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How should we determine the distribution of psychological traits—such as Theory of Mind, episodic memory, and metacognition—throughout the Animal kingdom? Researchers have long worried about the distorting effects of anthropomorphic bias on this comparative project. A purported corrective against this bias was offered as a cornerstone of comparative psychology by C. Lloyd Morgan in his famous “Canon”. Also dangerous, however, is a distinct bias that loads the deck against animal mentality: our tendency to tie the competence criteria for cognitive capacities to an exaggerated sense of typical human performance. I dub this error “anthropofabulation”, since it combines anthropocentrism with confabulation about our own prowess. Anthropofabulation has long distorted the debate about animal minds, but it is a bias that has been little discussed and against which the Canon provides no protection. Luckily, there is a venerable corrective against anthropofabulation: a principle offered long ago by David Hume, which I call “Hume’s Dictum”. In this paper, I argue that Hume’s Dictum deserves a privileged place next to Morgan’s Canon in the methodology of comparative psychology, illustrating my point through a discussion of the debate over Theory of Mind in nonhuman animals.

Recent studies purported to demonstrate that chimpanzees, monkeys and corvids possess a basic Theory of Mind, the ability to attribute mental states like seeing to others. However, these studies remain controversial because they share a common confound: the conspecific’s line of gaze, which could serve as an associative cue. Here, we show that ravens Corvus corax take into account the visual access of others, even when they cannot see a conspecific. Specifically, we find that ravens guard their caches against discovery in response to the sounds of conspecifics when a peephole is open but not when it is closed. Our results suggest that ravens can generalize from their own perceptual experience to infer the possibility of being seen. These findings confirm and unite previous work, providing strong evidence that ravens are more than mere behaviour-readers. Theory of Mind experiments in animals have not previously discounted the possibility that individuals follow their competitors′ behavioural cues. Here, Bugnyar et al. show that ravens consider the possibility that they are being watched when caching food, even when they cannot see a conspecific competitor.

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.

Deep neural networks are currently the most widespread and successful technology in artificial intelligence. However, these systems exhibit bewildering new vulnerabilities: most notably a susceptibility to adversarial examples. Here, I review recent empirical research on adversarial examples that suggests that deep neural networks may be detecting in them features that are predictively useful, though inscrutable to humans. To understand the implications of this research, we should contend with some older philosophical puzzles about scientific reasoning, helping us to determine whether these features are reliable targets of scientific investigation or just the distinctive processing artefacts of deep neural networks. DNN classifiers are vulnerable to small, specific perturbations in an input that seem benign to humans. To understand this phenomenon, Buckner argues that it may be necessary to treat the patterns that DNNs detect in these adversarial examples as artefacts, which may contain predictive information.

A surge of empirical research demonstrating flexible cognition in animals and young infants has raised interest in the possibility of rational decision-making in the absence of language. A venerable position, which I here call “Classical Inferentialism”, holds that nonlinguistic agents are incapable of rational inferences. Against this position, I defend a model of nonlinguistic inferences that shows how they could be practically rational. This model vindicates the Lockean idea that we can intuitively grasp rational connections between thoughts by developing the Davidsonian idea that practical inferences are at bottom categorization judgments. From this perspective, we can see how similarity-based categorization processes widely studied in human and animal psychology might count as practically rational. The solution involves a novel hybrid of internalism and externalism: intuitive inferences are psychologically rational (in the explanatory sense) given the intensional sensitivity of the similarity assessment to the internal structure of the agent’s reasons for acting, but epistemically rational (in the justificatory sense) given an ecological fit between the features matched by that assessment and the structure of the agent’s environment. The essay concludes by exploring empirical results that show how nonlinguistic agents can be sensitive to these similarity assessments in a way that grants them control over their opaque judgments.

I here critique the application of the traditional, similarity-based account of natural kinds to debates in psychology. A challenge to such accounts of kindhood—familiar from the study of biological species—is a metaphysical phenomenon that I call 'transitional gradation': the systematic progression of slightly modified transitional forms between related candidate kinds. Where such gradation proliferates, it renders the selection of similarity criteria for kinds arbitrary. Reflection on general features of learning—especially on the gradual revision of concepts throughout the acquisition of expertise—shows that even the strongest candidates for similarity-based kinds in psychology exhibit systematic transitional gradation. As a result, philosophers of psychology should abandon discussion of kindhood, or explore non-similarity based accounts.

Landmarks play a crucial role in bootstrapping both spatial and temporal cognition. Given the similarity in the underlying demands of representing spatial and temporal relations, we ask here whether animals can be trained to reason about temporal relations by providing them with temporal landmark cues, proposing a line of future research complementary to those suggested by the authors.

The functionalist approach to kinds has suffered recently due to its association with law-based approaches to induction and explanation. Philosophers of science increasingly view nomological methods as inappropriate for the special sciences like psychology and biology, which has led to a surge of interest in approaches to natural kinds that are more obviously compatible with mechanistic and model-based methods, especially homeostatic property cluster theory. But can the functionalist approach to kinds be weaned off its dependency on laws? Dan Weiskopf has recently offered a reboot of the functionalist program by replacing its nomological commitments with a model-based approach more closely derived from practice in psychology. Roughly, Weiskopf holds that the natural kinds of psychology will be the functional properties that feature in many empirically successful cognitive models, and that those properties need not be localizable to parts of an underlying mechanism. I here skeptically examine the three modeling practices that Weiskopf thinks introduce such non-localizable properties: fictionalization, reification, and functional abstraction. In each case, I argue that recognizing functional properties introduced by these practices as autonomous kinds comes at clear cost to those explanations' counterfactual explanatory power. At each step, a tempting functionalist response is parochialism: to hold that the false or omitted counterfactuals fall outside the modeler's explanatory aims, and so should not be counted against functional kinds. I conclude by noting the dangers this attitude poses to scientific disagreement, inviting functionalists to better articulate how the individuation conditions for functional kinds might outstrip the perspective of a single modeler.

Cognition was conceived of as internal information-processing which somehow went beyond classical and operant conditioning. Virtually every aspect of the initial sketch of the nature of cognition and cognitive explanation has now been challenged. In particular, cognitive science now faces at least three areas of open crisis wherein researchers surveying the same data are unable to reach a consensus as to whether the causes of the behaviors observed should count as genuinely ‘cognitive’: (1) the debate over whether cognition is strictly internal—that is, whether it is brain-bound or extends into the body and environment; (2) a crisis in comparative psychology over whether cognition is really mutually-exclusive with association—given that new and more powerful associative models can clear paradigm benchmarks for cognition; (3) a debate as to whether even “low-level” behaviors might be cognitive—given recent work revealing a surprising degree of flexibility and control at the level of“automatic” processing.

The Darwinian protolanguage hypothesis is one of the most popular theories of the evolution of human language. According to this hypothesis, language evolved through a three stage process involving general increases in intelligence, the emergence of grammatical structure as a result of sexual selection on protomusical songs, and finally the attachment of meaning to the components of those songs. The strongest evidence for the second stage of this process has been considered to be birdsong, and as a result researchers have investigated the existence of various forms of grammar in the production and comprehension of songs by birds. Here, we argue that mating dances are another relevant source of sexually-selected complexity that has until now been largely overlooked by proponents of Darwinian protolanguage, focusing especially on the dances of long-tailed manakins. We end by sketching several lines of research that should be pursued to determine the relevance of mating dances to the evolution of language.