This article describes an integrated theory of analogical access and mapping, instantiated in a

The use of analogy in human thinking is examined from the perspective of a multiconstraint theory, which postu-lates 3 basic types of constraints: similarity, structure, and purpose. The operation of these constraints is appar-ent in laboratory experiments on analogy and in natural-istic settings, including politics, psychotherapy, and sci-entific research. The multiconstraint theory has been im-plemented in detailed computational simulations of the analogical human mind. ~ ~ any parents know that young children take comfort in getting a kiss on an injury to "make it better. " Little Aaron, aged 24 months, would routinely come to his mother saying things like, "I bump my head. Kiss it. " But one morning, for the first time ever, the tables turned. While his mother was dressing

Recent constraint-satisfaction models of explanation, analogy, and decision making claim that these processes are influenced by bidirectional constraints that promote coherence. College students were asked to reach a verdict in a complex legal case involving multiple conflicting arguments, including alternative analogies to the target case. Participants rated agreement with the individual arguments both in isolation before seeing the case, and again after reaching a verdict. Assessments of the individual arguments (including the competing analogies) shifted so as to cohere with their emerging verdict. Information about the character of the defendant in the initial case triggered a cascade of "spreading coherence", influencing decisions made about a subsequent case involving very different legal issues. Participants ' memory for their initial positions also shifted so as to cohere with their final positions. The coherence shifts were simulated by a constraint satisfaction model. The results demonstrate that an alogical process of constraint satisfaction can transform highly ambiguous inputs into coherent decisions. Bidirectional Reasoning 3 One of the most deep-rooted assumptions about human reasoning is that the flow of

This article describes the path-mapping theory of how humans integrate analogical mapping and general problem solving. The theory posits that humans represent analogs with declarative roles, map analogs by lower-level retrieval of analogous role paths, and coordinate mappings with higher-level organizational knowledge. Implemented in the ACT-R cognitive architecture, the path-mapping theory enables models of analogical mapping behavior to incorporate and interface with other problem-solving knowledge. Path-mapping models thus can include task-specific skills such as encoding analogs or generating responses, and can make behavioral predictions at the level of real-world metrics such as latency or correctness. We show that the path-mapping theory can successfully account for the major phenomena addressed by previous theories of analogy. We also describe a path-mapping model that can account for subjects’ incremental eye-movement and typing behavior in a story-mapping task. We discuss extensions and implications of this work to other areas of analogy and problem-solving research.

The ways in which analogy was used in a nonexperimental environment—politics—was investigated. We used the framework developed in analogy research to analyze the selection of analogical sources in political discourse. We took all the analogies reported in newspapers during the final week of a referendum campaign in Canada and analyzed the features of the different analogies used. We identified 234 analogies and analyzed the range over which analogies were used, semantic categories of analogies, goals of the analogizer, and emotional connotation of the analogies. Our results reveal that analogy was frequently used, that over two-thirds of the analogical sources were nonpolitical, and that many of the sources had strong emotional connotations. Furthermore, the goal of the analogizer influenced the selection of sources. We conclude that characteristics of the audience and emotionality of the source analog are important features in the selection of source analogs.

Although commentators periodically declare that Freud is dead, his repeated burials lie on shaky grounds. Critics typically attack an archaic version of psychodynamic theory that most clinicians similarly consider obsolete. Central to contemporary psychodynamic theory is a series of propositions about (a) unconscious cognitive, affective, and motivational processes; (b) ambivalence and the tendency for affective and motivational dynamics to operate in parallel and produce compromise solutions; (c) the origins of many personality and social dispositions in childhood; (d) mental representations of the self, others, and relationships; and (e) developmental dynamics. An enormous body of research in cognitive, social, developmental, and personality psychology now supports many of these propositions. Freud's scientific legacy has implications for a wide range of domains in psychology, such as integration of affective and motivational constraints into connectionist models in cognitive science. Freud, like Elvis, has been dead for a number of years but continues to be cited with some regularity. Although the majority of clinicians report that they rely to some degree upon psychodynamic 1 principles

Some inferences are seemingly automatic (reflexive; Shastri & Ajjanagadde, 1993), whereas others require more effort (i.e., are reflective). We present the beginnings of an integrated account of reflexive and reflective reasoning, based on the LISA model of analogical reasoning (Hummel & Holyoak, 1997). The account holds that reflexive inferences are those that can be generated automatically based on existing knowledge in long-term memory, whereas reflective inferences require explicit structuremapping and therefore demand greater attention and working memory. According to this account, reflexive inferences manifest themselves in the semantic encoding of objects and predicates, whereas reflective inferences manifest themselves as explicit propositions. In contrast to reflexive inferences, which are equally reflexive, reflective inferences may require more or less effort. We present preliminary simulation results demonstrating that both kinds of inference can be modeled in a single architecture for representing propositional knowledge.

We propose to characterize structural similarity between source and target problems by the type and size of their structural overlap. Size of structural overlap is captured by a measure of graph-distance. We investigated the influence of structural overlap on transfer success in analogical problem solving in two experiments. In both experiments, for a fixed source problem one of five target problems had to be solved. In the first experiment, target problems varied in superficial and structural similarity to the source. In the case of isomorphic source/target relations superficial similarity had no impact on transfer success while for a partial isomorphic target solution success was only high if source and target had identical surface attributes. In the second experiment, surface of source and target were kept identical and different types of structural source/target relations were investigated: For problems with a high structural overlap source inclusive and target exhaustive source/ta...

Theories of analogical reasoning have viewed relational structure as the dominant determinant of analogical mapping and inference, while assigning lesser importance to similarity between individual objects. An experiment is reported in which these two sources of constraints on analogy are placed in competition under conditions of high relational complexity. Results demonstrate equal importance for relational structure and object similarity, both in analogical mapping and in inference generation. The human data were successfully simulated using a computational analogy model (LISA) that treats both relational correspondences and object similarity as soft constraints that operate within a limited-capacity working memory; but not with a model (SME) that treats relational structure as pre-eminent. Analogies provide a valuable source of new inferences and a means of expanding knowledge. Analogical reasoning is generally viewed as involving four major subprocesses: (1) retrieving an appropriate source analogue from long-term memory to compare with a novel target analogue, (2) mapping elements of the two analogues, (3) making inferences about the target as a function of its mapping to the source, and (4) using the source and target together to induce a more general schema or rule (e.g., Carbonell, 1983;

Can the use of mobile phones by low computer literacy populations help them also learn computing? This paper argues that it can help. We are claiming that theories of metaphors and mental models, as well as the role of social networks in the diffusion of innovation, suggest that people with low computer literacy could use their experience