Cognitive architectures have often been applied to data from individual experiments. In this paper, I develop an ACT-R reader that can model a much larger set of data, eye-tracking corpus data. It is shown that the resulting model has a good fit to the data for the considered low-level processes. Unlike previous related works, the model achieves the fit by estimating free parameters of ACT-R using Bayesian estimation and Markov-Chain Monte Carlo techniques, rather than by relying on the (...) mix of manual selection + default values. The method used in the paper is generalizable beyond this particular model and data set and could be used on other ACT-R models. (shrink)

The parameters governing our behavior are in constant flux. Accurately capturing these dynamics in cognitive models poses a challenge to modelers. Here, we demonstrate a mapping of ACT-R's declarative memory onto the linear ballistic accumulator (LBA), a mathematical model describing a competition between evidence accumulation processes. We show that this mapping provides a method for inferring individual ACT-R parameters without requiring the modeler to build and fit an entire ACT-R model. Existing parameter estimation methods for the LBA can (...) be used, instead of the computationally expensive parameter sweeps that are traditionally done. We conduct a parameter recovery study to confirm that the LBA can recover ACT-R parameters from simulated data. Then, as a proof of concept, we use the LBA to estimate ACT-R parameters from an empirical dataset. The resulting parameter estimates provide a cognitively meaningful explanation for observed differences in behavior over time and between individuals. In addition, we find that the mapping between ACT-R and LBA lends a more concrete interpretation to ACT-R's latency factor parameter, namely as a measure of response caution. This work contributes to a growing movement towards integrating formal modeling approaches in cognitive science. (shrink)

Model validation in computational cognitive psychology often relies on methods drawn from the testing of theories in experimental physics. However, applications of these methods to computational models in typical cognitive experiments can hide multiple, plausible sources of variation arising from human participants and from stochastic cognitive theories, encouraging a “model fixed, data variable” paradigm that makes it difficult to interpret model predictions and to account for individual differences. This article proposes a likelihood‐based, “data fixed, model variable” paradigm in which models (...) are treated as stochastic processes in experiments with participant‐to‐participant variation that can be applied to a broad range of mechanistic cognitive architectures. This article discusses the implementation and implications of this view in model validation, with a concrete focus on a simple class of ACT‐R models of cognition. This article is not intended as a recipe for broad application of these preliminary, proof‐of‐concept methods, but as a framework for communication between statisticians searching for interesting problems in the cognitive modeling sphere, and cognitive modelers interested in generalizing from deterministic to stochastic model validation, in the face of random variation in human experimental data. (shrink)

Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub‐tasks to work on in parallel. Underlying the human ability to collaborate is theory‐of‐mind (ToM), the ability to infer the hidden mental states that drive others to act. Here, we develop Bayesian Delegation, a decentralized multi‐agent learning mechanism with these abilities. Bayesian Delegation enables agents to rapidly infer the hidden intentions of others by (...) inverse planning. We test Bayesian Delegation in a suite of multi‐agent Markov decision processes inspired by cooking problems. On these tasks, agents with Bayesian Delegation coordinate both their high‐level plans (e.g., what sub‐task they should work on) and their low‐level actions (e.g., avoiding getting in each other's way). When matched with partners that act using the same algorithm, Bayesian Delegation outperforms alternatives. Bayesian Delegation is also a capable ad hoc collaborator and successfully coordinates with other agent types even in the absence of prior experience. Finally, in a behavioral experiment, we show that Bayesian Delegation makes inferences similar to human observers about the intent of others. Together, these results argue for the centrality of ToM for successful decentralized multi‐agent collaboration. (shrink)

This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress of (...) computational modeling in cognitive science. In this article, we investigate the capability and role of modern fitting methods—including Bayesian optimization and approximate Bayesian computation—and contrast them to some more commonly used methods: grid search and Nelder–Mead optimization. Our investigation consists of a reanalysis of the fitting of two previous computational models: an Adaptive Control of Thought—Rational model of skill acquisition and a computational rationality model of visual search. The results contrast the efficiency and informativeness of the methods. A key advantage of the Bayesian methods is the ability to estimate the uncertainty of fitted parameter values. We conclude that approximate Bayesian computation is (a) efficient, (b) informative, and (c) offers a path to reproducible results. (shrink)

Consistent with Barbey & Sloman (B&S), it is proposed that performance on Bayesian inference tasks is well explained by nested sets theory (NST). However, contrary to those authors' view, it is proposed that NST does better by dispelling with dual-systems assumptions. This article examines why, and sketches out a series of NST's core principles, which were not previously defined.

The parameters governing our behavior are in constant flux, and capturing these dynamics in cognitive models remains a challenge. We demonstrate how a mapping between ACT‐R's model of declarative memory and the linear ballistic accumulator enables efficient estimation of memory parameters from data. The resulting estimates provide a cognitively meaningful explanation for observed differences in behavior over time and between individuals.

Many clinical and psychological constructs are conceptualized to have multivariate higher-order constructs that give rise to multidimensional lower-order traits. Although recent measurement models and computing algorithms can accommodate item response data with a higher-order structure, there are few measurement models and computing techniques that can be employed in the context of complex research synthesis, such as meta-analysis of individual participant data or integrative data analysis. The current study was aimed at modeling complex item responses that can arise when underlying domain-specific, (...) lower-order traits are hierarchically related to multiple higher-order traits for individual participant data from multiple studies. We formulated a multi-group, multivariate higher-order item response theory (HO-IRT) model from a Bayesian perspective and developed a new Markov chain Monte Carlo (MCMC) algorithm to simultaneously estimate the (a) structural parameters of the first- and second-order latent traits across multiple groups and (b) item parameters of the model. Results from a simulation study support the feasibility of the MCMC algorithm. From the analysis of real data, we found that a bivariate HO-IRT model with different correlation/covariance structures for different studies fit the data best, compared to a univariate HO-IRT model or other alternate models with unreasonable assumptions (i.e., the same means and covariances across studies). Although more work is needed to further develop the method and to disseminate it, the multi-group multivariate HO-IRT model holds promise to derive a common metric for individual participant data from multiple studies in research synthesis studies for robust inference and for new discoveries. (shrink)

An experiment was conducted to test the effectiveness of brief instruction in information structuring (i.e., representing and integrating information) for improving the coherence of probability judgments and binary choices among intelligence analysts. Forty-three analysts were presented with comparable sets of Bayesian judgment problems before and immediately after instruction. After instruction, analysts’ probability judgments were more coherent (i.e., more additive and compliant with Bayes theorem). Instruction also improved the coherence of binary choices regarding category membership: after instruction, subjects were more (...) likely to invariably choose the category to which they assigned the higher probability of a target’s membership. The research provides a rare example of evidence-based validation of effectiveness in instruction to improve the statistical assessment skills of intelligence analysts. Such instruction could also be used to improve the assessment quality of other types of experts who are required to integrate statistical information or make probabilistic assessments. (shrink)

The paper provides a formal proof that efficient estimates of parameters, which vary as as little as possible when measurements are repeated, may be expected to provide more accurate predictions. The definition of predictive accuracy is motivated by the work of Akaike (1973). Surprisingly, the same explanation provides a novel solution for a well known problem for standard theories of scientific confirmation — the Ravens Paradox. This is significant in light of the fact that standard Bayesian analyses of (...) the paradox fail to account for the predictive utility of universal laws like All ravens are black. (shrink)

In this article, we consider estimation of the parameters of a generalized Pareto distribution and some lifetime indices such as those relating to reliability and hazard rate functions when the failure data are progressive first-failure censored. Both classical and Bayesian techniques are obtained. In the Bayesian framework, the point estimations of unknown parameters under both symmetric and asymmetric loss functions are discussed, after having been estimated using the conjugate gamma and discrete priors for the shape and (...) scale parameters, respectively. In addition, both exact and approximate confidence intervals as well as the exact confidence region for the estimators are constructed. A practical example using a simulated data set is analyzed. Finally, the performance of Bayes estimates is compared with that of maximum likelihood estimates through a Monte Carlo simulation study. (shrink)

In most decision-making situations, there is a plethora of information potentially available to people. Deciding what information to gather and what to ignore is no small feat. How do decision makers determine in what sequence to collect information and when to stop? In two experiments, we administered a version of the German cities task developed by Gigerenzer and Goldstein (1996), in which participants had to decide which of two cities had the larger population. Decision makers were not provided with the (...) names of the cities, but they were able to collect different kinds of cues for both response alternatives (e.g., “Does this city have a university?”) before making a decision. Our experiments differed in whether participants were free to determine the number of cues they examined. We demonstrate that a novel model, using hierarchical latent mixtures and Bayesian inference (Lee & Newell, 2011) provides a more complete description of the data from both experiments than simple conventional strategies, such as the take–the–best or the Weighted Additive heuristics. (shrink)

Meta-learning is even more broadly relevant to the study of inductive biases than Binz et al. suggest: Its implications go beyond the extensions to rational analysis that they discuss. One noteworthy example is that meta-learning can act as a bridge between the vector representations of neural networks and the symbolic hypothesis spaces used in many Bayesian models.

Definitions of syncopation share two characteristics: the presence of a meter or analogous hierarchical rhythmic structure and a displacement or contradiction of that structure. These attributes are translated in terms of a Bayesian theory of syncopation, where the syncopation of a rhythm is inferred based on a hierarchical structure that is, in turn, learned from the ongoing musical stimulus. Several experiments tested its simplest possible implementation, with equally weighted priors associated with different meters and independence of auditory events, which (...) can be decomposed into two terms representing note density and deviation from a metric hierarchy. A computational simulation demonstrated that extant measures of syncopation fall into two distinct factors analogous to the terms in the simple Bayesian model. Next, a series of behavioral experiments found that perceived syncopation is significantly related to both terms, offering support for the general Bayesian construction of syncopation. However, we also found that the prior expectations associated with different metric structures are not equal across meters and that there is an interaction between density and hierarchical deviation, implying that auditory events are not independent from each other. Together, these findings provide evidence that syncopation is a manifestation of a form of temporal expectation that can be directly represented in Bayesian terms and offer a complementary, feature‐driven approach to recent Bayesian models of temporal prediction. (shrink)

Bayesian paradigm has been widely acknowledged as a coherent approach to learning putative probability model structures from a finite class of candidate models. Bayesian learning is based on measuring the predictive ability of a model in terms of the corresponding marginal data distribution, which equals the expectation of the likelihood with respect to a prior distribution for model parameters. The main controversy related to this learning method stems from the necessity of specifying proper prior distributions for all (...) unknown parameters of a model, which ensures a complete determination of the marginal data distribution. Even for commonly used models, subjective priors may be difficult to specify precisely, and therefore, several automated learning procedures have been suggested in the literature. Here we introduce a novel Bayesian learning method based on the predictive entropy of a probability model, that can combine both subjective and objective probabilistic assessment of uncertain quantities in putative models. It is shown that our approach can avoid some of the limitations of the earlier suggested objective Bayesian methods. (shrink)

Infant language learners are faced with the difficult inductive problem of determining how new words map to novel or known objects in their environment. Bayesian inference models have been successful at using the sparse information available in natural child-directed speech to build candidate lexicons and infer speakers’ referential intentions. We begin by asking how a Bayesian model optimized for monolingual input generalizes to new monolingual or bilingual corpora and find that, especially in the case of the bilingual (...) input, the model shows a significant decrease in performance. In the next experiment, we propose the ME Model, a modified Bayesian model, which approximates infants’ mutual exclusivity bias to support the differential demands of monolingual and bilingual learning situations. The extended model is assessed using the same corpora of real child-directed speech, showing that its performance is more robust against varying input and less dependent than the Intentional Model on optimization of its parsimony parameter. We argue that both monolingual and bilingual demands on word learning are important considerations for a computational model, as they can yield significantly different results than when only one such context is considered. (shrink)

Some theorists, ranging from W. James to contemporary psychologists, have argued that forgetting is the key to proper functioning of memory. The authors elaborate on the notion of beneficial forgetting by proposing that loss of information aids inference heuristics that exploit mnemonic information. To this end, the authors bring together 2 research programs that take an ecological approach to studying cognition. Specifically, they implement fast and frugal heuristics within the ACT-R cognitive architecture. Simulations of the recognition heuristic, which relies (...) on systematic failures of recognition to infer which of 2 objects scores higher on a criterion value, demonstrate that forgetting can boost accuracy by increasing the chances that only 1 object is recognized. Simulations of the fluency heuristic, which arrives at the same inference on the basis of the speed with which objects are recognized, indicate that forgetting aids the discrimination between the objects' recognition speeds. (shrink)

Viewing the brain as an organ of approximate Bayesian inference can help us understand how it represents the self. We suggest that inferred representations of the self have a normative function: to predict and optimise the likely outcomes of social interactions. Technically, we cast this predict-and-optimise as maximising the chance of favourable outcomes through active inference. Here the utility of outcomes can be conceptualised as prior beliefs about final states. Actions based on interpersonal representations can therefore be (...) understood as minimising surprise – under the prior belief that one will end up in states with high utility. Interpersonal representations thus serve to render interactions more predictable, while the affective valence of interpersonal inference renders self-perception evaluative. Distortions of self-representation contribute to major psychiatric disorders such as depression, personality disorder and paranoia. The approach we review may therefore operationalise the study of interpersonal representations in pathological states. (shrink)

We criticise Shepard's notions of “invariance” and “universality,” and the incorporation of Shepard's work on inference into the general framework of his paper. We then criticise Tenenbaum and Griffiths' account of Shepard (1987b), including the attributed likelihood function, and the assumption of “weak sampling.” Finally, we endorse Barlow's suggestion that minimum message length (MML) theory has useful things to say about the Bayesian inference problems discussed by Shepard and Tenenbaum and Griffiths. [Barlow; Shepard; Tenenbaum & Griffiths].

Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a (...) single novel stimulus, and for stimuli that can be represented as points in a continuous metric psychological space. Here we recast Shepard's theory in a more general Bayesian framework and show how this naturally extends his approach to the more realistic situation of generalizing from multiple consequential stimuli with arbitrary representational structure. Our framework also subsumes a version of Tversky's set-theoretic model of similarity, which is conventionally thought of as the primary alternative to Shepard's continuous metric space model of similarity and generalization. This unification allows us not only to draw deep parallels between the set-theoretic and spatial approaches, but also to significantly advance the explanatory power of set-theoretic models. Key Words: additive clustering; Bayesian inference; categorization; concept learning; contrast model; features; generalization; psychological space; similarity. (shrink)

Many of the problems studied in cognitive science are inductive problems, requiring people to evaluate hypotheses in the light of data. The key to solving these problems successfully is having the right inductive biases—assumptions about the world that make it possible to choose between hypotheses that are equally consistent with the observed data. This article explores a novel experimental method for identifying the biases that guide human inductive inferences. The idea behind this method is simple: This article uses the responses (...) produced by a participant on one trial to generate the stimuli that either they or another participant will see on the next. A formal analysis of this “iterated learning” procedure, based on the assumption that the learners are Bayesian agents, predicts that it should reveal the inductive biases of these learners, as expressed in a prior probability distribution over hypotheses. This article presents a series of experiments using stimuli based on a well-studied set of category structures, demonstrating that iterated learning can be used to reveal the inductive biases of human learners. (shrink)

The study tests the hypothesis that conditional probability judgments can be influenced by causal links between the target event and the evidence even when the statistical relations among variables are held constant. Three experiments varied the causal structure relating three variables and found that (a) the target event was perceived as more probable when it was linked to evidence by a causal chain than when both variables shared a common cause; (b) predictive chains in which evidence is a cause of (...) the hypothesis gave rise to higher judgments than diagnostic chains in which evidence is an effect of the hypothesis; and (c) direct chains gave rise to higher judgments than indirect chains. A Bayesian learning model was applied to our data but failed to explain them. An explanation-based hypothesis stating that statistical information will affect judgments only to the extent that it changes beliefs about causal structure is consistent with the results. (shrink)

Although much human action serves as proof that irrational behavior is remarkably common, certain forms of irrationality--most notably, incontinent action and self-deception--pose such difficult theoretical problems that philosophers have rejected them as logically or psychologically impossible. Here, Mele shows that, and how, incontinent action and self-deception are indeed possible. Drawing upon recent experimental work in the psychology of action and inference, he advances naturalized explanations of akratic action and self-deception while resolving the paradoxes around which the philosophical literature revolves. (...) In addition, he defends an account of self-control, argues that "strict" akratic action is an insurmountable obstacle for traditional belief-desire models of action-explanation, and explains how a considerably modified model accommodates action of this sort. (shrink)

Can the output of human cognition be predicted from the assumption that it is an optimal response to the information-processing demands of the environment? A methodology called rational analysis is described for deriving predictions about cognitive phenomena using optimization assumptions. The predictions flow from the statistical structure of the environment and not the assumed structure of the mind. Bayesian inference is used, assuming that people start with a weak prior model of the world which they integrate with experience (...) to develop stronger models of specific aspects of the world. Cognitive performance maximizes the difference between the expected gain and cost of mental effort. Memory performance can be predicted on the assumption that retrieval seeks a maximal trade-off between the probability of finding the relevant memories and the effort required to do so; in categorization performance there is a similar trade-off between accuracy in predicting object features and the cost of hypothesis formation; in casual inference the trade-off is between accuracy in predicting future events and the cost of hypothesis formation; and in problem solving it is between the probability of achieving goals and the cost of both external and mental problem-solving search. The implemention of these rational prescriptions in neurally plausible architecture is also discussed. (shrink)

We introduce a graphical framework for Bayesian inference that is sufficiently general to accommodate not just the standard case but also recent proposals for a theory of quantum Bayesian inference wherein one considers density operators rather than probability distributions as representative of degrees of belief. The diagrammatic framework is stated in the graphical language of symmetric monoidal categories and of compact structures and Frobenius structures therein, in which Bayesian inversion boils down to transposition with respect (...) to an appropriate compact structure. We characterize classical Bayesian inference in terms of a graphical property and demonstrate that our approach eliminates some purely conventional elements that appear in common representations thereof, such as whether degrees of belief are represented by probabilities or entropie quantities. We also introduce a quantum-like calculus wherein the Frobenius structure is noncommutative and show that it can accommodate Leifer's calculus of 'conditional density operators'. The notion of conditional independence is also generalized to our graphical setting and we make some preliminary connections to the theory of Bayesian networks. Finally, we demonstrate how to construct a graphical Bayesian calculus within any dagger compact category. (shrink)

A large body of research in cognitive psychology and neuroscience draws on Bayesian statistics to model information processing within the brain. Many theorists have noted that this research seems to be in tension with a large body of experimental results purportedly documenting systematic deviations from Bayesian updating in human belief formation. In response, proponents of the Bayesian brain hypothesis contend that Bayesian models can accommodate such results by making suitable assumptions about model parameters. To make (...) progress in this debate, I argue that it is fruitful to focus not on specific experimental results but rather on what I call the ‘sources of epistemic irrationality’ in human cognition. I identify four such sources and I explore whether and, if so, how Bayesian models can be reconciled with them: processing costs, evolutionary suboptimality, motivated cognition, and error management. 1 Introduction 2 The Bayesian Brain 3 The Problem of Epistemic Irrationality 3.1 Bayesian inference and rationality 3.2 Intuitive Bayesian inference 4 Sources of Epistemic Irrationality 4.1 Processing costs 4.2 Evolutionary suboptimality 4.3 Motivational influences 4.4 Error management 5 Conclusion. (shrink)

The plausibility of uncommon events and miracles based on testimony of such an event has been much discussed. When analyzing the probabilities involved, it has mostly been assumed that the common events can be taken as data in the calculations. However, we usually have only testimonies for the common events. While this difference does not have a significant effect on the inductive part of the inference, it has a large influence on how one should view the reliability of testimonies. (...) In this work, a full Bayesian solution is given for the more realistic case, where one has a large number of testimonies for a common event and one testimony for an uncommon event. A free-running parameter is given for the unreliability of testimony, to be determined from data. It is seen that, in order for there to be a large amount of testimonies for a common event, the testimonies will probably be quite reliable. For this reason, because the testimonies are quite reliable based on the testimonies for the common events, the probability for the uncommon event, given a testimony for it, is also higher. Perhaps surprisingly, in the simple case, the increase in plausibility from testimony for the uncommon events is of the same magnitude as the decrease in plausibility from induction. In summary, one should be more open-minded when considering the plausibility of uncommon events. (shrink)

The theory of personal probability needs to be developed as a logic of credibility in order to provide an adequate basis for the theories of scientific inference and rational decision making. But standard systems of personal probability impose formal structures on probability relationships which are too restrictive to qualify them as logics of credibility. Moreover, some rules for conditional probability have no justification as principles of credibility. A formal system of qualitative probability which is free of these defects and (...) which leads to a presuppositionless representation theorem is sketched. (shrink)

Epistemic logic with its possible worlds semantic model is a powerful framework that allows us to represent an agent’s information not only about propositional facts, but also about her own information. Nevertheless, agents represented in this framework are logically omniscient: their information is closed under logical consequence. This property, useful in some applications, is an unrealistic idealisation in some others. Many proposals to solve this problem focus on weakening the properties of the agent’s information, but some authors have argued that (...) solutions of this kind are not completely adequate because they do not look at the heart of the matter: the actions that allow the agent to reach such omniscient state. Recent works have explored how acts of observation, inference, consideration and forgetting affect an agent’s implicit and explicit knowledge; the present work focuses on acts that affect an agent’s implicit and explicit beliefs. It starts by proposing a framework in which these two notions can be represented, and then it looks into their dynamics, first by reviewing the existing notion of belief revision, and then by introducing a rich framework for representing diverse forms of inference that involve both knowledge and beliefs. (shrink)

A research program is announced, and initial, exciting progress described. Many inference problems, poorly modeled by some traditional approaches, are surprisingly well handled by kinds of simple-minded Bayesian approximations. Fuller Bayesian approaches are typically more accurate but rarely are they either fast or frugal. Open issues include codifying when to use which heuristic and to give detailed evolutionary explanations.

In lieu of an abstract, here is a brief excerpt of the content:Vice and Naturalistic OntologyChristopher R. Williams (bio)Keywordscausality, criminality, determinism, medical model, positivismThese questions have been posed: Is vice (encompassing criminal and other wrongful conduct) best regarded as “sick” behavior, “immoral” behavior, or some other type altogether? Are we to understand vice in natural-medical terms, or are we better served by utilizing a moral framework? Is criminality reducible to and best categorized as a metaphysical type the essential features of (...) which are neurological or otherwise biological in nature? Or, perhaps, is criminality closer to a (non-natural) sociological type, the essential features of which can be understood only through reference to social norms and cultural conventions? These are torturously involved philosophical and theoretical questions that have given rise to an equally torturous volume of literature. Without pretending to do justice to these questions or their corresponding literature, several key considerations that must be inserted into and accounted for within the discursive pursuit of these inquiries are worth noting.Where a naturalistic ontology is employed within the study of vice, we reduce the intricacies of deviant behavioral phenomena to what we presume to be their underlying medical/biological properties. It is worth remembering that behavior itself cannot rightfully be said to be “sick”; rather, what we mean to say is that the behavior in question—in our case, various forms of “vice”—is symptomatic of some underlying illness or medical/biological condition. The presumed underlying sickness and the behavior itself are distinct, ontologically linked only through their theorized relationship to one another. This, then, is a key assumption of the medical approach to vice and its ontological classification as a natural-medical kind: that there exists a relationship between the behavior (a symptom) and the underlying illness or medical condition, and that the relationship is of the causal kind. Vice, it is suggested, is a change in state that is caused by (or, at the very least, is a direct consequence of) sickness or illness. Second, to legitimate such attributions, the illness supplying the requisite causal force must be identifiable and its causal connection to the behavior must be or be capable of being sufficiently explicated. In other words, once we have identified the relevant medical/biological properties and the behavior to which they stand in causal relatedness, we must be prepared to characterize this dynamic—we must be able to show exactly how and why such and such a condition gives rise to such and such a behavior.Problematically, causation itself is and has always been a contentious metaphysical specimen. Hume (1955) reminded us of the troubling reality [End Page 39] that we cannot ever “see” one thing cause or necessitate another. We are left, rather, to merely presume the existence of causal relationships between things. How is it, exactly, that disorder in neurodevelopment can necessitate certain behavioral outcomes? Even if we can “see” (in the empirical sense) the disorder and equally “see” the behavior, we cannot of course witness the precise causal action of the former upon the latter. Even if we are content inferring causal action in such cases, we still need to demonstrate the basic conditions of causality. To show that Condition X is causally related Behavior Y, we need to show that they are regularly associated with one another, that Condition X temporally preceded Behavior Y and, most challengingly, that no other conditions or factors have an intervening (e.g., additive, synergistic) influence. Hypothetically, if we were to posit a causal relationship between schizotypal personality traits and violent behavior, we would need to establish: (1) that schizotypal traits and violent behavior regularly coexist; (2) that those traits presented before the manifestation of the violent behavior; and (3) that no other factors (biological, psychological, and/or sociological) contributed in any meaningful way to those manifestations. We would thus need to systematically rule out family dynamics, income, education, intelligence, substance use, and so forth, as contributing factors. If, in turn, we were to determine that the presence of schizotypal traits alone is not sufficient—even if a necessary condition—to produce violent behavior but, rather, that those traits act in concert with other variables to produce the behavior, then we would not truly be... (shrink)

An influential argument is that mental processes can be explained at three different levels of analysis: the functional, algorithmic, and implementation level. Identity attribution (the process whereby an identity is attributed to another individual or to the self) has been rarely explored at the functional level. To address this, here I propose a theory of identity attribution grounded on Bayesian inference, being the latter a well-established functional perspective in cognitive science. The theory posits that an identity is inferred (...) based on observations about a target’s features, about the context, and about motivational factors. This inference can be made based upon multiple sources of observations, with prior beliefs becoming more prominent when observations are fewer in number. The theory offers an interpretation of key processes driving identity attribution, potentially providing a platform for integrating different perspectives on identity in psychology and sociology. (shrink)

Scientists often study hypotheses that they know to be false. This creates an interpretive problem for Bayesians because the probability assigned to a hypothesis is typically interpreted as the probability that the hypothesis is true. I argue that solving the interpretive problem requires coming up with a new semantics for Bayesian inference. I present and contrast two new semantic frameworks, and I argue that both of them support the claim that there is pervasive pragmatic encroachment on whether a (...) given Bayesian probability assignment is rational. (shrink)

The browser wars case is a useful springboard for considering the principle of positive competition and the proper regulation of platform technologies. There are lessons to be culled about policy, the application of antitrust law, and the parameters of fair competition. We argue that despite Microsofts opportunistic exploitation of its proprietary code, policy makers should resist the temptation to mandate an open source code model. Vigilant anti-trust enforcement is a preferable alternative. But courts must refrain from using antitrust law (...) to favor small innovators or to constrain the economic protections inherent in intellectual property rights, since those rights are still needed to encourage innovation. On the other hand, despite the exigencies of competition, platform leaders should respect the norm of fair competition and refrain from the impulse to tilt the platform in their favor. Preservation of robust intellectual property rights, deft application of antitrust statutes, and principled competition represent the best prescription for social wealth in the New Economy. (shrink)

The full Bayesian signi/cance test (FBST) for precise hypotheses is presented, with some illustrative applications. In the FBST we compute the evidence against the precise hypothesis. We discuss some of the theoretical properties of the FBST, and provide an invariant formulation for coordinate transformations, provided a reference density has been established. This evidence is the probability of the highest relative surprise set, “tangential” to the sub-manifold (of the parameter space) that defines the null hypothesis.

In recent years, Bayesian probability theory has emerged not only as a powerful tool for building computational theories of vision, but also as a general paradigm for studying human visual perception. This book provides an introduction to and critical analysis of the Bayesian paradigm. Leading researchers in computer vision and experimental vision science describe general theoretical frameworks for modeling vision, detailed applications to specific problems and implications for experimental studies of human perception. The book provides a dialogue between (...) different perspectives both within chapters, which draw on insights from experimental and computational work, and between chapters, through commentaries written by the contributors on each other's work. Students and researchers in cognitive and visual science will find much to interest them in this thought-provoking collection. (shrink)

The question posed by Dunn and Kirsner (D&K) is an instance of a more general one: What can we infer from data? One answer, if we are talking about logically valid deductive inference, is that we cannot infer theories from data. A theory is supposed to explain the data and so cannot be a mere summary of the data to be explained. The truth of an explanatory theory goes beyond the data and so is never logically guaranteed by the (...) data. This is not just a point about cognitive neuropsychology, or even about psychology in general. It is a familiar point about all science. (shrink)

Cognitive approaches contribute to our understanding of delusions by providing an explanatory framework that extends beyond the personal level to the sub personal level of information-processing systems. According to one influential cognitive approach, two factors are required to account for the content of a delusion, its initial adoption as a belief, and its persistence. This chapter reviews Bayesian developments of the two-factor framework.

This volume is a revival and updating of the rationalism initiated by the Cartesian cogito. Even the four main divisions of the work give evidence of this: Perception, the Real World, Real Mind, and the Suprarational. The order of treatment is not identical in every respect with that of Descartes, but the four main themes are indubitably Cartesian. While the protagonist is Descartes, the antagonist to whom this volume is consciously addressed is the empiricist and the positivist. Professor Robinson seems (...) intent on convincing the contemporary positivist that science is primarily an affair of reason and secondarily one of experience. He enlists two formidable allies of Descartes to elucidate and solidify his position. One he finds in the same century as Descartes, the redoubtable Leibniz; the other he takes from the twentieth century, Bertrand Russell. The problem of perception is resolved by the joint solution of Leibniz and Russell, a solution which rejects a naive realism in favor of a metarealism. The singular attraction of this volume is the personal dedication and expertise which Professor Robinson brings to this subject. His exploration and defense of his rationalistic premise brings him to examine epistemic issues and paradoxes which have cast long shadows over the history of modern and contemporary philosophy in Western civilization. These are the issues with which the inheritors of the Cartesian cogito have been wrestling: Spinoza, Leibniz, Locke, Berkeley, Hume, and Kant. Another very attractive aspect of this volume is that whereas Professor Robinson’s thesis has its protagonist and antagonist, it does not explicitly address itself to these. They remain more in the background as the audience to whom the book is addressed. They are not called on explicitly to engage in the exposition. In this sense, the book is not presented as a professorial treatise with copious footnotes and an extensive bibliography. It is presented as a personal exposition engaging in the act of philosophizing within the letter and spirit of a rationalistic revival. In spite of this personal and personalized renewal of rationalism on the part of Professor Robinson, it should not be construed that the book is written either for the layman or by a layman. It is a methodical and technical exposition replete with the coinage of new terms befitting the spirit of a philosophy operating within the parameters of a self-enclosed and self-contained scientism. Professor Robinson presents his book as a viable alternative to the monopoly which the empiricist and positivist have exercised within the domain of the philosophy of science.—R.E.D. (shrink)

Probability updating via Bayes' rule often entails extensive informational and computational requirements. In consequence, relatively few practical applications of Bayesian adaptive control techniques have been attempted. This paper discusses an alternative approach to adaptive control, Bayesian in spirit, which shifts attention from the updating of probability distributions via transitional probability assessments to the direct updating of the criterion function, itself, via transitional utility assessments. Results are illustrated in terms of an adaptive reinvestment two-armed bandit problem.

The discovery of conjunctive causes--factors that act in concert to produce or prevent an effect--has been explained by purely covariational theories. Such theories assume that concomitant variations in observable events directly license causal inferences, without postulating the existence of unobservable causal relations. This article discusses problems with these theories, proposes a causal-power theory that overcomes the problems, and reports empirical evidence favoring the new theory. Unlike earlier models, the new theory derives (a) the conditions under which covariation implies conjunctive causation (...) and (b) functions relating observable events to unobservable conjunctive causal strength. This psychological theory, which concerns simple cases involving 2 binary candidate causes and a binary effect, raises questions about normative statistics for testing causal hypotheses regarding categorical data resulting from discrete variables. (shrink)

We are often justified in acting on the basis of evidential confirmation. I argue that such evidence supports belief in non-quantificational generic generalizations, rather than universally quantified generalizations. I show how this account supports, rather than undermines, a Bayesian account of confirmation. Induction from confirming instances of a generalization to belief in the corresponding generic is part of a reasoning instinct that is typically (but not always) correct, and allows us to approximate the predictions that formal epistemology would make.

Cognitive approaches contribute to our understanding of delusions by providing an explanatory framework that extends beyond the personal level to the sub personal level of information-processing systems. According to one influential cognitive approach, two factors are required to account for the content of a delusion, its initial adoption as a belief, and its persistence. This chapter reviews Bayesian developments of the two-factor framework.

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:Empty Words: Buddhist Philosophy and Cross-Cultural InterpretationEdward R. FallsEmpty Words: Buddhist Philosophy and Cross-Cultural Interpretation. By Jay L. Garfield. Oxford and New York: Oxford University Press, 2002. 306 + xi pp.Jay L. Garfield's Empty Words is a collection of (mostly) previously published essays bearing on the interpretation of Buddhist thought. Emphasizing the Indo-Tibetan tradition while indebted to Euro-American philosophy, Empty Words belongs in a class with books such (...) as Tom Tillemans's Scripture, Logic and Language: Essays on Dharmakīrti and His Tibetan Successors (1999) and Matthew Kapstein's Reason's Traces: Identity and Interpretation in Indian and Tibetan Buddhist Thought (2001). Below is a précis of Empty Words, followed by remarks on some points that especially caught this reviewer's interest.The essays in Empty Words are gathered into three sections according to subject matter: (1) Madhyamaka, (2) Yogācāra, and (3) Ethics and Hermeneutics. There are five essays in section 1, clustered around three themes: Nāgārjuna's theory of causality (ch. 2, 4), parallels between Madhyamaka and the European skeptical tradition (ch. 1), and paradox in Madhyamaka (ch. 3, 5).Section 2 begins with an essay interpreting Vasubandhu on the three natures and three naturelessnesses, arguing that "Vasubandhu subordinates the naturelessnesses [End Page 196] to the natures pedagogically and describes them as characteristics of, but not (with the exception of the third) identical to the three natures" (p. 121). As for the other three essays in section 2, chapter 7 consists of a translation of and commentary on Vasubandhu's Trisvabhāvanirdes´a, chapter 8 presents an interesting experiment in cross-cultural interpretation in which the history of European idealism is viewed through a Buddhist idealist lens, and chapter 9 (one of the two previously unpublished essays in Empty Words—ch. 14 is the other) attempts to bring together Garfield's views on the limits of expressibility in Madhyamaka and his interpretation of the three natures in Yogācāra.Section 3 includes three essays concerning issues in ethics or political philosophy and two essays discussing the hermeneutical issues involved when Euro-American and Tibetan philosophers attempt to understand each other's traditions. The topics of the former essays are as follows. Is the Dalai Lama's advocacy of human rights (which are grounded in liberal moral theory) compatible with Buddhist ethics, which emphasizes a compassion-based morality (ch. 10)? Are Buddhist values and democracy incompatible (ch. 11)? How does Samdhong Rinpoche's approach to nonviolence relate to Gandhi's (ch. 12)? In the final two essays of the book, Garfield advocates a strategy of cross-cultural interpretation which would avoid the pitfall of orientalism, drawing heavily on Gadamerian-Heideggerian hermeneutics.The remarks that follow bear exclusively on points from essays in section 1 on Madhyamaka. In chapter 4, Garfield critiques the claim, attributed to Dharmakīrti (by rGyal tshab in his commentary on the pramānasiddhi chapter of Pramānavarttika) that belief in rebirth is necessary for the cultivation of bodhicitta. Garfield argues that if this claim is correct, then the "I" in the bodhisattva vow is doing real metaphysical work, functioning as the basis for a real causal relation, contra Nāgārjuna's analysis of causality (which is supposed to involve the rejection of real causal powers). Otherwise, a simpler account of bodhicitta would suffice, in which striving for personal enlightenment could be replaced by striving for someone's attaining enlightenment some time in the future. On this account, practitioners would resolve to contribute to the accumulation of causes of someone's eventually attaining enlightenment, where this accumulation of causes turns out to be something like "universal moral improvement."But what if practitioners just do not know how to bring about universal moral improvement? Garfield acknowledges that buddhas sometimes act counter to the "moral" good, and this suggests an important cognitive difference between buddhas and ordinary beings: ordinary beings can only (fallibly) infer what the best thing to do is, whereas buddhas simply know. Hence, the importance, from a practitioner's standpoint, of the Buddha's teaching, which is a path of personal attainment (and who knows whether it is incidentally a... (shrink)

I argue that if deliberation is incompatible with (fore)knowing what one is going to do at the time of the deliberation, then God cannot deliberate. However, this thesis cannot be used to show either that God cannot act intentionally or that human persons cannot deliberate. Further, I have suggested that though omniscience is incompatible with deliberation, it is not incompatible with either some speculation or knowing something on the grounds of inference.

Some arguments are good; others are not. How can we tell the difference? This article advances three proposals as a partial answer to this question. The proposals are keyed to arguments conditioned by different degrees of uncertainty: mild, where the argument’s premises are hedged with point-valued probabilities; moderate, where the premises are hedged with interval probabilities; and severe, where the premises are hedged with non-numeric plausibilities such as ‘very likely’ or ‘unconfirmed’. For mild uncertainty, the article proposes to apply a (...) principle referred to as ‘Jeffrey’s rule’, for the principle is a generalization of Jeffrey conditionalization. For moderate uncertainty, the proposal is to extend Jeffrey’s rule for use with probability intervals. For severe uncertainty, the article proposes that even when lack of probabilistic information prevents the application of Jeffrey’s rule, the rule can be adapted to these conditions with the aid of a suitable plausibility measure. Together, the three proposals introduce an approach to argument evaluation that complements established frameworks for evaluating arguments: deductive soundness, informal logic, argumentation schemes, pragma-dialectics, and Bayesian inference. Nevertheless, this approach can be looked at as a generalization of the truth and validity conditions of the classical criterion for sound argumentation. (shrink)