Schedule for Semester 2, 2026

4th August

Dominic Murphy (University of Sydney)

Social construction, social causation and social meaning

Sally Haslanger (1995) influentially distinguished between causal and constitutive varieties of social construction. According to her, something is socially causally constructed iff social factors play a causal role in bringing it into existence or, to some substantial extent, in its being the way it is. But setting things issue up like this makes pretty much everything in recorded history into a social construction because almost everything has some social causes. I think this makes the idea of social construction uninformative. I will consider some ways of differentiating between social construction proper and the effects of social processes considered more generically. I will also make a case for recognising a further category: social meaning, seen as the interpretation by a culture of existing phenomena regardless of their causes. So just as a natural phenomenon can be open to interpretation, something can be both a social construction and also open to further interpretation and contest

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11th August

[No meeting]

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18th August

Raphaël Millière (University of Oxford)

A Bayesian Framework for Comparative Cognitive Science (joint work with Jennifer Hu)

Suppose a language model and human subjects both achieve high performance on a test designed to measure a given cognitive ability (e.g., analogical reasoning or theory of mind). Should we conclude that they both have that ability to the same degree? Performance data alone can’t settle this question. Cognitive evaluation is an inverse problem: the target ability is a latent variable that is not directly observed, and many hidden causes can produce the same pattern of performance. Memorization, surface cues, and response bias can enable good performance without the target ability, while auxiliary task demands or measurement error can result in observed failure despite the ability. We propose a Bayesian framework to formalise the evaluation of cognitive capacities in humans, animals, and machines. In this framework, a pattern of performance provides evidence only through a comparison. The observed result supports attributing an ability to the extent that it was more expected under a predictive model of the subject with the ability than under a model of the subject without it. This ratio (the Bayes factor) depends on modelling credible routes to success and failure under each hypothesis, and comes apart from prior beliefs about whether subject has the ability. It follows that the same pattern of evidence can carry different evidence for different subjects. This framework explains what makes a cognitive test diagnostic, and how to choose which experiment to perform next as function of the uncertainty it is expected to remove about the subject. In particular, it suggests that the relative evidential weight of behavioural and mechanistic experiments is quite different for human subjects and for artificial neural networks. Beyond the framework’s value as a regulative ideal for comparative cognitive science, we review some of its practical implications for experimental design.

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25th August

Tom Montefiore (Macquarie University) and Darius Parvizi-Wayne (Macquarie University)

Creativity and Proximal Intentions: Rethinking the Intentionality Criterion for Creativity in Humans and AI

Modern AI systems can produce works which at least seem as creative as those produced by humans. It is therefore unsurprising that the question of whether AI systems can be genuinely creative has emerged. A common approach is to appeal to the absence of distal intentions in order to reject AI creativity, but we argue that the activation and fulfilment of distal intentions is neither necessary for creativity, nor sufficient for the type of intentionality needed for genuine creativity. Instead, we develop an alternative focus on proximal intentions, the activation and fulfilment of which is necessary for genuine creativity, and sufficient for the type of intentionality needed for genuine creativity. Further, we show that the relevant proximal intentions need not be held overtly in the mind during the creative process but can also be held subdoxastically. Finally, we argue that AI systems do not possess proximal intentions and therefore cannot be genuinely creative.

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1st September

Laura Candiotto (University of Pardubice)

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8th September

Matt Simms (Cambridge University)

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15th September

Paul Griffiths (University of Sydney)

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22nd September

[No meeting - Mid-semester break]

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29th September

[No meeting - Mid-semester break]

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6th October

Altheer Alkhalfa (ACU)

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13th October

Jess Whyte (UNSW)

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20th October

Millicent Churcher (ACU)

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27th October

Dalia Nassar (University of Sydney)

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3rd November

Michael Kirchhoff (University of Wollongong)

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