I describe steps toward “deep lexical acquisition” based on naive theories, motivated by modern results of developmental psychology. I argue that today’s machine learning paradigm is inappropriate to take these steps. Instead we must develop computational accounts of naive theory representations, mechanisms of theory acquisition, and the mapping of naive theories to lexicalizable concepts. This will enable our theories to describe the flexibility of the human conceptual apparatus.

This research reports on a cognitive neuroscientific examination of whether trade-off aversion explains the attraction effect. The principal study involves the neuroimaging of participants engaging in choice tasks while their cerebral activity is recorded. The authors examine whether the presence of a third (normatively irrelevant) alternative yields relatively less activation in areas of the brain associated with negative emotion than the activation during choice tasks involving two equally (un)attractive options. The results support the claim that trade-off choice sets are associated with relatively greater negative emotion. The authors discuss the implications of the research for marketing theory and methodology, as well as for managerial practice in the corporate and public policy arenas.

Rozenblit and Keil (2002) claim that people are subject to an illusion of explanatory depth (IOED) whereby they believe they understand the world in greater detail, coherence and depth than they actually do. In the present research, we questioned Rozenblit and Keil’s conclusions in two ways. First, we tested whether people might overestimate their explanatory knowledge as a result of misconstruing how to initially rate their understanding of stimuli. We found that when directed to consider the physical-mechanical processes of stimuli instead of their functional affordances, participants did in fact offer more accurate estimates of understanding relative to their explanatory performance. Second, we tested whether the explanations participants proffer are misleadingly shallow. We predicted that by encouraging a more relational encoding of stimuli, participants would be able to produce better explanations. However, the results showed that participants ’ explanations remained shallow after relationally encoding stimuli.

The significant role of explanation in learning and generalization is ubiquitous and well documented: explanation promotes student learning in educational settings, drives conceptual development in young children, is accorded a central role in theories of conceptual representation, and has a long history in artificial intelligence. Despite this, relatively little is known about the precise mechanisms that underlie explanation’s effects, and there is a paucity of discourse between the disciplines of cognitive science that study explanation. This interdisciplinary symposium brings together key researchers from education, development, cognitive psychology and computer science to synthesize the progress from these disciplines, forging connections between ongoing research programs to identify promising future directions.

I describe steps toward "deep lexical acquisition" based on naive theories, motivated by modern results of developmental psychology. I argue that today's machine learning paradigm is inappropriate to take these steps. Instead we must develop computational accounts of naive theory representations, mechanisms of theory acquisition, and the mapping of naive theories to lexicalizable concepts. This will enable our theories to describe the flexibility of the human conceptual apparatus.

The literature on concept development is highly contentious because there is a lot at stake. The processes that give rise to categories are at the very core of how we understand human cognition. In broad strokes, the debate is about whether categories reflect internal representations that are highly stable symbolic proposition-like and manipulated via logical operators or, whether they are probabilistic, context-dependent, and derived from bundles of correlated features and ordinary processes of perceiving and remembering (for reviews, see, Komatsu, 1992; Murphy & Medin, 1989; E. Smith, 1989; E. Smith & Medin, 1981. The literature appears to cycle through these two classes of accounts, advancing with each pass through but never quite leaving these two general points of view. Many of the contentious issues in the developmental literature on concepts and categories are variants of this debate. Accordingly, this review begins with a brief history of theories of categories. This is as history of back-and-forth transitions between a focus on more the more stable and the more probabilistic aspects of categories and it is a debate that is not resolved. However, by either view, categories result from internal representations that capture the structure in the world. Accordingly, the review of the developmental literature is organized with respect to recent advances in understanding outside-the-mind factors that organize and recruit the cognitive processes that create categories: the statistical regularities in the learning environment, the cognitive tasks and the nested time scales of the internal processes they recruit, and the body which is the interface between the external world and cognition. Back – and – forth theories. 2 Traditionally, categories are viewed as discrete bounded things that are stable over time and context. In this view, categories are enduringly real, object-like, truly out there in the world and also in our heads. Thus, theorists in this tradition write about categories being acquired, discovered, and possessed. The boundedness and stability expected of categories is well exemplified in the following quote from Keil (1994): Shared mental structures are assumed to be constant across repeated categorizations of the same set of instances and different from other categorizations. When I think