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Distinct relationships of parietal and prefrontal cortices to evidence accumulation.

Hanks TD, Kopec CD, Brunton BW, Duan CA, Erlich JC, Brody CD - Nature (2015)

Bottom Line: Gradual accumulation of evidence is thought to be fundamental for decision-making, and its neural correlates have been found in several brain regions.Classical analyses uncovered correlates of accumulating evidence, similar to previous observations in primates and also similar across the two regions.Our results place important constraints on the circuit logic of brain regions involved in decision-making.

View Article: PubMed Central - PubMed

Affiliation: 1] Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, USA [2] Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, USA.

ABSTRACT
Gradual accumulation of evidence is thought to be fundamental for decision-making, and its neural correlates have been found in several brain regions. Here we develop a generalizable method to measure tuning curves that specify the relationship between neural responses and mentally accumulated evidence, and apply it to distinguish the encoding of decision variables in posterior parietal cortex and prefrontal cortex (frontal orienting fields, FOF). We recorded the firing rates of neurons in posterior parietal cortex and FOF from rats performing a perceptual decision-making task. Classical analyses uncovered correlates of accumulating evidence, similar to previous observations in primates and also similar across the two regions. However, tuning curve assays revealed that while the posterior parietal cortex encodes a graded value of the accumulating evidence, the FOF has a more categorical encoding that indicates, throughout the trial, the decision provisionally favoured by the evidence accumulated so far. Contrary to current views, this suggests that premotor activity in the frontal cortex does not have a role in the accumulation process, but instead has a more categorical function, such as transforming accumulated evidence into a discrete choice. To probe causally the role of FOF activity, we optogenetically silenced it during different time points of the trial. Consistent with a role in committing to a categorical choice at the end of the evidence accumulation process, but not consistent with a role during the accumulation itself, a behavioural effect was observed only when FOF silencing occurred at the end of the perceptual stimulus. Our results place important constraints on the circuit logic of brain regions involved in decision-making.

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Expected response profiles and click-triggered averages based on graded and categorical encodingsa, Using stimuli corresponding to what was presented to the rats, individual trial responses r(t) were calculated based on the graded encoding r(t) = r = k1a(t)+k2, where a(t) is the decision variable obtained from the behavioral model. Then, responses were averaged across trials sorting based on mean stimulus strength with the same exact method as described for Figure 1. b, Same as (a) for a categorical encoding r(t) = k1sign(a(t))+k2. The ratio of the positive-to-negative encoding changes over time for each condition, leading to a ramping response profile. This highlights the point that ramping response profiles alone are not a sufficient demonstration of a gradual accumulation process. c, The black line shows the model prediction for the click-triggered average based on a graded encoding of accumulated evidence (r = k1a+k2) with a 0.2 s mean encoding lag to match the response lag of PPC. The gray line shows the data from Fig. 1. d, Same as (c) for a categorical encoding of accumulated evidence (r = k1sign(a)+k2) with a 0.1 s mean encoding lag to match the response lag of FOF. For both schemes, the encoding lag was taken from a Gaussian distribution with a 0.02 s standard deviation to account for variability in lag across neurons.
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Figure 10: Expected response profiles and click-triggered averages based on graded and categorical encodingsa, Using stimuli corresponding to what was presented to the rats, individual trial responses r(t) were calculated based on the graded encoding r(t) = r = k1a(t)+k2, where a(t) is the decision variable obtained from the behavioral model. Then, responses were averaged across trials sorting based on mean stimulus strength with the same exact method as described for Figure 1. b, Same as (a) for a categorical encoding r(t) = k1sign(a(t))+k2. The ratio of the positive-to-negative encoding changes over time for each condition, leading to a ramping response profile. This highlights the point that ramping response profiles alone are not a sufficient demonstration of a gradual accumulation process. c, The black line shows the model prediction for the click-triggered average based on a graded encoding of accumulated evidence (r = k1a+k2) with a 0.2 s mean encoding lag to match the response lag of PPC. The gray line shows the data from Fig. 1. d, Same as (c) for a categorical encoding of accumulated evidence (r = k1sign(a)+k2) with a 0.1 s mean encoding lag to match the response lag of FOF. For both schemes, the encoding lag was taken from a Gaussian distribution with a 0.02 s standard deviation to account for variability in lag across neurons.

Mentions: A direct comparison of the time-averaged tuning curves showed a significantly larger slope at the zero-crossing for FOF compared to PPC (Fig. 3c; PPC: 0.058 ± 0.003, FOF: 0.158 ± 0.015, mean ± 95% CI), which indicates a sharper transition of firing rate between negative and positive accumulator values for FOF. This difference was robust to variations in the time constant of integration and the value of a “sticky” decision commitment bound in the behavioral model of evidence accumulation, variations in neural response latencies, and whether the behavioral model was fit individually to each rat or to the aggregate behavior for all rats (Extended Data Fig. 3 and 4). The difference between PPC and FOF was also apparent at the level of individual neurons. We computed the steepness of each neuron’s tuning curve, and while there was overlap between the PPC and the FOF populations, there was a significant shift towards greater steepness for the distribution of FOF neurons compared to PPC neurons (p<0.001, Extended Data Fig. 5). This difference in encoding between PPC and FOF was consistent both with the response profiles (Fig. 1c,d) and the click-triggered average responses, including the diminishing trend for the FOF (Extended Data Fig. 6). The difference in encoding between PPC and FOF was not maintained after the end of the decision process. During the period of motor preparation, the FOF maintained a categorical encoding, while the graded encoding in the PPC converged to a categorical encoding (Fig. 3d,e).


Distinct relationships of parietal and prefrontal cortices to evidence accumulation.

Hanks TD, Kopec CD, Brunton BW, Duan CA, Erlich JC, Brody CD - Nature (2015)

Expected response profiles and click-triggered averages based on graded and categorical encodingsa, Using stimuli corresponding to what was presented to the rats, individual trial responses r(t) were calculated based on the graded encoding r(t) = r = k1a(t)+k2, where a(t) is the decision variable obtained from the behavioral model. Then, responses were averaged across trials sorting based on mean stimulus strength with the same exact method as described for Figure 1. b, Same as (a) for a categorical encoding r(t) = k1sign(a(t))+k2. The ratio of the positive-to-negative encoding changes over time for each condition, leading to a ramping response profile. This highlights the point that ramping response profiles alone are not a sufficient demonstration of a gradual accumulation process. c, The black line shows the model prediction for the click-triggered average based on a graded encoding of accumulated evidence (r = k1a+k2) with a 0.2 s mean encoding lag to match the response lag of PPC. The gray line shows the data from Fig. 1. d, Same as (c) for a categorical encoding of accumulated evidence (r = k1sign(a)+k2) with a 0.1 s mean encoding lag to match the response lag of FOF. For both schemes, the encoding lag was taken from a Gaussian distribution with a 0.02 s standard deviation to account for variability in lag across neurons.
© Copyright Policy - permissions-link
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4835184&req=5

Figure 10: Expected response profiles and click-triggered averages based on graded and categorical encodingsa, Using stimuli corresponding to what was presented to the rats, individual trial responses r(t) were calculated based on the graded encoding r(t) = r = k1a(t)+k2, where a(t) is the decision variable obtained from the behavioral model. Then, responses were averaged across trials sorting based on mean stimulus strength with the same exact method as described for Figure 1. b, Same as (a) for a categorical encoding r(t) = k1sign(a(t))+k2. The ratio of the positive-to-negative encoding changes over time for each condition, leading to a ramping response profile. This highlights the point that ramping response profiles alone are not a sufficient demonstration of a gradual accumulation process. c, The black line shows the model prediction for the click-triggered average based on a graded encoding of accumulated evidence (r = k1a+k2) with a 0.2 s mean encoding lag to match the response lag of PPC. The gray line shows the data from Fig. 1. d, Same as (c) for a categorical encoding of accumulated evidence (r = k1sign(a)+k2) with a 0.1 s mean encoding lag to match the response lag of FOF. For both schemes, the encoding lag was taken from a Gaussian distribution with a 0.02 s standard deviation to account for variability in lag across neurons.
Mentions: A direct comparison of the time-averaged tuning curves showed a significantly larger slope at the zero-crossing for FOF compared to PPC (Fig. 3c; PPC: 0.058 ± 0.003, FOF: 0.158 ± 0.015, mean ± 95% CI), which indicates a sharper transition of firing rate between negative and positive accumulator values for FOF. This difference was robust to variations in the time constant of integration and the value of a “sticky” decision commitment bound in the behavioral model of evidence accumulation, variations in neural response latencies, and whether the behavioral model was fit individually to each rat or to the aggregate behavior for all rats (Extended Data Fig. 3 and 4). The difference between PPC and FOF was also apparent at the level of individual neurons. We computed the steepness of each neuron’s tuning curve, and while there was overlap between the PPC and the FOF populations, there was a significant shift towards greater steepness for the distribution of FOF neurons compared to PPC neurons (p<0.001, Extended Data Fig. 5). This difference in encoding between PPC and FOF was consistent both with the response profiles (Fig. 1c,d) and the click-triggered average responses, including the diminishing trend for the FOF (Extended Data Fig. 6). The difference in encoding between PPC and FOF was not maintained after the end of the decision process. During the period of motor preparation, the FOF maintained a categorical encoding, while the graded encoding in the PPC converged to a categorical encoding (Fig. 3d,e).

Bottom Line: Gradual accumulation of evidence is thought to be fundamental for decision-making, and its neural correlates have been found in several brain regions.Classical analyses uncovered correlates of accumulating evidence, similar to previous observations in primates and also similar across the two regions.Our results place important constraints on the circuit logic of brain regions involved in decision-making.

View Article: PubMed Central - PubMed

Affiliation: 1] Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, USA [2] Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, USA.

ABSTRACT
Gradual accumulation of evidence is thought to be fundamental for decision-making, and its neural correlates have been found in several brain regions. Here we develop a generalizable method to measure tuning curves that specify the relationship between neural responses and mentally accumulated evidence, and apply it to distinguish the encoding of decision variables in posterior parietal cortex and prefrontal cortex (frontal orienting fields, FOF). We recorded the firing rates of neurons in posterior parietal cortex and FOF from rats performing a perceptual decision-making task. Classical analyses uncovered correlates of accumulating evidence, similar to previous observations in primates and also similar across the two regions. However, tuning curve assays revealed that while the posterior parietal cortex encodes a graded value of the accumulating evidence, the FOF has a more categorical encoding that indicates, throughout the trial, the decision provisionally favoured by the evidence accumulated so far. Contrary to current views, this suggests that premotor activity in the frontal cortex does not have a role in the accumulation process, but instead has a more categorical function, such as transforming accumulated evidence into a discrete choice. To probe causally the role of FOF activity, we optogenetically silenced it during different time points of the trial. Consistent with a role in committing to a categorical choice at the end of the evidence accumulation process, but not consistent with a role during the accumulation itself, a behavioural effect was observed only when FOF silencing occurred at the end of the perceptual stimulus. Our results place important constraints on the circuit logic of brain regions involved in decision-making.

Show MeSH
Related in: MedlinePlus