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We have previously reported results showing that when children can identify the critical structural relations in a scene analogy problem, development of their ability to reason analogically interacts with both relational complexity and featural distraction (Richland, Morrison & Holyoak, 2004, in press). In this paper we present computer simulations in LISA (Hummel & Holyoak, 1997, 2003) demonstrating that both relational complexity and featural distraction effects can be parsimoniously accounted for by a simple change in inhibition in the model. This result is similar to data and simulations of analogy performance in patients with damage to prefrontal cortex (Morrison et al., 2004) and older adults (Viskontas et al., 2004), two other populations whose cognitive performance is associated with decreases in inhibitory control in working memory. These results lend support to the hypothesis that the development of inhibitory control in working memory is a critical factor in children’s ability to perform relational reasoning. Children’s development of analogical reasoning allows them to notice correspondences and make inferences about relationally similar phenomena across contexts. This greatly enriches children’s capacity for transfer of learning and schema abstraction, two essential aspects of children’s learning and cognitive development (Chen, Sanchez &

Analogical reasoning is a complex form of reasoning in which concepts from one situation are mapped onto another situation resulting in new inferences and explanations. More

The present research explored the role of working memory (WM) in unsupervised category learning, learning without an external tutor or even knowing that categories exist, by investigating its role using a pattern-sequence manipulation. A pattern-sequence manipulation compares learning when items from categories are presented together (blocked) versus when the items are presented in random order (mixed). Experiment 1 extended the pattern-sequence manipulation to assess category knowledge separate from paired-associate learning. Participants performed equally well on new and studied items, supporting the hypothesis that the pattern-sequence manipulation results in the acquisition of category information, not simply memory for item-feature associations. Experiment 2 introduced a WM factor, administering the method used in Experiment 1 to a group of high and low WM span participants. High WM span was predicted to interact with the pattern-sequence effect to produce greater learning when the items were blocked than mixed. There was reliable support for a role of WM span in the discovery and acquisition of category knowledge, but this role was different from the one hypothesized. The high WM span participants exhibited higher overall accuracies than the low WM span participants. This result supports a role for WM in unsupervised category learning, but did not benefit more from the pattern-sequence effect than did the low WM span participants as predicted. Implications for theories of category learning and WM are discussed.

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of metaphorical and analogical reasoning in controlled contexts, less work has been conducted on model capturing less structured, everyday relational reasoning. This paper describes computational metaphor identification (CMI), which uses computational linguistic techniques to identify patterns in written text indicative of potential conceptual metaphors. The paper presents an overview of CMI, followed by sample results from two different corpora: middle school students ' science writing and political blogs from during the 2008 US election. These results demonstrate CMI's capacity to identify linguistic patterns potentially indicative of deep conceptual metaphors that could subtly yet powerfully influence reasoning.

This paper presents a series of simulations performed with the AMBR model that demonstrate how deduction, induction, and analogy can emerge from the interaction of several simple mechanisms. First, a case of deductive reasoning is demonstrated when a problem is solved based on general knowledge. The system represents the target in different ways depending on the goal, and different solutions are generated. Second, the constructed solutions of the problems are remembered and later on used as a base for remote analogy. Finally, on the basis of the analogy made, a generalized solution of the class of problems is induced. One important characteristic of the model is that representation of the task, problem-solving, and learning are not viewed as separate modules. Instead, they are different aspects of one and the same joined work of the basic mechanisms of the architecture.

Kotovsky and Gentner (1996) showed that presenting progressively aligned examples helped children discover relational similarities: Comparisons based on initially exemplars helped the discovery of higher-order relational similarities. We investigated whether progressive alignment can aid learning of relational categories with either a deterministic (in which one relation reliably predicts category membership) or a probabilistic structure (in which each relation predicts category membership with 75 % reliability). Progressive alignment helped participants learn relational categories with the deterministic structure. However, progressive alignment did not help participants learn the probabilistic relational categories. The results show that learning relational categories with a deterministic structure can be improved by progressive alignment, consistent with previous findings (e.g., Kotovsky & Gentner, 1996), but also support previous findings suggesting that relational categories are represented as a schemas, which are learned by a process of intersection discovery that fails catastrophically with

In this paper we proposed a new classification of analogical mechanisms of representational change and gathered evidence of the operation of one of the new ones that we proposed: recategorization of events. We carried out two experiments to assess whether an analogy can trigger the recategorization of a target analog (TA). More specifically, the experiments were designed to test whether a TA not initially regarded as a member of a schema relational category can be perceived as belonging to such category as a result of being paired with a base analog (BA) consisting of a typical exemplar of that

One of the most important aspects of human reasoning is our ability to robustly adapt to new situations, tasks, and domains. Current AI systems exhibit brittleness when faced with new situations and domains. This work explores how structure mapping models of analogical processing allow for the robust reuse of domain knowledge. This work focuses on two analogical methods to reuse existing knowledge in novel situations and domains. The first method, analogical model formulation, applies analogy to the task of model formulation. Model formulation is the process of moving from a scenario or system description to a formal vocabulary of abstractions and causal models that can be used effectively for problem-solving. Analogical model formulation uses prior examples to determine which abstractions, assumptions, quantities, equations, and causal models are applicable in new situations within the same domain. By employing examples, the range of an analogical model formulation system is extendable by adding additional example-specific models. The robustness of this method for reasoning and learning is evaluated in a series of experiments in two domains, everyday physical reasoning with sketches and textbook physics problem-solving. The second method, domain transfer via analogy, is a task-level model of cross-domain analogical learning. DTA helps overcome brittleness by allowing abstract domain