Direct injection of noise to the visual cortex decreases accuracy but increases decision confidence

Human-like dissociations between confidence and accuracy in convolutional neural networks

Prior research has shown that manipulating stimulus energy by changing both stimulus contrast and variability results in confidence-accuracy dissociations in humans. Specifically, even when performance is matched, higher stimulus energy leads to higher confidence. The most common explanation for this effect is the positive evidence heuristic where confidence neglects evidence that disconfirms the choice. However, an alternative explanation is the signal-and-variance-increase hypothesis, according to which these dissociations arise from low-level changes in the separation and variance of perceptual representations. Because artificial neural networks lack built-in confidence heuristics, they can serve as a test for the necessity of confidence heuristics in explaining confidence-accuracy dissociations. Therefore, we tested whether confidence-accuracy dissociations induced by stimulus energy manipulations emerge naturally in convolutional neural networks (CNNs). We found that, across three different energy manipulations, CNNs produced confidence-accuracy dissociations similar to those found in humans. This effect was present for a range of CNN architectures from shallow 4-layer networks to very deep ones, such as VGG-19 and ResNet -50 pretrained on ImageNet. Further, we traced back the reason for the confidence-accuracy dissociations in all CNNs to the same signal-and-variance increase that has been proposed for humans: higher stimulus energy increased the separation and variance of the CNNs' internal representations leading to higher confidence even for matched accuracy. These findings cast doubt on the necessity of the positive evidence heuristic to explain human confidence and establish CNNs as promising models for adjudicating between low-level, stimulus-driven and high-level, cognitive explanations of human behavior.

Natural statistics support a rational account of confidence biases

Previous work has sought to understand decision confidence as a prediction of the probability that a decision will be correct, leading to debate over whether these predictions are optimal, and whether they rely on the same decision variable as decisions themselves. This work has generally relied on idealized, low-dimensional models, necessitating strong assumptions about the representations over which confidence is computed. To address this, we used deep neural networks to develop a model of decision confidence that operates directly over high-dimensional, naturalistic stimuli. The model accounts for a number of puzzling dissociations between decisions and confidence, reveals a rational explanation of these dissociations in terms of optimization for the statistics of sensory inputs, and makes the surprising prediction that, despite these dissociations, decisions and confidence depend on a common decision variable.

Common computations in automatic cue combination and metacognitive confidence reports

Appropriate perceptual decision making necessitates the accurate estimation and use of sensory uncertainty. Such estimation has been studied in the context of both low-level multisensory cue combination and metacognitive estimation of confidence, but it remains unclear whether the same computations underlie both sets of uncertainty estimation. We created visual stimuli with low vs. high overall motion energy, such that the high-energy stimuli led to higher confidence but lower accuracy in a visual-only task. Importantly, we tested the impact of the low- and high-energy visual stimuli on auditory motion perception in a separate task. Despite being irrelevant to the auditory task, both visual stimuli impacted auditory judgments presumably via automatic low-level mechanisms. Critically, we found that the high-energy visual stimuli influenced the auditory judgments more strongly than the low-energy visual stimuli. This effect was in line with the confidence but contrary to the accuracy differences between the high- and low-energy stimuli in the visual-only task. These effects were captured by a simple computational model that assumes common computational principles underlying both confidence reports and multisensory cue combination. Our results reveal a deep link between automatic sensory processing and metacognitive confidence reports, and suggest that vastly different stages of perceptual decision making rely on common computational principles.

Challenging the fixed-criterion model of perceptual decision-making

Perceptual decision-making is often conceptualized as the process of comparing an internal decision variable to a categorical boundary or criterion. How the mind sets such a criterion has been studied from at least two perspectives. One idea is that the criterion is a fixed quantity. In work on subjective phenomenology, the notion of a fixed criterion has been proposed to explain a phenomenon called "subjective inflation"-a form of metacognitive mismatch in which observers overestimate the quality of their sensory representation in the periphery or at unattended locations. A contrasting view emerging from studies of perceptual decision-making is that the criterion adjusts to the level sensory uncertainty and is thus sensitive to variations in attention. Here, we mathematically demonstrate that previous empirical findings supporting subjective inflation are consistent with either a fixed or a flexible decision criterion. We further lay out specific task properties that are necessary to make inferences about the flexibility of the criterion: (i) a clear mapping from decision variable space to stimulus feature space and (ii) an incentive for observers to adjust their decision criterion as uncertainty changes. Recent work satisfying these requirements has demonstrated that decision criteria flexibly adjust according to uncertainty. We conclude that the fixed-criterion model of subjective inflation is poorly tenable.

Human perceptual and metacognitive decision-making rely on distinct brain networks

Perceptual decisions depend on the ability to exploit available sensory information in order to select the most adaptive option from a set of alternatives. Such decisions depend on the perceptual sensitivity of the organism, which is generally accompanied by a corresponding level of certainty about the choice made. Here, by use of corticocortical paired associative transcranial magnetic stimulation protocol (ccPAS) aimed at inducing plastic changes, we shaped perceptual sensitivity and metacognitive ability in a motion discrimination task depending on the targeted network, demonstrating their functional dissociation. Neurostimulation aimed at boosting V5/MT+-to-V1/V2 back-projections enhanced motion sensitivity without impacting metacognition, whereas boosting IPS/LIP-to-V1/V2 back-projections increased metacognitive efficiency without impacting motion sensitivity. This double-dissociation provides causal evidence of distinct networks for perceptual sensitivity and metacognitive ability in humans.

Dynamic influences on static measures of metacognition

Humans differ in their capability to judge choice accuracy via confidence judgments. Popular signal detection theoretic measures of metacognition, such as M-ratio, do not consider the dynamics of decision making. This can be problematic if response caution is shifted to alter the tradeoff between speed and accuracy. Such shifts could induce unaccounted-for sources of variation in the assessment of metacognition. Instead, evidence accumulation frameworks consider decision making, including the computation of confidence, as a dynamic process unfolding over time. Using simulations, we show a relation between response caution and M-ratio. We then show the same pattern in human participants explicitly instructed to focus on speed or accuracy. Finally, this association between M-ratio and response caution is also present across four datasets without any reference towards speed. In contrast, when data are analyzed with a dynamic measure of metacognition, v-ratio, there is no effect of speed-accuracy tradeoff.

A robust confidence-accuracy dissociation via criterion attraction

Many studies have shown that confidence and accuracy can be dissociated in a variety of tasks. However, most of these dissociations involve small effect sizes, occur only in a subset of participants, and include a reaction time (RT) confound. Here, I develop a new method for inducing confidence-accuracy dissociations that overcomes these limitations. The method uses an external noise manipulation and relies on the phenomenon of criterion attraction where criteria for different tasks become attracted to each other. Subjects judged the identity of stimuli generated with either low or high external noise. The results showed that the two conditions were matched on accuracy and RT but produced a large difference in confidence (effect appeared for 25 of 26 participants, effect size: Cohen's d = 1.9). Computational modeling confirmed that these results are consistent with a mechanism of criterion attraction. These findings establish a new method for creating conditions with large differences in confidence without differences in accuracy or RT. Unlike many previous studies, however, the current method does not lead to differences in subjective experience and instead produces robust confidence-accuracy dissociations by exploiting limitations in post-perceptual, cognitive processes.

Bayesian inference with incomplete knowledge explains perceptual confidence and its deviations from accuracy

In perceptual decisions, subjects infer hidden states of the environment based on noisy sensory information. Here we show that both choice and its associated confidence are explained by a Bayesian framework based on partially observable Markov decision processes (POMDPs). We test our model on monkeys performing a direction-discrimination task with post-decision wagering, demonstrating that the model explains objective accuracy and predicts subjective confidence. Further, we show that the model replicates well-known discrepancies of confidence and accuracy, including the hard-easy effect, opposing effects of stimulus variability on confidence and accuracy, dependence of confidence ratings on simultaneous or sequential reports of choice and confidence, apparent difference between choice and confidence sensitivity, and seemingly disproportionate influence of choice-congruent evidence on confidence. These effects may not be signatures of sub-optimal inference or discrepant computational processes for choice and confidence. Rather, they arise in Bayesian inference with incomplete knowledge of the environment.

Variance misperception under skewed empirical noise statistics explains overconfidence in the visual periphery

Perceptual confidence typically corresponds to accuracy. However, observers can be overconfident relative to accuracy, termed "subjective inflation." Inflation is stronger in the visual periphery relative to central vision, especially under conditions of peripheral inattention. Previous literature suggests inflation stems from errors in estimating noise (i.e., "variance misperception"). However, despite previous Bayesian hypotheses about metacognitive noise estimation, no work has systematically explored how noise estimation may critically depend on empirical noise statistics, which may differ across the visual field, with central noise distributed symmetrically but peripheral noise positively skewed. Here, we examined central and peripheral vision predictions from five Bayesian-inspired noise-estimation algorithms under varying usage of noise priors, including effects of attention. Models that failed to optimally estimate noise exhibited peripheral inflation, but only models that explicitly used peripheral noise priors-but used them incorrectly-showed increasing peripheral inflation under increasing peripheral inattention. Further, only one model successfully captured previous empirical results, which showed a selective increase in confidence in incorrect responses under performance reductions due to inattention accompanied by no change in confidence in correct responses; this was the model that implemented Bayesian estimation of peripheral noise, but using an (incorrect) symmetric rather than the correct positively skewed peripheral noise prior. Our findings explain peripheral inflation, especially under inattention, and suggest future experiments that might reveal the noise expectations used by the visual metacognitive system.

The effects of positive or negative self-talk on the alteration of brain functional connectivity by performing cognitive tasks

Self-talk can improve cognitive performance, but the underlying mechanism of such improvement has not been investigated. This study aimed to elucidate the effects of self-talks on functional connectivity associated with cognitive performance. We used the short form of Progressive Matrices Test (sRPM) to measure differences in performance improvements between self-respect and self-criticism. Participants were scanned using functional magnetic resonance imaging in the following order: baseline, during-sRPM1, post-sRPM1, self-respect or self-criticism, during-sRPM2, and post-sRPM2. Analysis was conducted to identify the self-talks' modulatory effects on the reward-motivation, default mode, and central-executive networks. Increase in sRPM2 score compared to sRPM1 score was observed only after self-criticism. The self-talk-by-repetition interaction effect was not found for during-sRPM, but found for post-sRPM; decreased nucleus accumbens-based connectivity was shown after self-criticism compared with self-respect. However, the significant correlations between the connectivity change and performance change appeared only in the self-respect group. Our findings showed that positive self-talk and negative self-talk differently modulate brain states concerning cognitive performance. Self-respect may have both positive and negative effects due to enhanced executive functions and inaccurate confidence, respectively, whereas self-criticism may positively affect cognitive performance by inducing a less confident state that increases internal motivation and attention.

Contrasting contributions of movement onset and duration to self-evaluation of sensorimotor timing performance

Movement execution is not always optimal. Understanding how humans evaluate their own motor decisions can give us insights into their suboptimality. Here, we investigated how humans time the action of synchronizing an arm movement with a predictable visual event and how well they can evaluate the outcome of this action. On each trial, participants had to decide when to start (reaction time) and for how long to move (movement duration) to reach a target on time. After each trial, participants judged the confidence they had that their performance on that trial was better than average. We found that participants mostly varied their reaction time, keeping the average movement duration short and relatively constant across conditions. Interestingly, confidence judgements reflected deviations from the planned reaction time and were not related to planned movement duration. In two other experiments, we replicated these results in conditions where the contribution of sensory uncertainty was reduced. In contrast to confidence judgements, when asked to make an explicit estimation of their temporal error, participants' estimates were related in a similar manner to both reaction time and movement duration. In summary, humans control the timing of their actions primarily by adjusting the delay to initiate the action, and they estimate their confidence in their action from the difference between the planned and executed movement onset. Our results highlight the critical role of the internal model for the self‐evaluation of one's motor performance.

Is the primary visual cortex necessary for blindsight-like behavior? Review of transcranial magnetic stimulation studies in neurologically healthy individuals

The visual pathways that bypass the primary visual cortex (V1) are often assumed to support visually guided behavior in humans in the absence of conscious vision. This conclusion is largely based on findings on patients: V1 lesions cause blindness but sometimes leave some visually guided behaviors intact-this is known as blindsight. With the aim of examining how well the findings on blindsight patients generalize to neurologically healthy individuals, we review studies which have tried to uncover transcranial magnetic stimulation (TMS) induced blindsight. In general, these studies have failed to demonstrate a completely unconscious blindsight-like capacity in neurologically healthy individuals. A possible exception to this is TMS-induced blindsight of stimulus presence or location. Because blindsight in patients is often associated with some form of introspective access to the visual stimulus, and blindsight may be associated with neural reorganization, we suggest that rather than revealing a dissociation between visually guided behavior and conscious seeing, blindsight may reflect preservation or partial recovery of conscious visual perception after the lesion.

Dual strategies in human confidence judgments

Although confidence is commonly believed to be an essential element in decision-making, it remains unclear what gives rise to one's sense of confidence. Recent Bayesian theories propose that confidence is computed, in part, from the degree of uncertainty in sensory evidence. Alternatively, observers can use physical properties of the stimulus as a heuristic to confidence. In the current study, we developed ideal observer models for either hypothesis and compared their predictions against human data obtained from psychophysical experiments. Participants reported the orientation of a stimulus, and their confidence in this estimate, under varying levels of internal and external noise. As predicted by the Bayesian model, we found a consistent link between confidence and behavioral variability for a given stimulus orientation. Confidence was higher when orientation estimates were more precise, for both internal and external sources of noise. However, we observed the inverse pattern when comparing between stimulus orientations: although observers gave more precise orientation estimates for cardinal orientations (a phenomenon known as the oblique effect), they were more confident about oblique orientations. We show that these results are well explained by a strategy to confidence that is based on the perceived amount of noise in the stimulus. Altogether, our results suggest that confidence is not always computed from the degree of uncertainty in one's perceptual evidence but can instead be based on visual cues that function as simple Heuristics to confidence.

Are children's judgments of another's accuracy linked to their metacognitive confidence judgments?

The world can be a confusing place, which leads to a significant challenge: how do we figure out what is true? To accomplish this, children possess two relevant skills: reasoning about the likelihood of their own accuracy (metacognitive confidence) and reasoning about the likelihood of others' accuracy (mindreading). Guided by Signal Detection Theory and Simulation Theory, we examine whether these two self- and other-oriented skills are one in the same, relying on a single cognitive process. Specifically, Signal Detection Theory proposes that confidence in a decision is purely derived from the imprecision of that decision, predicting a tight correlation between decision accuracy and confidence. Simulation Theory further proposes that children attribute their own cognitive experience to others when reasoning socially. Together, these theories predict that children's self and other reasoning should be highly correlated and dependent on decision accuracy. In four studies (N = 374), children aged 4-7 completed a confidence reasoning task and selective social learning task each designed to eliminate confounding language and response biases, enabling us to isolate the unique correlation between self and other reasoning. However, in three of the four studies, we did not find that individual differences on the two tasks correlated, nor that decision accuracy explained performance. These findings suggest self and other reasoning are either independent in childhood, or the result of a single process that operates differently for self and others.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11409-021-09263-x.

Tuned inhibition in perceptual decision-making circuits can explain seemingly suboptimal confidence behavior

Current dominant views hold that perceptual confidence reflects the probability that a decision is correct. Although these views have enjoyed some empirical support, recent behavioral results indicate that confidence and the probability of being correct can be dissociated. An alternative hypothesis suggests that confidence instead reflects the magnitude of evidence in favor of a decision while being relatively insensitive to the evidence opposing the decision. We considered how this alternative hypothesis might be biologically instantiated by developing a simple neural network model incorporating a known property of sensory neurons: tuned inhibition. The key idea of the model is that the level of inhibition that each accumulator unit receives from units with the opposite tuning preference, i.e. its inhibition 'tuning', dictates its contribution to perceptual decisions versus confidence judgments, such that units with higher tuned inhibition (computing relative evidence for different perceptual interpretations) determine perceptual discrimination decisions, and units with lower tuned inhibition (computing absolute evidence) determine confidence. We demonstrate that this biologically plausible model can account for several counterintuitive findings reported in the literature where confidence and decision accuracy dissociate. By comparing model fits, we further demonstrate that a full complement of behavioral data across several previously published experimental results-including accuracy, reaction time, mean confidence, and metacognitive sensitivity-is best accounted for when confidence is computed from units without, rather than units with, tuned inhibition. Finally, we discuss predictions of our results and model for future neurobiological studies. These findings suggest that the brain has developed and implements this alternative, heuristic theory of perceptual confidence computation by relying on the diversity of neural resources available.

Overlapping and unique neural circuits are activated during perceptual decision making and confidence

The period of making a perceptual decision is often followed by a period of rating confidence where one evaluates the likely accuracy of the initial decision. However, it remains unclear whether the same or different neural circuits are engaged during periods of perceptual decision making and confidence report. To address this question, we conducted two functional MRI experiments in which we dissociated the periods related to perceptual decision making and confidence report by either separating their respective regressors or asking for confidence ratings only in the second half of the experiment. We found that perceptual decision making and confidence reports gave rise to activations in large and mostly overlapping brain circuits including frontal, parietal, posterior, and cingulate regions with the results being remarkably consistent across the two experiments. Further, the confidence report period activated a number of unique regions, whereas only early sensory areas were activated for the decision period across the two experiments. We discuss the possible reasons for this overlap and explore their implications about theories of perceptual decision making and visual metacognition.

Sources of Metacognitive Inefficiency

Confidence judgments are typically less informative about one's accuracy than they could be; a phenomenon we call metacognitive inefficiency. We review the existence of different sources of metacognitive inefficiency and classify them into four categories based on whether the corruption is due to: (i) systematic or nonsystematic influences, and (ii) the input to or the computation of the metacognitive system. Critically, the existence of different sources of metacognitive inefficiency provides an alternative explanation for behavioral findings typically interpreted as evidence for domain-specific (and against domain-general) metacognitive systems. We argue that, contrary to the dominant assumption in the field, metacognitive failures are not monolithic and suggest that understanding the sources of metacognitive inefficiency should be a primary goal of the science of metacognition.

Single-Neuron Correlates of Decision Confidence in the Human Medial Temporal Lobe

The human medial temporal lobe (MTL) has been suggested to play a role in valuation. However, little is known about its role in binary decisions and metacognition. We performed two decision-making tasks while recording from neurons in the human MTL. During a break, subjects consumed their preferred food item to satiation and subsequently repeated both tasks. We identified both a persistent and a transient modulation of the neural activity. Two independent subpopulations of neurons showed a persistent correlation of their firing rates with either decision confidence or reaction times. Importantly, the changes in confidence and reaction time between experimental sets were accompanied by a correlated change in the neural activity, and this correlation lasted as long as it was relevant for the behavioral task. Previous studies have suggested a transient modulation of the neural activity in the human MTL correlated with subjective value. However, in our study, neither subjective value nor unsigned value could explain this transient activity better than the nutritional features of the stimuli, calling into question the role of the human MTL in valuation.

Unconscious reinforcement learning of hidden brain states supported by confidence

Can humans be trained to make strategic use of latent representations in their own brains? We investigate how human subjects can derive reward-maximizing choices from intrinsic high-dimensional information represented stochastically in neural activity. Reward contingencies are defined in real-time by fMRI multivoxel patterns; optimal action policies thereby depend on multidimensional brain activity taking place below the threshold of consciousness, by design. We find that subjects can solve the task within two hundred trials and errors, as their reinforcement learning processes interact with metacognitive functions (quantified as the meaningfulness of their decision confidence). Computational modelling and multivariate analyses identify a frontostriatal neural mechanism by which the brain may untangle the 'curse of dimensionality': synchronization of confidence representations in prefrontal cortex with reward prediction errors in basal ganglia support exploration of latent task representations. These results may provide an alternative starting point for future investigations into unconscious learning and functions of metacognition.

Confidence as a Priority Signal

When dealing with multiple tasks, we must establish the order in which to tackle them. In multiple experiments, including a preregistered replication (Ns = 16-105), we found that confidence, or the subjective accuracy of decisions, acts as a priority signal, both when ordering responses about tasks already completed or ordering tasks yet to be completed. Specifically, when participants categorized perceptual stimuli along two dimensions, they tended to first give the decision associated with higher confidence. When participants selected which of two tasks they wanted to perform first, they were slightly biased toward the task associated with higher confidence. This finding extends to nonperceptual decisions (mental calculation) and cannot be reduced to effects of task difficulty, response accuracy, response availability, or implicit demands. Our results thus support the role of confidence as a priority signal, thereby suggesting a new way in which it may regulate human behavior.

The suboptimality of perceptual decision making with multiple alternatives

It is becoming widely appreciated that human perceptual decision making is suboptimal but the nature and origins of this suboptimality remain poorly understood. Most past research has employed tasks with two stimulus categories, but such designs cannot fully capture the limitations inherent in naturalistic perceptual decisions where choices are rarely between only two alternatives. We conduct four experiments with tasks involving multiple alternatives and use computational modeling to determine the decision-level representation on which the perceptual decisions are based. The results from all four experiments point to the existence of robust suboptimality such that most of the information in the sensory representation is lost during the transformation to a decision-level representation. These results reveal severe limits in the quality of decision-level representations for multiple alternatives and have strong implications about perceptual decision making in naturalistic settings.

The nature of metacognitive inefficiency in perceptual decision making

Humans have the metacognitive ability to judge the accuracy of their own decisions via confidence ratings. A substantial body of research has demonstrated that human metacognition is fallible but it remains unclear how metacognitive inefficiency should be incorporated into a mechanistic model of confidence generation. Here we show that, contrary to what is typically assumed, metacognitive inefficiency depends on the level of confidence. We found that, across 5 different data sets and 4 different measures of metacognition, metacognitive ability decreased with higher confidence ratings. To understand the nature of this effect, we collected a large dataset of 20 subjects completing 2,800 trials each and providing confidence ratings on a continuous scale. The results demonstrated a robustly nonlinear zROC curve with downward curvature, despite a decades-old assumption of linearity. This pattern of results was reproduced by a new mechanistic model of confidence generation, which assumes the existence of lognormally distributed metacognitive noise. The model outperformed competing models either lacking metacognitive noise altogether or featuring Gaussian metacognitive noise. Further, the model could generate a measure of metacognitive ability which was independent of confidence levels. These findings establish an empirically validated model of confidence generation, have significant implications about measures of metacognitive ability, and begin to reveal the underlying nature of metacognitive inefficiency. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Spontaneous Brain Oscillations and Perceptual Decision-Making

Making rapid decisions on the basis of sensory information is essential to everyday behaviors. Why, then, are perceptual decisions so variable despite unchanging inputs? Spontaneous neural oscillations have emerged as a key predictor of trial-to-trial perceptual variability. New work casting these effects in the framework of models of perceptual decision-making has driven novel insight into how the amplitude of spontaneous oscillations impact decision-making. This synthesis reveals that the amplitude of ongoing low-frequency oscillations (<30 Hz), particularly in the alpha-band (8-13 Hz), bias sensory responses and change conscious perception but not, surprisingly, the underlying sensitivity of perception. A key model-based insight is that various decision thresholds do not adapt to alpha-related changes in sensory activity, demonstrating a seeming suboptimality of decision mechanisms in tracking endogenous changes in sensory responses.

Post-training TMS abolishes performance improvement and releases future learning from interference

The period immediately after the offset of visual training is thought to be critical for memory consolidation. Nevertheless, we still lack direct evidence for the causal role of this period to perceptual learning of either previously or subsequently trained material. To address these issues, we had human subjects complete two consecutive trainings with different tasks (detecting different Gabor orientations). We applied continuous theta burst stimulation (cTBS) to either the visual cortex or a control site (vertex) immediately after the offset of the first training. In the vertex cTBS condition, subjects showed improvement on the first task but not on the second task, suggesting the presence of anterograde interference. Critically, cTBS to the visual cortex abolished the performance improvement on the first task and released the second training from the anterograde interference. These results provide causal evidence for a role of the immediate post-training period in the consolidation of perceptual learning.

A neural circuit model of decision uncertainty and change-of-mind

Decision-making is often accompanied by a degree of confidence on whether a choice is correct. Decision uncertainty, or lack in confidence, may lead to change-of-mind. Studies have identified the behavioural characteristics associated with decision confidence or change-of-mind, and their neural correlates. Although several theoretical accounts have been proposed, there is no neural model that can compute decision uncertainty and explain its effects on change-of-mind. We propose a neuronal circuit model that computes decision uncertainty while accounting for a variety of behavioural and neural data of decision confidence and change-of-mind, including testable model predictions. Our theoretical analysis suggests that change-of-mind occurs due to the presence of a transient uncertainty-induced choice-neutral stable steady state and noisy fluctuation within the neuronal network. Our distributed network model indicates that the neural basis of change-of-mind is more distinctively identified in motor-based neurons. Overall, our model provides a framework that unifies decision confidence and change-of-mind.

We make decisions with varying degrees of confidence and, if our confidence in a decision falls, we may change our mind. Here, the authors present a neuronal circuit model to account for how change of mind occurs under particular low-confidence conditions.

The mixed-polarity benefit of stereopsis arises in early visual cortex

Depth perception is better when observers view stimuli containing a mixture of bright and dark visual features. It is currently unclear where in the visual system sensory processing benefits from the availability of different contrast polarity. To address this question, we applied transcranial magnetic stimulation to the visual cortex to modulate normal neural activity during processing of single- or mixed-polarity random-dot stereograms. In line with previous work, participants gave significantly better depth judgments for mixed-polarity stimuli. Stimulation of early visual cortex (V1/V2) significantly increased this benefit for mixed-polarity stimuli, and it did not affect performance for single-polarity stimuli. Stimulation of disparity responsive areas V3a and LO had no effect on perception. Our findings show that disparity processing in early visual cortex gives rise to the mixed-polarity benefit. This is consistent with computational models of stereopsis at the level of V1 that produce a mixed polarity benefit.

Comparing Bayesian and non-Bayesian accounts of human confidence reports

Humans can meaningfully report their confidence in a perceptual or cognitive decision. It is widely believed that these reports reflect the Bayesian probability that the decision is correct, but this hypothesis has not been rigorously tested against non-Bayesian alternatives. We use two perceptual categorization tasks in which Bayesian confidence reporting requires subjects to take sensory uncertainty into account in a specific way. We find that subjects do take sensory uncertainty into account when reporting confidence, suggesting that brain areas involved in reporting confidence can access low-level representations of sensory uncertainty, a prerequisite of Bayesian inference. However, behavior is not fully consistent with the Bayesian hypothesis and is better described by simple heuristic models that use uncertainty in a non-Bayesian way. Both conclusions are robust to changes in the uncertainty manipulation, task, response modality, model comparison metric, and additional flexibility in the Bayesian model. Our results suggest that adhering to a rational account of confidence behavior may require incorporating implementational constraints.

Humans incorporate attention-dependent uncertainty into perceptual decisions and confidence

Perceptual decisions are better when they take uncertainty into account. Uncertainty arises not only from the properties of sensory input but also from cognitive sources, such as different levels of attention. However, it is unknown whether humans appropriately adjust for such cognitive sources of uncertainty during perceptual decision-making. Here we show that, in a task in which uncertainty is relevant for performance, human categorization and confidence decisions take into account uncertainty related to attention. We manipulated uncertainty in an orientation categorization task from trial to trial using only an attentional cue. The categorization task was designed to disambiguate decision rules that did or did not depend on attention. Using formal model comparison to evaluate decision behavior, we found that category and confidence decision boundaries shifted as a function of attention in an approximately Bayesian fashion. This means that the observer's attentional state on each trial contributed probabilistically to the decision computation. This responsiveness of an observer's decisions to attention-dependent uncertainty should improve perceptual decisions in natural vision, in which attention is unevenly distributed across a scene.

Human metacognition across domains: insights from individual differences and neuroimaging

Metacognition is the capacity to evaluate and control one's own cognitive processes. Metacognition operates over a range of cognitive domains, such as perception and memory, but the neurocognitive architecture supporting this ability remains controversial. Is metacognition enabled by a common, domain-general resource that is recruited to evaluate performance on a variety of tasks? Or is metacognition reliant on domain-specific modules? This article reviews recent literature on the domain-generality of human metacognition, drawing on evidence from individual differences and neuroimaging. A meta-analysis of behavioral studies found that perceptual metacognitive ability was correlated across different sensory modalities, but found no correlation between metacognition of perception and memory. However, evidence for domain-generality from behavioral data may suffer from a lack of power to identify correlations across model parameters indexing metacognitive efficiency. Neuroimaging data provide a complementary perspective on the domain-generality of metacognition, revealing co-existence of neural signatures that are common and distinct across tasks. We suggest that such an architecture may be appropriate for "tagging" generic feelings of confidence with domain-specific information, in turn forming the basis for priors about self-ability and modulation of higher-order behavioral control.

Suboptimality in perceptual decision making

Human perceptual decisions are often described as optimal. Critics of this view have argued that claims of optimality are overly flexible and lack explanatory power. Meanwhile, advocates for optimality have countered that such criticisms single out a few selected papers. To elucidate the issue of optimality in perceptual decision making, we review the extensive literature on suboptimal performance in perceptual tasks. We discuss eight different classes of suboptimal perceptual decisions, including improper placement, maintenance, and adjustment of perceptual criteria; inadequate tradeoff between speed and accuracy; inappropriate confidence ratings; misweightings in cue combination; and findings related to various perceptual illusions and biases. In addition, we discuss conceptual shortcomings of a focus on optimality, such as definitional difficulties and the limited value of optimality claims in and of themselves. We therefore advocate that the field drop its emphasis on whether observed behavior is optimal and instead concentrate on building and testing detailed observer models that explain behavior across a wide range of tasks. To facilitate this transition, we compile the proposed hypotheses regarding the origins of suboptimal perceptual decisions reviewed here. We argue that verifying, rejecting, and expanding these explanations for suboptimal behavior - rather than assessing optimality per se - should be among the major goals of the science of perceptual decision making.

When uncertain, does human self-motion decision-making fully utilize complete information?

When forced to choose humans often feel uncertain. Investigations of human perceptual decision-making often employ signal detection theory, which assumes that even when uncertain all available information is fully utilized. However, other studies have suggested or assumed that, when uncertain, human subjects guess totally at random, ignoring available information. When uncertain, do humans simply guess totally at random? Or do humans fully utilize complete information? Or does behavior fall between these two extremes yielding "above chance" performance without fully utilizing complete information? While it is often assumed complete information is fully utilized, even when uncertain, to our knowledge this has never been experimentally confirmed. To answer this question, we combined numerical simulations, theoretical analyses, and human studies performed using a self-motion direction-recognition perceptual decision-making task (did I rotate left or right?). Subjects were instructed to make forced-choice binary (left/right) and trinary (left/right/uncertain) decisions when cued following each stimulus. Our results show that humans 1) do not guess at random when uncertain and 2) make binary and trinary decisions equally well. These findings show that humans fully utilize complete information when uncertain for our perceptual decision-making task. This helps unify signal detection theory and other models of forced-choice decision-making which allow for uncertain responses. NEW & NOTEWORTHY Humans make many perceptual decisions every day. But what if we are uncertain? While many studies assume that humans fully utilize complete information, other studies have suggested and/or assumed that when we're uncertain and forced to decide, information is not fully utilized. While humans tend to perform above chance when uncertain, no earlier study has tested whether available information is fully utilized. Our results show that humans make fully informed decisions even when uncertain.

Transcranial magnetic stimulation to visual cortex induces suboptimal introspection

Blindsight patients with damage to the visual cortex can discriminate objects but report no conscious visual experience. This provides an intriguing opportunity to allow the study of subjective awareness in isolation from objective performance capacity. However, blindsight is rare, so one promising way to induce the effect in neurologically intact observers is to apply transcranial magnetic stimulation (TMS) to the visual cortex. Here, we used a recently-developed criterion-free method to conclusively rule out an important alternative interpretation of TMS-induced performance without awareness: that TMS-induced blindsight may be just due to conservative reporting biases for conscious perception. Critically, using this criterion-free paradigm we have previously shown that introspective judgments were optimal even under visual masking. However, here under TMS, observers were suboptimal, as if they were metacognitively blind to the visual disturbances caused by TMS. We argue that metacognitive judgments depend on observers' internal statistical models of their own perceptual systems, and introspective suboptimality arises when external perturbations abruptly make those models invalid - a phenomenon that may also be happening in actual blindsight.

Should metacognition be measured by logistic regression?

Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria.

Heuristic use of perceptual evidence leads to dissociation between performance and metacognitive sensitivity

Zylberberg et al. [Zylberberg, Barttfeld, & Sigman (Frontiers in Integrative Neuroscience, 6; 79, 2012), Frontiers in Integrative Neuroscience 6:79] found that confidence decisions, but not perceptual decisions, are insensitive to evidence against a selected perceptual choice. We present a signal detection theoretic model to formalize this insight, which gave rise to a counter-intuitive empirical prediction: that depending on the observer's perceptual choice, increasing task performance can be associated with decreasing metacognitive sensitivity (i.e., the trial-by-trial correspondence between confidence and accuracy). The model also provides an explanation as to why metacognitive sensitivity tends to be less than optimal in actual subjects. These predictions were confirmed robustly in a psychophysics experiment. In a second experiment we found that, in at least some subjects, the effects were replicated even under performance feedback designed to encourage optimal behavior. However, some subjects did show improvement under feedback, suggesting the tendency to ignore evidence against a selected perceptual choice may be a heuristic adopted by the perceptual decision-making system, rather than reflecting inherent biological limitations. We present a Bayesian modeling framework that explains why this heuristic strategy may be advantageous in real-world contexts.

The influence of evidence volatility on choice, reaction time and confidence in a perceptual decision

Many decisions are thought to arise via the accumulation of noisy evidence to a threshold or bound. In perception, the mechanism explains the effect of stimulus strength, characterized by signal-to-noise ratio, on decision speed, accuracy and confidence. It also makes intriguing predictions about the noise itself. An increase in noise should lead to faster decisions, reduced accuracy and, paradoxically, higher confidence. To test these predictions, we introduce a novel sensory manipulation that mimics the addition of unbiased noise to motion-selective regions of visual cortex, which we verified with neuronal recordings from macaque areas MT/MST. For both humans and monkeys, increasing the noise induced faster decisions and greater confidence over a range of stimuli for which accuracy was minimally impaired. The magnitude of the effects was in agreement with predictions of a bounded evidence accumulation model.

DOI:http://dx.doi.org/10.7554/eLife.17688.001

Many of our decisions are made on the basis of imperfect or ‘noisy’ information. A longstanding goal in neuroscience is to work out how such noise affects three aspects of decision-making: the accuracy (or appropriateness) of a choice, the speed at which the choice is made, and the decision-maker’s confidence that they have chosen correctly.

One theory of decision-making is that the brain simultaneously accumulates evidence for each of the options it is considering, until one option exceeds a threshold and is declared the ‘winner’. This theory is known as bounded evidence accumulation. It predicts that increasing the noisiness of the available information decreases the accuracy of decisions made in response. Counterintuitively, it also predicts that such an increase in noise speeds up decision-making and increases confidence levels.

Zylberberg et al. have now tested these predictions experimentally by getting human volunteers and monkeys to perform a series of trials where they had to decide whether a set of randomly moving dots moved to the left or to the right overall. Using a newly developed method, the noisiness of the dot motion could be changed between trials. The effectiveness of this technique was confirmed by recording the activity of neurons in the region of the monkey brain that processes visual motion information.

After each trial, the humans rated their confidence in their decision. By comparison, the monkeys could indicate that they were not confident in a decision by opting for a guaranteed small reward on certain trials (instead of the larger reward they received when they correctly indicated the direction of motion of the dots).

In both humans and monkeys, increasing the noisiness associated with the movement of the dots led to faster and more confident decision-making, just as the bounded evidence accumulation framework predicts. Furthermore, the results presented by Zylberberg et al. suggest that the brain does not always gauge how reliable evidence is in order to fine-tune decisions.

Now that the role of noise in decision-making is better understood, future experiments could attempt to reveal how artificial manipulations of the brain contribute both information and noise to a decision. Other experiments might ascertain when the brain can learn that noisy information should invite slower, more cautious decisions.

DOI:http://dx.doi.org/10.7554/eLife.17688.002

Visual Confidence

Visual confidence refers to an observer's ability to judge the accuracy of her perceptual decisions. Even though confidence judgments have been recorded since the early days of psychophysics, only recently have they been recognized as essential for a deeper understanding of visual perception. The reluctance to study visual confidence may have come in part from obtaining convincing experimental evidence in favor of metacognitive abilities rather than just perceptual sensitivity. Some effort has thus been dedicated to offer different experimental paradigms to study visual confidence in humans and nonhuman animals. To understand the origins of confidence judgments, investigators have developed two competing frameworks. The approach based on signal decision theory is popular but fails to account for response times. In contrast, the approach based on accumulation of evidence models naturally includes the dynamics of perceptual decisions. These models can explain a range of results, including the apparently paradoxical dissociation between performance and confidence that is sometimes observed.

Causal evidence for frontal cortex organization for perceptual decision making

Although recent research has shown that the frontal cortex has a critical role in perceptual decision making, an overarching theory of frontal functional organization for perception has yet to emerge. Perceptual decision making is temporally organized such that it requires the processes of selection, criterion setting, and evaluation. We hypothesized that exploring this temporal structure would reveal a large-scale frontal organization for perception. A causal intervention with transcranial magnetic stimulation revealed clear specialization along the rostrocaudal axis such that the control of successive stages of perceptual decision making was selectively affected by perturbation of successively rostral areas. Simulations with a dynamic model of decision making suggested distinct computational contributions of each region. Finally, the emergent frontal gradient was further corroborated by functional MRI. These causal results provide an organizational principle for the role of frontal cortex in the control of perceptual decision making and suggest specific mechanistic contributions for its different subregions.

Low attention impairs optimal incorporation of prior knowledge in perceptual decisions

When visual attention is directed away from a stimulus, neural processing is weak and strength and precision of sensory data decreases. From a computational perspective, in such situations observers should give more weight to prior expectations in order to behave optimally during a discrimination task. Here we test a signal detection theoretic model that counter-intuitively predicts subjects will do just the opposite in a discrimination task with two stimuli, one attended and one unattended: when subjects are probed to discriminate the unattended stimulus, they rely less on prior information about the probed stimulus' identity. The model is in part inspired by recent findings that attention reduces trial-by-trial variability of the neuronal population response and that they use a common criterion for attended and unattended trials. In five different visual discrimination experiments, when attention was directed away from the target stimulus, subjects did not adjust their response bias in reaction to a change in stimulus presentation frequency despite being fully informed and despite the presence of performance feedback and monetary and social incentives. This indicates that subjects did not rely more on the priors under conditions of inattention as would be predicted by a Bayes-optimal observer model. These results inform and constrain future models of Bayesian inference in the human brain.

The signal processing architecture underlying subjective reports of sensory awareness

What is the relationship between perceptual information processing and subjective perceptual experience? Empirical dissociations between stimulus identification performance and subjective reports of stimulus visibility are crucial for shedding light on this question. We replicated a finding that metacontrast masking can produce such a dissociation (Lau and Passingham, 2006), and report a novel finding that this paradigm can also dissociate stimulus identification performance from the efficacy with which visibility ratings predict task performance. We explored various hypotheses about the relationship between perceptual task performance and visibility rating by implementing them in computational models and using formal model comparison techniques to assess which ones best captured the unusual patterns in the data. The models fell into three broad categories: Single Channel models, which hold that task performance and visibility ratings are based on the same underlying source of information; Dual Channel models, which hold that there are two independent processing streams that differentially contribute to task performance and visibility rating; and Hierarchical models, which hold that a late processing stage generates visibility ratings by evaluating the quality of early perceptual processing. Taking into account the quality of data fitting and model complexity, we found that Hierarchical models perform best at capturing the observed behavioral dissociations. Because current theories of visual awareness map well onto these different model structures, a formal comparison between them is a powerful approach for arbitrating between the different theories.

Confidence Leak in Perceptual Decision Making

People live in a continuous environment in which the visual scene changes on a slow timescale. It has been shown that to exploit such environmental stability, the brain creates a continuity field in which objects seen seconds ago influence the perception of current objects. What is unknown is whether a similar mechanism exists at the level of metacognitive representations. In three experiments, we demonstrated a robust intertask confidence leak-that is, confidence in one's response on a given task or trial influencing confidence on the following task or trial. This confidence leak could not be explained by response priming or attentional fluctuations. Better ability to modulate confidence leak predicted higher capacity for metacognition as well as greater gray matter volume in the prefrontal cortex. A model based on normative principles from Bayesian inference explained the results by postulating that observers subjectively estimate the perceptual signal strength in a stable environment. These results point to the existence of a novel metacognitive mechanism mediated by regions in the prefrontal cortex.

Low Intensity TMS Enhances Perception of Visual Stimuli

BACKGROUND

Transcranial magnetic stimulation (TMS) is a popular functional mapping tool in cognitive and sensory neuroscience. While strong TMS typically degrades performance, two recent studies have demonstrated that weak TMS, delivered to visual cortex, can improve performance on simple visual tasks. The improvement was interpreted as the summation of visually-evoked and TMS-elicited neuronal activity in visual cortex, but the nature of this interaction remains unclear.

OBJECTIVE

The present experiments sought to determine whether these weak pulses of TMS assist subjects to see the visual stimulus itself or create a distinct "melded" percept that may not be recognizable as the visual stimulus.

METHODS

We measured contrast thresholds in an orientation discrimination task in which participants reported the orientation (left or right) of gratings tilted 45° from vertical.

RESULTS

Weak TMS improved sensitivity for identifying gratings, suggesting that TMS sums with but preserves orientation information so that the subject can recognize the visual stimulus. We explain the effect using a mechanism of non-linear transduction of sensory signals in the brain.

CONCLUSIONS

The capability of low-intensity TMS to augment the neural signal while preserving information encoded in the stimulus can be employed as a novel approach to study the neural correlates of consciousness by selectively "pushing" an unconscious stimulus into consciousness.

A decisional account of subjective inflation of visual perception at the periphery

Human peripheral vision appears vivid compared to foveal vision; the subjectively perceived level of detail does not seem to drop abruptly with eccentricity. This compelling impression contrasts with the fact that spatial resolution is substantially lower at the periphery. A similar phenomenon occurs in visual attention, in which subjects usually overestimate their perceptual capacity in the unattended periphery. We have previously shown that at identical eccentricity, low spatial attention is associated with liberal detection biases, which we argue may reflect inflated subjective perceptual qualities. Our computational model suggests that this subjective inflation occurs because under the lack of attention, the trial-by-trial variability of the internal neural response is increased, resulting in more frequent surpassing of a detection criterion. In the current work, we hypothesized that the same mechanism may be at work in peripheral vision. We investigated this possibility in psychophysical experiments in which participants performed a simultaneous detection task at the center and at the periphery. Confirming our hypothesis, we found that participants adopted a conservative criterion at the center and liberal criterion at the periphery. Furthermore, an extension of our model predicts that detection bias will be similar at the center and at the periphery if the periphery stimuli are magnified. A second experiment successfully confirmed this prediction. These results suggest that, although other factors contribute to subjective inflation of visual perception in the periphery, such as top-down filling-in of information, the decision mechanism may be relevant too.

Weighting mean and variability during confidence judgments

Humans can not only perform some visual tasks with great precision, they can also judge how good they are in these tasks. However, it remains unclear how observers produce such metacognitive evaluations, and how these evaluations might be dissociated from the performance in the visual task. Here, we hypothesized that some stimulus variables could affect confidence judgments above and beyond their impact on performance. In a motion categorization task on moving dots, we manipulated the mean and the variance of the motion directions, to obtain a low-mean low-variance condition and a high-mean high-variance condition with matched performances. Critically, in terms of confidence, observers were not indifferent between these two conditions. Observers exhibited marked preferences, which were heterogeneous across individuals, but stable within each observer when assessed one week later. Thus, confidence and performance are dissociable and observers’ confidence judgments put different weights on the stimulus variables that limit performance.

The effect of self-efficacy on visual discrimination sensitivity

Can subjective belief about one's own perceptual competence change one's perception? To address this question, we investigated the influence of self-efficacy on sensory discrimination in two low-level visual tasks: contrast and orientation discrimination. We utilised a pre-post manipulation approach whereby two experimental groups (high and low self-efficacy) and a control group made objective perceptual judgments on the contrast or the orientation of the visual stimuli. High and low self-efficacy were induced by the provision of fake social-comparative performance feedback and fictional research findings. Subsequently, the post-manipulation phase was performed to assess changes in visual discrimination thresholds as a function of the self-efficacy manipulations. The results showed that the high self-efficacy group demonstrated greater improvement in visual discrimination sensitivity compared to both the low self-efficacy and control groups. These findings suggest that subjective beliefs about one's own perceptual competence can affect low-level visual processing.

Effects of cortical microstimulation on confidence in a perceptual decision

Decisions are often associated with a degree of certainty, or confidence--an estimate of the probability that the chosen option will be correct. Recent neurophysiological results suggest that the central processing of evidence leading to a perceptual decision also establishes a level of confidence. Here we provide a causal test of this hypothesis by electrically stimulating areas of the visual cortex involved in motion perception. Monkeys discriminated the direction of motion in a noisy display and were sometimes allowed to opt out of the direction choice if their confidence was low. Microstimulation did not reduce overall confidence in the decision but instead altered confidence in a manner that mimicked a change in visual motion, plus a small increase in sensory noise. The results suggest that the same sensory neural signals support choice, reaction time, and confidence in a decision and that artificial manipulation of these signals preserves the quantitative relationship between accumulated evidence and confidence.