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Value dynamics affect choice preparation during decision-making

August 2023
Nature Neuroscience 26(9):1-9

August 2023
26(9):1-9

Authors:

During decision-making, neurons in the orbitofrontal cortex (OFC) sequentially represent the value of each option in turn, but it is unclear how these dynamics are translated into a choice response. One brain region that may be implicated in this process is the anterior cingulate cortex (ACC), which strongly connects with OFC and contains many neurons that encode the choice response. We investigated how OFC value signals interacted with ACC neurons encoding the choice response by performing simultaneous high-channel count recordings from the two areas in nonhuman primates. ACC neurons encoding the choice response steadily increased their firing rate throughout the decision-making process, peaking shortly before the time of the choice response. Furthermore, the value dynamics in OFC affected ACC ramping—when OFC represented the more valuable option, ACC ramping accelerated. Because OFC tended to represent the more valuable option more frequently and for a longer duration, this interaction could explain how ACC selects the more valuable response.

Task and behavior a, Subjects fixated on a cue to initiate each trial. They were presented with one (forced choice) or two (free choice) pictures. After indicating their choice with a lever, they received the corresponding juice amount with the probability Preward. b, Subjects learned 16 pictures associated with different juice amounts and reward probabilities. c, Subjects were more likely to choose pictures associated with larger and more probable rewards. Using the same arrangement as b, the color indicates the percent of free trials on which each picture was chosen when available. d, To train the direction decoder, we split the pictures into equal-sized groups from the lowest expected value (value bin = 1) to the highest (value bin = 4). Groupings were the same for both subjects; the same arrangement as in b. e, Choice response times on free (green) and forced (gray) trials. f, The effects of maximum value and value difference on reaction times. To visualize the unique contributions of each of these predictors, response times were modeled as a linear function of one parameter, and the residuals (mean ± s.e.m.) were plotted as a function of the other parameter.

…

Single-neuron encoding of choice and value during the 500-ms preceding choice a, ACC firing rates were modeled via linear regressions as a linear combination of the maximum value (blue), choice direction (yellow) and trial type (red) in overlapping 100-ms windows stepped by 25 ms. Most neurons were significantly predicted by at least one factor. Significance was defined as P < 0.01 for at least 100 ms. Statistical tests were two-sided. b, Same as a but for OFC neurons. c, Comparison of the prevalence of different types of encoding between ACC and OFC. In subject C, selective neurons were more common in ACC than in OFC, but there was little difference in subject G. Asterisks indicate significant differences (χ² test, P < 0.01). NS, not significant.

…

ACC ramping activity around the time of the choice response a, Mean posterior probability when decoding the chosen direction from ACC or OFC neuronal firing rates. Decoding performance gradually increased and was much stronger in ACC than OFC, peaking shortly before the lever movement. The red rectangle indicates the period where we tested the influence of value signals on the ACC choice response. Error bars denote the s.e.m. b, Activity of direction-selective neurons around the time of the choice response, showing z-scored firing rates for when the subject makes a choice in either the contralateral or ipsilateral direction, defined relative to the hemisphere from which the neurons were recorded. Neurons are sorted according to the onset of the contralateral (top row) or ipsilateral (bottom row) choice response to illustrate similarity in the dynamics of the single-neuron responses across the two different choice directions. c, The single-neuron dynamics of the choice response, as measured by the time of the onset and peak of firing, are correlated across the two different choice directions. The magnitude of the response is not correlated.

…

Value decoding in OFC and ACC a, We trained an LDA decoder to predict the value (1–4) on forced trials from neuronal firing rates. We applied the decoder weights to compute the posterior probability for each value in individual free trials in overlapping windows of 20 ms stepped by 5 ms. States are sustained periods (≥35 ms) of confidence decoding of a specific value. In both OFC and ACC, we observed multiple flips between chosen and unchosen value states of each trial. b, We observed more chosen (red) than unchosen (blue) than unavailable (gray) value states in both OFC and ACC, as determined from a one-way analysis of variance with post hoc t-tests, *P < 0.01 and **P < 0.0001. Error bars denote the s.e. of the mean number of states observed in a single trial (n = 3,865 trials for both ACC and OFC in subject C; n = 3,247 trials for both ACC and OFC in subject G). c, We observed longer chosen than unchosen than unavailable value states. Conventions are the same as b. Error bars denote the s.e. of the mean duration of each observed state. In subject C’s OFC, we observed 6,107 chosen states, 3,045 unchosen states and 2,259 unavailable states. In subject G’s OFC, we observed 6,420 chosen states, 3,877 unchosen states and 3,251 unavailable states. In subject C’s ACC, we observed 6,265 chosen states, 3,037 unchosen states and 2,450 unavailable states. In subject G’s ACC, we observed 5,762 chosen states, 3,651 unchosen states and 3,228 unavailable states. d, Choice response times were faster when the chosen value (red) was decoded more strongly and slower when the unchosen value (blue) was decoded more strongly. We built a linear regression for response time with chosen and unchosen decoding strength as predictors. Bold lines indicate significance at P < 0.01 value decoding. e, The posterior probabilities associated with value states were significantly positively correlated between OFC and ACC. We calculated the Pearson correlation coefficient for each session separately, and we then plotted the mean of these correlations. We assessed significance at each time point by performing a one-sample t-test against zero on the Fisher-transformed correlation coefficients. The t-tests were two-sided. To correct for multiple comparisons, we adjusted our significance level so that correlations had to be significantly different from zero (assessed at P < 0.01 and indicated by the thick horizontal lines) for more than 20 ms. This criterion ensured that there were no significant correlations in the 500 ms before picture onset. Inter-regional value correlations were significant and positive in both subjects shortly after the choice options became available. Error bars denote the s.e. of the mean of the correlation coefficients.

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OFC value integration a, Model of how OFC value state integration might affect the ramping ACC choice direction signal (left). We predict that DIRACC will increase faster following chosen (high value) states than unchosen (low value) OFC value states. The null hypothesis is that OFC value and DIRACC are independent signals. b, DIRACC aligned to the onset of chosen or unchosen OFC value states. The thick gray line indicates significant differences between the states (P < 0.01, two-sided two-sample t-tests). Error bars denote the 99% confidence intervals of the bootstrapped distributions. c, Same as b, except aligned to the onset of ACC value states. Error bars denote the 99% confidence intervals of the bootstrapped distributions. d, Difference in the effect of chosen versus unchosen value states on DIRACC aligned to ACC value states as compared to OFC states during 100 ms before the onset of the state. Positive values indicate a bigger difference in ACC, showing that the difference in DIRACC typically occurs before the onset of ACC value states, but not before OFC value states. The vertical lines indicate the first percentile of the bootstrapped distribution.

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Nature Neuroscience | Voume 26 | September 2023 | 1575–1583 1575

nature neuroscience

https://doi.org/10.1038/s41593-023-01407-3

Article

Value dynamics affect choice preparation

during decision-making

Zuzanna Z. Balewski1,3, Thomas W. Elston1,3, Eric B. Knudsen1

& Joni D. Wallis 1,2

During decision-making, neurons in the orbitofrontal cortex (OFC)

sequentially represent the value of each option in turn, but it is unclear how

these dynamics are translated into a choice response. One brain region that

may be implicated in this process is the anterior cingulate cortex (ACC),

which strongly connects with OFC and contains many neurons that encode

the choice response. We investigated how OFC value signals interacted with

ACC neurons encoding the choice response by performing simultaneous

high-channel count recordings from the two areas in nonhuman primates.

ACC neurons encoding the choice response steadily increased their ring

rate throughout the decision-making process, peaking shortly before

the time of the choice response. Furthermore, the value dynamics in OFC

aected ACC ramping—when OFC represented the more valuable option,

ACC ramping accelerated. Because OFC tended to represent the more

valuable option more frequently and for a longer duration, this interaction

could explain how ACC selects the more valuable response.

A wealth of evidence demonstrates the necessity of the orbitofron-

tal cortex (OFC) for value-based decision-making. Patients with OFC

damage show specific deficits in value-based decision-making

, while

electrical microstimulation of OFC in humans

and monkeys

3,4

selec-

tively impairs value-based decision-making. Despite this, there is less

evidence that OFC is involved in selecting the correct response to real-

ize the decision. OFC only weakly connects with motor areas

and its

neurons only weakly encode the choice response

6–8

. At the population

level, although OFC alternately represents the value of each available

option9,10, it does not appear to represent a specific option at the time

that the choice response occurs.

One area that could have an important role in translating

value-based decisions into actions is the anterior cingulate cortex

(ACC). Like OFC, ACC neurons strongly encode the value of antici-

pated outcomes, but unlike OFC, they also often encode the choice

response

7,11–15

. In addition, ACC strongly connects with both OFC

5,16

and motor areas in the medial frontal cortex, such as the cingulate

motor area

17,18

. Stimulation of ACC in humans evokes movements

and an urgency to act

. ACC seems to be particularly important when

the cost of action must be factored into the decision. Lesions of ACC

impair effort-based decisions21,22 and neuronal tuning in ACC reflects

the value of anticipated outcomes, discounted by the effort necessary

to obtain them23.

We, therefore, aimed to determine whether the dynamics of OFC

value signals influenced neurons in ACC that encoded the choice

response. One clue as to how this might occur is that more valuable

options tend to be represented more frequently and for longer dura-

tion

9,10

. Consequently, a downstream area that integrated the OFC

value dynamics would be able to select the more valuable option.

ACC neurons that encoded the choice response tended to increase

their firing rate throughout the decision, peaking shortly before the

choice response. To determine whether this ramping was affected by

the value dynamics in OFC, we carried out high-channel count record-

ings simultaneously from OFC and ACC while monkeys performed a

value-based decision-making task.

Results

We taught two monkeys (subjects C and G) to use a bidirectional

lever to select either one (forced choice trials) or between two (free

choice trials) available pictures for the corresponding juice outcome

Received: 20 June 2022

Accepted: 17 July 2023

Published online: 10 August 2023

Check for updates

1Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, CA, USA. 2Department of Psychology, University of California at Berkeley,

Berkeley, CA, USA. 3These authors contributed equally: Zuzanna Z. Balewski, Thomas W. Elston. e-mail: wallis@berkeley.edu

Content courtesy of Springer Nature, terms of use apply. Rights reserved

Context-dependent decision-making in the primate hippocampal-prefrontal circuit

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What is good in one scenario might be bad in another. Despite the ubiquity of such contextual reasoning in everyday choice, how the brain flexibly utilizes different valuation schemes across contexts remains unknown. We addressed this question by monitoring neural activity from the hippocampus (HPC) and orbitofrontal cortex (OFC) of two monkeys performing a state-dependent choice task. We found that HPC neurons encoded state information as it became available and then, at the time of choice, relayed this information to OFC via theta synchronization. During choice, OFC represented value in a state-dependent manner: many OFC neurons uniquely coded for value in only one state but not the other. This suggests a functional dissociation whereby HPC encodes contextual information that is broadcast to OFC via theta synchronization to select a state-appropriate value subcircuit, thus allowing for contextual reasoning in value-based choice.

Reward Circuit Local Field Potential Modulations Precede Risk Taking

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Risk taking behavior is a symptom of multiple neuropsychiatric disorders and often lacks effective treatments. Reward circuitry regions including the amygdala, orbitofrontal cortex, insula, and anterior cingulate have been implicated in risk-taking by neuroimaging studies. Electrophysiological activity associated with risk taking in these regions is not well understood in humans. Further characterizing the neural signalling that underlies risk-taking may provide therapeutic insight into disorders associated with risk-taking. Eleven patients with pharmacoresistant epilepsy who underwent stereotactic electroencephalography with electrodes in the amygdala, orbitofrontal cortex, insula, and/or anterior cingulate participated. Patients participated in a gambling task where they wagered on a visible playing card being higher than a hidden card, betting $5 or $20 on this outcome, while local field potentials were recorded from implanted electrodes. We used cluster-based permutation testing to identify reward prediction error signals, defined as signals associated with unexpected reward, by comparing oscillatory power following unexpected and expected rewards. We also used cluster-based permutation testing to compare power preceding high and low bets in high-risk (<50% chance of winning) trials and two-way ANOVA with bet and risk level to identify signals associated with risky, risk averse, and optimized decisions. We used linear mixed effects models to evaluate the relationship between reward prediction error and risky decision signals across trials, and a linear regression model for associations between risky decision signal power and Barratt Impulsiveness Scale scores for each patient. Reward prediction error signals were identified in the amygdala (p=0.0066), anterior cingulate (p=0.0092), and orbitofrontal cortex (p=6.0E-4, p=4.0E-4). Risky decisions were predicted by increased oscillatory power in high-gamma frequency range during card presentation in the orbitofrontal cortex (p=0.0022), and by increased power following bet cue presentation across the theta-to-beta range in the orbitofrontal cortex (p=0.0022), high-gamma in the anterior cingulate (p=0.0004), and high-gamma in the insula (p=0.0014). Risk averse decisions were predicted by decreased orbitofrontal cortex gamma power (p=2.0E-4). Optimized decisions that maximized earnings were preceded by decreases across the theta-to-beta range in orbitofrontal cortex (p=2.0E-4), all frequencies in amygdala (p=2.0E-4), and theta-to-beta range, and low-gamma in insula (p=4.0E-4). Insula risky decision power was associated with orbitofrontal cortex high-gamma reward prediction error signal (p=0.0048) and with patient impulsivity (p=0.00478). Our findings identify and help characterize reward circuitry activity predictive of risk-taking in humans. These findings may serve as potential biomarkers to inform the development of novel treatment strategies such as closed loop neuromodulation for disorders of risk taking.

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Adaptive value-guided decision-making requires weighing up the costs and benefits of pursuing an available opportunity. Though neurons across frontal cortical-basal ganglia circuits have been repeatedly shown to represent decision-related parameters, it is unclear whether and how this information is co-ordinated. To address this question, we performed large-scale single unit recordings across medial/orbital frontal cortex and basal ganglia as rats decided whether varying reward payoffs outweighed the associated physical effort costs. We found that single neurons across the circuit could encode different combinations of the canonical decision variables reward, effort and choice. Co-active cell assemblies - ensembles of neurons that repeatedly co-activated within short time windows (<25ms) within and across structures - were able to provide representations of the same decision variables through the synchronisation of individual neurons with different coding properties. Together, these findings demonstrate a hierarchical encoding structure for cost-benefit computations, where individual neurons with diverse encoding properties are coordinated into larger, lower dimensional spaces within and across brain regions to that can encode decision parameters on the millisecond timescale.

Network computations underlying learning from symbolic gains and losses

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Reinforcement learning (RL) engages a network of areas, including the orbitofrontal cortex (OFC), ventral striatum (VS), amygdala (AMY), and mediodorsal thalamus (MDt). This study examined RL mediated by gains and losses of symbolic reinforcers across this network. Monkeys learned to select options that led to gaining tokens and avoid options that led to losing tokens. Tokens were cashed out for juice rewards periodically. We found that task-relevant information was distributed across the network. However, examination of the way in which information was encoded differed, with VS showing increased responses to appetitive outcomes, OFC differentiating primary and symbolic reinforcers, and AMY responding to the salience of outcomes. In addition, analysis of network activity showed that symbolic reinforcement was calculated by temporal differentiation of accumulated tokens. This process was mediated by dynamics within the OFC-MDt-VS circuit. Thus, we provide a neurocomputational account of learning from symbolic gains and losses.

The Representation of Decision Variables in Orbitofrontal Cortex is Longitudinally Stable

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Feb 2024

The computation and comparison of subjective values underlying economic choices rely on the orbitofrontal cortex (OFC). In this area, distinct groups of neurons encode the value of individual options, the binary choice outcome, and the chosen value. These variables capture both the input and the output of the choice process, suggesting that the cell groups found in OFC constitute the building blocks of a decision circuit. Here we show that this neural circuit is longitudinally stable. Using two-photon calcium imaging, we recorded from mice choosing between different juice flavors. Recordings of individual cells continued for up to 20 weeks. For each cell and each pair of sessions, we compared the activity profiles using cosine similarity, and we assessed whether the cell encoded the same variable in both sessions. These analyses revealed a high degree of stability and a modest representational drift. A quantitative estimate indicated this drift would not randomize the circuit within the animal’s lifetime.

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Neuronal activity in dorsal anterior cingulate cortex during economic choices under variable action costs

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eLife

The role of the dorsal anterior cingulate cortex (ACCd) in decision making has often been discussed but remains somewhat unclear. On the one hand, numerous studies implicated this area in decisions driven by effort or action cost. On the other hand, work on economic choices between goods (under fixed action costs) found that neurons in ACCd encoded only post-decision variables. To advance our understanding of the role played by this area in decision making, we trained monkeys to choose between different goods (juice types) offered in variable amounts and with different action costs. Importantly, the task design dissociated computation of the action cost from planning of any particular action. Neurons in ACCd encoded the chosen value and the binary choice outcome in several reference frames (chosen juice, chosen cost, chosen action). Thus, this area provided a rich representation of post-decision variables. In contrast to the OFC, neurons in ACCd did not represent pre-decision variables such as individual offer values in any reference frame. Hence, ongoing decisions are unlikely guided by ACCd. Conversely, neuronal activity in this area might inform subsequent actions.

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Significance Decision making involves the evaluation of the options, and the current popular theory puts the orbitofrontal cortex (OFC) in a central role in this process. However, by using a sophisticated task design in which the decision making requires both evidence accumulation over time and a stimulus-to-action transformation, we found that the OFC neuronal activity reflected neither. Both processes were instead represented in the dorsolateral prefrontal cortex (DLPFC), and the stimulus-to-action transformation occurred before the evidence accumulation. Our study argued for a more limited role of the OFC in value-based decision making than previously proposed and indicated how the OFC and the DLPFC may work together to compute value during decision making.

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In the eighteenth century, Daniel Bernoulli, Adam Smith and Jeremy Bentham proposed that economic choices rely on the computation and comparison of subjective values¹. This hypothesis continues to inform modern economic theory² and research in behavioural economics³, but behavioural measures are ultimately not sufficient to verify the proposal⁴. Consistent with the hypothesis, when agents make choices, neurons in the orbitofrontal cortex (OFC) encode the subjective value of offered and chosen goods⁵. Value-encoding cells integrate multiple dimensions6–9, variability in the activity of each cell group correlates with variability in choices10,11 and the population dynamics suggests the formation of a decision¹². However, it is unclear whether these neural processes are causally related to choices. More generally, the evidence linking economic choices to value signals in the brain13–15 remains correlational¹⁶. Here we show that neuronal activity in the OFC is causal to economic choices. We conducted two experiments using electrical stimulation in rhesus monkeys (Macaca mulatta). Low-current stimulation increased the subjective value of individual offers and thus predictably biased choices. Conversely, high-current stimulation disrupted both the computation and the comparison of subjective values, and thus increased choice variability. These results demonstrate a causal chain linking subjective values encoded in OFC to valuation and choice.

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Outcome-guided behavior requires knowledge about the current value of expected outcomes. Such behavior can be isolated in the reinforcer devaluation task, which assesses the ability to infer the current value of specific rewards after devaluation. Animal lesion studies demonstrate that orbitofrontal cortex (OFC) is necessary for normal behavior in this task, but a causal role for human OFC in outcome-guided behavior has not been established. Here, we used sham-controlled, non-invasive, continuous theta-burst stimulation (cTBS) to temporarily disrupt human OFC network activity by stimulating a site in the lateral prefrontal cortex that is strongly connected to OFC prior to devaluation of food odor rewards. Subjects in the sham group appropriately avoided Pavlovian cues associated with devalued food odors. However, subjects in the stimulation group persistently chose those cues, even though devaluation of food odors themselves was unaffected by cTBS. This behavioral impairment was mirrored in changes in resting-state functional magnetic resonance imaging (rs-fMRI) activity such that subjects in the stimulation group exhibited reduced OFC network connectivity after cTBS, and the magnitude of this reduction was correlated with choices after devaluation. These findings demonstrate the feasibility of indirectly targeting the human OFC with non-invasive cTBS and indicate that OFC is specifically required for inferring the value of expected outcomes.

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Humans and other animals often show a strong desire to know the uncertain rewards their future has in store, even when they cannot use this information to influence the outcome. However, it is unknown how the brain predicts opportunities to gain information and motivates this information-seeking behavior. Here we show that neurons in a network of interconnected subregions of primate anterior cingulate cortex and basal ganglia predict the moment of gaining information about uncertain rewards. Spontaneous increases in their information prediction signals are followed by gaze shifts toward objects associated with resolving uncertainty, and pharmacologically disrupting this network reduces the motivation to seek information. These findings demonstrate a cortico-basal ganglia mechanism responsible for motivating actions to resolve uncertainty by seeking knowledge about the future. Animals resolve uncertainty by seeking knowledge about the future. How the brain controls this is unclear. The authors show that a network including primate anterior cingulate cortex and basal ganglia encodes opportunities to gain information about uncertain rewards and mediates information seeking.

Visual fixation patterns during economic choice reflect covert valuation processes that emerge with learning

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Significance Where we direct our gaze can have a big impact on what we choose. However, where we choose to gaze during the decision process is not well-characterized, despite the important role it plays. In our study, monkeys performed a simple decision-making experiment where they were free to look around a computer screen showing choice options. They then indicated their economic choice with a joystick movement. When choice options appeared, monkeys rapidly gazed toward more valuable and novel stimuli—suggesting there is a system that orients gaze toward important information. However, despite the gaze preference for novel stimuli, subjects did not prefer to choose them. This suggests the mechanisms governing value-guided attentional capture and value-guided choice are dissociable.

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We make complex decisions using both fast judgments and slower, more deliberative reasoning. For example, during value-based decision-making, animals make rapid value-guided orienting eye movements after stimulus presentation that bias the upcoming decision. The neural mechanisms underlying these processes remain unclear. To address this, we recorded from the caudate nucleus and orbitofrontal cortex while animals made value-guided decisions. Using population-level decoding, we found a rapid, phasic signal in caudate that predicted the choice response and closely aligned with animals’ initial orienting eye movements. In contrast, the dynamics in orbitofrontal cortex were more consistent with a deliberative system serially representing the value of each available option. The phasic caudate value signal and the deliberative orbitofrontal value signal were largely independent from each other, consistent with value-guided orienting and value-guided decision-making being independent processes.

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Neurons in primate lateral prefrontal cortex (LPFC) play a critical role in working memory (WM) and cognitive strategies. Consistent with adaptive coding models, responses of these neurons are not fixed but flexibly adjust on the basis of cognitive demands. However, little is known about how these adjustments affect population codes. Here, we investigated ensemble coding in LPFC while monkeys implemented different strategies in a WM task. Although single neurons were less tuned when monkeys used more stereotyped strategies, task information could still be accurately decoded from neural populations. This was due to changes in population codes that distributed information among a greater number of neurons, each contributing less to the overall population. Moreover, this shift occurred for task-relevant, but not irrelevant, information. These results demonstrate that cognitive strategies that impose structure on information held in mind rearrange population codes in LPFC, such that information becomes more distributed among neurons in an ensemble.

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Closed-Loop Theta Stimulation in the Orbitofrontal Cortex Prevents Reward-Based Learning

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Neuronal oscillations in the frontal cortex have been hypothesized to play a role in the organization of high-level cognition. Within the orbitofrontal cortex (OFC), there is a prominent oscillation in the theta frequency (4–8 Hz) during reward-guided behavior, but it is unclear whether this oscillation has causal significance. One methodological challenge is that it is difficult to manipulate theta without affecting other neural signals, such as single-neuron firing rates. A potential solution is to use closed-loop control to record theta in real time and use this signal to control the application of electrical microstimulation to the OFC. Using this method, we show that theta oscillations in the OFC are critically important for reward-guided learning and that they are driven by theta oscillations in the hippocampus (HPC). The ability to disrupt OFC computations via spatially localized and temporally precise stimulation could lead to novel treatment strategies for neuropsychiatric disorders involving OFC dysfunction.

Value dynamics affect choice preparation during decision-making

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