Neurological syndromes in which consciousness seems to malfunction, such as temporal lobe epilepsy, visual scotomas, Charles Bonnet syndrome, and synesthesia offer valuable clues about the normal functions of consciousness and ‘qualia’. An investigation into these syndromes reveals, we argue, that qualia are different from other brain states in that they possess three functional characteristics, which we state in the form of ‘three laws of qualia’. First, they are irrevocable: I cannot simply decide to start seeing the sunset as green, or (...) feel pain as if it were an itch; second, qualia do not always produce the same behaviour: given a set of qualia, we can choose from a potentially infinite set of possible behaviours to execute; and third, qualia endure in short-term memory, as opposed to non-conscious brain states involved in the on-line guidance of behaviour in real time. We suggest that qualia have evolved these and other attributes because of their role in facilitating non-automatic, decision-based action. We also suggest that the apparent epistemic barrier to knowing what qualia another person is experiencing can be overcome by using a ‘bridge’ of neurons; and we offer a hypothesis about the relation between qualia and one's sense of self. (shrink)

Analogy and similarity are central phenomena in human cognition, involved in processes ranging from visual perception to conceptual change. To capture this centrality requires that a model of comparison must be able to integrate with other processes and handle the size and complexity of the representations required by the tasks being modeled. This paper describes extensions to Structure-Mapping Engine since its inception in 1986 that have increased its scope of operation. We first review the basic SME algorithm, describe psychological evidence (...) for SME as a process model, and summarize its role in simulating similarity-based retrieval and generalization. Then we describe five techniques now incorporated into the SME that have enabled it to tackle large-scale modeling tasks: Greedy merging rapidly constructs one or more best interpretations of a match in polynomial time: O); Incremental operation enables mappings to be extended as new information is retrieved or derived about the base or target, to model situations where information in a task is updated over time; Ubiquitous predicates model the varying degrees to which items may suggest alignment; Structural evaluation of analogical inferences models aspects of plausibility judgments; Match filters enable large-scale task models to communicate constraints to SME to influence the mapping process. We illustrate via examples from published studies how these enable it to capture a broader range of psychological phenomena than before. (shrink)

Work on analogy has been done from a number of disciplinary perspectives throughout the history of Western thought. This work is a multidisciplinary guide to theorizing about analogy. It contains 1,406 references, primarily to journal articles and monographs, and primarily to English language material. classical through to contemporary sources are included. The work is classified into eight different sections (with a number of subsections). A brief introduction to each section is provided. Keywords and key expressions of importance to research on (...) analogy are discussed in the introductory material. Electronic resources for conducting research on analogy are listed as well. (shrink)

In the novel Die Automate (Hoffmann, 1957 ed.), the German writer Ernst Theodor Amadeus Hoffmann (1776-1822) describes different kinds of musical automatons which play music with expression and musicality. The whole novel is based on the effect of the automatic performances on the feelings of the two personages and on the appearance of musical automatons which simulate, with their musical skill and expressiveness, the structures of the knowledge and feelings of human beings.

We present a model of similarity‐based retrieval that attempts to capture three seemingly contradictory psychological phenomena: (a) structural commonalities are weighed more heavily than surface commonalities in similarity judgments for items in working memory; (b) in retrieval, superficial similarity is more important than structural similarity; and yet (c) purely structural (analogical) remindings e sometimes experienced. Our model, MAC/FAC, explains these phenomena in terms of a two‐stage process. The first stage uses a computationally cheap, non‐structural matcher to filter candidate long‐term memory (...) items. It uses content vectors, a redundant encoding of structured representations whose dot product estimates how well the corresponding structural representations will match. The second stage uses SME (structure‐mapping engine) to compute structural matches on the handful of items found by the first stage. We show the utility of the MAC/FAC model through a series of computational experiments: (a) We demonstrate that MAC/FAC can model patterns of access found in psychological data; (b) we argue via sensitivity analyses that these simulation results rely on the theory; and (c) we compare the performance of MAC/FAC with ARCS, an alternate model of similarity‐based retrieval, and demonstrate that MAC/FAC explains the data better than ARCS. Finally, we discuss limitations and possible extensions of the model, relationships with other recent retrieval models, and place MAC/FAC in the context of other recent work on the nature of similarity. (shrink)

This research investigates the development of analogy: In particular, we wish to study the development of systematicity in analogy. Systematicity refers to the mapping of systems of mutually constraining relations, such as causal chains or chains of implication. A preference for systematic mappings is a central aspect of analogical processing in adults (Gentner, 1980, 1983). This research asks two questions: Does systematicity make analogical mapping easier? And, if so, when, developmentally, do children become able to utilize systematicity?Children aged 5–7 and (...) 8–10 acted out stories with toy characters. Then they were asked to act out the same stories with new characters. Two variables were manipulated: systematicity, or the degree of explicit causal structure in the original stories, and the transparency of the object‐mappings. Transparency was manipulated by varying the similarity between the original characters and the corresponding new characters: it was included in order to vary the difficulty of the transfer task. If children can utilize systematicity, then their transfer accuracy should be greater for systematic stories.The results show: (1) As expected, transparency strongly influenced transfer accuracy (for both age groups, transfer accuracy dropped sharply as the object correspondences became less transparent); and (2) for the older group, there was also a strong effect of systematicity and an interaction between the two variables. Given a systematic story, 9‐year‐olds could transfer it accurately regardless of the transparency of the object correspondence. (shrink)

We discuss several aspects of legal arguments, primarily arguments about the meaning of statutes. First, we discuss how the requirements of argument guide the specification and selection of supporting cases and how an existing case base influences argument formation. Second, we present,our evolving taxonomy of patterns of actual legal argument. This taxonomy builds upon our much earlier work on argument moves and also on our more recent analysis of how cases are used to support arguments for the interpretation of legal (...) statutes. Third, we show how the theory of argument used by CABARET, a hybrid case-based/rule-based reasoner, can support many of the argument patterns in our taxonomy. (shrink)

High-level perception--”the process of making sense of complex data at an abstract, conceptual level--”is fundamental to human cognition. Through high-level perception, chaotic environmen- tal stimuli are organized into the mental representations that are used throughout cognitive pro- cessing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dis- missal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models--”notably (...) BACON, a model of scientific discovery, and the Structure-Mapping Engine, a model of analogical thought--”and argue that these are flawed pre- cisely because they downplay the role of high-level perception. Further, we argue that perceptu- al processes cannot be separated from other cognitive processes even in principle, and therefore that traditional artificial-intelligence models cannot be defended by supposing the existence of a --œrepresentation module--� that supplies representations ready-made. Finally, we describe a model of high-level perception and analogical thought in which perceptual processing is integrated with analogical mapping, leading to the flexible build-up of representations appropriate to a given context. (shrink)

A theory of analogy must describe how the meaning of an analogy is derived from the meanings of its parts. In the structure‐mapping theory, the interpretation rules are characterized as implicit rules for mapping knowledge about a base domain into a target domain. Two important features of the theory are (a) the rules depend only on syntactic properties of the knowledge representation, and not on the specific content of the domains; and (b) the theoretical framework allows analogies to be distinguished (...) cleanly from literal similarity statements, applications of abstractions, and other kinds of comparisons.Two mapping principles are described: (a) Relations between objects, rather than attributes of objects, are mapped from base to target; and (b) The particular relations mapped are determined by systematicity, as defined by the existence of higher‐order relations. (shrink)

Max Wertheimer, in his classic Productive Thinking, linked understanding to transfer: Understanding is important because it provides the ability to generalize the solution of one problem to apply to another. Recent work in human and machine learning has led to the development of a new class of generalization mechanism, called here analysis‐based generalization, which can be used to provide a concrete account of the linkage Wertheimer suggested: these mechanisms all, in different ways, use understanding of examples in the generalization process. (...) In this paper I review this class of mechanism, and describe a method for causal attribution that can produce the analyses of examples that the generalization methods require, in the domain of simple procedures in human‐computer interaction. This causal analysis method is linked with analysis‐based generalization to form EXPL, an implemented model which is a concrete, though limited, instontiation of Wertheimer s scheme. EXPL constructs an understanding of an example procedure and generalizes it on the basis of that understanding. Results of an empirical study suggest that some of EXPL's attribution heuristics are used by people, and that while a subclass of analysis‐based methods, called superstitious methods, seem to provide a more plausible account of people's generalization under the conditions of the study than a contrasting class of rationalistic methods, at least some participants appear to use methods from both classes. The results also show that explanation‐based methods, which rely on comprehensive domain theories, must be used in conjunction with a means for extending the domain theory. If thus enhanced, explanation‐based methods are able to mimic the effects of other analysis‐based methods, and can provide a good account of the data, though combinations of other methods must also be considered. Finally, I return to Wertheimer s ideas to argue that none of the current analysis‐based generalization methods fully captures Wertheimer s notion of understanding. Proper choice among different possible analyses of an example is crucial for Wertheimer, but I argue that this problem may be beyond the reach of learning systems. (shrink)