Biomedical subjects

Stephen T Hammett

Publications and source records attributed to Stephen T Hammett.

7 recordsLinked to original sources

PubMed2006

Perceived direction of plaid motion is not predicted by component speeds.

It has been shown that the perceived direction of a plaid with components of unequal contrast is biased towards the direction of the higher-contrast component [Stone, L. S., Watson, A. B., & Mulligan, J. B. (1990). Effect of contrast on the perceived direction of a moving plaid. Vision Research 30, 1049-1067]. It was proposed that this effect is due to the influence of contrast on the perceived speed of the plaid components. This led to the conclusion that perceived plaid direction is computed by the intersection of constraints (IOC) of the perceived speed of the components rather than their physical speeds. We tested this proposal at a wider range of component speeds (2-16deg/s) than used previously, across which the effect of contrast on perceived speed is seen to reverse. We find that across this range, perceived plaid direction cannot be predicted either by a model which takes the IOC of physical or perceived component speed. Our results are consistent with an explanation of 2D motion perception proposed by [Bowns, L. (1996). Evidence for a feature tracking explanation of why Type II plaids move in the vector sum direction at short durations. Vision Research, 36, 3685-3694.] in which the motion of the zero-crossing edges of the features in the stimulus contribute to the perceived direction of motion.

Contrast Sensitivity↗

PubMed2006

Perceptual distortions of speed at low luminance: evidence inconsistent with a Bayesian account of speed encoding.

Our perception of speed has been shown to be distorted under a number of viewing conditions. Recently the well-known reduction of perceived speed at low contrast has led to Bayesian models of speed perception that account for these distortions with a slow speed 'prior'. To test the predictive, rather than the descriptive, power of the Bayesian approach we have investigated perceived speed at low luminance. Our results indicate that, for the mesopic and photopic range (0.13-30 cd m(-2)) the perceived speed of lower luminance patterns is virtually unaffected at low speeds (<4 deg s(-1)) but is over-estimated at higher speeds (>4 deg s(-1)). We show here that the results can be accounted for by an extension to a simple ratio model of speed encoding [Hammett, S. T., Champion, R. A., Morland, A. & Thompson, P. G. (2005). A ratio model of perceived speed in the human visual system. Proceedings of Royal Society B, 262, 2351-2356.] that takes account of known changes in neural responses as a function of luminance, contrast and temporal frequency. The results are not consistent with current Bayesian approaches to modelling speed encoding that postulate a slow speed prior.

Bayes Theorem↗

PubMed2005

A ratio model of perceived speed in the human visual system.

The perceived speed of moving images changes over time. Prolonged viewing of a pattern (adaptation) leads to an exponential decrease in its perceived speed. Similarly, responses of neurones tuned to motion reduce exponentially over time. It is tempting to link these phenomena. However, under certain conditions, perceived speed increases after adaptation and the time course of these perceptual effects varies widely. We propose a model that comprises two temporally tuned mechanisms whose sensitivities reduce exponentially over time. Perceived speed is taken as the ratio of these filters' outputs. The model captures increases and decreases in perceived speed following adaptation and describes our data well with just four free parameters. Whilst the model captures perceptual time courses that vary widely, parameter estimates for the time constants of the underlying filters are in good agreement with estimates of the time course of adaptation of direction selective neurones in the mammalian visual system.

Humans↗

PubMed2005

Speed can go up as well as down at low contrast: implications for models of motion perception.

It is well-known that reducing the contrast of a slow moving stimulus reduces its apparent speed. [Thompson, P. (1982). Perceived rate of movement depends on contrast. Vision Research, 22, 377-380.] report of this finding also suggested that at speeds above 8 cycles/s reducing contrast increased perceived speed. However in a later report, Stone and Thompson (1992), using a more rigorous, forced-choice procedure, failed to collect reliable data at these higher speeds. Here, we confirm that faster moving stimuli can appear to move faster than their true speed at low contrasts and we propose a physiologically plausible ratio model that unlike recent Bayesian models (e.g. Weiss, Y., Simoncelli, E. P., & Adelson, E. H. (2002). Motion illusions as optimal percepts. Nature Neuroscience, 5, 598-604) can account well for the results.

Bayes Theorem↗

PubMed2003

Motion sharpening and contrast: gain control precedes compressive non-linearity?

Blurred edges appear sharper in motion than when they are stationary. We (Vision Research 38 (1998) 2108) have previously shown how such distortions in perceived edge blur may be accounted for by a model which assumes that luminance contrast is encoded by a local contrast transducer whose response becomes progressively more compressive as speed increases. If the form of the transducer is fixed (independent of contrast) for a given speed, then a strong prediction of the model is that motion sharpening should increase with increasing contrast. We measured the sharpening of periodic patterns over a large range of contrasts, blur widths and speeds. The results indicate that whilst sharpening increases with speed it is practically invariant with contrast. The contrast invariance of motion sharpening is not explained by an early, static compressive non-linearity alone. However, several alternative explanations are also inconsistent with these results. We show that if a dynamic contrast gain control precedes the static non-linear transducer then motion sharpening, its speed dependence, and its invariance with contrast, can be predicted with reasonable accuracy.

Contrast Sensitivity↗

PubMed2003

Motion, flash, and flicker: a unified spatiotemporal model of perceived edge sharpening.

Blurred edges appear sharper in motion than when they are stationary. We proposed a model of this motion sharpening that invokes a local, nonlinear contrast transducer function (Hammett et al, 1998 Vision Research 38 2099-2108). Response saturation in the transducer compresses or 'clips' the input spatial waveform, rendering the edges as sharper. To explain the increasing distortion of drifting edges at higher speeds, the degree of nonlinearity must increase with speed or temporal frequency. A dynamic contrast gain control before the transducer can account for both the speed dependence and approximate contrast invariance of motion sharpening (Hammett et al, 2003 Vision Research, in press). We show here that this model also predicts perceived sharpening of briefly flashed and flickering edges, and we show that the model can account fairly well for experimental data from all three modes of presentation (motion, flash, and flicker). At moderate durations and lower temporal frequencies the gain control attenuates the input signal, thus protecting it from later compression by the transducer. The gain control is somewhat sluggish, and so it suffers both a slow onset, and loss of power at high temporal frequencies. Consequently, brief presentations and high temporal frequencies of drift and flicker are less protected from distortion, and show greater perceptual sharpening.

Form Perception↗

PubMed2002

Seeing blur: 'motion sharpening' without motion.

It is widely supposed that things tend to look blurred when they are moving fast. Previous work has shown that this is true for sharp edges but, paradoxically, blurred edges look sharper when they are moving than when stationary. This is 'motion sharpening'. We show that blurred edges also look up to 50% sharper when they are presented briefly (8-24 ms) than at longer durations (100-500 ms) without motion. This argues strongly against high-level models of sharpening based specifically on compensation for motion blur. It also argues against a recent, low-level, linear filter model that requires motion to produce sharpening. No linear filter model can explain our finding that sharpening was similar for sinusoidal and non-sinusoidal gratings, since linear filters can never distort sine waves. We also conclude that the idea of a 'default' assumption of sharpness is not supported by experimental evidence. A possible source of sharpening is a nonlinearity in the contrast response of early visual mechanisms to fast or transient temporal changes, perhaps based on the magnocellular (M-cell) pathway. Our finding that sharpening is not diminished at low contrast sets strong constraints on the nature of the nonlinearity.

Humans↗