Metric error monitoring as a component of metacognitive processing.

Metacognitive processing constitutes one of the contemporary target domains in consciousness research. Error monitoring (the ability to correctly report one's own errors without feedback) is considered one of the functional outcomes of metacognitive processing. Error monitoring is traditionally investigated as part of categorical decisions where choice accuracy is a binary construct (choice is either correct or incorrect). However, recent studies revealed that this ability is characterized by metric features (i.e., direction and magnitude) in temporal, spatial, and numerical domains. Here, we discuss methodological approaches to investigating metric error monitoring in both humans and non-human animals and review their findings. The potential neural substrates of metric error monitoring measures are also discussed. This new scope of metacognitive processing can help improve our current understanding of conscious processing from a new perspective. Thus, by summarizing and discussing the perspectives, findings, and common applications in the metric error monitoring literature, this paper aims to provide a guideline for future research.

Altered temporal awareness during Covid-19 pandemic.

Social isolation during the COVID-19 pandemic had profound effects on human well-being. A handful of studies have focused on how time perception was altered during the COVID-19 pandemic, while no study has tested whether temporal metacognition is also affected by the lockdown. We examined the impact of long-term social isolation during the COVID-19 pandemic on the ability to monitor errors in timing performance. We recruited 1232 participants from 12 countries during lockdown, 211 of which were retested "post-pandemic" for within-group comparisons. We also tested a new group of 331 participants during the "post-pandemic" period and compared their data to those of 1232 participants tested during the lockdown (between-group comparison). Participants produced a 3600 ms target interval and assessed the magnitude and direction of their time production error. Both within and between-group comparisons showed reduced metric error monitoring performance during the lockdown, even after controlling for government-imposed stringency indices. A higher level of reported social isolation also predicted reduced temporal error monitoring ability. Participants produced longer duration during lockdown compared to post-lockdown (again controlling for government stringency indices). We reason that these effects may be underlain by altered biological and behavioral rhythms during social isolation experienced during the COVID-19 pandemic. Understanding these effects is crucial for a more complete characterization of the cognitive consequences of long-term social isolation.

Neurocomputational Models of Interval Timing: Seeing the Forest for the Trees.

Extracting temporal regularities and relations from experience/observation is critical for organisms' adaptiveness (communication, foraging, predation, prediction) in their ecological niches. Therefore, it is not surprising that the internal clock that enables the perception of seconds-to-minutes-long intervals (interval timing) is evolutionarily well-preserved across many species of animals. This comparative claim is primarily supported by the fact that the timing behavior of many vertebrates exhibits common statistical signatures (e.g., on-average accuracy, scalar variability, positive skew). These ubiquitous statistical features of timing behaviors serve as empirical benchmarks for modelers in their efforts to unravel the processing dynamics of the internal clock (namely answering how internal clock "ticks"). In this chapter, we introduce prominent (neuro)computational approaches to modeling interval timing at a level that can be understood by general audience. These models include Treisman's pacemaker accumulator model, the information processing variant of scalar expectancy theory, the striatal beat frequency model, behavioral expectancy theory, the learning to time model, the time-adaptive opponent Poisson drift-diffusion model, time cell models, and neural trajectory models. Crucially, we discuss these models within an overarching conceptual framework that categorizes different models as threshold vs. clock-adaptive models and as dedicated clock/ramping vs. emergent time/population code models.

The timing database: An open-access, live repository for interval timing studies.

Interval timing refers to the ability to perceive and remember intervals in the seconds to minutes range. Our contemporary understanding of interval timing is derived from relatively small-scale, isolated studies that investigate a limited range of intervals with a small sample size, usually based on a single task. Consequently, the conclusions drawn from individual studies are not readily generalizable to other tasks, conditions, and task parameters. The current paper presents a live database that presents raw data from interval timing studies (currently composed of 68 datasets from eight different tasks incorporating various interval and temporal order judgments) with an online graphical user interface to easily select, compile, and download the data organized in a standard format. The Timing Database aims to promote and cultivate key and novel analyses of our timing ability by making published and future datasets accessible as open-source resources for the entire research community. In the current paper, we showcase the use of the database by testing various core ideas based on data compiled across studies (i.e., temporal accuracy, scalar property, location of the point of subjective equality, malleability of timing precision). The Timing Database will serve as the repository for interval timing studies through the submission of new datasets.

Mice make temporal inferences about novel locations based on previously learned spatiotemporal contingencies.

Animals learn multiple spatiotemporal contingencies and organize their anticipatory responses accordingly. The representational/computational capacity that underlies such spatiotemporally guided behaviors is not fully understood. To this end, we investigated whether mice make temporal inferences of novel locations based on previously learned spatiotemporal contingencies. We trained 18 C57BL/6J mice to anticipate reward after three different intervals at three different locations and tested their temporal expectations of a reward at five locations simultaneously, including two locations that were not previously associated with reward delivery but adjacent to the previously trained locations. If mice made spatiotemporal inferences, they were expected to interpolate between duration pairs associated with previously reinforced hoppers surrounding the novel hopper. We found that the maximal response rate at the novel locations indeed fell between the two intervals reinforced at the surrounding hoppers. We argue that this pattern of responding might be underlain by spatially constrained Bayesian computations.

Count-based decision-making in mice: numerosity vs. stimulus control.

Numerical and temporal control of behavior is ubiquitous across many species of animals. Recent studies showed that in the presence of reliable discriminative stimuli, mice ignore temporal relations and probabilistic information but when discriminative stimuli become non-informative, the same mice can spontaneously start relying on previously experienced time intervals and probabilities. Similar dynamics do not readily generalize to counting behavior since the response-outcome contingency functions differ when reinforcement depends on the number vs. timing of responding. In the current study, mice (N = 32) learned to press two different levers 10 (few) or 20 (many) times, while the active lever was signaled by a light stimulus. The probability of the few/many trials was manipulated between groups. During testing, the informative value of light stimulus was eliminated by signaling both few- and many-levers. In a quarter of training trials, mice ignored the discriminative stimulus and adopted a numerical decision strategy (starting to respond on the few-option and then switching to the many-option in many trials) that was sensitive to probabilistic information. The frequency but not the probability-sensitive parametrization of switching behavior changed when the discriminative stimulus became non-informative in testing. These findings suggest that there is a relatively strong representational control over counting behavior even in conditions that afford strong stimulus control.

Numerical averaging in mice.

Rodents can be trained to associate different durations with different stimuli (e.g., light/sound). When the associated stimuli are presented together, maximal responding is observed around the average of individual durations (akin to averaging). The current study investigated whether mice can also average independently trained numerosities. Mice were initially trained to make 10 or 20 lever presses on a single (run) lever to obtain a reward and each fixed-ratio schedule was signaled either with an auditory or visual stimulus. Then, mice were trained to press another lever to obtain the reward after they responded on the run lever for the minimum number of presses [Fixed Consecutive Number (FCN)-10 or -20 trials] signaled by the corresponding discriminative stimulus. Following this training, FCN trials with the compound stimulus were introduced to test the counting behavior of mice when they encountered conflicting information regarding the number of responses required to obtain the reward. Our results showed that the numbers of responses on these compound test trials were around the average of the number of responses in FCN-10 and FCN-20 trials particularly when the auditory stimulus was associated with a fewer number of required responses. The counting strategy explained the behavior of the majority of the mice in the FCN-Compound test trials (as opposed to the timing strategy). The number of responses in FCN-Compound trials was accounted for equally well by the arithmetic, geometric, and Bayesian averages of the number of responses observed in FCN-10 and FCN-20 trials.

Timing behavior in genetic murine models of neurological and psychiatric diseases.

How timing behavior is altered in different neurodevelopmental and neurodegenerative disorders is a contemporary research question. Genetic murine models (GMM) that offer high construct validity also serve as useful tools to investigate this question. But the literature on timing behavior of different GMMs largely remains to be consolidated. The current paper addresses this gap by reviewing studies that have been conducted with GMMs of neurodevelopmental (e.g. ADHD, schizophrenia, autism spectrum disorder), neurodegenerative disorders (e.g., Alzheimer's disease, Huntington's disease) as well as circadian and other mutant lines. The review focuses on those studies that specifically utilized the peak interval procedure to improve the comparability of findings both within and between different disease models. The reviewed studies revealed timing deficits that are characteristic of different disorders. Specifically, Huntington's disease models had weaker temporal control over the termination of their anticipatory responses, Alzheimer's disease models had earlier timed responses, schizophrenia models had weaker temporal control, circadian mutants had shifted timed responses consistent with shifts in the circadian periods. The differences in timing behavior were less consistent for other conditions such as attention deficit and hyperactivity disorder and mutations related to intellectual disability. We discuss the implications of these findings for the neural basis of an internal stopwatch. Finally, we make methodological recommendations for future research for improving the comparability of the timing behavior across different murine models.

Etanercept rescues cognitive deficits, depression-like symptoms, and spike-wave discharge incidence in WAG/Rij rat model of absence epilepsy.

Pro-inflammatory cytokines have been shown to be associated with the development of seizures in the WAG/Rij rat model of absence epilepsy. Importantly, WAG/Rij rats also exhibit cognitive deficits and depression-like behaviors. It is possible that pro-inflammatory cytokines mediate these comorbid conditions of absence epilepsy given their well-established effects on cognition and affective responses. The current study investigated the potential therapeutic effect of etanercept (tumor necrosis factor inhibitor) on cognitive impairment, depression-like behavior, and spike-wave discharges (SWDs) typically observed in the WAG/Rij rats. Eight-month-old male WAG/Rij rats and Wistar controls were tested in Morris water maze (MWM), passive avoidance (PA), forced swimming, sucrose preference, and locomotor activity tests, and electroencephalogram (EEG) recordings were taken from a separate group of WAG/Rij rats after 8 weeks of etanercept or vehicle treatment. Consistent with earlier work, WAG/Rij rats exhibited cognitive deficits and depression-like behavior. From these, the cognitive deficits and despair-like behavior were rescued by etanercept administration, which also reduced the frequency of SWDs without affecting their duration. Our results support the hypothesis that pro-inflammatory cytokines mediate the absence seizures and comorbid symptoms of absence epilepsy.

Temporal error monitoring with directional error magnitude judgements: a robust phenomenon with no effect of being watched.

A key aspect of metacognition is the ability to monitor performance. A recent line of work has shown that error-monitoring ability captures both the magnitude and direction of timing errors, thereby pointing at the metric composition of error monitoring [e.g., Akdogan and Balci (J Exp Psychol https://dx.doi.org/10.1037/xge0000265 , 2017)]. These studies, however, primarily used a composite variable that combined isolated measures of ordinal confidence ratings (as a proxy for error magnitude judgement) and "shorter/longer than the target" judgements. In two experiments we tested temporal error monitoring (TEM) performance with a more direct measure of directional error magnitude rating on a continuum. The second aim of this study is to test if TEM performance is modulated by the feeling of being watched that was previously shown to influence metacognitive-like monitoring processes. We predicted that being watched would improve TEM performance, particularly in participants with high timing precision (a proxy for high task mastery), and disrupt TEM performance in participants with low timing precision (a proxy for low task mastery). In both experiments, we found strong evidence for TEM ability. However, we did not find any reliable effect of the social stimulus on TEM performance. In short, our results demonstrate that metric error monitoring is a robust metacognitive phenomenon, which is not sensitive to social influence.

Synchrony and Complexity in State-Related EEG Networks: An Application of Spectral Graph Theory.

The brain may be considered as a synchronized dynamic network with several coherent dynamical units. However, concerns remain whether synchronizability is a stable state in the brain networks. If so, which index can best reveal the synchronizability in brain networks? To answer these questions, we tested the application of the spectral graph theory and the Shannon entropy as alternative approaches in neuroimaging. We specifically tested the alpha rhythm in the resting-state eye closed (rsEC) and the resting-state eye open (rsEO) conditions, a well-studied classical example of synchrony in neuroimaging EEG. Since the synchronizability of alpha rhythm is more stable during the rsEC than the rsEO, we hypothesized that our suggested spectral graph theory indices (as reliable measures to interpret the synchronizability of brain signals) should exhibit higher values in the rsEC than the rsEO condition. We performed two separate analyses of two different datasets (as elementary and confirmatory studies). Based on the results of both studies and in agreement with our hypothesis, the spectral graph indices revealed higher stability of synchronizability in the rsEC condition. The k-mean analysis indicated that the spectral graph indices can distinguish the rsEC and rsEO conditions by considering the synchronizability of brain networks. We also computed correlations among the spectral indices, the Shannon entropy, and the topological indices of brain networks, as well as random networks. Correlation analysis indicated that although the spectral and the topological properties of random networks are completely independent, these features are significantly correlated with each other in brain networks. Furthermore, we found that complexity in the investigated brain networks is inversely related to the stability of synchronizability. In conclusion, we revealed that the spectral graph theory approach can be reliably applied to study the stability of synchronizability of state-related brain networks.

Monitoring line length reproduction errors.

Previous work revealed that humans can keep track of the direction and degree of errors in their temporal and numerical reproductions/estimations. Given the behavioral and psychophysical commonalities to various magnitudes and the implication of an overlapping neuroanatomical locus for their representation, we hypothesized that participants would capture the direction of errors and confidence ratings would track the magnitude of errors in line-length reproductions. In two experiments, participants reproduced various target lengths as accurately as possible, and reported the direction of their errors and provided confidence ratings for their reproductions. The isolated analysis of these two second-order judgments showed that participants can correctly report the direction of errors in their line-length reproductions and subjective confidence decreases as the magnitude of errors increases. These results show that humans can robustly keep track of the direction of errors in their line-length reproductions and their subjective confidence corroborates the magnitude of these errors.

Interval timing is disrupted in female 5xFAD mice: An indication of altered memory processes.

Temporal information processing in the seconds-to-minutes range is disrupted in patients with Alzheimer's disease (AD). In this study, we investigated the timing behavior of the 5xFAD mouse model of AD in the peak interval (PI) procedure. Nine-month-old female mice were trained with sucrose solution reinforcement for their first response after a fixed-interval (FI) and tested in the inter-mixed non-reinforced PI trials that lasted longer than FI. Timing performance indices were estimated from steady-state timed anticipatory nose-poking responses in the PI trials. We found that the time of maximal reward expectancy (peak time) of the 5xFAD mice was significantly earlier than that of the wild-type (WT) controls with no differences in other indices of timing performance. These behavioral differences corroborate the findings of previous studies on the disruption of temporal associative memory abilities of 5xFAD mice and can be accounted for by the scalar timing theory based on altered long-term memory consolidation of temporal information in the 5xFAD mice. This is the first study to directly show an interval timing phenotype in a genetic mouse model of AD.

Metric error monitoring in the numerical estimates.

Recent studies have shown that participants can keep track of the magnitude and direction of their errors while reproducing target intervals (Akdogan & Balci, 2017) and producing numerosities with sequentially presented auditory stimuli (Duyan & Balci, 2018). Although the latter work demonstrated that error judgments were driven by the number rather than the total duration of sequential stimulus presentations, the number and duration of stimuli are inevitably correlated in sequential presentations. This correlation empirically limits the purity of the characterization of "numerical error monitoring". The current work expanded the scope of numerical error monitoring as a form of "metric error monitoring" to numerical estimation based on simultaneously presented array of stimuli to control for temporal correlates. Our results show that numerical error monitoring ability applies to magnitude estimation in these more controlled experimental scenarios underlining its ubiquitous nature.

Mice plan decision strategies based on previously learned time intervals, locations, and probabilities.

Animals can shape their timed behaviors based on experienced probabilistic relations in a nearly optimal fashion. On the other hand, it is not clear if they adopt these timed decisions by making computations based on previously learnt task parameters (time intervals, locations, and probabilities) or if they gradually develop their decisions based on trial and error. To address this question, we tested mice in the timed-switching task, which required them to anticipate when (after a short or long delay) and at which of the two delay locations a reward would be presented. The probability of short trials differed between test groups in two experiments. Critically, we first trained mice on relevant task parameters by signaling the active trial with a discriminative stimulus and delivered the corresponding reward after the associated delay without any response requirement (without inducing switching behavior). During the test phase, both options were presented simultaneously to characterize the emergence and temporal characteristics of the switching behavior. Mice exhibited timed-switching behavior starting from the first few test trials, and their performance remained stable throughout testing in the majority of the conditions. Furthermore, as the probability of the short trial increased, mice waited longer before switching from the short to long location (experiment 1). These behavioral adjustments were in directions predicted by reward maximization. These results suggest that rather than gradually adjusting their time-dependent choice behavior, mice abruptly adopted temporal decision strategies by directly integrating their previous knowledge of task parameters into their timed behavior, supporting the model-based representational account of temporal risk assessment.

Spontaneous integration of temporal information: implications for representational/computational capacity of animals.

How do animals adapt their behaviors to changing conditions? This question relates to the debate between associative versus representational/computational approaches in cognitive science. An influential line of research that has significantly shaped the conceptual development of animal learning over decades has primarily focused on the role of associative dynamics with little-to-no ascription of representational/combinatorial capacities. The common assumption of these models is that behavioral adjustments are incremental and they result from updating of associations based on actions and their outcomes, without encoding the critical information serving as the determinant(s) of such contingencies (e.g., time in interval schedules, number in ratio schedules). On the other hand, an independent line of research provides evidence for behavioral phenomena that cannot be readily accounted for by the conventional associationist approach. In this paper, we will review different sets of findings particularly in the area of interval timing that suggest the ability of animals to make swift spontaneous computations on subjective quantities and incorporate them into their behavior. Findings of these studies constitute empirical challenges for the associationist approaches to behavioral flexibility. We argue that interval timing is a fertile ground for the formulation of critical tests of different theoretical approaches to animal behavior.

Discarding optimality: Throwing out the baby with the bathwater?

Rahnev & Denison (R&D) argue against normative theories and in favor of a more descriptive "standard observer model" of perceptual decision making. We agree with the authors in many respects, but we argue that optimality (specifically, reward-rate maximization) has proved demonstrably useful as a hypothesis, contrary to the authors' claims.

Inhibition of Pre-Supplementary Motor Area by Continuous Theta Burst Stimulation Leads to More Cautious Decision-making and More Efficient Sensory Evidence Integration.

Decisions are made based on the integration of available evidence. The noise in evidence accumulation leads to a particular speed-accuracy tradeoff in decision-making, which can be modulated and optimized by adaptive decision threshold setting. Given the effect of pre-SMA activity on striatal excitability, we hypothesized that the inhibition of pre-SMA would lead to higher decision thresholds and an increased accuracy bias. We used offline continuous theta burst stimulation to assess the effect of transient inhibition of the right pre-SMA on the decision processes in a free-response two-alternative forced-choice task within the drift diffusion model framework. Participants became more cautious and set higher decision thresholds following right pre-SMA inhibition compared with inhibition of the control site (vertex). Increased decision thresholds were accompanied by an accuracy bias with no effects on post-error choice behavior. Participants also exhibited higher drift rates as a result of pre-SMA inhibition compared with the vertex inhibition. These results, in line with the striatal theory of speed-accuracy tradeoff, provide evidence for the functional role of pre-SMA activity in decision threshold modulation. Our results also suggest that pre-SMA might be a part of the brain network associated with the sensory evidence integration.

Mice optimize timed decisions about probabilistic outcomes under deadlines.

Optimal performance in temporal decisions requires the integration of timing uncertainty with environmental statistics such as probability or cost functions. Reward maximization under response deadlines constitutes one of the most stringent examples of these problems. The current study investigated whether and how mice can optimize their timing behavior in a complex experimental setting under a response deadline in which reward maximization required the integration of timing uncertainty with a geometrically increasing probability/decreasing cost function. Mice optimized their performance under seconds-long response deadlines when the underlying function was reward probability but approached this level of performance when the underlying function was reward cost, only under the assumption of logarithmically scaled subjective costs. The same subjects were then tested in a timed response inhibition task characterized by response rules that conflicted with the initial task, not responding earlier than a schedule as opposed to not missing the deadline. Irrespective of original test groups, mice optimized the timing of their inhibitory control in the second experiment. These results provide strong support for the ubiquity of optimal temporal risk assessment in mice.