Biology subjects

McDermott, J. H.

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

27 records · Page 2Linked to original sources

bioRxiv2020

Deep neural network models of sound localization reveal how perception is adapted to real-world environments

Mammals localize sounds using information from their two ears. Localization in real-world conditions is challenging, as echoes provide erroneous information, and noises mask parts of target sounds. To better understand real-world localization we equipped a deep neural network with human ears and trained it to localize sounds in a virtual environment. The resulting model localized accurately in realistic conditions with noise and reverberation, outperforming alternative systems that lacked human ears. In simulated experiments, the network exhibited many features of human spatial hearing: sensitivity to monaural spectral cues and interaural time and level differences, integration across frequency, and biases for sound onsets. But when trained in unnatural environments without either reverberation, noise, or natural sounds, these performance characteristics deviated from those of humans. The results show how biological hearing is adapted to the challenges of real-world environments and illustrate how artificial neural networks can extend traditional ideal observer models to real-world domains.

neuroscience↗

bioRxiv2020

Causal inference in environmental sound recognition

Sound is caused by physical events in the world. Do humans infer these causes when recognizing sound sources? We tested whether the recognition of common environmental sounds depends on the inference of a basic physical variable - the source intensity (i.e., the power that produces a sound). A sources intensity can be inferred from the intensity it produces at the ear and its distance, which is normally conveyed by reverberation. Listeners could thus use intensity at the ear and reverberation to constrain recognition by inferring the underlying source intensity. Alternatively, listeners might separate these acoustic cues from their representation of a sounds identity in the interest of invariant recognition. We compared these two hypotheses by measuring recognition accuracy for sounds with typically low or high source intensity (e.g., pepper grinders vs. trucks) that were presented across a range of intensities at the ear or with reverberation cues to distance. The recognition of low-intensity sources (e.g., pepper grinders) was impaired by high presentation intensities or reverberation that conveyed distance, either of which imply high source intensity. Neither effect occurred for high-intensity sources. The results suggest that listeners implicitly use the intensity at the ear along with distance cues to infer a sources power and constrain its identity. The recognition of real-world sounds thus appears to depend upon the inference of their physical generative parameters, even generative parameters whose cues might otherwise be separated from the representation of a sounds identity.

animal behavior and cognition↗

bioRxiv2020

Efficient codes for memory determine pitch representations

Perceptual systems have finite memory resources and must store incoming signals in compressed formats. To explore whether representations of a sounds pitch might derive from this need for compression, we compared discrimination of harmonic and inharmonic sounds across delays. In contrast to inharmonic spectra, harmonic spectra can be summarized, and thus compressed, using their fundamental frequency (f0). Participants heard two sounds and judged which was higher. Despite being comparable for sounds presented back-to-back, discrimination was better for harmonic than inharmonic stimuli when sounds were separated in time, implicating memory representations unique to harmonic sounds. Patterns of individual differences (correlations between thresholds in different conditions) indicated that listeners use different representations depending on the time delay between sounds, directly comparing the spectra of temporally adjacent sounds, but transitioning to comparing f0s across delays. The need to store sound in memory appears to determine reliance on f0-based pitch, and may explain its importance in music, in which listeners must extract relationships between notes separated in time.

neuroscience↗

bioRxiv2020

Cortical music selectivity does not require musical training

Recent work has shown that human auditory cortex contains neural populations anterior and posterior to primary auditory cortex that respond selectively to music. However, it is unknown how this selectivity for music arises. To test whether musical training is necessary, we measured fMRI responses to 192 natural sounds in 10 people with almost no musical training. When voxel responses were decomposed into underlying components, this group exhibited a music-selective component that was very similar in response profile and anatomical distribution to that previously seen in individuals with moderate musical training. We also found that musical genres that were less familiar to our participants (e.g., Balinese gamelan) produced strong responses within the music component, as did drum clips with rhythm but little melody, suggesting that these neural populations are broadly responsive to music as a whole. Our findings demonstrate that the signature properties of neural music selectivity do not require musical training to develop, showing that the music-selective neural populations are a fundamental and widespread property of the human brain. NEW & NOTEWORTHYWe show that music-selective neural populations are clearly present in people without musical training, demonstrating that they are a fundamental and widespread property of the human brain. Additionally, we show music-selective neural populations respond strongly to music from unfamiliar genres as well as music with rhythm but little pitch information, suggesting that they are broadly responsive to music as a whole.

neuroscience↗

bioRxiv2019

Intracranial recordings from human auditory cortex reveal a neural population selective for musical song

How are neural representations of music organized in the human brain? While neuroimaging has suggested some segregation between responses to music and other sounds, it remains unclear whether finer-grained organization exists within the domain of music. To address this question, we measured cortical responses to natural sounds using intracranial recordings from human patients and inferred canonical response components using a data-driven decomposition algorithm. The inferred components replicated many prior findings including distinct neural selectivity for speech and music. Our key novel finding is that one component responded nearly exclusively to music with singing. Song selectivity was not explainable by standard acoustic features and was co-located with speech- and music-selective responses in the middle and anterior superior temporal gyrus. These results suggest that neural representations of music are fractionated into subpopulations selective for different types of music, at least one of which is specialized for the analysis of song.

neuroscience↗

bioRxiv2019

Illusory sound texture reveals multi-second statistical completion in auditory scene analysis

Sound sources in the world are experienced as stable even when intermittently obscured, implying perceptual completion mechanisms that \"fill in\" missing sensory information. We demonstrate a filling-in phenomenon in which the brain extrapolates the statistics of background sounds (textures) over periods of several seconds when they are interrupted by another sound, producing vivid percepts of illusory texture. The effect differs from previously described completion effects in that 1) the extrapolated sound must be defined statistically given the stochastic nature of texture, and 2) in lasting much longer, enabling introspection and facilitating assessment of the underlying representation. Illusory texture appeared to be integrated into texture statistic estimates indistinguishably from actual texture, suggesting that it is represented similarly to actual texture. The illusion appears to represent an inference about whether the background is likely to continue during concurrent sounds, providing a stable representation of the environment despite unstable sensory evidence.

neuroscience↗

bioRxiv2019

Ecological origins of perceptual grouping principles in the auditory system

Events and objects in the world must be inferred from sensory signals to support behavior. Because sensory measurements are temporally and spatially local, the estimation of an object or event can be viewed as the grouping of these measurements into representations of their common causes. Per-ceptual grouping is believed to reflect internalized regularities of the natural environment, yet grouping cues have traditionally been identified using informal observation, and investigated using artificial stim-uli. The relationship of grouping to natural signal statistics has thus remained unclear, and additional or alternative cues remain possible. Here we derive auditory grouping cues by measuring and summarizing statistics of natural sound features. Feature co-occurrence statistics reproduced established cues but also revealed previously unappreciated grouping principles. The results suggest that auditory grouping is adapted to natural stimulus statistics, show how these statistics can reveal novel grouping phenomena, and provide a framework for studying grouping in natural signals.

neuroscience↗

bioRxiv2018

Pitch perception is adapted to species-specific cochlear filtering

Pitch perception is critical for recognizing speech, music and animal vocalizations, but its neurobiological basis remains unsettled, in part because of divergent results from different species. We used a combination of behavioural measurements and cochlear modelling to investigate whether species-specific differences exist in the cues used to perceive pitch and whether these can be accounted for by differences in the auditory periphery. Ferrets performed a pitch discrimination task well whenever temporal envelope cues were robust, but not when resolved harmonics only were available. By contrast, human listeners exhibited the opposite pattern of results on an analogous task, consistent with previous studies. Simulated cochlear responses in the two species suggest that the relative salience of the two types of pitch cues can be attributed to differences in cochlear filter bandwidths. Cross-species variation in pitch perception may therefore reflect the constraints of estimating a sounds fundamental frequency given species-specific cochlear tuning.

neuroscience↗

bioRxiv2018

Neural responses to natural and model-matched stimuli reveal distinct computations in primary and non-primary auditory cortex

A central goal of sensory neuroscience is to construct models that can explain neural responses to complex, natural stimuli. As a consequence, sensory models are often tested by comparing neural responses to natural stimuli with model responses to those stimuli. One challenge is that distinct model features are often correlated across natural stimuli, and thus model features can predict neural responses even if they do not in fact drive them. Here we propose a simple alternative for testing a sensory model: we synthesize stimuli that yield the same model response as a natural stimulus, and test whether the natural and \"model-matched\" stimulus elicit the same neural response. We used this approach to test whether a common model of auditory cortex - in which spectrogram-like peripheral input is processed by linear spectrotemporal filters - can explain fMRI responses in humans to natural sounds. Prior studies have that shown that this model has good predictive power throughout auditory cortex, but this finding could reflect stimulus-driven correlations. We observed that fMRI voxel responses to natural and model-matched stimuli were nearly equivalent in primary auditory cortex, but that non-primary regions showed highly divergent responses to the two sound sets, suggesting that neurons in non-primary regions extract higher-order properties not made explicit by traditional models. This dissociation between primary and non-primary regions was not clear from model predictions due to the influence of stimulus-driven response correlations. Our methodology enables stronger tests of sensory models and could be broadly applied in other domains.\n\nAuthor SummaryModeling neural responses to natural stimuli is a core goal of sensory neuroscience. Here we propose a new approach for testing sensory models: we synthesize a \"model-matched\" stimulus that yields the same model response as a natural stimulus, and test whether it produces the same neural response. We used model-matching to test whether a standard model of auditory cortex can explain human cortical responses measured with fMRI. Model-matched stimuli produced nearly equivalent voxel responses in primary auditory cortex, but highly divergent responses in non-primary regions. This dissociation was not evident using more standard approaches for model testing, and suggests that non-primary regions compute higher-order stimulus properties not captured by traditional models. The methodology could be broadly applied in other domains.

neuroscience↗