Vicente Sierra-Vázquez

Low spatial frequency filtering modulates early brain processing of affective complex pictures

Recent research on affective processing has suggested that low spatial frequency information of fearful faces provide rapid emotional cues to the amygdala, whereas high spatial frequencies convey fine-grained information to the fusiform gyrus, regardless of emotional expression. In the present experiment, we examined the effects of low (LSF, <15 cycles/image width) and high spatial frequency filtering (HSF, >25 cycles/image width) on brain processing of complex pictures depicting pleasant, unpleasant, and neutral scenes. Event-related potentials (ERP), percentage of recognized stimuli and response times were recorded in 19 healthy volunteers. Behavioral results indicated faster reaction times in response to unpleasant LSF than to unpleasant HSF pictures. Unpleasant LSF pictures and pleasant unfiltered pictures also elicited significant enhancements of P1 amplitudes at occipital electrodes as compared to neutral LSF and unfiltered pictures, respectively; whereas no significant effects of affective modulation were found for HSF pictures. Moreover, mean ERP amplitudes in the time between 200 and 500ms post-stimulus were significantly greater for affective (pleasant and unpleasant) than for neutral unfiltered pictures; whereas no significant affective modulation was found for HSF or LSF pictures at those latencies. The fact that affective LSF pictures elicited an enhancement of brain responses at early, but not at later latencies, suggests the existence of a rapid and preattentive neural mechanism for the processing of motivationally relevant stimuli, which could be driven by LSF cues. Our findings confirm thus previous results showing differences on brain processing of affective LSF and HSF faces, and extend these results to more complex and social affective pictures.

Do Channels Shift their Tuning Towards Lower Spatial Frequencies in the Periphery

Space-variant multichannel spatial vision models include either anchored channels whose units (sensors) share their tuning frequency, or shifting channels whose sensors shift their tuning towards lower frequencies in the periphery. Each type of model embodies a different type of structural organization across eccentricity. Anchored- and shifting-channel models are tested in this paper against empirical data from five types of relevant detection experiment: measurements of the local sine-wave contrast sensitivity function (CSF) at several eccentricities using (a) fixed apertures, (b) apertures scaled with eccentricity or (c) fixed number of cycles, (d) measurements of foveal sensitivity as a function of aperture size, and (e) measurements of the contrast sensitivity gradient across the visual field. Each type of model is shown to predict a different outcome in each type of experiment. A review of empirical research reveals that three of the five experiment types have yielded two distinct sets of results, each of which is consistent with the predictions from one of the types of model, while the two other types of experiment have always yielded similar results which support anchored-channel models. Further scrutiny of the models reveals that the distinction between anchored and shifting channels is more apparent than real, as model predictions are only determined by how sensor gain is assumed to change with eccentricity and tuning frequency. Two alternative sensor gain functions are identified and interpreted in terms of two versions of the cortical magnification theory of spatial vision. Altogether, these two functions account for all extant data on the five types of experiment, suggesting individual differences in the functional organization of the human visual system across eccentricity.

Application of Riesz transforms to the isotropic AM-PM decomposition of geometrical-optical illusion images

The existence of a special second-order mechanism in the human visual system, able to demodulate the envelope of visual stimuli, suggests that spatial information contained in the image envelope may be perceptually relevant. The Riesz transform, a natural isotropic extension of the Hilbert transform to multidimensional signals, was used here to demodulate band-pass filtered images of well-known visual illusions of length, size, direction, and shape. We show that the local amplitude of the monogenic signal or envelope of each illusion image conveys second-order information related to image holistic spatial structure, whereas the local phase component conveys information about the spatial features. Further low-pass filtering of the illusion image envelopes creates physical distortions that correspond to the subjective distortions perceived in the illusory images. Therefore the envelope seems to be the image component that physically carries the spatial information about these illusions. This result contradicts the popular belief that the relevant spatial information to perceive geometrical-optical illusions is conveyed only by the lower spatial frequencies present in their Fourier spectrum.

The Effect of Spatial-Frequency Filtering on the Visual Processing of Global Structure

In three experiments we measured reaction times (RTs) and error rates in identifying the global structure of spatially filtered stimuli whose spatial-frequency content was selected by means of three types of 2-D isotropic filters (Butterworth of order 2, Butterworth of order 10, and a filters with total or partial Gaussian spectral profile). In each experiment, low-pass (LP), band-pass (BP), and high-pass (HP) filtered stimuli, with nine centre or cut-off spatial frequencies, were used. Irrespective of the type of filter, the experimental results showed that: (a) RTs to stimuli with low spatial frequencies were shorter than those to stimuli with medium or high spatial frequencies, (b) RTs to LP filtered stimuli were nearly constant, but they increased in a non-monotonic way with the filter centre spatial frequency in BP filtered stimuli and with the filter cut-off frequency in HP filtered stimuli, and (c) the identification of the global pattern occurred with all visible stimuli used, including BP and HP images without low spatial frequencies. To remove the possible influence of the energy, a fourth experiment was conducted with Gaussian filtered stimuli of equal contrast power (crms = 0.065). Similar results to those described above were found for stimuli with spatial-frequency content higher than 2 cycles deg−1. A model of isotropic first-order visual channels collecting the stimulus spectral energy in all orientations explains the RT data. A subsequent second-order nonlinear amplitude demodulation process, applied to the output of the most energetic first-order channel, could explain the perception of global structure of each spatially filtered stimulus, including images lacking low spatial frequencies.

The effect of white-noise mask level on sinewave contrast detection thresholds and the critical-band-masking model.

It is known that visual noise added to sinusoidal gratings changes the typical U-shaped threshold curve which becomes flat in log-log scale for frequencies below 10c/deg when gratings are masked with white noise of high power spectral density level. These results have been explained using the critical-band-masking (CBM) model by supposing a visual filter-bank of constant relative bandwidth. However, some psychophysical and biological data support the idea of variable octave bandwidth. The CBM model has been used here to explain the progressive change of threshold curves with the noise mask level and to estimate the bandwidth of visual filters. Bayesian staircases were used in a 2IFC paradigm to measure contrast thresholds of horizontal sinusoidal gratings (0.25-8 c/deg) within a fixed Gaussian window and masked with one-dimensional, static, broadband white noise with each of five power density levels. Raw data showed that the contrast threshold curve progressively shifts upward and flattens out as the mask noise level increases. Theoretical thresholds from the CBM model were fitted simultaneously to the data at all five noise levels using visual filters with log-Gaussian gain functions. If we assume a fixed-channel detection model, the best fit was obtained when the octave bandwidth of visual filters decreases as a function of peak spatial frequency.

Power spectrum model of visual masking: simulations and empirical data

In the study of the spatial characteristics of the visual channels, the power spectrum model of visual masking is one of the most widely used. When the task is to detect a signal masked by visual noise, this classical model assumes that the signal and the noise are previously processed by a bank of linear channels and that the power of the signal at threshold is proportional to the power of the noise passing through the visual channel that mediates detection. The model also assumes that this visual channel will have the highest ratio of signal power to noise power at its output. According to this, there are masking conditions where the highest signal-to-noise ratio (SNR) occurs in a channel centered in a spatial frequency different from the spatial frequency of the signal (off-frequency looking). Under these conditions the channel mediating detection could vary with the type of noise used in the masking experiment and this could affect the estimation of the shape and the bandwidth of the visual channels. It is generally believed that notched noise, white noise and double bandpass noise prevent off-frequency looking, and high-pass, low-pass and bandpass noises can promote it independently of the channels shape. In this study, by means of a procedure that finds the channel that maximizes the SNR at its output, we performed numerical simulations using the power spectrum model to study the characteristics of masking caused by six types of one-dimensional noise (white, high-pass, low-pass, bandpass, notched, and double bandpass) for two types of channels shape (symmetric and asymmetric). Our simulations confirm that (1) high-pass, low-pass, and bandpass noises do not prevent the off-frequency looking, (2) white noise satisfactorily prevents the off-frequency looking independently of the shape and bandwidth of the visual channel, and interestingly we proved for the first time that (3) notched and double bandpass noises prevent off-frequency looking only when the noise cutoffs around the spatial frequency of the signal match the shape of the visual channel (symmetric or asymmetric) involved in the detection. In order to test the explanatory power of the model with empirical data, we performed six visual masking experiments. We show that this model, with only two free parameters, fits the empirical masking data with high precision. Finally, we provide equations of the power spectrum model for six masking noises used in the simulations and in the experiments.

Visual Chimaeras Obtained with the Riesz Transform

Similar to an auditory chimaera (Smith et al, 2002 Nature 416 87–90), a visual chimaera can be defined as a synthetic image which has the fine spatial structure of one natural image and the envelope of another image in each spatial frequency band. Visual chimaeras constructed in this way could be useful to vision scientists interested in the study of interactions between first-order and second-order visual processing. Although it is almost trivial to generate 1-D chimaeras by means of the Hilbert transform and the analytic signal, problems arise in multidimensional signals like images given that the partial directional Hilbert transform and current 2-D demodulation algorithms are anisotropic or orientation-variant procedures. Here, we present a computational procedure to synthesise visual chimaeras by means of the Riesz transform—an isotropic generalisation of the Hilbert transform for multidimensional signals—and the associated monogenic signal—the vector-valued function counterpart of the analytic signal in which the Riesz transform replaces the Hilbert transform. Examples of visual chimaeras are shown for same/different category images.

The optimal motion stimulus: comments on Watson and Turano (1995)

Watson and Turano (Vision Research 1995;35:325-336) described experimental research aimed at determining the motion stimulus that the visual system detects best. They reported conflicting results in the determination of the optimal spatial size and they interpreted them as an effect of probability summation. They also reported disagreement with earlier results of Watson et al. (Nature 1983;302:419-422). This study shows (i) that probability summation is not responsible for those results and (ii) that they can be explained as a consequence of the method that was used to search for the optimal stimulus.