Jacob Beck

Analogue Magnitude Representations: A Philosophical Introduction

Empirical discussions of mental representation appeal to a wide variety of representational kinds. Some of these kinds, such as the sentential representations underlying language use and the pictorial representations of visual imagery, are thoroughly familiar to philosophers. Others have received almost no philosophical attention at all. Included in this latter category are analogue magnitude representations, which enable a wide range of organisms to primitively represent spatial, temporal, numerical, and related magnitudes. This article aims to introduce analogue magnitude representations to a philosophical audience by rehearsing empirical evidence for their existence and analysing their format, their content, and the computations they support. 1   Background    1.1   Evidence of analogue magnitude representations    1.2   Weber’s law    1.3   Scepticism about analogue magnitude representations 2   Format    2.1   Carey’s analogy    2.2   Neural realization    2.3   Analogue representation    2.4   Analogue magnitude representation components 3   Content    3.1   Do analogue magnitude representations have representational content?    3.2   What do analogue magnitude representations represent?    3.3   What content types do analogue magnitude representations have? 4   Computations    4.1   Arithmetic computation    4.2   Practical deliberation 5   Conclusion 1   Background    1.1   Evidence of analogue magnitude representations    1.2   Weber’s law    1.3   Scepticism about analogue magnitude representations    1.1   Evidence of analogue magnitude representations    1.2   Weber’s law    1.3   Scepticism about analogue magnitude representations 2   Format    2.1   Carey’s analogy    2.2   Neural realization    2.3   Analogue representation    2.4   Analogue magnitude representation components    2.1   Carey’s analogy    2.2   Neural realization    2.3   Analogue representation    2.4   Analogue magnitude representation components 3   Content    3.1   Do analogue magnitude representations have representational content?    3.2   What do analogue magnitude representations represent?    3.3   What content types do analogue magnitude representations have?    3.1   Do analogue magnitude representations have representational content?    3.2   What do analogue magnitude representations represent?    3.3   What content types do analogue magnitude representations have? 4   Computations    4.1   Arithmetic computation    4.2   Practical deliberation    4.1   Arithmetic computation    4.2   Practical deliberation 5   Conclusion

Marking the Perception–Cognition Boundary: The Criterion of Stimulus-Dependence

ABSTRACT Philosophy, scientific psychology, and common sense all distinguish perception from cognition. While there is little agreement about how the perception–cognition boundary ought to be drawn, one prominent idea is that perceptual states are dependent on a stimulus, or are stimulus-dependent, in a way that cognitive states are not. This paper seeks to develop this idea in a way that can accommodate two apparent counterexamples: hallucinations, which are prima facie perceptual yet stimulus-independent; and demonstrative thoughts, which are prima facie cognitive yet stimulus-dependent. The payoff is not only a specific proposal for marking the perception–cognition boundary, but also a deeper understanding of the natures of hallucination and demonstrative thought.

Why we can’t say what animals think

Realists about animal cognitive representations confront a puzzle. If animals have real, contentful cognitive representations, why can’t anyone say precisely what the contents of those representations are? I argue that realists can best resolve this puzzle by appealing to differences in the format of animal cognition and human language.

Can bootstrapping explain concept learning

Susan Careys account of Quinean bootstrapping has been heavily criticized. While it purports to explain how important new concepts are learned, many commentators complain that it is unclear just what bootstrapping is supposed to be or how it is supposed to work. Others allege that bootstrapping falls prey to the circularity challenge: it cannot explain how new concepts are learned without presupposing that learners already have those very concepts. Drawing on discussions of concept learning from the philosophical literature, this article develops a detailed interpretation of bootstrapping that can answer the circularity challenge. The key to this interpretation is the recognition of computational constraints, both internal and external to the mind, which can endow empty symbols with new conceptual roles and thus new contents.