Recent models using simple signaling games provide a theoretical setting for investigating the evolutionary connection between signaling and behavioral plasticity. These models have shown that plasticity is typically eliminated in common-interest signaling games. In many real cases of signaling, however, interests do not align. Here, I present a model of the evolution of plasticity in signaling games and consider games of common, opposed, and partially aligned interests. I find that the setting of partial common interest is most conducive to the evolution of plasticity. Plastic individuals succeed by learning when to deceive others and when to trust others’ signals.

Drawing on models of communication due to Lewis and Skyrms, I contrast sender-receiver systems as they appear within and between organisms, and as they function in the bridging of space and time. Within the organism, memory can be seen as the sending of messages over time, communication between stages as opposed to spatial parts. Psychological memory and genetic memory are compared with respect to their relations to a sender-receiver model. Some puzzles about “genetic information” can be resolved by seeing the genome as a cell-level memory with no sender.

Recent work on the evolution of signaling systems provides a novel way of thinking about genetic information, where information is passed between genes in a regulatory network. I use examples from evolutionary developmental biology to show how information can be created in these networks and how it can be reused to produce rapid phenotypic change.

What are the functional units of the brain? If the function of the brain is to process information-carrying signals, then the functional units will be the senders and receivers of those signals. Neurons have been the default candidate, with action potentials as the signals. But there are alternatives: synapses fit the action potential picture more cleanly, and glial activities (e.g., in astrocytes) might also be characterized as signaling. Are synapses or nonneuronal cells better candidates to play the role of functional units? Will informational signaling still be the best model for brain function if we move beyond the neuron doctrine?

Recent work combining cognitive neuroscience with computational modeling suggests that distributed patterns of neural firing may represent probability distributions. This article asks, what makes it the case that distributed patterns of firing, as well as carrying information about (correlating with) probability distributions over worldly parameters, represent such distributions? In examples of probabilistic population coding, it is the way information is used in downstream processing so as to lead to successful behavior. In these cases content depends on factors beyond bare information, contra Brian Skyrms’s view that representational content can be fully characterized in information-theoretic terms.

In this paper I argue that certain methodological conventions within psychophysics reflect a continued uncertainty over the reliability of subjects’ introspective judgements. Firstly, different psychophysical methods do not rely equally on the introspective capabilities of experimental subjects. I contrast “minimally introspective” tasks with “introspection-reliant” ones. It is only in the latter that introspection can be said to have a nontrivial role in the subjects’ performance. Secondly, my distinction maps onto a number of important methodological divisions in vision science. The introspection-reliant categorization captures many of the tasks typically considered less able to yield useful information regarding the processes underlying visual sensation.

While philosophical discussions of first-person methods often turn on the veridicality of first-person reports, I argue that more attention should be paid to the circumstances and aims of their experimental production in the science of perception. After pointing to the ‘constructedness’ of first-person reports, I raise questions about the criteria by which to judge whether they illuminate something about the nature of perception. I illustrate this point with a historical debate between Gestalt psychologists and atomists, both of whom used first-person methods to investigate perception, but who disagreed deeply over the epistemic value of their respective first-person data.

Some experiments in perceptual psychology measure perceivers’ phenomenal experiences of objects versus their cognitive assessments of object properties. Analyzing such experiments, this article responds to Pizlo’s claim that much work on shape constancy before 1985 confused problems of shape ambiguity with problems of shape constancy. Pizlo fails to grasp the logic of experimental designs directed toward phenomenal aspects of shape constancy. In the domain of size perception, Granrud’s studies of size constancy in children and adults distinguish phenomenal from cognitive factors.

Are objects in convex passenger-side mirrors “closer than they appear”? If one adapts to inverting goggles, does the world go back to looking the way it was before, or does the world look approximately the same throughout the course of adaptation, only losing its normative sense of wrongness? The answers to these empirical, introspective questions might help cast light on the classic philosophical debate about the degree of resemblance between our visual experience of reality and things as they are in themselves.

Considerable methodological difficulties abound in neuroimaging, and several philosophers of science have recently called into question the potential of neuroimaging studies to contribute to our knowledge of human cognition. These skeptical accounts suggest that functional hypotheses are underdetermined by neuroimaging data. I apply Mayo’s error-statistical account to clarify the evidential import of neuroimaging data and the kinds of inferences it can reliably support. Thus, we can answer the question “What can we reliably learn from neuroimaging?” and make sense of how this knowledge can contribute to novel construals of cognition.

Neuroimaging studies of the resting state continue to gather philosophical and scientific attention. Most discussions assume an identification between resting-state activity and activity in the so-called default mode network. I argue we should resist this identification, structuring my discussion around a dilemma first posed by Morcom and Fletcher. I offer an alternative view of rest as a state dominated by long-term processes and show how interaction effects might thereby let rest shed light on short-term changes in activation.

I argue that ductal carcinoma in situ (DCIS), the tumor most commonly diagnosed by breast mammography, cannot be confidently classified as cancer, that is, as pathological. This is because there may not be dysfunction present in DCIS—as I argue based on its high prevalence and the small amount of risk it conveys—and thus DCIS may not count as a disease by dysfunction-requiring approaches, such as Boorse’s biostatistical theory and Wakefield’s harmful dysfunction account. Patients should decide about treatment for DCIS based on the risks it poses and the risks and benefits of treatment, not on its disease status.

Although cases are central to the epistemic practices utilized within clinical medicine, they appear to be limited in their ability to provide evidence about causal relations because they provide detailed accounts of particular patients without explicit filtering of those attributes most likely to be relevant for explaining the phenomena observed. This paper uses a series of recent case reports to explore the role of cases in causal attribution in medical diagnosis. It is argued that cases are brought together by practitioners to generate causal attributions and testable predictions using a manipulability view of causation.

This paper addresses the problem of how scientific knowledge, which is always locally generated, becomes accepted in other sites. The analysis suggests that there are a small number of strategies that enable scientists to resituate knowledge and that these strategies are generic: they are not restricted to specific disciplines or modes of doing science but rather are found in a variety of different forms across the sciences.

It has become customary among philosophers and biologists to claim that folk racial classification has no biological basis. This paper attempts to debunk that view. In this paper I show that ‘race’, as used in current US race talk, picks out a biologically real entity. I do this by first showing that ‘race’, in this use, is not a kind term, but a proper name for a set of human population groups. Next, using recent human genetic clustering results, I show that this set of human population groups is a partition of human populations that I call ‘the Blumenbach partition’.

This paper distinguishes three concepts of “race”: bio-genomic cluster/race, biological race, and social race. We map out realism, antirealism, and conventionalism about each of these, in three important historical episodes: Frank Livingstone and Theodosius Dobzhansky in 1962, A. W. F. Edwards’s 2003 response to Lewontin’s 1972 paper, and contemporary discourse. Semantics is especially crucial to the first episode, while normativity is central to the second. Upon inspection, each episode also reveals a variety of commitments to the metaphysics of race. We conclude by interrogating the relevance of these scientific discussions for political positions and a post-racial future.