Eliminative materialism is the thesis that our common-sense conception of psychological phenomena constitutes a radically false theory, a theory so fundamentally defective that both the principles and the ontology of that theory will eventually be displaced, rather than smoothly reduced, by completed neuroscience. Our mutual understanding and even our introspection may then be reconstituted within the conceptual framework of completed neuroscience, a theory we may expect to be more powerful by far than the common-sense psychology it displaces, and more substantially (...) integrated within physical science generally. My purpose in this paper is to explore these projections, especially as they bear on (1) the principal elements of common-sense psychology: the propositional attitudes (beliefs, desires, etc.), and (2) the conception of rationality in which these elements figure. (shrink)

Philosophical Issues, Volume 30, Issue 1, Page 273-292, October 2020.

On a now orthodox view, humans and many other animals possess a “number sense,” or approximate number system, that represents number. Recently, this orthodox view has been subject to numerous critiques that question whether the ANS genuinely represents number. We distinguish three lines of critique – the arguments from congruency, confounds, and imprecision – and show that none succeed. We then provide positive reasons to think that the ANS genuinely represents numbers, and not just non-numerical confounds or exotic substitutes for (...) number, such as “numerosities” or “quanticals,” as critics propose. In so doing, we raise a neglected question: numbers of what kind? Proponents of the orthodox view have been remarkably coy on this issue. But this is unsatisfactory since the predictions of the orthodox view, including the situations in which the ANS is expected to succeed or fail, turn on the kind of number being represented. In response, we propose that the ANS represents not only natural numbers, but also non-natural rational numbers. It does not represent irrational numbers, however, and thereby fails to represent the real numbers more generally. This distances our proposal from existing conjectures, refines our understanding of the ANS, and paves the way for future research. (shrink)

This paper refines a controversial proposal: that core systems belong to a perceptual kind, marked out by the format of its representational outputs. Following Susan Carey, this proposal has been understood in terms of core representations having an iconic format, like certain paradigmatically perceptual outputs. I argue that they don’t, but suggest that the proposal may be better formulated in terms of a broader analogue format type. Formulated in this way, the proposal accommodates the existence of genuine icons in perception, (...) and avoids otherwise troubling objections. (shrink)

Chomsky proposes a reformulation of the theory of transformational generative grammar that takes recent developments in the descriptive analysis of particular ...

Most of us create and use a panoply of non-sentential representations throughout our ordinary lives: we regularly use maps to navigate, charts to keep track of complex patterns of data, and diagrams to visualize logical and causal relations among states of affairs. But philosophers typically pay little attention to such representations, focusing almost exclusively on language instead. In particular, when theorizing about the mind, many philosophers assume that there is a very tight mapping between language and thought. Some analyze utterances (...) as the outer vocalizations of inner thoughts (e.g. Grice 1957, Devitt 2005), while others treat thought as a form of inner speech (e.g. Sellars 1956/1997, Carruthers 2002). But even philosophers who take no stand on the relative priority of language and thought still tend to individuate mental states in terms of the sentences we use to ascribe them. Indeed, Dummett (1993) claims that it is constitutive of analytic philosophy that it approaches the mind by way of language. In many ways, this linguistic model is salutary. Our thoughts are often intimately intertwined with their linguistic expression, and public language does provide a comparatively tractable proxy for, and a window into, the messier realm of thought. However, an exclusive focus on thought as it is expressed in language threatens to leave other sorts of thought unexplained, or even to blind us to their possibility. In particular, many cognitive ethologists and psychologists find it useful to talk about humans, chimpanzees, birds, rats, and even bees as employing cognitive maps. We need to make sense of this way of talking about minds as well as more familiar sentential descriptions. In what follows, I investigate the theoretical and practical possibility of non-sentential thought. Ultimately, I am most interested in the contours of distinctively human thought: what forms does human thought take, and how do those different forms interact? How does human thought compare with that of other animals? In this essay, however, I focus on a narrower and more basic theoretical question: could thought occur in maps? Many philosophers are convinced that in some important sense, thought per se must be language-like.. (shrink)

I argue that we can reconcile two seemingly incompatible traditions for thinking about concepts. On the one hand, many cognitive scientists assume that the systematic redeployment of representational abilities suffices for having concepts. On the other hand, a long philosophical tradition maintains that language is necessary for genuinely conceptual thought. I argue that on a theoretically useful and empirically plausible concept of 'concept', it is necessary and sufficient for conceptual thought that a thinker be able to entertain many of the (...) potential thoughts produced by recombining her representational abilities apart from a direct confrontation with the states of affairs being represented. Such representational abilities support a cognitive engagement with the world that is flexible, abstract, and active. (shrink)

Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been largely silent on this technology so far. This is remarkable, given that deep learning neural networks have blown past predicted upper limits on artificial intelligence performance—recognizing complex objects in natural photographs and defeating world champions in strategy games as complex as Go and chess—yet there remains no universally accepted explanation (...) as to why they work so well. This article provides an introduction to these networks as well as an opinionated guidebook on the philosophical significance of their structure and achievements. It argues that deep learning neural networks differ importantly in their structure and mathematical properties from the shallower neural networks that were the subject of so much philosophical reflection in the 1980s and 1990s. The article then explores several different explanations for their success and ends by proposing three areas of inquiry that would benefit from future engagement by philosophers of mind and science. (shrink)

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in predicting (...) brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data. (shrink)

In Thinking without Words I develop a philosophical framework for treating some animals and human infants as genuine thinkers. This paper outlines the aspects of this account that are most relevant to those working in animal ethics. There is a range of different levels of cognitive sophistication in different animal species, in addition to limits to the types of thought available to non-linguistic creatures, and it may be important for animal ethicists to take this into account in exploring issues of (...) moral significance and the obligations that we might or might not have to non-human animals. (shrink)

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the (...) brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components – not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored. (shrink)

Influential work on reasoning and decision-making has popularised the idea that sound reasoning requires correction of fast, intuitive thought processes by slower and more demanding deliberation. We present seven studies that question this corrective view of human thinking. We focused on the very problem that has been widely featured as the paradigmatic illustration of the corrective view, the well-known bat-and-ball problem. A two-response paradigm in which people were required to give an initial response under time pressure and cognitive load allowed (...) us to identify the presumed intuitive response that preceded the final response given after deliberation. Across our studies, we observe that correct final responses are often non-corrective in nature. Many reasoners who manage to answer the bat-and-ball problem correctly after deliberation already solved it correctly when they reasoned under conditions that minimised deliberation in the initial response phase. This suggests that sound bat-and-ball reasoners do not necessarily need to deliberate to correct their intuitions; their intuitions are often already correct. Pace the corrective view, findings suggest that in these cases, they deliberate to verify correct intuitive insights. (shrink)

Dual process models of higher cognition have become very influential in the cognitive sciences. The popular Default-Interventionist model has long favoured a serial view on the interaction between...

How can we think about things in the outside world? There is still no widely accepted theory of how mental representations get their meaning. In light of pioneering research, Nicholas Shea develops a naturalistic account of the nature of mental representation with a firm focus on the subpersonal representations that pervade the cognitive sciences.

Over the past fifty years the philosophy of language and mind has been dominated by a nondescriptivist approach to content and reference. This book attempts to recast and systematize that approach by offering an indexical model in terms of mental files. According to Recanati, we refer through mental files, the function of which is to store information derived through certain types of contextual relation the subject bears to objects in his or her environment. The reference of a file is determined (...) relationally, not satisfactionally, so a file is not to be equated to the body of (mis-)information it contains. Mental files are the mental equivalent of singular terms, and the reference of linguistic expressions is inherited from that of the files we associate with them. On this approach, mental files, the vehicles of singular thought, do all the work of so-called 'modes of presentation' in Fregean and neo-Russellian theories. (shrink)

I argue that to have an attitude is, primarily, (1.) to have a dispositional profile that matches, to an appropriate degree and in appropriate respects, a stereotype for that attitude, typically grounded in folk psychology, and secondarily, (2.) in some cases also to meet further stereotypical attitude-specific conditions. To have an attitude, on the account I will recommend here, is mainly a matter of being apt to interact with the world in patterns that ordinary people would regard as characteristic of (...) having that attitude. I contrast this view with alternative views that treat having an attitude as a matter of possessing some particular internally stored representational content or some other "deep" structural feature. (shrink)

This paper concerns the role of the structural properties of representations in determining the nature of their content. I take as a starting point Fodor's (2007) and Heck's (2007) recent arguments making the iconic structure of perceptual representations essential in establishing their content as content of a different (nonconceptual) kind. I argue that the prima facie state–content error this strategy seems to display is nothing but a case of “state–content error error,” i.e., the mistake of considering that the properties that (...) characterize the type of content certain representations have are indeed independent of their structural properties. I also consider another objection to the general strategy, namely that it falls short of showing that the nonconceptual content of perceptual experiences thus established is the kind of content that figures in true explanations of intentional behavior. I concede this point in respect of Fodor's version of the strategy, while denying that it has any bearing on Heck's approach. The success of this objection in Heck's case ultimately depends, I argue, upon an unjustified commitment to a certain empirically suspect model of perception. (shrink)

Many debates in philosophy focus on whether folk or scientific psychological notions pick out cognitive natural kinds. Examples include memory, emotions and concepts. A potentially interesting type of kind is: kinds of mental representations (as opposed, for example, to kinds of psychological faculties). In this chapter we outline a proposal for a theory of representational kinds in cognitive science. We argue that the explanatory role of representational kinds in scientific theories, in conjunction with a mainstream approach to explanation in cognitive (...) science, suggest that representational kinds are multi-level. This is to say that representational kinds’ properties cluster at different levels of explanation and allow for intra- and inter-level projections. (shrink)

Unconscious logical inference seems to rely on the syntactic structures of mental representations (Quilty-Dunn & Mandelbaum 2018). Other transitions, such as transitions using iconic representations and associative transitions, are harder to assimilate to syntax-based theories. Here we tackle these difficulties head on in the interest of a fuller taxonomy of mental transitions. Along the way we discuss how icons can be compositional without having constituent structure, and expand and defend the “symmetry condition” on Associationism (the idea that associative links and (...) transitions are perfectly symmetric). In the end, we show how a BIT (“bare inferential transition”) theory can cohabitate with these other non-inferential mental transitions. (shrink)

Good reasoning can lead to success; bad reasoning can lead to catastrophe. Yet, it's not obvious how we reason, and why we make mistakes. This new book by one of the pioneers of the field, Philip Johnson-Laird, looks at the mental processes that underlie our reasoning. It provides the most accessible account yet of the science of reasoning.

Rational Rules argues that moral learning can be understood in terms of general-purpose rational learning procedures. Nichols provides statistical learning accounts of some fundamental aspects of moral development, combining aspects of traditional empiricist and rationalist approaches.

Paul M. Pietroski presents an ambitious new account of human languages as generative procedures that respect substantive constraints. He argues that meanings are neither concepts nor extensions, and sentences do not have truth conditions; meanings are composable instructions for how to access and assemble concepts of a special sort.

Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence. According to an article in The New York Times, the course on artificial intelligence is (...) “one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world.” One of the other two courses, an introduction to database software, is being taught by Pearson author Dr. Jennifer Widom. Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle™, NOOK™, and the iPhone®/iPad®. (shrink)

It is generally accepted that there is something special about reasoning by using mental images. The question of how it is special, however, has never been satisfactorily spelled out, despite more than thirty years of research in the post-behaviorist tradition. This article considers some of the general motivation for the assumption that entertaining mental images involves inspecting a picture-like object. It sets out a distinction between phenomena attributable to the nature of mind to what is called the cognitive architecture, and (...) ones that are attributable to tacit knowledge used to simulate what would happen in a visual situation. With this distinction in mind, the paper then considers in detail the widely held assumption that in some important sense images are spatially displayed or are depictive, and that examining images uses the same mechanisms that are deployed in visual perception. I argue that the assumption of the spatial or depictive nature of images is only explanatory if taken literally, as a claim about how images are physically instantiated in the brain, and that the literal view fails for a number of empirical reasons – for example, because of the cognitive penetrability of the phenomena cited in its favor. Similarly, while it is arguably the case that imagery and vision involve some of the same mechanisms, this tells us very little about the nature of mental imagery and does not support claims about the pictorial nature of mental images. Finally, I consider whether recent neuroscience evidence clarifies the debate over the nature of mental images. I claim that when such questions as whether images are depictive or spatial are formulated more clearly, the evidence does not provide support for the picture-theory over a symbol-structure theory of mental imagery. Even if all the empirical claims were true, they do not warrant the conclusion that many people have drawn from them: that mental images are depictive or are displayed in some (possibly cortical) space. Such a conclusion is incompatible with what is known about how images function in thought. We are then left with the provisional counterintuitive conclusion that the available evidence does not support rejection of what I call the “null hypothesis”; namely, that reasoning with mental images involves the same form of representation and the same processes as that of reasoning in general, except that the content or subject matter of thoughts experienced as images includes information about how things would look. (shrink)

At least since Locke, philosophers and psychologists have usually held that concepts arise out of sensory perceptions, thoughts are built from concepts, and language enables speakers to convey their thoughts to hearers. Christopher Gauker holds that this tradition is mistaken about both concepts and language. The mind cannot abstract the building blocks of thoughts from perceptual representations. More generally, we have no account of the origin of concepts that grants them the requisite independence from language. Gauker's alternative is to show (...) that much of cognition consists in thinking by means of mental imagery, without the help of concepts, and that language is a tool by which interlocutors coordinate their actions in pursuit of shared goals. Imagistic cognition supports the acquisition and use of this tool, and when the use of this tool is internalized, it becomes the very medium of conceptual thought. (shrink)

Fodor advocates a view of cognitive processes as computations defined over the language of thought (or Mentalese). Even among those who endorse Mentalese, considerable controversy surrounds its representational format. What semantically relevant structure should scientific psychology attribute to Mentalese symbols? Researchers commonly emphasize logical structure, akin to that displayed by predicate calculus sentences. To counteract this tendency, I discuss computational models of navigation drawn from probabilistic robotics. These models involve computations defined over cognitive maps, which have geometric rather than logical (...) structure. They thereby demonstrate the possibility of rational cognitive processes in an exclusively non-logical representational medium. Furthermore, they offer much promise for the empirical study of animal navigation. (shrink)

My topic is the intelligent guidance of action. In this paper I offer an empirically grounded case for four ideas: that [a] cognitive processes of practical reasoning play a key role in the intelligent guidance of action, [b] these processes could not do so without significant enabling work done by both perception and the motor system, [c] the work done by perceptual and motor systems can be characterized as the generation of information specialized for action guidance, which in turn suggests (...) that [d] the cognitive processes of practical reasoning that play a key role in the guidance of intelligent action are not the abstract, syllogistic ones philosophers often treat as the paradigm of practical reasoning. Rather, these cognitive processes are constrained by, and work well with, the specialized concepts outputted by perception and the feedback outputted by sensorimotor processes. (shrink)

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

Dual Process Theories of reasoning posit that judgments are mediated by both fast, automatic processes and more deliberate, analytic ones. A critical, but unanswered question concerns the issue of monitoring and control: When do reasoners rely on the first, intuitive output and when do they engage more effortful thinking? We hypothesised that initial, intuitive answers are accompanied by a metacognitive experience, called the Feeling of Rightness, which can signal when additional analysis is needed. In separate experiments, reasoners completed one of (...) four tasks: conditional reasoning, a three-term variant of conditional reasoning, problems used to measure base rate neglect, or a syllogistic reasoning task. For each task, participants were instructed to provide an initial, intuitive response to the problem along with an assessment of the rightness of that answer. They were then allowed as much time as needed to reconsider their initial answer and provide a final answer. In each experiment, we observed a robust relationship between the FOR and two measures of analytic thinking: low FOR was associated with longer rethinking times and an increased probability of answer change. In turn, FOR judgments were consistently predicted by the fluency with which the initial answer was produced, providing a link to the wider literature on metamemory. These data support a model in which a metacognitive judgment about a first, initial model determines the extent of analytic engagement. (shrink)

One hundred and three participants solved conflict and non-conflict versions of four reasoning tasks using a two-response procedure: a base rate task, a causal reasoning task, a denominator neglect task, and a categorical syllogisms task. Participants were asked to give their first, intuitive answer, to make a Feeling of Rightness judgment, and then were given as much time as needed to rethink their answer. They also completed a standardized measure of IQ and the actively open-minded thinking questionnaire. The FORs of (...) both high- and low-capacity reasoners were responsive to conflict, such that FORs were lower for conflict relative to non-conflict problems. Consistent with the quantity hypothesis, high-capacity reasoners made a greater distinction between conflict and non-conflict items on measures of Type 2 thinking, namely, rethinking time and probability of changing answers. In contrast to the quality hypothesis, however, this rethinking time did not advantage the ability of the high-capacity group to produce normative answers, except for the base rate task. Indeed, we observed that the correlation between capacity and the probability of normative answers emerged at the initial response, rather than after rethinking. (shrink)

Research on human infants has begun to shed light on early-developing processes for segmenting perceptual arrays into objects. Infants appear to perceive objects by analyzing three-dimensional surface arrangements and motions. Their perception does not accord with a general tendency to maximize figural goodness or to attend to nonaccidental geometric relations in visual arrays. Object perception does accord with principles governing the motions of material bodies: Infants divide perceptual arrays into units that move as connected wholes, that move separately from one (...) another, that tend to maintain their size and shape over motion, and that tend to act upon each other only on contact. These findings suggest that o general representation of object unity and boundaries is interposed between representations of surfaces and representations of objects of familiar kinds. The processes that construct this representation may be related to processes of physical reasoning. (shrink)