Responses are strengthened by consequences having to do with the survival of individuals and species. With respect to the provenance of behavior, we know more about ontogenic than phylogenic contingencies. The contingencies responsible for unlearned behavior acted long ago. This remoteness affects our scientific methods, both experimental and conceptual. Until we have identified he variables responsible for an event, we tend to invent causes. Explanatory entities such as “instincts,” “drives,” and “traits” still survive. Unable to show how organisms can behave (...) effectively under complex circumstances, we endow them with special abilities permitting them to do so.Behavior exhibited by most members of a species is often accepted as inherited if all members were not likely to have been exposed to relevant ontogenic contingencies. When contingencies are not obvious, it is perhaps unwise to call any behavior either inherited or acquired, as the examples of churring in honey guides and following in imprinted ducklings show. Nor can the relative importance of phylogenic and ontogenic contingencies be argued from instances in which unlearned or learned behavior intrudes or dominates. Intrusions occur in both directions.Behavior influenced by its consequences seems directed toward the future, but only past effects are relevant. The mere fact that behavior is adaptive does not indicate whether phylogenic or ontogenic processes have been responsible for it. Examples include the several possible provenances of imitation, aggression, and communication. The generality of such concepts limits their usefulness. A more specific analysis is needed if we are to deal effectively with the two kinds of contingencies and their products. (shrink)

This article responds to two unresolved and crucial problems of cognitive science: (1) What is actually accomplished by functions of the nervous system that we ordinarily describe in the intentional idiom? and (2) What makes the information processing involved in these functions semantic? It is argued that, contrary to the assumptions of many cognitive theorists, the computational approach does not provide coherent answers to these problems, and that a more promising start would be to fall back on mathematical communication theory (...) and, with the help of evolutionary biology and neurophysiology, to attempt a characterization of the adaptive processes involved in visual perception. Visual representations are explained as patterns of cortical activity that are enabled to focus on objects in the changing visual environment by constantly adjusting to maintain levels of mutual information between pattern and object that are adequate for continuing perceptual control. In these terms, the answer proposed to (1) is that the intentional functions of vision are those involved in the establishment and maintenance of such representations, and to (2) that semantic features are added to the information processes of vision with the focus on objects that these representations accomplish. The article concludes with proposals for extending this account of intentionality to the higher domains of conceptualization and reason, and with speculation about how semantic information-processing might be achieved in mechanical systems. (shrink)

Alan Turing’s influence on subsequent research in artificial intelligence is undeniable. His proposed test for intelligence remains influential. In this paper, I propose to analyze his conception of intelligence by relying on traditional close reading and language technology. The Turing test is interpreted as an instance of conceptual engineering that rejects the role of the previous linguistic usage, but appeals to intuition pumps instead. Even though many conceive his proposal as a prime case of operationalism, it is more plausibly viewed (...) as a stepping stone toward a future theoretical construal of intelligence in mechanical terms. To complete this picture, his own conceptual network is analyzed through the lens of distributional semantics over the corpus of his written work. As it turns out, Turing’s conceptual engineering of the notion of intelligence is indeed quite similar to providing a precising definition with the aim of revising the usage of the concept. However, that is not its ultimate aim: Turing is after a rich theoretical understanding of thinking in mechanical, i.e., computational, terms. (shrink)