Proper linear models are those in which predictor variables are given weights such that the resulting linear composite optimally predicts some criterion of interest; examples of proper linear models are standard regression analysis, discriminant function analysis, and ridge regression analysis. Research summarized in P. Meehl's book on clinical vs statistical prediction and research stimulated in part by that book indicate that when a numerical criterion variable is to be predicted from numerical predictor variables, proper linear models outperform clinical intuition. Improper (...) linear models are those in which the weights of the predictor variables are obtained by some nonoptimal method. The present article presents evidence that even such improper linear models are superior to clinical intuition when predicting a numerical criterion from numerical predictors. In fact, unit weighting is quite robust for making such predictions. The application of unit weights to decide what bullet the Denver Police Department should use is described; some technical, psychological, and ethical resistances to using linear models in making social decisions are considered; and arguments that could weaken these resistances are presented. (shrink)

Many have argued that early visual processing is encapsulated from the influence of higher-level goals, expectations, and knowledge of the world. Here we confront the main arguments offered in support of such a view, showing that they are unpersuasive. We also present evidence of top–down influences on early vision, emphasizing data from cognitive neuroscience. Our conclusion is that encapsulation is not a defining feature of visual processing. But we take this conclusion to be quite modest in scope, readily incorporated into (...) mainstream vision science. (shrink)

Computation is a process of making explicit, information that was implicit. In computing 5 as the solution to ∛125, for example, we move from a description that is not explicitly about 5 to one that is. We are drawing out numerical consequences to the description ∛125. We are extracting information implicit in the problem statement. Can we precisely state the difference between information thati s implicit in a state, structure or process and information that is explicit?

Abstract: According to the Veridicality Thesis, information requires truth. On this view, smoke carries information about there being a fire only if there is a fire, the proposition that the earth has two moons carries information about the earth having two moons only if the earth has two moons, and so on. We reject this Veridicality Thesis. We argue that the main notions of information used in cognitive science and computer science allow A to have information about the obtaining of (...) p even when p is false. (shrink)

Advocates of dynamical systems theory (DST) sometimes employ revolutionary rhetoric. In an attempt to clarify how DST models differ from others in cognitive science, I focus on two issues raised by DST: the role for representations in mental models and the conception of explanation invoked. Two features of representations are their role in standing-in for features external to the system and their format. DST advocates sometimes claim to have repudiated the need for stand-ins in DST models, but I argue that (...) they are mistaken. Nonetheless, DST does offer new ideas as to the format of representations employed in cognitive systems. With respect to explanation, I argue that some DST models are better seen as conforming to the covering-law conception of explanation than to the mechanistic conception of explanation implicit in most cognitive science research. But even here, I argue, DST models are a valuable complement to more mechanistic cognitive explanations. (shrink)

In this lucid portrayal of human behavior, Fred Dretske provides an original account of the way reasons function in the causal explanation of behavior.

A theory of information is developed in which the informational content of a signal (structure, event) can be specified. This content is expressed by a sentence describing the condition at a source on which the properties of a signal depend in some lawful way. Information, as so defined, though perfectly objective, has the kind of semantic property (intentionality) that seems to be needed for an analysis of cognition. Perceptual knowledge is an information-dependent internal state with a content corresponding to the (...) information producing it. This picture of knowledge captures most of what makes knowledge an important cpistcmological notion. It also avoids many of the problems infecting traditional justificational accounts of knowledge (knowledge as [justified, true belief those (unlike knowledge) having a content that can be either true or false (e.g., belief) – are described in terms of the way internal (presumably neural) structures acquire during learning a certain information-carrying role. The content of these structures (whether true or false) is identified with the kind of information they were developed to carry. (shrink)

Functional neuroimaging (NI) technologies like Positron Emission Tomography and functional Magnetic Resonance Imaging (fMRI) have revolutionized neuroscience, and provide crucial tools to link cognitive psychology and traditional neuroscientific models. A growing discipline of 'neurophilosophy' brings fMRI evidence to bear on traditional philosophical issues such as weakness of will, moral psychology, rational choice, social interaction, free will, and consciousness. NI has also attracted critical attention from psychologists and from philosophers of science. I review debates over the evidential status of fMRI, including (...) the differences between brain scans and ordinary images, the legitimacy of forward inference and reverse inference, and deductive versus probabilistic accounts of NI evidence. I conclude with a discussion of fMRI as exploratory rather than confirmatory evidence, linking this debate to the growing literature on cognitive ontology. (shrink)