ArticleeLife2019
Population rate-coding predicts correctly that human sound localization depends on sound intensity.
Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Spatial coding of conspecifics in the electrosensory system: Early segregation of information streams.PloS one · 2026Article
- The Rapid Decline in Interaural-Time-Difference Sensitivity for Pure Tones Can Be Explained by Peripheral Filtering.Journal of the Association for Research in Otolaryngology : JARO · 2024Article
- Amplification of input differences by dynamic heterogeneity in the spiral ganglion.Journal of neurophysiology · 2022Article
- Population rate-coding predicts correctly that human sound localization depends on sound intensity.eLife · 2019Article
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Authors and funding
3 authors.
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Abstract
Human sound localization is an important computation performed by the brain. Models of sound localization commonly assume that sound lateralization from interaural time differences is level invariant. Here we observe that two prevalent theories of sound localization make opposing predictions. The labelled-line model encodes location through tuned representations of spatial location and predicts that perceived direction is level invariant. In contrast, the hemispheric-difference model encodes location through spike-rate and predicts that perceived direction becomes medially biased at low sound levels. Here, behavioral experiments find that softer sounds are perceived closer to midline than louder sounds, favoring rate-coding models of human sound localization. Analogously, visual depth perception, which is based on interocular disparity, depends on the contrast of the target. The similar results in hearing and vision suggest that the brain may use a canonical computation of location: encoding perceived location through population spike rate relative to baseline.
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Registered trials
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