A Defense of Pure Connectionism

Connectionism is an approach to neural-networks-based cognitive modeling that encompasses the recent deep learning movement in artificial intelligence. It came of age in the 1980s, with its roots in cybernetics and earlier attempts to model the brain as a system of simple parallel processors. Connectionist models center on statistical inference within neural networks with empirically learnable parameters, which can be represented as graphical models. More recent approaches focus on learning and inference within hierarchical generative models. Contra influential and ongoing critiques, I argue in this dissertation that the connectionist approach to cognitive science possesses in principle (and, as is becoming increasingly clear, in practice) the resources to model even the most rich and distinctly human cognitive capacities, such as abstract, conceptual thought and natural language comprehension and production. Consonant with much previous philosophical work on connectionism, I argue that a core principle—that proximal representations in a vector space have similar semantic values—is the key to a successful connectionist account of the systematicity and productivity of thought, language, and other core cognitive phenomena. My work here differs from preceding work in philosophy in several respects: (1) I compare a wide variety of connectionist responses to the systematicity challenge and isolate two main strands that are both historically important and reflected in ongoing work today: (a) vector symbolic architectures and (b) (compositional) vector space semantic models; (2) I consider very recent applications of these approaches, including their deployment on large-scale machine learning tasks such as machine translation; (3) I argue, again on the basis mostly of recent developments, for a continuity in representation and processing across natural language, image processing and other domains; (4) I explicitly link broad, abstract features of connectionist representation to recent proposals in cognitive science similar in spirit, such as hierarchical Bayesian and free energy minimization approaches, and offer a single rebuttal of criticisms of these related paradigms; (5) I critique recent alternative proposals that argue for a hybrid Classical (i.e. serial symbolic)/statistical model of mind; (6) I argue that defending the most plausible form of a connectionist cognitive architecture requires rethinking certain distinctions that have figured prominently in the history of the philosophy of mind and language, such as that between word- and phrase-level semantic content, and between inference and association

Classical and Connectionist Models: Levels of Description

To begin, I introduce an analysis of interlevel relations that allows us to offer an initial characterization of the debate about the way classical and connectionist models relate. Subsequently, I examine a compatibility thesis and a conditional claim on this issue. With respect to the compatibility thesis, I argue that, even if classical and connectionist models are not necessarily incompatible, the emergence of the latter seems to undermine the best arguments for the Language of Thought Hypothesis, which is essential to the former. I attack the conditional claim of connectionism to eliminativism, presented by Ramsey et al. (1990), by discrediting their discrete characterization of common-sense psychological explanations and pointing to the presence of a moderate holistic constraint. Finally, I conclude that neither of the arguments considered excludes the possibility of viewing connectionist models as forming a part of a representational theory of cognition that dispenses with the Language of Thought Hypothesis

Towards explanatory pluralism in cognitive science

This thesis seeks to shed light on the intricate relationships holding between the various explanatory frameworks currently used within cognitive science. The driving question of this philosophical investigation concerns the nature and structure of cognitive explanation. More specifically, I attempt to clarify whether the sort of scientific explanations proposed for various cognitive phenomena at different levels of analysis or abstraction differ in significant ways from the explanations offered in other areas of scientific inquiry, such as biology, chemistry, or even physics. Thus, what I will call the problem of cognitive explanation, asks whether there is a distinctive feature that characterises cognitive explanations and distinguishes them from the explanatory schemas utilised in other scientific domains. I argue that the explanatory pluralism encountered within the daily practice of cognitive scientists has an essential normative dimension. The task of this thesis is to demonstrate that pluralism is an appropriate standard for the general explanatory project associated with cognitive science, which further implies defending and promoting the development of multiple explanatory schemas in the empirical study of cognitive phenomena

Linguistic Competence and New Empiricism in Philosophy and Science

The topic of this dissertation is the nature of linguistic competence, the capacity to understand and produce sentences of natural language. I defend the empiricist account of linguistic competence embedded in the connectionist cognitive science. This strand of cognitive science has been opposed to the traditional symbolic cognitive science, coupled with transformational-generative grammar, which was committed to nativism due to the view that human cognition, including language capacity, should be construed in terms of symbolic representations and hardwired rules. Similarly, linguistic competence in this framework was regarded as being innate, rule-governed, domain-specific, and fundamentally different from performance, i.e., idiosyncrasies and factors governing linguistic behavior. I analyze state-of-the-art connectionist, deep learning models of natural language processing, most notably large language models, to see what they can tell us about linguistic competence. Deep learning is a statistical technique for the classification of patterns through which artificial intelligence researchers train artificial neural networks containing multiple layers that crunch a gargantuan amount of textual and/or visual data. I argue that these models suggest that linguistic competence should be construed as stochastic, pattern-based, and stemming from domain-general mechanisms. Moreover, I distinguish syntactic from semantic competence, and I show for each the ramifications of the endorsement of a connectionist research program as opposed to the traditional symbolic cognitive science and transformational-generative grammar. I provide a unifying front, consisting of usage-based theories, a construction grammar approach, and an embodied approach to cognition to show that the more multimodal and diverse models are in terms of architectural features and training data, the stronger the case is for the connectionist linguistic competence. I also propose to discard the competence vs. performance distinction as theoretically inferior so that a novel and integrative account of linguistic competence originating in connectionism and empiricism that I propose and defend in the dissertation could be put forward in scientific and philosophical literature

What does semantic tiling of the cortex tell us about semantics?

Recent use of voxel-wise modeling in cognitive neuroscience suggests that semantic maps tile the cortex. Although this impressive research establishes distributed cortical areas active during the conceptual processing that underlies semantics, it tells us little about the nature of this processing. While mapping concepts between Marr's computational and implementation levels to support neural encoding and decoding, this approach ignores Marr's algorithmic level, central for understanding the mechanisms that implement cognition, in general, and conceptual processing, in particular. Following decades of research in cognitive science and neuroscience, what do we know so far about the representation and processing mechanisms that implement conceptual abilities? Most basically, much is known about the mechanisms associated with: (1) features and frame representations, (2) grounded, abstract, and linguistic representations, (3) knowledge-based inference, (4) concept composition, and (5) conceptual flexibility. Rather than explaining these fundamental representation and processing mechanisms, semantic tiles simply provide a trace of their activity over a relatively short time period within a specific learning context. Establishing the mechanisms that implement conceptual processing in the brain will require more than mapping it to cortical (and sub-cortical) activity, with process models from cognitive science likely to play central roles in specifying the intervening mechanisms. More generally, neuroscience will not achieve its basic goals until it establishes algorithmic-level mechanisms that contribute essential explanations to how the brain works, going beyond simply establishing the brain areas that respond to various task conditions

Predictive processing and mental representation

According to some (e.g. Friston, 2010) predictive processing (PP) models of cognition have the potential to offer a grand unifying theory of cognition. The framework defines a flexible architecture governed by one simple principle – minimise error. The process of Bayesian inference used to achieve this goal results in an ongoing flow of prediction that both makes sense of perception and unifies it with action. Such a provocative and appealing theory naturally has caused ripples in philosophical circles, prompting several commentaries (e.g. Hohwy, 2012; Clark, 2016). This thesis tackles one outstanding philosophical problem in relation to PP – the question of mental representation. In attempting to understand the nature of mental representations in PP systems I touch on several contentious points in philosophy of cognitive science, including the explanatory power of mechanisms vs. dynamics, the internalism vs. externalism debate, and the knotty problem of proper biological function. Exploring these issues enables me to offer a speculative solution to the question of mental representation in PP systems, with further implications for understanding mental representation in a broader context. The result is a conception of mind that is deeply continuous with life. With an explanation of how normativity emerges in certain classes of self-maintaining systems of which cognitive systems are a subset. We discover the possibility of a harmonious union between mechanics and dynamics necessary for making sense of PP systems, each playing an indispensable role in our understanding of their internal representations

Similarity space theories and the problem of concept acquisition.

232 p.Uno de los principales problemas a los que se enfrenta el empirismo es el de explicar cómo se adquieren los elementos más básicos de los conceptos, sin recurrir para ello a elementos innatos preexistentes. El propósito de esta tesis es mostrar que los mejores argumentos nativistas en contra de la posibilidad de aprender conceptos (primitivos) dependen de la asunción de que los constituyentes de los conceptos deben estar disponibles de antemano, como entrada de los procesos de adquisición. No obstante, mostraré que nada obliga a aceptar esa asunción (de precedencia). De hecho, presentaré un modelo en donde los elementos constitutivos de un concepto resultan del mismo proceso de aprendizaje en virtud del cual ese concepto se adquiere. Mi propuesta está basada en una teoría de espacios de similaridad articulada mediante prototipos. Además pruebo: (A) que dos nociones distintas de concepto deben distinguirse en este tipo de aproximación, a saber, conceptos como almacenamiento y conceptos como instanciación; y (B) que una propuesta como ésta reúne virtudes tanto del ámbito invariantista como del contextualista. Argumento también que, si los conceptos son dependientes del contexto ¿según sostiene el contextualismo¿, entonces los conceptos instanciados carecen de persistencia mínima y, por ello, no pueden ser una representación de sus categorías asociadas