Nick Chater

TL;DR: The experimental data is reassessed in the light of a Bayesian model of optimal data selection in inductive hypothesis testing that suggests that reasoning in hypothesis-testing tasks may be rational rather than subject to systematic bias.

TL;DR: This work concludes that a biologically determined UG is not evolutionarily viable, and suggests that apparently arbitrary aspects of linguistic structure may result from general learning and processing biases deriving from the structure of thought processes, perceptuo-motor factors, cognitive limitations, and pragmatics.

TL;DR: The case is made that cognition in general, and human everyday reasoning in particular, is best viewed as solving probabilistic, rather than logical, inference problems, and the wider “probabilistic turn” in cognitive science and artificial intelligence is considered.

TL;DR: The model embodies 4 main claims: temporal memory--traces of items are represented in memory partly in terms of their temporal distance from the present, scale-similarity--similar mechanisms govern retrieval from memory over many different timescales, local distinctiveness--performance on a range of memory tasks is determined by interference from near psychological neighbors, and interference-based forgetting.

TL;DR: A theory of decision by sampling (DbS) in which, in contrast with traditional models, there are no underlying psychoeconomic scales is presented and it is assumed that an attribute's subjective value is constructed from a series of binary, ordinal comparisons to a sample of attribute values drawn from memory.