Affective valence is increasingly thought to be the common currency underlying all forms of intuitive, non-discursive decision making, in both humans and other animals. And it is thought to constitute the good or bad (pleasant or unpleasant) aspects of all desires, emotions, and moods. This article contrasts two theories of valence. According to one, valence is an experience-directed imperative (‘more of this!’ or ‘less of this!’); according to the other, valence is a representation of adaptive value or disvalue. The latter is shown to provide the better account.

The violation of Bell inequalities seems to establish an important fact about the world: that it is non-local. However, this result relies on the assumption of the statistical independence of the measurement settings with respect to potential past events that might have determined them. Superdeterminism refers to the view that a local, and determinist, account of Bell inequality violations is possible by rejecting this assumption of statistical independence. We examine and clarify various problems with superdeterminism, looking in particular at its consequences on the nature of scientific laws and scientific reasoning. We argue that the view requires a neo-Humean account of at least some laws and creates a significant problem for the use of statistical independence in other parts of physics and science more generally.

Supporters of the autonomy of higher-level causation (or explanation) often appeal to proportionality, arguing that higher-level causes are more proportional than their lower-level realizers. Recently, measures based on information theory and causal modelling have been proposed that allow one to shed new light on proportionality and the related notion of specificity. In this article we apply ideas from this literature to the issue of higher- versus lower-level causation (and explanation). Surprisingly, proportionality turns out to be irrelevant for the question of whether higher-level causes (or explanations) can be autonomous; specificity is a much more informative notion for this purpose.

There is, among philosophers involved in the debate concerning the nature and epistemology of understanding, a general recognition that this state has a grasping component that accounts for some of its distinctive features. This article defends the view that this component of understanding consists of a knowledge of how to use a particular explanation to account for a given phenomenon. How a subject acquires this knowledge is examined and the result of this examination is shown to highlight the particular contribution of the grasping component of understanding to the distinctive features of this state.

We explore the contribution made by oscillatory, synchronous neural activity to representation in the brain. We closely examine six prominent examples of brain function in which neural oscillations play a central role, and identify two levels of involvement that these oscillations take in the emergence of representations: enabling (when oscillations help to establish a communication channel between sender and receiver, or are causally involved in triggering a representation) and properly representational (when oscillations are a constitutive part of the representation). We show that even an idealized informational sender–receiver account of representation makes the representational status of oscillations a non-trivial matter, which depends on rather minute empirical details.

Truthlikeness is a property of a theory or a proposition that represents its closeness to the truth. According to Niiniluoto, truthlikeness for quantitative deterministic laws can be defined by the Minkowski metric. I present some counterexamples to the definition and argue that it fails because it considers truthlikeness for quantitative deterministic laws to be just a function of accuracy, but an accurate law can be wrong about the actual ‘structure’ or ‘behaviour’ of the system it intends to describe. I develop a modification of Niiniluoto’s proposal that defines truthlikeness for quantitative deterministic laws according to two parameters: accuracy and nomicity. The presented proposal solves the counterexamples and defines a new way of understanding scientific progress.

The last 5 years have seen a series of remarkable achievements in deep-neural-network-based artificial intelligence research, and some modellers have argued that their performance compares favourably to human cognition. Critics, however, have argued that processing in deep neural networks is unlike human cognition for four reasons: they are (i) data-hungry, (ii) brittle, and (iii) inscrutable black boxes that merely (iv) reward-hack rather than learn real solutions to problems. This article rebuts these criticisms by exposing comparative bias within them, in the process extracting some more general lessons that may also be useful for future debates.

When scientists or science reporters communicate research results to the public, this often involves ethical and epistemic risks. One such risk arises when scientific claims cause cognitive or behavioural changes in the audience that contribute to the self-fulfilment of these claims. I argue that the ethical and epistemic problems that such self-fulfilment effects may pose are much broader and more common than hitherto appreciated. Moreover, these problems are often due to a specific psychological phenomenon that has been neglected in the research on science communication: many people tend to conform to ‘descriptive norms’, that is, norms capturing (perceptions of) what others commonly do, think, or feel. Because of this tendency, science communication may frequently produce significant social harm. I contend that scientists have a responsibility to assess the risk of this potential harm and consider adopting strategies to mitigate it. I introduce one such strategy and argue that its implementation is independently well motivated by the fact that it helps improve scientific accuracy.

Relative to digital computation, analogue computation has been neglected in the philosophical literature. To the extent that attention has been paid to analogue computation, it has been misunderstood. The received view—that analogue computation has to do essentially with continuity—is simply wrong, as shown by careful attention to historical examples of discontinuous, discrete analogue computers. Instead of the received view, I develop an account of analogue computation in terms of a particular type of analogue representation that allows for discontinuity. This account thus characterizes all types of analogue computation, whether continuous or discrete. Furthermore, the structure of this account can be generalized to other types of computation: analogue computation essentially involves analogue representation, whereas digital computation essentially involves digital representation. Besides being a necessary component of a complete philosophical understanding of computation in general, understanding analogue computation is important for computational explanation in contemporary neuroscience and cognitive science.

Advances in developmental genetics and evo-devo in the last several decades have enabled the growth of novel developmental approaches to the classic theme of homology. These approaches depart from the more standard phylogenetic view by contending that homology between morphological characters depends on developmental-genetic individuation and explanation. This article provides a systematic re-examination of the relationship between developmental and phylogenetic homology in light of current evidence from developmental and evolutionary genetics and genomics. I present a qualitative model of the processes that cause de-coupling of morphological and molecular evolution by developmental system drift (DSD), and hypothesize that DSD is a widespread and regular stochastic process that is predictable from parameters such as population size, pleiotropy, and regulatory redundancy. These theoretical findings support an integrative approach in which models of DSD aid in determining when developmental-genetic explanations of homology apply and when other explanations such as stabilizing selection are needed. I argue that a phylogenetic monism about the definition and criteria of homology supports the integration of different explanations into a theory of homology better than pluralism does.

Monothematic delusions are delusions whose contents pertain to a single subject matter. Examples include Capgras delusion, the delusion that a loved one has been replaced by an impostor, and Cotard delusion, the delusion that one is dead or does not exist. Two-factor accounts of such delusions hold that they are the result of both an experiential deficit—for instance, flattened affect—coupled with an aberrant cognitive response to that deficit. In this article, we develop a new expressivist two-factor account of delusion. In contrast to existing endorsement and explanationist accounts, which treat delusions as either explanations or endorsements of the contents of these disordered experiences, we hold that delusional beliefs have an expressive function: they characterize, in impressionistic terms, what the subject’s experience is like for them. We show how our account improves upon existing two-factor views in explaining the central features of monothematic delusions, in particular how delusional subjects fall short of the ideal of rationality.