Robert Charles Cummins

This chapter describes the prospects for a cognitive science of ‘meaning’ and ‘content’. It considers ‘meaning’ as a property of linguistic expressions or acts, while ‘content’ is described as a property of mental representations and indicator signals. The chapter concludes with explanations for reasons why it is dangerous to think of contents developed by representations and indicator signals as ‘meanings’. One reason suggests that a theory of content is a semantics for content, implying that representations and indicator signals required by cognitive science don't have logical forms and are not candidates for truth-conditional semantics.

This chapter discusses the debate over systematicity that concerns the formal conditions a scheme of mental representation must satisfy in order to explain the systematicity of thought. The systematicity of thought is assumed to be a pervasive property of minds and can be characterized as anyone who can think systematic variants of the same thought. One example of systematicity is where anyone who can think of the alleged fact that ‘John loves Mary’ can also think that ‘Mary loves John’, implying that the thought ‘Mary loves John’ is a systematic variant of the thought ‘John loves Mary’.

The pressure for reduction in science is an artifact of what we call the nomic conception of science (NCS): the idea that the content of science is a collection of laws, together with the deductive-nomological model of explanation. NCS in effect identifies explanation with reduction, thus making no room for the explanatory autonomy of function-analytical explanations. When we replace NCS with something more descriptively accurate, however, we find that the kind of explanatory autonomy of functional-analytic explanations is ubiquitous in the sciences. Key to showing this is a distinction between horizontal and vertical explanation. Horizontal explanations explain the capacities of a complex system by appeal to the design of the system. Vertical explanations, by contrast, explain how a design is implemented in a system. We argue that the distinction between horizontal and vertical explanations provides us with a better picture of the relationship between functional analysis and mechanistic explanation.

This chapter points out the difficulties of teleosemantics, such as its inability to account for unexploited content. It explains the basis behind the theory that any content adequate to ground representationalist theories in cognitive science must allow unexploited content and describes teleosemantic theories that cannot accommodate unexploited content. The chapter concludes with a discussion of the existence and importance of unexploited content that has been obscured by the failure to distinguish ‘representation’ from ‘indication’.

This chapter discusses the relation between ‘representation’ and ‘indication’, as the two kinds of mental content. ‘Representation’ is an element in a scheme of semantically individuated types whose tokens are structurally transformed by mental processes, while ‘indication’ is a distinction between the mechanism that does the detection and the process that indicates the target has been detected. The chapter concludes with an analysis of the differences between ‘representation’ and ‘indication’, such as ‘indication’ being transitive while ‘representation’ is not.

It is commonly supposed that evolutionary explanations of cognitive phenomena involve the assumption that the capacities to be explained are both innate and modular. This is understandable: independent selection of a trait requires that it be both heritable and largely decoupled from other ”nearby’ traits. Cognitive capacities realized as innate modules would certainly satisfy these contraints. A viable evolutionary cognitive psychology, however, requires neither extreme nativism nor modularity, though it is consistent with both. In this paper, we seek to show that rather weak assumptions about innateness and modularity are consistent with evolutionary explanations of cognitive capacities. Evolutionary pressures can affect the degree to which the development of a capacity is canalized by biasing acquisition/learning in ways that favor development of concepts and capacities that proved adaptive to an organism’s ancestors.

The thesis of this paper is that the causal theory of mental content (hereafter CT) is incompatible with an elementary fact of perceptual psychology, namely, that the detection of distal properties generally requires the mediation of a “theory.” I shall call this fact the nontransducibility of distal properties (hereafter NTDP). The argument proceeds in two stages. The burden of stage one is that, taken together, CT and the language of thought hypothesis (hereafter LOT) are incompatible with NTDP. The burden of stage two is that acceptance of CT requires acceptance of LOT as well. It follows that CT is incompatible with NTDP. I organize things in this way in part because it makes the argument easier to understand, and in part because the stage-two thesis—that CT entails LOT—has some independent interest and is therefore worth separating from the rest of the argument.

In exploring the nature of psychological explanation, this book looks at how psychologists theorize about the human ability to calculate, to speak a language and the like. It shows how good theorizing explains or tries to explain such abilities as perception and cognition. It recasts the familiar explanations of "intelligence" and "cognitive capacity" as put forward by philosophers such as Fodor, Dennett, and others in terms of a theory of explanation that makes established doctrine more intelligible to professionals and their students.In particular, the book shows that vestigial adherence to the positivists' D-N model has distorted the view of philosophers of science about what psychologists (and biologists) do and has masked the real nature of explanation. Major sections in the book cover Analysis and Subsumption; Functional Analysis; Understanding Cognitive Capacities; and Historical Reflections.Robert Cummins is Associate Professor of Philosophy at the University of Illinois, Chicago Circle. A Bradford Book.

In this paper, we introduce a novel difficulty for teleosemantics, viz., its inability to account for what we call unexploited content—content a representation has, but which the system that harbors it is currently unable to exploit. In section two, we give a characterization of teleosemantics. Since our critique does not depend on any special details that distinguish the variations in the literature, the characterization is broad, brief and abstract. In section three, we explain what we mean by unexploited content, and argue that any theory of content adequate to ground representationalist theories in cognitive science must allow for it.1 In section four, we show that teleosemantic theories of the sort we identify in section two cannot accommodate unexploited content, and are therefore unacceptable if intended as attempts to ground representationalist cognitive science. Finally, in section five, we speculate that the existence and importance of unexploited content has likely been obscured by a failure to distinguish representation from indication, and by a tendency to think of representation as reference.

I.1. Two reasons for studying inference. Inference is studied for two distinct reasons: for its bearing on justification and for its bearing on learning. By and large, philosophy has focused on the role of inference in justification, leaving its role in learning to psychology and artificial intelligence. This difference of role leads to a difference of conception. An inference based theory of learning does not require a conception of inference according to which a good inference is one that justifies its conclusion, whereas, obviously, an inference based theory of justification does require such a conception.1 Because of its focus on normative issues of justification, philosophy has taken a retrospective approach to inference, whereas a focus on learning naturally leads to a prospective approach. A focus on learning leads us to ask, "Given what is known, what should be inferred? How can what is known lead, via inference, to new knowledge?" A focus on justification has led philosophers to concentrate instead on a retrospective question: "Given a belief, can it be validly inferred from what is known? How can what is known justify, via inference, a new belief?" Thus, for philosophy, inference can be regarded as permissive: one needn't worry about what to infer, only about whether what has been arrived at somehow or other is or can be inferentially justified. A theory of learning, on the other hand, requires a conception of inference that is directive, for the problem of inference based learning is precisely the problem of what to infer.

The current debate over systematicity concerns the formal conditions a scheme of mental representation must satisfy in order to explain the systematicity of thought.1 The systematicity of thought is assumed to be a pervasive property of minds, and can be characterized (roughly) as follows: anyone who can think T can think systematic variants of T, where the systematic variants of T are found by permuting T’s constituents. So, for example, it is an alleged fact that anyone who can think the thought that John loves Mary can think the thought that Mary loves John, where the latter thought is a systematic variant of the former.

This paper considers two ways functions figure into scientific explanations: (i) via laws?events are causally explained by subsuming those events under functional laws; and (ii) via designs?capacities are explained by specifying the functional design of a system. We argue that a proper understanding of how functions figure into design explanations of capacities makes it clear why such functions are ill-suited to figure into functional-cum-causal law explanations of events, as those explanations are typically understood. We further argue that a proper understanding of how functions enter into design explanations of capacities enables us to show why two prominent objections to functionalism in the philosophy mind?the argument from metaphysically necessary effects (Bennett, 2007; Rupert, 2006) and causal exclusion (Kim, 1993, 1998; Malcolm, 1968)?are misguided when interpreted as posing a threat to functional explanation in science across the board. If those arguments pose a threat at all, they pose it to instances of (i); however, a great number of the functional explanations we find in psychology?and the sciences generally?are instances of (ii)

A viable evolutionary cognitive psychology requires that specific cognitive capacities be (a) heritable and (b) ‘quasi-independent’ from other heritable traits. They must be heritable because there can be no selection for traits that are not. They must be quasi-independent from other heritable traits, since adaptive variations in a specific cognitive capacity could have no distinctive consequences for fitness if effecting those variations required widespread changes in other unrelated traits and capacities as well. These requirements would be satisfied by innate cognitive modules, as the dominant paradigm in evolutionary cognitive psychology assumes. However, those requirements would also be satisfied by heritable learning biases, perhaps in the form of architec- tural or chronotopic constraints, that operated to increase the canalization of specific cognitive capacities in the ancestral environment (Cummins and Cummins 1999). As an organism develops, cognitive capacities that are highly canalized as the result of heritable learning biases might result in an organism that is behaviourally quite similar to an organism whose innate modules come on line as the result of various environ- mental triggers. Taking this possibility seriously is increasingly important as the case against innate cognitive modules becomes increasingly strong.

The Knowledge Argument of Frank Jackson has not persuaded physicalists, but their replies have not dispelled the intuition that someone raised in a black and white environment gains genuinely new knowledge when she sees colors for the first time. In what follows, we propose an explanation of this particular kind of knowledge gain that displays it as genuinely new, but orthogonal to both physicalism and phenomenology. We argue that Mary’s case is an instance of a common phenomenon in which something new is learned as the result of exploiting representational resources that were not previously exploited, and that this results in gaining genuinely new information.