Nick Byrd

We ought to treat others’ moral views with respect, even when we disagree. But what does that mean? This paper articulates a moral obligation to make ourselves open to sincere moral persuasion by others. Doing so allows us to participate in valuable relationships of reciprocal respect for agency. Yet this proposal can sound tritely agreeable. To explore its full implications, the paper applies the general obligation to one of the most challenging topics of moral disagreement: the morality of abortion. I consider and reject arguments that abortion decisions have special features exempting them from the obligation to be open to moral persuasion. Further, I argue that viewing fetal ultrasound images can accomplish morally persuasion. Accordingly, in at least some cases a woman seeking abortion has an obligation to view fetal ultrasound images as a means of being open to moral persuasion. However, this conclusion does not support recent laws compelling women seeking abortion to view ultrasound images; such laws are in fact incompatible with the respect for agency that underwrites the obligation to be open to persuasion.

This article examines the problem of mental partitioning and mental conflict in Han 漢 dynasty sources. It begins by outlining two Greek psychological models—the Platonic tripartite model and the Stoic monistic model—and explains the connection between the two psychological models and their differing descriptions of mental conflict. It then analyzes passages from a seldom discussed text, the _Extended Reflections_ (_Shenjian_ 申鑒), written by the Eastern Han thinker X un Yue 荀悅. A combined analysis of the _Extended Reflections_ with fragments from other Han dynasty thinkers uncovers debates on mental partitioning and mental conflict that are in some ways analogous to those found in Greek philosophical literature. One view posits that the human psyche is composed of two parts capable of producing conflicting motivations. The other view sees all actions as stemming from a single psychological center. The conclusions of this article offer new perspectives on early Chinese moral psychology that have important implications for modern research on early Chinese philosophy.

The rise of experimental philosophy has placed metaphilosophical questions, particularly those concerning concepts, at the center of philosophical attention. X-phi offers empirically rigorous methods for identifying conceptual content, but what exactly it contributes towards evaluating conceptual content remains unclear. We show how x-phi complements Rudolf Carnap’s underappreciated methodology for concept determination, explication. This clarifies and extends x-phi’s positive philosophical import, and also exhibits explication’s broad appeal. But there is a potential problem: Carnap’s account of explication was limited to empirical and logical concepts, but many concepts of interest to philosophers are essentially normative. With formal epistemology as a case study, we show how x-phi assisted explication can apply to normative domains.

Philosophers of science debate the proper role of non-epistemic value judgements in scientific reasoning. Many modern authors oppose the value free ideal, claiming that we should not even try to get scientists to eliminate all such non-epistemic value judgements from their reasoning. W. E. B. Du Bois, on the other hand, has a defence of the value free ideal in science that is rooted in a conception of the proper place of science in a democracy. In particular, Du Bois argues that the value free ideal must be upheld in order to, first, retain public trust in science and, second, ensure that those best placed to make use of scientifically acquired information are able to do so. This latter argument turns out to relate Du Bois’ position on the value free ideal in science to his defence of epistemic democracy. In this essay I elaborate, motivate, and relate to the modern debate, Du Bois’ under-appreciated defence of the value free ideal.

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain.

In 2018 Academic Placement Data and Analysis ran a survey of doctoral students and recent graduates on the topics of diversity and inclusivity in collaboration with the Graduate Student Council and Data Task Force of the American Philosophical Association. We submitted a preliminary report in Fall 2018 that describes the origins and procedure of the survey [1]. This is our final report on the survey. We first discuss the demographic profile of our survey participants and compare it to the United States general population, its doctoral students, and APA membership, finding several areas of underrepresentation (i.e. gender, race/ethnicity, socioeconomic, and veteran status). We then discuss the results of questions regarding diversity and inclusivity. We find, for instance, that participant comfort in philosophy depends on gender, sexuality, race/ethnicity, disability, and language status and that participants most often mentioned the theme of diversity when asked how philosophy could be more inclusive. Finally, we discuss the results of questions related to graduate program and placement. We find, for example, that underrepresented graduates are both less likely to recommend their graduate program to others and less likely to prefer an academic job. We close by making some recommendations for the APA and for the discipline based on our findings.

How are biases encoded in our representations of social categories? Philosophical and empirical discussions of implicit bias overwhelmingly focus on salient or statistical associations between target features and representations of social categories. These are the sorts of associations probed by the Implicit Association Test and various priming tasks. In this paper, we argue that these discussions systematically overlook an alternative way in which biases are encoded, that is, in the dependency networks that are part of our representations of social categories. Dependency networks encode information about how features in a conceptual representation depend on each other. This information determines the degree of centrality of a feature for a conceptual representation. Importantly, centrally encoded biases systematically disassociate from those encoded in salient-statistical associations. Furthermore, the degree of centrality of a feature determines its cross-contextual stability: in general, the more central a feature is for a concept, the more likely it is to survive into a wide array of cognitive tasks involving that concept. Accordingly, implicit biases that are encoded in the central features of concepts are predicted to be more resilient across different tasks and contexts. As a result, the distinction between centrally encoded and salient-statistical biases has important theoretical and practical implications.

Many scientists believe that there is a uniform, interdisciplinary method for the prac- tice of good science. The paradigmatic examples, however, are drawn from classical ex- perimental science. Insofar as historical hypotheses cannot be tested in controlled labo- ratory settings, historical research is sometimes said to be inferior to experimental research. Using examples from diverse historical disciplines, this paper demonstrates that such claims are misguided. First, the reputed superiority of experimental research is based upon accounts of scientific methodology (Baconian inductivism or falsificationism) that are deeply flawed, both logically and as accounts of the actual practices of scientists. Second, although there are fundamental differences in methodology between experimental scien- tists and historical scientists, they are keyed to a pervasive feature of nature, a time asymmetry of causation. As a consequence, the claim that historical science is methodo- logically inferior to experimental science cannot be sustained.