Robert Cummins

This chapter focuses on the critical applications of truism. Truism implies that the knowledge of the world is limited by representational, perceptual, and conceptual resources and that ideas are derived from perceptual or reflective experience. According to John Locke, George Berkeley, and David Hume, truism are ideas derived from sense and reflection and limited by concepts and percepts. The chapter concludes with a review of the experiment inspired by Immanuel Kant's _Aesthetic_ and perceptual application of the Cubic system in analyzing truism.

This chapter discusses the reasons for skepticism about the dominant massive innate modularity paradigm in relation to the learning-bias-and-canalization (LBC) framework. The formulation and analysis of LBC meets objections to massive modularity and questions about conceptual clarity and explanatory force, explaining the criticisms of evolutionary psychology for requiring massive innate modularity and for being conceptually vague. The chapter concludes with an analysis of the recent developments in LBC interactionist synthesis and the LBC framework.

This chapter focuses on the theory of cognition called computationalism, a philosophy introduced by John Haugeland which implies that the mind is an automatic formal system. It discusses the method of differences that responds to how something conscious can have the Nagel property as a result of exercising computational capacities. The method of differences build something whose only significant similarity to the human brain is computational architecture and see if a behaving system has the Nagel property or not.

This chapter outlines the semantic arguments for the incompatibility of connectionism and the Rationale Constraint, which are based on the idea that connectionist representations don't represent rationales that justify the system's cognitive capacities. It focuses on Paul Smolensky's issues in the philosophy of psychology, which claims that the Rationale Constraint is incompatible with connectionism in its most interesting form and that connectionism is a viable framework for the explanation of cognition. This chapter concludes with an analysis of the incompatibility thesis, which contains the semantic and computational arguments of the conflict between connectionism and the Rationale Constraint.

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’.

This chapter explains the argument of Jerry Fodor that the human scheme of mental representation exhibits a classical structure, implying that the systematicity of thought and language is best explained on the hypothesis that mental representation is classical. It discusses the idea of a classical representational scheme that provides structural representations of linguistic expressions and requires the tokening of constituents of a complex representation. The chapter concludes with Fodor's discussion of systematicity, which suggests that classical representation is structural and requires a complex proposition.

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 talks about the causal theory of mental content (CT), which is incompatible with an elementary fact of perceptual psychology called the nontransducibility of distal properties (NTDP). It focuses on two stages of arguments. The first stage implies that the conjunction of CT and the language of thought hypothesis (LOT) are conflicting with NTDP, while the second stage indicates that the acceptance of CT requires acceptance of LOT. Since distal properties are not transducible and the argument uses only a generic form of CT, CT is considered false in all forms.

This chapter discusses the deductive nomological (DN) model of explanation and ‘capacities’, as the two primary concepts of psychological explanation. The DN model, developed by Carl G. Hempel and Paul Oppenheim, implies that scientific explanation is subsumption under natural law and that explanation of scientific data requires appeal to certain hypothesis. On the other hand, capacities and their associated incidental effects are developed by appeal to a combination of functional analysis and realization, and formation of influential explanatory frameworks in psychology to generate scientific explanation.

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 begins by discussing the factors that are prominent in motivating interest in evolutionary approaches to cognition and reviews criticisms against some of the approaches. It is followed by the characterization of how evolutionary explanations of cognitive phenomena that appeal to innate modules are interpreted. The chapter concludes with an explanation of the relation between natural selection and cognitive development that is responsive to innate modules and compatible with an evolutionary description of specialized and independent cognitive mechanisms in adult organisms.

This chapter talks about Donald Davidson's article _Truth and Meaning_, which revolutionized the conception of how truth and meaning are related. In _Truth and Meaning_, Davidson put forward the bold conjecture that meanings are satisfaction conditions and that a Tarskian theory of truth for a language is a theory of meaning. Davidson proposed that the finite base of a Tarskian theory, together with familiar combinatorics, would explain how a language with unbounded expressive capacity could be learned with finite means.

This chapter focuses on John Haugeland's theory of intentionality and correlative theory of objectivity. It compares the points of agreement between Haugeland's theories and Robert Cummin's 1996 publication _Representations, Targets, and Attitudes_ (RTA). RTA distinguishes between the target a representation has on a given occasion of its application and takes representation deployment to be the business of intenders. On the other hand, Haugeland's theory of intentionality determines the target of a representation on a given occasion and specifies what targets are possible for a system to have.

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.

This chapter talks about inexplicit information, a form of information that exists in a system without benefit of any symbolic structure. It distinguishes and clarifies control-implicit information and domain-implicit information as the two types of inexplicit information. The control-implicit type describes information that is implicit in the ‘logic’ or ‘structure’ of the flow of control, while the domain-implicit type is information of a system that is lodged in the environment. The chapter concludes with a description of the explanatory role and program-executing features of the CHESS system.

This chapter analyses intentional realism and the philosophy of science, followed by a description of belief attributions. It focuses on Fred Dretske's theory, which implies that mental states get representational content during a learning period when mentality becomes perfect indicators of the properties they represent. Fred Dretske's theory runs the risk that no mental states have representational content compared to a deed, indicating that no mental state is a perfect indicator of states of affairs. The chapter concludes with a review of folk psychology, which explains the concepts and implications of folk psychology once proven true and explanatory.

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.

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.