This paper begins with a brief review of SME and MAC/FAC, our simulations of matching and retrieval. Next I lay out several arguments for exploring analogy in the large, including why it is now very feasible and what we can learn by such explorations. A new constraint on cognitive simulations, the Integration Constraint, is proposed: A cognitive simulation of some aspect of analogical processing should be usable as a component in larger-scale cognitive simulations. I believe that the implications of this new constraint for cognitive simulation of analogy are far-reaching. After that, two explorations of larger-scale phenomena are described. First, I describe a theoretical framework in which we model common sense reasoning as an interplay of analogical and first-principles reasoning. Second, I describe how SME and MAC/FAC have been used in a case-based coach that is accessible to engineering thermodynamics students worldwide via electronic mail. These examples show that exploring analogy in the large can provide new insights and new challenges to our simulations. Finally, the broader implications of this approach are discussed.

The dynamics model, which is based on L. Talmy’s (1988) theory of force dynamics, characterizes causation as a pattern of forces and a position vector. In contrast to counterfactual and probabilistic models, the dynamics model naturally distinguishes between different cause-related concepts and explains the induction of causal relationships from single observations. Support for the model is provided in experiments in which participants categorized 3-D animations of realistically rendered objects with trajectories that were wholly determined by the force vectors entered into a physics simulator. Experiments 1–3 showed that causal judgments are based on several forces, not just one. Experiment 4 demonstrated that people compute the resultant of forces using a qualitative decision rule. Experiments 5 and 6 showed that a dynamics approach extends to the representation of social causation. Implications for the relationship between causation and time are discussed.

Three studies examined the claim that hand movements can facilitate imagery for object rotations but that this facilitation depends on people's model of the situation. In Experiment 1, physically turning a block without vision reduced mental rotation times compared with imagining the same rotation without bodily movement. In Experiment 2, pulling a string from a spool facilitated participants ' mental rotation of an object sitting on the spool. In Experiment 3, depending on participants ' model of the spool, the exact same pulling movement facilitated or interfered with the exact same imagery transformation. Results of Experiments 2 and 3 indicate that the geometric characteristics of an action do not specify the trajectory of an imagery transformation. Instead, they point to people's ability to model the tools that mediate between motor activity and its environmental consequences and to transfer tool knowledge to a new situation. From hinges to hand drills, a distinctively human talent is the construction and use of multipart tools (Diamond, 1997). The purpose of this article is to explore a competence that may be responsible for this talent, namely, people's ability to imagine the environmental consequences of their actions. During tool use, objects often change position, for example, when a wrench turns a bolt. Imagery may help people anticipate the displacements that result from tool use. The literature on imagery points to this possibility, but it has primarily explored the functions of imagery for recognizing objects and spatial arrays displaced by an experimenter

Abstract. There is much empirical evidence showing that factors other than the relative positions of objects in Euclidean space are important in the comprehension of a wide range of spatial prepositions in English and other languages. We first the overview the functional geometric framework (Coventry & Garrod, 2004) which puts “what ” and “where ” information together to underpin the situation specific meaning of spatial terms. We then outline an implementation of this framework. The computational model for the processing of visual scenes and the identification of the appropriate spatial preposition consists of three main modules: (1) Vision Processing, (2) Elman Network, (3) Dual-Route Network. Mirroring data from experiments with human participants, we show that the model is both able to predict what will happen to objects in a scene, and use these judgements to influence the appropriateness of over/under/above/below to describe where objects are located in the scene. Extensions of the model to other prepositions and quantifiers are discussed. 1

Dynamics and the perception of causal events To imagine possible events, we use our knowledge of causal relationships. To look deep into the past and infer events that were not witnessed, we use causal relationships as well. We also use causal knowledge to infer what can not be directly seen in the present, for instance, the existence of planets around distant stars, or the presence of subatomic particles. Knowledge of causal relationships allows us to go beyond the immediate here and now. In this chapter I introduce a new theoretical framework for how this very basic concept might be mentally represented. In effect, I propose an epistemological theory of causation, that is, a theory that specifies the nature of people’s knowledge of causation, the notion of causation used in everyday language and reasoning. In philosophy, epistemological theories are often contrasted with metaphysical theories, theories about the nature of reality. Since people’s concepts of causation are assumed to be in error, most metaphysical theories of causation seek to reform rather than describe the concept of CAUSE in people’s heads, (see Mackie, 1974; Dowe, 2000). Theories of causation in psychology have followed suit by linking people’s representations of causation to the outward

parisdescartes.fr People often perform spontaneous body movements during spatial tasks such as giving complex directions or orienting themselves on maps. How are these spontaneous gestures related to spatial problem-solving? We measured spontaneous movements during a perspective-taking task inspired by map reading. Analyzing the motion data to isolate rotation and translation components of motion in specific geometric relation to the task, we found out that most participants executed spontaneous miniature rotations of the head that were significantly related to the main task parameter. These head rotations were as if participants were trying to align themselves with the orientation on the map either in the image plane or on the ground plane, but with tiny amplitudes, typically below 1 % of the actual movements. Our results are consistent with a model of sensorimotor prediction driving spatial reasoning. The efference copy of planned movements triggers this prediction mechanism. The movements themselves may then be mostly inhibited; the small spontaneous gestures that we measure are the visible traces of these planned but inhibited actions.