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.

Modeling recent positive selection using identity-by-descent segments.

Recent positive selection can result in an excess of long identity-by-descent (IBD) haplotype segments overlapping a locus. The statistical methods that we propose here address three major objectives in studying selective sweeps: scanning for regions of interest, identifying possible sweeping alleles, and estimating a selection coefficient s. First, we implement a selection scan to locate regions with excess IBD rates. Second, we estimate the allele frequency and location of an unknown sweeping allele by aggregating over variants that are more abundant in an inferred outgroup with excess IBD rate versus the rest of the sample. Third, we propose an estimator for the selection coefficient and quantify uncertainty using the parametric bootstrap. Comparing against state-of-the-art methods in extensive simulations, we show that our methods are more precise at estimating s when s≥0.015. We also show that our 95% confidence intervals contain s in nearly 95% of our simulations. We apply these methods to study positive selection in European ancestry samples from the Trans-Omics for Precision Medicine project. We analyze eight loci where IBD rates are more than four standard deviations above the genome-wide median, including LCT where the maximum IBD rate is 35 standard deviations above the genome-wide median. Overall, we present robust and accurate approaches to study recent adaptive evolution without knowing the identity of the causal allele or using time series data.

Beyond Neyman-Pearson: E-values enable hypothesis testing with a data-driven alpha.

A standard practice in statistical hypothesis testing is to mention the P-value alongside the accept/reject decision. We show the advantages of mentioning an e-value instead. With P-values, it is not clear how to use an extreme observation (e.g. [Formula: see text]) for getting better frequentist decisions. With e-values it is straightforward, since they provide Type-I risk control in a generalized Neyman-Pearson setting with the decision task (a general loss function) determined post hoc, after observation of the data-thereby providing a handle on "roving [Formula: see text]'s." When Type-II risks are taken into consideration, the only admissible decision rules in the post hoc setting turn out to be e-value-based. Similarly, if the loss incurred when specifying a faulty confidence interval is not fixed in advance, standard confidence intervals and distributions may fail, whereas e-confidence sets and e-posteriors still provide valid risk guarantees. Sufficiently powerful e-values have by now been developed for a range of classical testing problems. We discuss the main challenges for wider development and deployment.

Confidence of probabilistic predictions modulates the cortical response to pain.

Pain typically evolves over time, and the brain needs to learn this temporal evolution to predict how pain is likely to change in the future and orient behavior. This process is termed temporal statistical learning (TSL). Recently, it has been shown that TSL for pain sequences can be achieved using optimal Bayesian inference, which is encoded in somatosensory processing regions. Here, we investigate whether the confidence of these probabilistic predictions modulates the EEG response to noxious stimuli, using a TSL task. Confidence measures the uncertainty about the probabilistic prediction, irrespective of its actual outcome. Bayesian models dictate that the confidence about probabilistic predictions should be integrated with incoming inputs and weight learning, such that it modulates the early components of the EEG responses to noxious stimuli, and this should be captured by a negative correlation: when confidence is higher, the early neural responses are smaller as the brain relies more on expectations/predictions and less on sensory inputs (and vice versa). We show that participants were able to predict the sequence transition probabilities using Bayesian inference, with some forgetting. Then, we find that the confidence of these probabilistic predictions was negatively associated with the amplitude of the N2 and P2 components of the vertex potential: the more confident were participants about their predictions, the smaller the vertex potential. These results confirm key predictions of a Bayesian learning model and clarify the functional significance of the early EEG responses to nociceptive stimuli, as being implicated in confidence-weighted statistical learning.

Calibrating Bayesian Decoders of Neural Spiking Activity.

Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, which provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. Using data from males, we examine (1) decoding the direction of grating stimuli from spike recordings in the primary visual cortex in monkeys, (2) decoding movement direction from recordings in the primary motor cortex in monkeys, (3) decoding natural images from multiregion recordings in mice, and (4) decoding position from hippocampal recordings in rats. For each setting, we characterize the overconfidence, and we describe a possible method to correct miscalibration post hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain-machine interfaces that more accurately reflect confidence levels when identifying external variables.

Double Dissociation of Spontaneous Alpha-Band Activity and Pupil-Linked Arousal on Additive and Multiplicative Perceptual Gain.

Perception is a probabilistic process dependent on external stimulus properties and one's internal state. However, which internal states influence perception and via what mechanisms remain debated. We studied how spontaneous alpha-band activity (8-13 Hz) and pupil fluctuations impact visual detection and confidence across stimulus contrast levels (i.e., the contrast response function, CRF). In human subjects of both sexes, we found that low prestimulus alpha power induced an "additive" shift in the CRF, whereby stimuli were reported present more frequently at all contrast levels, including contrast of zero (i.e., false alarms). Conversely, prestimulus pupil size had a "multiplicative" effect on detection such that stimuli occurring during large pupil states (putatively corresponding to higher arousal) were perceived more frequently as contrast increased. Signal detection modeling reveals that alpha power changes detection criteria equally across the CRF but not detection sensitivity (d'), whereas pupil-linked arousal modulated sensitivity, particularly for higher contrasts. Interestingly, pupil size and alpha power were positively correlated, meaning that some of the effect of alpha on detection may be mediated by pupil fluctuations. However, pupil-independent alpha still induced an additive shift in the CRF corresponding to a criterion effect. Our data imply that low alpha boosts detection and confidence by an additive factor, rather than by a multiplicative scaling of contrast responses, a profile which captures the effect of pupil-linked arousal. We suggest that alpha power and arousal fluctuations have dissociable effects on behavior. Alpha reflects the baseline level of visual excitability, which can vary independent of arousal.

Neural Representations of Sensory Uncertainty and Confidence Are Associated with Perceptual Curiosity.

Humans are immensely curious and motivated to reduce uncertainty, but little is known about the neural mechanisms that generate curiosity. Curiosity is inversely associated with confidence, suggesting that it is triggered by states of low confidence (subjective uncertainty), but the neural mechanisms of this link, have been little investigated. Inspired by studies of sensory uncertainty, we hypothesized that visual areas provide multivariate representations of uncertainty, which are read out by higher-order structures to generate signals of confidence and, ultimately, curiosity. We scanned participants (17 female, 15 male) using fMRI while they performed a new task in which they rated their confidence in identifying distorted images of animals and objects and their curiosity to see the clear image. We measured the activity evoked by each image in the occipitotemporal cortex (OTC) and devised a new metric of "OTC Certainty" indicating the strength of evidence this activity conveys about the animal versus object categories. We show that, perceptual curiosity peaked at low confidence and OTC Certainty negatively correlated with curiosity, establishing a link between curiosity and a multivariate representation of sensory uncertainty. Moreover, univariate (average) activity in two frontal areas-vmPFC and ACC-correlated positively with confidence and negatively with curiosity, and the vmPFC mediated the relationship between OTC Certainty and curiosity. The results reveal novel mechanisms through which uncertainty about an event generates curiosity about that event.

Brain Network Interconnectivity Dynamics Explain Metacognitive Differences in Listening Behavior.

Complex auditory scenes pose a challenge to attentive listening, rendering listeners slower and more uncertain in their perceptual decisions. How can we explain such behaviors from the dynamics of cortical networks that pertain to the control of listening behavior? We here follow up on the hypothesis that human adaptive perception in challenging listening situations is supported by modular reconfiguration of auditory-control networks in a sample of N = 40 participants (13 males) who underwent resting-state and task functional magnetic resonance imaging (fMRI). Individual titration of a spatial selective auditory attention task maintained an average accuracy of ∼70% but yielded considerable interindividual differences in listeners' response speed and reported confidence in their own perceptual decisions. Whole-brain network modularity increased from rest to task by reconfiguring auditory, cinguloopercular, and dorsal attention networks. Specifically, interconnectivity between the auditory network and cinguloopercular network decreased during the task relative to the resting state. Additionally, interconnectivity between the dorsal attention network and cinguloopercular network increased. These interconnectivity dynamics were predictive of individual differences in response confidence, the degree of which was more pronounced after incorrect judgments. Our findings uncover the behavioral relevance of functional cross talk between auditory and attentional-control networks during metacognitive assessment of one's own perception in challenging listening situations and suggest two functionally dissociable cortical networked systems that shape the considerable metacognitive differences between individuals in adaptive listening behavior.

Value Estimation versus Effort Mobilization: A General Dissociation between Ventromedial and Dorsomedial Prefrontal Cortex.

Deciding on a course of action requires both an accurate estimation of option values and the right amount of effort invested in deliberation to reach sufficient confidence in the final choice. In a previous study, we have provided evidence, across a series of judgment and choice tasks, for a dissociation between the ventromedial prefrontal cortex (vmPFC), which would represent option values, and the dorsomedial prefrontal cortex (dmPFC), which would represent the duration of deliberation. Here, we first replicate this dissociation and extend it to the case of an instrumental learning task, in which 24 human volunteers (13 women) choose between options associated with probabilistic gains and losses. According to fMRI data recorded during decision-making, vmPFC activity reflects the sum of option values generated by a reinforcement learning model and dmPFC activity the deliberation time. To further generalize the role of the dmPFC in mobilizing effort, we then analyze fMRI data recorded in the same participants while they prepare to perform motor and cognitive tasks (squeezing a handgrip or making numerical comparisons) to maximize gains or minimize losses. In both cases, dmPFC activity is associated with the output of an effort regulation model, and not with response time. Taken together, these results strengthen a general theory of behavioral control that implicates the vmPFC in the estimation of option values and the dmPFC in the energization of relevant motor and cognitive processes.

Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors.

Machine learning including modern deep learning models has been extensively used in drug design and screening. However, reliable prediction of molecular properties is still challenging when exploring out-of-domain regimes, even for deep neural networks. Therefore, it is important to understand the uncertainty of model predictions, especially when the predictions are used to guide further experiments. In this study, we explored the utility and effectiveness of evidential uncertainty in compound screening. The evidential Graphormer model was proposed for uncertainty-guided discovery of KDM1A/LSD1 inhibitors. The benchmarking results illustrated that (i) Graphormer exhibited comparative predictive power to state-of-the-art models, and (ii) evidential regression enabled well-ranked uncertainty estimates and calibrated predictions. Subsequently, we leveraged time-splitting on the curated KDM1A/LSD1 dataset to simulate out-of-distribution predictions. The retrospective virtual screening showed that the evidential uncertainties helped reduce false positives among the top-acquired compounds and thus enabled higher experimental validation rates. The trained model was then used to virtually screen an independent in-house compound set. The top 50 compounds ranked by two different ranking strategies were experimentally validated, respectively. In general, our study highlighted the importance to understand the uncertainty in prediction, which can be recognized as an interpretable dimension to model predictions.

Model-agnostic confidence measurement for aggregating multimodal ensemble models in automatic diagnostic systems.

Automatic diagnostic systems (ADSs) have been garnering increased attention because they can alleviate the workload of clinicians by assisting in diagnosis and offering low-cost access to healthcare for people in medically underserved areas. ADS can suggest potential diseases by analyzing a patient's self-report. Previous research on ADS has leveraged diagnostic case data from various patients and medical knowledge to diagnose diseases, with multimodal ensemble methods proving particularly effective. However, the existing multimodal ensemble method combines the probabilities of different models in the aggregating process, which can not properly combine the probabilities that are produced by different criteria. To address these issues, we propose an effective aggregation framework for multimodal ensembles that can properly aggregate model-agnostic confidence scores and predictions from each model. Our framework transforms probability scores from different criteria into unified aggregation rule-based scores and reflects the gap between the probabilities that may be blurred in the aggregation process through the confidence score. In particular, The proposed confidence measurement method employs a post-analysis approach with the developed model or algorithm, making it adaptable in a model-agnostic manner and suitable for multimodal ensemble learning that utilizes heterogeneous prediction results. Our experimental results demonstrate that our framework outperforms existing approaches by more effectively leveraging the strengths of each ensemble member.

Predicting drug-target interactions by measuring confidence with consistent causal neighborhood interventions.

Predicting drug-target interactions (DTI) is a crucial stage in drug discovery and development. Understanding the interaction between drugs and targets is essential for pinpointing the specific relationship between drug molecules and targets, akin to solving a link prediction problem using information technology. While knowledge graph (KG) and knowledge graph embedding (KGE) methods have been rapid advancements and demonstrated impressive performance in drug discovery, they often lack authenticity and accuracy in identifying DTI. This leads to increased misjudgment rates and reduced efficiency in drug development. To address these challenges, our focus lies in refining the accuracy of DTI prediction models through KGE, with a specific emphasis on causal intervention confidence measures (CI). These measures aim to assess triplet scores, enhancing the precision of the predictions. Comparative experiments conducted on three datasets and utilizing 9 KGE models reveal that our proposed confidence measure approach via causal intervention, significantly improves the accuracy of DTI link prediction compared to traditional approaches. Furthermore, our experimental analysis delves deeper into the embedding of intervention values, offering valuable insights for guiding the design and development of subsequent drug development experiments. As a result, our predicted outcomes serve as valuable guidance in the pursuit of more efficient drug development processes.

Neural correlates of retrospective memory confidence during face-name associative learning.

The ability to accurately assess one's own memory performance during learning is essential for adaptive behavior, but the brain mechanisms underlying this metamemory function are not well understood. We investigated the neural correlates of memory accuracy and retrospective memory confidence in a face-name associative learning task using magnetoencephalography in healthy young adults (n = 32). We found that high retrospective confidence was associated with stronger occipital event-related fields during encoding and widespread event-related fields during retrieval compared to low confidence. On the other hand, memory accuracy was linked to medial temporal activities during both encoding and retrieval, but only in low-confidence trials. A decrease in oscillatory power at alpha/beta bands in the parietal regions during retrieval was associated with higher memory confidence. In addition, representational similarity analysis at the single-trial level revealed distributed but differentiable neural activities associated with memory accuracy and confidence during both encoding and retrieval. In summary, our study unveiled distinct neural activity patterns related to memory confidence and accuracy during associative learning and underscored the crucial role of parietal regions in metamemory.

fMRI evidence of movement familiarization effects on recognition memory in professional dancers.

Dual-process theories propose that recognition memory involves recollection and familiarity; however, the impact of motor expertise on memory recognition, especially the interplay between familiarity and recollection, is relatively unexplored. This functional magnetic resonance imaging study used videos of a dancer performing International Latin Dance Styles as stimuli to investigate memory recognition in professional dancers and matched controls. Participants observed and then reported whether they recognized dance actions, recording the level of confidence in their recollections, whereas blood-oxygen-level-dependent signals measured encoding and recognition processes. Professional dancers showed higher accuracy and hit rates for high-confidence judgments, whereas matched controls exhibited the opposite trend for low-confidence judgments. The right putamen and precentral gyrus showed group-based moderation effects, especially for high-confidence (vs. low-confidence) action recognition in professional dancers. During action recognition, the right superior temporal gyrus and insula showed increased activation for accurate recognition and high-confidence retrieval, particularly in matched controls. These findings highlighting enhanced action memory of professional dancers-evident in their heightened recognition confidence-not only supports the dual-processing model but also underscores the crucial role of expertise-driven familiarity in bolstering successful recollection. Additionally, they emphasize the involvement of the action observation network and frontal brain regions in facilitating detailed encoding linked to intention processing.

Metacognition and Confidence: A Review and Synthesis.

Determining the psychological, computational, and neural bases of confidence and uncertainty holds promise for understanding foundational aspects of human metacognition. While a neuroscience of confidence has focused on the mechanisms underpinning subpersonal phenomena such as representations of uncertainty in the visual or motor system, metacognition research has been concerned with personal-level beliefs and knowledge about self-performance. I provide a road map for bridging this divide by focusing on a particular class of confidence computation: propositional confidence in one's own (hypothetical) decisions or actions. Propositional confidence is informed by the observer's models of the world and their cognitive system, which may be more or less accurate-thus explaining why metacognitive judgments are inferential and sometimes diverge from task performance. Disparate findings on the neural basis of uncertainty and performance monitoring are integrated into a common framework, and a new understanding of the locus of action of metacognitive interventions is developed.

Preserved Metacognition for Undetected Visuomotor Deviations.

Humans can successfully correct deviations of movements without conscious detection of such deviations, suggesting limited awareness of movement details. We ask whether such limited awareness impairs confidence (metacognition). We recorded functional magnetic resonance imaging data while 31 human female and male participants detected cursor deviations during a visuomotor reaching task and rated their confidence retrospectively. We show that participants monitor a summary statistic of the unfolding visual feedback (the peak cursor error) to detect visuomotor deviations and adjust their confidence ratings, even when they report being unaware of a deviation. Crucially, confidence ratings were as metacognitively efficient for aware and unaware deviations. At the neural level, activity in the ventral striatum tracks high confidence, whereas a broad network encodes cursor error but not confidence. These findings challenge the notion of limited conscious action monitoring and uncover how humans monitor their movements as they unfold, even when unaware of ongoing deviations.SIGNIFICANCE STATEMENT We are unaware of the small corrections we apply to our movements as long as our goals are achieved. Here, although we replicate the finding that participants deny perceiving small deviations they correct, we show that their confidence reliably reflects the presence or absence of a deviation. This observation shows they can metacognitively monitor the presence of a deviation, even when they deny perceiving it. We also describe the hemodynamic correlates of confidence ratings. Our study questions the extent to which humans are unaware of the details of their movements; describes a plausible mechanism for metacognition in a visuomotor task, along with its neural correlates; and has important implications for the construction of the sense of self.

Neural mechanisms underpinning metacognitive shifts driven by non-informative predictions.

Humans constantly make predictions and such predictions allow us to prepare for future events. Yet, such benefits may come with drawbacks as premature predictions may potentially bias subsequent judgments. Here we examined how prediction influences our perceptual decisions and subsequent confidence judgments, on scenarios where the predictions were arbitrary and independent of the identity of the upcoming stimuli. We defined them as invalid and non-informative predictions. Behavioral results showed that, such non-informative predictions biased perceptual decisions in favor of the predicted choice, and such prediction-induced perceptual bias further increased the metacognitive efficiency. The functional MRI results showed that activities in the medial prefrontal cortex (mPFC) and subgenual anterior cingulate cortex (sgACC) encoded the response consistency between predictions and perceptual decisions. Activity in mPFC predicted the strength of this congruency bias across individuals. Moreover, the parametric encoding of confidence in putamen was modulated by prediction-choice consistency, such that activity in putamen was negatively correlated with confidence rating after inconsistent responses. These findings suggest that predictions, while made arbitrarily, orchestrate the neural representations of choice and confidence judgment.

Learning to discriminate the eye-of-origin during continuous flash suppression.

Helmholtz asked whether one could discriminate which eye is the origin of one's perception merely based on the retinal signals. Studies to date showed that participants' ability to tell the eye-of-origin most likely depends on contextual cues. Nevertheless, it has been shown that exogenous attention can enhance performance for monocularly presented stimuli. We questioned whether adults can be trained to discriminate the eye-of-origin of their perceptions and if this ability depends on the strength of the monocular channels. We used attentional feed-forward training to improve the subject's eye-of-origin discrimination performance with voluntary attention. During training, participants received a binocular cue to inform them of the eye-of-origin of an upcoming target. Using continuous flash suppression, we also measured the signal strength of the monocular targets to see any possible modulations related to the cues. We collected confidence ratings from the participants about their eye-of-origin judgements to study in further detail whether metacognition has access to this information. Our results show that, even though voluntary attention did not alter the strength of the monocular channels, eye-of-origin discrimination performance improved following the training. A similar pattern was observed for confidence. The results from the feedforward attentional training and the increase in subjective confidence point towards a high-level decisional mechanism being responsible for the eye-of-origin judgements. We propose that this high-level process is informed by subtle sensory cues such as the differences in luminance or contrast in the two monocular channels.

The neural representation of metacognition in preferential decision-making.

Humans regularly assess the quality of their judgements, which helps them adjust their behaviours. Metacognition is the ability to accurately evaluate one's own judgements, and it is assessed by comparing objective task performance with subjective confidence report in perceptual decisions. However, for preferential decisions, assessing metacognition in preference-based decisions is difficult because it depends on subjective goals rather than the objective criterion. Here, we develop a new index that integrates choice, reaction time, and confidence report to quantify trial-by-trial metacognitive sensitivity in preference judgements. We found that the dorsomedial prefrontal cortex (dmPFC) and the right anterior insular were more activated when participants made bad metacognitive evaluations. Our study suggests a crucial role of the dmPFC-insula network in representing online metacognitive sensitivity in preferential decisions.

Confidence maps for reliable estimation of proton density fat fraction and R 2 * in the liver.

PURPOSE: The objective was to develop a fully automated algorithm that generates confidence maps to identify regions valid for analysis of quantitative proton density fat fraction (PDFF) and R 2 * $$ {R}_2^{\ast } $$ maps of the liver, generated with chemical shift-encoded MRI (CSE-MRI). Confidence maps are urgently needed for automated quality assurance, particularly with the emergence of automated segmentation and analysis algorithms. METHODS: Confidence maps for both PDFF and R 2 * $$ {R}_2^{\ast } $$ maps are generated based on goodness of fit, measured by normalized RMS error between measured complex signals and the CSE-MRI signal model. Based on Cramér-Rao lower bound and Monte-Carlo simulations, normalized RMS error threshold criteria were developed to identify unreliable regions in quantitative maps. Simulation, phantom, and in vivo clinical studies were included. To analyze the clinical data, a board-certified radiologist delineated regions of interest (ROIs) in each of the nine liver segments for PDFF and R 2 * $$ {R}_2^{\ast } $$ analysis in consecutive clinical CSE-MRI data sets. The percent area of ROIs in areas deemed unreliable by confidence maps was calculated to assess the impact of confidence maps on real-world clinical PDFF and R 2 * $$ {R}_2^{\ast } $$ measurements. RESULTS: Simulations and phantom studies demonstrated that the proposed algorithm successfully excluded regions with unreliable PDFF and R 2 * $$ {R}_2^{\ast } $$ measurements. ROI analysis by the radiologist revealed that 2.6% and 15% of the ROIs were placed in unreliable areas of PDFF and R 2 * $$ {R}_2^{\ast } $$ maps, as identified by confidence maps. CONCLUSION: A proposed confidence map algorithm that identifies reliable areas of PDFF and R 2 * $$ {R}_2^{\ast } $$ measurements from CSE-MRI acquisitions was successfully developed. It demonstrated technical and clinical feasibility.