brain regions that encode the value of individual items from those that accumulate those values for value-based choices. It shows that distinct brain regions carry signals for currently evaluated and previously accumulated evidence. The study provides solid evidence in support of most of its claims, albeit with current minor weaknesses concerning the evidence in favour of gaze-modulation of the fMRI signal. The work will be of interest to neuroscientists working on attention and decision-making.
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Reviewer #1 (Public review):
Summary:
This study builds upon a major theoretical account of value-based choice, the 'attentional drift diffusion model' (aDDM), and examines whether and how this might be implemented in the human brain using functional magnetic resonance imaging (fMRI). The aDDM states that the process of internal evidence accumulation across time should be weighted by the decision maker's gaze, with more weight being assigned to the currently fixated item. The present study aims to test whether there are (a) regions of the brain where signals related to the currently presented value are affected by the participant's gaze; (b) regions of the brain where previously accumulated information is weighted by gaze.
To examine this, the authors developed a novel paradigm that allowed them to dissociate currently and previously …
Reviewer #1 (Public review):
Summary:
This study builds upon a major theoretical account of value-based choice, the 'attentional drift diffusion model' (aDDM), and examines whether and how this might be implemented in the human brain using functional magnetic resonance imaging (fMRI). The aDDM states that the process of internal evidence accumulation across time should be weighted by the decision maker's gaze, with more weight being assigned to the currently fixated item. The present study aims to test whether there are (a) regions of the brain where signals related to the currently presented value are affected by the participant's gaze; (b) regions of the brain where previously accumulated information is weighted by gaze.
To examine this, the authors developed a novel paradigm that allowed them to dissociate currently and previously presented evidence, at a timescale amenable to measuring neural responses with fMRI. They asked participants to choose between bundles or 'lotteries' of food times, which they revealed sequentially and slowly to the participant across time. This allowed modelling of the haemodynamic response to each new observation in the lottery, separately for previously accumulated and currently presented evidence.
Using this approach, they find that regions of the brain supporting valuation (vmPFC and ventral striatum) have responses reflecting gaze-weighted valuation of the currently presented item, whereas regions previously associated with evidence accumulation (preSMA and IPS) have responses reflecting gaze-weighted modulation of previously accumulated evidence.
Strengths:
A major strength of the current paper is the design of the task, nicely allowing the researchers to examine evidence accumulation across time despite using a technique with poor temporal resolution. The dissociation between currently presented and previously accumulated evidence in different brain regions in GLM1 (before gaze-weighting), as presented in Figure 5, is already compelling. The result that regions such as preSMA respond positively to |AV| (absolute difference in accumulated value) is particularly interesting, as it would seem that the 'decision conflict' account of this region's activity might predict the exact opposite result. Additionally, the behaviour has been well modelled at the end of the paper when examining temporal weighting functions across the multiple samples.
Weaknesses:
The results relating to gaze-weighting in the fMRI signal could do with some further explication to become more complete. A major concern with GLM2, which looks at the same effects as GLM1 but now with gaze-weighting, is that these gaze-weighted regressors may be (at least partially) correlated with their non-gaze-weighted counterparts (e.g., SVgaze will correlate with SV). But the non-gaze-weighted regressors have been excluded from this model. In other words, the authors are not testing for effects of gaze-weighting of value signals *over and above* the base effects of value in this model. In my mind, this means that the GLM2 results could simply be a replication of the findings from GLM1 at present. GLM3 is potentially a stronger test, as it includes the value signals and the interaction with gaze in the same model. But here, while the link to the currently attended item is quite clear (and a replication of Lim et al, 2011), the link to previously accumulated evidence is a bit contorted, depending upon the interpretation of a behavioural regression to interpret the fMRI evidence. The results from GLM3 are also, by the authors' own admission, marginal in places.
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Reviewer #2 (Public review):
Summary:
In this paper, the authors seek to disentangle brain areas that encode the subjective value of individual stimuli/items (input regions) from those that accumulate those values into decision variables (integrators) for value-based choice. The authors used a novel task in which stimulus presentation was slowed down to ensure that such a dissociation was possible using fMRI despite its relatively low temporal resolution. In addition, the authors leveraged the fact that gaze increases item value, providing a means of distinguishing brain regions that encode decision variables from those that encode other quantities such as conflict or time-on-task. The authors adopt a region-of-interest approach based on an extensive previous literature and found that the ventral striatum and vmPFC correlated with the …
Reviewer #2 (Public review):
Summary:
In this paper, the authors seek to disentangle brain areas that encode the subjective value of individual stimuli/items (input regions) from those that accumulate those values into decision variables (integrators) for value-based choice. The authors used a novel task in which stimulus presentation was slowed down to ensure that such a dissociation was possible using fMRI despite its relatively low temporal resolution. In addition, the authors leveraged the fact that gaze increases item value, providing a means of distinguishing brain regions that encode decision variables from those that encode other quantities such as conflict or time-on-task. The authors adopt a region-of-interest approach based on an extensive previous literature and found that the ventral striatum and vmPFC correlated with the item values and not their accumulation, whereas the pre-SMA, IPS, and dlPFC correlated more strongly with their accumulation. Further analysis revealed that the pre-SMA was the only one of the three integrator regions to also exhibit gaze modulation.
Strengths:
The study uses a highly innovative design and addresses an important and timely topic. The manuscript is well-written and engaging, while the data analysis appears highly rigorous.
Weaknesses:
With 23 subjects, the study has relatively low statistical power for fMRI.
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Author response:
eLife Assessment
This important study uses an innovative task design combined with eye tracking and fMRI to distinguish brain regions that encode the value of individual items from those that accumulate those values for value-based choices. It shows that distinct brain regions carry signals for currently evaluated and previously accumulated evidence. The study provides solid evidence in support of most of its claims, albeit with current minor weaknesses concerning the evidence in favour of gaze-modulation of the fMRI signal. The work will be of interest to neuroscientists working on attention and decision-making.
We thank the Editor and Reviewers for their summary of the strengths of our study, and for their thoughtful review and feedback on our manuscript. We plan to undertake some additional analyses suggested by …
Author response:
eLife Assessment
This important study uses an innovative task design combined with eye tracking and fMRI to distinguish brain regions that encode the value of individual items from those that accumulate those values for value-based choices. It shows that distinct brain regions carry signals for currently evaluated and previously accumulated evidence. The study provides solid evidence in support of most of its claims, albeit with current minor weaknesses concerning the evidence in favour of gaze-modulation of the fMRI signal. The work will be of interest to neuroscientists working on attention and decision-making.
We thank the Editor and Reviewers for their summary of the strengths of our study, and for their thoughtful review and feedback on our manuscript. We plan to undertake some additional analyses suggested by the Reviewers to bolster the evidence in favor of gaze-modulation of the fMRI signal.
Reviewer #1 (Public review):
Summary:
This study builds upon a major theoretical account of value-based choice, the 'attentional drift diffusion model' (aDDM), and examines whether and how this might be implemented in the human brain using functional magnetic resonance imaging (fMRI). The aDDM states that the process of internal evidence accumulation across time should be weighted by the decision maker's gaze, with more weight being assigned to the currently fixated item. The present study aims to test whether there are (a) regions of the brain where signals related to the currently presented value are affected by the participant's gaze; (b) regions of the brain where previously accumulated information is weighted by gaze.
To examine this, the authors developed a novel paradigm that allowed them to dissociate currently and previously presented evidence, at a timescale amenable to measuring neural responses with fMRI. They asked participants to choose between bundles or 'lotteries' of food times, which they revealed sequentially and slowly to the participant across time. This allowed modelling of the haemodynamic response to each new observation in the lottery, separately for previously accumulated and currently presented evidence.
Using this approach, they find that regions of the brain supporting valuation (vmPFC and ventral striatum) have responses reflecting gaze-weighted valuation of the currently presented item, whereas regions previously associated with evidence accumulation (preSMA and IPS) have responses reflecting gaze-weighted modulation of previously accumulated evidence.
Strengths:
A major strength of the current paper is the design of the task, nicely allowing the researchers to examine evidence accumulation across time despite using a technique with poor temporal resolution. The dissociation between currently presented and previously accumulated evidence in different brain regions in GLM1 (before gaze-weighting), as presented in Figure 5, is already compelling. The result that regions such as preSMA respond positively to |AV| (absolute difference in accumulated value) is particularly interesting, as it would seem that the 'decision conflict' account of this region's activity might predict the exact opposite result. Additionally, the behaviour has been well modelled at the end of the paper when examining temporal weighting functions across the multiple samples.
Thank you!
Weaknesses:
The results relating to gaze-weighting in the fMRI signal could do with some further explication to become more complete. A major concern with GLM2, which looks at the same effects as GLM1 but now with gaze-weighting, is that these gaze-weighted regressors may be (at least partially) correlated with their non-gaze-weighted counterparts (e.g., SVgaze will correlate with SV). But the non-gaze-weighted regressors have been excluded from this model. In other words, the authors are not testing for effects of gaze-weighting of value signals *over and above* the base effects of value in this model. In my mind, this means that the GLM2 results could simply be a replication of the findings from GLM1 at present. GLM3 is potentially a stronger test, as it includes the value signals and the interaction with gaze in the same model. But here, while the link to the currently attended item is quite clear (and a replication of Lim et al, 2011), the link to previously accumulated evidence is a bit contorted, depending upon the interpretation of a behavioural regression to interpret the fMRI evidence. The results from GLM3 are also, by the authors' own admission, marginal in places.
We thank the Reviewer for their thoughtful critique. We acknowledge that our formulation of GLM2 does not test for the effects of gaze-weighted value signals beyond the base effects of value, only in place of the base effects of value. In our revision, we plan to examine alternative ways of quantifying the relative importance of gaze in these results.
Reviewer #2 (Public review):
Summary:
In this paper, the authors seek to disentangle brain areas that encode the subjective value of individual stimuli/items (input regions) from those that accumulate those values into decision variables (integrators) for value-based choice. The authors used a novel task in which stimulus presentation was slowed down to ensure that such a dissociation was possible using fMRI despite its relatively low temporal resolution. In addition, the authors leveraged the fact that gaze increases item value, providing a means of distinguishing brain regions that encode decision variables from those that encode other quantities such as conflict or time-on-task. The authors adopt a region-of-interest approach based on an extensive previous literature and found that the ventral striatum and vmPFC correlated with the item values and not their accumulation, whereas the pre-SMA, IPS, and dlPFC correlated more strongly with their accumulation. Further analysis revealed that the pre-SMA was the only one of the three integrator regions to also exhibit gaze modulation.
Strengths:
The study uses a highly innovative design and addresses an important and timely topic. The manuscript is well-written and engaging, while the data analysis appears highly rigorous.
Weaknesses:
With 23 subjects, the study has relatively low statistical power for fMRI.
We thank the Reviewer for their comments on the strengths of the manuscript, and for highlighting an important limitation. We agree that the number of participants in the study, after exclusions, was lower than your typical fMRI study. However, it is important to note that we do have a lot of data for each subject. Due to our relatively fast, event-related design, we have on average 65 trials per subject (SD = 18) and 5.95 samples per trial (SD = 4.03), for an average of 387 observations per subject (SD = 18). Our model-based analysis looks for very specific neural time courses across these ~387 observations, giving us substantial power to detect our effects of interest. Still, we acknowledge that our small number of subjects does still limit our power and our ability to generalize to other subjects. We plan to add the following disclaimer to the Discussion section:
“Together with our limited sample size (n = 23), we may not have had adequate statistical power required to observe consistent effects. Additional research with larger sample sizes is needed to resolve this issue.”
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