A Bayesian mind is, at its core, a rational mind. Bayesianism is thus well-suited to predict and explain mental processes that best exemplify our ability to be rational. However, evidence from belief acquisition and change appears to show that we do not acquire and update information in a Bayesian way. Instead, the principles of belief acquisition and updating seem grounded in maintaining a psychological immune system rather than in approximating a Bayesian processor.

A widely shared view in the cognitive sciences is that discovering and assessing explanations of cognitive phenomena whose production involves uncertainty should be done in a Bayesian framework. One assumption supporting this modelling choice is that Bayes provides the best approach for representing uncertainty. However, it is unclear that Bayes possesses special epistemic virtues over alternative modelling frameworks, since a systematic comparison has yet to be attempted. Currently, it is then premature to assert that cognitive phenomena involving uncertainty are best (...) explained within the Bayesian framework. As a forewarning, progress in cognitive science may be hindered if too many scientists continue to focus their efforts on Bayesian modelling, which risks to monopolize scientific resources that may be better allocated to alternative approaches. (shrink)

While Bayesian models have been applied to an impressive range of cognitive phenomena, methodological challenges have been leveled concerning their role in the program of rational analysis. The focus of the current article is on computational impediments to probabilistic inference and related puzzles about empirical confirmation of these models. The proposal is to rethink the role of Bayesian methods in rational analysis, to adopt an independently motivated notion of rationality appropriate for computationally bounded agents, and to explore broad conditions under (...) which Bayesian agents would be rational. The proposal is illustrated with a characterization of costs inspired by thermodynamics. (shrink)

Nonreductive physicalism faces serious problems regarding causal exclusion, causal heterogeneity, and the nature of realization. In this paper I advance solutions to each of those problems. The proposed solutions all depend crucially on embracing modal counterpart theory. Hence, the paper’s thesis: counterpart theory saves nonreductive physicalism. I take as my inspiration the view that mental tokens are constituted by physical tokens in the same way statues are constituted by lumps of clay. I break from other philosophers who have pursued this (...) line, however, in that I hold that constitution is identity. Much of the value of the comparison to statues and lumps is that it calls to mind the resources used to defend constitution-as-identity, most notably that of counterpart theory. Along the way, I discuss the virtues of a trope ontology, modal objections to token identity theories, the prospects of conditional analyses of causal powers, the subset account of realization, and the grounding problem. I also endorse a novel, empirical argument in favor of counterpart theory. (shrink)

A concern for individual differences has been missing from the Great Rationality Debate in cognitive science—the debate about how much irrationality to attribute to human cognition. There are individual differences in rational thinking that are less than perfectly correlated with individual differences in intelligence because intelligence and rationality occupy different conceptual locations in models of cognition. A tripartite extension of currently popular dual-process theories is presented in this chapter that illustrates how intelligence and rationality are theoretically separate concepts. The chapter (...) concludes by showing how this tripartite model of mind, taken in the context of studies of individual differences, can help to resolve the Great Rationality Debate. (shrink)

This article described three heuristics that are employed in making judgements under uncertainty: representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and adjustment from an anchor, which is usually employed in numerical prediction when a relevant value (...) is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty. (shrink)

Subjective probability plays an increasingly important role in many fields concerned with human cognition and behavior. Yet there have been significant criticisms of the idea that probabilities could actually be represented in the mind. This paper presents and elaborates a view of subjective probability as a kind of sampling propensity associated with internally represented generative models. The resulting view answers to some of the most well known criticisms of subjective probability, and is also supported by empirical work in neuroscience and (...) behavioral psychology. The repercussions of the view for how we conceive of many ordinary instances of subjective probability, and how it relates to more traditional conceptions of subjective probability, are discussed in some detail. (shrink)

It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces the phenomenon to be (...) explained, the kind of unification afforded by the Bayesian framework to cognitive science does not necessarily reveal aspects of a mechanism. Bayesian unification, nonetheless, can place fruitful constraints on causal–mechanical explanation. 1 Introduction2 What a Great Many Phenomena Bayesian Decision Theory Can Model3 The Case of Information Integration4 How Do Bayesian Models Unify?5 Bayesian Unification: What Constraints Are There on Mechanistic Explanation?5.1 Unification constrains mechanism discovery5.2 Unification constrains the identification of relevant mechanistic factors5.3 Unification constrains confirmation of competitive mechanistic models6 ConclusionAppendix. (shrink)

This is a survey of recent debates concerning the normativity of belief. We explain what the thesis that belief is normative involves, consider arguments for and against that thesis, and explore its bearing on debates in metaethics.

(2013). Why humans are (sometimes) less rational than other animals: Cognitive complexity and the axioms of rational choice. Thinking & Reasoning: Vol. 19, No. 1, pp. 1-26. doi: 10.1080/13546783.2012.713178.

I argue that an adequate account of non-reductive realization must guarantee satisfaction of a certain condition on the token causal powers associated with (instances of) realized and realizing entities---namely, what I call the 'Subset Condition on Causal Powers' (first introduced in Wilson 1999). In terms of states, the condition requires that the token powers had by a realized state on a given occasion be a proper subset of the token powers had by the state that realizes it on that occasion. (...) Accounts of non-reductive realization conforming to this condition are implementing what I call 'the powers-based subset strategy'. I focus on the crucial case involving mental and brain states; the results may be generalized, as appropriate. I first situate and motivate the strategy by attention to the problem of mental causation; I make the case, in schematic terms, that implementation of the strategy makes room (contra Kim 1989, 1993, 1998, and elsewhere) for mental states to be ontologically and causally autonomous from their realizing physical states, without inducing problematic causal overdetermination, and compatible with both Physicalism and Non-reduction; and I show that several contemporary accounts of non-reductive realization (in terms of functional realization, parthood, and the determinable/determinate relation) are plausibly seen as implementing the strategy. As I also show, implementation of the powers-based strategy does not require endorsement of any particular accounts of either properties or causation---indeed, a categoricalist contingentist Humean can implement the strategy. The schematic location of the strategy in the space of available responses to the problem of mental (more generally, higher-level) causation, as well as the fact that the schema may be metaphysically instantiated, strongly suggests that the strategy is, appropriately generalized and instantiated, sufficient and moreover necessary for non-reductive realization. I go on to defend the sufficiency and necessity claims against a variety of objections, considering, along the way, how the powers-based subset strategy fares against competing accounts of purportedly non-reductive realization in terms of supervenience, token identity, and constitution. (shrink)

Normativity involves two kinds of relation. On the one hand, there is the relation of being a reason for. This is a relation between a fact and an attitude. On the other hand, there are relations specified by requirements of rationality. These are relations among a person's attitudes, viewed in abstraction from the reasons for them. I ask how the normativity of rationality—the sense in which we ‘ought’ to comply with requirements of rationality—is related to the normativity of reasons—the sense (...) in which we ‘ought’ to have the attitudes what we have conclusive reason to have. The normativity of rationality is not straightforwardly that of reasons, I argue; there are no reasons to comply with rational requirements in general. First, this would lead to ‘bootstrapping’, because, contrary to the claims of John Broome, not all rational requirements have ‘wide scope’. Second, it is unclear what such reasons to be rational might be. Finally, we typically do not, and in many cases could not, treat rational requirements as reasons. Instead, I suggest, rationality is only apparently normative, and the normativity that it appears to have is that of reasons. According to this ‘Transparency Account’, rational requirements govern our responses to our beliefs about reasons. The normative ‘pressure’ that we feel, when rational requirements apply to us, derives from these beliefs: from the reasons that, as it seems to us, we have. (shrink)

Note: this is the first published presentation and defense of the 'proper subset strategy' for making sense of non-reductive physicalism or the associated notion of realization; this is sometimes, inaccurately, called "Shoemaker's subset strategy"; if people could either call it the 'subset strategy' or better yet, add my name to the mix I would appreciate it. Horgan claims that physicalism requires "superdupervenience" -- supervenience plus robust ontological explanation of the supervenient in terms of the base properties. I argue that Horgan's (...) account fails to rule out physically unacceptable emergence. I rather suggest that this and other unacceptable possibilities may be ruled out by requiring that each individual causal power in the set associated with a given supervenient property be numerically identical with a causal power in the set associated with its base property. I go on to show that a wide variety of physicalist accounts, both reductive and non-reductive, are implicitly or explicitly designed to meet this condition, and so are more similar than they seem. In particular, non-reductive physicalism accounts typically appeal to a relation plausibly ensuring that the powers of a higher-level property are a proper subset of those of its physical base property. (shrink)

What is Functionalism? Functionalism is one of the major proposals that have been offered as solutions to the mind/body problem. Solutions to the mind/body problem usually try to answer questions such as: What is the ultimate nature of the mental? At the most general level, what makes a mental state mental? Or more specifically, What do thoughts have in common in virtue of which they are thoughts? That is, what makes a thought a thought? What makes a pain a pain? (...) Cartesian Dualism said the ultimate nature of the mental was to be found in a special mental substance. Behaviorism identified mental states with behavioral dispositions; physicalism in its most influential version identifies mental states with brain states. Functionalism says that mental states are constituted by their causal relations to one another and to sensory inputs and behavioral outputs. Functionalism is one of the major theoretical developments of Twentieth Century analytic philosophy, and provides the conceptual underpinnings of much work in cognitive science. (shrink)

It is often said, metaphorically, that belief "aims" at the truth. This paper proposes a normative interpretation of this metaphor. First, the notion of "epistemic norms" is clarified, and reasons are given for the view that epistemic norms articulate essential features of the beliefs that are subject to them. Then it is argued that all epistemic norms--including those that specify when beliefs count as rational, and when they count as knowledge--are explained by a fundamental norm of correct belief, which requires (...) that, if one considers a proposition at all, one should believe it if and only if it is true. (shrink)

Are there really beliefs? Or are we learning (from neuroscience and psychology, presumably) that, strictly speaking, beliefs are figments of our imagination, items in a superceded ontology? Philosophers generally regard such ontological questions as admitting just two possible answers: either beliefs exist or they don't. There is no such state as quasi-existence; there are no stable doctrines of semi-realism. Beliefs must either be vindicated along with the viruses or banished along with the banshees. A bracing conviction prevails, then, to the (...) effect that when it comes to beliefs (and other mental items) one must be either a realist or an eliminative materialist. (shrink)

Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent burgeoning of theoretical developments and online (...) corpus creation has enabled large models to be tested, revealing probabilistic constraints in processing, undermining acquisition arguments based on a perceived poverty of the stimulus, and suggesting fruitful links with probabilistic theories of categorization and ambiguity resolution in perception. (shrink)

Bayesian psychology follows an old instrumentalist tradition most infamously illustrated by Osiander's preface to Copernicus's masterpiece. Jones & Love's (J&L's) criticisms are, if anything, understated, and their proposals overoptimistic.

Jaegwon Kim’s causal inheritance principle says that the causal powers of a mental property instance are identical with the causal powers of its particular physical realizer. Sydney Shoemaker’s subset account of realization is at odds with Kim’s principle: it says that a mental property instance has fewer causal powers than Kim’s principle entails. In this paper, I argue that the subset account should be rejected because it has intolerable consequences for mental causation, consequences that are avoided by accepting causal inheritance. (...) I develop my argument in part by drawing on one of the central debates that has arisen regarding the extended mind hypothesis defended by Andy Clark and David Chalmers. (shrink)

“Intentional content,” as I understand it, is whatever serves as the object of “propositional” attitude verbs, such as “think,” “judge,” “represent,” “prefer” (whether or not these objects are “propositions”). These verbs are standardly used to pick out the intentional states invoked to explain the states and behavior of people and many animals. I shall take the “normativity of the intentional,” or “Normativism,” to be the claim that any adequate theory of intentional states involves considerations of value not essentially involved in (...) the natural sciences. Thus, according to Normativism, whether or not someone thinks that fish sleep, or even can represent fish at all, depends upon making a judgment about the person’s goodness or rationality, of a sort that would not be involved in merely determining whether or not fish in fact sleep. (shrink)

The aim of this chapter is to defend the claim that “the intentional is normative” against a number of objections, including those that Georges Rey has presented in his contribution to this volume. First, I give a quick sketch of the principal argument that I have used to support this claim, and briefly comment on Rey’s criticisms of this argument. Next, I try to answer the main objections that have been raised against this claim. First, it may seem that the (...) claim that “the intentional is normative” is just hopelessly Panglossian: doesn’t this claim just wilfully ignore all the mountains of evidence that we have for the sheer ubiquity and pervasiveness of human irrationality? Secondly, the claim that intentional mental states are essentially normative seems to be intended as a purely philosophical, non-empirical account of the nature of these mental states: but why should we think that purely philosophical reflection can tell us anything interesting about the nature of the mind -- shouldn’t we look to empirical psychology to enlighten us about such matters? I argue that neither of these objections succeeds in undermining my version of the claim that "the intentional is normative". (shrink)

© 2017 Psychonomic Society, Inc.Cognitive biases, such as the anchoring bias, pose a serious challenge to rational accounts of human cognition. We investigate whether rational theories can meet this challenge by taking into account the mind’s bounded cognitive resources. We asked what reasoning under uncertainty would look like if people made rational use of their finite time and limited cognitive resources. To answer this question, we applied a mathematical theory of bounded rationality to the problem of numerical estimation. Our analysis (...) led to a rational process model that can be interpreted in terms of anchoring-and-adjustment. This model provided a unifying explanation for ten anchoring phenomena including the differential effect of accuracy motivation on the bias towards provided versus self-generated anchors. Our results illustrate the potential of resource-rational analysis to provide formal theories that can unify a wide range of empirical results and reconcile the impressive capacities of the human mind with its apparently irrational cognitive biases. (shrink)

There is currently a significant amount of interest in understanding and developing theories of realization. Naturally arguments have arisen about the adequacy of some theories over others. Many of these arguments have a point. But some can be resolved by seeing that the theories of realization in question are not genuine competitors because they fall under different conceptual traditions with different but compatible goals. I will first describe three different conceptual traditions of realization that are implicated by the arguments under (...) discussion. I will then examine the arguments, from an older complaint by Norman Malcolm against a familiar functional theory to a recent argument by Thomas Polger against an assortment of theories that traffic in inherited causal powers, showing how they can be resolved by situating the theories under their respective conceptual traditions. (shrink)

Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children recognizing relevant associations among events, and responding based on those associations. To examine this claim, in Experiments 1 and 2, children were introduced to a “blicket detector”, a machine that lit up and played music when certain objects were placed upon it. Children observed patterns of contingency between objects and the machine’s activation that (...) required them to use indirect evidence to make causal inferences. Critically, associative models either made no predictions, or made incorrect predictions about these inferences. In general, children were able to make these inferences, but some developmental differences between 3- and 4- year-olds were found. We suggest that children’s causal inferences are not based on recognizing associations, but rather that children develop a mechanism for Bayesian structure learning. Experiment 3 explicitly tests a prediction of this account. Children were asked to make an inference about ambiguous data based on the base-rate of certain events occurring. Fouryear-olds, but not 3-year-olds were able to make this inference. (shrink)

In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and standard assumptions about optimality: People often appear to make decisions based on just one or a few samples from the appropriate posterior probability distribution, rather than using the full distribution. Although sampling-based approximations are a common way to implement Bayesian (...) inference, the very limited numbers of samples often used by humans seem insufficient to approximate the required probability distributions very accurately. Here, we consider this discrepancy in the broader framework of statistical decision theory, and ask: If people are making decisions based on samples—but as samples are costly—how many samples should people use to optimize their total expected or worst-case reward over a large number of decisions? We find that under reasonable assumptions about the time costs of sampling, making many quick but locally suboptimal decisions based on very few samples may be the globally optimal strategy over long periods. These results help to reconcile a large body of work showing sampling-based or probability matching behavior with the hypothesis that human cognition can be understood in Bayesian terms, and they suggest promising future directions for studies of resource-constrained cognition. (shrink)