Reason without much language

Language is more than a system used for interpersonal communication. Linguistic representations can also form a part of reasoning in other cognitive domains. However, it is unclear whether the role of language in non-verbal domains is a necessary one, or whether it represents an optional resource that is recruited under demanding or highly intentional processing conditions. The possible role of language in categorisation, belief reasoning, calculation and cross-domain integration is explored, together with the various sources of evidence that can inform debates on language–thought relationships. Evidence from comparative and developmental psychology, together with that from neuroscience and ‘virtual language impairment’ (verbal shadowing) suggests reduced or absent language resource can disrupt performance in non-verbal domains. Similarly results of some investigations of people with developmental or acquired language impairments suggest an association with broader cognitive impairment. However, there is a substantial and growing body of evidence from across experimental fields indicating autonomy between language and reasoning. Residual reasoning in the face of severe aphasia is described, together with possible objections to the evidence from aphasia informing language and thought debates

With Language in Mind

Does language have a role to play in conceptual development, and if so, what is that role? Understanding the contents of another person’s mind parallels the development in early childhood of mental state language. Does the conceptual understanding get reflected in and drive the language development, or does the language allow the representation of propositional attitudes like belief? The paper reviews the evidence and sets up the terms of the debate, focusing on the syntax for mental states. It also asks whether syntax development could serve as a scaffold for other concepts that are described by propositions rather than labels. Finally, it reviews experimentation on the syntax of embedded clauses, where subtle phenomena are acquired for which it is impossible to imagine nonverbal counterparts: here, language is human thinking

Epistemic Mentalizing and Causal Cognition Across Agents and Objects

This dissertation examines mentalizing abilities, causal reasoning, and the interactions thereof. Minds are so much more than false beliefs, yet much of the existing research on mentalizing has placed a disproportionately large emphasis on this one aspect of mental life. The first aim of this dissertation is to examine whether representing others’ knowledge states relies on more fundamentally basic cognitive processes than representations of their mere beliefs. Using a mixture of behavioral and brain measures across five experiments, I find evidence that we can represent others\u27 knowledge quicker and using fewer neural resources than when representing others’ beliefs. To be considered a representation of knowledge rather than belief, both mentalizer and mentalizee must accept the propositional content being represented as factive (Kiparsky & Kiparsky, 2014; Williamson, 2002). As such, my results suggest that representing the mental states of others may be cognitively easier when the content of which does not need to be decoupled from one’s own existing view of reality. Our perception of other minds can influence how we attribute causality for certain events. The second aim of this dissertation is to explore how perceptions of agency and prescriptive social norms influence our intuitions of how agents and objects cause events in the world. Using physics simulations and 3D anthropomorphic stimuli, the results of three experiments show that agency, itself, does not make agents more causal to an outcome than objects. Instead, causal judgments about agents and objects differ as a function of the counterfactuals they respectively afford. Furthermore, I show that what distinguishes the counterfactuals we use to make causal attributions to agents and objects are the prescriptions we hold for how they should behave. Why do we say a fire occurred because of a lightning strike, rather than the necessary presence of oxygen? The answer involves our normative expectations of the probability of lightning strikes and the relative ubiquity of oxygen (Icard et al., 2017). The third aim of this dissertation explores the gradation of causal judgments across multiple contributing events that each vary in their statistical probability. I contribute to ongoing theoretical debates about how humans select causes in experimental philosophy and cognitive science by introducing a publicly available dataset containing 47,970 causal attribution ratings collected from 1,599 adult participants and structured around four novel configurations of causal relationships. By quantitatively manipulating the influence of normality, I systematically explore the continuous space of a causal event’s probability from unlikely to certain. It is my hope that this benchmark dataset may serve as a growing testbed for diverging theoretical models proposing to characterize how humans answer the question: Why