Syllogisms (e.g. “All A are B; All B are C; What is true about A and C?”) are a long‐studied area of human reasoning. Riesterer, Brand, and Ragni compare a variety of models to human performance and show that not only do current models have a lot of room for improvement, but more importantly a large part of this improvement must come from examining individual differences in performance.

How individuals reason deductively has concerned researchers for many years. Yet, it is still unclear whether, and if so how, participants’ reasoning performance changes over time. In two test sessions one week apart, we examined how the syllogistic reasoning performance of 100 participants changed within and between sessions. Participants’ reasoning performance increased during the first session. A week later, they started off at the same level of reasoning performance but did not further improve. The reported performance gains were only found (...) for logically valid, but not for invalid syllogisms indicating a bias against responding that ‘no valid conclusion’ follows from the premises. Importantly, we demonstrate that participants substantially varied in the strength of the temporal performance changes and explored how individual characteristics, such as participants’ personality and cognitive ability, relate to these interindividual differences. Together, our findings contradict common assumptions that reasoning performance only reflects a stable inherent ability. (shrink)

After 100+ years of studying syllogistic reasoning, what have we learned? Well, Riesterer and colleagues suggest that we have learned to throw away most of the data! If that seems like a bad idea to you then, be assured, that the authors agree with you. The sad fact is that the conclusion of “No Valid Conclusion” (NVC) is one of the most frequently selected responses in syllogistic reasoning but these “majority data” have been ignored by most researchers. Riesterer and colleagues (...) introduce heuristic rules for predicting NVC and provide directions to modelers on how to up their game. (shrink)

The knowledge representation and reasoning of both humans and artificial systems often involves conditionals. A conditional connects a consequence which holds given a precondition. It can be easily recognized in natural languages with certain key words, like “if” in English. A vast amount of literature in both fields, both artificial intelligence and psychology, deals with the questions of how such conditionals can be best represented and how these conditionals can model human reasoning. On the other hand, findings in the psychology (...) of reasoning, such as those in the Suppression Task, have led to a paradigm shift from the monotonicity assumptions in human inferences towards nonmonotonic reasoning. Nonmonotonic reasoning is sensitive for information change, that is, inferences are drawn cautiously such that retraction of previous information is not required with the addition of new information. While many formalisms of nonmonotonic reasoning have been proposed in the field of Artificial Intelligence, their capability to model properties of human reasoning has not yet been extensively investigated. In this paper, we analyzed systematically from both a formal and an empirical perspective the power of formal nonmonotonic systems to model possible explicit defeaters, as in the Suppression Task, and more implicit conditional rules that trigger nonmonotonic reasoning by the keywords in such rules. The results indicated that the classical evaluation for the correctness of inferences has to be extended in the three major aspects regarding the inference system, the knowledge base, and possible assumed exceptions for the rule. (shrink)

We apply three state‐of‐the‐art models for syllogistic reasoning to data from experiments where participants received feedback for their conclusions in order to demonstrate the use of model parameters to derive new hypotheses and present possible explanations for the feedback effect.