This article examines how Modern theories of mind remain even in some materialistic and hence ontologically anti-dualistic views; and shows how Dewey's pragmatism, anticipating Merleau-Ponty, 4E cognitive scientists and especially enactivism, repudiates these theories. Throughout I place Dewey’s thought in the context of scientific inquiry, both recent and historical and including the cognitive as well as traditional sciences; and I show how he incorporated sciences of his day into his thought, while also anticipating enactive cognitive science. While emphasizing Dewey’s continued (...) relevance, my main goal is to show how his scientifically informed account of perception and cognition combats skepticism propagated by certain scientific visions, exacerbated by commonplace notions about mind, that jointly suggest that human beings lack genuine access to reality. (shrink)

We outline a framework of multilevel neurocognitive mechanisms that incorporates representation and computation. We argue that paradigmatic explanations in cognitive neuroscience fit this framework and thus that cognitive neuroscience constitutes a revolutionary break from traditional cognitive science. Whereas traditional cognitive scientific explanations were supposed to be distinct and autonomous from mechanistic explanations, neurocognitive explanations aim to be mechanistic through and through. Neurocognitive explanations aim to integrate computational and representational functions and structures across multiple levels of organization in order to explain (...) cognition. To a large extent, practicing cognitive neuroscientists have already accepted this shift, but philosophical theory has not fully acknowledged and appreciated its significance. As a result, the explanatory framework underlying cognitive neuroscience has remained largely implicit. We explicate this framework and demonstrate its contrast with previous approaches. (shrink)

The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology – namely, Behaviorism and evolutionary psychology – that set aside mechanistic explanations or make use of optimality assumptions. Through (...) these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements. (shrink)

The use of the computer metaphor has led to the proposal of mind architecture (Pylyshyn 1984; Newell 1990) as a model of the organization of the mind. The dualist computational model, however, has, since the earliest days of psychological functionalism, required that the concepts mind architecture and brain architecture be remote from each other. The development of both connectionism and neurocomputational science, has sought to dispense with this dualism and provide general models of consciousness – a uniform cognitive architecture –, (...) which is in general reductionist, but which retains the computer metaphor. This paper examines, in the first place, the concepts of mind architecture and brain architecture, in order to evaluate the syntheses which have recently been offered. It then moves on to show how modifications which have been made to classical functionalist mind architectures, with the aim of making them compatible with brain architectures, are unable to resolve some of the most serious problems of functionalism. Some suggestions are given as to why it is not possible to relate mind structures and brain structures by using neurocomputational approaches, and finally the question is raised of the validity of reductionism in a theory which sets out to unite mind and brain architectures. (shrink)

According to the dominant computational approach in cognitive science, cognitive agents are digital computers; according to the alternative approach, they are dynamical systems. This target article attempts to articulate and support the dynamical hypothesis. The dynamical hypothesis has two major components: the nature hypothesis (cognitive agents are dynamical systems) and the knowledge hypothesis (cognitive agents can be understood dynamically). A wide range of objections to this hypothesis can be rebutted. The conclusion is that cognitive systems may well be dynamical systems, (...) and only sustained empirical research in cognitive science will determine the extent to which that is true. (shrink)

Theories related to cognitive sciences, Human-in-the-loop Cyber-physical systems, data analysis for decision-making, and computational ethics make clear the need to create transdisciplinary learning, research, and application strategies to bring coherence to the paradigm of a truly human-oriented technology. Autonomous objects assume more responsibilities for individual and collective phenomena, they have gradually filtered into routines and require the incorporation of ethical practice into the professions related to the development, modeling, and design of algorithms. To make this possible, it is pertinent and (...) urgent to bring them closer to the problems and approaches of the humanities. Increasingly transdisciplinary research must be part of the construction of systems that provide developers and scientists with the necessary bases to understand the ethical debate and therefore their commitment to society. This article considers two theories as articulating axes: Blumenberg’s, coming from the field of philosophy, for whom the process of technification and especially the implementation of mathematical models in their algorithmic form leads to an emptying of meaning and therefore makes programmers who implement their functions to be alien to the concerns that gave them origin; Daston’s, belonging to the field of the history of science and according to which the division of labor in the processes of technification of the calculation implies a kind of subordination in which those who implement the inventions of a small group of privileged mathematicians ignore the procedures that put them into operation. Given these two theories, the black box models prevalent in AI development, and the urgency of establishing explanatory frameworks for the development of computational ethics, this article exposes the need to give a voice to the cultural history of consciousness for promoting the discussions around the implementation of mathematical algorithms. The paper takes as a reference the different points of view that have emerged around the study of technological ethics, its applicability, management, and design. It criticizes the current state of studies from a humanistic perspective and explains how the historical perspective allows promoting the training of software engineers, developers and creators so that they assume intuitions and moral values in the development of their work. Specifically, it aims to expose how cultural history, applied to the study of consciousness and its phenomena, makes those involved in this technological revolution aware of the effect that they, through their algorithms, have on society in general and on human beings in particular. (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)