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Experimental studies investigating logical reasoning performance show very high error rates of up to 80% and more. Previous research identified scalar inferences of the sentences of logical arguments as a major error source. We present new analytical tools to quantify the impact of scalar inferences on syllogistic reasoning. Our proposal builds on a new classification of Aristotelian syllogisms and a closely linked classification of reasoning behaviors/strategies.
We argue that the variation in error rates across syllogistic reasoning tasks is in part due to individual variation: reasoners follow different reasoning strategies and these strategies play out differently for syllogisms of different classes.
We bring experimental considerations to bear on the structure of comparatives and on our understanding of how quantifiers are processed. At issue are mismatches between the standard view of quantifier processing cost and results from speeded verification experiments with comparative quantifiers. We build our case in several steps:
1. We show that the standard view, which attributes processing cost to the verification process, accounts for some aspects of the data, but fails to cover the main effect of monotonicity on measured behavior. We derive a prediction of this view for comparatives, and show that it is not borne out.
2. We consider potential reasons - experimental and theoretical - for this theory-data mismatch.
3. We describe a new processing experiment with comparative quantifiers, designed to address the experimental concerns. Its results still point to the inadequacy of the standard view.
4. We review the semantics of comparative constructions and their potential processing implications. 5. We revise the definition of quantifier processing cost and tie it to the number of Downward Entailing (DE) operators at Logical Form (LF). We show how this definition successfully reconciles the theory-data mismatch. 6. The emerging picture calls for a distinction between the complexity of verified representations and the complexity of the verification process itself.