Severity of Test (SoT) is an alternative to Popper's logical falsification that solves a number of problems of the logical view. It was presented by Popper himself in 1963. SoT is a less sophisticated probabilistic model of hypothesis testing than Oaksford & Chater's (O&C's) information gain model, but it has a number of striking similarities. Moreover, it captures the intuition of everyday hypothesis testing.

Human cognition requires coping with a complex and uncertain world. This suggests that dealing with uncertainty may be the central challenge for human reasoning. In Bayesian Rationality we argue that probability theory, the calculus of uncertainty, is the right framework in which to understand everyday reasoning. We also argue that probability theory explains behavior, even on experimental tasks that have been designed to probe people's logical reasoning abilities. Most commentators agree on the centrality of uncertainty; some suggest that there is (...) a residual role for logic in understanding reasoning; and others put forward alternative formalisms for uncertain reasoning, or raise specific technical, methodological, or empirical challenges. In responding to these points, we aim to clarify the scope and limits of probability and logic in cognitive science; explore the meaning of the explanation of cognition; and re-evaluate the empirical case for Bayesian rationality. (shrink)

Noam Chomsky claims that we know the grammatical principles of our languages in pretty much the same sense that we know ordinary things about the world (e.g. facts), a view about linguistic knowledge that I term ''cognitivism''. In much recent philosophy of linguistics (including that sympathetic to Chomsky's general approach to language), cognitivism has been rejected in favour of an account of grammatical competence as some or other form of mental mechanism, describable at various levels of abstraction (''non-cognitivism''). I argue (...) for cognitivism and against non-cognitivism. First, I show that the distinction between competence and performance in current linguistics is as clearly made as ever it was, in spite of recent interest in linguistic processing modules. Second, I use these facts about the practice of theoretical linguistics to refute various proposals for a non-cognitivist construal of grammatical competence, and to support cognitivism by reflecting on the inapplicability of a multi-level account of linguistic competence. Cognitivism is then defended against several objections centring around the problems of rational integration and conceptualization of grammatical knowledge. Finally, the conception of competence argued for in relation to linguistics is placed in the larger context of cognitive science research and its implications for philosophy of mind. (shrink)

Evolutionary psychology is widely understood as involving an integration of evolutionary theory and cognitive psychology, in which the former promises to revolutionise the latter. In this paper, I suggest some reasons to doubt that the assumptions of evolutionary theory and of cognitive psychology are as directly compatible as is widely assumed. These reasons relate to three different problems of specifying adaptive functions as the basis for characterising cognitive mechanisms: the disjunction problem, the grain problem and the environment problem. Each of (...) these problems can be understood as arising from incommensurate characterisations of the nature and role of 'the environment' in the two approaches. Purported solutions to the problems appear to require detailed information concerning the EEA (environment of evolutionary adaptedness), with the disjunction problem placing the lowest requirement, the environment problem placing the highest requirement, and the grain problem placing an intermediate one. In each case, such information is not likely to be forthcoming, because it may require iterating through successively more distant EEA's with no principled stopping point. This produces a dilemma for evolutionary psychology - either to solve these apparently insoluble problems, or to attempt to avoid them but in doing so forego detailed evolutionary constraints on cognition. (shrink)

The connection between idealizations, competence and multi-level explanations in cognitive psychology is discussed, in response to Patterson's ([1998]) reply to Franks ([1995]). I argue that idealizations are inherent in competence explanations and as a result, such explanations cannot be formulated in the multi-level terms widely used in the cognitive sciences. Patterson's argument was that neither competence nor performance involve idealizations, and, since they are separate 'systems', it is inappropriate to apply a single multi-level explanation to them. I suggest that there (...) is evidence that, although competence and performance are very often explicated in terms of levels of description, both none the less involve idealizations. However, I also suggest that Patterson's argument rests on confounding the demarcation of cognitive explanations with the demarcation of cognitive systems. Hence, even if competence and performance are different levels of a single system, questions concerning idealizations still arise when they are combined in an explanation. (shrink)

We consider approaches to explanation within the cognitive sciences that begin with Marr's computational level or Marr's implementational level and argue that each is subject to fundamental limitations which impair their ability to provide adequate explanations of cognitive phenomena. For this reason, it is argued, explanation cannot proceed at either level without tight coupling to the algorithmic and representation level. Even at this level, however, we argue that additional constraints relating to the decomposition of the cognitive system into a set (...) of interacting subfunctions are required. Integrated cognitive architectures that permit abstract specification of the functions of components and that make contact with the neural level provide a powerful bridge for linking the algorithmic and representational level to both the computational level and the implementational level. (shrink)

The Darwinian protolanguage hypothesis is one of the most popular theories of the evolution of human language. According to this hypothesis, language evolved through a three stage process involving general increases in intelligence, the emergence of grammatical structure as a result of sexual selection on protomusical songs, and finally the attachment of meaning to the components of those songs. The strongest evidence for the second stage of this process has been considered to be birdsong, and as a result researchers have (...) investigated the existence of various forms of grammar in the production and comprehension of songs by birds. Here, we argue that mating dances are another relevant source of sexually-selected complexity that has until now been largely overlooked by proponents of Darwinian protolanguage, focusing especially on the dances of long-tailed manakins. We end by sketching several lines of research that should be pursued to determine the relevance of mating dances to the evolution of language. (shrink)

In this paper, I describe grounds for dissatisfaction with certain aspects of the sciences of animal cognition and argue that a turn toward mathematical modeling of animal cognition is warranted. I consider some objections to this call and argue that the implications of such a turn are not as drastic for ordinary, commonsense understanding of animal minds as they might seem.

There are two distinct approaches to Bayesian modelling in cognitive science. Black-box approaches use Bayesian theory to model the relationship between the inputs and outputs of a cognitive system without reference to the mediating causal processes; while mechanistic approaches make claims about the neural mechanisms which generate the outputs from the inputs. This paper concerns the relationship between these two approaches. We argue that the dominant trend in the philosophical literature, which characterizes the relationship between black-box and mechanistic approaches to (...) Bayesian cognitive science in terms of the dichotomy between instrumentalism and realism, is misguided. We propose that the two distinctions are orthogonal: black-box and mechanistic approaches to Bayesian modelling can each be given either an instrumentalist or a realist interpretation. We argue that the current tendency to conflate black-box approaches with instrumentalism and mechanistic approaches with realism stems from unwarranted assumptions about the nature of scientific explanation, the ontological commitments of scientific theories, and the role of abstraction and idealization in scientific models. We challenge each of these assumptions to reframe the debates over Bayesian modelling in cognitive science. (shrink)

Theories of consciousness divide over whether perceptual consciousness is rich or sparse in specific representational content and whether it requires cognitive access. These two issues are often treated in tandem because of a shared assumption that the representational capacity of cognitive access is fairly limited. Recent research on working memory challenges this shared assumption. This paper argues that abandoning the assumption undermines post-cue-based “overflow” arguments, according to which perceptual conscious is rich and does not require cognitive access. Abandoning it also (...) dissociates the rich/sparse debate from the access question. The paper then explores attempts to reformulate overflow theses in ways that don’t require the assumption of limited capacity. Finally, it discusses the problem of relating seemingly non-probabilistic perceptual consciousness to the probabilistic representations posited by the models that challenge conceptions of cognitive access as capacity-limited. (shrink)