Threat Expectation Does Not Improve Perceptual Discrimination despite Causing Heightened Priority Processing in the Frontoparietal Network

Threat cues have been widely shown to elicit increased sensory and attentional neural processing. However, whether this enhanced recruitment leads to measurable behavioral improvements in perception is still in question. Here, we adjudicate between two opposing theories: that threat cues do or do not enhance perceptual sensitivity. We created threat stimuli by pairing one direction of motion in a random dot kinematogram with an aversive sound. While in the MRI scanner, 46 subjects (both men and women) completed a cued (threat/safe/neutral) perceptual decision-making task where they indicated the perceived motion direction of each moving dot stimulus. We found strong evidence that threat cues did not increase perceptual sensitivity compared with safe and neutral cues. This lack of improvement in perceptual decision-making ability occurred despite the threat cue resulting in widespread increases in frontoparietal BOLD activity, as well as increased connectivity between the right insula and the frontoparietal network. These results call into question the intuitive claim that expectation automatically enhances our perception of threat and highlight the role of the frontoparietal network in prioritizing the processing of threat-related environmental cues.

Behavior can be decoded across the cortex when individual differences are considered

Group-level analyses have typically associated behavioral signatures with a constrained set of brain areas. Here we show that two behavioral metrics - reaction time (RT) and confidence - can be decoded across the cortex when each individual is considered separately. Subjects (N=50) completed a perceptual decision-making task with confidence. We built models decoding trial-level RT and confidence separately for each subject using the activation patterns in one brain area at a time after splitting the entire cortex into 200 regions of interest (ROIs). At the group level, we replicated previous results by showing that both RT and confidence could be decoded from a small number of ROIs (12.0% and 3.5%, respectively). Critically, at the level of the individual, both RT and confidence could be decoded from most brain regions even after Bonferroni correction (90.0% and 72.5%, respectively). Surprisingly, we observed that many brain regions exhibited opposite brain-behavior relationships across individuals, such that, for example, higher activations predicted fast RTs in some subjects but slow RTs in others. These results were further replicated in a second dataset. Lastly, we developed a simple test to determine the robustness of decoding performance, which showed that several hundred trials per subject are required for robust decoding. These results show that behavioral signatures can be decoded from a much broader range of cortical areas than previously recognized and suggest the need to study the brain-behavior relationship at both the group and the individual level.

How do humans give confidence? A comprehensive comparison of process models of perceptual metacognition

Humans have the metacognitive ability to assess the accuracy of their decisions via confidence judgments. Several computational models of confidence have been developed but not enough has been done to compare these models, making it difficult to adjudicate between them. Here, we compare 14 popular models of confidence that make various assumptions, such as confidence being derived from postdecisional evidence, from positive (decision-congruent) evidence, from posterior probability computations, or from a separate decision-making system for metacognitive judgments. We fit all models to three large experiments in which subjects completed a basic perceptual task with confidence ratings. In Experiments 1 and 2, the best-fitting model was the lognormal meta noise (LogN) model, which postulates that confidence is selectively corrupted by signal-dependent noise. However, in Experiment 3, the positive evidence (PE) model provided the best fits. We evaluated a new model combining the two consistently best-performing models-LogN and the weighted evidence and visibility (WEV). The resulting model, which we call logWEV, outperformed its individual counterparts and the PE model across all data sets, offering a better, more generalizable explanation for these data. Parameter and model recovery analyses showed mostly good recoverability but with important exceptions carrying implications for our ability to discriminate between models. Finally, we evaluated each model's ability to explain different patterns in the data, which led to additional insight into their performances. These results comprehensively characterize the relative adequacy of current confidence models to fit data from basic perceptual tasks and highlight the most plausible mechanisms underlying confidence generation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Multiple brain activation patterns for the same task

Meaningful variation in internal states that impacts cognition and behavior remains challenging to discover and characterize. Here we leveraged trial-to-trial fluctuations in the brain-wide signal recorded using functional MRI to test if distinct sets of brain regions are activated on different trials when accomplishing the same task. Across three different perceptual decision-making experiments, we estimated the brain activations for each trial. We then clustered the trials based on their similarity using modularity-maximization, a data-driven classification method. In each experiment, we found multiple distinct but stable subtypes of trials, suggesting that the same task can be accomplished in the presence of widely varying brain activation patterns. Surprisingly, in all experiments, one of the subtypes exhibited strong activation in the default mode network, which is typically thought to decrease in activity during tasks that require externally focused attention. The remaining subtypes were characterized by activations in different task-positive areas. The default mode network subtype was characterized by behavioral signatures that were similar to the other subtypes exhibiting activation with task-positive regions. Finally, in a fourth experiment, we tested whether multiple activation patterns would also appear for a qualitatively different, working memory task. We again found multiple subtypes of trials with differential activation in frontoparietal control, dorsal attention, and ventral attention networks. Overall, these findings demonstrate that the same cognitive tasks are accomplished through multiple brain activation patterns.

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.

Think twice: Re-assessing confidence improves visual metacognition

Metacognition is a fundamental feature of human behavior that has adaptive functional value. Current understanding of the factors that influence metacognition remains incomplete, and we lack protocols to improve metacognition. Here, we introduce a two-step confidence choice paradigm to test whether metacognitive performance may improve by asking subjects to reassess their initial confidence. Previous work on perceptual and mnemonic decision-making has shown that (type 1) perceptual sensitivity benefits from reassessing the primary choice, however, it is not clear whether such an effect occurs for type 2 confidence choices. To test this hypothesis, we ran two separate online experiments, in which participants completed a type 1 task followed by two consecutive confidence choices. The results of the two experiments indicated that metacognitive sensitivity improved after re-evaluation. Since post-decisional evidence accumulation following the first confidence choice is likely to be minimal, this metacognitive improvement is better accounted for by an attenuation of metacognitive noise during the process of confidence generation. Thus, here we argue that metacognitive noise may be filtered out by additional post-decisional processing, thereby improving metacognitive sensitivity. We discuss the ramifications of these findings for models of metacognition and for developing protocols to train and manipulate metacognitive processes.

Task learning is subserved by a domain-general brain network

One of the most important human faculties is the ability to acquire not just new memories but the capacity to perform entirely new tasks. However, little is known about the brain mechanisms underlying the learning of novel tasks. Specifically, it is unclear to what extent learning of different tasks depends on domain-general and/or domain-specific brain mechanisms. Here human subjects (n = 45) learned to perform 6 new tasks while undergoing functional MRI. The different tasks required the engagement of perceptual, motor, and various cognitive processes related to attention, expectation, speed-accuracy tradeoff, and metacognition. We found that a bilateral frontoparietal network was more active during the initial compared with the later stages of task learning, and that this effect was stronger for task variants requiring more new learning. Critically, the same frontoparietal network was engaged by all 6 tasks, demonstrating its domain generality. Finally, although task learning decreased the overall activity in the frontoparietal network, it increased the connectivity strength between the different nodes of that network. These results demonstrate the existence of a domain-general brain network whose activity and connectivity reflect learning for a variety of new tasks, and thus may underlie the human capacity for acquiring new abilities.

Common computations for metacognition and meta-metacognition

Recent evidence shows that people have the meta-metacognitive ability to evaluate their metacognitive judgments of confidence. However, it is unclear whether meta-metacognitive judgments are made by a different system and rely on a separate set of computations compared to metacognitive judgments. To address this question, we asked participants (N = 36) to perform a perceptual decision-making task and provide (i) an object-level, Type-1 response about the identity of the stimulus; (ii) a metacognitive, Type-2 response (low/high) regarding their confidence in their Type-1 decision; and (iii) a meta-metacognitive, Type-3 response (low/high) regarding the quality of their Type-2 rating. We found strong evidence for the existence of Type-3, meta-metacognitive ability. In a separate condition, participants performed an identical task with only a Type-1 response followed by a Type-2 response given on a 4-point scale. We found that the two conditions produced equivalent results such that the combination of binary Type-2 and binary Type-3 responses acts similar to a 4-point Type-2 response. Critically, while Type-2 evaluations were subject to metacognitive noise, Type-3 judgments were made at no additional cost. These results suggest that it is unlikely that there is a distinction between Type-2 and Type-3 systems (metacognition and meta-metacognition) in perceptual decision-making and, instead, a single system can be flexibly adapted to produce both Type-2 and Type-3 evaluations recursively.

Quantifying the contribution of subject and group factors in brain activation

Research in neuroscience often assumes universal neural mechanisms, but increasing evidence points toward sizeable individual differences in brain activations. What remains unclear is the extent of the idiosyncrasy and whether different types of analyses are associated with different levels of idiosyncrasy. Here we develop a new method for addressing these questions. The method consists of computing the within-subject reliability and subject-to-group similarity of brain activations and submitting these values to a computational model that quantifies the relative strength of group- and subject-level factors. We apply this method to a perceptual decision-making task (n = 50) and find that activations related to task, reaction time, and confidence are influenced equally strongly by group- and subject-level factors. Both group- and subject-level factors are dwarfed by a noise factor, though higher levels of smoothing increases their contributions relative to noise. Overall, our method allows for the quantification of group- and subject-level factors of brain activations and thus provides a more detailed understanding of the idiosyncrasy levels in brain activations.

The timing of confidence computations in human prefrontal cortex

Knowing when confidence computations take place is critical for building a mechanistic understanding of the neural and computational bases of metacognition. Yet, even though a substantial amount of research has focused on revealing the neural correlates and computations underlying human confidence judgments, very little is known about the timing of confidence computations. To understand when confidence is computed, we delivered single pulses of transcranial magnetic stimulation (TMS) at different times after stimulus presentation while subjects judged the orientation of a briefly presented visual stimulus and provided a confidence rating. TMS was delivered to either the right dorsolateral prefrontal cortex (DLPFC) in the experimental group or to vertex in the control group. We found that TMS to right DLPFC, but not to vertex, led to increased confidence in the absence of changes to accuracy or metacognitive efficiency. Critically, equivalent levels of confidence increase occurred for TMS delivered between 200 and 500 msec after stimulus presentation. These results suggest that confidence computations occur during a broad window that begins before the perceptual decision has been fully made and thus provide important constraints for theories of confidence generation.

Brain signatures indexing variation in internal processing during perceptual decision-making

Brain activity is highly variable during a task. Discovering, characterizing, and linking variability in brain activity to internal processes has primarily relied on experimental manipulations. However, changes in internal processing could arise from many factors independent of experimental conditions. Here we utilize a data-driven clustering method based on modularity-maximation to identify consistent spatial-temporal EEG activity patterns across individual trials. Subjects (N = 25) performed a motion discrimination task with six interleaved levels of coherence. Clustering identified two discrete subtypes of trials with different patterns of activity. Surprisingly, Subtype 1 occurred more frequently in trials with lower motion coherence but was associated with faster response times. Computational modeling suggests that Subtype 1 was characterized by a lower threshold for reaching a decision. These results highlight across-trial variability in decision processes traditionally hidden to experimenters and provide a method for identifying endogenous brain state variability relevant to cognition and behavior.

Using serial dependence to predict confidence across observers and cognitive domains

Our perceptual system appears hardwired to exploit regularities of input features across space and time in seemingly stable environments. This can lead to serial dependence effects whereby recent perceptual representations bias current perception. Serial dependence has also been demonstrated for more abstract representations, such as perceptual confidence. Here, we ask whether temporal patterns in the generation of confidence judgments across trials generalize across observers and different cognitive domains. Data from the Confidence Database across perceptual, memory, and cognitive paradigms was reanalyzed. Machine learning classifiers were used to predict the confidence on the current trial based on the history of confidence judgments on the previous trials. Cross-observer and cross-domain decoding results showed that a model trained to predict confidence in the perceptual domain generalized across observers to predict confidence across the different cognitive domains. The recent history of confidence was the most critical factor. The history of accuracy or Type 1 reaction time alone, or in combination with confidence, did not improve the prediction of the current confidence. We also observed that confidence predictions generalized across correct and incorrect trials, indicating that serial dependence effects in confidence generation are uncoupled to metacognition (i.e., how we evaluate the precision of our own behavior). We discuss the ramifications of these findings for the ongoing debate on domain-generality versus domain-specificity of metacognition.

Threat expectation does not improve perceptual discrimination despite causing heightened priority processing in the frontoparietal network

Threat cues have been widely shown to elicit increased sensory and attentional neural processing. However, whether this enhanced recruitment leads to measurable behavioral improvements in perception is still in question. Here we adjudicate between two opposing theories: that threat cues do or do not enhance perceptual sensitivity. We created threat stimuli by pairing one direction of motion in a random dot kinematogram with an aversive sound. While in the MRI scanner, 46 subjects (both men and women) completed a cued (threat/safe/neutral) perceptual decision-making task where they indicated the perceived motion direction of each moving dots stimulus. We found strong evidence that threat cues did not increase perceptual sensitivity compared to safe and neutral cues. This lack of improvement in perceptual decision-making ability occurred despite the threat cue resulting in widespread increases in frontoparietal BOLD activity, as well as increased connectivity between the right insula and the frontoparietal network. These results call into question the intuitive claim that expectation automatically enhances our perception of threat, and highlight the role of the frontoparietal network in prioritizing the processing of threat-related environmental cues.

Confidence response times: Challenging postdecisional models of confidence

Even though the nature of confidence computations has been the topic of intense interest, little attention has been paid to what confidence response times (cRTs) reveal about the underlying confidence computations. Several previous studies found cRTs to be negatively correlated with confidence in the group as a whole and consequently hypothesized the existence of an intrinsic relationship of cRT with confidence for all subjects. This hypothesis was further used to support postdecisional models of confidence that predict that cRT and confidence should always be negatively correlated. Here we test the alternative hypothesis that cRT is driven by the frequency of confidence responses such that the most frequent confidence ratings are inherently made faster regardless of whether they are high or low. We examined cRTs in three large data sets from the Confidence Database and found that the lowest cRTs occurred for the most frequent confidence rating. In other words, subjects who gave high confidence ratings most frequently had negative confidence-cRT relationships, whereas subjects who gave low confidence ratings most frequently had positive confidence-cRT relationships. In addition, we found a strong across-subject correlation between response time and cRT, suggesting that response speed for both the decision and the confidence rating is influenced by a common factor. Our results show that cRT is not intrinsically linked to confidence and strongly challenge several postdecisional models of confidence.

Stimulation along the anterior-posterior axis of lateral frontal cortex reduces visual serial dependence

Serial dependence is an attractive pull that recent perceptual history exerts on current judgments. Theory suggests that this bias is due to a form of short-term plasticity prevalent specifically in the frontal lobe. We sought to test the importance of the frontal lobe to serial dependence by disrupting neural activity along its lateral surface during two tasks with distinct perceptual and motor demands. In our first experiment, stimulation of the lateral prefrontal cortex (LPFC) during an oculomotor delayed response task decreased serial dependence only in the first saccade to the target, whereas stimulation posterior to the LPFC decreased serial dependence only in adjustments to eye position after the first saccade. In our second experiment, which used an orientation discrimination task, stimulation anterior to, in, and posterior to the LPFC all caused equivalent decreases in serial dependence. In this experiment, serial dependence occurred only between stimuli at the same location; an alternation bias was observed across hemifields. Frontal stimulation had no effect on the alternation bias. Transcranial magnetic stimulation to parietal cortex had no effect on serial dependence in either experiment. In summary, our experiments provide evidence for both functional differentiation (Experiment 1) and redundancy (Experiment 2) in frontal cortex with respect to serial dependence.

Brain signatures indexing variation in internal processing during perceptual decision-making

Brain activity is highly variable even while performing the same cognitive task with consequences for performance. Discovering, characterizing, and linking variability in brain activity to internal processes has primarily relied on experimentally inducing changes (e.g., via attention manipulation) to identify neuronal and behavioral consequences or studying spontaneous changes in ongoing brain dynamics. However, changes in internal processing could arise from many factors, such as variation in strategy or arousal, that are independent of experimental conditions. Here we utilize a data-driven clustering method based on modularity-maximation to identify consistent spatial-temporal EEG activity patterns across individual trials and relate this activity to behavioral performance. Subjects (N = 25) performed a motion direction discrimination task with six interleaved levels of motion coherence. Modularity-maximization based clustering identified two discrete spatial-temporal clusters, or subtypes, of trials with different patterns of brain activity. Surprisingly, even though Subtype 1 occurred more frequently with lower motion coherence, it was nonetheless associated with faster response times. Computational modeling suggests that Subtype 1 was characterized by a lower threshold for reaching a decision. These results highlight trial-to-trial variability in decision processes usually masked to experimenters and provide a method for identifying endogenous brain state variability relevant to cognition and behavior.

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.

The timing of confidence computations in human prefrontal cortex

Knowing when confidence computations take place is critical for building mechanistic understanding of the neural and computational bases of metacognition. Yet, even though substantial amount of research has focused on revealing the neural correlates and computations underlying human confidence judgments, very little is known about the timing of confidence computations. Subjects judged the orientation of a briefly presented visual stimulus and provided a confidence rating regarding the accuracy of their decision. We delivered single pulses of transcranial magnetic stimulation (TMS) at different times after stimulus presentation. TMS was delivered to either dorsolateral prefrontal cortex (DLPFC) in the experimental group or to vertex in the control group. We found that TMS to DLPFC, but not to vertex, led to increased confidence in the absence of changes to accuracy or metacognitive ability. Critically, equivalent levels of confidence increase occurred for TMS delivered between 200 and 500 ms after stimulus presentation. These results suggest that confidence computations occur during a broad window that begins before the perceptual decision has been fully made and thus provide important constraints for theories of confidence generation.

Consensus Goals in the Field of Visual Metacognition

Despite the tangible progress in psychological and cognitive sciences over the last several years, these disciplines still trail other more mature sciences in identifying the most important questions that need to be solved. Reaching such consensus could lead to greater synergy across different laboratories, faster progress, and increased focus on solving important problems rather than pursuing isolated, niche efforts. Here, 26 researchers from the field of visual metacognition reached consensus on four long-term and two medium-term common goals. We describe the process that we followed, the goals themselves, and our plans for accomplishing these goals. If this effort proves successful within the next few years, such consensus building around common goals could be adopted more widely in psychological science.

Across-subject correlation between confidence and accuracy: A meta-analysis of the Confidence Database

If one friend confidently tells us to buy Product A while another friend thinks that Product B is better but is not confident, we may go with the advice of our confident friend. Should we? The relationship between people's confidence and accuracy has been of great interest in many fields, especially in high-stakes situations like eyewitness testimony. However, there is still little consensus about how much we should trust someone's overall confidence level. Here, we examine the across-subject relationship between average accuracy and average confidence in 213 unique datasets from the Confidence Database. This approach allows us to empirically address this issue with unprecedented statistical power and check for the presence of various moderators. We find an across-subject correlation between average accuracy and average confidence of R = .22. Importantly, this relationship is much stronger for memory than for perception tasks ("domain effect"), as well as for confidence scales with fewer points ("granularity effect"). These results show that we should take one's confidence seriously (and perhaps buy Product A) and suggest several factors that moderate the relative consistency of how people make confidence judgments.

Visual metacognition: Measures, models, and neural correlates

Visual metacognition is the ability to evaluate one's performance on visual perceptual tasks. The field of visual metacognition unites the long tradition of visual psychophysics with the younger field of metacognition research. This article traces the historical roots of the field and reviews progress in the areas of (a) constructing appropriate measures of metacognitive ability, (b) developing computational models, and (c) revealing the neural correlates of visual metacognition. First, I review the most popular measures of metacognitive ability with an emphasis on their psychophysical properties. Second, I examine the empirical targets for modeling, the dominant modeling frameworks and the assumed computations underlying visual metacognition. Third, I explore the progress on understanding the neural correlates of visual metacognition by focusing on anatomical and functional studies, as well as causal manipulations. What emerges is a picture of substantial progress on constructing measures, developing models, and revealing the neural correlates of metacognition, but very little integration between these three areas of inquiry. I then explore the deep, intrinsic links between the three areas of research and argue that continued progress requires the recognition and exploitation of these links. Throughout, I discuss the implications of progress in visual metacognition for other areas of metacognition research, and pinpoint specific advancements that could be adopted by researchers working in other subfields of metacognition. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The Impact of Feedback on Perceptual Decision-Making and Metacognition: Reduction in Bias but No Change in Sensitivity

It is widely believed that feedback improves behavior, but the mechanisms behind this improvement remain unclear. Different theories postulate that feedback has either a direct effect on performance through automatic reinforcement mechanisms or only an indirect effect mediated by a deliberate change in strategy. To adjudicate between these competing accounts, we performed two large experiments on human adults (total N = 518); approximately half the participants received trial-by-trial feedback on a perceptual task, whereas the other half did not receive any feedback. We found that feedback had no effect on either perceptual or metacognitive sensitivity even after 7 days of training. On the other hand, feedback significantly affected participants' response strategies by reducing response bias and improving confidence calibration. These results suggest that the beneficial effects of feedback stem from allowing people to adjust their strategies for performing the task and not from direct reinforcement mechanisms, at least in the domain of perception.

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.

Evidence for Transcranial Magnetic Stimulation Induced Functional Connectivity Oscillations in the Brain

Transcranial magnetic stimulation (TMS) is an effective research tool to elucidate mechanisms of function in the brain. Despite its widespread use, very few studies have looked at dynamic functional connectivity responses to TMS. This work performs an exploratory analysis of dynamic functional network connectivity (dynFNC) to evaluate evidence of brain response to TMS. Results show clear functional dynamic patterns categorized by frequency. Some patterns appear to be more directly linked to TMS, but there is one pattern that might be a TMS-independent response to the excitation. This first look presents an analysis methodology and important results to consider in future research.

Examining the robustness of the relationship between metacognitive efficiency and metacognitive bias

We recently found a positive relationship between estimates of metacognitive efficiency and metacognitive bias. However, this relationship was only examined on a within-subject level and required binarizing the confidence scale, a technique that introduces methodological difficulties. Here we examined the robustness of the positive relationship between estimates of metacognitive efficiency and metacognitive bias by conducting two different types of analyses. First, we developed a new within-subject analysis technique where the original n-point confidence scale is transformed into two different (n-1)-point scales in a way that mimics a naturalistic change in confidence. Second, we examined the across-subject correlation between metacognitive efficiency and metacognitive bias. Importantly, for both types of analyses, we not only established the direction of the effect but also computed effect sizes. We applied both techniques to the data from three tasks from the Confidence Database (N > 400 in each). We found that both approaches revealed a small to medium positive relationship between metacognitive efficiency and metacognitive bias. These results demonstrate that the positive relationship between metacognitive efficiency and metacognitive bias is robust across several analysis techniques and datasets, and have important implications for future research.

Response Bias Reflects Individual Differences in Sensory Encoding

Humans exhibit substantial biases in their decision making even in simple two-choice tasks, but the origin of these biases remains unclear. I hypothesized that one source of bias could be individual differences in sensory encoding. Specifically, if one stimulus category gives rise to an internal-evidence distribution with higher variability, then responses should optimally be biased against that stimulus category. Therefore, response bias may reflect a previously unappreciated subject-to-subject difference in the variance of the internal-evidence distributions. I tested this possibility by analyzing data from three different two-choice tasks (ns = 443, 443, and 498). For all three tasks, response bias moved in the direction of the optimal criterion determined by each subject's idiosyncratic internal-evidence variability. These results demonstrate that seemingly random variations in response bias can be driven by individual differences in sensory encoding and are thus partly explained by normative strategies.

Transcranial magnetic stimulation alters multivoxel patterns in the absence of overall activity changes

Transcranial magnetic stimulation (TMS) has become one of the major tools for establishing the causal role of specific brain regions in perceptual, motor, and cognitive processes. Nevertheless, a persistent limitation of the technique is the lack of clarity regarding its precise effects on neural activity. Here, we examined the effects of TMS intensity and frequency on concurrently recorded blood‐oxygen‐level‐dependent (BOLD) signals at the site of stimulation. In two experiments, we delivered TMS to the dorsolateral prefrontal cortex in human subjects of both sexes. In Experiment 1, we delivered a series of pulses at high (100% of motor threshold) or low (50% of motor threshold) intensity, whereas, in Experiment 2, we always used high intensity but delivered stimulation at four different frequencies (5, 8.33, 12.5, and 25 Hz). We found that the TMS intensity and frequency could be reliably decoded using multivariate analysis techniques even though TMS had no effect on the overall BOLD activity at the site of stimulation in either experiment. These results provide important insight into the mechanisms through which TMS influences neural activity.

Awake suppression after brief exposure to a familiar stimulus

Newly learned information undergoes a process of awake reactivation shortly after the learning offset and we recently demonstrated that this effect can be observed as early as area V1. However, reactivating all experiences can be wasteful and unnecessary, especially for familiar stimuli. Therefore, here we tested whether awake reactivation occurs differentially for new and familiar stimuli. Subjects completed a brief visual task on a stimulus that was either novel or highly familiar due to extensive prior training on it. Replicating our previous results, we found that awake reactivation occurred in V1 for the novel stimulus. On the other hand, brief exposure to the familiar stimulus led to 'awake suppression' such that neural activity patterns immediately after exposure to the familiar stimulus diverged from the patterns associated with that stimulus. Further, awake reactivation was observed selectively in V1, whereas awake suppression had similar strength across areas V1-V3. These results are consistent with the presence of a competition between local awake reactivation and top-down awake suppression, with suppression becoming dominant for familiar stimuli.

Qualitative speed-accuracy tradeoff effects that cannot be explained by the diffusion model under the selective influence assumption

It is often thought that the diffusion model explains all effects related to the speed-accuracy tradeoff (SAT) but this has previously been examined with only a few SAT conditions or only a few subjects. Here we collected data from 20 subjects who performed a perceptual discrimination task with five different difficulty levels and five different SAT conditions (5000 trials/subject). We found that the five SAT conditions produced robustly U-shaped curves for (i) the difference between error and correct response times (RTs), (ii) the ratio of the standard deviation and mean of the RT distributions, and (iii) the skewness of the RT distributions. Critically, the diffusion model where only drift rate varies with contrast and only boundary varies with SAT could not account for any of the three U-shaped curves. Further, allowing all parameters to vary across conditions revealed that both the SAT and difficulty manipulations resulted in substantial modulations in every model parameter, while still providing imperfect fits to the data. These findings demonstrate that the diffusion model cannot fully explain the effects of SAT and establishes three robust but challenging effects that models of SAT should account for.

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.

Confidence in the Real World

Humans often assign confidence to multioption decisions, but most computational research only uses two-alternative tasks. In a new study, Li and Ma begin to reveal the mechanisms of confidence generation in multialternative tasks. This research should inspire further experiments on how humans assign confidence judgments in real-world situations.

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).

The Confidence Database

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects.

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.

Erratum: How experimental procedures influence estimates of metacognitive ability

[This corrects the article DOI: 10.1093/nc/niz009.][This corrects the article DOI: 10.1093/nc/niz009.].

Predictive cues reduce but do not eliminate intrinsic response bias

Predictive cues induce large changes in people's choices by biasing responses towards the expected stimulus category. At the same time, even in the absence of predictive cues, humans often exhibit substantial intrinsic response biases. Despite the ubiquity of both of these biasing effects, it remains unclear how predictive cues interact with intrinsic bias. To understand the nature of this interaction, we examined data across three previous experiments that featured a combination of neutral cues (revealing intrinsic biases) and predictive cues. We found that predictive cues decreased the intrinsic bias to about half of its original size. This result held both when bias was quantified as the criterion location estimated using signal detection theory and as the probability of choosing a particular stimulus category. Our findings demonstrate that predictive cues reduce but do not eliminate intrinsic response bias, testifying to both the malleability and rigidity of intrinsic biases.

How experimental procedures influence estimates of metacognitive ability

It is becoming widely appreciated that higher stimulus sensitivity trivially increases estimates of metacognitive sensitivity. Therefore, meaningful comparisons of metacognitive ability across conditions and observers necessitates equating stimulus sensitivity. To achieve this, one common approach is to use a continuous staircase that runs throughout the duration of the experiment under the assumption that this procedure has no influence on the estimated metacognitive ability. Here we critically examine this assumption. Using previously published data, we find that, compared to using a single level of stimulus contrast, staircase techniques lead to inflated estimates of metacognitive ability across a wide variety of measures including area under the type 2 ROC curve, the confidence-accuracy correlation phi, meta-d′, meta-d′/d′, and meta-d′–d′. Furthermore, this metacognitive inflation correlates with the degree of stimulus variability experienced by each subject. These results suggest that studies using a staircase approach are likely to report inflated estimates of metacognitive ability. Furthermore, we argue that similar inflation likely occurs in the presence of variability in task difficulty caused by other factors such as fluctuations in alertness or gradual improvement on the task. We offer practical solutions to these issues, both in the design and analysis of metacognition experiments.

Resolving Age-Related Differences in Working Memory: Equating Perception and Attention Makes Older Adults Remember as Well as Younger Adults

OBJECTIVES

Older adults show clear deficits in working memory functioning. Here, we investigate the often-reported decline in focus switching, that is, the ability to shift items from the focus of attention into working memory, and back. Specifically, we examined whether equating subjects on early processing (perception and attention) might ameliorate the deficit.

METHOD

We examined 1-Back and 2-Back performance in younger and older adults, with line segments of different orientation as the stimuli. Stimuli were calibrated depending on each individual's 75% threshold for 1-Back performance. Subjects made match/mismatch judgments.

RESULTS

After the calibration on 1-Back performance, no age-related differences were found on either accuracy or sensitivity in the 2-Back task. Additionally, when investigating focus-switch trials versus non-focus-switch trials in a random-order 2-Back task, older adults were more efficient at switching the focus of attention than younger adults.

DISCUSSION

These results provide evidence for the view that age-related limitations in focus switching in working memory are caused (at least in part) by changes in early processing (perception and attention), suggesting that (at least some of the) age-related differences in working memory functioning may be due to shifts in trade-off between early processing and memory-related processing.