Many human behaviors are discussed in terms of discrete categories. Quantizing behavior in this fashion may provide important traction for understanding the complexities of human experience, but it also may bias understanding of phenomena and associated mechanisms. One example of this is absolute pitch (AP), which is often treated as a discrete trait that is either present or absent (i.e., with easily identifiable near-perfect “genuine” AP possessors and at-chance non-AP possessors) despite emerging evidence that pitch-labeling ability is not all-or-nothing. We used a large-scale online assessment to test the discrete model of AP, specifically by measuring how intermediate performers related to the typically defined “non-AP” and “genuine AP” populations. Consistent with prior research, individuals who performed at-chance (non-AP) reported beginning musical instruction much later than the near-perfect AP participants, and the highest performers were more likely to speak a tonal language than were the lowest performers (though this effect was not as statistically robust as one would expect from prior research). Critically, however, these developmental factors did not differentiate the near-perfect AP performers from the intermediate AP performers. Gaussian mixture modeling supported the existence of two performance distributions–the first distribution encompassed both the intermediate and near-perfect AP possessors, whereas the second distribution encompassed only the at-chance participants. Overall, these results provide support for conceptualizing intermediate levels of pitch-labeling ability along the same continuum as genuine AP-level pitch labeling ability—in other words, a continuous distribution of AP skill among all above-chance performers rather than discrete categories of ability. Expanding the inclusion criteria for AP makes it possible to test hypotheses about the mechanisms that underlie this ability and relate this ability to more general cognitive mechanisms involved in other abilities.
The frequency-following response (FFR) provides a measure of phase-locked auditory encoding in humans and has been used to study subcortical processing in the auditory system. While effects of experience on the FFR have been reported, few studies have examined whether individual differences in early sensory encoding have measurable effects on human performance. Absolute pitch (AP), the rare ability to label musical notes without reference notes, provides an excellent model system for testing how early neural encoding supports specialized auditory skills. Results show that the FFR predicts pitch labelling performance better than traditional measures related to AP (age of music onset, tonal language experience, pitch adjustment and just-noticeable-difference scores). Moreover, the stimulus type used to elicit the FFR (tones or speech) impacts predictive performance in a manner that is consistent with prior research. Additionally, the FFR predicts labelling performance for piano tones better than unfamiliar sine tones. Taken together, the FFR reliably distinguishes individuals based on their explicit pitch labeling abilities, which highlights the complex dynamics between sensory processing and cognition.
Sense of agency (SoA), the conscious sensation that "I" am causing my own bodily movements, is a fundamental feature of human experience and a core facet of self-awareness. While prior work has measured participants' SoA over action consequences (e.g., a tone resulting from a button press), the neural mechanisms underlying SoA over action itself remains poorly understood since control of one's own muscles is normally unambiguous. In this work, we preempt subjects' own movements during a reaction time task using electrical muscle stimulation (EMS). On each trial, subjects report whether their movements were self-caused or caused by EMS. Then, by adaptively adjusting the timing of EMS delivery using Bayesian Optimization, we caused subjects to erroneously attribute roughly half of such involuntary (EMS-induced) movements to their own volition. Comparing trials with illusory agency to those without a sense of agency, we identify multivariate patterns of EEG activity that predict trial-by-trial SoA over EMS-caused movements across subjects. We show these EEG-derived predictors transition from transient to sustained dynamics as predicted by dual-process theories of action monitoring. In an exploratory analysis, we additionally decode SoA from nonlinear fractal measures of trial-by-trial brain activity. Results suggest that while the complexity (i.e., fractal dimension) of activity throughout the cortex is robustly higher when subjects experience agency over a behavior, but electrodes over motor regions appear to be selectively less sensitive to local perturbation (i.e., suppressed long-range correlations) when this sense of agency is absent. This finding is consistent with an account of SoA as a mechanism that gates the motor system from irrelevant sensation.
The ability to generalize rapidly across specific experiences is vital for robust recognition of new patterns, especially in speech perception considering acoustic-phonetic pattern variability. Behavioral research has demonstrated that listeners are rapidly able to generalize their experience with a talker’s speech and quickly improve understanding of a difficult-to-understand talker without prolonged practice, e.g., even after a single training session. Here, we examine the differences in neural responses to generalized versus rote learning in auditory cortical processing by training listeners to understand a novel synthetic talker using a Pretest-Posttest design with electroencephalography (EEG). Participants were trained using either (1) a large inventory of words where no words repeated across the experiment (generalized learning) or (2) a small inventory of words where words repeated (rote learning). Analysis of long-latency auditory evoked potentials at Pretest and Posttest revealed that while rote and generalized learning both produce rapid changes in auditory processing, the nature of these changes differed. In the context of adapting to a talker, generalized learning is marked by an amplitude reduction in the N1-P2 complex and by the presence of a late-negative (LN) wave in the auditory evoked potential following training. Rote learning, however, is marked only by temporally later source configuration changes. The early N1-P2 change, found only for generalized learning, suggests that generalized learning relies on the attentional system to reorganize the way acoustic features are selectively processed. This change in relatively early sensory processing (i.e. during the first 250ms) is consistent with an active processing account of speech perception, which proposes that the ability to rapidly adjust to the specific vocal characteristics of a new talker (for which rote learning is rare) relies on attentional mechanisms to adaptively tune early auditory processing sensitivity.Statement of SignificancePrevious research on perceptual learning has typically examined neural responses during rote learning: training and testing is carried out with the same stimuli. As a result, it is not clear that findings from these studies can explain learning that generalizes to novel patterns, which is critical in speech perception. Are neural responses to generalized learning in auditory processing different from neural responses to rote learning? Results indicate rote learning of a particular talker’s speech involves brain regions focused on the memory encoding and retrieving of specific learned patterns, whereas generalized learning involves brain regions involved in reorganizing attention during early sensory processing. In learning speech from a novel talker, only generalized learning is marked by changes in the N1-P2 complex (reflective of secondary auditory cortical processing). The results are consistent with the view that robust speech perception relies on the fast adjustment of attention mechanisms to adaptively tune auditory sensitivity to cope with acoustic variability.
Cognitive scientists differentiate the “minimal self” – subjective experiences of agency and ownership in our sensorimotor interactions with the world – and the “narrative self” that encompasses those beliefs about the self that are sustained over time. How exactly moment-to-moment experiences are integrated into narrative beliefs, however, remains an open question. We administered a battery of sensorimotor tasks and surveys to index subjects’ (n = 195) propensity to classify stimuli as self-caused and their metacognitive monitoring of such agency judgements, and we compared these behavioral metrics to trait-level beliefs about their own agency. Subjects who were less sensitive to sensory control cues in the sensorimotor tasks also reported lower trait-level agency beliefs. Importantly, however, this relationship all but disappears when controlling for metacognitive accuracy. These results suggest narrative beliefs about self-agency are not just the sum of individual experiences of self-causation but rather the product of a metacognitively-driven integration process.
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