Neuronal evidence for good-based economic decisions under variable action costs

Dorsal raphe neurons integrate the values of reward amount, delay, and uncertainty in multi-attribute decision-making

The dorsal raphe nucleus (DRN) is implicated in psychiatric disorders that feature impaired sensitivity to reward amount, impulsivity when facing reward delays, and risk-seeking when confronting reward uncertainty. However, it has been unclear whether and how DRN neurons signal reward amount, reward delay, and reward uncertainty during multi-attribute value-based decision-making, where subjects consider these attributes to make a choice. We recorded DRN neurons as monkeys chose between offers whose attributes, namely expected reward amount, reward delay, and reward uncertainty, varied independently. Many DRN neurons signaled offer attributes, and this population tended to integrate the attributes in a manner that reflected monkeys' preferences for amount, delay, and uncertainty. After decision-making, in response to post-decision feedback, these same neurons signaled signed reward prediction errors, suggesting a broader role in tracking value across task epochs and behavioral contexts. Our data illustrate how the DRN participates in value computations, guiding theories about the role of the DRN in decision-making and psychiatric disease.

Neural mechanisms of information seeking

We ubiquitously seek information to make better decisions. Particularly in the modern age, when more information is available at our fingertips than ever, the information we choose to collect determines the quality of our decisions. Decision neuroscience has long adopted empirical approaches where the information available to decision-makers is fully controlled by the researchers, leaving neural mechanisms of information seeking less understood. Although information seeking has long been studied in the context of the exploration-exploitation trade-off, recent studies have widened the scope to investigate more overt information seeking in a way distinct from other decision processes. Insights gained from these studies, accumulated over the last few years, raise the possibility that information seeking is driven by the reward system signaling the subjective value of information. In this piece, we review findings from the recent studies, highlighting the conceptual and empirical relationships between distinct literatures, and discuss future research directions necessary to establish a more comprehensive understanding of how individuals seek information as a part of value-based decision-making.

Parallel processing of value-related information during multi-attribute decisions

When choosing between options with multiple attributes, do we integrate all attributes into a unified measure for comparison, or does the comparison also occur at the level of each attribute, involving parallel processes that can dynamically influence each other? What happens when independent sensory features all carry information about the same decision factor, such as reward value? To investigate these questions, we asked human participants to perform a two-alternative forced choice reaching task in which the reward value of a target was indicated by two visual attributes-its brightness ("bottom-up," BU feature) and its orientation ("top-down," TD feature). If decisions always occur after the integration of both features, there should be no difference in the reaction time (RT) regardless of the attribute combinations that drove the choice. Counter to that prediction, RT distributions depended on the attribute combinations of given targets and the choices made by participants. RTs were shortest when both attributes were congruent or when the choice was based on the bottom-up feature, and longer when the attributes were in conflict (favoring opposite options). In conflict trials, nearly two-thirds of participants made faster decisions when choosing the option favored by the bottom-up feature than when choosing the top-down-favored option. We also observed mid-reach changes-of-mind in a subset of conflict trials, mostly changing from the bottom-up to the top-down-favored target. These data suggest that multi-attribute value-based decisions are better explained by a distributed process including competition among different features than by a competition based on a single, integrated estimate of value.NEW & NOTEWORTHY We show that during value-based decisions, humans do not always use all reward-related information to make their choice, but instead can "jump the gun" using partial information. In particular, when different sources of information were in conflict, early decisions were mostly based on fast bottom-up information, and sometimes followed by corrective changes-of-mind based on slower top-down information. This supports parallel decision processes among different information sources, as opposed to a single integrated "common currency."

Dorsal raphe neurons signal integrated value during multi-attribute decision-making

The dorsal raphe nucleus (DRN) is implicated in psychiatric disorders that feature impaired sensitivity to reward amount, impulsivity when facing reward delays, and risk-seeking when grappling with reward uncertainty. However, whether and how DRN neurons signal reward amount, reward delay, and reward uncertainty during multi-attribute value-based decision-making, where subjects consider all these attributes to make a choice, is unclear. We recorded DRN neurons as monkeys chose between offers whose attributes, namely expected reward amount, reward delay, and reward uncertainty, varied independently. Many DRN neurons signaled offer attributes. Remarkably, these neurons commonly integrated offer attributes in a manner that reflected monkeys' overall preferences for amount, delay, and uncertainty. After decision-making, in response to post-decision feedback, these same neurons signaled signed reward prediction errors, suggesting a broader role in tracking value across task epochs and behavioral contexts. Our data illustrate how DRN participates in integrated value computations, guiding theories of DRN in decision-making and psychiatric disease.

Emotion, motivation, decision-making, the orbitofrontal cortex, anterior cingulate cortex, and the amygdala

The orbitofrontal cortex and amygdala are involved in emotion and in motivation, but the relationship between these functions performed by these brain structures is not clear. To address this, a unified theory of emotion and motivation is described in which motivational states are states in which instrumental goal-directed actions are performed to obtain rewards or avoid punishers, and emotional states are states that are elicited when the reward or punisher is or is not received. This greatly simplifies our understanding of emotion and motivation, for the same set of genes and associated brain systems can define the primary or unlearned rewards and punishers such as sweet taste or pain. Recent evidence on the connectivity of human brain systems involved in emotion and motivation indicates that the orbitofrontal cortex is involved in reward value and experienced emotion with outputs to cortical regions including those involved in language, and is a key brain region involved in depression and the associated changes in motivation. The amygdala has weak effective connectivity back to the cortex in humans, and is implicated in brainstem-mediated responses to stimuli such as freezing and autonomic activity, rather than in declarative emotion. The anterior cingulate cortex is involved in learning actions to obtain rewards, and with the orbitofrontal cortex and ventromedial prefrontal cortex in providing the goals for navigation and in reward-related effects on memory consolidation mediated partly via the cholinergic system.

Bounded rational decision-making models suggest capacity-limited concurrent motor planning in human posterior parietal and frontal cortex

While traditional theories of sensorimotor processing have often assumed a serial decision-making pipeline, more recent approaches have suggested that multiple actions may be planned concurrently and vie for execution. Evidence for the latter almost exclusively stems from electrophysiological studies in posterior parietal and premotor cortex of monkeys. Here we study concurrent prospective motor planning in humans by recording functional magnetic resonance imaging (fMRI) during a delayed response task engaging movement sequences towards multiple potential targets. We find that also in human posterior parietal and premotor cortex delay activity modulates both with sequence complexity and the number of potential targets. We tested the hypothesis that this modulation is best explained by concurrent prospective planning as opposed to the mere maintenance of potential targets in memory. We devise a bounded rationality model with information constraints that optimally assigns information resources for planning and memory for this task and determine predicted information profiles according to the two hypotheses. When regressing delay activity on these model predictions, we find that the concurrent prospective planning strategy provides a significantly better explanation of the fMRI-signal modulations. Moreover, we find that concurrent prospective planning is more costly and thus limited for most subjects, as expressed by the best fitting information capacities. We conclude that bounded rational decision-making models allow relating both behavior and neural representations to utilitarian task descriptions based on bounded optimal information-processing assumptions.

When the future is uncertain, it can be beneficial to concurrently plan several action possibilities in advance. Electrophysiological research found evidence in monkeys that brain regions in posterior parietal and promotor cortex are indeed capable of planning several actions in parallel. We now used fMRI to study brain activity in these brain regions in humans. For our analyses we applied bounded rationality models that optimally assign information resources to fMRI activity in a complex motor planning task. We find that theoretical information costs of concurrent prospective planning explained fMRI activity profiles significantly better than assuming alternative memory-based strategies. Moreover, exploiting the model allowed us to quantify the individual capacity limit for concurrent planning and to relate these individual limits to both subjects’ behavior and to their neural representations of planning.

Laser stimulation of the skin for quantitative study of decision-making and motivation

Neuroeconomics studies how decision-making is guided by the value of rewards and punishments. But to date, little is known about how noxious experiences impact decisions. A challenge is the lack of an aversive stimulus that is dynamically adjustable in intensity and location, readily usable over many trials in a single experimental session, and compatible with multiple ways to measure neuronal activity. We show that skin laser stimulation used in human studies of aversion can be used for this purpose in several key animal models. We then use laser stimulation to study how neurons in the orbitofrontal cortex (OFC), an area whose many roles include guiding decisions among different rewards, encode the value of rewards and punishments. We show that some OFC neurons integrated the positive value of rewards with the negative value of aversive laser stimulation, suggesting that the OFC can play a role in more complex choices than previously appreciated.

Theta but not beta activity is modulated by freedom of choice during action selection

Large-scale neurophysiological markers of action competition have been almost exclusively investigated in the context of instructed choices, hence it remains unclear whether these markers also apply to free choices. This study aimed to compare the specific brain dynamics underlying instructed and free decisions. Electroencephalography (EEG) was recorded while 31 participants performed a target selection task; the choice relied either on stimulus-response mappings (instructed) or on participants' preferences (free). Choice difficulty was increased by introducing distractors in the informative stimulus in instructed choices, and by presenting targets with similar motor costs in free choices. Results revealed that increased decision difficulty was associated with higher reaction times (RTs) in instructed choices and greater choice uncertainty in free choices. Midfrontal EEG theta (4-8 Hz) power increased with difficulty in instructed choices, but not in free choices. Although sensorimotor beta (15-30 Hz) power was correlated with RTs, it was not significantly influenced by choice context or difficulty. These results suggest that midfrontal theta power may specifically increase with difficulty in externally-driven choices, whereas sensorimotor beta power may be predictive of RTs in both externally- and internally-driven choices.

The orbitofrontal cortex represents advantageous choice in the Iowa gambling task

A good‐based model, the central neurobiological model of economic decision‐making, proposes that the orbitofrontal cortex (OFC) represents binary choice outcome, that is, the chosen good. A good is defined by a group of determinants characterizing the conditions in which the commodity is offered, including commodity type, cost, risk, time delay, and ambiguity. Previous studies have found that the OFC represents the binary choice outcome in decision‐making tasks involving commodity type, cost, risk, and delay. Real‐life decisions are often complex and involve uncertainty, rewards, and penalties; however, whether the OFC represents binary choice outcomes in a complex decision‐making situation, for example, Iowa gambling task (IGT), remains unclear. Here, we propose that the OFC represents binary choice outcome, that is, advantageous choice versus disadvantageous choice, in the IGT. We propose two hypotheses: first, the activity pattern in the human OFC represents an advantageous choice; and second, choice induces an OFC‐related functional network. Using functional magnetic resonance imaging and advanced machine‐learning tools, we found that the OFC represented an advantageous choice in the IGT. The OFC representation of advantageous choice was related to decision‐making performance. Choice modulated the functional connectivity between the OFC and the superior medial gyrus. In conclusion, the OFC represents an advantageous choice during the IGT. In the framework of a good‐based model, the results extend the role of the OFC to complex decision‐making situation when making a binary choice.

Logistic analysis of choice data: A primer

Logistic regressions were developed in economics to model individual choice behavior. In recent years, they have become an important tool in decision neuroscience. Here, I describe and discuss different logistic models, emphasizing the underlying assumptions and possible interpretations. Logistic models may be used to quantify a variety of behavioral traits, including the relative subjective value of different goods, the choice accuracy, risk attitudes, and choice biases. More complex logistic models can be used for choices between good bundles, in cases of nonlinear value functions, and for choices between multiple options. Finally, logistic models can quantify the explanatory power of neuronal activity on choices, thus providing a valid alternative to receiver operating characteristic (ROC) analyses.

The Neural Instantiation of an Abstract Cognitive Map for Economic Choice

Since the discovery of cognitive maps in rodent hippocampus (HC), the cognitive map has evolved from originally referring to spatial representations encoding locations and objects in Euclidean spaces to a general low-dimensional organization of information along selected feature dimensions. A cognitive map includes hypothetical constructs that bridge between environmental stimuli and the final overt behavior. To neuroeconomists, utility and utility functions are such constructs with neurobiological basis that drive choice behavior. Emergence of distinct functional neuron groups in the primate orbitofrontal cortex (OFC) during simple economic choice indicates the formation of an abstract cognitive map for organizing information of goods for value computation. Experimental evidence suggests that organization of neuronal activity in such cognitive map reflects the abstraction of core task features. Thus, such map can be adapted to accommodate economic choices under various task contexts.

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

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.

A Reinforcement Learning Approach to Understanding Procrastination: Does Inaccurate Value Approximation Cause Irrational Postponing of a Task?

Procrastination is the voluntary but irrational postponing of a task despite being aware that the delay can lead to worse consequences. It has been extensively studied in psychological field, from contributing factors, to theoretical models. From value-based decision making and reinforcement learning (RL) perspective, procrastination has been suggested to be caused by non-optimal choice resulting from cognitive limitations. Exactly what sort of cognitive limitations are involved, however, remains elusive. In the current study, we examined if a particular type of cognitive limitation, namely, inaccurate valuation resulting from inadequate state representation, would cause procrastination. Recent work has suggested that humans may adopt a particular type of state representation called the successor representation (SR) and that humans can learn to represent states by relatively low-dimensional features. Combining these suggestions, we assumed a dimension-reduced version of SR. We modeled a series of behaviors of a "student" doing assignments during the school term, when putting off doing the assignments (i.e., procrastination) is not allowed, and during the vacation, when whether to procrastinate or not can be freely chosen. We assumed that the "student" had acquired a rigid reduced SR of each state, corresponding to each step in completing an assignment, under the policy without procrastination. The "student" learned the approximated value of each state which was computed as a linear function of features of the states in the rigid reduced SR, through temporal-difference (TD) learning. During the vacation, the "student" made decisions at each time-step whether to procrastinate based on these approximated values. Simulation results showed that the reduced SR-based RL model generated procrastination behavior, which worsened across episodes. According to the values approximated by the "student," to procrastinate was the better choice, whereas not to procrastinate was mostly better according to the true values. Thus, the current model generated procrastination behavior caused by inaccurate value approximation, which resulted from the adoption of the reduced SR as state representation. These findings indicate that the reduced SR, or more generally, the dimension reduction in state representation, can be a potential form of cognitive limitation that leads to procrastination.

Action Costs Rapidly and Automatically Interfere with Reward-Based Decision-Making in a Reaching Task

It is widely assumed that we select actions we value the most. While the influence of rewards on decision-making has been extensively studied, evidence regarding the influence of motor costs is scarce. Specifically, how and when motor costs are integrated in the decision process is unclear. Twenty-two right-handed human participants performed a reward-based target selection task by reaching with their right arm toward one of two visual targets. Targets were positioned in different directions according to biomechanical preference, such that one target was systematically associated with a lower motor cost than the other. Only one of the two targets was rewarded, either in a congruent or incongruent manner with respect to the associated motor cost. A timed-response paradigm was used to manipulate participants' reaction times (RT). Results showed that when the rewarded target carried the highest motor cost, movements produced at short RT (<350 ms) were deviated toward the other (i.e., non-rewarded, low-cost (LC) target). In this context participants needed an additional 150-ms delay to reach the same percentage of rewarded trials as when the LC target was rewarded. Crucially, motor costs affected the total earnings of participants. These results demonstrate a robust interference of motor costs in a simple reward-based decision-making task. They point to the rapid and automatic integration of motor costs at an early stage of processing, potentially through the direct modulation of competing action representations in parieto-frontal regions. The progressive overcoming of this bias with increasing RT is likely achieved through top-down signaling pertaining to expected rewards.

Call for a more balanced approach to understanding orbital frontal cortex function

Orbital frontal cortex (OFC) research has historically emphasized the function of this associative cortical area within top-down theoretical frameworks. This approach has largely focused on mapping OFC activity onto human-defined psychological or cognitive constructs and has often led to OFC circuitry bearing the weight of entire theoretical frameworks. New techniques and tools developed in the last decade have made it possible to revisit long-standing basic science questions in neuroscience and answer them with increasing sophistication. We can now study and specify the genetic, molecular, cellular, and circuit architecture of a brain region in much greater detail, which allows us to piece together how they contribute to emergent circuit functions. For instance, adopting such systematic and unbiased bottom-up approaches to elucidating the function of the visual system has paved the way to building a greater understanding of the spectrum of its computational capabilities. In the same vein, we argue that OFC research would benefit from a more balanced approach that also places focus on novel bottom-up investigations into OFC's computational capabilities. Furthermore, we believe that the knowledge gained by employing a more bottom-up approach to investigating OFC function will ultimately allow us to look at OFC's dysfunction in disease through a more nuanced biological lens. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The cingulate cortex and limbic systems for emotion, action, and memory

Evidence is provided for a new conceptualization of the connectivity and functions of the cingulate cortex in emotion, action, and memory. The anterior cingulate cortex receives information from the orbitofrontal cortex about reward and non-reward outcomes. The posterior cingulate cortex receives spatial and action-related information from parietal cortical areas. It is argued that these inputs allow the cingulate cortex to perform action-outcome learning, with outputs from the midcingulate motor area to premotor areas. In addition, because the anterior cingulate cortex connects rewards to actions, it is involved in emotion; and because the posterior cingulate cortex has outputs to the hippocampal system, it is involved in memory. These apparently multiple different functions of the cingulate cortex are related to the place of this proisocortical limbic region in brain connectivity.

Transforming the Choice Outcome to an Action Plan in Monkey Lateral Prefrontal Cortex: A Neural Circuit Model

In economic decisions, we make a good-based choice first, then we transform the outcome into an action to obtain the good. To elucidate the network mechanisms for such transformation, we constructed a neural circuit model consisting of modules representing choice, integration of choice with target locations, and the final action plan. We examined three scenarios regarding how the final action plan could emerge in the neural circuit and compared their implications with experimental data. Our model with heterogeneous connectivity predicts the coexistence of three types of neurons with distinct functions, confirmed by analyzing the neural activity in the lateral prefrontal cortex (LPFC) of behaving monkeys. We obtained a much more distinct classification of functional neuron types in the ventral than the dorsal region of LPFC, suggesting that the action plan is initially generated in ventral LPFC. Our model offers a biologically plausible neural circuit architecture that implements good-to-action transformation during economic choice.