Tenenbaum and Griffiths (2001) have proposed that their Bayesian model of generalisation unifies Shepard’s (1987) and Tversky’s (1977) similarity-based explanations of two distinct patterns of generalisation behaviours by reconciling them under a single coherent task analysis. I argue that this proposal needs refinement: instead of unifying the heterogeneous notion of psychological similarity, the Bayesian approach unifies generalisation by rendering the distinct patterns of behaviours informationally relevant. I suggest that generalisation as a Bayesian inference should be seen as a complement to, (...) instead of a replacement of, similarity-based explanations. Furthermore, I show that the unificatory powers of the Bayesian model of generalisation can contribute to the selection of one of these models of psychological similarity. (shrink)

Character judgments play an important role in our everyday lives. However, decades of empirical research on trait attribution suggest that the cognitive processes that generate these judgments are prone to a number of biases and cognitive distortions. This gives rise to a skeptical worry about the epistemic foundations of everyday characterological beliefs that has deeply disturbing and alienating consequences. In this paper, I argue that this skeptical worry is misplaced: under the appropriate informational conditions, our everyday character-trait judgments are in (...) fact quite trustworthy. I then propose a mindreading-based model of the socio-cognitive processes underlying trait attribution that explains both why these judgments are initially unreliable, and how they eventually become more accurate. (shrink)

Fodor argued that learning a concept by hypothesis testing would involve an impossible circularity. I show that Fodor's argument implicitly relies on the assumption that actually φ-ing entails an ability to φ. But this assumption is false in cases of φ-ing by luck, and just such luck is involved in testing hypotheses with the kinds of generative random sampling methods that many cognitive scientists take our minds to use. Concepts thus can be learned by hypothesis testing without circularity, and it (...) is plausible that this is how humans in fact acquire at least some of their concepts. (shrink)

Reading in the Brain (Les neurones de la lecture, 2007) examined the origins of human reading abilities in the light of contemporary cognitive neuroscience. It argued that reading acquisition, in all cultures, recycles preexisting cortical circuits dedicated to invariant visual recognition, and that the organization of these circuits imposes strong constraints on the invention and cultural evolution of writing systems. In this article, seven years later, I briefly review new experimental evidence, particularly from brain imaging studies of illiterate adults, which (...) indicates that reading acquisition invades culturally universal cortical circuits and competes with their prior function, including mirror-invariant visual recognition and face processing. In response to my critics, I emphasize how brain plasticity and brain constraints can be reconciled within the Bayesian perspective on learning. I also discuss the importance of a newly discovered gesture system in reading and writing. Finally, I argue that there is consistent evidence for deep cross-cultural universals in writing systems, as well as for the multiple subtypes of dyslexia that are expected given the broad set of areas recruited by the reading task. (shrink)

The rise of Bayesianism in cognitive science promises to shape the debate between nativists and empiricists into more productive forms—or so have claimed several philosophers and cognitive scientists. The present paper explicates this claim, distinguishing different ways of understanding it. After clarifying what is at stake in the controversy between nativists and empiricists, and what is involved in current Bayesian cognitive science, the paper argues that Bayesianism offers not a vindication of either nativism or empiricism, but one way to talk (...) precisely and transparently about the kinds of mechanisms and representations underlying the acquisition of psychological traits without a commitment to an innate language of thought. (shrink)

The rise of Bayesianism in cognitive science promises to shape the debate between nativists and empiricists into more productive forms—or so have claimed several philosophers and cognitive scientists. The present paper explicates this claim, distinguishing different ways of understanding it. After clarifying what is at stake in the controversy between nativists and empiricists, and what is involved in current Bayesian cognitive science, the paper argues that Bayesianism offers not a vindication of either nativism or empiricism, but one way to talk (...) precisely and transparently about the kinds of mechanisms and representations underlying the acquisition of psychological traits without a commitment to an innate language of thought. (shrink)

Name der Zeitschrift: Cognitive Linguistics Jahrgang: 27 Heft: 4 Seiten: 493-505.

This paper argues that neurolinguistics has the potential to yield insights that can feed back into corpus-based Cognitive Linguistics. It starts by discussing how far the cognitive realism of probabilistic statements derived from corpus data currently goes. Against this background, it argues that the cognitive realism of usage-based models could be further enhanced through deeper engagement with neurolinguistics, but also highlights a number of common misconceptions about what neurolinguistics can and cannot do for linguistic theorizing.

In recent works, Kind has argued that imagination is a skill, since it possesses the two hallmarks of skill: improvability by practice, and control. I agree with Kind that and are indeed hallmarks of skill, and I also endorse her claim that imagination is a skill in virtue of possessing these two features. However, in this paper, I argue that Kind’s case for imagination’s being a skill is unsatisfactory, since it lacks robust empirical evidence. Here, I will provide evidence for (...) by considering data from mental rotation experiments and for by considering data from developmental experiments. I conclude that imagination is a skill, but there is a further pressing question of how the cognitive architecture of imagination has to be structured to make this possible. I begin by considering how can be implemented sub-personally. I argue that this can be accounted for by positing a selection mechanism which selects content from memory representations to be recombined into imaginings, using Bayesian generation. I then show that such an account can also explain. On this basis, I hold that not only is imagination a skill, but that it is also plausibly implemented sub-personally by a Bayesian selection mechanism. (shrink)

Modularity theorists have challenged that there are, or could be, general learning mechanisms that explain theory-of-mind development. In response, supporters of the ‘scientific theory-theory’ account of theory-of-mind development have appealed to children's use of auxiliary hypotheses and probabilistic causal modeling. This article argues that these general learning mechanisms are not sufficient to meet the modularist's challenge. The article then explores an alternative domain-general learning mechanism by proposing that children grasp the concept belief through the progressive alignment of relational structure that (...) occurs as a result of structural-comparison. The article also explores the implications of the proposed account for Fodor's puzzle of conceptual learning. (shrink)

Analogical cognition refers to the ability to detect, process, and learn from relational similarities. The study of analogical and similarity cognition is widely considered one of the ‘success stories’ of cognitive science, exhibiting convergence across many disciplines on foundational questions. Given the centrality of analogy to mind and knowledge, it would benefit philosophers investigating topics in epistemology and the philosophies of mind and language to become familiar with empirical models of analogical cognition. The goal of this essay is to describe (...) recent empirical work on analogical cognition as well as model applications to philosophical topics. Topics to be discussed include the epistemological distinction between implicit knowledge and explicit knowledge, the debate between empiricists and nativists, the frame problem, expertise, creativity and autism, cognitive architecture, and relational knowledge. Particular attention is given to Dedre Gentner and colleague’s structure-mapping theory – the most developed and widely accepted model of analogical cognition. (shrink)

Recent work in folk metaethics finds a correlation between perceived consensus about a moral claim and meta-ethical judgments about whether the claim is universally or only relatively true. We argue that consensus can provide evidence for meta-normative claims, such as whether a claim is universally true. We then report several experiments indicating that people use consensus to make inferences about whether a claim is universally true. This suggests that people's beliefs about relativism and universalism are partly guided by evidence-based reasoning. (...) In a final study, we show that the rejection of universalism does not generate a simple subjectivism but is associated with a more moderate relativism on which highly atypical positions are regarded as mistaken. (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)

Several evolutionary accounts of human social cognition posit that language has co-evolved with the sophisticated mindreading abilities of modern humans. It has also been argued that these mindreading abilities are the product of cultural, rather than biological, evolution. Taken together, these claims suggest that the evolution of language has played an important role in the cultural evolution of human social cognition. Here we present a new computational model which formalises the assumptions that underlie this hypothesis, in order to explore how (...) language and mindreading interact through cultural evolution. This model treats communicative behaviour as an interplay between the context in which communication occurs, an agent’s individual perspective on the world, and the agent’s lexicon. However, each agent’s perspective and lexicon are private mental representations, not directly observable to other agents. Learners are therefore confronted with the task of jointly inferring the lexicon and perspective of their cultural parent, based on their utterances in context. Simulation results show that given these assumptions, an informative lexicon evolves not just under a pressure to be successful at communicating, but also under a pressure for accurate perspective-inference. When such a lexicon evolves, agents become better at inferring others’ perspectives; not because their innate ability to learn about perspectives changes, but because sharing a language with others helps them to do so. (shrink)

Both mindreading and stereotyping are forms of social cognition that play a pervasive role in our everyday lives, yet too little attention has been paid to the question of how these two processes are related. This paper offers a theory of the influence of stereotyping on mental-state attribution that draws on hierarchical predictive coding accounts of action prediction. It is argued that the key to understanding the relation between stereotyping and mindreading lies in the fact that stereotypes centrally involve character-trait (...) attributions, which play a systematic role in the action–prediction hierarchy. On this view, when we apply a stereotype to an individual, we rapidly attribute to her a cluster of generic character traits on the basis of her perceived social group membership. These traits are then used to make inferences about that individual’s likely beliefs and desires, which in turn inform inferences about her behavior. (shrink)

Young children spend a large portion of their time pretending about non-real situations. Why? We answer this question by using the framework of Bayesian causal models to argue that pretending and counterfactual reasoning engage the same component cognitive abilities: disengaging with current reality, making inferences about an alternative representation of reality, and keeping this representation separate from reality. In turn, according to causal models accounts, counterfactual reasoning is a crucial tool that children need to plan for the future and learn (...) about the world. Both planning with causal models and learning about them require the ability to create false premises and generate conclusions from these premises. We argue that pretending allows children to practice these important cognitive skills. We also consider the prevalence of unrealistic scenarios in children's play and explain how they can be useful in learning, despite appearances to the contrary. (shrink)

Bayes’ inference cannot reliably account for uncertainty in mental processes. The reason is that Bayes’ inference is based on the assumption that the order in which the relevant features are evaluated is indifferent, which is not the case in most of mental processes. Instead of Bayes’ rule, a more general, probabilistic rule of inference capable of accounting for these order effects is established. This new rule of inference can be used to improve the current Bayesian models of cognition. Moreover, it (...) should play an essential role in the search for artificial emotional intelligence. (shrink)

Surprise—operationalized as looking time—has a long history in developmental research, providing a window into the perception and cognition of infants. Recently, however, a number of developmental researchers have considered infants’ and children's surprise in its own right. This article reviews empirical evidence and computational models of complex statistical inferences underlying surprise, and discusses how these findings relate to the role that surprise appears to play as a catalyst for learning.

Natural languages make prolific use of conventional constituent‐ordering patterns to indicate “who did what to whom,” yet the mechanisms through which these regularities arise are not well understood. A series of recent experiments demonstrates that, when prompted to express meanings through silent gesture, people bypass native language conventions, revealing apparent biases underpinning word order usage, based on the semantic properties of the information to be conveyed. We extend the scope of these studies by focusing, experimentally and computationally, on the interpretation (...) of silent gesture. We show cross‐linguistic experimental evidence that people use variability in constituent order as a cue to obtain different interpretations. To illuminate the computational principles that govern interpretation of non‐conventional communication, we derive a Bayesian model of interpretation via biased inductive inference and estimate these biases from the experimental data. Our analyses suggest people's interpretations balance the ambiguity that is characteristic of emerging language systems, with ordering preferences that are skewed and asymmetric, but defeasible. (shrink)

Natural languages make prolific use of conventional constituent‐ordering patterns to indicate “who did what to whom,” yet the mechanisms through which these regularities arise are not well understood. A series of recent experiments demonstrates that, when prompted to express meanings through silent gesture, people bypass native language conventions, revealing apparent biases underpinning word order usage, based on the semantic properties of the information to be conveyed. We extend the scope of these studies by focusing, experimentally and computationally, on the interpretation (...) of silent gesture. We show cross‐linguistic experimental evidence that people use variability in constituent order as a cue to obtain different interpretations. To illuminate the computational principles that govern interpretation of non‐conventional communication, we derive a Bayesian model of interpretation via biased inductive inference and estimate these biases from the experimental data. Our analyses suggest people's interpretations balance the ambiguity that is characteristic of emerging language systems, with ordering preferences that are skewed and asymmetric, but defeasible. (shrink)

Whilst the topic of representations is one of the key topics in philosophy of mind, it has only occasionally been noted that representations and representational features may be gradual. Apart from vague allusions, little has been said on what representational gradation amounts to and why it could be explanatorily useful. The aim of this paper is to provide a novel take on gradation of representational features within the neuroscientific framework of predictive processing. More specifically, we provide a gradual account of (...) two features of structural representations: structural similarity and decoupling. We argue that structural similarity can be analysed in terms of two dimensions: number of preserved relations and state space granularity. Both dimensions can take on different values and hence render structural similarity gradual. We further argue that decoupling is gradual in two ways. First, we show that different brain areas are involved in decoupled cognitive processes to a greater or lesser degree depending on the cause (internal or external) of their activity. Second, and more importantly, we show that the degree of decoupling can be further regulated in some brain areas through precision weighting of prediction error. We lastly argue that gradation of decoupling (via precision weighting) and gradation of structural similarity (via state space granularity) are conducive to behavioural success. (shrink)

Tenenbaum and Griffiths (Behavioral and Brain Sciences 24(4):629–640, 2001) have proposed that their Bayesian model of generalisation unifies Shepard’s (Science 237(4820): 1317–1323, 1987) and Tversky’s (Psychological Review 84(4): 327–352, 1977) similarity-based explanations of two distinct patterns of generalisation behaviours by reconciling them under a single coherent task analysis. I argue that this proposal needs refinement: instead of unifying the heterogeneous notion of psychological similarity, the Bayesian approach unifies generalisation by rendering the distinct patterns of behaviours informationally relevant. I suggest that (...) generalisation as a Bayesian inference should be seen as a complement to, instead of a replacement of, similarity-based explanations. Furthermore, I show that the unificatory powers of the Bayesian model of generalisation can contribute to the selection of one of these models of psychological similarity. (shrink)

Classical (Bayesian) probability (CP) theory has led to an influential research tradition for modeling cognitive processes. Cognitive scientists have been trained to work with CP principles for so long that it is hard even to imagine alternative ways to formalize probabilities. However, in physics, quantum probability (QP) theory has been the dominant probabilistic approach for nearly 100 years. Could QP theory provide us with any advantages in cognitive modeling as well? Note first that both CP and QP theory share the (...) fundamental assumption that it is possible to model cognition on the basis of formal, probabilistic principles. But why consider a QP approach? The answers are that (1) there are many well-established empirical findings (e.g., from the influential Tversky, Kahneman research tradition) that are hard to reconcile with CP principles; and (2) these same findings have natural and straightforward explanations with quantum principles. In QP theory, probabilistic assessment is often strongly context- and order-dependent, individual states can be superposition states (that are impossible to associate with specific values), and composite systems can be entangled (they cannot be decomposed into their subsystems). All these characteristics appear perplexing from a classical perspective. However, our thesis is that they provide a more accurate and powerful account of certain cognitive processes. We first introduce QP theory and illustrate its application with psychological examples. We then review empirical findings that motivate the use of quantum theory in cognitive theory, but also discuss ways in which QP and CP theories converge. Finally, we consider the implications of a QP theory approach to cognition for human rationality. (shrink)

Absolute linguistic universals are often justified by cross-linguistic analysis: If all observed languages exhibit a property, the property is taken to be a likely universal, perhaps specified in the cognitive or linguistic systems of language learners and users. In many cases, these patterns are then taken to motivate linguistic theory. Here, we show that cross-linguistic analysis will very rarely be able to statistically justify absolute, inviolable patterns in language. We formalize two statistical methods—frequentist and Bayesian—and show that in both it (...) is possible to find strict linguistic universals, but that the numbers of independent languages necessary to do so is generally unachievable. This suggests that methods other than typological statistics are necessary to establish absolute properties of human language, and thus that many of the purported universals in linguistics have not received sufficient empirical justification. (shrink)

The informativity of a computational model of language acquisition is directly related to how closely it approximates the actual acquisition task, sometimes referred to as the model's cognitive plausibility. We suggest that though every computational model necessarily idealizes the modeled task, an informative language acquisition model can aim to be cognitively plausible in multiple ways. We discuss these cognitive plausibility checkpoints generally and then apply them to a case study in word segmentation, investigating a promising Bayesian segmentation strategy. We incorporate (...) cognitive plausibility by using an age-appropriate unit of perceptual representation, evaluating the model output in terms of its utility, and incorporating cognitive constraints into the inference process. Our more cognitively plausible model shows a beneficial effect of cognitive constraints on segmentation performance. One interpretation of this effect is as a synergy between the naive theories of language structure that infants may have and the cognitive constraints that limit the fidelity of their inference processes, where less accurate inference approximations are better when the underlying assumptions about how words are generated are less accurate. More generally, these results highlight the utility of incorporating cognitive plausibility more fully into computational models of language acquisition. (shrink)

This article explores some of the philosophical implications of the Bayesian modeling paradigm. In particular, it focuses on the ramifications of the fact that Bayesian models pre‐specify an inbuilt hypothesis space. To what extent does this pre‐specification correspond to simply ‘‘building the solution in''? I argue that any learner must have a built‐in hypothesis space in precisely the same sense that Bayesian models have one. This has implications for the nature of learning, Fodor's puzzle of concept acquisition, and the role (...) of modeling in cognitive science. (shrink)

It is an old and venerable idea in philosophy that morality is built into us, and this nativist view has seen a resurgence of late. Indeed, the prevailing systematic account of how we acquire complex moral representations is a nativist view inspired by arguments in Chomskyan linguistics. In this article, I review the leading argument for moral nativism – the poverty of the moral stimulus. I defend a systematic empiricist alternative that draws on the resources of statistical learning. Such an (...) empiricist account, I argue, promises to explain much of the complexity of people’s moral and other normative attitudes. (shrink)

Over the last several decades, there has been a wealth of illuminating work on processes implicated in social cognition. Much less has been done in articulating how we learn the contours of particular concepts deployed in social cognition, like the concept MENTALISTIC AGENT. Recent developments in learning theory afford new tools for approaching these questions. In this article, I describe some rudimentary ways in which learning theoretic considerations can illuminate philosophically important aspects of the MENTALISTIC AGENT concept. I maintain that (...) MENTALISTIC AGENT is an essentialized concept and that learning-theoretic considerations help explain why the concept is not tied to particular traits. (shrink)

Do you know you are not being massively deceived by an evil demon? That is a familiar skeptical challenge. Less familiar is this question: How do you have a conception of knowledge on which the evil demon constitutes a prima facie challenge? Recently several philosophers have suggested that our responses to skeptical scenarios can be explained in terms of heuristics and biases. We offer an alternative explanation, based in learning theory. We argue that, given the evidence available to the learner, (...) it would be rational for the learner to infer an infallibilist conception of knowledge. (shrink)

People draw subtle distinctions in the normative domain. But it remains unclear exactly what gives rise to such distinctions. On one prominent approach, emotion systems trigger non-utilitarian judgments. The main alternative, inspired by Chomskyan linguistics, suggests that moral distinctions derive from an innate moral grammar. In this article, we draw on Bayesian learning theory to develop a rational learning account. We argue that the ‘size principle’, which is implicated in word learning, can also explain how children would use scant and (...) equivocal evidence to interpret candidate rules as applying more narrowly than utilitarian rules. (shrink)

A full account of purposive action must appeal not only to propositional attitude states like beliefs, desires, and intentions, but also to motor representations, i.e., non-propositional states that are thought to represent, among other things, action outcomes as well as detailed kinematic features of bodily movements. This raises the puzzle of how it is that these two distinct types of state successfully coordinate. We examine this so-called “Interface Problem”. First, we clarify and expand on the nature and role of motor (...) representations in explaining intentional action. Next, we characterize the respective functions of intentions and motor representations, the differences in representational format and content that these imply, and the interface challenge these differences in turn raise. We then evaluate Butterfill and Sinigaglia’s recent answer to this interface challenge, according to which intentions refer to action outcomes by way of demonstrative deference to motor representations. We present some worries for this proposal, arguing that, among other things, it implicitly presupposes a solution to the problem, and so cannot help to resolve it. Finally, we suggest that we may make some progress on this puzzle by positing a “content-preserving causal process” taking place between intentions and motor representations, and we offer a proposal for how this might work. (shrink)

The French sociological tradition has long regarded the ‘individual’ as a reality situated outside its area of intellection and investigation. According to Durkheim, the individual is a psychological object par excellence. Sociology has thus long favored the study of collectives (groups, classes, categories, institutions, microcosms), suggesting that the individual was a reality which, in itself, fell short of the social. The article discusses a method from the mid-1990s of researching sociology at an individual scale. This approach is essentially embedded in (...) the French sociological tradition, from Durkheim to Bourdieu via Halbwachs, despite the inflections and criticisms it might have of this tradition, while also drawing on the main theoretical knowledge of Norbert Elias’ relational and process-focused sociology. From empirical realization in methodological and theoretical reflexivity, this research program has progressed in dialog with various types of scientific knowledge more classically oriented toward the individual and their mental realities, such as cultural psychology, psychoanalysis, cognitive psychology or the neurosciences. (shrink)

Inspired by, and in close relation with, the contributions of this special issue, Kuperberg elegantly links event comprehension, production, and learning. She proposes an overarching hierarchical generative framework of processing events enabling us to make sense of the world around us and to interact with it in a competent manner.

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)

ABSTRACTJames Woodward and John Allman [2007, 2008] and Peter Railton [2014, 2016] argue that our moral intuitions are products of sophisticated rational learning systems. I investigate the implications that this discovery has for intuition-based philosophical methodologies. Instead of vindicating the conservative use of intuitions in philosophy, I argue that what I call the rational learning strategy fails to show philosophers are justified in appealing to their moral intuitions in philosophical arguments without giving reasons why those intuitions are trustworthy. Despite the (...) fact that our intuitions are outputs of surprisingly sophisticated learning mechanisms, we do not have reason to unreflectively trust them when offering arguments in moral philosophy. (shrink)

Debunking arguments aim to undermine common sense beliefs by showing that they are not explanatorily or causally linked to the entities they are purportedly about. Rarely are facts about the aetiology of common sense beliefs invoked for the opposite aim, that is, to support the reality of entities that furnish our manifest image of the world. Here I undertake this sort of un-debunking project. My focus is on the metaphysics of ordinary physical objects. I use the view of perception as (...) approximate Bayesian inference to show how representations of ordinary objects can be extracted from sensory input in a rational and truth-tracking manner. Drawing an analogy between perception construed as Bayesian hypothesis testing and scientific inquiry, I sketch out how some of the intuitions that traditionally inspired arguments for scientific realism also find application with regards to proverbial tables and chairs. (shrink)

How are new Bayesian hypotheses generated within the framework of predictive processing? This explanatory framework purports to provide a unified, systematic explanation of cognition by appealing to Bayes rule and hierarchical Bayesian machinery alone. Given that the generation of new hypotheses is fundamental to Bayesian inference, the predictive processing framework faces an important challenge in this regard. By examining several cognitive‐level and neurobiological architecture‐inspired models of hypothesis generation, we argue that there is an essential difference between the two types of (...) models. Cognitive‐level models do not specify how they can be implemented in brains and include structures and assumptions that are external to the predictive processing framework. By contrast, neurobiological architecture‐inspired models, which aim to better resemble brain processes, fail to explain important capacities of cognition, such as categorization and few‐shot learning. The “scaling‐up” challenge for proponents of predictive processing is to explain the relationship between these two types of models using only the theoretical and conceptual machinery of Bayesian inference. (shrink)