Correctly understood, teleosemantics is the claim that “representation” is a function term. Things are called “representations” if they have a certain kind of function or telos and perform it in a certain kind of way. This claim is supported with a discussion and proposals about the function of a representation and of how representations perform that function. These proposals have been retrieved by putting together current descriptions from the literature on neural representations with earlier explorations of the features common to (...) most things we are inclined to call representations as these were assessed in Millikan. Of interest is the degree to which these independent sources converge. I conclude that there is no need to employ any new or technical sense of the term “representation” for it to play an important role in neuroscience. (shrink)

Neuroclassicism is the view that cognition is explained by “classical” computing mechanisms in the nervous system that exhibit a clear demarcation between processing machinery and read–write memory. The psychologist C. R. Gallistel has mounted a sophisticated defense of neuroclassicism by drawing from ethology and computability theory to argue that animal brains necessarily contain read–write memory mechanisms. This argument threatens to undermine the “connectionist” orthodoxy in contemporary neuroscience, which does not seem to recognize any such mechanisms. In this paper I argue (...) that the neuroclassicist critique rests on a misunderstanding of how computability theory constrains theorizing about natural computing mechanisms. (shrink)

The main thesis of this paper is that two prevailing theories about cognitive penetration are too extreme, namely, the view that cognitive penetration is pervasive and the view that there is a sharp and fundamental distinction between cognition and perception, which precludes any type of cognitive penetration. These opposite views have clear merits and empirical support. To eliminate this puzzling situation, we present an alternative theoretical approach that incorporates the merits of these views into a broader and more nuanced explanatory (...) framework. A key argument we present in favor of this framework concerns the evolution of intentionality and perceptual capacities. An implication of this argument is that cases of cognitive penetration must have evolved more recently and that this is compatible with the cognitive impenetrability of early perceptual stages of processing information. A theoretical approach that explains why this should be the case is the consciousness and attention dissociation framework. The paper discusses why concepts, particularly issues concerning concept acquisition, play an important role in the interaction between perception and cognition. (shrink)

La discusión acerca de funciones es de larga data en filosofía. Normalmente se describe a la revolución científica del siglo XVII como eliminando las causas finales y la teleología de la física. Sin embargo, el lenguaje funcional cumple un papel central en ciertas áreas de la práctica biológica. Esto ha llevado a muchos filósofos a intentar elucidar el concepto de función, en algunos casos para defender la relevancia de estos usos, en otros para mostrar que se trata de meras formas (...) de hablar que podrían ser eliminadas sin pérdida de ningún tipo de información relevante. El objetivo principal de este trabajo es mostrar la necesidad de apelar a conceptos funcionales para una reconstrucción adecuada de la teoría de la selección natural darwiniana. Pues, se utiliza lenguaje funcional en la conceptualización del campo de aplicación de esta teoría, y tal campo debe ser determinable con independencia la teoría de la selección natural. (shrink)

Many philosophers and psychologists have attempted to elucidate the nature of mental representation by appealing to notions like isomorphism or abstract structural resemblance. The ‘structural representations’ that these theorists champion are said to count as representations by virtue of functioning as internal models of distal systems. In his 2007 book, Representation Reconsidered, William Ramsey endorses the structural conception of mental representation, but uses it to develop a novel argument against representationalism, the widespread view that cognition essentially involves the manipulation of (...) mental representations. Ramsey argues that although theories within the ‘classical’ tradition of cognitive science once posited structural representations, these theories are being superseded by newer theories, within the tradition of connectionism and cognitive neuroscience, which rarely if ever appeal to structural representations. Instead, these theories seem to be explaining cognition by invoking so-called ‘receptor representations’, which, Ramsey claims, aren’t genuine representations at all—despite being called representations, these mechanisms function more as triggers or causal relays than as genuine stand-ins for distal systems. I argue that when the notions of structural and receptor representation are properly explicated, there turns out to be no distinction between them. There only appears to be a distinction between receptor and structural representations because the latter are tacitly conflated with the ‘mental models’ ostensibly involved in offline cognitive processes such as episodic memory and mental imagery. While structural representations might count as genuine representations, they aren’t distinctively mental representations, for they can be found in all sorts of non-intentional systems such as plants. Thus to explain the kinds of offline cognitive capacities that have motivated talk of mental models, we must develop richer conceptions of mental representation than those provided by the notions of structural and receptor representation. (shrink)

We situate the debate on intentionality within the rise of cognitive neuroscience and argue that cognitive neuroscience can explain intentionality. We discuss the explanatory significance of ascribing intentionality to representations. At first, we focus on views that attempt to render such ascriptions naturalistic by construing them in a deflationary or merely pragmatic way. We then contrast these views with staunchly realist views that attempt to naturalize intentionality by developing theories of content for representations in terms of information and biological function. (...) We echo several other philosophers by arguing that these theories over-generalize unless they are constrained by a theory of the functional role of representational vehicles. This leads to a discussion of the functional roles of representations, and how representations might be realized in the brain. We argue that there’s work to be done to identify a distinctively mental kind of representation. We close by sketching a way forward for the project of naturalizing intentionality. This will not be achieved simply by ascribing the content of mental states to generic neural representations, but by identifying specific neural representations that explain the puzzling intentional properties of mental states. (shrink)

This paper aims to clarify the relationship between consciousness and attention through theoretical considerations about evolution. Specifically, we will argue that the empirical findings on attention and the basic considerations concerning the evolution of the different forms of attention demonstrate that consciousness and attention must be dissociated regardless of which definition of these terms one uses. To the best of our knowledge, no extant view on the relationship between consciousness and attention has this advantage. Because of this characteristic, this paper (...) presents a principled and neutral way to settle debates concerning the relationship between consciousness and attention, without falling into disputes about the meaning of these terms. A decisive conclusion of this approach is that extreme views on the relationship between consciousness and attention must be rejected, including identity and full dissociation views. There is an overlap between the two within conscious attention, but developing a full understanding of this mechanism requires further empirical investigations. (shrink)

Biolinguistic inquiry investigates the human language faculty as an internal biological property. This article traces the development of biolinguistics from its early philosophical origins through its reformulation during the cognitive revolution of the 1950s and outlines my views on where the biolinguistic enterprise stands today. The growth of language in the individual, it is suggested, depends on (i) genetic factors, (ii) experience, and (iii) principles that are not specific to the faculty of language. The best current explanation of how language (...) is recursively generated is through Merge, an operation that takes objects already constructed, and reconstructs a new object from them, generating a 'language of thought', perhaps in a manner close to optimal (relying on principles of category (iii)), with externalization (hence communication) a secondary process. The concluding section of the article offers several objectives for future research in the field. (shrink)

In recent years, the family of algorithms collected under the term ``deep learning'' has revolutionized artificial intelligence, enabling machines to reach human-like performances in many complex cognitive tasks. Although deep learning models are grounded in the connectionist paradigm, their recent advances were basically developed with engineering goals in mind. Despite of their applied focus, deep learning models eventually seem fruitful for cognitive purposes. This can be thought as a kind of biological exaptation, where a physiological structure becomes applicable for a (...) function different from that for which it was selected. In this paper, it will be argued that it is time for cognitive science to seriously come to terms with deep learning, and we try to spell out the reasons why this is the case. First, the path of the evolution of deep learning from the connectionist project is traced, demonstrating the remarkable continuity, and the differences as well. Then, it will be considered how deep learning models can be useful for many cognitive topics, especially those where it has achieved performance comparable to humans, from perception to language. It will be maintained that deep learning poses questions that cognitive sciences should try to answer. One of such questions is the reasons why deep convolutional models that are disembodied, inactive, unaware of context, and static, are by far the closest to the patterns of activation in the brain visual system. (shrink)

The historical debate on representation in cognitive science and neuroscience construes representations as theoretical posits and discusses the degree to which we have reason to posit them. We reject the premise of that debate. We argue that experimental neuroscientists routinely observe and manipulate neural representations in their laboratory. Therefore, neural representations are as real as neurons, action potentials, or any other well-established entities in our ontology.

This paper proposes a model for implementation of intrinsic natural language sentence meaning in a physical language understanding system, where 'intrinsic' is understood as 'independent of meaning ascription by system-external observers'. The proposal is that intrinsic meaning can be implemented as a point attractor in the state space of a nonlinear dynamical system with feedback which is generated by temporally sequenced inputs. It is motivated by John Searle's well known (Behavioral and Brain Sciences, 3: 417–57, 1980) critique of the then-standard (...) and currently still influential computational theory of mind (CTM), the essence of which was that CTM representations lack intrinsic meaning because that meaning is dependent on ascription by an observer_._ The proposed dynamical model comprises a collection of interacting artificial neural networks, and constitutes a radical simplification of the principle of compositional phrase structure which is at the heart of the current standard view of sentence semantics because it is computationally interpretable as a finite state machine. (shrink)

Macphail’s “null hypothesis,” that there are no differences in intelligence, qualitative, or quantitative, between non-human vertebrates has been controversial. This controversy can be useful if it encourages interest in acquiring a detailed understanding of how non-human animals express flexible problem-solving capacity (“intelligence”), but limiting the discussion to vertebrates is too arbitrary. As an example, we focus here on Portia, a spider with an especially intricate predatory strategy and a preference for other spiders as prey. We review research on pre-planned detours, (...) expectancy violation, and a capacity to solve confinement problems where, in each of these three contexts, there is experimental evidence of innate cognitive capacities and reliance on internal representation. These cognitive capacities are related to, but not identical to, intelligence. When discussing intelligence, as when discussing cognition, it is more useful to envisage a continuum instead of something that is simply present or not; in other words, a continuum pertaining to flexible problem-solving capacity for “intelligence” and a continuum pertaining to reliance on internal representation for “cognition.” When envisaging a continuum pertaining to intelligence, Daniel Dennett’s notion of four Creatures (Darwinian, Skinnerian, Popperian, and Gregorian) is of interest, with the distinction between Skinnerian and Popperian Creatures being especially relevant when considering Portia. When we consider these distinctions, a case can be made for Portia being a Popperian Creature. Like Skinnerian Creatures, Popperian Creatures express flexible problem solving capacity, but the manner in which this capacity is expressed by Popperian Creatures is more distinctively cognitive. (shrink)

Working memory limits are best defined in terms of the complexity of the relations that can be processed in parallel. Complexity is defined as the number of related dimensions or sources of variation. A unary relation has one argument and one source of variation; its argument can be instantiated in only one way at a time. A binary relation has two arguments, two sources of variation, and two instantiations, and so on. Dimensionality is related to the number of chunks, because (...) both attributes on dimensions and chunks are independent units of information of arbitrary size. Studies of working memory limits suggest that there is a soft limit corresponding to the parallel processing of one quaternary relation. More complex concepts are processed by or In segmentation, tasks are broken into components that do not exceed processing capacity and can be processed serially. In conceptual chunking, representations are to reduce their dimensionality and hence their processing load, but at the cost of making some relational information inaccessible. Neural net models of relational representations show that relations with more arguments have a higher computational cost that coincides with experimental findings on higher processing loads in humans. Relational complexity is related to processing load in reasoning and sentence comprehension and can distinguish between the capacities of higher species. The complexity of relations processed by children increases with age. Implications for neural net models and theories of cognition and cognitive development are discussed. (shrink)

A distinction is made between two definitions of animal cognition: the one most frequently employed in cognitive sciences considers cognition as extracting and processing information; a more phenomenologically inspired model considers it as attributing to a form of the outside world a significance, linked to the state of the animal. The respective fields of validity of these two models are discussed along with the limitations they entail, and the questions they pose to evolutionary biologists are emphasized. This is followed by (...) a presentation of a general overview of what might be the study of the evolution of knowledge in animals. (shrink)

After Tractatus Logico-Philosophicus, Wittgenstein realized that elementary propositions may logically conflict with each other, due to the fact that the most elementary measurements may contradict each other. This led to the view that logic consists of various calculi. A calculus consists of measurement scales, each scale being a rule for the application of numbers. These scales determine logical relationships between elementary propositions by reason of arithmetical relations. Attempts to reject Wittgenstein's change in viewpoint, which ignore the relevance of measurement and (...) arithmetic, are remiss. In this light, I discuss Sarah Moss’s criticism of intermediate Wittgenstein. (shrink)