General mechanisms of task engagement in the primate frontal cortex.

Staying engaged is necessary to maintain goal-directed behaviors. Despite this, engagement exhibits continuous, intrinsic fluctuations. Even in experimental settings, animals, unlike most humans, repeatedly and spontaneously move between periods of complete task engagement and disengagement. We, therefore, looked at behavior in male macaques (macaca mulatta) in four tasks while recording fMRI signals. We identified consistent autocorrelation in task disengagement. This made it possible to build models capturing task-independent engagement. We identified task general patterns of neural activity linked to impending sudden task disengagement in mid-cingulate gyrus. By contrast, activity centered in perigenual anterior cingulate cortex (pgACC) was associated with maintenance of performance across tasks. Importantly, we carefully controlled for task-specific factors such as the reward history and other motivational effects, such as response vigor, in our analyses. Moreover, we showed pgACC activity had a causal link to task engagement: transcranial ultrasound stimulation of pgACC changed task engagement patterns.

Goal commitment is supported by vmPFC through selective attention.

When striking a balance between commitment to a goal and flexibility in the face of better options, people often demonstrate strong goal perseveration. Here, using functional MRI (n = 30) and lesion patient (n = 26) studies, we argue that the ventromedial prefrontal cortex (vmPFC) drives goal commitment linked to changes in goal-directed selective attention. Participants performed an incremental goal pursuit task involving sequential decisions between persisting with a goal versus abandoning progress for better alternative options. Individuals with stronger goal perseveration showed higher goal-directed attention in an interleaved attention task. Increasing goal-directed attention also affected abandonment decisions: while pursuing a goal, people lost their sensitivity to valuable alternative goals while remaining more sensitive to changes in the current goal. In a healthy population, individual differences in both commitment biases and goal-oriented attention were predicted by baseline goal-related activity in the vmPFC. Among lesion patients, vmPFC damage reduced goal commitment, leading to a performance benefit.

Frontopolar cortex represents complex features and decision value during choice between environments.

Important decisions often involve choosing between complex environments that define future item encounters. Despite its importance for adaptive behavior and distinct computational challenges, decision-making research primarily focuses on item choice, ignoring environment choice altogether. Here we contrast previously studied item choice in ventromedial prefrontal cortex with lateral frontopolar cortex (FPl) linked to environment choice. Furthermore, we propose a mechanism for how FPl decomposes and represents complex environments during decision making. Specifically, we trained a choice-optimized, brain-naive convolutional neural network (CNN) and compared predicted CNN activation with actual FPl activity. We showed that the high-dimensional FPl activity decomposes environment features to represent the complexity of an environment to make such choice possible. Moreover, FPl functionally connects with posterior cingulate cortex for guiding environment choice. Further probing FPl's computation revealed a parallel processing mechanism in extracting multiple environment features.

Neural responses in macaque prefrontal cortex are linked to strategic exploration.

Humans have been shown to strategically explore. They can identify situations in which gathering information about distant and uncertain options is beneficial for the future. Because primates rely on scarce resources when they forage, they are also thought to strategically explore, but whether they use the same strategies as humans and the neural bases of strategic exploration in monkeys are largely unknown. We designed a sequential choice task to investigate whether monkeys mobilize strategic exploration based on whether information can improve subsequent choice, but also to ask the novel question about whether monkeys adjust their exploratory choices based on the contingency between choice and information, by sometimes providing the counterfactual feedback about the unchosen option. We show that monkeys decreased their reliance on expected value when exploration could be beneficial, but this was not mediated by changes in the effect of uncertainty on choices. We found strategic exploratory signals in anterior and mid-cingulate cortex (ACC/MCC) and dorsolateral prefrontal cortex (dlPFC). This network was most active when a low value option was chosen, which suggests a role in counteracting expected value signals, when exploration away from value should to be considered. Such strategic exploration was abolished when the counterfactual feedback was available. Learning from counterfactual outcome was associated with the recruitment of a different circuit centered on the medial orbitofrontal cortex (OFC), where we showed that monkeys represent chosen and unchosen reward prediction errors. Overall, our study shows how ACC/MCC-dlPFC and OFC circuits together could support exploitation of available information to the fullest and drive behavior towards finding more information through exploration when it is beneficial.

Temporal scaling of human scalp-recorded potentials.

Much of human behavior is governed by common processes that unfold over varying timescales. Standard event-related potential analysis assumes fixed-duration responses relative to experimental events. However, recent single-unit recordings in animals have revealed neural activity scales to span different durations during behaviors demanding flexible timing. Here, we employed a general linear modeling approach using a combination of fixed-duration and variable-duration regressors to unmix fixed-time and scaled-time components in human magneto-/electroencephalography (M/EEG) data. We use this to reveal consistent temporal scaling of human scalp-recorded potentials across four independent electroencephalogram (EEG) datasets, including interval perception, production, prediction, and value-based decision making. Between-trial variation in the temporally scaled response predicts between-trial variation in subject reaction times, demonstrating the relevance of this temporally scaled signal for temporal variation in behavior. Our results provide a general approach for studying flexibly timed behavior in the human brain.

The effect of apathy and compulsivity on planning and stopping in sequential decision-making.

Real-life decision-making often comprises sequences of successive decisions about whether to take opportunities as they are encountered or keep searching for better ones instead. We investigated individual differences related to such sequential decision-making and link them especially to apathy and compulsivity in a large online sample (discovery sample: n = 449 and confirmation sample: n = 756). Our cognitive model revealed distinct changes in the way participants evaluated their environments and planned their own future behaviour. Apathy was linked to decision inertia, i.e., automatically persisting with a sequence of searches for longer than appropriate given the value of searching. Thus, despite being less motivated, they did not avoid the effort associated with longer searches. In contrast, compulsivity was linked to self-reported insensitivity to the cost of continuing with a sequence of searches. The objective measures of behavioural cost insensitivity were clearly linked to compulsivity only in the discovery sample. While the confirmation sample showed a similar effect, it did not reach significance. Nevertheless, in both samples, participants reported awareness of such bias (experienced as "overchasing"). In addition, this awareness made them report preemptively avoiding situations related to the bias. However, we found no evidence of them actually preempting more in the task, which might mean a misalignment of their metacognitive beliefs or that our behavioural measures were incomplete. In summary, individual variation in distinct, fundamental aspects of sequential decision-making can be linked to variation in 2 measures of behavioural traits associated with psychological illness in the normal population.

Spatiotemporally resolved multivariate pattern analysis for M/EEG.

An emerging goal in neuroscience is tracking what information is represented in brain activity over time as a participant completes some task. While electroencephalography (EEG) and magnetoencephalography (MEG) offer millisecond temporal resolution of how activity patterns emerge and evolve, standard decoding methods present significant barriers to interpretability as they obscure the underlying spatial and temporal activity patterns. We instead propose the use of a generative encoding model framework that simultaneously infers the multivariate spatial patterns of activity and the variable timing at which these patterns emerge on individual trials. An encoding model inversion maps from these parameters to the equivalent decoding model, allowing predictions to be made about unseen test data in the same way as in standard decoding methodology. These SpatioTemporally Resolved MVPA (STRM) models can be flexibly applied to a wide variety of experimental paradigms, including classification and regression tasks. We show that these models provide insightful maps of the activity driving predictive accuracy metrics; demonstrate behaviourally meaningful variation in the timing of pattern emergence on individual trials; and achieve predictive accuracies that are either equivalent or surpass those achieved by more widely used methods. This provides a new avenue for investigating the brain's representational dynamics and could ultimately support more flexible experimental designs in the future.

Distinct Causal Influences of Dorsolateral Prefrontal Cortex and Posterior Parietal Cortex in Multiple-Option Decision Making.

Our knowledge about neural mechanisms underlying decision making is largely based on experiments that involved few options. However, it is more common in daily life to choose between many options, in which processing choice information selectively is particularly important. The current study examined whether the dorsolateral prefrontal cortex (dlPFC) and posterior parietal cortex (PPC) are of particular importance to multiple-option decision making. Sixty-eight participants received anodal high definition-transcranial direct current stimulation (HD-tDCS) to focally enhance dlPFC or PPC in a double-blind sham-controlled design. Participants then performed a multiple-option decision making task. We found longer fixations on poorer options were related to less optimal decisions. Interestingly, this negative impact was attenuated after applying anodal HD-tDCS over dlPFC, especially in choices with many options. This suggests that dlPFC has a causal role in filtering choice-irrelevant information. In contrast, these effects were absent after participants received anodal HD-tDCS over PPC. Instead, the choices made by these participants were more biased towards the best options presented on the side contralateral to the stimulation. This suggests PPC has a causal role in value-based spatial selection. To conclude, the dlPFC has a role in filtering undesirable options, whereas the PPC emphasizes the desirable contralateral options.

Constructing Others' Beliefs from One's Own Using Medial Frontal Cortex.

Many daily choices are based on one's own knowledge. However, when predicting other people's behavior, we need to consider the differences between our knowledge and other people's presumed knowledge. Social agents need a mechanism to use privileged information for their own behavior but exclude it from predictions of others. Using fMRI, we investigated the neural implementation of such social and personal predictions in healthy human volunteers of both sexes by manipulating privileged and shared information. The medial frontal cortex appeared to have an important role in flexibly making decisions using privileged information for oneself or predicting others' behavior. Specifically, we show that ventromedial PFC tracked the state of the world independent of the type of decision (personal, social), whereas dorsomedial regions adjusted their frame of reference to the use of privileged or shared information. Sampling privileged evidence not available to another person also relied on specific interactions between temporoparietal junction area and frontal pole.SIGNIFICANCE STATEMENT What we know about the minds of others and how we use that information is crucial to understanding social interaction. Mentalizing, or reading the minds of others, is argued to be particularly well developed in the human and crucially affected in some disorders. However, the intractable nature of human interactions makes it very difficult to study these processes. Here, we present a way to objectively quantify the information people have about others and to investigate how their brain deals with this information. This shows that people use similar areas in the brain related to nonsocial decision-making when making decisions in social situations and modify this information processing by the knowledge about others use these to modify their information processing according to the knowledge of others.

Multiple systems in macaques for tracking prediction errors and other types of surprise.

Animals learn from the past to make predictions. These predictions are adjusted after prediction errors, i.e., after surprising events. Generally, most reward prediction errors models learn the average expected amount of reward. However, here we demonstrate the existence of distinct mechanisms for detecting other types of surprising events. Six macaques learned to respond to visual stimuli to receive varying amounts of juice rewards. Most trials ended with the delivery of either 1 or 3 juice drops so that animals learned to expect 2 juice drops on average even though instances of precisely 2 drops were rare. To encourage learning, we also included sessions during which the ratio between 1 and 3 drops changed. Additionally, in all sessions, the stimulus sometimes appeared in an unexpected location. Thus, 3 types of surprising events could occur: reward amount surprise (i.e., a scalar reward prediction error), rare reward surprise, and visuospatial surprise. Importantly, we can dissociate scalar reward prediction errors-rewards that deviated from the average reward amount expected-and rare reward events-rewards that accorded with the average reward expectation but that rarely occurred. We linked each type of surprise to a distinct pattern of neural activity using functional magnetic resonance imaging. Activity in the vicinity of the dopaminergic midbrain only reflected surprise about the amount of reward. Lateral prefrontal cortex had a more general role in detecting surprising events. Posterior lateral orbitofrontal cortex specifically detected rare reward events regardless of whether they followed average reward amount expectations, but only in learnable reward environments.

Dopamine Modulates Dynamic Decision-Making during Foraging.

The mesolimbic dopaminergic system exerts a crucial influence on incentive processing. However, the contribution of dopamine in dynamic, ecological situations where reward rates vary, and decisions evolve over time, remains unclear. In such circumstances, current (foreground) reward accrual needs to be compared continuously with potential rewards that could be obtained by traveling elsewhere (background reward rate), to determine the opportunity cost of staying versus leaving. We hypothesized that dopamine specifically modulates the influence of background, but not foreground, reward information when making a dynamic comparison of these variables for optimal behavior. On a novel foraging task based on an ecological account of animal behavior (marginal value theorem), human participants of either sex decided when to leave locations in situations where foreground rewards depleted at different rates, either in rich or poor environments with high or low background reward rates. In line with theoretical accounts, people's decisions to move from current locations were independently modulated by changes in both foreground and background reward rates. Pharmacological manipulation of dopamine D2 receptor activity using the agonist cabergoline significantly affected decisions to move on, specifically modulating the effect of background reward rates. In particular, when on cabergoline, people left patches in poor environments much earlier. These results demonstrate a role of dopamine in signaling the opportunity cost of rewards, not value per se. Using this ecologically derived framework, we uncover a specific mechanism by which D2 dopamine receptor activity modulates decision-making when foreground and background reward rates are dynamically compared.SIGNIFICANCE STATEMENT Many decisions, across economic, political, and social spheres, involve choices to "leave". Such decisions depend on a continuous comparison of a current location's value, with that of other locations you could move on to. However, how the brain makes such decisions is poorly understood. Here, we developed a computerized task, based around theories of how animals make decisions to move on when foraging for food. Healthy human participants had to decide when to leave collecting financial rewards in a location, and travel to collect rewards elsewhere. Using a pharmacological manipulation, we show that the activity of dopamine in the brain modulates decisions to move on, with people valuing other locations differently depending on their dopaminergic state.

The macaque anterior cingulate cortex translates counterfactual choice value into actual behavioral change.

The neural mechanisms mediating sensory-guided decision-making have received considerable attention, but animals often pursue behaviors for which there is currently no sensory evidence. Such behaviors are guided by internal representations of choice values that have to be maintained even when these choices are unavailable. We investigated how four macaque monkeys maintained representations of the value of counterfactual choices-choices that could not be taken at the current moment but which could be taken in the future. Using functional magnetic resonance imaging, we found two different patterns of activity co-varying with values of counterfactual choices in a circuit spanning the hippocampus, the anterior lateral prefrontal cortex and the anterior cingulate cortex. Anterior cingulate cortex activity also reflected whether the internal value representations would be translated into actual behavioral change. To establish the causal importance of the anterior cingulate cortex for this translation process, we used a novel technique, transcranial focused ultrasound stimulation, to reversibly disrupt anterior cingulate cortex activity.

Prospection, Perseverance, and Insight in Sequential Behavior.

Real-world decisions have benefits occurring only later and dependent on additional decisions taken in the interim. We investigated this in a novel decision-making task in humans (n = 76) while measuring brain activity with fMRI (n = 24). Modeling revealed that participants computed the prospective value of decisions: they planned their future behavior taking into account how their decisions might affect which states they would encounter and how they themselves might respond in these states. They considered their own likely future behavioral biases (e.g., failure to adapt to changes in prospective value) and avoided situations in which they might be prone to such biases. Three neural networks in adjacent medial frontal regions were linked to distinct components of prospective decision making: activity in dorsal anterior cingulate cortex, area 8 m/9, and perigenual anterior cingulate cortex reflected prospective value, anticipated changes in prospective value, and the degree to which prospective value influenced decisions.

State-change decisions and dorsomedial prefrontal cortex: the importance of time.

Different kinds of decision making can be categorized by their differential effect on the agent's current and future states as well as the computational challenges they pose. Here, we draw a distinction between within-state and state-change decision-making, and propose that a dedicated decision mechanism exists in dorsomedial prefrontal cortex (dmPFC) that is specialized for state-change decisions. We set out a formal framework in which state change decisions may be made on the basis of the integrated momentary reward rate, over the intended time to be spent in a state. A key feature of this framework is that reward rate is expressed as a function of continuous time. We argue that dmPFC is suited for this type of decision making partly due to its ability to track the passage of time. This proposed function of dmPFC is placed in contrast to other evaluative systems such as the orbitofrontal cortex, which is important for careful deliberation within a specific model-space or option-space and within a decision strategy.

Simultaneous representation of a spectrum of dynamically changing value estimates during decision making.

Decisions are based on value expectations derived from experience. We show that dorsal anterior cingulate cortex and three other brain regions hold multiple representations of choice value based on different timescales of experience organized in terms of systematic gradients across the cortex. Some parts of each area represent value estimates based on recent reward experience while others represent value estimates based on experience over the longer term. The value estimates within these areas interact with one another according to their temporal scaling. Some aspects of the representations change dynamically as the environment changes. The spectrum of value estimates may act as a flexible selection mechanism for combining experience-derived value information with other aspects of value to allow flexible and adaptive decisions in changing environments.

(Reinforcement?) Learning to forage optimally.

Foraging effectively is critical to the survival of all animals and this imperative is thought to have profoundly shaped brain evolution. Decisions made by foraging animals often approximate optimal strategies, but the learning and decision mechanisms generating these choices remain poorly understood. Recent work with laboratory foraging tasks in humans suggest their behaviour is poorly explained by model-free reinforcement learning, with simple heuristic strategies better describing behaviour in some tasks, and in others evidence of prospective prediction of the future state of the environment. We suggest that model-based average reward reinforcement learning may provide a common framework for understanding these apparently divergent foraging strategies.

Beyond negative valence: 2-week administration of a serotonergic antidepressant enhances both reward and effort learning signals.

To make good decisions, humans need to learn about and integrate different sources of appetitive and aversive information. While serotonin has been linked to value-based decision-making, its role in learning is less clear, with acute manipulations often producing inconsistent results. Here, we show that when the effects of a selective serotonin reuptake inhibitor (SSRI, citalopram) are studied over longer timescales, learning is robustly improved. We measured brain activity with functional magnetic resonance imaging (fMRI) in volunteers as they performed a concurrent appetitive (money) and aversive (effort) learning task. We found that 2 weeks of citalopram enhanced reward and effort learning signals in a widespread network of brain regions, including ventromedial prefrontal and anterior cingulate cortex. At a behavioral level, this was accompanied by more robust reward learning. This suggests that serotonin can modulate the ability to learn via a mechanism that is independent of stimulus valence. Such effects may partly underlie SSRIs' impact in treating psychological illnesses. Our results highlight both a specific function in learning for serotonin and the importance of studying its role across longer timescales.

Excitation and inhibition in anterior cingulate predict use of past experiences.

Dorsal anterior cingulate cortex (dACC) mediates updating and maintenance of cognitive models of the world used to drive adaptive reward-guided behavior. We investigated the neurochemical underpinnings of this process. We used magnetic resonance spectroscopy in humans, to measure levels of glutamate and GABA in dACC. We examined their relationship to neural signals in dACC, measured with fMRI, and cognitive task performance. Both inhibitory and excitatory neurotransmitters in dACC were predictive of the strength of neural signals in dACC and behavioral adaptation. Glutamate levels were correlated, first, with stronger neural activity representing information to be learnt about the tasks' costs and benefits and, second, greater use of this information in the guidance of behavior. GABA levels were negatively correlated with the same neural signals and the same indices of behavioral influence. Our results suggest that glutamate and GABA in dACC affect the encoding and use of past experiences to guide behavior.

Value, search, persistence and model updating in anterior cingulate cortex.

Dorsal anterior cingulate cortex (dACC) carries a wealth of value-related information necessary for regulating behavioral flexibility and persistence. It signals error and reward events informing decisions about switching or staying with current behavior. During decision-making, it encodes the average value of exploring alternative choices (search value), even after controlling for response selection difficulty, and during learning, it encodes the degree to which internal models of the environment and current task must be updated. dACC value signals are derived in part from the history of recent reward integrated simultaneously over multiple time scales, thereby enabling comparison of experience over the recent and extended past. Such ACC signals may instigate attentionally demanding and difficult processes such as behavioral change via interactions with prefrontal cortex. However, the signal in dACC that instigates behavioral change need not itself be a conflict or difficulty signal.

Predictive decision making driven by multiple time-linked reward representations in the anterior cingulate cortex.

In many natural environments the value of a choice gradually gets better or worse as circumstances change. Discerning such trends makes predicting future choice values possible. We show that humans track such trends by comparing estimates of recent and past reward rates, which they are able to hold simultaneously in the dorsal anterior cingulate cortex (dACC). Comparison of recent and past reward rates with positive and negative decision weights is reflected by opposing dACC signals indexing these quantities. The relative strengths of time-linked reward representations in dACC predict whether subjects persist in their current behaviour or switch to an alternative. Computationally, trend-guided choice can be modelled by using a reinforcement-learning mechanism that computes a longer-term estimate (or expectation) of prediction errors. Using such a model, we find a relative predominance of expected prediction errors in dACC, instantaneous prediction errors in the ventral striatum and choice signals in the ventromedial prefrontal cortex.