The aim of this paper is to give an account of what it is to internalize a rule. I claim that internalization is the process of redistributing the burden of instruction from the teacher to the student. The process is complete when instruction is no longer needed, and the rule has reshaped perceptual classification of the circumstances in which it applies. Teaching a rule is the initiation of this process. We internalize rules by simulating instruction coming from someone else. Running (...) these simulations enables us to toggle between the perspective of the instructor and our own perspective. By doing this we coordinate our perspectives with that of the teacher. The account given here provides a deeper explanation of why internalizing a rule involves the dispositions and reactive attitudes proponents of Rule Consequentialism often say it does, why moral reflection is variably demanding, how intergenerational moral progress is made possible by our cognitive architectures, and why the adoption of a rule should be understood in terms of teaching that rule. (shrink)

Theories of rational decision making often abstract away from computational and other resource limitations faced by real agents. An alternative approach known as resource rationality puts such matters front and center, grounding choice and decision in the rational use of finite resources. Anticipated by earlier work in economics and in computer science, this approach has recently seen rapid development and application in the cognitive sciences. Here, the theory of rationality plays a dual role, both as a framework for normative assessment (...) and as a source of scientific hypotheses about how mental processes in fact work. The latter project, often called rational analysis, depends for its success on a fine-grained characterization of the computational problem facing a decision maker, which may in turn depend on realistic assumptions about what the relevant agent is like. As a consequence, resource rationality involves a delicate, but often fruitful interplay between the normative and the descriptive. (shrink)

This article comprehensively examines how information processing relates to attention and consciousness. We argue that no current theoretical framework investigating consciousness has a satisfactory and holistic account of their informational relationship. Our key theoretical contribution is showing how the dissociation between consciousness and attention must be understood in informational terms in order to make the debate scientifically sound. No current theories clarify the difference between attention and consciousness in terms of information. We conclude with two proposals to advance the debate. (...) First, neurobiological homeostatic processes need to be more explicitly associated with conscious information processing, since information processed through attention is algorithmic, rather than being homeostatic. Second, to understand subjectivity in informational terms, we must define information uniqueness in consciousness (e.g., irreproducible information, biologically encrypted information). These approaches could help cognitive scientists better understand conflicting accounts of the neural correlates of consciousness and work toward a more unified theoretical framework. (shrink)

The medium‐independence of computational descriptions has shaped common conceptions of computational explanation. So long as our goal is to explain how a system successfully carries out its computations, then we only need to describe the abstract series of operations that achieve the desired input–output mapping, however they may be implemented. It is argued that this abstract conception of computational explanation cannot be applied to so‐called real‐time computing systems, in which meeting temporal deadlines imposed by the systems with which a device (...) interfaces are constitutive of the computing tasks that a device performs. Instead, real‐time computing reveals the need for alternative conceptions of computational explanation, as well as computational implementation, that eschew medium‐independence. (shrink)

Nowadays, philosophers and scientists tend to agree that, even though human and artificial intelligence work quite differently, they can still illuminate aspects of each other, and knowledge in one domain can inspire progress in the other. For instance, the notion of “artificial” or “synthetic” phenomenology has been gaining some traction in recent AI research. In this paper, we ask the question: what (if anything) is the use of thinking about phenomenology in the context of AI, and in particular machine learning? (...) We will isolate one sense of “phenomenology”, namely the sense in which it is commonly understood within analytic philosophy of perception. Then, we will give examples of projects within sensory substitution and restoration science that rely heavily on machine learning and to which, according to us, phenomenology in the sense specified makes a relevant contribution. Finally, we will shed some light on what this contribution looks like and why it is important. (shrink)

Linguistic representations are taken by some to be representations of something, specifically of Standard Linguistic Entities, such as phonemes, clauses, noun phrases etc. This perspective takes them to be intentional. Rey (2021) further argues that the SLEs themselves are inexistent. Here I argue that linguistic representations are simply structures, abstractions of brain states, and hence not intentional, and show how they nevertheless connect to the systems that use them.

Recent work in cognitive modelling has found that most of the data that has been cited as evidence for the dual-process theory (DPT) of reasoning is best explained by non-linear, “monotonic” one-process models (Stephens et al., 2018, 2019). In this paper, I consider an important caveat of this research: it uses models that are committed to unrealistic assumptions about how effectively task conditions can isolate Type-1 and Type-2 reasoning. To avoid this caveat, I develop a coordinated theoretical, experimental, and modelling (...) strategy to better test DPT. First, I propose that Type-1 and Type-2 reasoning are defined as reasoning that precedes and follows metacognitive control, respectively. Second, I argue that reasoning that precedes and follows metacognitive control can be effectively isolated using debiasing paradigms that manipulate metacognitive heuristics (e.g., processing fluency) to prevent or trigger metacognitive control, respectively. Third, I argue that monotonic modelling can allow us to decisively test DPT only when we use them to analyse data from this particular kind of debiasing paradigm. (shrink)

Human behavior and thought often exhibit a familiar pattern of within group similarity and between group difference. Many of these patterns are attributed to cultural differences. For much of the history of its investigation into behavior and thought, however, cognitive science has been disproportionately focused on uncovering and explaining the more universal features of human minds—or the universal features of minds in general. -/- This entry charts out the ways in which this has changed over recent decades. It sketches the (...) motivation behind the cultural turn in cognitive science, and situates some of its central findings with respect to the questions that animate it and the debates that it has inspired. Woven throughout the entry are examples of how the cognitive science of culture, and especially its elevated concern with different forms of diversity and variation, continues to influence and be influenced by philosophers. -/- One cluster of philosophical work falls within the traditional subject matter of philosophy of science, in this case of the cognitive and social sciences. Philosophers have analyzed and assessed the methods and evidence central to the scientific study of cognition and culture, and have offered conceptual scrutiny, clarification, and synthesis. Research in a second vein sees philosophers themselves contributing more directly to cognitive scientific projects, (co)constructing theories, helping build computational models, even gathering empirical data. A third kind of work is naturalistic philosophy or philosophy of nature, wherein philosophers seek to use results from the cognitive science of culture to inform or transform debates over long-standing philosophical questions, including questions about the nature of philosophy and philosophical methodology itself. (shrink)

The use of "levels of abstraction" in philosophical analysis (levellism) has recently come under attack. In this paper, we argue that a refined version of epistemological levellism should be retained as a fundamental method, which we call the method of abstraction. After a brief introduction, in section two we make clear the nature and applicability of the (epistemological) method of levels of abstraction. In section three, we show the fruitfulness of the new method by applying it to five case studies: (...) the concept of agenthood, the Turing test, the definition of emergence, quantum observation and decidable observation. In section four, we further characterise and support the method by distinguishing it from three other forms of "levellism": (i) levels of organisation; (ii) levels of explanation and (iii) conceptual schemes. In this context, we also briefly address the problems of relativism and antirealism. In the conclusion, we indicate some of the work that lies ahead, two potential limitations of the method and some results that have already been obtained by applying the method to some long-standing philosophical problems. (shrink)

The main purpose of this dissertation is to examine critically and discuss the role of imagination in science and religion, with particular emphasis on its possible epistemic, creative, and meaning-making functions. In order to answer my research questions, I apply theories and concepts from contemporary philosophy of mind on scientific and religious practices. This framework allows me to explore the mental state of imagination, not as an isolated phenomenon but, rather, as one of many mental states that co-exist and interplay (...) in our cogntive architecture. Based on the philosophical discourse of philosophy of mind, four types of imagination are indentified and conceptualized: sensory, propositional, experiential, and creative imagination. These categories are then employed on five phenomena that can be found in scientific and religious environments: metaphors, models, thought experiments, aspect perception, and - in the religious case - rituals. In relation to the concept of religious "seeing" I consider how imaginings may influence visionary experiences and visualization, and compare these phenomena with cases of scientific visualization and eureka experiences. In regard to scientific and religious models, a distinction is made between, on the one hand, two notions of truth and, on the other hand, truth-independent meaning-making. In light of these categories, I differentiate between, and critically discuss, the use of imagination in doxastic, non-doxastic and fictionalist accounts. In light of this investigation, I formulate and defend the position of interactivism, which acknowledges a constant interplay between different attitudes and mental states. In my examination of rituals and scientific and religious thought experiments, special attention is given to the mental capacity to recreate the experiences that are entailed in an imagined scenario. At the end of the investigation, I consider the possible impact that my study might have on how we view science and religion as well as the dialogue bewteen these two fields. (shrink)

Double dissociations between perceivable colors and physical properties of colored objects have led many philosophers to endorse relationalist accounts of color. I argue that there are analogous double dissociations between attitudes of belief—the beliefs that people attribute to each other in everyday life—and intrinsic cognitive states of belief—the beliefs that some cognitive scientists posit as cogs in cognitive systems—pitched at every level of psychological explanation. These dissociations provide good reason to refrain from conflating attitudes of belief with intrinsic cognitive states (...) of belief. I suggest that interpretivism provides an attractive account of the former. Like colors, attitudes of belief evolved to be ecological signifiers, not cogs in cognitive systems. (shrink)

This essay explores the connection between theories of the self and theories of self-knowledge, arguing (a) that empirical results strongly support a certain negative thesis about the self, a thesis about what the self isn’t, and (b) that a more promising account of the self makes available unorthodox – but likely apt – ways of characterizing self-knowledge. Regarding (a), I argue that the human self does not appear at a personal level the autonomous (or quasi-autonomous) status of which might provide (...) a natural home for a self that can be investigated reliably from the first-person perspective, independent of the empirical sciences. Regarding (b), I contend that the most promising alternative view of the self is revisionary: the self is to be identified with the cognitive system as a whole, the relatively integrated collection of mechanisms that produces intelligent behavior (Rupert 2009, 2010, 2019). The cognitive system teems with reliable, though not necessarily perfect, indicators (cf. Dretske 1988) of its own properties or of the properties of its proper parts, many of which are available for detection by, or the control of, further processes, such as motor control. I argue that indicating states should be treated as potential vehicles of self-knowledge, regardless of whether they are truth-evaluable states, such as beliefs. The investigation of self and self-knowledge frames discussion of a final topic, of some gravity: the way in which self-knowledge might contribute to self-improvement. In this regard, I emphasize the efficacy of certain forms of alignment between, on the one hand, elements of the cognitive system corresponding to a more commonsense-based conception of the self and, on the other hand, processes associated with what is frequently referred to as ‘implicit’ cognitive processing (Evans and Frankish 2009). (shrink)

A venerable view holds that a border between perception and cognition is built into our cognitive architecture and that this imposes limits on the way information can flow between them. While the deliverances of perception are freely available for use in reasoning and inference, there are strict constraints on information flow in the opposite direction. Despite its plausibility, this approach to the perception-cognition border has faced criticism in recent years. This article develops an updated version of the architectural approach, which (...) I call the dimension restriction hypothesis (DRH). According to DRH, perceptual processes are constrained to compute over a bounded range of dimensions, while cognitive processes are not. This view allows that perception is cognitively penetrable, but places strict limits on the varieties of penetration that can occur. The article argues that DRH enjoys both theoretical and empirical support, and also defends the view against several objections. (shrink)

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

The debate about the nature of the representational format of concepts seems to have reached an impasse. The debate faces two fundamental problems. Firstly, amodalists (i.e., those who argue that concepts are represented by amodal symbols) and modalists (i.e., those who see concepts as involving crucially representations including sensorimotor information) claim that the same empirical evidence is compatible with their views. Secondly, there is no shared understanding of what a modal or amodal format amounts to. Both camps recognize that the (...) two formats play essential roles in higher cognition, leading to an increasing number of hybrid proposals. In this paper, I argue that the existence of those fundamental problems should make us suspicious about a modal/amodal dichotomy. Also, I suggest that hybrid approaches, as they currently stand, do not provide suitable solutions to the impasse. Instead, we should overcome the dichotomy and treat the modal/amodal distinction as a graded phenomenon. I illustrate this hypothesis with an example of a cognitive-computational model of concepts based on the Predictive Processing framework. (shrink)

Decision-making has traditionally been modelled as a serial process, consisting of a number of distinct stages. The traditional account assumes that an agent first acquires the necessary perceptual evidence, by constructing a detailed inner repre- sentation of the environment, in order to deliberate over a set of possible options. Next, the agent considers her goals and beliefs, and subsequently commits to the best possible course of action. This process then repeats once the agent has learned from the consequences of her (...) actions and subsequently updated her beliefs. Under this interpretation, the agent’s body is considered merely as a means to report the decision, or to acquire the relevant goods. However, embodied cognition argues that an agent’s body should be understood as a proper part of the decision-making pro- cess. Accepting this principle challenges a number of commonly held beliefs in the cognitive sciences, but may lead to a more unified account of decision-making. This thesis explores an embodied account of decision-making using a recent frame- work known as predictive processing. This framework has been proposed by some as a functional description of neural activity. However, if it is approached from an embodied perspective, it can also offer a novel account of decision-making that ex- tends the scope of our explanatory considerations out beyond the brain and the body. We explore work in the cognitive sciences that supports this view, and argue that decision theory can benefit from adopting an embodied and predictive perspective. (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)

While philosophers have worried about mental causation for centuries, worries about the causal relevance of conscious phenomena are also increasingly featuring in neuroscientific literature. Neuroscientists have regarded the threat of epiphenomenalism as interesting primarily because they have supposed that it entails free will scepticism. However, the steps that get us from a premise about the causal irrelevance of conscious phenomena to a conclusion about free will are not entirely clear. In fact, if we examine popular philosophical accounts of free will, (...) we find, for the most part, nothing to suggest that free will is inconsistent with the presence of unconscious neural precursors to choices. It is only if we adopt highly non-naturalistic assumptions about the mind (e.g. if we embrace Cartesian dualism and locate free choice in the non-physical realm) that it seems plausible to suppose that the neuroscientific data generates a threat to free will. (shrink)

Computational complexity theory is a subfield of computer science originating in computability theory and the study of algorithms for solving practical mathematical problems. Amongst its aims is classifying problems by their degree of difficulty — i.e., how hard they are to solve computationally. This paper highlights the significance of complexity theory relative to questions traditionally asked by philosophers of mathematics while also attempting to isolate some new ones — e.g., about the notion of feasibility in mathematics, the $\mathbf{P} \neq \mathbf{NP}$ (...) problem and why it has proven hard to resolve, and the role of non-classical modes of computation and proof. (shrink)

Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. Explainable Artificial Intelligence aims to develop analytic techniques that render opaque computing systems transparent, but lacks a normative framework with which to evaluate these techniques’ explanatory successes. The aim of the present discussion is to develop such a framework, paying particular attention to different stakeholders’ distinct explanatory requirements. Building on an analysis of “opacity” from (...) philosophy of science, this framework is modeled after accounts of explanation in cognitive science. The framework distinguishes between the explanation-seeking questions that are likely to be asked by different stakeholders, and specifies the general ways in which these questions should be answered so as to allow these stakeholders to perform their roles in the Machine Learning ecosystem. By applying the normative framework to recently developed techniques such as input heatmapping, feature-detector visualization, and diagnostic classification, it is possible to determine whether and to what extent techniques from Explainable Artificial Intelligence can be used to render opaque computing systems transparent and, thus, whether they can be used to solve the Black Box Problem. (shrink)

Whether perception involves the manipulation of representations is currently heavily debated. The embedded view advanced by Nico Orlandi seeks a middle passage between representationalism and radical enactivism. In this paper I argue for a non-representational take on EV. I argue that this is the best way to resolve the objections EV has received from both representationalists and non-representationalists. I analyze this debate, and distinguish four sorts of objections: the objection of the wrongfully cut middleman, the argument against explanatory exclusionism, the (...) case for scientific benefits of representations, and the charge of inconsistent ascription of representational status in EV. I argue that the middleman was never cut in EV, and is controversial to boot, otherwise equal, non-representational explanations have primacy over representational explanations, due to the lack of naturalistic grounds for representations and the unnecessarily ascribed cognitive load to the system. Further, I show that puts the cart before the horse, and the arguments on offer are viciously circular. However, the final objection, lays bare a deeper issue for EV. At the cost of giving up the middle position, however, the explanatory tools already available to EV can be shown to cover the work initially thought to require representation. I conclude that EV is best altered to be a non-representational theory of perception. (shrink)

Short‐term memory in vision is typically thought to divide into at least two memory stores: a short, fragile, high‐capacity store known as iconic memory, and a longer, durable, capacity‐limited store known as visual working memory (VWM). This paper argues that iconic memory stores icons, i.e., image‐like perceptual representations. The iconicity of iconic memory has significant consequences for understanding consciousness, nonconceptual content, and the perception–cognition border. Steven Gross and Jonathan Flombaum have recently challenged the division between iconic memory and VWM by (...) arguing against the idea of capacity limits in favor of a flexible resource‐based model of short‐term memory. I argue that, while VWM capacity is probably governed by flexible resources rather than a sharp limit, the two memory stores should still be distinguished by their representational formats. Iconic memory stores icons, while VWM stores discursive (i.e., language‐like) representations. I conclude by arguing that this format‐based distinction between memory stores entails that prominent views about consciousness and the perception–cognition border will likely have to be revised. (shrink)

The counterfactual account of physical computation is simple and, for the most part, very attractive. However, it is usually thought to trivialize the notion of physical computation insofar as it implies ‘limited pancomputationalism’, this being the doctrine that every deterministic physical system computes some function. Should we bite the bullet and accept limited pancomputationalism, or reject the counterfactual account as untenable? Jack Copeland would have us do neither of the above. He attempts to thread a path between the two horns (...) of the dilemma by buttressing the counterfactual account with extra conditions intended to block certain classes of deterministic physical systems from qualifying as physical computers. His theory is called the ‘algorithm execution account’. Here we show that the algorithm execution account entails limited pancomputationalism, despite Copeland’s argument to the contrary. We suggest, partly on this basis, that the counterfactual account should be accepted as it stands, pancomputationalist warts and all. (shrink)

In this article, I analyze how the relationship of developmental psychology with general psychology and cognitive science has unfolded. This historical analysis will provide a background for a critical examination of the present state of the art. I shall argue that the study of human mind is inherently connected with the study of its development. From the beginning of psychology as a discipline, general psychology and developmental psychology have followed parallel and relatively separated paths. This separation between adult and child (...) studies has also persisted with the emergence of cognitive science. The reason is due essentially to methodological problems that have involved not only research methods but also the very object of inquiry. At present, things have evolved in many ways. Psychology and cognitive science have enlarged their scope to include change process and the interaction between mind and environment. On the other hand, the possibility of using experimental methods to study infancy has allowed us to realize the complexity of young humans. These facts have paved the way for new possibilities of convergence, which are eliciting interesting results, despite a number of ongoing problems related to methods. (shrink)

Experiences like pains, pleasures, and emotions have affective phenomenal character: they feel pleasant or unpleasant. Imperativism proposes to explain affective phenomenal character by appeal to imperative content, a kind of intentional content that directs rather than describes. We argue that imperativism is on the right track, but has been developed in the wrong way. There are two varieties of imperativism on the market: first-order and higher-order. We show that neither is successful, and offer in their place a new theory: reflexive (...) imperativism. Our proposal is that an experience P feels pleasant in virtue of being constituted by a Command with reflexive imperative content, while an experience U feels unpleasant in virtue of being constituted by a Command with reflexive imperative content : More of P!Less of U! If you need a slogan: experiences have affective phenomenal character in virtue of commanding us Get more of me! Get less of me! (shrink)

The paper offers a solution to the generality problem for a reliabilist epistemology, by developing an “algorithm and parameters” scheme for type-individuating cognitive processes. Algorithms are detailed procedures for mapping inputs to outputs. Parameters are psychological variables that systematically affect processing. The relevant process type for a given token is given by the complete algorithmic characterization of the token, along with the values of all the causally relevant parameters. The typing that results is far removed from the typings of folk (...) psychology, and from much of the epistemology literature. But it is principled and empirically grounded, and shows good prospects for yielding the desired epistemological verdicts. The paper articulates and elaborates the theory, drawing out some of its consequences. Toward the end, the fleshed-out theory is applied to two important case studies: hallucination and cognitive penetration of perception. (shrink)

Autonomist accounts of cognitive science suggest that cognitive model building and theory construction (can or should) proceed independently of findings in neuroscience. Common functionalist justifications of autonomy rely on there being relatively few constraints between neural structure and cognitive function (e.g., Weiskopf, 2011). In contrast, an integrative mechanistic perspective stresses the mutual constraining of structure and function (e.g., Piccinini & Craver, 2011; Povich, 2015). In this paper, I show how model-based cognitive neuroscience (MBCN) epitomizes the integrative mechanistic perspective and concentrates (...) the most revolutionary elements of the cognitive neuroscience revolution (Boone & Piccinini, 2016). I also show how the prominent subset account of functional realization supports the integrative mechanistic perspective I take on MBCN and use it to clarify the intralevel and interlevel components of integration. (shrink)

In this work, it is shown that given a correct understanding of the nature of reference and of linguistic meaning generally, it is possible to produce non-revisionist analyses of the nature of -/- *Perceptual content, *Mental content generally, *Logical equivalence, *Logical dependence generally, *Counterfactual truth, *The causal efficacy of mental states, and *Our knowledge of ourselves and of the external world. -/- In addition, set-theoretic interpretations of several semantic concepts are put forth. These concepts include truth, falsehood, negation, and conjunction.

A collection of papers presented at the First International Summer Institute in Cognitive Science, University at Buffalo, July 1994, including the following papers: ** Topological Foundations of Cognitive Science, Barry Smith ** The Bounds of Axiomatisation, Graham White ** Rethinking Boundaries, Wojciech Zelaniec ** Sheaf Mereology and Space Cognition, Jean Petitot ** A Mereotopological Definition of 'Point', Carola Eschenbach ** Discreteness, Finiteness, and the Structure of Topological Spaces, Christopher Habel ** Mass Reference and the Geometry of Solids, Almerindo E. Ojeda (...) ** Defining a 'Doughnut' Made Difficult, N .M. Gotts ** A Theory of Spatial Regions with Indeterminate Boundaries, A.G. Cohn and N.M. Gotts ** Mereotopological Construction of Time from Events, Fabio Pianesi and Achille C. Varzi ** Computational Mereology: A Study of Part-of Relations for Multi-media Indexing, Wlodek Zadrozny and Michelle Kim. (shrink)

In this paper, the Author reviewed the typical objections against the claim that brains are computers, or, to be more precise, information-processing mechanisms. By showing that practically all the popular objections are based on uncharitable interpretations of the claim, he argues that the claim is likely to be true, relevant to contemporary cognitive science, and non-trivial.

Perceptual systems respond to proximal stimuli by forming mental representations of distal stimuli. A central goal for the philosophy of perception is to characterize the representations delivered by perceptual systems. It may be that all perceptual representations are in some way proprietarily perceptual and differ from the representational format of thought (Dretske 1981; Carey 2009; Burge 2010; Block ms.). Or it may instead be that perception and cognition always trade in the same code (Prinz 2002; Pylyshyn 2003). This paper rejects (...) both approaches in favor of perceptual pluralism, the thesis that perception delivers a multiplicity of representational formats, some proprietary and some shared with cognition. The argument for perceptual pluralism marshals a wide array of empirical evidence in favor of iconic (i.e., image-like, analog) representations in perception as well as discursive (i.e., language-like, digital) perceptual object representations. (shrink)

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory (...) to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year. (shrink)

Is action-guiding vision cognitively penetrable? More specifically, is the visual processing that guides our goal-directed actions sensitive to semantic information from cognitive states? This paper critically examines a recent family of arguments whose aim is to challenge a widespread and influential view in philosophy and cognitive science: the view that action-guiding vision is cognitively impenetrable. I argue, in response, that while there may very well be top–down causal influences on action-guiding vision, they should not be taken to be an instance (...) of cognitive penetration. Assuming otherwise is to assign a computational role to the influencing states that they cannot perform. Although questions about cognitive penetrability are ultimately empirical, the issues addressed in this paper are largely philosophical. The discussion here highlights an important set of considerations that help better understand the relations between cognition, vision, and action. (shrink)

It is often thought that consciousness has a qualitative dimension that cannot be tracked by science. Recently, however, some philosophers have argued that this worry stems not from an elusive feature of the mind, but from the special nature of the concepts used to describe conscious states. Marc Champagne draws on the neglected branch of philosophy of signs or semiotics to develop a new take on this strategy. The term “semiotics” was introduced by John Locke in the modern period – (...) its etymology is ancient Greek, and its theoretical underpinnings are medieval. Charles Sanders Peirce made major advances in semiotics, so he can act as a pipeline for these forgotten ideas. Most philosophers know Peirce as the founder of American pragmatism, but few know that he also coined the term “qualia,” which is meant to capture the intrinsic feel of an experience. Since pragmatic verification and qualia are now seen as conflicting commitments, Champagne endeavors to understand how Peirce could (or thought he could) have it both ways. The key, he suggests, is to understand how humans can insert distinctions between features that are always bound. Recent attempts to take qualities seriously have resulted in versions of panpsychism, but Champagne outlines a more plausible way to achieve this. So, while semiotics has until now been the least known branch of philosophy ending in –ics, his book shows how a better understanding of that branch can move one of the liveliest debates in philosophy forward. (shrink)

A large body of compelling evidence has been accumulated demonstrating that embodiment – the agent’s physical setup, including its shape, materials, sensors and actuators – is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool (...) of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe. (shrink)

Complex systems are to be seen as typically having multiple levels of organization. For instance, in the behavioural and cognitive sciences, there has been a long lasting trend, promoted by the seminal work of David Marr, putting focus on three distinct levels of analysis: the computational level, accounting for the What and Why issues, the algorithmic and the implementational levels specifying the How problem. However, the tremendous developments in neuroscience knowledge about processes at different scales of organization together with the (...) complexity of today cognitive theories suggest that there will hardly be only three levels of explanation. Instead, there will be many different degrees of commitments corresponding to the different granularities—from high-level (behavioural) models to low-level (neural and molecular) models of the cognitive research program. For instance, Bayesian approaches, that are usually advocated for formalizing Marr’s computational level and rational behaviour, have even been adopted to model synaptic plasticity and axon guidance by molecular gradients. As a result, we can consider the behavioural scientist as dealing with models at a multiplicity of levels. The purpose of this Research Topic in Frontiers in Theoretical and Philosophical Psychology is to promote an approach to the role of the levels and explanation and models which is of interest for cognitive scientists, neuroscientists, psychologists, behavioural scientists, and philosophers of science. (shrink)

Evidence from cognitive science supports the claim that humans and other animals see the world as divided into objects. Although this claim is widely accepted, it remains unclear whether the mechanisms of visual reference have representational content or are directly instantiated in the functional architecture. I put forward a version of the former approach that construes object files as icons for objects. This view is consistent with the evidence that motivates the architectural account, can respond to the key arguments against (...) representational accounts, and has explanatory advantages. I draw general lessons for the philosophy of perception and the naturalization of intentionality. (shrink)

Traditionally, philosophers have appealed to the phenomenological similarity between visual experience and visual imagery to support the hypothesis that there is significant overlap between the perceptual and imaginative domains. The current evidence, however, is inconclusive: while evidence from transcranial brain stimulation seems to support this conclusion, neurophysiological evidence from brain lesion studies (e.g., from patients with brain lesions resulting in a loss of mental imagery but not a corresponding loss of perception and vice versa) indicates that there are functional and (...) anatomical dissociations between mental imagery and perception. Assuming that the mental imagery and perception do not overlap, at least, to the extent traditionally assumed, then the question arises as to what exactly mental imagery is and whether it parallels perception by proceeding via several functionally distinct mechanisms. In this review, we argue that even though there may not be a shared mechanism underlying vision for perception and conscious imagery, there is an overlap between the mechanisms underlying vision for action and unconscious visual imagery. On the basis of these findings, we propose a modification of Kosslyn’s model of imagery that accommodates unconscious imagination and explore possible explanations of the quasi-pictorial phenomenology of conscious visual imagery in light of the fact that its underlying neural substrates and mechanisms typically are distinct from those of visual experience. (shrink)

As an emerging discipline, neuroeconomics faces considerable methodological and practical challenges. In this paper, I suggest that these challenges can be understood by exploring the similarities and dissimilarities between the emergence of neuroeconomics and the emergence of cognitive and computational neuroscience two decades ago. From these parallels, I suggest the major challenge facing theory formation in the neural and behavioural sciences is that of being under-constrained by data, making a detailed understanding of physical implementation necessary for theory construction in neuroeconomics. (...) Rather than following a top-down strategy, neuroeconomists should be pragmatic in the use of available data from animal models, information regarding neural pathways and projections, computational models of neural function, functional imaging and behavioural data. By providing convergent evidence across multiple levels of organization, neuroeconomics will have its most promising prospects of success. (shrink)

There are many branches of philosophy called “the philosophy of X,” where X = disciplines ranging from history to physics. The philosophy of artificial intelligence has a long history, and there are many courses and texts with that title. Surprisingly, the philosophy of computer science is not nearly as well-developed. This article proposes topics that might constitute the philosophy of computer science and describes a course covering those topics, along with suggested readings and assignments.

Expertise is a cognitive achievement that clearly involves experience and learning, and often requires explicit, time-consuming training specific to the relevant domain. It is also intuitive that this kind of achievement is, in a rich sense, genuinely perceptual. Many experts—be they radiologists, bird watchers, or fingerprint examiners—are better perceivers in the domain(s) of their expertise. The goal of this paper is to motivate three related claims, by substantial appeal to recent empirical research on perceptual expertise: Perceptual expertise is genuinely perceptual (...) and genuinely cognitive, and this phenomenon reveals how we can become epistemically better perceivers. These claims are defended against sceptical opponents that deny significant top-down or cognitive effects on perception, and opponents who maintain that any such effects on perception are epistemically pernicious. (shrink)

Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to implement sophisticated forms of human intelligence in machines. This research attempts to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable once we overcome short-term engineering challenges. We believe, however, that phenomenal consciousness cannot be implemented in machines. This becomes (...) clear when considering emotions and examining the dissociation between consciousness and attention in humans. While we may be able to program ethical behavior based on rules and machine learning, we will never be able to reproduce emotions or empathy by programming such control systems—these will be merely simulations. Arguments in favor of this claim include considerations about evolution, the neuropsychological aspects of emotions, and the dissociation between attention and consciousness found in humans. Ultimately, we are far from achieving artificial consciousness. (shrink)

This paper explores the possibility of building a coherent knowledge base for philosophyof design in IS research. After identifying and categorizing what is considered as designissues in the IS field, we propose a taxonomy of design abstractions in the form of a meta-theoretical structure. Based on the proposed hierarchical taxonomy, we have conducteda concept-centric literature review on four leading IS research journals publishedbetween January 2011 and July 2016. Our result shows that the IS field struggles withmisalignment of philosophy of design (...) issues, including constructing design theory,research positioning, and an inconsistent knowledge base in design subject areas such asethics and aesthetics. It is suggested that integrating philosophy of design into IS researchcould contribute to the development of a coherent body of design knowledge. We concludeby proposing a model both to study and integrate philosophy of design into the ISresearch field. (shrink)