Andreas Stephens

Epistemological contextualism states that propositions about knowledge, expressed in sentences like “S knows that P,” are context-sensitive. Schaffer (2005) examines whether one of Lewis’ (1996), Cohen’s (1988) and DeRose’s (1995) influential contextualist accounts is preferable to the others. According to Schaffer, Lewis’ theory of relevant alternatives succeeds as a linguistic basis for contextualism and as an explanation of what the parameter that shifts with context is, while Cohen’s theory of thresholds and DeRose’s theory of standards fail. This paper argues that Schaffer’s analysis is unsatisfactory since it fails to show that thresholds and standards cannot cope with skepticism, as it is ultimately the conversation participants who control how the conversation plays out. Moreover, Schaffer fails to show that gradability is of no importance in inquiries.

We defend two theses: (1) Knowledge how and knowledge that are two distinct forms of knowledge, and; (2) Stanley-style intellectualism is neuro-psychologically implausible. Our naturalistic argument for the distinction between knowledge how and knowledge that is based on a consideration of the nature of slips and basic activities. We further argue that Stanley’s brand of intellectualism has certain ontological consequences that go against modern cognitive neuroscience and psychology. We tie up our line of thought by showing that input from cognitive neuroscience and psychology, on multiple levels of analysis, cohere in supporting the distinction between two separate forms of knowledge. The upshot is a neuro-psychologically plausible understanding of knowledge.

For quite some time, cognitive science has offered philosophy an opportunity to address central problems with an arsenal of relevant theories and empirical data. However, even among those naturalistically inclined, it has been hard to find a universally accepted way to do so. In this article, we offer a case study of how cognitive-science input can elucidate an epistemological issue that has caused extensive debate. We explore Jason Stanley’s idea of the practical grasp of a propositional truth and present naturalistic arguments against his reductive approach to knowledge. We argue that a plausible interpretation of cognitive-science input concerning knowledge—even if one accepts that knowledge how is partly propositional—must involve an element of knowing how to act correctly upon the proposition; and this element of knowing how to act correctly cannot itself be propositional.

The value problem for knowledge is the problem of explaining why knowledge is more valuable than mere true belief. The problem arises for reliabilism in particular, i.e., the externalist view that knowledge amounts to reliably acquired true belief. Goldman and Olsson argue that knowledge, in this sense, is more valuable than mere true belief due to the higher likelihood of future true beliefs (produced by the same reliable process) in the case of knowledge. They maintain that their solution works given four empirical assumptions, which they claim hold “normally.” However, they do not show that their assumptions are externalistically acceptable; nor do they provide detailed evidence for their normality claim. We address these remaining gaps in Goldman and Olsson’s solution in a constructive spirit. In particular, we suggest an externalist interpretation of the assumptions such that they essentially spell out what it means for a broad range of organisms capable of belief-like representations to be epistemically adapted to their environment. Our investigation also sheds light on the circumstances in which the assumptions fail to hold and knowledge therefore fails to have extra value in Goldman and Olsson’s sense. The upshot is a deeper understanding of their solution as a contribution to naturalized epistemology and a strengthened case for its empirical validity.

Hilary Kornblith argues that many traditional philosophical accounts involve problematic views of reflection (understood as second-order mental states). According to Kornblith, reflection does not add reliability, which makes it unfit to underlie a separate form of knowledge. We show that a broader understanding of reflection, encompassing Type 2 processes, working memory, and episodic long-term memory, can provide philosophy with elucidating input that a restricted view misses. We further argue that reflection in fact often does add reliability, through generalizability, flexibility, and creativity that is helpful in newly encountered situations, even if the restricted sense of both reflection and knowledge is accepted. And so, a division of knowledge into one reflexive (animal) form and one reflective form remains a plausible, and possibly fruitful, option.

This paper argues that intelligence can be approximated by the ability to produce accurate predictions. It is further argued that general intelligence can be approximated by context dependent predictive abilities combined with the ability to use working memory to abstract away contextual information. The flexibility associated with general intelligence can be understood as the ability to use selective attention to focus on specific aspects of sensory impressions to identify patterns, which can then be used to predict events in novel situations and environments. The argumentation synthesizes Godfrey-Smith’s environmental complexity theory, adding the notion of niche broadness as well as changes concerning the view of cognition and control, and Hohwy’s predictive mind theory, making explicit the significance of accuracy as a composite of trueness and precision where the nervous system acts as a distributed controller motivating actions that keep the body in homeostasis.

This paper argues that intelligence can be approximated by the ability to produce accurate predictions. It is further argued that general intelligence can be approximated by context dependent predictive abilities combined with the ability to use working memory to abstract away contextual information. The flexibility associated with general intelligence can be understood as the ability to use selective attention to focus on specific aspects of sensory impressions to identify patterns, which can then be used to predict events in novel situations and environments. The argumentation synthesizes Godfrey-Smith’s environmental complexity theory, adding the notion of niche broadness as well as changes concerning the view of cognition and control, and Hohwy’s predictive mind theory, making explicit the significance of accuracy as a composite of trueness and precision where the nervous system acts as a distributed controller motivating actions that keep the body in homeostasis.

The generality problem is commonly considered to be a critical difficulty for reliabilism. In this paper, we present a dynamical perspective on the problem in the spirit of naturalized epistemology. According to this outlook, it is worth investigating how token belief-forming processes instantiate specific types in the biological agent’s cognitive architecture and background experience, consisting in the process of attractor-guided neural activation. While our discussion of the generality problem assigns “scientific types” to token processes, it represents a unified account in the sense that it incorporates contextual and common sense features emphasized by other authors.

Numerous species use different forms of communication in order to successfully interact in their respective environment. This article seeks to elucidate limitations of the classical conduit metaphor by investigating communication from the perspectives of biology and artificial neural networks. First, communication is a biological natural phenomenon, found to be fruitfully grounded in an organism’s embodied structures and memory system, where specific abilities are tied to procedural, semantic, and episodic long-term memory as well as to working memory. Second, the account explicates differences between non-verbal and verbal communication and shows how artificial neural networks can communicate by means of ontologically non-committal modelling. This approach enables new perspectives of communication to emerge regarding both sender and receiver. It is further shown that communication features gradient properties that are plausibly divided into a reflexive and a reflective form, parallel to knowledge and reflection.

Hilary Kornblith argues that many traditional philosophical accounts involve problematic views of reflection. According to Kornblith, reflection does not add reliability, which makes it unfit to underlie a separate form of knowledge. We show that a broader understanding of reflection, encompassing Type 2 processes, working memory, and episodic long-term memory, can provide philosophy with elucidating input that a restricted view misses. We further argue that reflection in fact often does add reliability, through generalizability, flexibility, and creativity that is helpful in newly encountered situations, even if the restricted sense of both reflection and knowledge is accepted. And so, a division of knowledge into one reflexive form and one reflective form remains a plausible, and possibly fruitful, option.

A recent naturalistic epistemological account suggests that there are three nested basic forms of knowledge: procedural knowledge-how, conceptual knowledge-what, and propositional knowledge-that. These three knowledge-forms are grounded in cognitive neuroscience and are mapped to procedural, semantic, and episodic long-term memory respectively. This article investigates and integrates the neuroscientifically grounded account with knowledge-accounts from cognitive ethology and cognitive psychology. It is found that procedural and semantic memory, on a neuroscientific level of analysis, matches an ethological reliabilist account. This formation also matches System 1 from dual process theory on a psychological level, whereas the addition of episodic memory, on the neuroscientific level of analysis, can account for System 2 on the psychological level. It is furthermore argued that semantic memory and the cognitive ability of categorization are linked to each other, and that they can be fruitfully modeled within a conceptual spaces framework.

In an attempt to address some long-standing issues of epistemology, Hilary Kornblith proposes that knowledge is a natural kind the identification of which is the unique responsibility of one particular science: cognitive ethology. As Kornblith sees it, the natural kind thus picked out is knowledge as construed by reliabilism. Yet the claim that cognitive ethology has this special role has not convinced all critics. The present article argues that knowledge plays a causal and explanatory role within many of our more fruitful current theories, diverging from the reliabilist conception even in disciplines that are closely related to cognitive ethology, and thus still dealing with knowledge as a natural as opposed to a social phenomenon, where special attention will be given to cognitive neuroscience. However, rather than discarding the natural kind approach altogether, it is argued that many of Kornblith’s insights can in fact be preserved within a framework that is both naturalist and pluralist.

Within analytic philosophy, induction has been seen as a problem concerning inferences that have been analysed as relations between sentences. In this article, we argue that induction does not primarily concern relations between sentences, but between properties and categories. We outline a new approach to induction that is based on two theses. The first thesis is epistemological. We submit that there is not only knowledge-how and knowledge-that, but also knowledge-what. Knowledge-what concerns relations between properties and categories and we argue that it cannot be reduced to knowledge-that. We support the partition of knowledge by mapping it onto the long-term memory systems: procedural, semantic and episodic memory. The second thesis is that the role of inductive reasoning is to generate knowledge-what. We use conceptual spaces to model knowledge-what and the relations between properties and categories involved in induction.

Within analytic philosophy, induction has been seen as a problem concerning inferences that have been analysed as relations between sentences. In this article, we argue that induction does not primarily concern relations between sentences, but between properties and categories. We outline a new approach to induction that is based on two theses. The first thesis is epistemological. We submit that there is not only knowledge-how and knowledge-that, but also knowledge-what. Knowledge-what concerns relations between properties and categories and we argue that it cannot be reduced to knowledge-that. We support the partition of knowledge by mapping it onto the long-term memory systems: procedural, semantic and episodic memory. The second thesis is that the role of inductive reasoning is to generate knowledge-what. We use conceptual spaces to model knowledge-what and the relations between properties and categories involved in induction.