• Publications
  • Influence
A temporal ratio model of memory.
TLDR
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.
A rational analysis of the selection task as optimal data selection.
TLDR
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.
Précis of Bayesian Rationality: The Probabilistic Approach to Human Reasoning
TLDR
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.
The Probability Heuristics Model of Syllogistic Reasoning
TLDR
PHM suggests that syllogistic reasoning performance may be determined by simple but rational informational strategies justified by probability theory rather than by logic.
Decision by sampling
Language as shaped by the brain.
TLDR
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.
Distributional Information: A Powerful Cue for Acquiring Syntactic Categories
TLDR
It is demonstrated empirically that distributional information provides a powerful cue to syntactic category membership, which can be exploited by a variety of simple, psychologically plausible mechanisms.
Probabilities and polarity biases in conditional inference.
TLDR
A probabilistic computational level model of conditional inference is proposed that can explain polarity biases in conditional inference and three experiments revealed that, consistent with this Probabilistic account, when high-probability categories are used instead of negations, a high- Probability conclusion effect is observed.
Individual decision-making
  • N. Chater
  • Computer Science
    Delivering Better Policies Through Behavioural…
  • 16 April 2019
Reconciling simplicity and likelihood principles in perceptual organization.
  • N. Chater
  • Philosophy
    Psychological review
  • 1 July 1996
TLDR
Drawing on mathematical results in A. N. Kolmogorov's complexity theory, the author argues that simplicity and likelihood are not in competition, but are identical.
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