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For techniques diffusing as commodities, sales form a critical medium of technological communication which facilitates secondary invention. While sales may also influence the incentives to invent, I argue that the technical learning associated with selling provides a fuller account of invention for the case of the sewing machine in the United States. A study of some 3,500 patents and forty-eight city directories shows that as sales expanded in extent and location, so did patenting by first-time inventors. Moreover, patent use generated a flow of information back to inventors which increased the likelihood and extent of repeat patenting. In these ways, sales sustained technological change as a cumulative process.

ABSTRACTMachine learning methods are currently the object of considerable study by the artificial intelligence community. Research on machine learning carries implications for decision making in that it seeks computational methods that mimic input‐output behaviors found in classes of decision‐making examples. At the same time, research in statistics and econometrics has resulted in the development of qualitative‐response models that can be applied to the same kind of problems addressed by machine‐learning models—particularly those that involve a classification decision. This paper presents the theoretical structure of a generalized qualitative‐response model and compares its performance to two seminal machine‐learning models in two problem domains associated with audit decision making. The results suggest that the generalized qualitative‐response model may be a useful alternative for certain problem domains.

Differentiation of perceptual invariants is proposed as a theoretical approach to explain skill transfer for control at the human-machine interface. I propose that sensitivity to perceptual invariants is enhanced during learning and that this sensitivity forms the basis for transfer of skill from one task to another. The hypothesis implies that detection and discrimination of critical features, patterns, and dimensions of difference are important for learning and for transfer. This account goes beyond other similarity conceptions of transfer. To the extent that those conceptions are specific, they cannot account for effects in which performance is better following training on tasks that are less rather than more similar to the criterion task. In essence, this is a theory about the central role of low-dimensional informational patterns for control of behavior within a high-dimensional environment, and about the adjustment of an actor's sensitivity to changes in those low-dimensional patterns.