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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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Related in: MedlinePlus

Behavioral model, following Brunton et al. (2013)a, At each timepoint, the accumulator a (black trace) represents an estimate of the “Right” vs “Left” evidence accrued so far. At stimulus end, the model decides “Right” if a > Þ (the decision borderline) and “Left” otherwise, where Þ is a free parameter. Light grey traces indicate alternate runs with different instantiations of model noise. These example trajectories are for illustrative purposes; the model estimates the full probability distribution of a at each timepoint.Right  (Left ) clicks change the value of a by positive (negative) impulses of magnitude C. is a diffusion constant, parameterizing noise in a.parameterizes noise associated with each click.λ parameterizes consistent drift in the decision variable a. In the “leaky” case (λ < 0, illustrated), drift is towards a = 0, and later clicks impact the decision more than earlier clicks. In the “unstable” case (λ > 0), drift is away from a =0, and earlier clicks impact the decision more than later clicks. The time constant of the accumulation process is τ = 1/λ.B is the height of “sticky” decision bounds. If a reaches either bound, it leads to decision commitment before the end of the stimulus and later clicks have no impact on the choice.ϕ, τϕ parameterize sensory adaptation by defining the dynamics of C. Immediately after a click, the magnitude C is multiplied by ϕ. C then recovers towards an unadapted value of 1 with time constant τϕ. Facilitation thus corresponds to ϕ > 1, while depression corresponds to ϕ < 1.These properties are implemented by the following equations:if /a/ B then da/dt = 0; elseda=σadW+(δt,tR·ηR·C-δt,tL·ηL·C)dt+λadtwhereδt,tR,L are delta functions at the times of the auditory clicks.η are i.i.d. gaussian variables drawn from N(1,σs).dW is a white-noise Wiener process.Adaptation dynamics are given by:dCdt=1-Cτϕ+(ϕ-1)C(δt,tR+δt,tL)In addition, a lapse rate parameterizes the fraction of trials on which a random response is made.b, The behavioral model provides an estimate of the evolution of the distribution of a for each trial, with color representing probability density for both panels. The forward version of the model (left panel) estimates the distribution of a at each timepoint based entirely on the click times and model parameters. Leftward (green) clicks push the distribution more negative and rightward (red) clicks push it more positive. The final value obtained by a at the end of the trial dictates the choice. In this example, the distribution of the final value of a is more heavily weighted towards negative values because there were more leftward than rightward clicks for this trial. A better estimate of the distribution of a can be obtained by also taking into account the subject’s choice made at the end of the trial (right panel). The final choice constrains the distribution of a at the final timepoint to have all its mass to one side of the decision boundary (in this example trial, despite the many leftward clicks, the subject chose right, and thus at the final timepoint, all the probability mass is at a>0). This constraint is then propagated backwards in time, to obtain the distribution of accumulator values at each timepoint that is consistent both with the stimulus clicks and the subject’s final choice. The final result is an estimate of the distribution of a at each timepoint that takes into account the click times, the model parameters, and the subject’s choice.c, Illustration of non-sticky versus sticky decision bounds. Upper panel: response of an accumulator to a sequence of downward (green arrows) and upward (red arrows) impulses when the bound parameter B is 2.5 and the bounds are not sticky. The fourth downward arrow (green) has no impact, because the bounds have been reached, and a cannot go beyond them. But subsequent upward arrows (red) do have an impact, because they do not push against the bounds. Lower panel: response of an accumulator with the same parameters, receiving the same sequence of impulses, but when the bounds are sticky. Impulses subsequent to reaching the bound have no effect. We fit our rats’ behavior data to this non-sticky bound model. We found that, similar to the version with the sticky bounds, the accumulation time constants were long (/τ/=1//λ/= 1.0 ± 0.2 s, mean ± SEM across rats), and the bounds were high (17.1 ± 2.2, mean ± SEM across rats). Such high bounds once again indicate that the bounds have minimal impact (consistent with this, the difference between the sticky and non-sticky bound models turned out to be negligible).
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Figure 6: Behavioral model, following Brunton et al. (2013)a, At each timepoint, the accumulator a (black trace) represents an estimate of the “Right” vs “Left” evidence accrued so far. At stimulus end, the model decides “Right” if a > Þ (the decision borderline) and “Left” otherwise, where Þ is a free parameter. Light grey traces indicate alternate runs with different instantiations of model noise. These example trajectories are for illustrative purposes; the model estimates the full probability distribution of a at each timepoint.Right (Left ) clicks change the value of a by positive (negative) impulses of magnitude C. is a diffusion constant, parameterizing noise in a.parameterizes noise associated with each click.λ parameterizes consistent drift in the decision variable a. In the “leaky” case (λ < 0, illustrated), drift is towards a = 0, and later clicks impact the decision more than earlier clicks. In the “unstable” case (λ > 0), drift is away from a =0, and earlier clicks impact the decision more than later clicks. The time constant of the accumulation process is τ = 1/λ.B is the height of “sticky” decision bounds. If a reaches either bound, it leads to decision commitment before the end of the stimulus and later clicks have no impact on the choice.ϕ, τϕ parameterize sensory adaptation by defining the dynamics of C. Immediately after a click, the magnitude C is multiplied by ϕ. C then recovers towards an unadapted value of 1 with time constant τϕ. Facilitation thus corresponds to ϕ > 1, while depression corresponds to ϕ < 1.These properties are implemented by the following equations:if /a/ B then da/dt = 0; elseda=σadW+(δt,tR·ηR·C-δt,tL·ηL·C)dt+λadtwhereδt,tR,L are delta functions at the times of the auditory clicks.η are i.i.d. gaussian variables drawn from N(1,σs).dW is a white-noise Wiener process.Adaptation dynamics are given by:dCdt=1-Cτϕ+(ϕ-1)C(δt,tR+δt,tL)In addition, a lapse rate parameterizes the fraction of trials on which a random response is made.b, The behavioral model provides an estimate of the evolution of the distribution of a for each trial, with color representing probability density for both panels. The forward version of the model (left panel) estimates the distribution of a at each timepoint based entirely on the click times and model parameters. Leftward (green) clicks push the distribution more negative and rightward (red) clicks push it more positive. The final value obtained by a at the end of the trial dictates the choice. In this example, the distribution of the final value of a is more heavily weighted towards negative values because there were more leftward than rightward clicks for this trial. A better estimate of the distribution of a can be obtained by also taking into account the subject’s choice made at the end of the trial (right panel). The final choice constrains the distribution of a at the final timepoint to have all its mass to one side of the decision boundary (in this example trial, despite the many leftward clicks, the subject chose right, and thus at the final timepoint, all the probability mass is at a>0). This constraint is then propagated backwards in time, to obtain the distribution of accumulator values at each timepoint that is consistent both with the stimulus clicks and the subject’s final choice. The final result is an estimate of the distribution of a at each timepoint that takes into account the click times, the model parameters, and the subject’s choice.c, Illustration of non-sticky versus sticky decision bounds. Upper panel: response of an accumulator to a sequence of downward (green arrows) and upward (red arrows) impulses when the bound parameter B is 2.5 and the bounds are not sticky. The fourth downward arrow (green) has no impact, because the bounds have been reached, and a cannot go beyond them. But subsequent upward arrows (red) do have an impact, because they do not push against the bounds. Lower panel: response of an accumulator with the same parameters, receiving the same sequence of impulses, but when the bounds are sticky. Impulses subsequent to reaching the bound have no effect. We fit our rats’ behavior data to this non-sticky bound model. We found that, similar to the version with the sticky bounds, the accumulation time constants were long (/τ/=1//λ/= 1.0 ± 0.2 s, mean ± SEM across rats), and the bounds were high (17.1 ± 2.2, mean ± SEM across rats). Such high bounds once again indicate that the bounds have minimal impact (consistent with this, the difference between the sticky and non-sticky bound models turned out to be negligible).

Mentions: We used the behavioral model previously developed for this task10 to obtain trial-by-trial, moment-by-moment estimates of the accumulating evidence, denoted by a(t) (Extended Data Fig. 2, Extended Data Table 1). Together with the simultaneously recorded firing rates r(t), this enabled the estimation of “tuning curves” that specify, for each point in time during the stimulus, how r depends on a (Fig. 2).


Distinct relationships of parietal and prefrontal cortices to evidence accumulation.

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

Behavioral model, following Brunton et al. (2013)a, At each timepoint, the accumulator a (black trace) represents an estimate of the “Right” vs “Left” evidence accrued so far. At stimulus end, the model decides “Right” if a > Þ (the decision borderline) and “Left” otherwise, where Þ is a free parameter. Light grey traces indicate alternate runs with different instantiations of model noise. These example trajectories are for illustrative purposes; the model estimates the full probability distribution of a at each timepoint.Right  (Left ) clicks change the value of a by positive (negative) impulses of magnitude C. is a diffusion constant, parameterizing noise in a.parameterizes noise associated with each click.λ parameterizes consistent drift in the decision variable a. In the “leaky” case (λ < 0, illustrated), drift is towards a = 0, and later clicks impact the decision more than earlier clicks. In the “unstable” case (λ > 0), drift is away from a =0, and earlier clicks impact the decision more than later clicks. The time constant of the accumulation process is τ = 1/λ.B is the height of “sticky” decision bounds. If a reaches either bound, it leads to decision commitment before the end of the stimulus and later clicks have no impact on the choice.ϕ, τϕ parameterize sensory adaptation by defining the dynamics of C. Immediately after a click, the magnitude C is multiplied by ϕ. C then recovers towards an unadapted value of 1 with time constant τϕ. Facilitation thus corresponds to ϕ > 1, while depression corresponds to ϕ < 1.These properties are implemented by the following equations:if /a/ B then da/dt = 0; elseda=σadW+(δt,tR·ηR·C-δt,tL·ηL·C)dt+λadtwhereδt,tR,L are delta functions at the times of the auditory clicks.η are i.i.d. gaussian variables drawn from N(1,σs).dW is a white-noise Wiener process.Adaptation dynamics are given by:dCdt=1-Cτϕ+(ϕ-1)C(δt,tR+δt,tL)In addition, a lapse rate parameterizes the fraction of trials on which a random response is made.b, The behavioral model provides an estimate of the evolution of the distribution of a for each trial, with color representing probability density for both panels. The forward version of the model (left panel) estimates the distribution of a at each timepoint based entirely on the click times and model parameters. Leftward (green) clicks push the distribution more negative and rightward (red) clicks push it more positive. The final value obtained by a at the end of the trial dictates the choice. In this example, the distribution of the final value of a is more heavily weighted towards negative values because there were more leftward than rightward clicks for this trial. A better estimate of the distribution of a can be obtained by also taking into account the subject’s choice made at the end of the trial (right panel). The final choice constrains the distribution of a at the final timepoint to have all its mass to one side of the decision boundary (in this example trial, despite the many leftward clicks, the subject chose right, and thus at the final timepoint, all the probability mass is at a>0). This constraint is then propagated backwards in time, to obtain the distribution of accumulator values at each timepoint that is consistent both with the stimulus clicks and the subject’s final choice. The final result is an estimate of the distribution of a at each timepoint that takes into account the click times, the model parameters, and the subject’s choice.c, Illustration of non-sticky versus sticky decision bounds. Upper panel: response of an accumulator to a sequence of downward (green arrows) and upward (red arrows) impulses when the bound parameter B is 2.5 and the bounds are not sticky. The fourth downward arrow (green) has no impact, because the bounds have been reached, and a cannot go beyond them. But subsequent upward arrows (red) do have an impact, because they do not push against the bounds. Lower panel: response of an accumulator with the same parameters, receiving the same sequence of impulses, but when the bounds are sticky. Impulses subsequent to reaching the bound have no effect. We fit our rats’ behavior data to this non-sticky bound model. We found that, similar to the version with the sticky bounds, the accumulation time constants were long (/τ/=1//λ/= 1.0 ± 0.2 s, mean ± SEM across rats), and the bounds were high (17.1 ± 2.2, mean ± SEM across rats). Such high bounds once again indicate that the bounds have minimal impact (consistent with this, the difference between the sticky and non-sticky bound models turned out to be negligible).
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getmorefigures.php?uid=PMC4835184&req=5

Figure 6: Behavioral model, following Brunton et al. (2013)a, At each timepoint, the accumulator a (black trace) represents an estimate of the “Right” vs “Left” evidence accrued so far. At stimulus end, the model decides “Right” if a > Þ (the decision borderline) and “Left” otherwise, where Þ is a free parameter. Light grey traces indicate alternate runs with different instantiations of model noise. These example trajectories are for illustrative purposes; the model estimates the full probability distribution of a at each timepoint.Right (Left ) clicks change the value of a by positive (negative) impulses of magnitude C. is a diffusion constant, parameterizing noise in a.parameterizes noise associated with each click.λ parameterizes consistent drift in the decision variable a. In the “leaky” case (λ < 0, illustrated), drift is towards a = 0, and later clicks impact the decision more than earlier clicks. In the “unstable” case (λ > 0), drift is away from a =0, and earlier clicks impact the decision more than later clicks. The time constant of the accumulation process is τ = 1/λ.B is the height of “sticky” decision bounds. If a reaches either bound, it leads to decision commitment before the end of the stimulus and later clicks have no impact on the choice.ϕ, τϕ parameterize sensory adaptation by defining the dynamics of C. Immediately after a click, the magnitude C is multiplied by ϕ. C then recovers towards an unadapted value of 1 with time constant τϕ. Facilitation thus corresponds to ϕ > 1, while depression corresponds to ϕ < 1.These properties are implemented by the following equations:if /a/ B then da/dt = 0; elseda=σadW+(δt,tR·ηR·C-δt,tL·ηL·C)dt+λadtwhereδt,tR,L are delta functions at the times of the auditory clicks.η are i.i.d. gaussian variables drawn from N(1,σs).dW is a white-noise Wiener process.Adaptation dynamics are given by:dCdt=1-Cτϕ+(ϕ-1)C(δt,tR+δt,tL)In addition, a lapse rate parameterizes the fraction of trials on which a random response is made.b, The behavioral model provides an estimate of the evolution of the distribution of a for each trial, with color representing probability density for both panels. The forward version of the model (left panel) estimates the distribution of a at each timepoint based entirely on the click times and model parameters. Leftward (green) clicks push the distribution more negative and rightward (red) clicks push it more positive. The final value obtained by a at the end of the trial dictates the choice. In this example, the distribution of the final value of a is more heavily weighted towards negative values because there were more leftward than rightward clicks for this trial. A better estimate of the distribution of a can be obtained by also taking into account the subject’s choice made at the end of the trial (right panel). The final choice constrains the distribution of a at the final timepoint to have all its mass to one side of the decision boundary (in this example trial, despite the many leftward clicks, the subject chose right, and thus at the final timepoint, all the probability mass is at a>0). This constraint is then propagated backwards in time, to obtain the distribution of accumulator values at each timepoint that is consistent both with the stimulus clicks and the subject’s final choice. The final result is an estimate of the distribution of a at each timepoint that takes into account the click times, the model parameters, and the subject’s choice.c, Illustration of non-sticky versus sticky decision bounds. Upper panel: response of an accumulator to a sequence of downward (green arrows) and upward (red arrows) impulses when the bound parameter B is 2.5 and the bounds are not sticky. The fourth downward arrow (green) has no impact, because the bounds have been reached, and a cannot go beyond them. But subsequent upward arrows (red) do have an impact, because they do not push against the bounds. Lower panel: response of an accumulator with the same parameters, receiving the same sequence of impulses, but when the bounds are sticky. Impulses subsequent to reaching the bound have no effect. We fit our rats’ behavior data to this non-sticky bound model. We found that, similar to the version with the sticky bounds, the accumulation time constants were long (/τ/=1//λ/= 1.0 ± 0.2 s, mean ± SEM across rats), and the bounds were high (17.1 ± 2.2, mean ± SEM across rats). Such high bounds once again indicate that the bounds have minimal impact (consistent with this, the difference between the sticky and non-sticky bound models turned out to be negligible).
Mentions: We used the behavioral model previously developed for this task10 to obtain trial-by-trial, moment-by-moment estimates of the accumulating evidence, denoted by a(t) (Extended Data Fig. 2, Extended Data Table 1). Together with the simultaneously recorded firing rates r(t), this enabled the estimation of “tuning curves” that specify, for each point in time during the stimulus, how r depends on a (Fig. 2).

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