Konstantinos Voudouris

In this article, we summarise and critically evaluate Ryan Nefdt’s The Philosophy of Theoretical Linguistics: A Contemporary Outlook (2024). In this book, Nefdt brings the tools of philosophy to bear on contemporary linguistics, targeting perennial debates in syntax, semantics, pragmatics, phonology, and the evolution of language. In so doing, Nefdt sketches several tantalising paths for progress on these topics. Although some of Nefdt’s arguments are underdeveloped, this book serves as a worthwhile introduction to the philosophy of linguistics.

Artificial Intelligence is making rapid and remarkable progress in the development of more sophisticated and powerful systems. However, the acknowledgement of several problems with modern machine learning approaches has prompted a shift in AI benchmarking away from task-oriented testing towards ability-oriented testing, in which AI systems are tested on their capacity to solve certain kinds of novel problems. The Animal-AI Environment is one such benchmark which aims to apply the ability-oriented testing used in comparative psychology to AI systems. Here, we present the first direct human-AI comparison in the Animal-AI Environment, using children aged 6–10. We found that children of all ages were significantly better than a sample of 30 AIs across most of the tests we examined, as well as performing significantly better than the two top-scoring AIs, “ironbar” and “Trrrrr,” from the Animal-AI Olympics Competition 2019. While children and AIs performed similarly on basic navigational tasks, AIs performed significantly worse in more complex cognitive tests, including detour tasks, spatial elimination tasks, and object permanence tasks, indicating that AIs lack several cognitive abilities that children aged 6–10 possess. Both children and AIs performed poorly on tool-use tasks, suggesting that these tests are challenging for both biological and non-biological machines.

Hypotheses about how and why animals behave the way they do are frequently labelled as either associative or cognitive. This has been taken as evidence for a fundamental distinction between two kinds of behavioural processes. However, there is significant disagreement about how to define this distinction and whether it ought to be rejected entirely. Rather than seeking a definition of the associative-cognitive distinction, or advocating for its rejection, I argue that it is an artefact of the way that comparative psychologists generate hypotheses. I suggest that hypotheses for non-human animal behaviour are often generated by analogy with hypotheses drawn from human psychology and associative learning theory, a justifiable strategy since analogies help to establish the pursuit-worthiness of a hypothesis. Any apparent distinction is a misleading characterisation of what is a complex web of hypotheses that explain diverse behavioural phenomena. The analogy view of the distinction has three advantages. It motivates the apparent existence of the distinction based on a common inference strategy in science, analogical reasoning. It accounts for why the distinction has been difficult to articulate, because of the diversity of possible analogies. Finally, it delimits the role of the distinction in downstream inferences about animal behaviour.