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Organisms have evolved diverse behavioural strategies that enhance the likelihood of encountering and assessing mates

Sleep, waking, locomotion, and attention are associated with cell-type specific changes in neocortical activity. The effect of brain state on circuit output requires understanding of how neuromodulators influence specific neuronal classes and their synapses, with normal patterns of neuromodulator release from endogenous sources. We investigated the state-dependent modulation of a ubiquitous feedforward inhibitory motif in mouse sensory cortex, local pyramidal (Pyr) inputs onto somatostatin-expressing (SST) interneurons. Paired whole-cell recordings in acute brain slices and in vivo showed that Pyr-to-SST synapses are remarkably weak, with failure rates approaching 80%. Pharmacological screening revealed that cholinergic agonists uniquely enhance synaptic efficacy. Brief, optogenetically-gated acetylcholine release dramatically enhanced Pyr-to-SST input, via nicotinic receptors and presynaptic PKA signalling. Importantly, endogenous acetylcholine release preferentially activated nicotinic, not muscarinic receptors, thus differentiating drug effects from endogenous neurotransmission. Brain state, synapse-specific unmasking of synapses may be a powerful way to functionally rewire cortical circuits dependent on behavioral demands.

Emotional processes are central to behavior, yet their deeply subjective nature has been a challenge for neuroscientific study as well as for psychiatric diagnosis. Here we explore the relationships between subjective feelings and their underlying brain circuits from a computational perspective. We apply recent insights from systems neuroscience-approaching subjective behavior as the result of mental computations instantiated in the brain-to the study of emotions. We develop the hypothesis that emotions are the product of neural computations whose motor role is to reallocate bodily resources mostly gated by smooth muscles. This "emotor" control system is analagous to the more familiar motor control computations that coordinate skeletal muscle movements. To illustrate this framework, we review recent research on "confidence." Although familiar as a feeling, confidence is also an objective statistical quantity: an estimate of the probability that a hypothesis is correct. This model-based approach helped reveal the neural basis of decision confidence in mammals and provides a bridge to the subjective feeling of confidence in humans. These results have important implications for psychiatry, since disorders of confidence computations appear to contribute to a number of psychopathologies. More broadly, this computational approach to emotions resonates with the emerging view that psychiatric nosology may be best parameterized in terms of disorders of the cognitive computations underlying complex behavior.Los procesos emocionales son centrales para la conducta, pero su naturaleza intensamente subjetiva ha sido un desafío para el estudio neurocientífico y el diagnóstico psiquiátrico. En este artículo se exploran las relaciones entre los sentimientos subjetivos y los circuitos cerebrales subyacentes desde una perspectiva computacional. Para el estudio de las emociones se aplican conocimientos recientes de la neurociencia de sistemas, planteándose la conducta subjetiva como el resultado de cálculos mentales que se ejemplifican concretamente en el cerebro. Se desarrolla la hipótesis que las emociones son el producto de cálculos neurales cuyo papel motor es redistribuir los recursos corporales regulados por los músculos lisos, análogo a los cálculos del control motor que coordinan los movimientos del músculo esquelético. Para ilustrar este modelo se revisa la investigación reciente sobre la “certidumbre”. Aunque ésta es familiar como un sentimiento, la certidumbre es también una magnitud estadística objetiva: una estimación de la probabilidad de que una hipótesis sea correcta. Este enfoque basado en el modelo ayuda a revelar las bases neurales de la certidumbre en las decisiones en mamíferos y construye un enlace al sentimiento subjetivo de certidumbre en humanos. Estos resultados tienen importantes implicancias para la psiquiatría, ya que los trastornos de los cálculos de certidumbre parecen contribuir a numerosas psicopatologías. En un sentido más amplio, este enfoque computacional de las emociones se hace eco de la visión emergente de la nosología psiquiátrica que puede ser mejor parametrizada en términos de trastornos de los cálculos cognitivos subyacentes a las conductas complejas.Les processus émotionnels sont au cœur du comportement, pourtant leur nature subjective profonde s'est révélée être une difficulté pour les études neuroscientifiques comme pour le diagnostic psychiatrique. Nous examinons ici les relations entre les sentiments subjectifs et leurs circuits cérébraux d'un point de vue computationnel. Nous appliquons les connaissances récentes de la neuroscience des systèmes - en considérant le comportement subjectif comme résultat de computations mentales générées dans le cerveau - à l'étude des émotions. Nous développons l'hypothèse que les émotions sont le produit de computations neuronales dont le rôle moteur est de réattribuer les ressources corporelles principalement contrôlées par les muscles lisses. Ce système de contrôle moteur émotionnel est semblable aux calculs plus familiers du contrôle moteur qui coordonnent les mouvements musculaires du squelette. Pour illustrer cette perspective, nous examinons la recherche récente sur la «confiance». Bien que connue comme sentiment, la confiance est aussi une quantité statistique objective: une estimation de la probabilité qu'une hypothèse est exacte. Cette approche fondée sur un modèle permet de révéler les bases neuronales de la confiance décisionnaire chez les mammifères et fournit une passerelle au sentiment subjectif de confiance chez les humains. Les implications de ces résultats en psychiatrie sont importantes, puisque des troubles de computations de confiance semblent contribuer à de nombreuses psychopathologies. Plus largement, cette approche computationnelle des émotions fait émerger la possibilité d'une nosologie psychiatrique pouvant être mieux paramétrée en termes de troubles des computations cognitives sous-tendant des comportements complexes.

One of the principal mechanisms by which illness can affect driving safety is by impairing cognition. Nevertheless, despite the substantial evidence demonstrating cognitive impairment in chronic kidney disease (CKD), little is known about the effects of CKD on driving safety.Investigate the current national medical guidelines and research literature with respect to CKD and driving safety.Medline, CINAHL, PEDro, Scopus as of August 2017. The most up to date national driving guidelines and available information provided by the provincial and territorial ministries of transportation across Canada.Fives studies of driving fitness in patients with CKD have been published with minimal data available for patients at early stages of the disease. Amongst these studies, only two come from an era when modern end stage renal disease therapies were routinely provided. The first study demonstrated that 40% of 186 surveyed patients on hemodialysis felt uncomfortable driving and that 1/3 of patients were involved in motor vehicle collisions (MVC) since starting dialysis. Of the patients who felt comfortable driving, more than 75% were found to be at increased driving risk. The second study reported that 15% of patients on hemodialysis were involved in MVCs over a three year span and that the "Am I A Safe Driver" assessment tool by the American Medical Association may not capture all patients at high driving risk. Despite these alarming numbers, national guidelines place few driving restrictions on this patient population and only 3 of 11 available provincial or territorial driving forms include kidney disease as a category that physicians should consider when assessing medical fitness to drive.Our review is limited by the lack of randomized control studies evaluating the effects of CKD on driving safety.Our review demonstrates that driving safety in this patient population remains poorly understood. The limited evidence that does exist, however, suggests that these patients are at substantial risk for unsafe driving. Future research is necessary to determine the impact of CKD-associated cognitive impairment on driving risk, and to parse out the contributions of CKD and its various treatments to driving impairment.La réduction de la vigilance engendrée par la maladie est un des principaux mécanismes par lesquels celle-ci peut affecter la sécurité au volant. Cependant, malgré des données probantes faisant état de troubles cognitifs associés à l’insuffisance rénale chronique (IRC), on en sait peu sur l’incidence de l’IRC sur la conduite.Examiner les travaux de recherche et les recommandations médicales nationales en matière de sécurité routière en contexte d’IRC.Ont été consultés 1- les articles traitant du sujet publiés en date d’août 2017 sur Medline, CINAHL, PEDro et Scopus; 2- les plus récentes recommandations routières nationales et l’information fournie par les ministères des transports provinciaux et territoriaux du Canada.Cinq études faisant état des aptitudes de conduite de patients atteints d’IRC ont été publiées. Ces études contenaient toutefois peu de données concernant les patients atteints des premiers stades de la maladie. Seules deux études étaient datées d’une époque où on appliquait systématiquement les traitements modernes de l’insuffisance rénale terminale. La première mentionnait que 40 % des 186 patients hémodialysés sondés se disaient mal à l’aise de conduire, et que le tiers avait été impliqué dans un accident de la route depuis le début de leurs traitements de dialyse. Parmi les patients qui se disaient à l’aise de conduire, plus de 75 % se sont avérés des conducteurs à risque. La deuxième étude rapportait que 15 % des patients hémodialysés avaient été impliqués dans une collision automobile sur une période de trois ans. Cette étude ajoutait que l’outil d’évaluationNotre revue est limitée par le manque d’études contrôlées à répartition aléatoire évaluant l’effet de l’IRC sur la conduite.Notre revue démontre que la sécurité au volant demeure mal comprise au sein de la population de patients hémodialysés. Les données examinées, quoique parcimonieuses, suggèrent que ces patients posent un risque substantiel à la sécurité routière. Des études additionnelles sont nécessaires pour évaluer l’incidence des troubles cognitifs associés à l’IRC sur les risques d’accidents de la route, et pour établir un lien entre l’IRC (et ses divers modes de traitement) sur la réduction des aptitudes de conduite.

The brain creates internal representations of the external world in the form of neural activity, which is structured to support adaptive behavior. In many cortical regions, individual neurons respond to specific features that are matched to the function of each region and statistics of the world. In frontal cortex, however, neurons display baffling complexity, responding to a mixture of sensory, motor and other variables. Here we use an integrated new approach to understanding the architecture of higher-order cortical representations, and use this approach to show that discrete groups of orbitofrontal cortex (OFC) neurons encode distinct decision variables. Using rats engaged in a complex task combining perceptual and value guided decisions, we found that OFC neurons can be grouped into distinct, categorical response types. These categorical representations map directly onto decision-variables of a choice model explaining our behavioral data, such as reward size, decision confidence and integrated value. We propose that, like sensory neurons, frontal neurons form a sparse and over complete population representation aligned to the natural statistics of the world – in this case spanning the space of decision-variables required for optimal behavior.

High spatiotemporal resolution deep-brain optical excitation for optogenetics would enable activation of specific neural populations and in-depth study of neural circuits. Conventionally, a single fiber is used to flood light into a large area of the brain with limited resolution. The scalability of silicon photonics could enable neural excitation over large areas with single-cell resolution similar to electrical probes. However, active control of these optical circuits has yet to be demonstrated for optogenetics. Here we demonstrate the first active integrated optical switch for neural excitation at 473 nm, enabling control of multiple beams for deep-brain neural stimulation. Using a silicon nitride waveguide platform, we develop a cascaded Mach-Zehnder interferometer (MZI) network located outside the brain to direct light to 8 different grating emitters located at the tip of the neural probe. We use integrated platinum microheaters to induce a local thermo-optic phase shift in the MZI to control the switch output. We measure an ON/OFF extinction ratio of >8dB for a single switch and a switching speed of 20 microseconds. We characterize the optical output of the switch by imaging its excitation of fluorescent dye. Finally, we demonstrate in vivo single-neuron optical activation from different grating emitters using a fully packaged device inserted into a mouse brain. Directly activated neurons showed robust spike firing activities with low first-spike latency and small jitter. Active switching on a nanophotonic platform is necessary for eventually controlling highly-multiplexed reconfigurable optical circuits, enabling high-resolution optical stimulation in deep-brain regions.

Central to the organization of behavior is the ability to predict the values of outcomes to guide choices. The accuracy of such predictions is honed by a teaching signal that indicates how incorrect a prediction was (“reward prediction error,” RPE). In several reinforcement learning contexts, such as Pavlovian conditioning and decisions guided by reward history, this RPE signal is provided by midbrain dopamine neurons. In many situations, however, the stimuli predictive of outcomes are perceptually ambiguous. Perceptual uncertainty is known to influence choices, but it has been unclear whether or how dopamine neurons factor it into their teaching signal. To cope with uncertainty, we extended a reinforcement learning model with a belief state about the perceptually ambiguous stimulus; this model generates an estimate of the probability of choice correctness, termed decision confidence. We show that dopamine responses in monkeys performing a perceptually ambiguous decision task comply with the model’s predictions. Consequently, dopamine responses did not simply reflect a stimulus’ average expected reward value but were predictive of the trial-to-trial fluctuations in perceptual accuracy. These confidence-dependent dopamine responses emerged prior to monkeys’ choice initiation, raising the possibility that dopamine impacts impending decisions, in addition to encoding a post-decision teaching signal. Finally, by manipulating reward size, we found that dopamine neurons reflect both the upcoming reward size and the confidence in achieving it. Together, our results show that dopamine responses convey teaching signals that are also appropriate for perceptual decisions.

In the mammalian cerebral cortex the diversity of interneuronal subtypes underlies a division of labour subserving distinct modes of inhibitory control. A unique mode of inhibitory control may be provided by inhibitory neurons that specifically suppress the firing of other inhibitory neurons. Such disinhibition could lead to the selective amplification of local processing and serve the important computational functions of gating and gain modulation. Although several interneuron populations are known to target other interneurons to varying degrees, little is known about interneurons specializing in disinhibition and their in vivo function. Here we show that a class of interneurons that express vasoactive intestinal polypeptide (VIP) mediates disinhibitory control in multiple areas of neocortex and is recruited by reinforcement signals. By combining optogenetic activation with single-cell recordings, we examined the functional role of VIP interneurons in awake mice, and investigated the underlying circuit mechanisms in vitro in auditory and medial prefrontal cortices. We identified a basic disinhibitory circuit module in which activation of VIP interneurons transiently suppresses primarily somatostatin- and a fraction of parvalbumin-expressing inhibitory interneurons that specialize in the control of the input and output of principal cells, respectively. During the performance of an auditory discrimination task, reinforcement signals (reward and punishment) strongly and uniformly activated VIP neurons in auditory cortex, and in turn VIP recruitment increased the gain of a functional subpopulation of principal neurons. These results reveal a specific cell type and microcircuit underlying disinhibitory control in cortex and demonstrate that it is activated under specific behavioural conditions.

Neurons in the prefrontal cortex exhibit diverse behavioural correlates, an observation that has been attributed to cell-type diversity. To link identified neuron types with network and behavioural functions, we recorded from the two largest genetically defined inhibitory interneuron classes, the perisomatically targeting parvalbumin (PV) and the dendritically targeting somatostatin (SOM) neurons in anterior cingulate cortex of mice performing a reward foraging task. Here we show that PV and a subtype of SOM neurons form functionally homogeneous populations showing a double dissociation between both their inhibitory effects and behavioural correlates. Out of several events pertaining to behaviour, a subtype of SOM neurons selectively responded at reward approach, whereas PV neurons responded at reward leaving and encoded preceding stay duration. These behavioural correlates of PV and SOM neurons defined a behavioural epoch and a decision variable important for foraging (whether to stay or to leave), a crucial function attributed to the anterior cingulate cortex. Furthermore, PV neurons could fire in millisecond synchrony, exerting fast and powerful inhibition on principal cell firing, whereas the inhibitory effect of SOM neurons on firing output was weak and more variable, consistent with the idea that they respectively control the outputs of, and inputs to, principal neurons. These results suggest a connection between the circuit-level function of different interneuron types in regulating the flow of information and the behavioural functions served by the cortical circuits. Moreover, these observations bolster the hope that functional response diversity during behaviour can in part be explained by cell-type diversity.

Background Total hip arthroplasty revision for a fractured ceramic bearing is rare but offers unique challenges. The purpose of this review was to provide a summary of existing literature on fractured ceramic bearings. Methods Two authors performed a literature search of the MEDLINE OVID and PubMed databases with the following search terms: ceramic, fracture, total hip arthroplasty, and revision. Results The search identified 228 articles of which 199 were selected for review. Conclusions It is mandatory to perform a complete synovectomy and thorough debridement of the fractured ceramic fragments. A well-fixed acetabular component should be removed if either the locking mechanism is damaged or the component is malpositioned. If the femoral stem taper is damaged, the femoral stem should be removed. However, if minimal damage is present, the femoral stem may be retained and revised using a fourth generation ceramic head with a titanium sleeve. Metal bearings should be avoided and revision with ceramic bearings should be performed whenever possible.

Hypothesis/introduction:Renal fibrovascular injury often persists in chronic kidney disease patients treated with renin-angiotensin system blockers. Intriguingly, early outgrowth cell-derived factor infusion also inhibits chronic renal injury. We sought to determine whether early outgrowth cell-derived factor administration provides further renoprotection when added to renin-angiotensin system blockade.Materials and methods:Conditioned medium was generated by incubating rat early outgrowth cells with serum-free endothelial basal medium-2 to collect their secreted factors. Subtotal nephrectomy rats received enalapril 0.5 mg/L in drinking water or placebo, beginning 8 weeks post-surgery. Four weeks later, enalapril-treated rats received intravenous injections of either conditioned medium or control endothelial basal medium-2 for 2 weeks. Glomerular filtration rate, urinary protein excretion and renal structure were assessed 4 weeks later at 16 weeks post-surgery.Results:Enalapril-treated subtotal nephrectomy rats receiving control endothelial basal medium-2 injections experienced only partial renoprotection when compared to vehicle-treated subtotal nephrectomy rats. In contrast, conditioned medium infusion, when administered in addition to enalapril, attenuated the progression of renal dysfunction in subtotal nephrectomy rats, improving glomerular filtration rate and reducing proteinuria without affecting blood pressure.Conclusions:Early outgrowth cell-derived factors exert additive renoprotective effects on top of angiotensin-converting enzyme inhibitor therapy in experimental chronic kidney disease, providing the rationale for clinical trials of early outgrowth cell-based therapies for chronic kidney disease.

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure. DOI: http://dx.doi.org/10.7554/eLife.10989.001, eLife digest Many neuroscience experiments today involve using electrodes to record from the brain of an animal, such as a mouse or a monkey, while the animal performs a task. The goal of such experiments is to understand how a particular brain region works. However, modern experimental techniques allow the activity of hundreds of neurons to be recorded simultaneously. Analysing such large amounts of data then becomes a challenge in itself. This is particularly true for brain regions such as the prefrontal cortex that are involved in the cognitive processes that allow an animal to acquire knowledge. Individual neurons in the prefrontal cortex encode many different types of information relevant to a given task. Imagine, for example, that an animal has to select one of two objects to obtain a reward. The same group of prefrontal cortex neurons will encode the object presented to the animal, the animal’s decision and its confidence in that decision. This simultaneous representation of different elements of a task is called a ‘mixed’ representation, and is difficult to analyse. Kobak, Brendel et al. have now developed a data analysis tool that can ‘demix’ neural activity. The tool breaks down the activity of a population of neurons into its individual components. Each of these relates to only a single aspect of the task and is thus easier to interpret. Information about stimuli, for example, is distinguished from information about the animal’s confidence levels. Kobak, Brendel et al. used the demixing tool to reanalyse existing datasets recorded from several different animals, tasks and brain regions. In each case, the tool provided a complete, concise and transparent summary of the data. The next steps will be to apply the analysis tool to new datasets to see how well it performs in practice. At a technical level, the tool could also be extended in a number of different directions to enable it to deal with more complicated experimental designs in future. DOI: http://dx.doi.org/10.7554/eLife.10989.002

The mouse is an important model system for investigating the neural circuits mediating behavior. Because of advances in imaging and optogenetic methods, head-fixed mouse preparations provide an unparalleled opportunity to observe and control neural circuits. To investigate how neural circuits produce behavior, these methods need to be paired with equally well-controlled and monitored behavioral paradigms. Here, we introduce the choice ball, a response device that enables two-alternative forced-choice (2AFC) tasks in head-fixed mice based on the readout of lateral paw movements. We demonstrate the advantages of the choice ball by training mice in the random-click task, a two-choice auditory discrimination behavior. For each trial, mice listened to binaural streams of Poisson-distributed clicks and were required to roll the choice ball laterally toward the side with the greater click rate. In this assay, mice performed hundreds of trials per session with accuracy ranging from 95% for easy stimuli (large interaural click-rate contrast) to near chance level for low-contrast stimuli. We also show, using the record of individual paw strokes, that mice often reverse decisions they have already initiated and that decision reversals correlate with improved performance. The choice ball enables head-fixed 2AFC paradigms, facilitating the circuit-level analysis of sensory processing, decision making, and motor control in mice.

Confidence judgements, self-assessments about the quality of a subject's knowledge, are considered a central example of metacognition. Prima facie, introspection and self-report appear the only way to access the subjective sense of confidence or uncertainty. Contrary to this notion, overt behavioural measures can be used to study confidence judgements by animals trained in decision-making tasks with perceptual or mnemonic uncertainty. Here, we suggest that a computational approach can clarify the issues involved in interpreting these tasks and provide a much needed springboard for advancing the scientific understanding of confidence. We first review relevant theories of probabilistic inference and decision-making. We then critically discuss behavioural tasks employed to measure confidence in animals and show how quantitative models can help to constrain the computational strategies underlying confidence-reporting behaviours. In our view, post-decision wagering tasks with continuous measures of confidence appear to offer the best available metrics of confidence. Since behavioural reports alone provide a limited window into mechanism, we argue that progress calls for measuring the neural representations and identifying the computations underlying confidence reports. We present a case study using such a computational approach to study the neural correlates of decision confidence in rats. This work shows that confidence assessments may be considered higher order, but can be generated using elementary neural computations that are available to a wide range of species. Finally, we discuss the relationship of confidence judgements to the wider behavioural uses of confidence and uncertainty.

Nature regulars give their recommendations for relaxed, inspiring holiday reading and viewing — from climate-change history to Isaac Newton the detective.

Most forms of chronic, progressive kidney disease are characterized by fibrosis whereby the prototypical prosclerotic growth factor, transforming growth factor β (TGF-β), is thought to play a pivotal role. With the recent understanding that TGF-β's canonical signaling pathway may be modified by acetylation as well as phosphorylation, we explored the role of the NAD + -dependent lysine deacetylase, sirtuin 1 (SIRT1) in fibrogenesis in the cell culture, animal model, and human settings. In vitro , the increase in collagen production that results from TGF-β1 stimulation was ameliorated by the allosteric modifier of Sirt1 deacetylase, SRT3025, in association with a reduction in Smad3 reporter activity. In the remnant kidney model (subtotally or 5/6 nephrectomized rats) that develops progressive kidney disease in association with TGF-β overexpression, administration of SRT3025 attenuated glomerular filtration rate decline and proteinuria without affecting blood pressure. Glomerulosclerosis and tubulointerstitial fibrosis were similarly reduced with Sirt1 activation as were cardiac structure and function in this rodent model of primary kidney and secondary cardiac disease. Relating these findings to the human setting, we noted a reduction in SIRT1 mRNA in kidney biopsies obtained from individuals with focal glomerulosclerosis. Together these studies highlight the potential of SIRT1 activation as a therapeutic strategy in progressive, fibrotic kidney disease.

We present a new on-chip platform for neural excitation that is high bandwidth and high resolution. We demonstrate neural excitation in vivo in the visual cortex and the hippocampus by combining the platform with a recording electrode.

When facing uncertainty, adaptive behavioral strategies demand that the brain performs probabilistic computations. In this probabilistic framework, the notion of certainty and confidence would appear to be closely related, so much so that it is tempting to conclude that these two concepts are one and the same. We argue that there are computational reasons to distinguish between these two concepts. Specifically, we propose that confidence should be defined as the probability that a decision or a proposition, overt or covert, is correct given the evidence, a critical quantity in complex sequential decisions. We suggest that the term certainty should be reserved to refer to the encoding of all other probability distributions over sensory and cognitive variables. We also discuss strategies for studying the neural codes for confidence and certainty and argue that clear definitions of neural codes are essential to understanding the relative contributions of various cortical areas to decision making.

The Cas9/CRISPR system is a powerful tool for studying gene function. Here we describe a method that allows temporal control of Cas9/CRISPER activity based on conditional CAS9 destabilization. We demonstrate that fusing an FKBP12-derived destabilizing domain to Cas9 enables conditional rapid and reversible Cas9 expression in vitro and efficient gene-editing in the presence of a guide RNA. Further, we show that this strategy can be easily adapted to co-express, from the same promoter, DD-Cas9 with any other gene of interest, without the latter being co-modulated. In particular, when co-expressed with inducible Cre-ERT2, our system enables parallel, independent manipulation of alleles targeted by Cas9 and traditional recombinase with single-cell specificity. We anticipate this platform will be used for the systematic identification of essential genes and the interrogation of genes functional interactions.

Decision confidence is a forecast about the probability that a decision will be correct. From a statistical perspective, decision confidence can be defined as the Bayesian posterior probability that the chosen option is correct based on the evidence contributing to it. Here, we used this formal definition as a starting point to develop a normative statistical framework for decision confidence. Our goal was to make general predictions that do not depend on the structure of the noise or a specific algorithm for estimating confidence. We analytically proved several interrelations between statistical decision confidence and observable decision measures, such as evidence discriminability, choice, and accuracy. These interrelationships specify necessary signatures of decision confidence in terms of externally quantifiable variables that can be empirically tested. Our results lay the foundations for a mathematically rigorous treatment of decision confidence that can lead to a common framework for understanding confidence across different research domains, from human and animal behavior to neural representations.