Megan A. K. Peters

Here we examined how physical-effort induced arousal may have a differential impact on perception and metacognition in older adults. While arousal has been shown to enhance some aspects of perceptual performance in young adults, it can also disrupt metacognitive performance. However, both perceptual and metacognitive functions decline with age, alongside deterioration of neural systems that regulate arousal (e.g., Locus Coeruleus-Norepinephrine; LC-NE). Thus, it is unclear how physical-effort induced arousal influences perceptual performance and metacognitive efficiency in older adults. To test this, we had participants complete two perceptual discrimination tasks (auditory and visual), which involved judging whether two sequential stimuli were the same or different, and subsequently rating their confidence in their same/different response. At the start of each trial, arousal was manipulated by instructing participants to maintain either a low or high hand grip force on a dynamometer. We fit psychometric functions to characterize perceptual performance between grip levels and used the Relative Psychometric Function (RPF) analysis framework to characterize metacognitive ability while controlling for perceptual performance. Across tasks, high grip increased psychometric thresholds, but not psychometric slopes. On the other hand, metacognitive efficiency increased under high grip and was numerically larger in the auditory task, which appeared to be driven by increased error awareness during the easiest stimulus level. Together, these findings suggest that acute arousal can disrupt perceptual performance but enhance metacognitive efficiency in older adults.

Assessing similarity between objects is a fundamental cognitive process, and similarity judgments can assess many different attributes, including objective properties (color, shape) or subjective appraisals (preference, scariness). Here, we investigated how psychological similarity across eight different properties relates to neural representational geometries and artificial models. We first collected large-scale pairwise similarity ratings for 280 objects. Using representational similarity analysis (RSA) and multidimensional scaling, we identified three mental representational spaces: one defined by multiple object features and general similarity, a second by affective evaluations, and a third by color. Combining fMRI and RSA in 80 participants, we found that object feature ratings significantly correlated with representational structures in visual and prefrontal cortices. Unexpectedly, affective ratings also correlated with patterns in visual cortices. Crucially, regional variability strongly shaped these correspondences: visual cortices exhibited high noise ceilings and consistent alignment with behavior, whereas associative regions showed greater across-subjects variability and weaker correlations. Finally, algorithms and neural networks with high classification performance on specific properties strongly correlated with behavioral and fMRI RSA matrices. Together, these results demonstrate that similarity is a multidimensional construct that comprises multiple representational spaces whose neural correspondence is shaped by representational content, regional variability, and modeling methodology.

We build upon Haueis and Colaço’s account in two ways. First, we argue that the backgrounding of metabolism was a pragmatic response to a data granularity problem faced by traditional cognitive modelers. Then, we suggest that equivalence demonstrations offer a promising pathway for incrementally introducing metabolic considerations into extant modeling practice.

Humans learn multiple probabilistic regularities at once, and often without feedback, which may be aided by metacognitive evaluations throughout learning. To understand this learning-metacognition relationship, we must first characterize how both capacities co-evolve as environmental structures are internalized, especially when feedback is unavailable. Here, participants learned a two-level probabilistic environment in which stimulus co-occurrences were embedded within a sequence, using unfamiliar fractals and familiar line drawings in a two-session, within-subjects design (N=63; N=50 completed both sessions). For each stimulus set, we measured learning across six repeating exposure-test cycles, using a cover task during exposure phases and forced-choice questions in test phases; test questions also included confidence judgments. Results revealed stimulus- and structure-dependent learning and metacognitive signatures. First, learning dynamics differed across stimuli: fractal sequence test accuracy outpaced co-occurrence accuracy, but vice versa for line drawings. Further, confidence increased across blocks, and was higher for line drawings as well as co-occurrences and first but not second sequence selections. Finally, both metacognitive sensitivity and test accuracy increased in successive exposure-test cycles for fractals, but metacognitive sensitivity remained stagnant for line drawings even as test accuracy improved. These results demonstrate that structure learning and metacognitive processes are highly context- and complexity-dependent rather than being completely generalizable. This study informs future directions for refining theories of learning without feedback, including how people evaluate their own learning and uncertainty and the interaction of both cognitive abilities with task design, stimuli, and environmental structures.

Our feelings of perceptual confidence appear largely driven by the magnitude of evidence supporting our choices, dubbed a metacognitive ‘positive evidence bias’. This bias is typically studied through manipulating objective (physical) evidence, but it is unclear whether it also occurs when perceptual evidence is illusory. Here, using a spatial two-alternative forced choice task plus confidence reports, we successfully used the ‘convex hull’ numerosity illusion to manipulate decisions by making dot patches appear more numerous when their spatial extent (“convex hull”) was increased. However, confidence effects interacted with the convex hull’s “congruence” with actual numerosity: in ‘congruent’ conditions (larger convex hull accompanying the more numerous patch), convex hull strongly biased confidence, but in ‘incongruent’ conditions (accompanying the less numerous patch), it exerted little effect. These findings demonstrate that illusory evidence differentially influences objective choices versus subjective confidence, and suggest observers may treat illusory evidence no differently from veridical evidence making choices and metacognitively evaluating them.

Debates about artificial consciousness are often framed in terms of verdicts: whether a given system is or is not conscious, and which criteria or theories can deliver such judgments. This paper argues that this framing obscures a more fundamental epistemic challenge. Consciousness science has largely been developed within a narrow population – humans – under background assumptions that support evidential generalization within that domain but do not automatically extend to nonhuman animals or artificial systems. When our line of research expands to assess new populations that differ radically in embodiment, internal organization, and implementation, the central problem should be not only detection but also discovery: whether there even exists a stable cluster of properties that can support explanation and generalization across heterogeneous systems, which we might then call “consciousness”.Here, I build on the iterative natural-kind (INK) strategy proposed by Bayne and Shea (2020) (see also (Bayne et al., 2024a, 2024b)) to lay out a roadmap for a proximity-guided extension of this approach. The INK strategy treats consciousness as a candidate natural kind whose structure is to be discovered through iterative revision of indicators, mappings between those indicators and the “natural kind” of consciousness, and the underlying natural kind itself. However, in its abstract form, INK leaves open how evidential updates should be accomplished under population shift. The present paper argues that proximity relations – to be understood as multidimensional similarities and differences between systems (Peters, n.d.) – provide the missing structure. By conditioning belief updates on explicit judgments about proximity within a Bayesian latent variable discovery model, a proximity-guided INK strategy renders several potential limitations of the INK strategy as visible, interrogable features such that they themselves can be objects of scientific investigation. The result is a methodological roadmap for investigating consciousness under uncertainty, focused not only on delivering verdicts but also on clarifying what kinds of discovery about the natural kind of “consciousness” may be possible.

Fleming and Michel suggest that conscious vision reflects a reality monitoring (RM) mechanism that evolved to separate planning from perception. Here, I critically examine the RM decision policy implied by the authors – a slow, winner-take-all strategy tagging only one first-order representation as “real.” Specifically, I explore how expectations about environmental or reality-tagging stability may mask alternative RM decision policies, with important implications for learning and the evolutionary emergence and function of consciousness.

Psychophysics seeks to quantitatively characterize relationships between objective properties of the world and subjective properties of perception. However, traditional approaches fall short of identifying quantitative relationships among psychological processes. This latter goal is particularly important when certain processes may depend on each other, such as for subjective experience and task performance: Typically, better performance is accompanied by stronger subjective experiences of clarity, vividness, or confidence. But is the relationship between performance and subjective experience fixed, or can it vary, for example, by task or attentional demands? Such questions are key for better understanding psychological processes in general and subjective experience in particular. Here, we develop and showcase a new psychophysical method designed to answer such questions: relative psychometric function (RPF) analysis, which characterizes the nonlinear psychometric relationships between psychological processes and how these relationships change under different circumstances (e.g., experimental manipulations). We demonstrate the advantages of RPF analysis using a data set in which human subjects discriminated random dot kinematograms which varied in motion coherence and overall dot density (dots per visual degree) and rated confidence. RPF analysis revealed systematic changes in the relationship between performance and two subjective measures (confidence and metacognitive sensitivity) due to dot density and task design choices. These empirical results showcase how RPF analysis may reveal changes in quantitative relationships between any two psychological measures in future studies: performance, vividness, clarity, reaction time, confidence, and more. To enable the scientific community to use RPF analysis on their data, we also present our open-source RPF toolbox. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Perceptual confidence tracks decision accuracy, yet it is also systematically shaped by both stimulus properties and spatial location. Using a luminance comparison task, we assessed how multiple stimulus dimensions influence observers' relative brightness judgments and perceptual confidence. These dimensions included visual field location (eccentricity and polar angle) in Experiments 1 and 2, and additionally stimulus noise and overall stimulus strength in Experiment 1. We applied the recently developed relative psychometric function approach to characterize these dimensions' effects on confidence across a range of matched task accuracies, rather than at a single fixed performance level. Observers’ confidence increased with both stimulus noise and visual eccentricity, even as performance range was matched across all conditions. These effects depended on stimulus strength -- i.e., the overall luminance of the stimuli -- revealing interactions among different sources of uncertainty in shaping metacognitive judgments. Together, these results extend previous accounts of the relationship between confidence and accuracy to reveal integration of multiple uncertainty sources and to include suprathreshold stimuli in brightness perception.