Exploring the dynamics of light adaptation: the effects of varying the flickering background's duration in the probed-sinewave paradigm

Denoising forced-choice detection data

Observers in a two-alternative forced-choice (2AFC) detection task face the need to produce a response at random (a guess) on trials in which neither presentation appeared to display a stimulus. Observers could alternatively be instructed to use a 'guess' key on those trials, a key that would produce a random guess and would also record the resultant correct or wrong response as emanating from a computer-generated guess. A simulation study shows that 'denoising' 2AFC data with information regarding which responses are a result of guesses yields estimates of detection threshold and spread of the psychometric function that are far more precise than those obtained in the absence of this information, and parallel the precision of estimates obtained with yes-no tasks running for the same number of trials. Simulations also show that partial compliance with the instructions to use the 'guess' key reduces the quality of the estimates, which nevertheless continue to be more precise than those obtained from conventional 2AFC data if the observers are still moderately compliant. An empirical study testing the validity of simulation results showed that denoised 2AFC estimates of spread were clearly superior to conventional 2AFC estimates and similar to yes-no estimates, but variations in threshold across observers and across sessions hid the benefits of denoising for threshold estimation. The empirical study also proved the feasibility of using a 'guess' key in addition to the conventional response keys defined in 2AFC tasks.

Invariant template matching in systems with spatiotemporal coding: A matter of instability

We consider the design principles of algorithms that match templates to images subject to spatiotemporal encoding. Both templates and images are encoded as temporal sequences of samplings from spatial patterns. Matching is required to be tolerant to various combinations of image perturbations. These include ones that can be modeled as parameterized uncertainties such as image blur, luminance, and, as special cases, invariant transformation groups such as translation and rotations, as well as unmodeled uncertainties (noise). For a system to deal with such perturbations in an efficient way, they are to be handled through a minimal number of channels and by simple adaptation mechanisms. These normative requirements can be met within the mathematical framework of weakly attracting sets. We discuss explicit implementation of this principle in neural systems and show that it naturally explains a range of phenomena in biological vision, such as mental rotation, visual search, and the presence of multiple time scales in adaptation. We illustrate our results with an application to a realistic pattern recognition problem.

Bayesian adaptive estimation of arbitrary points on a psychometric function

Bayesian adaptive methods have been extensively used in psychophysics to estimate the point at which performance on a task attains arbitrary percentage levels, although the statistical properties of these estimators have never been assessed. We used simulation techniques to determine the small-sample properties of Bayesian estimators of arbitrary performance points, specifically addressing the issues of bias and precision as a function of the target percentage level. The study covered three major types of psychophysical task (yes-no detection, 2AFC discrimination and 2AFC detection) and explored the entire range of target performance levels allowed for by each task. Other factors included in the study were the form and parameters of the actual psychometric function Psi, the form and parameters of the model function M assumed in the Bayesian method, and the location of Psi within the parameter space. Our results indicate that Bayesian adaptive methods render unbiased estimators of any arbitrary point on psi only when M=Psi, and otherwise they yield bias whose magnitude can be considerable as the target level moves away from the midpoint of the range of Psi. The standard error of the estimator also increases as the target level approaches extreme values whether or not M=Psi. Contrary to widespread belief, neither the performance level at which bias is null nor that at which standard error is minimal can be predicted by the sweat factor. A closed-form expression nevertheless gives a reasonable fit to data describing the dependence of standard error on number of trials and target level, which allows determination of the number of trials that must be administered to obtain estimates with prescribed precision.

The role of parametric assumptions in adaptive Bayesian estimation

Variants of adaptive Bayesian procedures for estimating the 5% point on a psychometric function were studied by simulation. Bias and standard error were the criteria to evaluate performance. The results indicated a superiority of (a) uniform priors, (b) model likelihood functions that are odd symmetric about threshold and that have parameter values larger than their counterparts in the psychometric function, (c) stimulus placement at the prior mean, and (d) estimates defined as the posterior mean. Unbiasedness arises in only 10 trials, and 20 trials ensure constant standard errors. The standard error of the estimates equals 0.617 times the inverse of the square root of the number of trials. Other variants yielded bias and larger standard errors.

Recovery from contrast adaptation matches ideal-observer predictions

Recovery from contrast adaptation was studied in psychophysical experiments. We measured detection thresholds for a test pulse presented on a photopic background as a function of the time after the offset of a high-contrast flicker of the background. The decrease of thresholds with time is well described by a power-law function. Thresholds for tests presented at 640 ms after the offset of the background contrast are still significantly elevated above the threshold measured when the observers have completely adapted to a steady background. We compare the psychophysical data with contrast estimates of ideal-observer models. A match between the results for human and ideal observers can be obtained when the ideal observer is limited by noise. For a quantitative match, we assume that the ideal observer performs a Bayesian calculation on its noise-perturbed input, sampled every 10-20 ms. For the Bayesian calculation we assume a prior probability distribution function for the input contrast that has a lower cutoff at the standard deviation of the noise.

Effects of luminance oscillations on simulated lightness discriminations

The speed of processes underlying lightness constancy was studied by having observers discriminate small differences in simulated lightness under an oscillating illumination. The period of oscillation varied from 0.25 to 120 sec. The target was a 1 degrees square which appeared for 150 msec at random intervals either directly against a uniform background or separated from the background by a 1 degrees dark gap. When the target and background were adjacent to each other, discrimination accuracy approached control levels (fixed illumination) at all but the shortest periods of oscillation. When the gap was introduced, accuracy increased as the period of oscillation increased, but never approached control levels. The results suggest that a fast local contrast mechanism is the primary mediator of lightness constancy for this task, but that there is also a slower mechanism that may be related to adaptation.

Comparing increment and decrement probes in the probed-sinewave paradigm

Using the probed-sinewave paradigm, we explore the differences between increment and decrement probes across a range of frequencies (approx. 1-19 Hz). In this paradigm, detection threshold is measured for a small test probe presented on a large sinusoidally flickering background (at eight different phases). Probe thresholds are very similar for increment and decrement probes, but there is a very small (and systematic) difference: increment thresholds are usually slightly higher relative to decrement thresholds during the part of the cycle when the background's intensity is increasing. Although Wilson's (1997, Vis. Neuro., 14, 403-423) model substantially underestimates the size of this difference, it predicts some phase dependency. However, the existence of On- and Off-pathways in Wilson's model is not important for these predictions. A recent model by Snippe, Poot, and van Hateren (2000, Vis. Neuro., 17, 449-462) may be able to predict this result by using explicit contrast-gain control rather than separate On- and Off-pathways. Auxiliary experiments measuring the perceived polarity of the probe provide a further argument suggesting that separate On- and Off-pathways are not useful in explaining increment and decrement probe thresholds.