Thresholds for apparent auditory motion induced by linearly changing interaural time differences

1976 ◽  
Vol 60 (S1) ◽  
pp. S102-S102
Author(s):  
C. M. Brandauer ◽  
Wayne Ward
Keyword(s):  
Auditory Motion ◽  
Interaural Time Differences

Sequential Stream Segregation Affects Localisation of Diotic Tones among Tones with Time-Varying Interaural Time Difference

Perception ◽  
10.1068/p6369 ◽  
2009 ◽  
Vol 38 (9) ◽  
pp. 1377-1385
Author(s):  
Takahiro Kawabe
Keyword(s):  
Interaural Time Difference ◽  
Stream Segregation ◽  
Frequency Difference ◽  
The Other ◽  
Time Varying ◽  
Not Given ◽  
Auditory Motion ◽  
Interaural Time Differences ◽  
Complex Sound ◽  
Sound Image

In this study, I examined how sequential stream segregation contributes to the detection of diotic tones among tones with time-varying interaural time differences (ITDs). Target (T) and distractor (D) tones, and a silent duration (–) formed a sequence (DTD–) and this sequence was presented repeatedly. A frequency difference was introduced between target and distractor tones. The distractor tones were also given time-varying ITDs to produce a percept of smooth auditory motion along the interaural axis. In half of the trials, the target tones were not given time-varying ITDs, and thus were diotically presented. The task of the listeners was to determine whether the repeated sequences of DTD–had target tones without motion. The sensitivity d′ for the detection of diotic target tones was higher with larger frequency differences. On the other hand, the criterion c was lower with larger frequency differences. In another session, I confirmed that proportions of reports “two streams” was positively and negatively correlated with d′ and c, respectively. The results indicate that the localisation of a sound image could be influenced by sequential stream segregation in complex sound environments.

Auditory motion aftereffects with varying interaural time differences

1997 ◽  
Vol 101 (5) ◽  
pp. 3105-3105
Author(s):  
Hisashi Uematsu ◽  
Makio Kashino ◽  
Tatsuya Hirahara
Keyword(s):  
Auditory Motion ◽  
Interaural Time Differences ◽  
Motion Aftereffects

scholarly journals Auditory motion affects visual biological motion processing

2007 ◽  
Vol 45 (3) ◽  
pp. 523-530 ◽  
Author(s):  
A. Brooks ◽  
R. van der Zwan ◽  
A. Billard ◽  
B. Petreska ◽  
S. Clarke ◽  
...  
Keyword(s):  
Biological Motion ◽  
Motion Processing ◽  
Auditory Motion

scholarly journals Population Coding of Interaural Time Differences in Gerbils and Barn Owls

2010 ◽  
Vol 30 (35) ◽  
pp. 11696-11702 ◽  
Author(s):  
N. A. Lesica ◽  
A. Lingner ◽  
B. Grothe
Keyword(s):  
Population Coding ◽  
Interaural Time Differences ◽  
Barn Owls

scholarly journals Population rate-coding predicts correctly that human sound localization depends on sound intensity

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Antje Ihlefeld ◽  
Nima Alamatsaz ◽  
Robert M Shapley
Keyword(s):  
Sound Localization ◽  
Sound Intensity ◽  
Spatial Location ◽  
Spike Rate ◽  
Behavioral Experiments ◽  
Interaural Time Differences ◽  
Sound Levels ◽  
Rate Coding ◽  
The Brain ◽  
Population Rate

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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