Behavior- and Modality-General Representation of Confidence in Orbitofrontal Cortex.

Every decision we make is accompanied by a sense of confidence about its likely outcome. This sense informs subsequent behavior, such as investing more-whether time, effort, or money-when reward is more certain. A neural representation of confidence should originate from a statistical computation and predict confidence-guided behavior. An additional requirement for confidence representations to support metacognition is abstraction: they should emerge irrespective of the source of information and inform multiple confidence-guided behaviors. It is unknown whether neural confidence signals meet these criteria. Here, we show that single orbitofrontal cortex neurons in rats encode statistical decision confidence irrespective of the sensory modality, olfactory or auditory, used to make a choice. The activity of these neurons also predicts two confidence-guided behaviors: trial-by-trial time investment and cross-trial choice strategy updating. Orbitofrontal cortex thus represents decision confidence consistent with a metacognitive process that is useful for mediating confidence-guided economic decisions.

Frontal cortex neuron types categorically encode single decision variables.

Individual neurons in many cortical regions have been found to encode specific, identifiable features of the environment or body that pertain to the function of the region1-3. However, in frontal cortex, which is involved in cognition, neural responses display baffling complexity, carrying seemingly disordered mixtures of sensory, motor and other task-related variables4-13. This complexity has led to the suggestion that representations in individual frontal neurons are randomly mixed and can only be understood at the neural population level14,15. Here we show that neural activity in rat orbitofrontal cortex (OFC) is instead highly structured: single neuron activity co-varies with individual variables in computational models that explain choice behaviour. To characterize neural responses across a large behavioural space, we trained rats on a behavioural task that combines perceptual and value-guided decisions. An unbiased, model-free clustering analysis identified distinct groups of OFC neurons, each with a particular response profile in task-variable space. Applying a simple model of choice behaviour to these categorical response profiles revealed that each profile quantitatively corresponds to a specific decision variable, such as decision confidence. Additionally, we demonstrate that a connectivity-defined cell type, orbitofrontal neurons projecting to the striatum, carries a selective and temporally sustained representation of a single decision variable: integrated value. We propose that neurons in frontal cortex, as in other cortical regions, form a sparse and overcomplete representation of features relevant to the region's function, and that they distribute this information selectively to downstream regions to support behaviour.

Recent Advances at the Interface of Neuroscience and Artificial Neural Networks.

Biological neural networks adapt and learn in diverse behavioral contexts. Artificial neural networks (ANNs) have exploited biological properties to solve complex problems. However, despite their effectiveness for specific tasks, ANNs are yet to realize the flexibility and adaptability of biological cognition. This review highlights recent advances in computational and experimental research to advance our understanding of biological and artificial intelligence. In particular, we discuss critical mechanisms from the cellular, systems, and cognitive neuroscience fields that have contributed to refining the architecture and training algorithms of ANNs. Additionally, we discuss how recent work used ANNs to understand complex neuronal correlates of cognition and to process high throughput behavioral data.

Serotonin Decreases the Gain of Visual Responses in Awake Macaque V1.

Serotonin, an important neuromodulator in the brain, is implicated in affective and cognitive functions. However, its role even for basic cortical processes is controversial. For example, in the mammalian primary visual cortex (V1), heterogenous serotonergic modulation has been observed in anesthetized animals. Here, we combined extracellular single-unit recordings with iontophoresis in awake animals. We examined the role of serotonin on well-defined tuning properties (orientation, spatial frequency, contrast, and size) in V1 of two male macaque monkeys. We find that in the awake macaque the modulatory effect of serotonin is surprisingly uniform: it causes a mainly multiplicative decrease of the visual responses and a slight increase in the stimulus-selective response latency. Moreover, serotonin neither systematically changes the selectivity or variability of the response, nor the interneuronal correlation unexplained by the stimulus ("noise-correlation"). The modulation by serotonin has qualitative similarities with that for a decrease in stimulus contrast, but differs quantitatively from decreasing contrast. It can be captured by a simple additive change to a threshold-linear spiking nonlinearity. Together, our results show that serotonin is well suited to control the response gain of neurons in V1 depending on the animal's behavioral or motivational context, complementing other known state-dependent gain-control mechanisms.SIGNIFICANCE STATEMENT Serotonin is an important neuromodulator in the brain and a major target for drugs used to treat psychiatric disorders. Nonetheless, surprisingly little is known about how it shapes information processing in sensory areas. Here we examined the serotonergic modulation of visual processing in the primary visual cortex of awake behaving macaque monkeys. We found that serotonin mainly decreased the gain of the visual responses, without systematically changing their selectivity, variability, or covariability. This identifies a simple computational function of serotonin for state-dependent sensory processing, depending on the animal's affective or motivational state.

Dopamine Receptors Influence Internally Generated Oscillations during Rule Processing in Primate Prefrontal Cortex.

Neural oscillations in distinct frequency bands in the prefrontal cortex (pFC) are associated with specialized roles during cognitive control. How dopamine modulates oscillations to structure pFC functions remains unknown. We trained macaques to switch between two numerical rules and recorded local field potentials from pFC while applying dopamine receptor targeting drugs using microiontophoresis. We show that the D1 and D2 family receptors (D1Rs and D2Rs, respectively) specifically altered internally generated prefrontal oscillations, whereas sensory-evoked potentials remained unchanged. Blocking D1Rs or stimulating D2Rs increased low-frequency theta and alpha oscillations known to be involved in learning and memory. In contrast, only D1R inhibition enhanced high-frequency beta oscillations, whereas only D2R stimulation increased gamma oscillations linked to top-down and bottom-up attentional processing. These findings suggest that dopamine alters neural oscillations relevant for executive functioning through dissociable actions at the receptor level.

Dopamine D2 Receptors Enhance Population Dynamics in Primate Prefrontal Working Memory Circuits.

Working memory is associated with persistent activity in the prefrontal cortex (PFC). The neuromodulator dopamine, which is released by midbrain neurons projecting into the frontal lobe, influences PFC neurons and networks via the dopamine D1 (D1R) and the D2 receptor (D2R) families. Although behavioral, clinical and computational evidence suggest an involvement of D2Rs in working memory, a neuronal explanation is missing. We report an enhancement of persistent working memory responses of PFC neurons after iontophoretically stimulating D2Rs in monkeys memorizing the number of items in a display. D2R activation improved working memory representation at the population level and increased population dynamics during the transition from visual to mnemonic representations. Computational modeling suggests that D2Rs act by modulating interneuron-to-pyramidal signaling. By increasing the population's response dynamics, D2Rs might put PFC networks in a more flexible state and enhance the neurons' working memory coding, thereby controlling dynamic cognitive control.

Dopamine regulates two classes of primate prefrontal neurons that represent sensory signals.

The lateral prefrontal cortex (PFC), a hub of higher-level cognitive processing, is strongly modulated by midbrain dopamine (DA) neurons. The cellular mechanisms have been comprehensively studied in the context of short-term memory, but little is known about how DA regulates sensory inputs to PFC that precede and give rise to such memory activity. By preparing recipient cortical circuits for incoming signals, DA could be a powerful determinant of downstream cognitive processing. Here, we tested the hypothesis that prefrontal DA regulates the representation of sensory signals that are required for perceptual decisions. In rhesus monkeys trained to report the presence or absence of visual stimuli at varying levels of contrast, we simultaneously recorded extracellular single-unit activity and applied DA to the immediate vicinity of the neurons by micro-iontophoresis. We found that DA modulation of prefrontal neurons is not uniform but tailored to specialized neuronal classes. In one population of neurons, DA suppressed activity with high temporal precision but preserved signal/noise ratio. Neurons in this group had short visual response latencies and comprised all recorded narrow-spiking, putative interneurons. In a distinct population, DA increased excitability and enhanced signal/noise ratio by reducing response variability. These neurons had longer visual response latencies and were composed exclusively of broad-spiking, putative pyramidal neurons. By gating sensory inputs to PFC and subsequently strengthening the representation of sensory signals, DA might play an important role in shaping how the PFC initiates appropriate behavior in response to changes in the sensory environment.

Magnetoplasmons in rotating dusty plasmas.

A rotating dusty plasma apparatus was constructed to provide the possibility of experimental emulation of extremely high magnetic fields by means of the Coriolis force, observable in a corotating measurement frame. We present collective excitation spectra for different rotation rates with a magnetic induction equivalent of up to 3200 T. We identify the onset of magnetoplasmon-equivalent mode dispersion in the rotating macroscopic two-dimensional single-layer dusty plasma. The experimental results are supported by molecular dynamics simulations of 2D magnetized Yukawa systems.

Dopamine receptor activation regulates reward expectancy signals during cognitive control in primate prefrontal neurons.

Dopamine neurons respond to reward-predicting cues but also modulate information processing in the prefrontal cortex essential for cognitive control. Whether dopamine controls reward expectation signals in prefrontal cortex that motivate cognitive control is unknown. We trained two male macaques on a working memory task while varying the reward size earned for successful task completion. We recorded neurons in lateral prefrontal cortex while simultaneously stimulating dopamine D1 receptor (D1R) or D2 receptor (D2R) families using micro-iontophoresis. We show that many neurons predict reward size throughout the trial. D1R stimulation showed mixed effects following reward cues but decreased reward expectancy coding during the memory delay. By contrast, D2R stimulation increased reward expectancy coding in multiple task periods, including cueing and memory periods. Stimulation of either dopamine receptors increased the neurons' selective responses to reward size upon reward delivery. The differential modulation of reward expectancy by dopamine receptors suggests that dopamine regulates reward expectancy necessary for successful cognitive control.

Cognition and the single neuron: How cell types construct the dynamic computations of frontal cortex.

Frontal cortex is thought to underlie many advanced cognitive capacities, from self-control to long term planning. Reflecting these diverse demands, frontal neural activity is notoriously idiosyncratic, with tuning properties that are correlated with endless numbers of behavioral and task features. This menagerie of tuning has made it difficult to extract organizing principles that govern frontal neural activity. Here, we contrast two successful yet seemingly incompatible approaches that have begun to address this challenge. Inspired by the indecipherability of single-neuron tuning, the first approach casts frontal computations as dynamical trajectories traversed by arbitrary mixtures of neurons. The second approach, by contrast, attempts to explain the functional diversity of frontal activity with the biological diversity of cortical cell-types. Motivated by the recent discovery of functional clusters in frontal neurons, we propose a consilience between these population and cell-type-specific approaches to neural computations, advancing the conjecture that evolutionarily inherited cell-type constraints create the scaffold within which frontal population dynamics must operate.

Apparent sunk cost effect in rational agents.

Rational decision makers aim to maximize their gains, but humans and other animals often fail to do so, exhibiting biases and distortions in their choice behavior. In a recent study of economic decisions, humans, mice, and rats were reported to succumb to the sunk cost fallacy, making decisions based on irrecoverable past investments to the detriment of expected future returns. We challenge this interpretation because it is subject to a statistical fallacy, a form of attrition bias, and the observed behavior can be explained without invoking a sunk cost-dependent mechanism. Using a computational model, we illustrate how a rational decision maker with a reward-maximizing decision strategy reproduces the reported behavioral pattern and propose an improved task design to dissociate sunk costs from fluctuations in decision valuation. Similar statistical confounds may be common in analyses of cognitive behaviors, highlighting the need to use causal statistical inference and generative models for interpretation.

Reinforcement biases subsequent perceptual decisions when confidence is low, a widespread behavioral phenomenon.

Learning from successes and failures often improves the quality of subsequent decisions. Past outcomes, however, should not influence purely perceptual decisions after task acquisition is complete since these are designed so that only sensory evidence determines the correct choice. Yet, numerous studies report that outcomes can bias perceptual decisions, causing spurious changes in choice behavior without improving accuracy. Here we show that the effects of reward on perceptual decisions are principled: past rewards bias future choices specifically when previous choice was difficult and hence decision confidence was low. We identified this phenomenon in six datasets from four laboratories, across mice, rats, and humans, and sensory modalities from olfaction and audition to vision. We show that this choice-updating strategy can be explained by reinforcement learning models incorporating statistical decision confidence into their teaching signals. Thus, reinforcement learning mechanisms are continually engaged to produce systematic adjustments of choices even in well-learned perceptual decisions in order to optimize behavior in an uncertain world.

Dopamine and Cognitive Control in Prefrontal Cortex.

Cognitive control, the ability to orchestrate behavior in accord with our goals, depends on the prefrontal cortex. These cognitive functions are heavily influenced by the neuromodulator dopamine. We review here recent insights exploring the influence of dopamine on neuronal response properties in prefrontal cortex (PFC) during ongoing behaviors in primates. This review suggests three major computational roles of dopamine in cognitive control: (i) gating sensory input, (ii) maintaining and manipulating working memory contents, and (iii) relaying motor commands. For each of these roles, we propose a neuronal microcircuit based on known mechanisms of action of dopamine in PFC, which are corroborated by computational network models. This conceptual approach accounts for the various roles of dopamine in prefrontal executive functioning.

The Neurobiology of Confidence: From Beliefs to Neurons.

How confident are you? As humans, aware of our subjective sense of confidence, we can readily answer. Knowing your level of confidence helps to optimize both routine decisions such as whether to go back and check if the front door was locked and momentous ones like finding a partner for life. Yet the inherently subjective nature of confidence has limited investigations by neurobiologists. Here, we provide an overview of recent advances in this field and lay out a conceptual framework that lets us translate psychological questions about subjective confidence into the language of neuroscience. We show how statistical notions of confidence provide a bridge between our subjective sense of confidence and confidence-guided behaviors in nonhuman animals, thus enabling the study of the underlying neurobiology. We discuss confidence as a core cognitive process that enables organisms to optimize behavior such as learning or resource allocation and that serves as the basis of metacognitive reasoning. These approaches place confidence on a solid footing and pave the way for a mechanistic understanding of how the brain implements confidence-based algorithms to guide behavior.

Dopamine receptors differentially enhance rule coding in primate prefrontal cortex neurons.

Flexibly applying abstract rules is a hallmark feature of executive functioning represented by prefrontal cortex (PFC) neurons. Prefrontal networks are regulated by the neuromodulator dopamine, but how dopamine modulates high-level executive functions remains elusive. In monkeys performing a rule-based decision task, we report that both dopamine D1 and D2 receptors facilitated rule coding of PFC neurons, albeit by distinct physiological mechanisms. Dopamine D1 receptor stimulation suppressed neuronal firing while increasing responses to the preferred rule, thereby enhancing neuronal rule coding. D2 receptor stimulation, instead, excited neuronal firing while suppressing responses to the nonpreferred rule, thus also enhancing neuronal rule coding. These findings highlight complementary modulatory contributions of dopamine receptors to the neuronal circuitry mediating executive functioning and goal-directed behavior.

Ethograms indicate stable well-being during prolonged training phases in rhesus monkeys used in neurophysiological research.

Awake, behaving rhesus monkeys are widely used in neurophysiological research. Neural signals are typically measured from monkeys trained with operant conditioning techniques to perform a variety of behavioral tasks in exchange for rewards. Over the past years, monkeys' psychological well-being during experimentation has become an increasingly important concern. We suggest objective criteria to explore whether training sessions during which the monkeys work under controlled water intake over many days might affect their behavior. With that aim, we analyzed a broad range of species-specific behaviors over several months ('ethogram') and used these ethograms as a proxy for the monkeys' well-being. Our results show that monkeys' behavior during training sessions is unaffected by the duration of training-free days in-between. Independently of the number of training-free days (two or nine days) with ad libitum food and water supply, the monkeys were equally active and alert in their home group cages during training phases. This indicates that the monkeys were well habituated to prolonged working schedules and that their well-being was stably ensured during the training sessions.