@article {419,
title = {Immediate inferences from quantified assertions},
journal = {Quarterly Journal of Experimental Psychology},
year = {2015},
month = {03/2015},
pages = {2073{\textendash}2096},
author = {Sangeet S. Khemlani and Max Lotstein and J. Gregory Trafton and Philip N. Johnson-Laird}
}
@proceedings {350,
title = {A mental model theory of set membership},
year = {2014},
month = {07/2014},
pages = {2489-2494},
publisher = {Cognitive Science Society},
address = {Quebec City, Canada},
abstract = {Assertions of set membership, such as Amy is an artist, should not be confused with those of set inclusion, such as All artists are bohemians. Membership is not a transitive relation, whereas inclusion is. Cognitive scientists have neglected the topic, and so we developed a theory of inferences yielding conclusions about membership, e.g., Amy is a bohemian, and about non-membership, Abbie is not an artist. The theory is implemented in a computer program, mReasoner, and it is based on mental models. The theory predicts that inferences that depend on a search for alternative models should be more difficult than those that do not. An experiment corroborated this prediction. The program contains a parameter, σ, which determines the probability of searching for alternative models. A search showed that its optimal value of .58 yielded a simulation that matched the participant{\textquoteright}s accuracy in making inferences. We discuss the results as a step towards a unified theory of reasoning about sets.},
author = {Sangeet S. Khemlani and Max Lotstein and Phil Johnson-Laird}
}
@article {348,
title = {Naive Probability: Model-Based Estimates of Unique Events},
journal = {Cognitive Science},
year = {2014},
abstract = {We describe a dual-process theory of how individuals estimate the probabilities of unique events, such as Hillary Clinton becoming U.S. President. It postulates that uncertainty is a guide to improbability. In its computer implementation, an intuitive system 1 simulates evidence in mental models and forms analog non-numerical representations of the magnitude of degrees of belief. This system has minimal computational power and combines evidence using a small repertoire of primitive operations. It resolves the uncertainty of divergent evidence for single events, for conjunctions of events, and for inclusive disjunctions of events, by taking a primitive average of non-numerical probabilities. It computes conditional probabilities in a tractable way, treating the given event as evidence that may be relevant to the probability of the dependent event. A deliberative system 2 maps the resulting representations into numerical probabilities. With access to working memory, it carries out arithmetical operations in combining numerical estimates. Experiments corroborated the theory{\textquoteright}s predictions. Participants concurred in estimates of real possibilities. They violated the complete joint probability distribution in the predicted ways, when they made estimates about conjunctions: P(A), P(B), P(A and B), disjunctions: P(A), P(B), P(A or B or both), and conditional probabilities P(A), P(B), P(B|A). They were faster to estimate the probabilities of compound propositions when they had already estimated the probabilities of each of their components. We discuss the implications of these results for theories of probabilistic reasoning.},
issn = {1551-6709},
doi = {10.1111/cogs.12193},
url = {http://onlinelibrary.wiley.com/doi/10.1111/cogs.12193/full},
author = {Sangeet S. Khemlani and Max Lotstein and Philip N. Johnson-Laird}
}
@conference {308,
title = {A Proces Model of Immediate Inferences},
booktitle = {Proceedings of the 11th International Conference on Cognitive Modeling},
year = {2012},
month = {04/2012},
publisher = {Universitatsverlag der TU Berlin},
organization = {Universitatsverlag der TU Berlin},
address = {Berlin},
author = {Sangeet S. Khemlani and J G Trafton and Max Lotstein and Philip N. Johnson-Laird}
}