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Contrast discrimination with pulse trains in pink noise
G B Henning1, C M Bird, F A Wichmann
1The Department of Experimental Psychology, University of Oxford, UK.
Summary
Adding pink noise improved detection of pulse-train gratings by making components equally detectable. However, noise did not affect contrast discrimination, suggesting internal noise sources in early vision models.
Area of Science:
- Vision Science
- Perceptual Psychology
- Computational Neuroscience
Background:
- Early vision models often consider internal noise sources that affect detection thresholds.
- Pulse-train gratings, composed of multiple harmonics, present a unique challenge for visual detection models.
- Understanding how noise influences the detectability of complex stimuli is crucial for refining these models.
Purpose of the Study:
- To investigate the detectability of pulse-train gratings compared to their sinusoidal components.
- To determine the effect of broadband pink noise on the detection and discrimination of pulse-train gratings.
- To explore the implications of these findings for models of internal noise in early visual processing.
Main Methods:
- Detection performance was measured using sinusoidal and pulse-train gratings.
- Broadband pink noise was added to equalize the detectability of pulse-train grating components.
- Contrast-discrimination experiments were conducted using pedestal or masking gratings.
Main Results:
- Pulse-train gratings were not more detectable than their most detectable component without added noise.
- Adding pink noise to equalize component detectability increased pulse-train grating detection by approximately fourfold.
- Noise did not alter discrimination performance for pulse-train gratings relative to sinusoidal components.
Conclusions:
- Internal noise equalization can significantly enhance the detectability of complex visual stimuli like pulse-train gratings.
- The differential effect of noise on detection versus discrimination suggests distinct underlying mechanisms.
- Findings provide insights into the nature of internal noise and its role in early visual processing models.