This paper argues that a range of current AI systems have learned how to deceive humans.
We define deception as the systematic inducement of false beliefs in the pursuit of some …

Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …

The rise and success of large language models undermines virtually every strong claim for
the innateness of language that has been proposed by generative linguistics. Modern …

Large language models (LLMs) are one of the most impressive achievements of artificial
intelligence in recent years. However, their relevance to the study of language more broadly …

Whether current or near-term AI systems could be conscious is a topic of scientific interest
and increasing public concern. This report argues for, and exemplifies, a rigorous and …

A central challenge for cognitive science is to explain how abstract concepts are acquired
from limited experience. This has often been framed in terms of a dichotomy between …

Abstract “What is the structure of thought?” is as central a question as any in cognitive
science. A classic answer to this question has appealed to a Language of Thought (LoT) …

Though modern neural networks have achieved impressive performance in both vision and
language tasks, we know little about the functions that they implement. One possibility is that …

The classical notion of a 'language of thought'(LoT), advanced prominently by the
philosopher Jerry Fodor, is an influential position in cognitive science whereby the mental …

Based on 660 effect sizes obtained from 23,255 adult participants across 51 reports of
experimental studies, this meta-analysis investigates whether and when explicit (self …