Mazviita Chirimuuta

What is colour vision for? In the popular imagination colour vision is for “seeing the colours” — adding hue to the achromatic world of shape, depth and motion. On this view colour vision plays little more than an ornamental role, lending glamour to an otherwise monochrome world. This idea has guided much theorising about colour within vision science and philosophy. However, we argue that a broader approach is needed. Recent research in the psychology of colour demonstrates that colour vision is integral to a variety of visual processes, helping us to perform many types of visual tasks. We discuss some of this research and consider its implications for philosophical theories of colour

While introspective methods went out of favour with the decline of Titchener’s analytic school, many important questions concern the rehabilitation of introspection in contemporary psychology. Hatfield rightly points out that introspective methods should not be confused with analytic ones, and goes on to describe their “ineliminable role” in perceptual psychology. Here I argue that certain methodological conventions within psychophysics reflect a continued uncertainty over appropriate use of subjects’ perceptual observations and the reliability of their introspective judgements. My first claim is that different psychophysical methods do not rely equally on the introspective capabilities of experimental subjects. I contrast “minimally-introspective” tasks with “introspection-heavy” ones. It is only in the latter, I argue, that introspection can be said to have a non-trivial role in the subjects’ performance. My second claim is that my rough-and-ready distinction maps onto a number of important “dichotomies” in vision science. Not coincidentally, the introspection-heavy categorisation captures many of the tasks typically considered less able to yield useful information regarding the processes underlying visual sensation.

The concept of the receptive field, first articulated by Hartline, is central to visual neuroscience. The receptive field of a neuron encompasses the spatial and temporal properties of stimuli that activate the neuron, and, as Hubel and Wiesel conceived of it, a neuron’s receptive field is static. This makes it possible to build models of neural circuits and to build up more complex receptive fields out of simpler ones. Recent work in visual neurophysiology is providing evidence that the classical receptive field is an inaccurate picture. The receptive field seems to be a dynamic feature of the neuron. In particular, the receptive field of neurons in V1 seems to be dependent on the properties of the stimulus. In this paper, we review the history of the concept of the receptive field and the problematic data. We then consider a number of possible theoretical responses to these data.

This article examines three candidate cases of non-causal explanation in computational neuroscience. I argue that there are instances of efficient coding explanation that are strongly analogous to examples of non-causal explanation in physics and biology, as presented by Batterman, Woodward, and Lange. By integrating Lange’s and Woodward’s accounts, I offer a new way to elucidate the distinction between causal and non-causal explanation, and to address concerns about the explanatory sufficiency of non-mechanistic models in neuroscience. I also use this framework to shed light on the dispute over the interpretation of dynamical models of the brain. _1_ Introduction _1.1_ Efficient coding explanation in computational neuroscience _1.2_ Defining non-causal explanation _2_ Case I: Hybrid Computation _3_ Case II: The Gabor Model Revisited _4_ Case III: A Dynamical Model of Prefrontal Cortex _4.1_ A new explanation of context-dependent computation _4.2_ Causal or non-causal? _5_ Causal and Non-causal: Does the Difference Matter?

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.

This paper addresses concerns raised recently by Datteri (Biol Philos 24:301–324, 2009) and Craver (Philos Sci 77(5):840–851, 2010) about the use of brain-extending prosthetics in experimental neuroscience. Since the operation of the implant induces plastic changes in neural circuits, it is reasonable to worry that operational knowledge of the hybrid system will not be an accurate basis for generalisation when modelling the unextended brain. I argue, however, that Datteri’s no-plasticity constraint unwittingly rules out numerous experimental paradigms in behavioural and systems neuroscience which also elicit neural plasticity. Furthermore, I propose that Datteri and Craver’s arguments concerning the limitations of prosthetic modelling in basic neuroscience, as opposed to neuroengineering, rests on too narrow a view of the ways models in neuroscience should be evaluated, and that a more pluralist approach is needed. I distinguish organisational validity of models from mechanistic validity. I argue that while prosthetic models may be deficient in the latter of these explanatory virtues because of neuroplasticity, they excel in the former since organisational validity tracks the extent to which a model captures coding principles that are invariant with plasticity. Changing the brain, I conclude, is one viable route towards explaining the brain