Jonathan W. Peirce

PsychoPy--Psychophysics software in Python.

The vast majority of studies into visual processing are conducted using computer display technology. The current paper describes a new free suite of software tools designed to make this task easier, using the latest advances in hardware and software. PsychoPy is a platform-independent experimental control system written in the Python interpreted language using entirely free libraries. PsychoPy scripts are designed to be extremely easy to read and write, while retaining complete power for the user to customize the stimuli and environment. Tools are provided within the package to allow everything from stimulus presentation and response collection (from a wide range of devices) to simple data analysis such as psychometric function fitting. Most importantly, PsychoPy is highly extensible and the whole system can evolve via user contributions. If a user wants to add support for a particular stimulus, analysis or hardware device they can look at the code for existing examples, modify them and submit the modifications back into the package so that the whole community benefits.

Generating stimuli for neuroscience using PsychoPy

PsychoPy is a software library written in Python, using OpenGL to generate very precise visual stimuli on standard personal computers. It is designed to allow the construction of as wide a variety of neuroscience experiments as possible, with the least effort. By writing scripts in standard Python syntax users can generate an enormous variety of visual and auditory stimuli and can interact with a wide range of external hardware (enabling its use in fMRI, EEG, MEG etc.). The structure of scripts is simple and intuitive. As a result, new experiments can be written very quickly, and trying to understand a previously written script is easy, even with minimal code comments. PsychoPy can also generate movies and image sequences to be used in demos or simulated neuroscience experiments. This paper describes the range of tools and stimuli that it provides and the environment in which experiments are conducted.

Profound Contrast Adaptation Early in the Visual Pathway

Prior exposure to a moving grating of high contrast led to a substantial and persistent reduction in the contrast sensitivity of neurons in the lateral geniculate nucleus (LGN) of macaque. This slow contrast adaptation was potent in all magnocellular (M) cells but essentially absent in parvocellular (P) cells and neurons that received input from S cones. Simultaneous recordings of M cells and the potentials of ganglion cells driving them showed that adaptation originated in ganglion cells. As expected from the spatiotemporal tuning of M cells, adaptation was broadly tuned for spatial frequency and lacked orientation selectivity. Adaptation could be induced by high temporal frequencies to which cortical neurons do not respond, but not by low temporal frequencies that can strongly adapt cortical neurons. Our observations confirm that contrast adaptation occurs at multiple levels in the visual system, and they provide a new way to reveal the function and perceptual significance of the M pathway.

The Impact of Suppressive Surrounds on Chromatic Properties of Cortical Neurons

Stimulation of the suppressive surround of a cortical neuron affects the responsivity and tuning of the classical receptive field (CRF) on several stimulus dimensions. In V1 and V2 of macaques prepared for acute electrophysiological experiments, we explored the chromatic sensitivity of the surround and its influence on the chromatic tuning of the CRF. We studied receptive fields of single neurons with patches of drifting grating of optimal spatial frequency and orientation and variable size, modulated along achromatic or isoluminant color directions. The responses of most neurons declined as the patch was enlarged beyond the optimal size (surround suppression). In V1 the suppression evoked by isoluminant gratings was less than one-half that evoked by achromatic gratings. Consequently, many cells were most sensitive to achromatic modulation when patches just covered the CRF but were most sensitive to isoluminant modulation when patches were enlarged to cover the suppressive surround. Non-oriented neurons that were strongly chromatically opponent generally lacked suppressive surrounds. In V2 most neurons showed equal surround suppression from isoluminant gratings and achromatic gratings. This makes the relative sensitivity of V2 neurons to achromatic and isoluminant gratings mainly independent of the size of the grating. We also measured the chromatic properties of the CRF in the presence of differently colored surrounds. In neither V1 nor V2 did the surround alter the chromatic tuning of the CRF. Cortical mechanisms sensitive to chromatic contrast seem to provide little input to the suppressive surrounds of V1 neurons but substantial input to those of V2 neurons.

The potential importance of saturating and supersaturating contrast response functions in visual cortex

Most cortical visual neurons do not respond linearly with contrast. Generally, they show saturated responses to stimuli of high contrast, a feature often characterized by a divisive normalization function. This nonlinearity is generally thought to be useful in focusing the dynamic response range of the neuron on a particular region of contrast space, optimizing contrast gain. Some neurons not only saturate but also supersaturate; at high contrast, the response of the neuron decreases rather than plateaus. Under the contrast gain control theory, these cells would seem to reflect a nonoptimal normalization pool that provides excessive inhibition to the neurons. Since very few data on supersaturation are available, this article examines the frequency with which such neurons occur in macaque visual cortex by considering an extension of the Naka-Rushton equation with the capacity to represent nonmonotonic functions. The prevalence of gain-control theories for saturation has occluded an additional computational function for saturation, namely, in detecting the conjunction of certain features. A saturating nonlinearity is a critical part of the selective detection of compound stimuli over their components. In this role, the existence of saturating contrast response functions might be considered necessary rather than simply optimal.

A comparison of fMRI adaptation and multivariate pattern classification analysis in visual cortex

Functional magnetic resonance imaging (fMRI) has become a ubiquitous tool in cognitive neuroscience. The technique allows noninvasive measurements of cortical responses in the human brain, but only on the millimeter scale. Because a typical voxel contains many thousands of neurons with varied properties, establishing the selectivity of their responses directly is impossible. In recent years, two methods using fMRI aimed at studying the selectivity of neuronal populations on a ‘subvoxel’ scale have been heavily used. The first technique, fMRI adaptation, relies on the observation that the blood oxygen level-dependent (BOLD) response in a given voxel is reduced after prolonged presentation of a stimulus, and that this reduction is selective to the characteristics of the repeated stimuli (adapters). The second technique, multivariate pattern analysis (MVPA), makes use of multivariate statistics to recover small biases in individual voxels in their responses to different stimuli. It is thought that these biases arise due to the uneven distribution of neurons (with different properties) sampled by the many voxels in the imaged volume. These two techniques have not been compared explicitly, however, and little is known about their relative sensitivities. Here, we compared fMRI results from orientation-specific visual adaptation and orientation–classification by MVPA, using optimized experimental designs for each, and found that the multivariate pattern classification approach was more sensitive to small differences in stimulus orientation than the adaptation paradigm. Estimates of orientation selectivity obtained with the two methods were, however, very highly correlated across visual areas.

Selective mechanisms for simple contours revealed by compound adaptation.

Neurons in the early stages of visual processing are often thought of as edge detectors for different orientations. Here we investigate the existence of detectors for specific combinations of edges-detectors for specific curvatures. Previous attempts to demonstrate such detectors through aftereffects have ultimately been explained by adaptation to local orientation rather than curvature per se. To control for local aftereffects, we adapted one patch of visual field to two adjacent gratings presented as an obtuse contour (compound patch), and another patch to the same component gratings presented alternately (component patch). In this way both patches are adapted equally to the local orientation components of the stimuli, but only the compound patch is adapted to the global contour. Thus any difference in adaptation between the patches must result from the presence of the contour as a global figure. We found that perceived contrast of probe stimuli was not differentially altered in the two patches. However, apparent curvature of the probes was consistently greater in the compound patch than in the component patch. This effect was considerably reduced by increasing the spatial separation of the component gratings. The results are consistent with curvature detectors involved in the perceptual grouping of edges.

Two expressions of surround suppression in V1 that arise independent of cortical mechanisms of suppression

The preferred stimulus size of a V1 neuron decreases with increases in stimulus contrast. It has been supposed that stimulus contrast is the primary determinant of such spatial summation in V1 cells, though the extent to which it depends on other stimulus attributes such as orientation and spatial frequency remains untested. We investigated this by recording from single cells in V1 of anaesthetized cats and monkeys, measuring size-tuning curves for high-contrast drifting gratings of optimal spatial configuration, and comparing these curves with those obtained at lower contrast or at sub-optimal orientations or spatial frequencies. For drifting gratings of optimal spatial configuration, lower contrasts produced less surround suppression resulting in increases in preferred size. High contrast gratings of sub-optimal spatial configuration produced more surround suppression than optimal low-contrast gratings, and as much or more surround suppression than optimal high-contrast gratings. For sub-optimal spatial frequencies, preferred size was similar to that for the optimal high-contrast stimulus, whereas for sub-optimal orientations, preferred size was smaller than that for the optimal high-contrast stimulus. These results indicate that, while contrast is an important determinant of spatial summation in V1, it is not the only determinant. Simulation of these experiments on a cortical receptive field modeled as a Gabor revealed that the small preferred sizes observed for non-preferred stimuli could result simply from linear filtering by the classical receptive field. Further simulations show that surround suppression in retinal ganglion cells and LGN cells can be propagated to neurons in V1, though certain properties of the surround seen in cortex indicate that it is not solely inherited from earlier stages of processing.

Selective mechanisms for complex visual patterns revealed by adaptation.

A great deal is known about the initial steps of visual processing. We know that humans have neural mechanisms selectively tuned to simple patterns of particular spatial frequencies and orientations. We also know that much later in the visual pathway, in inferotemporal cortex, cells respond to extremely complex visual patterns such as images of faces. Very little is known about intermediate levels of visual processing, where early visual signals are presumably combined to represent increasingly complex visual features. Here we show the existence of visual mechanisms in humans, tuned and selective to particular combinations of simple sinusoidal patterns, using a novel method of compound adaptation.

Cortical representation of color is binocular

It is widely believed that the cortical mechanisms of color vision are monocular because stereopsis is poor for isoluminant patterns. By measuring and comparing the chromatic tuning of binocular and monocular neurons in cortical areas V1 and V2 of macaque, we show that this is not the case. Not only are many color-preferring cells in early visual cortex well-driven binocularly, but their color preferences are unusually well-matched in the two eyes. The receptive fields of these neurons are well equipped to convey information about binocular surface color, but because they are insensitive to local spatial contrast they are ill-suited to convey information about stereoscopic depth. Our observations suggest that in early cortical processing, binocular depth and binocular surface color are represented by two different groups of neurons: one that encodes binocular spatial detail at the expense of binocular chromatic detail and another that does the reverse.