The capacity to process complex dynamic scenes is of critical importance in real life. For instance, traveling through a crowd while avoiding collisions and maintaining orientation and good motor control requires fluent and continuous perceptual-cognitive processing. It is well documented that effects of healthy aging can influence perceptual-cognitive processes (Faubert, 2002) and that the efficiency of such processes can improve with training even for older adults (Richards et al., 2006). Here we assess the capacity of older participants to improve their tracking speed thresholds in a dynamic, virtual reality environment. Results show that this capacity is significantly affected by healthy aging but that perceptual-cognitive training can significantly reduce age-related effects in older individuals, who show an identical learning function to younger healthy adults. Data support the notion that learning in healthy older persons is maintained for processing complex dynamic scenes.
External noise paradigms have been widely used to probe different levels of visual processing (Pelli & Farell, 1999). A basic assumption of this paradigm is that the processing strategy is noise-invariant, remaining the same in low and high external noise. We tested this assumption by examining crowding in a detection task where traditionally crowding has no effect. In the first experiment, we measured detection thresholds for a vertically oriented sine wave grating (target) surrounded by four sine wave gratings (flankers) that were either vertically or horizontally oriented. At low noise levels, the detection threshold for the target was unaffected by the orientation of the flankers--there was no crowding. Surprisingly, however, there was crowding for detection at high noise levels: the threshold increased for the similarly-oriented flankers. This suggests that high noise triggered a change in processing strategy, increasing the range of space or features over which the visual signal was sampled. In a second experiment, we evaluated the impact of the spatial and temporal window of the noise on this crowding effect. Although crowding was observed for detection when the spatial and/or temporal window of the noise was localized (i.e. identical to the signal window), no crowding was observed when the noise was spatially and temporally extended (i.e. continuously displayed, full screen dynamic noise). Our results show that certain spatiotemporal distributions of external noise can elicit a change in processing strategy, invalidating the noise-invariant assumption that underlies external noise paradigms. In contrast, spatiotemporally extended noise maintains the required noise-indifference, perhaps because it matches the characteristics of the internal noise that determines the contrast threshold in low noise.
Visual psychophysics often manipulates the contrast of an image on a digital display screen. A computer screen can display digital images with a relatively high spatiotemporal resolution. Indeed, most computer displays can produce relatively well defined images (typically, at least 1,024768 pixels) at a relatively high temporal resolution (60-200 images/sec). This high spatiotemporal resolution enables computers to display digital images resembling analog images. Indeed, if the spatial and/ or temporal resolution is great enough, there will be no significant differences between a digital and an analog image. For instance, a luminance grating, which ideally would vary continuously over space and/or time, varies in a discrete manner when presented on a digital display (see Figure 1) but appears to vary continuously if the spatial and/or temporal resolution is great enough. Hence, high-frequency luminance variations are spatiotemporally summed by the visual system and, therefore, undetected (Watson, Ahumada, & Farrell, 1983). The Grayscale Resolution ProblemAnalogously to the spatiotemporal resolution, the luminance intensity of each pixel is also discrete. The luminance of each pixel of a digital image sent to a display is defined by a digital value typically ranging between 0 and 255; these values are called digital-to-analog converter (DAC) values.For sake of simplicity, we will omit that there are three different color guns and will define each pixel color only by its luminance intensity. In other words, for any given pixel, we will assume that all three guns are set to the same DAC value. The DAC translates each value into a voltage, resulting in a given luminance intensity. Before psychophysical testing, the relation between the DAC value sent to the display (i for an integer) and the luminance intensity produced (d for a discrete value) is typically made linear (i.e., gamma corrected to be proportional to the DAC value):where L 255 represents the luminance intensity emitted when the DAC value is 255. The DAC values are integers ranging between 0 and 255, which limits the number of different displayable luminance intensities. However, the mathematical function defining the luminance intensity of each pixel of the stimulus [L(x, y, t) for the luminance intensity of the pixel spatially positioned at (x, y) at time t] is generally continuous. Knowing the relation between the DAC value and the displayed luminance intensity (Equation 1) enables the unit conversion of the luminance intensity of a given pixel [l; for simplicity, we will refer to a given pixel, which enables us to drop the spatiotemporal ARTICLESThe noisy-bit method for digital displays: Converting a 256 luminance resolution into a continuous resolution RÉMY ALLARD AND JOCELYN FAUBERT University of Montreal, Montreal, Quebec, CanadaVisual psychophysics often manipulates the contrast of the image on a digital display screen. Therefore, the limitation of the number of different luminance intensities displayable for most computers (typically, 256) is f...
To study the difference of sensitivity to luminance- (LM) and contrast-modulated (CM) stimuli, we compared LM and CM detection thresholds in LM- and CM-noise conditions. The results showed a double dissociation (no or little inter-attribute interaction) between the processing of these stimuli, which implies that both stimuli must be processed, at least at some point, by separate mechanisms and that both stimuli are not merged after a rectification process. A second experiment showed that the internal equivalent noise limiting the CM sensitivity was greater than the one limiting the carrier sensitivity, which suggests that the internal noise occurring before the rectification process is not limiting the CM sensitivity. These results support the hypothesis that a suboptimal rectification process partially explains the difference of LM and CM sensitivity.
Contrast sensitivity varies substantially as a function of spatial frequency and luminance intensity. The variation as a function of luminance intensity is well known and characterized by three laws that can be attributed to the impact of three internal noise sources: early spontaneous neural activity limiting contrast sensitivity at low luminance intensities (i.e. early noise responsible for the linear law), probabilistic photon absorption at intermediate luminance intensities (i.e. photon noise responsible for de Vries-Rose law) and late spontaneous neural activity at high luminance intensities (i.e. late noise responsible for Weber’s law). The aim of this study was to characterize how the impact of these three internal noise sources vary with spatial frequency and determine which one is limiting contrast sensitivity as a function of luminance intensity and spatial frequency. To estimate the impact of the different internal noise sources, the current study used an external noise paradigm to factorize contrast sensitivity into equivalent input noise and calculation efficiency over a wide range of luminance intensities and spatial frequencies. The impact of early and late noise was found to drop linearly with spatial frequency, whereas the impact of photon noise rose with spatial frequency due to ocular factors.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.