Modeling grating contrast discrimination dippers: The role of surround suppression.

We consider the role of nonlinear inhibition in physiologically realistic multineuronal models of V1 to predict the dipper functions from contrast discrimination experiments with sinusoidal gratings of different geometries. The dip in dipper functions has been attributed to an expansive transducer function, which itself is attributed to two nonlinear inhibitory mechanisms: contrast normalization and surround suppression. We ran five contrast discrimination experiments, with targets and masks of different sizes and configurations: small Gabor target/small mask, small target/large mask, large target/large mask, small target/in-phase annular mask, and small target/out-of-phase annular mask. Our V1 modeling shows that the results for small Gabor target/small mask, small target/large mask, large target/large mask configurations are easily explained only if the model includes surround suppression. This is compatible with the finding that an in-phase annular mask generates only little threshold elevation while the out-of-phase mask was more effective. Surrounding mask gratings cannot be equated with surround suppression at the receptive-field level. We examine whether normalization and surround suppression occur simultaneously (parallel model) or sequentially (a better reflection of neurophysiology). The Akaike Criterion Difference showed that the sequential model was better than the parallel, but the difference was small. The large target/large mask dipper experiment was not well fit by our models, and we suggest that this may reflect selective attention for its uniquely larger test stimulus. The best-fit model replicates some behaviors of single V1 neurons, such as the decrease in receptive-field size with increasing contrast.

Perceptual Pragmatism and the Naturalized Ontology of Color.

This paper considers whether there can be any such thing as a naturalized metaphysics of color-any distillation of the commitments of perceptual science with regard to color ontology. I first make some observations about the kinds of philosophical commitments that sometimes bubble to the surface in the psychology and neuroscience of color. Unsurprisingly, because of the range of opinions expressed, an ontology of color cannot simply be read off from scientists' definitions and theoretical statements. I next consider two alternative routes. First, conceptual pluralism inspired by Mark Wilson's analysis of scientific representation. I argue that these findings leave the prospects for a naturalized color ontology rather dim. Second, I outline a naturalized epistemology of perception. I ask how the correctness and informativeness of perceptual states is understood by contemporary perceptual science. I argue that the detectionist ideal of correspondence should be replaced by the pragmatic ideal of usefulness. I argue that this result has significant implications for the metaphysics of color.

Magnitude of perceived change in natural images may be linearly proportional to differences in neuronal firing rates.

We are studying how people perceive naturalistic suprathreshold changes in the colour, size, shape or location of items in images of natural scenes, using magnitude estimation ratings to characterise the sizes of the perceived changes in coloured photographs. We have implemented a computational model that tries to explain observers' ratings of these naturalistic differences between image pairs. We model the action-potential firing rates of millions of neurons, having linear and non-linear summation behaviour closely modelled on real VI neurons. The numerical parameters of the model's sigmoidal transducer function are set by optimising the same model to experiments on contrast discrimination (contrast 'dippers') on monochrome photographs of natural scenes. The model, optimised on a stimulus-intensity domain in an experiment reminiscent of the Weber-Fechner relation, then produces tolerable predictions of the ratings for most kinds of naturalistic image change. Importantly, rating rises roughly linearly with the model's numerical output, which represents differences in neuronal firing rate in response to the two images under comparison; this implies that rating is proportional to the neuronal response.

Coding of the contrasts in natural images by visual cortex (V1) neurons: a Bayesian approach.

Individual V1 neurons respond dynamically over only limited ranges of stimulus contrasts, yet we can discriminate contrasts over a wide range. Different V1 neurons cover different parts of the contrast range, and the information they provide must be pooled somehow. We describe a probabilistic pooling model that shows that populations of neurons with contrast responses like those in cat and monkey V1 would most accurately code contrasts in the range actually found in natural scenes. The pooling equation is similar to Bayes's equation; however, explicit inclusion of prior probabilities in the inference increases coding accuracy only slightly.