The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

After reviewing factors implicated in the generation of delusional beliefs, we conclude that whilst a perceptual aberration coupled with a particular type of attri‐butional bias may be necessary to explain the specific thematic content of a bizarre delusion, neither of these factors, whether in isolation or in combination, is sufficient to explain the presence of delusional beliefs. In contrast to bias models (theories which explain delusion formation in terms of extremes of normal reasoning biases), we advocate a deficit model of (...) delusion formation–that is, delusions arise when the normal cognitive system which people use to generate, evaluate, and then adopt beliefs is damaged. Mere bias we think inadequate to explain bizarre delusions which defy commonsense and persist despite overwhelming rational counter‐argument. In particular, we propose that two deficits must be present in the normal cognitive system to explain bizarre delusions: (1) there must be some damage to sensory and/or attentional‐orient‐ing mechanisms which causes an aberrant perception–this explains the bizarre content of the causal hypothesis generated to explain what is happening; and (2) there must also be a failure of normal belief evaluation–this explains why a hypothesis, implausible in the light of general commonsense, is adopted as belief. This latter deficit occurs, we suggest, when an individual is incapable of suspending the natural favoured status of direct first‐person evidence in order to critically evaluate hypotheses, given equal pri‐ority whether based on direct or indirect sources of information. In contrast, delusions with ‘ordinary’ content may arise when a single deficit of normal belief evaluation occurs in the context of an extreme (but normal) attentional bias, thus causing failure to critically evaluate hypotheses based on misperceptions and misintrepretations of ambiguous (but ordinary) first‐person experience. (shrink)

One of the most notable aspects of Fodor's work is that it articulates features not only of speculative cognitive architectures but also of current research in ...

Presupposing no familiarity with the technical concepts of either philosophy or computing, this clear introduction reviews the progress made in AI since the inception of the field in 1956. Copeland goes on to analyze what those working in AI must achieve before they can claim to have built a thinking machine and appraises their prospects of succeeding. There are clear introductions to connectionism and to the language of thought hypothesis which weave together material from philosophy, artificial intelligence and neuroscience. John (...) Searle's attacks on AI and cognitive science are countered and close attention is given to foundational issues, including the nature of computation, Turing Machines, the Church-Turing Thesis and the difference between classical symbol processing and parallel distributed processing. The book also explores the possibility of machines having free will and consciousness and concludes with a discussion of in what sense the human brain may be a computer. (shrink)

The theory of visual perception that Irvin Rock develops and supports in this book with numerous original experiments, views perception as the outcome of a process of unconscious inference, problem solving, and the building of structural descriptions of the external world.

Each of the chapters in this volume devotes considerable attention to defining and elaborating the notion of the frame problem-one of the hard problems of artificial intelligence. Not only do the chapters clarify the problems at hand, they shed light on the different approaches taken by those in artificial intelligence and by certain philosophers who have been concerned with related problems in their field. The book should therefore not be read merely as a discussion of the frame problem narrowly conceived, (...) but also as a general analysis of what could be a major challenge to the design of computer systems exhibiting general intelligence. (shrink)

Jerry Fodor argues against the widely held view that mental processes are largely computations, that the architecture of cognition is massively modular, and...

In this chapter I examine past and recent theories of unconscious inference. Most theorists have ascribed inferences to perception literally, not analogically, and I focus on the literal approach. I examine three problems faced by such theories if their commitment to unconscious inferences is taken seriously. Two problems concern the cognitive resources that must be available to the visual system (or a more central system) to support the inferences in question. The third problem focuses on how the conclusions of inferences (...) are supposed to explain the phenomenal aspects of visual experience, the looks of things. Finally, in comparing past and recent responses to these problems, I provide an assessment of the current prospects for inferential theories. (This paper is reprinted in Hatfield 2009, Perception and Cognition: Essays in the Philosophy of Psychology, Clarendon Press, 124-152.). (shrink)

This paper discusses abductive reasoning---that is, reasoning in which explanatory hypotheses are formed and evaluated. First, it criticizes two recent formal logical models of abduction. An adequate formalization would have to take into account the following aspects of abduction: explanation is not deduction; hypotheses are layered; abduction is sometimes creative; hypotheses may be revolutionary; completeness is elusive; simplicity is complex; and abductive reasoning may be visual and non-sentential. Second, in order to illustrate visual aspects of hypothesis formation, the paper describes (...) recent work on visual inference in archaeology. Third, in connection with the evaluation of explanatory hypotheses, the paper describes recent results on the computation of coherence. (shrink)