Search bioRxiv⌕ Search

Biology subjects

McDermott, J. H.

Publications and source records attributed to McDermott, J. H..

At least 19 recordsLinked to original sources
bioRxiv2026

Perceptual sensitivity is explained by optimization for ecological tasks

When asked to discriminate between two stimuli that differ along some dimension, such as intensity, frequency, or orientation, humans exhibit a smallest stimulus difference, or threshold, below which discrimination becomes impossible. These limits on perceptual sensitivity have traditionally been explained by internal noise. Contemporary theories instead link perceptual sensitivity to efficient coding of the natural environment but have not been comprehensively tested. Here, we measured the psychoacoustic sensitivity of a computational model optimized for everyday sound localization and recognition tasks. We used linear classifiers operating on the model's learned representations to estimate 1632 thresholds spanning the perceptual dimensions of sound. These model-derived thresholds closely matched thresholds measured behaviorally in humans on the same test battery. The results show that many classical psychophysical phenomena emerge from representations optimized for natural behavior and suggest that discrimination thresholds are determined by linear separability in these optimized representations, rather than intrinsic neural noise.

neuroscience↗
bioRxiv2026

Robustness to noise reveals cross-culturally consistent properties of pitch perception for harmonic and inharmonic sounds

Pitch is a building block of speech and music, but the extent to which pitch perception is shared across cultures is unclear. Evidence from Western participants suggests that pitch perception relies on multiple representations. For instance, harmonic tones are easier to discriminate in noise than inharmonic tones despite comparable discrimination in quiet, suggesting that different representations are used in noise and quiet. We tested whether these effects are present cross-culturally, comparing participants from the US and a Bolivian Amazonian Indigenous community (Tsimane). Participants heard two-note melodies and reproduced the melody by singing. Tones were either harmonic or inharmonic and were presented in noise or quiet. Both groups exhibited two characteristics of pitch perception previously seen in Western listeners: the direction of pitch changes could be reproduced with equal accuracy for harmonic and inharmonic tones in quiet but was better for harmonic than inharmonic tones in noise. However, replicating previous work, Tsimane vocal reproductions were much less likely to be related to the absolute pitch or chroma of the stimulus notes, differing from the tendency seen in Western participants to match pitch and/or chroma. Pitch and chroma matching behavior were more prominent in a subset of Tsimane whose responses to a demographic survey suggested greater integration with global and Bolivian markets and culture. The results demonstrate that the basic structure of pitch perception is shared across cultures despite other differences in pitch-related behavior that are plausibly driven by culture-specific experience.

neuroscience↗
bioRxiv2026

From sound to source: Human and model recognition of environmental sounds

Our ability to recognize sound sources in the world is critical to daily life, but is not well documented or understood in computational terms. We developed a large-scale behavioral benchmark of human environmental sound recognition, built models of sound recognition that operate on the acoustic signal, and used the benchmark to compare models to humans. The behavioral benchmark measured how sound recognition varied across source categories, audio distortions, and concurrent sound sources, all of which influenced recognition performance in humans. Artificial neural network models trained to recognize sounds in multi-source scenes reached near-human accuracy and qualitatively matched human patterns of performance in many conditions. By contrast, classifiers trained to recognize sounds from traditional models of the cochlea and auditory cortex produced worse matches to human performance. Models trained on larger datasets exhibited stronger alignment with both human behavior and brain responses. The results suggest that many aspects of human sound recognition emerge in systems optimized for the problem of real-world recognition.

neuroscience↗
bioRxiv2026

Intuitive knowledge of object acoustics enables perceptual separation of physical variables from impact sounds

Upon hearing objects collide, humans can estimate physical attributes such as material and mass. Although the physics of sound generation is well established, the inverse problem that listeners solve - of inferring physical parameters from sound - remains poorly understood. Classical accounts posit the use of acoustic cues that correlate with physical variables, but do not explain how humans might distinguish multiple concurrent physical causes. To study this problem, we built a probabilistic generative model of impact sounds, combining theoretical acoustics with statistics of object resonances measured from hundreds of everyday objects, and used it to synthesize and manipulate experimental stimuli. Humans accurately judged object properties from collision sounds. However, when both of the colliding objects varied, performance was impaired if the distribution of object resonances deviated from those measured in real-world objects. The results suggest that listeners use internal physical models to separate the acoustic contributions of objects in the world.

neuroscience↗
bioRxiv2025

Task-optimized models of sensory uncertainty reproduce human confidence judgments

Sensory input is often ambiguous, leading to uncertain interpretations of the external world. Estimates of perceptual uncertainty might be useful in guiding behavior, but it remains unclear whether humans explicitly represent uncertainty in naturalistic settings, and whether any such representations are normatively correct. Progress has been hindered by the absence of stimulus-computable models that estimate uncertainty. We developed a class of task-optimized models that generate probability distributions over perceptual estimates. To assess whether human uncertainty representations align with the models, we compared human confidence judgments, which might indirectly reflect uncertainty representations, to confidence judgments extracted from the models uncertainty. In both sound localization and pitch perception, human confidence varied systematically, being lower for stimuli that produced more variable estimates across trials. Human confidence tracked model confidence across conditions, suggesting that human uncertainty representations accurately reflect the actual uncertainty of perceptual estimation. The modeling framework is extensible to other perceptual domains.

neuroscience↗
bioRxiv2025

A deep learning framework for understanding cochlear implants

Sensory prostheses replace dysfunctional sensory organs with electrical stimulation but currently fail to restore normal perception. Outcomes may be limited by stimulation strategies, neural degeneration, or suboptimal decoding by the brain. We propose a deep learning framework to evaluate these issues by estimating best-case outcomes with task-optimized decoders operating on simulated prosthetic input. We applied the framework to cochlear implants - the standard treatment for deafness - by training artificial neural networks to recognize and localize sounds using simulated auditory nerve input. The resulting models exhibited speech recognition and sound localization that was worse than that of normal hearing listeners, and on par with the best human cochlear implant users, with similar results across the three main stimulation strategies in current use. Speech recognition depended heavily on the extent of decoder optimization for implant input, with lesser influence from other factors. The results identify performance limits of current devices and demonstrate a model-guided approach for understanding the limitations and potential of sensory prostheses.

neuroscience↗
bioRxiv2025

Optimized feature gains explain and predict successes and failures of human selective listening

Attention facilitates communication by enabling selective listening to sound sources of interest. However, little is known about why attentional selection succeeds in some conditions but fails in others. While neurophysiology implicates multiplicative feature gains in selective attention, it is unclear whether such gains can explain real-world attention-driven behavior. To investigate these issues, we optimized an artificial neural network with stimulus-computable, feature-based gains to recognize a cued talkers speech from binaural audio in "cocktail party" scenarios. Though not trained to mimic humans, the model matched human performance across diverse real-world conditions, exhibiting selection based both on voice qualities and spatial location. It also predicted novel attentional effects that we confirmed in human experiments, and exhibited signatures of "late selection" like those seen in human auditory cortex. The results suggest that human-like attentional strategies naturally arise from optimization of feature gains for selective listening, offering a normative account of the mechanisms--and limitations--of auditory attention.

neuroscience↗
bioRxiv2024

Privileged representational axes in biological and artificial neural networks

How do neurons code information? Recent work emphasizes properties of population codes, such as their geometry and decodable information, using measures that are blind to the native tunings (or axes) of neural responses. But might these representational axes matter, with some privileged systematically over others? To find out, we developed methods to test for alignment of neural tuning across brains and deep convolutional neural networks (DCNNs). Across both vision and audition, both brains and DCNNs consistently favored certain axes for representing the natural world. Moreover, the representational axes of DCNNs trained on natural inputs were aligned to those in perceptual cortices, such that axis-sensitive model-brain similarity metrics better differentiated competing models of biological sensory systems. We further show that coding schemes that privilege certain axes can reduce downstream wiring costs and improve generalization. These results motivate a new framework for understanding neural tuning in biological and artificial networks and its computational benefits.

neuroscience↗
bioRxiv2024

Models optimized for real-world tasks reveal the necessity of precise temporal coding in hearing

Neurons encode information in the timing of their spikes in addition to their firing rates. Spike timing is particularly precise in the auditory nerve, where action potentials phase lock to sound with sub-millisecond precision, but its behavioral relevance remains uncertain. We optimized machine learning models to perform real-world hearing tasks with simulated cochlear input, assessing the precision of auditory nerve spike timing needed to reproduce human behavior. Models with high-fidelity phase locking exhibited more human-like sound localization and speech perception than models without, consistent with an essential role in human hearing. However, the temporal precision needed to reproduce human-like behavior varied across tasks, as did the precision that benefited real-world task performance. These effects suggest that perceptual domains incorporate phase locking to different extents depending on the demands of real-world hearing. The results illustrate how optimizing models for realistic tasks can clarify the role of candidate neural codes in perception.

neuroscience↗
bioRxiv2024

Noise schemas aid hearing in noise

Human hearing is robust to noise, but the basis of this robustness is poorly understood. Several lines of evidence are consistent with the idea that the auditory system adapts to sound components that are stable over time, potentially achieving noise robustness by suppressing noise-like signals. Yet background noise often provides behaviorally relevant information about the environment, and thus seems unlikely to be completely discarded by the auditory system. Motivated by this observation, we explored whether noise robustness might instead be mediated by internal models of noise structure that could facilitate the separation of background noise from other sounds. We found that detection, recognition, and localization in real-world background noise was better for foreground sounds positioned later in a noise excerpt, with performance improving over the initial second of exposure to a noise. These results are consistent with both adaptation-based and model-based accounts, since both explanations require online noise estimation that should benefit from acquiring more samples. However, performance was also robust to interruptions in the background noise and was enhanced for intermittently recurring backgrounds, neither of which would be expected from known forms of adaptation. Additionally, the performance benefit observed for foreground sounds occurring later within a noise excerpt was reduced for recurring noises, suggesting that a noise representation is built up during exposure to a new background noise and then maintained in memory. These findings suggest noise robustness is supported by internal models--"noise schemas"--that are rapidly estimated, stored over time, and used to estimate other concurrent sounds.

neuroscience↗
bioRxiv2023

Bayesian auditory scene synthesis explains human perception of illusions and everyday sounds

Perception has long been envisioned to use an internal model of the world to explain the causes of sensory signals. However, such accounts have historically not been testable, typically requiring intractable search through the space of possible explanations. Using auditory scenes as a case study, we leveraged contemporary computational tools to infer explanations of sounds in a candidate internal model of the auditory world (ecologically inspired audio synthesizers). Model inferences accounted for many classic illusions. Unlike traditional accounts of auditory illusions, the model is applicable to any sound, and exhibited human-like perceptual organization for real world sound mixtures. The combination of stimulus-computability and interpretable model structure enabled rich falsification, revealing additional assumptions about sound generation needed to account for perception. The results show how generative models can account for the perception of both classic illusions and everyday sensory signals, and provide the basis on which to build theories of perception.

neuroscience↗
bioRxiv2022

Many but not all deep neural network audio models capture brain responses and exhibit hierarchical region correspondence

Models that predict brain responses to stimuli provide one measure of understanding of a sensory system, and have many potential applications in science and engineering. Deep artificial neural networks have emerged as the leading such predictive models of the visual system, but are less explored in audition. Prior work provided examples of audio-trained neural networks that produced good predictions of auditory cortical fMRI responses and exhibited correspondence between model stages and brain regions, but left it unclear whether these results generalize to other neural network models, and thus how to further improve models in this domain. We evaluated model-brain correspondence for publicly available audio neural network models along with in-house models trained on four different tasks. Most tested models out-predicted previous filter-bank models of auditory cortex, and exhibited systematic model-brain correspondence: middle stages best predicted primary auditory cortex while deep stages best predicted non-primary cortex. However, some state-of-the-art models produced substantially worse brain predictions. Models trained to recognize speech in background noise produced better brain predictions than models trained to recognize speech in quiet, potentially because hearing in noise imposes constraints on biological auditory representations. The training task influenced the prediction quality for specific cortical tuning properties, with best overall predictions resulting from models trained on multiple tasks. The results generally support the promise of deep neural networks as models of audition, though they also indicate that current models do not explain auditory cortical responses in their entirety.

neuroscience↗
bioRxiv2022

Model metamers illuminate divergences between biological and artificial neural networks

Deep neural network models of sensory systems are often proposed to learn representational transformations with invariances like those in the brain. To reveal these invariances we generated "model metamers" - stimuli whose activations within a model stage are matched to those of a natural stimulus. Metamers for state-of-the-art supervised and unsupervised neural network models of vision and audition were often completely unrecognizable to humans when generated from deep model stages, suggesting differences between model and human invariances. Targeted model changes improved human-recognizability of model metamers, but did not eliminate the overall human-model discrepancy. The human-recognizability of a models metamers was well predicted by their recognizability by other models, suggesting that models learn idiosyncratic invariances in addition to those required by the task. Metamer recognition dissociated from both traditional brain-based benchmarks and adversarial vulnerability, revealing a distinct failure mode of existing sensory models and providing a complementary benchmark for model assessment.

neuroscience↗
bioRxiv2022

PCYT2 controls muscle health and muscle aging

Muscle degeneration is the most prevalent cause for frailty and dependency in inherited diseases and ageing, affecting hundreds of millions of people. Elucidation of pathophysiological mechanisms, as well as effective treatments for muscle diseases represents an important goal in improving human health. Here, we show that phosphatidylethanolamine cytidyltransferase (PCYT2/ECT), the critical enzyme of the Kennedy branch of phosphatidylethanolamine (PE) synthesis pathway, has an essential role in muscle health. Human genetic deficiency in PCYT2 causes a severe disease with failure to thrive and progressive muscle weakness. Pcyt2 mutant zebrafish recapitulate the patient phenotypes, indicating that the role of PCYT2/PE in muscle is evolutionary conserved. Muscle specific Pcyt2 knockout mice exhibited failure to thrive, impaired muscle development, progressive muscle weakness, muscle loss, accelerated ageing, and reduced lifespan. Mechanistically, Pcyt2 deficiency affects mitochondrial bioenergetics and physicochemical properties of the myofiber membrane lipid bilayer, in particular under exercise strain. We also show that PCYT2 activity declines in the aging muscles of humans and mice. AAV-based delivery of PCYT2 rescued muscle weakness in Pcyt2 knock-out mice and, importantly, improved muscle strength in old mice, offering a novel therapeutic avenue for rare disease patients and muscle aging. Thus, PCYT2 plays a fundamental, specific, and conserved role in vertebrate muscle health, linking PCYT2 and PCYT2 synthesized PE lipids to severe muscle dystrophy, exercise intolerance and aging.

genetics↗
bioRxiv2022

Invariance in pitch perception

Information in speech and music is often conveyed through changes in fundamental frequency (f0), perceived by humans as "relative pitch". Relative pitch judgments are complicated by two facts. First, sounds can simultaneously vary in timbre due to filtering imposed by a vocal tract or instrument body. Second, relative pitch can be extracted in two ways: by measuring changes in constituent frequency components from one sound to another, or by estimating the f0 of each sound and comparing the estimates. We examined the effects of timbral differences on relative pitch judgments, and whether any invariance to timbre depends on whether judgments are based on constituent frequencies or their f0. Listeners performed up/down and interval discrimination tasks with pairs of spoken vowels, instrument notes, or synthetic tones, synthesized to be either harmonic or inharmonic. Inharmonic sounds lack a well-defined f0, such that relative pitch must be extracted from changes in individual frequencies. Pitch judgments were less accurate when vowels/instruments were different compared to when they were the same, and were biased by the associated timbre differences. However, this bias was similar for harmonic and inharmonic sounds, and was observed even in conditions where judgments of harmonic sounds were based on f0 representations. Relative pitch judgments are thus not invariant to timbre, even when timbral variation is naturalistic, and when such judgments are based on representations of f0.

neuroscience↗
bioRxiv2021

Music-selective cortex is sensitive to structure in both pitch and time

Music is distinguished from other natural sounds by the presence of relatively discrete notes, which are then organized across pitch and time to convey melody, harmony, and rhythm. Growing evidence suggests that small clusters of neural populations within anterior and posterior human non-primary auditory cortex respond selectively to musical structure. However, it is unclear whether this selectivity reflects short-term musical structure at the level of individual notes, and/or the patterning of notes in pitch and time. We used fMRI voxel decomposition to measure the response of music-selective and non-selective auditory neural populations to synthetic music and drum stimuli whose notes were scrambled in pitch and/or time, disrupting musical pattern structure while largely preserving note-level structure. We observed reliably stronger responses to music with intact pitch and temporal pattern structure in both anterior and posterior music-selective regions bilaterally, but little difference between intact and scrambled music in non-selective populations. Further, only music-selective populations showed reliably stronger responses to note-scrambled music compared with non-music sounds. These results suggest that musical structure involving both individual notes and their patterning over time is specifically represented in localized music-selective neural populations of human non-primary auditory cortex.

neuroscience↗
bioRxiv2020

Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception

Perception is thought to be shaped by the environments for which organisms are optimized. These influences are difficult to test in biological organisms but may be revealed by machine perceptual systems optimized under different conditions. We investigated environmental and physiological influences on pitch perception, whose properties are commonly linked to peripheral neural coding limits. We first trained artificial neural networks to estimate fundamental frequency from biologically faithful cochlear representations of natural sounds. The best-performing networks replicated many characteristics of human pitch judgments. To probe the origins of these characteristics, we then optimized networks given altered cochleae or sound statistics. Human-like behavior emerged only when cochleae had high temporal fidelity and when models were optimized for naturalistic sounds. The results suggest pitch perception is critically shaped by the constraints of natural environments in addition to those of the cochlea, illustrating the use of artificial neural networks to reveal underpinnings of behavior.

animal behavior and cognition↗
bioRxiv2020

Harmonicity aids hearing in noise

Hearing in noise is a core problem in audition, and a challenge for hearing-impaired listeners, yet the underlying mechanisms are poorly understood. We explored whether harmonic frequency relations, a signature property of many communication sounds, aid hearing in noise for normal hearing listeners. We measured detection thresholds in noise for tones and speech synthesized to have harmonic or inharmonic spectra. Harmonic signals were consistently easier to detect than otherwise identical inharmonic signals. Harmonicity also improved discrimination of sounds in noise. The largest benefits were observed for two-note up-down "pitch" discrimination and melodic contour discrimination, both of which could be performed equally well with harmonic and inharmonic tones in quiet, but which showed large harmonic advantages in noise. The results show that harmonicity facilitates hearing in noise, plausibly by providing a noise-robust pitch cue that aids detection and discrimination. Significance statementNoise is ubiquitous, and being able to hear in noise is critical to real-world behavior. We report that hearing in noise is aided by sensitivity to the harmonic frequency relations that occur in vocal communication signals and music: harmonic sounds are easier to hear in noise than inharmonic sounds. This effect was present in both musicians and non-musicians and for synthetic as well as natural sounds, suggesting a role in everyday hearing.

neuroscience↗