Dopamine regulates attitude toward risk.

Specific brain pathways can lower or raise the willingness of monkeys to take risks.

Quantifying Phase-Amplitude Modulation in Neural Data.

Phase-amplitude modulation (the modulation of the amplitude of higher frequency oscillations by the phase of lower frequency oscillations) is a specific type of cross-frequency coupling that has been observed in neural recordings from multiple species in a range of behavioral contexts. Given its potential importance, care must be taken with how it is measured and quantified. Previous studies have quantified phase-amplitude modulation by measuring the distance of the amplitude distribution from a uniform distribution. While this method is of general applicability, it is not targeted to the specific modulation pattern frequently observed with low-frequency oscillations. Here we develop a new method that has increased specificity to detect modulation in the sinusoidal shape commonly observed in neural data.

Decomposing Executive Function into Distinct Processes Underlying Human Decision Making.

Executive function (EF) consists of higher level cognitive processes including working memory, cognitive flexibility, and inhibition which together enable goal-directed behaviors. Many neurological disorders are associated with EF dysfunctions which can lead to suboptimal behavior. To assess the roles of these processes, we introduce a novel behavioral task and modeling approach. The gamble-like task, with sub-tasks targeting different EF capabilities, allows for quantitative assessment of the main components of EF. We demonstrate that human participants exhibit dissociable variability in the component processes of EF. These results will allow us to map behavioral outcomes to EEG recordings in future work in order to map brain networks associated with EF deficits. Clinical relevance- This work will allow us to quantify EF deficits and corresponding brain activity in patient populations in future work.

Primate anterior insular cortex represents economic decision variables proposed by prospect theory.

In humans, risk attitude is highly context-dependent, varying with wealth levels or for different potential outcomes, such as gains or losses. These behavioral effects have been modelled using prospect theory, with the key assumption that humans represent the value of each available option asymmetrically as a gain or loss relative to a reference point. It remains unknown how these computations are implemented at the neuronal level. Here we show that macaques, like humans, change their risk attitude across wealth levels and gain/loss contexts using a token gambling task. Neurons in the anterior insular cortex (AIC) encode the 'reference point' (i.e., the current wealth level of the monkey) and reflect 'loss aversion' (i.e., option value signals are more sensitive to change in the loss than in the gain context) as postulated by prospect theory. In addition, changes in the activity of a subgroup of AIC neurons correlate with the inter-trial fluctuations in choice and risk attitude. Taken together, we show that the primate AIC in risky decision-making may be involved in monitoring contextual information used to guide the animal's willingness to accept risk.

Multi-View Broad Learning System for Primate Oculomotor Decision Decoding.

Multi-view learning improves the learning performance by utilizing multi-view data: data collected from multiple sources, or feature sets extracted from the same data source. This approach is suitable for primate brain state decoding using cortical neural signals. This is because the complementary components of simultaneously recorded neural signals, local field potentials (LFPs) and action potentials (spikes), can be treated as two views. In this paper, we extended broad learning system (BLS), a recently proposed wide neural network architecture, from single-view learning to multi-view learning, and validated its performance in decoding monkeys' oculomotor decision from medial frontal LFPs and spikes. We demonstrated that medial frontal LFPs and spikes in non-human primate do contain complementary information about the oculomotor decision, and that the proposed multi-view BLS is a more effective approach for decoding the oculomotor decision than several classical and state-of-the-art single-view and multi-view learning approaches.

Decision Making: How Is Information Represented in Orbitofrontal Cortex?

Classically, specific orbitofrontal cortex (OFC) neurons are thought to represent attributes of specific decision options. A new model proposes instead that OFC neurons represent whichever option is currently attended. A recent study, however, tests these two models and rules out the 'current-focus-of-attention' model.

The formation of preference in risky choice.

A key question in decision-making is how people integrate amounts and probabilities to form preferences between risky alternatives. Here we rely on the general principle of integration-to-boundary to develop several biologically plausible process models of risky-choice, which account for both choices and response-times. These models allowed us to contrast two influential competing theories: i) within-alternative evaluations, based on multiplicative interaction between amounts and probabilities, ii) within-attribute comparisons across alternatives. To constrain the preference formation process, we monitored eye-fixations during decisions between pairs of simple lotteries, designed to systematically span the decision-space. The behavioral results indicate that the participants' eye-scanning patterns were associated with risk-preferences and expected-value maximization. Crucially, model comparisons showed that within-alternative process models decisively outperformed within-attribute ones, in accounting for choices and response-times. These findings elucidate the psychological processes underlying preference formation when making risky-choices, and suggest that compensatory, within-alternative integration is an adaptive mechanism employed in human decision-making.

Inactivation of Medial Frontal Cortex Changes Risk Preference.

Humans and other animals need to make decisions under varying degrees of uncertainty. These decisions are strongly influenced by an individual's risk preference; however, the neuronal circuitry by which risk preference shapes choice is still unclear [1]. Supplementary eye field (SEF), an oculomotor area within primate medial frontal cortex, is thought to be an essential part of the neuronal circuit underlying oculomotor decision making, including decisions under risk [2-5]. Consistent with this view, risk-related action value and monitoring signals have been observed in SEF [6-8]. However, such activity has also been observed in other frontal areas, including orbitofrontal [9-11], cingulate [12-14], and dorsal-lateral frontal cortex [15]. It is thus unknown whether the activity in SEF causally contributes to risky decisions, or whether it is merely a reflection of neural processes in other cortical regions. Here, we tested a causal role of SEF in risky oculomotor choices. We found that SEF inactivation strongly reduced the frequency of risky choices. This reduction was largely due to a reduced attraction to reward uncertainty and high reward gain, but not due to changes in the subjective estimation of reward probability or average expected reward. Moreover, SEF inactivation also led to increased sensitivity to differences between expected and actual reward during free choice. Nevertheless, it did not affect adjustments of decisions based on reward history.

First-Pass Processing of Value Cues in the Ventral Visual Pathway.

Real-world value often depends on subtle, continuously variable visual cues specific to particular object categories, like the tailoring of a suit, the condition of an automobile, or the construction of a house. Here, we used microelectrode recording in behaving monkeys to test two possible mechanisms for category-specific value-cue processing: (1) previous findings suggest that prefrontal cortex (PFC) identifies object categories, and based on category identity, PFC could use top-down attentional modulation to enhance visual processing of category-specific value cues, providing signals to PFC for calculating value, and (2) a faster mechanism would be first-pass visual processing of category-specific value cues, immediately providing the necessary visual information to PFC. This, however, would require learned mechanisms for processing the appropriate cues in a given object category. To test these hypotheses, we trained monkeys to discriminate value in four letter-like stimulus categories. Each category had a different, continuously variable shape cue that signified value (liquid reward amount) as well as other cues that were irrelevant. Monkeys chose between stimuli of different reward values. Consistent with the first-pass hypothesis, we found early signals for category-specific value cues in area TE (the final stage in monkey ventral visual pathway) beginning 81 ms after stimulus onset-essentially at the start of TE responses. Task-related activity emerged in lateral PFC approximately 40 ms later and consisted mainly of category-invariant value tuning. Our results show that, for familiar, behaviorally relevant object categories, high-level ventral pathway cortex can implement rapid, first-pass processing of category-specific value cues.

Neural Basis of Cognitive Control over Movement Inhibition: Human fMRI and Primate Electrophysiology Evidence.

Executive control involves the ability to flexibly inhibit or change an action when it is contextually inappropriate. Using the complimentary techniques of human fMRI and monkey electrophysiology in a context-dependent stop signal task, we found a functional double dissociation between the right ventrolateral prefrontal cortex (rVLPFC) and the bi-lateral frontal eye field (FEF). Different regions of rVLPFC were associated with context-based signal meaning versus intention to inhibit a response, while FEF activity corresponded to success or failure of the response inhibition regardless of the stimulus response mapping or the context. These results were validated by electrophysiological recordings in rVLPFC and FEF from one monkey. Inhibition of a planned behavior is therefore likely not governed by a single brain system as had been previously proposed, but instead depends on two distinct neural processes involving different sub-regions of the rVLPFC and their interactions with other motor-related brain regions.

Cortical control and performance monitoring of interrupting and redirecting movements.

Voluntary behaviour requires control mechanisms that ensure our ability to act independently of habitual and innate response tendencies. Electrophysiological experiments, using the stop-signal task in humans, monkeys and rats, have uncovered a core network of brain structures that is essential for response inhibition. This network is shared across mammals and seems to be conserved throughout their evolution. Recently, new research building on these earlier findings has started to investigate the interaction between response inhibition and other control mechanisms in the brain. Here we describe recent progress in three different areas: selectivity of movement inhibition across different motor systems, re-orientation of motor actions and action evaluation.This article is part of the themed issue 'Movement suppression: brain mechanisms for stopping and stillness'.

Hitting an uncertain target.

Experiments in which monkeys have to select or estimate the location of a target are revealing more about the role of the dorsal premotor cortex in decision making.

Supplementary Eye Field Encodes Confidence in Decisions Under Risk.

Choices are made with varying degrees of confidence, a cognitive signal representing the subjective belief in the optimality of the choice. Confidence has been mostly studied in the context of perceptual judgments, in which choice accuracy can be measured using objective criteria. Here, we study confidence in subjective value-based decisions. We recorded in the supplementary eye field (SEF) of monkeys performing a gambling task, where they had to use subjective criteria for placing bets. We found neural signals in the SEF that explicitly represent choice confidence independent from reward expectation. This confidence signal appeared after the choice and diminished before the choice outcome. Most of this neuronal activity was negatively correlated with confidence, and was strongest in trials on which the monkey spontaneously withdrew his choice. Such confidence-related activity indicates that the SEF not only guides saccade selection, but also evaluates the likelihood that the choice was optimal. This internal evaluation influences decisions concerning the willingness to bear later costs that follow from the choice or to avoid them. More generally, our findings indicate that choice confidence is an integral component of all forms of decision-making, whether they are based on perceptual evidence or on value estimations.

Sequential selection of economic good and action in medial frontal cortex of macaques during value-based decisions.

Value-based decisions could rely either on the selection of desired economic goods or on the selection of the actions that will obtain the goods. We investigated this question by recording from the supplementary eye field (SEF) of monkeys during a gambling task that allowed us to distinguish chosen good from chosen action signals. Analysis of the individual neuron activity, as well as of the population state-space dynamic, showed that SEF encodes first the chosen gamble option (the desired economic good) and only ~100 ms later the saccade that will obtain it (the chosen action). The action selection is likely driven by inhibitory interactions between different SEF neurons. Our results suggest that during value-based decisions, the selection of economic goods precedes and guides the selection of actions. The two selection steps serve different functions and can therefore not compensate for each other, even when information guiding both processes is given simultaneously.

The Decision Path Not Taken.

Real-life decisions often involve multiple intermediate choices among competing, interdependent options. Lorteije et al. (2015) introduce a new paradigm for dissecting the neural strategies underlying such decisions.

The role of supplementary eye field in goal-directed behavior.

The medial frontal cortex has been suggested to play a role in the control, monitoring, and selection of behavior. The supplementary eye field (SEF) is a cortical area within medial frontal cortex that is involved in the regulation of eye movements. Neurophysiological studies in the SEF of macaque monkeys have systematically investigated the role of SEF in various behavioral control and monitoring functions. Inhibitory control studies indicate that SEF neurons do not directly participate in the initiation of eye movements. Instead, recent value-based decision making studies suggest that the SEF participates in the control of eye movements by representing the context-dependent action values of all currently possible oculomotor behaviors. These action value signals in SEF would be useful in directing the activity distribution in more primary oculomotor areas, to guide decisions towards behaviorally optimal choices. SEF also does not participate in the fast, inhibitory control of eye movements in response to sudden changes in the task requirements. Instead, it participates in the long-term regulation of oculomotor excitability to adjust the speed-accuracy tradeoff. The context-dependent control signals found in SEF (including the action value signals) have to be learned and continuously adjusted in response to changes in the environment. This is likely the function of the large number of different response monitoring and evaluation signals in SEF. In conclusion, the overall function of SEF in goal-directed behavior seems to be the learning of context-dependent rules that allow predicting the likely consequences of different eye movements. This map of action value signals could be used so that eye movements are selected that best fulfill the current long-term goal of the agent.

Mechanisms underlying the influence of saliency on value-based decisions.

Objects in the environment differ in their low-level perceptual properties (e.g., how easily a fruit can be recognized) as well as in their subjective value (how tasty it is). We studied the influence of visual salience on value-based decisions using a two alternative forced choice task, in which human subjects rapidly chose items from a visual display. All targets were equally easy to detect. Nevertheless, both value and salience strongly affected choices made and reaction times. We analyzed the neuronal mechanisms underlying these behavioral effects using stochastic accumulator models, allowing us to characterize not only the averages of reaction times but their full distributions. Independent models without interaction between the possible choices failed to reproduce the observed choice behavior, while models with mutual inhibition between alternative choices produced much better results. Mutual inhibition thus is an important feature of the decision mechanism. Value influenced the amount of accumulation in all models. In contrast, increased salience could either lead to an earlier start (onset model) or to a higher rate (speed model) of accumulation. Both models explained the data from the choice trials equally well. However, salience also affected reaction times in no-choice trials in which only one item was present, as well as error trials. Only the onset model could explain the observed reaction time distributions of error trials and no-choice trials. In contrast, the speed model could not, irrespective of whether the rate increase resulted from more frequent accumulated quanta or from larger quanta. Visual salience thus likely provides an advantage in the onset, not in the processing speed, of value-based decision making.

Performance monitoring by presupplementary and supplementary motor area during an arm movement countermanding task.

A key component of executive control and decision making is the ability to use the consequences of chosen actions to update and inform the process of future action selection. Evaluative signals, which monitor the outcomes of actions, are critical for this ability. Signals related to the evaluation of actions have been identified in eye movement-related areas of the medial frontal cortex. Here we examined whether such evaluative signals are also present in areas of the medial frontal cortex related to arm movements. To answer this question, we recorded from cells in the supplementary motor area (SMA) and pre-SMA, while monkeys performed an arm movement version of the countermanding paradigm. SMA and pre-SMA have been implicated in the higher-order control of movement selection and execution, although their precise role within the skeletomotor control circuit is unclear. We found evaluative signals that encode information about the expected outcome of the reward, the actual outcome, and the mismatch between actual and intended outcome. These findings suggest that signals that monitor and evaluate movement outcomes are represented throughout the medial frontal cortex, playing a general role across effector systems. These evaluation signals supervise the relationship between intentional motor behavior and reward expectation and could be used to adaptively shape future goal-directed behavior.