Adult listeners rated the difficulty of hearing a single coherent stream in a sequence of high (H) and low (L) tones that alternated in a repetitive galloping pattern (HLH-HLH-HLH ...). They could hear the gallop when the sequence was perceived as a single stream, but when it segregated into two substreams, they heard H-H-... in one stream and L-L-... in the other. The onset-to-onset time of the tones, their duration, the interstimulus interval (lSI) between tones of the same frequency, and the frequency separation between H and L tones were varied. Subjects' ratings on a 7-point scale showed that the well-known effect of speed's increasing stream segregation is primarily due to its effect on the lSI between tones in the same frequency region. This has implications for several theories of streaming.When a sequence of tones, alternating between two frequency ranges, is speeded up, the tendency for the high and low tones to form separate auditory streams is increased. It has been proposed by Bregman (1990) that tones group by their proximity on a frequency-by-time surface. An increase of speed brings the tones closer together in time but does not reduce their frequency separations. This brings the consecutive tones of the same frequency closer together on the frequency-by-time surface, while leaving those of different frequencies almost as far away as they were before. This new proximity favors the grouping of a tone with the next one in the same frequency range even if the two tones are not consecutive, because the alternative grouping (with the tone that comes right after it but is ofa different frequency) requires grouping across a longer distance. So we see that temporal distance is very important. But what is the best way to measure temporal distance? The effect of speed could be due to a change in any of the four types of time intervals shown in Figure 1, which all become shorter when the speed is increased. (Note: SOA means stimulus onset asychrony-i.e., onset-to-onset time, and lSI is the label for interstimulus interval-offset-to-onset time.(1) SOA for consecutive tones in the same frequency range (SOAwithin). Note that in Figure 1 there are two different intervals of this type, one for each frequency, sincethe low tones occur less frequently than the high ones in the galloping pattern. (2) lSI for consecutive tones in the same frequency range (lSI-within). Again, there are two different intervals of this type, since the low tones occur less Support from the Natural Sciences and Engineering Research Council of Canada (Experiment I) and NIMH (Experiment 2) is gratefully acknowledged. We are also grateful for Lisa Weaver's assistance. Correspondence concerning this article should be addressed to A. S. Bregman, Department of Psychology, McGill University, 1205 Docteur Penfield Ave., Montreal QC H3A lBI, Canada (e-mail: bregman@hebb. psych.mcgill.ca).frequently than the high ones. (3) SOA for consecutive tones that cut across frequency ranges (SOA-across). (4) lSI for consecutive tones that cut across frequ...
. Two tonotopic areas, the primary auditory cortex (AI) and the anterior auditory field (AAF), are the primary cortical fields in the cat auditory system. They receive largely independent, concurrent thalamocortical projections from the different thalamic divisions despite their hierarchical equivalency. The parallel streams of thalamic inputs to AAF and AI suggest that AAF neurons may differ from AI neurons in physiological properties. Although a modular functional organization in cat AI has been well documented, little is known about the internal organization of AAF beyond tonotopy. We studied how basic receptive field parameters (RFPs) are spatially organized in AAF with single-and multiunit recording techniques. A distorted tonotopicity with an underrepresentation in midfrequencies (1 and 5 kHz) and an overrepresentation in the high-frequency range was found. Spectral bandwidth (Q-values) and response threshold were significantly correlated with characteristic frequency (CF). To understand whether AAF has a modular organization of RFPs, CF dependencies were eliminated by a nonparametric, local regression model, and the residuals (difference between the model and observed values) were evaluated. In a given isofrequency domain, clusters of low or high residual RFP values were interleaved for threshold, spectral bandwidth, and latency, suggesting a modular organization. However, RFP modules in AAF were not expressed as robustly as in AI. A comparison of RFPs between AAF and AI shows that AAF neurons were more broadly tuned and had shorter latencies than AI neurons. These physiological field differences are consistent with anatomical evidence of largely independent, concurrent thalamocortical projections in AI and AAF, which strongly suggest fieldspecific processing.
Once HDR displays were developed, a constant question persisted about how much dynamic range is needed for display. If one uses physical scene luminances or human visual system threshold detections to answer this question, the needed ranges are unachievable at exorbitant cost, and likely to remain so for decades. Therefore we designed studies to find the range that is preferred by human observers, and for suprathreshold appearances. Two studies address the diffuse reflective regions, and a third study tested preferences of highlight regions. Test images were specifically designed to test these limits without the perceptual conflicts that usually occur in these types of studies. For the diffuse range, we found displays capable of a dynamic range between 0.1 and 650 cd/m2 match the average preferences. However, to satisfy 90% of the population, a dynamic range from 0.005 to ∼3,000 cd/m2 is needed. Since a display should be able to produce values brighter than the diffuse white maximum, as in specular highlights and emissive sources, the highlight study concludes that the average preferred maximum luminance for highlight reproduction satisfying 50% of viewers is ∼2,500 cd/m2. This value increases to marginally over 20,000 cd/m2 when catering to 90%. Though there is some variability in preferred brightness between certain demographics, the call for a more capable display is still evident, as preferred luminances found in this study exceed even the best of consumer displays today.
Three studies demonstrate listeners' ability to use the rate of a sound's frequency change ͑velocity͒ to predict how the spectral path of the sound is likely to evolve, even in the event of an occlusion. Experiments 1 and 2 use a modified probe-signal method to measure attentional filters and demonstrate increased detection to sounds falling along implied paths of constant-linear velocity. Experiment 3 shows listeners perceive a suprathreshold tone as falling along a trajectory of constant velocity when the frequency is near to the region of greatest detection as measured in Experiments 1 and 2. Further, results show greater accuracy and decreased bias in the use of velocity information with increased exposure to a constant-velocity sound. As the duration of occlusion lengthens, results also show a downward shift ͑relative to a trajectory of constant velocity͒ in the frequency at which listeners' detection and experience of a continuous trajectory are greatest. A preliminary model of velocity processing is proposed to account for this downward shift. Results show listeners' use of velocity in extrapolating sounds with dynamically changing spectral and temporal properties and provide evidence for its role in perceptual auditory continuity within a noisy acoustic environment.
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