Predicting and Reflecting: A Dual Framework For Dual Process Theory (PhD Thesis), 2017. (original) (raw)

Predicting and Reflecting: A Dual Framework for Dual Process Theory

Dual Process Theory has increasingly gained fame as a framework for explaining evidence in reasoning and decision making tasks. This theory proposes there must be a sharp distinction in thinking to explain two clusters of correlational features. One cluster describes a fast and intuitive process (Type 1), while the other describes a slow andreflective one (Type 2), (see Evans, 2008; Evans & Stanovich, 2013; Kahneman, 2011). However, as Samuels (2009) has noted, there is a problem of determining why these group of features form clusters, more than what the labels Type (or system) 1 and 2 can capture, the unity problem. We understand there might be differences in the processingarchitecture that grounds each type of process, thus requiring distinct cognitive frameworks for each. We argue that the predictive processing approach (as held by Hohwy, 2013 and Clark, 2016) is a more suitable framework for Type 1 processing. Such an approach proposes cognition is in the job of attempting to p...

From representations in predictive processing to degrees of representational features

Minds and Machines

Whilst the topic of representations is one of the key topics in philosophy of mind, it has only occasionally been noted that representations and representational features may be gradual. Apart from vague allusions, little has been said on what representational gradation amounts to and why it could be explanatorily useful. The aim of this paper is to provide a novel take on gradation of representational features within the neuroscientific framework of predictive processing. More specifically, we provide a gradual account of two features of structural representations: structural similarity and decoupling. We argue that structural similarity can be analysed in terms of two dimensions: number of preserved relations and state space granularity. Both dimensions can take on different values and hence render structural similarity gradual. We further argue that decoupling is gradual in two ways. First, we show that different brain areas are involved in decoupled cognitive processes to a grea...

The metaphysics of Predictive Processing - A non-representational account

2022

This dissertation focuses on generative models in the Predictive Processing framework. It is commonly accepted that generative models are structural representations; i.e. physical particulars representing via structural similarity. Here, I argue this widespread account is wrong: when closely scrutinized, generative models appear to be non-representational control structures realizing an agent’s sensorimotor skills. The dissertation opens (Ch.1) introducing the Predictive Processing account of perception and action, and presenting some of its connectionist implementations, thereby clarifying the role generative models play in Predictive Processing. Subsequently, I introduce the conceptual framework guiding the research (ch.2). I briefly elucidate the metaphysics of representations, emphasizing the specific functional role played by representational vehicles within the systems of which they are part. I close the first half of the dissertation (Ch.3) introducing the claim that generative models are structural representations, and defending it from intuitive but inconclusive objections. I then move to the second half of the dissertation, switching from exposition to criticism. First (Ch.4), I claim that the argument allegedly establishing that generative models are structural representations is flawed beyond repair, for it fails to establish generative models are structurally similar to their targets. I then consider alternative ways to establish that structural similarity, showing they all either fail or violate some other condition individuating structural representations. I further argue (Ch.5) that the claim that generative models are structural representations would not be warranted even if the desired structural similarity were established. For, even if generative models were to satisfy the relevant definition of structural representation, it would still be wrong to consider them as representations. This is because, as currently defined, structural representations fail to play the relevant functional role of representations, and thus cannot be rightfully identified as representations in the first place. This conclusion prompts a direct examination of generative models, to determine their nature (Ch.6). I thus analyze the simplest generative model I know of: a neural network functioning as a robotic “brain” and allowing different robotic creatures to swiftly and intelligently interact with their environments. I clarify how these networks allow the robots to acquire and exert the relevant sensorimotor abilities needed to solve the various cognitive tasks the robots are faced with, and then argue that neither the entire architecture nor any of its parts can possibly qualify as representational vehicles. In this way, the structures implementing generative models are revealed to be non-representational structures that instantiate an agent’s relevant sensorimotor skills. I show that my conclusion generalizes beyond the simple example I considered, arguing that adding computational ingredients to the architecture, or considering altogether different implementations of generative models, will in no way force a revision of my verdict. I further consider and allay a number of theoretical worries that it might generate, and then briefly conclude the dissertation

Unification by Fiat: Arrested Development of Predictive Processing

Cognitive Science, 2020

Predictive processing (PP) has been repeatedly presented as a unificatory account of perception, action, and cognition. In this paper, we argue that this is premature: As a unifying theory, PP fails to deliver general, simple, homogeneous, and systematic explanations. By examining its current trajectory of development, we conclude that PP remains only loosely connected both to its computational framework and to its hypothetical biological underpinnings, which makes its fundamentals unclear. Instead of offering explanations that refer to the same set of principles, we observe sys- tematic equivocations in PP-based models, or outright contradictions with its avowed principles. To make matters worse, PP-based models are seldom empirically validated, and they are fre- quently offered as mere just-so stories. The large number of PP-based models is thus not evidence of theoretical progress in unifying perception, action, and cognition. On the contrary, we maintain that the gap between theory and its biological and computational bases contributes to the arrested development of PP as a unificatory theory. Thus, we urge the defenders of PP to focus on its critical problems instead of offering mere re-descriptions of known phenomena, and to validate their models against possible alternative explanations that stem from different theoretical assumptions. Otherwise, PP will ultimately fail as a unified theory of cognition.

Symbolic and Sub-Symbolic Representations in Computational Models of Human Cognition

Theory & Psychology, 2003

The debate over symbolic versus sub-symbolic representations of human cognition has been continuing for thirty years, with little indication of a resolution. The argument is this: Does the human cognitive system use symbols as a representation of knowledge, and does it process symbols and their respective constituents? Or does the human cognitive system use a distributed representation of knowledge, and is it somehow capable of processing this distributed representation of knowledge in a complex and meaningful way? This paper argues for an integrated symbolic and sub-symbolic approach to the representation of cognition. The lines of reasoning used as evidence to bolster this argument for an integrated approach are the cognitive architecture the Adaptive Character of Thought-Rational (ACT-R), and biology, where it is argued that symbolic and sub-symbolic representations of cognition are part of an intellectual continuum, with sub-symbolic representations at the low end and symbolic r...

Predictive Processing and the Representation Wars

Clark has recently suggested that predictive processing advances a theory of neural function with the resources to put an ecumenical end to the ''representation wars'' of recent cognitive science. In this paper I defend and develop this suggestion. First, I broaden the representation wars to include three foundational challenges to representational cognitive science. Second, I articulate three features of predictive processing's account of internal representation that distinguish it from more orthodox representationalist frameworks. Specifically, I argue that it posits a resemblance-based representational architecture with organism-relative contents that functions in the service of pragmatic success, not veridical representation. Finally , I argue that internal representation so understood is either impervious to the three anti-representationalist challenges I outline or can actively embrace them.

Dual processes, probabilities, and cognitive architecture

Mind & Society, 2012

It has been argued that dual process theories are not consistent with Oaksford and Chater's probabilistic approach to human reasoning Chater in Psychol Rev 101:608-631, 1994, 2007;, which has been characterised as a ''single-level probabilistic treatment[s]'' (Evans 2007). In this paper, it is argued that this characterisation conflates levels of computational explanation. The probabilistic approach is a computational level theory which is consistent with theories of general cognitive architecture that invoke a WM system and an LTM system. That is, it is a single function dual process theory which is consistent with dual process theories like Evans' (2007) that use probability logic (Adams 1998) as an account of analytic processes. This approach contrasts with dual process theories which propose an analytic system that respects standard binary truth functional logic (Heit and Rotello in J Exp Psychol