This paper addresses the apparent mismatch between the normative and descriptive literatures in the cognitive science of conditional reasoning. Descriptive psychological theories still regard material implication as the normative theory of the conditional. However, over the last 20 years in the philosophy of language and logic the idea that material implication can account for everyday indicative conditionals has been subject to severe criticism. The majority view is now apparently in favour of a subjective conditional probability interpretation. A comparative model fitting (...) exercise is presented that shows that a conditional probability model can explain as much of the data on abstract indicative conditional reasoning tasks as psychological theories that supplement material implication with various rationally unjustified processing assumptions. Consequently, when people are asked to solve laboratory reasoning tasks, they can be seen as simply generalising their everyday probabilistic reasoning strategies to this novel context. (shrink)

'The Probabilistic Mind' is a follow-up to the influential and highly cited 'Rational Models of Cognition'. It brings together developments in understanding how, and how far, high-level cognitive processes can be understood in rational terms, and particularly using probabilistic Bayesian methods.

Remarkable progress in the mathematics and computer science of probability has led to a revolution in the scope of probabilistic models. In particular, ‘sophisticated’ probabilistic methods apply to structured relational systems such as graphs and grammars, of immediate relevance to the cognitive sciences. This Special Issue outlines progress in this rapidly developing field, which provides a potentially unifying perspective across a wide range of domains and levels of explanation. Here, we introduce the historical and conceptual foundations of the approach, explore (...) how the approach relates to studies of explicit probabilistic reasoning, and give a brief overview of the field as it stands today. (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)

We present a theory of decision by sampling (DbS) in which, in contrast with traditional models, there are no underlying psychoeconomic scales. Instead, we assume that an attribute’s subjective value is constructed from a series of binary, ordinal comparisons to a sample of attribute values drawn from memory and is its rank within the sample. We assume that the sample reflects both the immediate distribution of attribute values from the current decision’s context and also the background, real-world distribution of attribute (...) values. DbS accounts for concave utility functions; losses looming larger than gains; hyperbolic temporal discounting; and the overestimation of small probabilities and the underestimation of large probabilities. Ó 2005 Elsevier Inc. All rights reserved. (shrink)

Much research on judgment and decision making has focussed on the adequacy of classical rationality as a description of human reasoning. But more recently it has been argued that classical rationality should also be rejected even as normative standards for human reasoning. For example, Gigerenzer and Goldstein and Gigerenzer and Todd argue that reasoning involves “fast and frugal” algorithms which are not justified by rational norms, but which succeed in the environment. They provide three lines of argument for this view, (...) based on: the importance of the environment; the existence of cognitive limitations; and the fact that an algorithm with no apparent rational basis, Take-the-Best, succeeds in an judgment task. We reconsider –, arguing that standard patterns of explanation in psychology and the social and biological sciences, use rational norms to explain why simple cognitive algorithms can succeed. We also present new computer simulations that compare Take-the-Best with other cognitive models. Although Take-the-Best still performs well, it does not perform noticeably better than the other models. We conclude that these results provide no strong reason to prefer Take-the-Best over alternative cognitive models. (shrink)

This interdisciplinary new work explores one of the central theoretical problems in linguistics: learnability. The authors, from different backgrounds---linguistics, philosophy, computer science, psychology and cognitive science-explore the idea that language acquisition proceeds through general purpose learning mechanisms, an approach that is broadly empiricist both methodologically and psychologically. Written by four researchers in the full range of relevant fields: linguistics, psychology, computer science, and cognitive science, the book sheds light on the central problems of learnability and language, and traces their implications (...) for key questions of theoretical linguistics and the study of language acquisition. (shrink)

Social interaction is both ubiquitous and central to understanding human behavior. Such interactions depend, we argue, on shared intentionality: the parties must form a common understanding of an ambiguous interaction (e.g., one person giving a present to another requires that both parties appreciate that a voluntary transfer of ownership is intended). Yet how can shared intentionality arise? Many well-known accounts of social cognition, including those involving “mind-reading,” typically fall into circularity and/or regress. For example, A’s beliefs and behavior may depend (...) on her prediction of B’s beliefs and behavior, but B’s beliefs and behavior depend in turn on her prediction of A’s beliefs and behavior. One possibility is to embrace circularity and take shared intentionality as imposing consistency conditions on beliefs and behavior, but typically there are many possible solutions and no clear criteria for choosing between them. We argue that addressing these challenges requires some form of we-reasoning, but that this raises the puzzle of how the collective agent (the “we”) arises from the individual agents. This puzzle can be solved by proposing that the will of the collective agent arises from a simulated process of bargaining: agents must infer what they would agree, were they able to communicate. This model explains how, and which, shared intentions are formed. We also propose that such “virtual bargaining” may be fundamental to understanding social interactions. (shrink)

An influential line of thinking in behavioral science, to which the two authors have long subscribed, is that many of society's most pressing problems can be addressed cheaply and effectively at the level of the individual, without modifying the system in which the individual operates. We now believe this was a mistake, along with, we suspect, many colleagues in both the academic and policy communities. Results from such interventions have been disappointingly modest. But more importantly, they have guided many (though (...) by no means all) behavioral scientists to frame policy problems in individual, not systemic, terms: To adopt what we call the “i-frame,” rather than the “s-frame.” The difference may be more consequential than i-frame advocates have realized, by deflecting attention and support away from s-frame policies. Indeed, highlighting the i-frame is a long-established objective of corporate opponents of concerted systemic action such as regulation and taxation. We illustrate our argument briefly for six policy problems, and in depth with the examples of climate change, obesity, retirement savings, and pollution from plastic waste. We argue that the most important way in which behavioral scientists can contribute to public policy is by employing their skills to develop and implement value-creating system-level change. (shrink)

Social interaction is both ubiquitous and central to understanding human behavior. Such interactions depend, we argue, on shared intentionality: the parties must form a common understanding of an ambiguous interaction. Yet how can shared intentionality arise? Many well-known accounts of social cognition, including those involving “mind-reading,” typically fall into circularity and/or regress. For example, A’s beliefs and behavior may depend on her prediction of B’s beliefs and behavior, but B’s beliefs and behavior depend in turn on her prediction of A’s (...) beliefs and behavior. One possibility is to embrace circularity and take shared intentionality as imposing consistency conditions on beliefs and behavior, but typically there are many possible solutions and no clear criteria for choosing between them. We argue that addressing these challenges requires some form of we-reasoning, but that this raises the puzzle of how the collective agent arises from the individual agents. This puzzle can be solved by proposing that the will of the collective agent arises from a simulated process of bargaining: agents must infer what they would agree, were they able to communicate. This model explains how, and which, shared intentions are formed. We also propose that such “virtual bargaining” may be fundamental to understanding social interactions. (shrink)

If Bayesian Fundamentalism existed, Jones & Love's (J&L's) arguments would provide a necessary corrective. But it does not. Bayesian cognitive science is deeply concerned with characterizing algorithms and representations, and, ultimately, implementations in neural circuits; it pays close attention to environmental structure and the constraints of behavioral data, when available; and it rigorously compares multiple models, both within and across papers. J&L's recommendation of Bayesian Enlightenment corresponds to past, present, and, we hope, future practice in Bayesian cognitive science.

British psychologists have been at the forefront of research into human reasoning for 40 years. This article describes some past research milestones within this tradition before outlining the major theoretical positions developed in the UK. Most British reasoning researchers have contributed to one or more of these positions. We identify a common theme that is emerging in all these approaches, that is, the problem of explaining how prior general knowledge affects reasoning. In our concluding comments we outline the challenges for (...) future research posed by this problem. (shrink)

Social interaction is both ubiquitous and central to understanding human behavior. Such interactions depend, we argue, on shared intentionality: the parties must form a common understanding of an ambiguous interaction (e.g., one person giving a present to another requires that both parties appreciate that a voluntary transfer of ownership is intended). Yet how can shared intentionality arise? Many well-known accounts of social cognition, including those involving “mind-reading,” typically fall into circularity and/or regress. For example, A’s beliefs and behavior may depend (...) on her prediction of B’s beliefs and behavior, but B’s beliefs and behavior depend in turn on her prediction of A’s beliefs and behavior. One possibility is to embrace circularity and take shared intentionality as imposing consistency conditions on beliefs and behavior, but typically there are many possible solutions and no clear criteria for choosing between them. We argue that addressing these challenges requires some form of we-reasoning, but that this raises the puzzle of how the collective agent (the “we”) arises from the individual agents. This puzzle can be solved by proposing that the will of the collective agent arises from a simulated process of bargaining: agents must infer what they would agree, were they able to communicate. This model explains how, and which, shared intentions are formed. We also propose that such “virtual bargaining” may be fundamental to understanding social interactions. (shrink)

Rational analysis (Anderson 1990, 1991a) is an empiricalprogram of attempting to explain why the cognitive system isadaptive, with respect to its goals and the structure of itsenvironment. We argue that rational analysis has two importantimplications for philosophical debate concerning rationality. First,rational analysis provides a model for the relationship betweenformal principles of rationality (such as probability or decisiontheory) and everyday rationality, in the sense of successfulthought and action in daily life. Second, applying the program ofrational analysis to research on human reasoning (...) leads to a radicalreinterpretation of empirical results which are typically viewed asdemonstrating human irrationality. (shrink)

Existing models of strategic decision making typically assume that only the attributes of the currently played game need be considered when reaching a decision. The results presented in this article demonstrate that the so-called “cooperativeness” of the previously played prisoner’s dilemma games influence choices and predictions in the current prisoner’s dilemma game, which suggests that games are not considered independently. These effects involved reinforcement-based assimilation to the previous choices and also a perceptual contrast of the present game with preceding games, (...) depending on the range and the rank of their cooperativeness. A. Parducci’s (1965) range frequency theory and H. Helson’s (1964) adaptation level theory are plausible theories of relative judgment of magnitude information, which could provide an account of these context effects. (shrink)

Judea Pearl has argued that counterfactuals and causality are central to intelligence, whether natural or artificial, and has helped create a rich mathematical and computational framework for formally analyzing causality. Here, we draw out connections between these notions and various current issues in cognitive science, including the nature of mental “programs” and mental representation. We argue that programs (consisting of algorithms and data structures) have a causal (counterfactual-supporting) structure; these counterfactuals can reveal the nature of mental representations. Programs can also (...) provide a causal model of the external world. Such models are, we suggest, ubiquitous in perception, cognition, and language processing. (shrink)

Recent research suggests that language evolution is a process of cultural change, in which linguistic structures are shaped through repeated cycles of learning and use by domain-general mechanisms. This paper draws out the implications of this viewpoint for understanding the problem of language acquisition, which is cast in a new, and much more tractable, form. In essence, the child faces a problem of induction, where the objective is to coordinate with others (C-induction), rather than to model the structure of the (...) natural world (N-induction). We argue that, of the two, C-induction is dramatically easier. More broadly, we argue that understanding the acquisition of any cultural form, whether linguistic or otherwise, during development, requires considering the corresponding question of how that cultural form arose through processes of cultural evolution. This perspective helps resolve the “logical” problem of language acquisition and has far-reaching implications for evolutionary psychology. (shrink)

This commentary focuses on three issues raised by Gigerenzer, Todd, and the ABC Research Group (1999). First, I stress the need for further experimental evidence to determine which heuristics people use in cognitive judgment tasks. Second, I question the scope of cognitive models based on simple heuristics, arguing that many aspects of cognition are too sophisticated to be modeled in this way. Third, I note the complementary role that rational explanation can play to Gigenerenzer et al.'s “ecological” analysis of why (...) heuristics succeed. (shrink)

The remarkable successes of the physical sciences have been built on highly general quantitative laws, which serve as the basis for understanding an enormous variety of specific physical systems. How far is it possible to construct universal principles in the cognitive sciences, in terms of which specific aspects of perception, memory, or decision making might be modelled? Following Shepard (e.g.,1987), it is argued that some universal principles may be attainable in cognitive science. Here, 2 examples are proposed: the simplicity principle (...) (which states that the cognitive system prefers patterns that provide simpler explanations of available data); and the scale‐invariance principle, which states that many cognitive phenomena are independent of the scale of relevant underlying physical variables, such as time, space, luminance, or sound pressure. This article illustrates how principles may be combined to explain specific cognitive processes by using these principles to derive SIMPLE, a formal model of memory for serial order (Brown, Neath, & Chater, 2007), and briefly mentions some extensions to models of identification and categorization. This article also considers the scope and limitations of universal laws in cognitive science. (shrink)

If human language must be squeezed through a narrow cognitive bottleneck, what are the implications for language processing, acquisition, change, and structure? In our target article, we suggested that the implications are far-reaching and form the basis of an integrated account of many apparently unconnected aspects of language and language processing, as well as suggesting revision of many existing theoretical accounts. With some exceptions, commentators were generally supportive both of the existence of the bottleneck and its potential implications. Many commentators (...) suggested additional theoretical and linguistic nuances and extensions, links with prior work, and relevant computational and neuroscientific considerations; some argued for related but distinct viewpoints; a few, though, felt traditional perspectives were being abandoned too readily. Our response attempts to build on the many suggestions raised by the commentators and to engage constructively with challenges to our approach. (shrink)

Mitchell et al. argue that conditioning phenomena may be better explained by high-level, rational processes, rather than by non-cognitive associative mechanisms. This commentary argues that this viewpoint is compatible with neuroscientific data, may extend to nonhuman animals, and casts computational models of reinforcement learning in a new light.

We agree with Heintz & Scott-Phillips that pragmatics does not supplement, but is prior to and underpins, language. Indeed, human non-linguistic communication is astonishingly rich, flexible, and subtle, as we illustrate through the game of charades, where people improvise communicative signals when linguistic channels are blocked. The route from non-linguistic charade-like communication to combinatorial language involves (1) local processes of conventionalization and grammaticalization and (2) spontaneous order arising from mutual constraints between different communicative signals.

Estimating the financial value of pain informs issues as diverse as the market price of analgesics, the cost-effectiveness of clinical treatments, compensation for injury, and the response to public hazards. Such costs are assumed to reflect a stable trade-off between relief of discomfort and money. Here, using an auction-based health market experiment, we show the price people pay for relief of pain is strongly determined by the local context of the market, determined either by recent intensities of pain, or their (...) immediately disposable income, but not overall wealth. The absence of a stable valuation metric suggests that the dynamic behaviour of health markets is not predictable from the static behaviour of individuals. We conclude that the results follow the dynamics of habit formation models of economic theory, and as such, the study provides the first scientific basis for this type of preference modelling. (shrink)

Cognitive science views thought as computation; and computation, by its very nature, can be understood in both rational and mechanistic terms. In rational terms, a computation solves some information processing problem (e.g., mapping sensory information into a description of the external world; parsing a sentence; selecting among a set of possible actions). In mechanistic terms, a computation corresponds to causal chain of events in a physical device (in engineering context, a silicon chip; in biological context, the nervous system). The discipline (...) is thus at the interface between two very different styles of explanation—as the papers in the current special issue well illustrate, it explores the interplay of rational and mechanistic forces. (shrink)