Daniel C. Burnston

Why Do Biologists Use So Many Diagrams

Diagrams have distinctive characteristics that make them an effective medium for communicating research findings, but they are even more impressive as tools for scientific reasoning. To explore this role, we examine diagrammatic formats that have been devised by biologists to (a) identify and illuminate phenomena involving circadian rhythms and (b) develop and modify mechanistic explanations of these phenomena.

Cognitive penetration and the cognition–perception interface

I argue that discussions of cognitive penetration have been insufficiently clear about (i) what distinguishes perception and cognition, and (ii) what kind of relationship between the two is supposed to be at stake in the debate. A strong reading, which is compatible with many characterizations of penetration, posits a highly specific and directed influence on perception. According to this view, which I call the “internal effect view” (IEV) a cognitive state penetrates a perceptual process if the presence of the cognitive state causes a change to the computation performed by the process, with the result being a distinct output. I produce a novel argument that this strong reading is false. On one well-motivated way of drawing the distinction between perceptual states and cognitive states, cognitive representations cannot play the computational role posited for them by IEV, vis-à-vis perception. This does not mean, however, that there are not important causal relationships between cognitive and perceptual states. I introduce an alternative view of these relationships, the “external effect view” (EEV). EEV posits that each cognitive state is associated with a broad range of possible perceptual outcomes, and biases perception towards any of those perceptual outcomes without determining specific perceptual contents. I argue that EEV captures the kinds of cases philosophers have thought to be evidence for IEV, and a wide range of other cases as well.

Interface problems in the explanation of action

When doing mental ontology, we must ask how to individuate distinct categories of mental states, and then, given that individuation, ask how states from distinct categories interact. One promising proposal for how to individuate cognitive from sensorimotor states is in terms of their representational form. On these views, cognitive representations are propositional in structure, while sensorimotor representations have an internal structure that maps to the perceptual and kinematic dimensions involved in an action context. This way of thinking has resulted in worries about the interface between cognition and sensorimotor systems – that is, about how representations of these distinct types might interact in performing actions. I claim that current solutions to the interface problem fail, because they have not sufficiently abandoned intuitions inspired by faculty psychology. In particular, current proposals seek to show how cognitive states can enforce prior decisions on sensorimotor systems. I argue that such “determination” views are the wrong kind of views to adopt, given the form distinction. Instead, I offer a proposal on which propositional representations can at best bias us toward certain kinds of action. This kind of view, I argue, appealingly distributes the explanation of action across distinctive contributions from cognitive and sensorimotor processing.

HIT on the Psychometric Approach

Traditionally, identity and supervenience have been proposed in philosophy of mind as metaphysical accounts of how mental activities (fully understood, as they might be at the end of science) relate to brain processes. Kievet et al. (this issue) suggest that to be relevant to cognitive neuroscience, these philosophical positions must make empirically testable claims and be evaluated accordingly—they cannot sit on the sidelines, awaiting the hypothetical completion of cognitive neuroscience. We agree with the authors on the importance of rendering these positions relevant to ongoing science. We disagree, however, with their proposal that a metaphysical relationship (identity or supervenience) should “serve as a means to conceptually organize and guide the analysis of neurological and behavioral data” (p. 69). Instead, we advance a different view of the goals of cognitive neuroscience and of the proper means of relating metaphysics and explanation. Our central objection to the psychometric approach deployed by Kievet et al. is that the formal models only account for correlations between variables (measurements) and do not aid in explaining phenomena. Cognitive neuroscience is concerned with the latter. We develop this point in the next section, in which we present what we find to be problematic in their proposed models. In the Identity Claims and Mechanistic Explanations in Cognitive Neuroscience section, we advance an account of what is required to explain phenomena: (a) providing an adequate description of a phenomenon, and (b) characterizing the mechanism responsible for it. In doing so we characterize a version of the identity theory—heuristic identity theory (HIT), which figures centrally in developing such explanations—and illustrate its role in what we take to be a prototypical example of research in cognitive neuroscience. Finally, in the Levels, Mechanisms and Identity Claims section, we turn to how levels and interlevel relations should be construed in a metaphysical account that fits the mission of cognitive neuroscience.

Computational neuroscience and localized neural function

In this paper I criticize a view of functional localization in neuroscience, which I call “computational absolutism” (CA). “Absolutism” in general is the view that each part of the brain should be given a single, univocal function ascription. Traditional varieties of absolutism posit that each part of the brain processes a particular type of information and/or performs a specific task. These function attributions are currently beset by physiological evidence which seems to suggest that brain areas are multifunctional—that they process distinct information and perform different tasks depending on context. Many theorists take this contextual variation as inimical to successful localization, and claim that we can avoid it by changing our functional descriptions to computational descriptions. The idea is that we can have highly generalizable and predictive functional theories if we can discover a single computation performed by each area regardless of the specific context in which it operates. I argue, drawing on computational models of perceptual area MT, that this computational version of absolutism fails to come through on its promises. In MT, the modeling field has not produced a univocal computational description, but instead a plurality of models analyzing different aspects of MT function. Moreover, CA cannot appeal to theoretical unification to solve this problem, since highly general models, on their own, neither explain nor predict what MT does in any particular context. I close by offering a perspective on neural modeling inspired by Nancy Cartwright’s and Margaret Morrison’s views of modeling in the physical sciences.

Correction to: Cognitive penetration and the cognition–perception interface

On page 3653, there is a mistake in the explanation of the Cornsweet illusion. In fact, the explanation is that the panel perceived as darker is facing towards the light source—in the case of this figure the light is coming from the right.

Real Patterns in Biological Explanation

In discussion of mechanisms, philosophers often debate about whether quantitative descriptions of generalizations or qualitative descriptions of operations are explanatorily fundamental. I argue that these debates have erred by conflating the explanatory roles of generalizations and patterns. Patterns are types of variations within or between quantities in a mechanism over time or across conditions. While these patterns must often be represented in addition to descriptions of operations in order to explain a phenomenon, they are not equivalent to generalizations because their explanatory role does not depend on any specific facts about their scope or domain of invariance.