Robin AA Ince’s research while affiliated with University of Glasgow and other places

Decision confidence, an internal estimate of how accurate our choices are, is essential for metacognitive self-evaluation and guides behaviour. However, it can be suboptimal and hence understanding the underlying neurocomputational mechanisms is crucial. To do so, it is essential to establish the extent to which both behavioural and neurophysiological measures of metacognition are reliable over time and shared across cognitive domains. The evidence regarding domain-generality of metacognition has been mixed, while the test-retest reliability of the most widely used metacognitive measures has not been reported. Here, in human participants of both sexes, we examined behavioural and electroencephalographic (EEG) measures of metacognition across two tasks that engage distinct cognitive domains – visual perception and semantic memory. The test-retest reliability of all measures was additionally tested across two experimental sessions. The results revealed a dissociation between metacognitive bias and efficiency, whereby only metacognitive bias showed strong test-retest reliability and domain-generality whilst metacognitive efficiency (measured by M-ratio) was neither reliable nor domain-general. Hence, overall confidence calibration (i.e., metacognitive bias) is a stable trait-like characteristic underpinned by domain-general mechanisms whilst metacognitive efficiency may rely on more domain-specific computations. Additionally, we found two distinct stimulus-locked EEG signatures related to the trial-by-trial fluctuations in confidence ratings during decision formation. A late event-related potential was reliably linked to confidence across cognitive domains, while evoked spectral power predicted confidence most reliably in the semantic knowledge domain. Establishing the reliability and domain-generality of neural predictors of confidence represents an important step in advancing our understanding of the mechanisms underlying self-evaluation. Significance Statement Understanding the mechanisms underlying metacognition is essential for addressing deficits in self-evaluation. Open questions exist regarding the domain-generality and reliability of both behavioural and neural measures of metacognition. We show that metacognitive bias is reliable across cognitive domains and time, whereas the most adopted measure of metacognitive efficiency is domain-specific and shows poor test-retest reliability. Hence, more reliable measures of metacognition, tailored to specific domains, are needed. We further show that decision confidence is linked to two EEG signatures: late event-related potentials and evoked alpha/beta spectral power. While the former predicts confidence in both perception and semantic knowledge domains, the latter is only reliably linked to knowledge confidence. These findings provide crucial insights into the computations underlying metacognition across domains.

This study investigates the dynamics of speech envelope tracking during speech production, listening and self listening. We use a paradigm in which participants listen to natural speech (Listening), produce natural speech (Speech Production), and listen to the playback of their own speech (Self-Listening), all while their neural activity is recorded with EEG. After time-locking EEG data collection and auditory recording and playback, we used a Gaussian copula mutual information measure to estimate the relationship between information content in the EEG and auditory signals. In the 2–10 Hz frequency range, we identified different latencies for maximal speech envelope tracking during speech production and speech perception. Maximal speech tracking takes place approximately 110 ms after auditory presentation during perception and 25 ms before vocalisation during speech production. These results describe a specific timeline for speech tracking in speakers and listeners in line with the idea of a speech chain and hence, delays in communication.

A key challenge in neuroimaging remains to understand where, when, and now particularly how human brain networks compute over sensory inputs to achieve behavior. To study such dynamic algorithms from mass neural signals, we recorded the magnetoencephalographic (MEG) activity of participants who resolved the classic XOR, OR, and AND functions as overt behavioral tasks (N = 10 participants/task, N-of-1 replications). Each function requires a different computation over the same inputs to produce the task-specific behavioral outputs. In each task, we found that source-localized MEG activity progresses through four computational stages identified within individual participants: (1) initial contralateral representation of each visual input in occipital cortex, (2) a joint linearly combined representation of both inputs in midline occipital cortex and right fusiform gyrus, followed by (3) nonlinear task-dependent input integration in temporal-parietal cortex, and finally (4) behavioral response representation in postcentral gyrus. We demonstrate the specific dynamics of each computation at the level of individual sources. The spatiotemporal patterns of the first two computations are similar across the three tasks; the last two computations are task specific. Our results therefore reveal where, when, and how dynamic network algorithms perform different computations over the same inputs to produce different behaviors.

Within neuroscience, psychology, and neuroimaging, the most frequently used statistical approach is null hypothesis significance testing (NHST) of the population mean. An alternative approach is to perform NHST within individual participants and then infer, from the proportion of participants showing an effect, the prevalence of that effect in the population. We propose a novel Bayesian method to estimate such population prevalence that offers several advantages over population mean NHST. This method provides a population-level inference that is currently missing from study designs with small participant numbers, such as in traditional psychophysics and in precision imaging. Bayesian prevalence delivers a quantitative population estimate with associated uncertainty instead of reducing an experiment to a binary inference. Bayesian prevalence is widely applicable to a broad range of studies in neuroscience, psychology, and neuroimaging. Its emphasis on detecting effects within individual participants can also help address replicability issues in these fields.

Facial expressions support effective social communication by dynamically transmitting complex, multi-layered messages, such as emotion categories and their intensity. How facial expressions achieve this signalling task remains unknown. Here, we address this question by identifying the specific facial movements that convey two key components of emotion communication – emotion classification (such as ‘happy,’ ‘sad’) and intensification (such as ‘very strong’) – in the six classic emotions (happy, surprise, fear, disgust, anger and sad). Using a data-driven, reverse correlation approach and an information-theoretic analysis framework, we identified in 60 Western receivers three communicative functions of face movements: those used to classify the emotion (classifiers), to perceive emotional intensity (intensifiers), and those serving the dual role of classifier and intensifier. We then validated the communicative functions of these face movements in a broader set of 18 complex facial expressions of emotion (including excited, shame, anxious, hate). We find that the timing of emotion classifier and intensifier face movements are temporally distinct, in which intensifiers peaked earlier or later than classifiers. Together, these results reveal the complexities of facial expressions as a signalling system, in which individual face movements serve specific communicative functions with a clear temporal structure.