Much has been written about anthropomorphic bias in the study of LLMs. Here we discuss various kinds of anthropocentric bias.

Under some conditions, we ought to trade interpretability for predictive power.

This talk describes some of the conceptual and methodological difficulties involved in articulating the cognitive capacities of large language models.

This talk describes theoretical constraints on current attempts to work decode mental content from brain data.

When we use methods from experimental psychology to test the capacities of LLMs, we are prone to transfer assumptions about the humane case to the LLM case, and to do so without justification. By drawing attention to these assumptions we can make more informed comparisons.

In order to tell whether someone is culpable for an action initiated by a brain-computer interface, it is not necessary to work out whether the brain-computer interface correctly decoded their intention.

Where ML models are used as the centerpiece of an epistemic classification procedure, reliability is not sufficient for ethical use. The nature of classification errors should be taken into account.

A popular article arguing that, when evaluating LLMs, anthropocentrism is just as misleading as anthropomorphism.

Reading minds is easier than you might think.

There can be an objective fact about the number of bits in a biological signal, despite the fact that the signal is receiver-relative.

Argument maps represent some kinds of arguments better than others.

In the brain, semantic information is intertwined with Shannon information.

Network models support novel forms of discovery, prediction, and explanation. They also raise a philosophical puzzle about unification.

Neural reuse helped to liberate humans from evolutionary constraints faced by our ancestors.

The concept of neural coding makes sense, if the codes can be learned by neurons.

If meta-cognition evolved, there is probably something like semi-meta-cognition.

Network representation compresses information about complex systems without abstracting away from the properties that make them complex.

If there are localized functions in the brain, they can only be articulated by abstracting away from functions associated with particular experimental tasks.