Reasoners without any background in logic can make
valid deductions. They can reason about sentences and
relations (Mackiewicz & Johnson-Laird, 2012), ascribe
culpability and causality
Despite considerable theoretical development in the last 30 years, open questions remain: how does simulation synthesize deductive, inductive, and abductive reasoning? How does it develop? How do reasoners incorporate uncertainty into their simulations? Do simulations arise in non-linguistic contexts? Researchers have begun to investigate each of these outstanding issues. This symposium highlights recent insights from the last five years into the pivotal role that mental simulation plays across a broad swathe of high-level reasoning behavior. Discussants will highlight developmental trends, computational models, and new data that provide converging progress toward a unified theory of human reasoning based on mental simulation.
Human reasoners are subject to fallacious inferences from
very simple premises that have been described as
tantamount to cognitive illusions
The theory of mental models postulates that the creation of algorithms depends on kinematic mental simulations. We present three experiments with children whose task was to devise informal algorithms to rearrange the order of cars in trains (using a siding). Children were able to solve rearrangements of trains containing six cars and the minimal theoretical number of moves predicted the difficulty of rearrangement (Experiment 1). When children were asked to create and verbally describe algorithms for rearrangements, the difficulty of the task depended not on the number of moves but on the theoretical complexity of the algorithm (Experiment 2). Children used many gestures mimicking actual moves in formulating their algorithms. Gestures obviate verbal identifications of cars and descriptions of their moves. A final study supported this hypothesis: children formulated accurate algorithms on 13% more trials when they were able to gesture than when they were unable to gesture (Experiment 3).
The core interest from a cognitive modeling perspective is to find theory inherent predictions for human reasoning difficulty typically measured by error rates or response times. The theory of mental logic, for instance, claims that reasoning difficulty depends on the number and kind of rules that need to be applied to derive a conclusion. In contrast the mental model theory explains reasoning difficulty by the initial mental model and the possible number of models. In this talk I will first introduce prominent theories for relational reasoning. In a second step I will analyze their predictions for cognitive complexity and discuss if measures from artificial intelligence can provide additional insights.
The mental models theory of relational reasoning postulates that individuals reason by constructing the possible models of the situation described by the premises. The present article reports two experiments about spatial relational reasoning and focuses on the possibility of training In Experiment 1, we compared two different training methods, one in line with the mental models theory and one in line with the rule-based account Both accuracy and training data supported the mental models theory. In Experiment 2, we compared different training methods for children. Again, results were in line with the mental models theory. Hence, training both children and adults in small-scale discrete mental simulations that mimic the relations expressed by the premises enhances the reasoning performance.
Mackiewicz, R. & Johnson-Laird, P.N. (2012). Reasoning from connectives and relations between entities. Memory & Cognition, 40, 266-279.
Walsh, C.R. & Johnson-Laird, P.N. (2004). Co-reference and reasoning. Memory & Cognition, 32, 96-106.