Nonmodular architectures of cognitive systems based on active inference

In psychology and neuroscience it is common to describe cognitive systems as input/output devices where perceptual and motor functions are implemented in a purely feedforward, open-loop fashion. On this view, perception and action are often seen as encapsulated modules with limited interaction between them. While embodied and enactive approaches to cognitive science have challenged the idealisation of the brain as an input/output device, we argue that even the more recent attempts to model systems using closed-loop architectures still heavily rely on a strong separation between motor and perceptual functions. Previously, we have suggested that the mainstream notion of modularity strongly resonates with the separation principle of control theory. In this work we present a minimal model of a sensorimotor loop implementing an architecture based on the separation principle. We link this to popular formulations of perception and action in the cognitive sciences, and show its limitations when, for instance, external forces are not modelled by an agent. These forces can be seen as variables that an agent cannot directly control, i.e., a perturbation from the environment or an interference caused by other agents. As an alternative approach inspired by embodied cognitive science, we then propose a nonmodular architecture based on active inference. We demonstrate the robustness of this architecture to unknown external inputs and show that the mechanism with which this is achieved in linear models is equivalent to integral control

[Commentary] Generative models as parsimonious descriptions of sensorimotor loops

The Bayesian brain hypothesis, predictive processing and variational free energy minimisation are typically used to describe perceptual processes based on accurate generative models of the world. However, generative models need not be veridical representations of the environment. We suggest that they can (and should) be used to describe sensorimotor relationships relevant for behaviour rather than precise accounts of the world

Literal Perceptual Inference

In this paper, I argue that theories of perception that appeal to Helmholtz’s idea of unconscious inference (“Helmholtzian” theories) should be taken literally, i.e. that the inferences appealed to in such theories are inferences in the full sense of the term, as employed elsewhere in philosophy and in ordinary discourse. In the course of the argument, I consider constraints on inference based on the idea that inference is a deliberate acton, and on the idea that inferences depend on the syntactic structure of representations. I argue that inference is a personal-level but sometimes unconscious process that cannot in general be distinguished from association on the basis of the structures of the representations over which it’s defined. I also critique arguments against representationalist interpretations of Helmholtzian theories, and argue against the view that perceptual inference is encapsulated in a module

Faculties and Modularity

While theorizing about mental faculties had been in decline throughout the nineteenth and early twentieth century, cognitivism and classical science brought back questions about the architecture of mind. Within this framework, Jerry Fodor developed a functionalist approach to what he called the “modularity of the mind.” While he believes that cognitive science can only explain the lower faculties of the mind, evolutionary psychology seizes on the notion of modularity and transforms it into the radical claim that the mind is modular all the way up. By comparison, recent approaches that take cognition to be embodied and situated have renewed the radical criticism of faculties or modules that was dominant from the nineteenth century onward. The concept of module is a naturalized successor of the traditional concept of faculty, as this chapter shows, and the debate about modules is centrally a debate about the possibility of naturalizing the mind

The Revolution will not be Optimised: Radical Enactivism, Extended Functionalism and the Extensive Mind

Optimising the 4E (embodied–embedded–extended–enactive) revolution in cognitive science arguably requires the rejection of two guiding commitments made by orthodox thinking in the field, namely that the material realisers of cognitive states and processes are located entirely inside the head (internalism), and that intelligent thought and action are to be explained in terms of the building and manipulation of content-bearing representations (representationalism). In other words, the full-strength 4E revolution would be secured only by a position that delivered externalism plus antirepresentationalism. I argue that one view in 4E space—extended functionalism—is appropriately poised to deliver externalism but not antirepresentationalism. By contrast, in the case of a competing 4E view—radical enactivism—even if that view can deliver antirepresentationalism, its pivotal notion of extensiveness falls short of establishing externalism. These conclusions are justified via an examination of, and by responding critically to, certain key arguments offered in support of their view (and against extended functionalism) by the radical enactivists

Learning action-oriented models through active inference

Converging theories suggest that organisms learn and exploit probabilistic models of their environment. However, it remains unclear how such models can be learned in practice. The open-ended complexity of natural environments means that it is generally infeasible for organisms to model their environment comprehensively. Alternatively, action-oriented models attempt to encode a parsimonious representation of adaptive agent-environment interactions. One approach to learning action-oriented models is to learn online in the presence of goal-directed behaviours. This constrains an agent to behaviourally relevant trajectories, reducing the diversity of the data a model need account for. Unfortunately, this approach can cause models to prematurely converge to sub-optimal solutions, through a process we refer to as a bad-bootstrap. Here, we exploit the normative framework of active inference to show that efficient action-oriented models can be learned by balancing goal-oriented and epistemic (information-seeking) behaviours in a principled manner. We illustrate our approach using a simple agent-based model of bacterial chemotaxis. We first demonstrate that learning via goal-directed behaviour indeed constrains models to behaviorally relevant aspects of the environment, but that this approach is prone to sub-optimal convergence. We then demonstrate that epistemic behaviours facilitate the construction of accurate and comprehensive models, but that these models are not tailored to any specific behavioural niche and are therefore less efficient in their use of data. Finally, we show that active inference agents learn models that are parsimonious, tailored to action, and which avoid bad bootstraps and sub-optimal convergence. Critically, our results indicate that models learned through active inference can support adaptive behaviour in spite of, and indeed because of, their departure from veridical representations of the environment. Our approach provides a principled method for learning adaptive models from limited interactions with an environment, highlighting a route to sample efficient learning algorithms

Embodied skillful performance: where the action is

© 2021, The Author(s). When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are smoothly performed without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist: that is, they cast skillful performance as a knowledge-driven process. Optimal motor control theory (OMCT), as representative par excellence of such approaches, casts skillful performance as an instruction, instantiated in the brain, that needs to be executed—a motor command. This paper aims to show the limitations of such instructionist approaches to skillful performance. We specifically address the question of whether the assumption of control-theoretic models is warranted. The first section of this paper examines the instructionist assumption, according to which skillful performance consists of the execution of theoretical instructions harnessed in motor representations. The second and third sections characterize the implementation of motor representations as motor commands, with a special focus on formulations from OMCT. The final sections of this paper examine predictive coding and active inference—behavioral modeling frameworks that descend, but are distinct, from OMCT—and argue that the instructionist, control-theoretic assumptions are ill-motivated in light of new developments in active inference

Two conceptions of the mind

Since the cognitive revolution during the last century the mind has been conceived of as being computer-like. Like a computer, the brain was assumed to be a physical structure (hardware) upon which a computational mind (software) was built. The mind was seen as a collection of independent programs which each have their own specific tasks, or modules. These modules took sensory input data and transduced it into language-like representations which were used in mental computations. Recently, a new conception of the mind has developed, grounded cognition. According to this model, sensory stimulus is saved in the original format in which it was received and recalled using association mechanisms. Rather than representations being language-like they are instead multimodal. The manipulation of these multimodal representations requires processing distributed throughout the brain. A new holistic model for mental architecture has developed in which the concerted activity of the brain\u27s modal systems produces functional systems which are intimately codependent with one another. The purpose of this thesis is to explore both the modular and multimodal theories of mental architecture. Each will be described in detail along with their supporting paradigms, cognitivism and grounded cognition. After my expositions I will offer support for my own position regarding these two theories before suggesting avenues for future research

How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition

A major goal of artificial intelligence (AI) is to create an agent capable of acquiring a general understanding of the world. Such an agent would require the ability to continually accumulate and build upon its knowledge as it encounters new experiences. Lifelong or continual learning addresses this setting, whereby an agent faces a continual stream of problems and must strive to capture the knowledge necessary for solving each new task it encounters. If the agent is capable of accumulating knowledge in some form of compositional representation, it could then selectively reuse and combine relevant pieces of knowledge to construct novel solutions. Despite the intuitive appeal of this simple idea, the literatures on lifelong learning and compositional learning have proceeded largely separately. In an effort to promote developments that bridge between the two fields, this article surveys their respective research landscapes and discusses existing and future connections between them