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S R Lehky

Publications and source records attributed to S R Lehky.

13 recordsLinked to original sources
PubMed2000

Deficits in visual feature binding under isoluminant conditions.

The contribution of the magnocellular stream to visual feature binding was examined psychophysically through the use of isoluminant stimuli. Subjects were presented with three briefly flashed colored letters arranged in an array and asked to identify the shape and color of the center letter. The rate of illusory conjunctions was much higher when the letters were isoluminant with a gray background, compared to when the letters were either brighter or dimmer. Over 90% of conjunction errors involved pairing the wrong shape with the correct color, rather than vice versa. Directing attention to the target location with a nonisoluminant cue did not reduce illusory conjunctions. High rates of binding errors under isoluminance are interpreted here in terms of abnormalities in visual form processing rather than an attentional effect. In another experiment designed to examine the role of synchrony in feature binding, the rate of illusory conjunctions was highest when flanking letters were presented before the central target letter and not synchronously.

Color Perception↗
PubMed2000

Fine discrimination of faces can be performed rapidly.

Here we measure the smallest change in a face that can be discriminated. A morphing algorithm mixed two faces in variable proportions to create a series of synthetic faces that each differed by a tiny amount. By selecting from this series, a test face could be chosen so as to reach a just noticeable difference from a sample face. Face-discrimination thresholds were about 7% of the average difference between two faces, as quantified by coefficients of a principal components decom position. This threshold remained constant as the duration of the test face was reduced from 1,000 to 100 msec, and rose quickly for shorter stimulus durations. The behavioral evidence presented here indicates that complex visual processing can be completed within the first 100 msec of the signal, suggesting involvement of feedforward neural mechanisms, and placing constraints on possible computational algorithms employed within the ventral visual pathways.

Algorithms↗
PubMed1999

Seeing white: Qualia in the context of decoding population codes.

When the nervous system is presented with multiple simultaneous inputs of some variable, such as wavelength or disparity, they can be combined to give rise to qualitatively new percepts that cannot be produced by any single input value. For example, there is no single wavelength that appears white. Many models of decoding neural population codes have problems handling multiple inputs, either attempting to extract a single value of the input parameter or, in some cases, registering the presence of multiple inputs without synthesizing them into something new. These examples raise a more general issue regarding the interpretation of population codes. We propose that population decoding involves not the extraction of specific values of the physical inputs, but rather a transformation from the input space to some abstract representational space that is not simply related to physical parameters. As a specific example, a four-layer network is presented that implements a transformation from wavelength to a high-level hue-saturation color space.

Color↗
PubMed1996

No binocular rivalry in the LGN of alert macaque monkeys.

Orthogonal drifting gratings were presented binocularly to alert macaque monkeys in an attempt to find neural correlates of binocular rivalry. Gratings were centered over lateral geniculate nucleus (LGN) receptive fields and the corresponding points for the opposite eye. The only task of the monkey was to fixate. We found no difference between the responses of LGN neurons under rivalrous and nonrivalrous conditions, as determined by examining the ratios of their respective power spectra. There was, however, a curious "temporal afterimage" effect in which cell responses continued to be modulated at the drift frequency of the grating for several seconds after the grating disappeared.

Animals↗
PubMed1995

Binocular rivalry is not chaotic.

Time series of the durations each eye was dominant during binocular rivalry were obtained psychophysically. The oscillations showed an adaptation effect with mean and standard deviations of rivalry dominance durations increasing as a square root function of time over the course of a trial. The data were corrected for this non-stationarity. Dominance durations had a log-normal probability distribution and the autocorrelation function revealed no short term correlations in the time series. In an attempt to distinguish whether the variability of durations was due to a deterministic, low-dimensional chaotic attractor or to a stochastic process, the data were subjected to two tests. The first was calculation of correlation dimensions and the second was nonlinear forecasting of the time series. Both tests included comparisons with randomized 'surrogate data' as controls. In neither case was there a large difference between test results for actual data and surrogate data. We conclude that chaos is not a major factor underlying variability in binocular rivalry.

Adaptation, Ocular↗
PubMed1992

Predicting responses of nonlinear neurons in monkey striate cortex to complex patterns.

The overwhelming majority of neurons in primate visual cortex are nonlinear. For those cells, the techniques of linear system analysis, used with some success to model retinal ganglion cells and striate simple cells, are of limited applicability. As a start toward understanding the properties of nonlinear visual neurons, we have recorded responses of striate complex cells to hundreds of images, including both simple stimuli (bars and sinusoids) as well as complex stimuli (random textures and 3-D shaded surfaces). The latter set tended to give the strongest response. We created a neural network model for each neuron using an iterative optimization algorithm. The recorded responses to some stimulus patterns (the training set) were used to create the model, while responses to other patterns were reserved for testing the networks. The networks predicted recorded responses to training set patterns with a median correlation of 0.95. They were able to predict responses to test stimuli not in the training set with a correlation of 0.78 overall, and a correlation of 0.65 for complex stimuli considered alone. Thus, they were able to capture much of the input/output transfer function of the neurons, even for complex patterns. Examining connection strengths within each network, different parts of the network appeared to handle information at different spatial scales. To gain further insights, the network models were inverted to construct "optimal" stimuli for each cell, and their receptive fields were mapped with high-resolution spots. The receptive field properties of complex cells could not be reduced to any simpler mathematical formulation than the network models themselves.

Animals↗
PubMed1990

Neural network model of visual cortex for determining surface curvature from images of shaded surfaces.

The visual system can extract information about shape from the pattern of light and dark surface shading on an object. Very little is known about how this is accomplished. We have used a learning algorithm to construct a neural network model that computes the principal curvatures and orientation of elliptic paraboloids independently of the illumination direction. Our chief finding is that receptive fields developed by units of such model network are surprisingly similar to some found in the visual cortex. It appears that neurons that can make use of the continuous gradations of shading have receptive fields similar to those previously interpreted as dealing with contours (i.e. 'bar' detectors or 'edge' detectors). This study illustrates the difficulty of deducing neuronal function within a network solely from receptive fields. It is also important to consider the pattern of connections a neuron makes with subsequent stages, which we call the 'projective field'.

Algorithms↗
PubMed1990

Neural model of stereoacuity and depth interpolation based on a distributed representation of stereo disparity.

We have developed a model for the representation of stereo disparity by a population of neurons that is based on tuning curves similar in shape to those measured physiologically (Poggio and Fischer, 1977). Signal detection analysis was applied to the model to generate predictions of depth discrimination thresholds. Agreement between the model and human psychophysical data was possible in this model only when the population size representing disparity in a small patch of visual field was in the range of about 20-200 units. Interval encoding and rate encoding were found to be inconsistent with these data. Psychophysical data on stereo interpolation (Westheimer, 1986a) suggest that there are short-range excitatory and long-range inhibitory interactions between disparity-tuned units at nearby spatial locations. We extended our population model of disparity coding at a single spatial location to include such lateral interactions. When there was a small disparity gradient between stimuli at 2 locations, units in the intermediate, unstimulated position developed a pattern of activity corresponding to the average of the 2 lateral disparities. When there was a large disparity gradient, units at the intermediate position developed a pattern of activity corresponding to an independent superposition of the 2 lateral disparities, so that both disparities were represented simultaneously. This mixed population pattern may underlie the perception of depth discontinuities and transparent surfaces. Similar types of distributed representations may be applicable to other parameters, such as orientation, motion, stimulus size, and motor coordinates.

Depth Perception↗
PubMed1988

Network model of shape-from-shading: neural function arises from both receptive and projective fields.

It is not known how the visual system is organized to extract information about shape from the continuous gradations of light and dark found on shaded surfaces of three-dimensional objects. To investigate this question, we used a learning algorithm to construct a neural network model which determines surface curvatures from images of simple geometrical surfaces. The receptive fields developed by units in the network were surprisingly similar to the actual receptive fields of neurons observed in the visual cortex which are commonly believed to be 'edge' or 'bar' detectors, but have never previously been associated with shading. Thus, our study illustrates the difficulty of trying to deduce neuronal function solely from determination of their receptive fields. It is also important to consider the connections a neuron makes with other neurons in subsequent stages of processing, which we call its 'projective field'.

Computer Simulation↗
PubMed1988

An astable multivibrator model of binocular rivalry.

The behavior of a neural network model for binocular rivalry is explored through the development of an analogy between it and an electronic astable multivibrator circuit. The model incorporates reciprocal feedback inhibition between signals from the left and the right eyes prior to binocular convergence. The strength of inhibitory coupling determines whether the system undergoes rivalrous oscillations or remains in stable fusion: strong coupling leads to oscillations, weak coupling to fusion. This implies that correlation between spatial patterns presented to the two eyes can affect the strength of binocular inhibition. Finally, computer simulations are presented which show that a reciprocal inhibition model can reproduce the stochastic behavior of rivalry. The model described is a counterexample to claims that reciprocal inhibition models as a class cannot exhibit many of the experimentally observed properties of rivalry.

Artificial Intelligence↗
PubMed1985

Temporal properties of visual channels measured by masking.

Threshold elevations for spatially localized test stimuli were measured as a function of the temporal frequency of a sinusoidal mask grating. Ten of these temporal-masking curves were determined, each using a test stimulus of different temporal frequency. It was found that these data could be accounted for by four temporal-tuning curves: one low-pass curve with a corner frequency of 8.0 Hz and three bandpass curves with bandwidths of 2.0-2.5 octaves and peaks all within the region 4.0-8.0 Hz. In another set of experiments, a series of 1 spatial-masking curves was determined, each using a test stimulus of different spatial frequency. Measurement of the entire series was repeated using 1.0- and 8.0-Hz mask temporal modulations. Spatial-masking data collected under the two temporal conditions could both be accounted for by a single set of seven spatial-tuning curves, without changing the shape of any of these curves. The data are therefore compatible with spatiotemporal separability. Finally, a large set of contrast-response curves (threshold elevation as a function of mask contrast) was collected. These curves were found to follow a power law whose exponent varied with the spatiotemporal conditions of the experiment.

Form Perception↗
PubMed1983

A model of binocular brightness and binaural loudness perception in humans with general applications to nonlinear summation of sensory inputs.

A single neural model is proposed to account for how responses of the two eyes and two ears combine to form the perception of binocular brightness and binaural loudness respectively. It involves nonlinear reciprocal feedback inhibition between left and right channels, followed by linear summation between the channels. Local circuit synaptic interactions are an important source of nonlinearity. The model combines inputs in a manner that approximates vector magnitude models in general. This suggests that the model can be applied to a variety of circumstances beyond the visual and auditory data discussed here.

Auditory Perception↗