Unifying perceptual and behavioral learning with a correlative subspace learning rule

Neural Dynamic Principles for an Intentional Embodied Agent

How situated embodied agents may achieve goals using knowledge is the classical question of natural and artificial intelligence. How organisms achieve this with their nervous systems is a central challenge for a neural theory of embodied cognition. To structure this challenge, we borrow terms from Searle's analysis of intentionality in its two directions of fit and six psychological modes (perception, memory, belief, intention-in-action, prior intention, desire). We postulate that intentional states are instantiated by neural activation patterns that are stabilized by neural interaction. Dynamic instabilities provide the neural mechanism for initiating and terminating intentional states and are critical to organizing sequences of intentional states. Beliefs represented by networks of concept nodes are autonomously learned and activated in response to desired outcomes. The neural dynamic principles of an intentional agent are demonstrated in a toy scenario in which a robotic agent explores an environment and paints objects in desired colors based on learned color transformation rules.

Synthetic consciousness: the distributed adaptive control perspective

Understanding the nature of consciousness is one of the grand outstanding scientific challenges. The fundamental methodological problem is how phenomenal first person experience can be accounted for in a third person verifiable form, while the conceptual challenge is to both define its function and physical realization. The distributed adaptive control theory of consciousness (DACtoc) proposes answers to these three challenges. The methodological challenge is answered relative to the hard problem and DACtoc proposes that it can be addressed using a convergent synthetic methodology using the analysis of synthetic biologically grounded agents, or quale parsing. DACtoc hypothesizes that consciousness in both its primary and secondary forms serves the ability to deal with the hidden states of the world and emerged during the Cambrian period, affording stable multi-agent environments to emerge. The process of consciousness is an autonomous virtualization memory, which serializes and unifies the parallel and subconscious simulations of the hidden states of the world that are largely due to other agents and the self with the objective to extract norms. These norms are in turn projected as value onto the parallel simulation and control systems that are driving action. This functional hypothesis is mapped onto the brainstem, midbrain and the thalamo-cortical and cortico-cortical systems and analysed with respect to our understanding of deficits of consciousness. Subsequently, some of the implications and predictions of DACtoc are outlined, in particular, the prediction that normative bootstrapping of conscious agents is predicated on an intentionality prior. In the view advanced here, human consciousness constitutes the ultimate evolutionary transition by allowing agents to become autonomous with respect to their evolutionary priors leading to a post-biological Anthropocene.This article is part of the themed issue 'The major synthetic evolutionary transitions'.

An embodied biologically constrained model of foraging: from classical and operant conditioning to adaptive real-world behavior in DAC-X

Animals successfully forage within new environments by learning, simulating and adapting to their surroundings. The functions behind such goal-oriented behavior can be decomposed into 5 top-level objectives: 'how', 'why', 'what', 'where', 'when' (H4W). The paradigms of classical and operant conditioning describe some of the behavioral aspects found in foraging. However, it remains unclear how the organization of their underlying neural principles account for these complex behaviors. We address this problem from the perspective of the Distributed Adaptive Control theory of mind and brain (DAC) that interprets these two paradigms as expressing properties of core functional subsystems of a layered architecture. In particular, we propose DAC-X, a novel cognitive architecture that unifies the theoretical principles of DAC with biologically constrained computational models of several areas of the mammalian brain. DAC-X supports complex foraging strategies through the progressive acquisition, retention and expression of task-dependent information and associated shaping of action, from exploration to goal-oriented deliberation. We benchmark DAC-X using a robot-based hoarding task including the main perceptual and cognitive aspects of animal foraging. We show that efficient goal-oriented behavior results from the interaction of parallel learning mechanisms accounting for motor adaptation, spatial encoding and decision-making. Together, our results suggest that the H4W problem can be solved by DAC-X building on the insights from the study of classical and operant conditioning. Finally, we discuss the advantages and limitations of the proposed biologically constrained and embodied approach towards the study of cognition and the relation of DAC-X to other cognitive architectures.

Visual anticipation biases conscious decision making but not bottom-up visual processing

Prediction plays a key role in control of attention but it is not clear which aspects of prediction are most prominent in conscious experience. An evolving view on the brain is that it can be seen as a prediction machine that optimizes its ability to predict states of the world and the self through the top-down propagation of predictions and the bottom-up presentation of prediction errors. There are competing views though on whether prediction or prediction errors dominate the formation of conscious experience. Yet, the dynamic effects of prediction on perception, decision making and consciousness have been difficult to assess and to model. We propose a novel mathematical framework and a psychophysical paradigm that allows us to assess both the hierarchical structuring of perceptual consciousness, its content and the impact of predictions and/or errors on conscious experience, attention and decision-making. Using a displacement detection task combined with reverse correlation, we reveal signatures of the usage of prediction at three different levels of perceptual processing: bottom-up fast saccades, top-down driven slow saccades and consciousnes decisions. Our results suggest that the brain employs multiple parallel mechanism at different levels of perceptual processing in order to shape effective sensory consciousness within a predicted perceptual scene. We further observe that bottom-up sensory and top-down predictive processes can be dissociated through cognitive load. We propose a probabilistic data association model from dynamical systems theory to model the predictive multi-scale bias in perceptual processing that we observe and its role in the formation of conscious experience. We propose that these results support the hypothesis that consciousness provides a time-delayed description of a task that is used to prospectively optimize real time control structures, rather than being engaged in the real-time control of behavior itself.

The why, what, where, when and how of goal-directed choice: neuronal and computational principles

The central problems that goal-directed animals must solve are: 'What do I need and Why, Where and When can this be obtained, and How do I get it?' or the H4W problem. Here, we elucidate the principles underlying the neuronal solutions to H4W using a combination of neurobiological and neurorobotic approaches. First, we analyse H4W from a system-level perspective by mapping its objectives onto the Distributed Adaptive Control embodied cognitive architecture which sees the generation of adaptive action in the real world as the primary task of the brain rather than optimally solving abstract problems. We next map this functional decomposition to the architecture of the rodent brain to test its consistency. Following this approach, we propose that the mammalian brain solves the H4W problem on the basis of multiple kinds of outcome predictions, integrating central representations of needs and drives (e.g. hypothalamus), valence (e.g. amygdala), world, self and task state spaces (e.g. neocortex, hippocampus and prefrontal cortex, respectively) combined with multi-modal selection (e.g. basal ganglia). In our analysis, goal-directed behaviour results from a well-structured architecture in which goals are bootstrapped on the basis of predefined needs, valence and multiple learning, memory and planning mechanisms rather than being generated by a singular computation.

A biologically based model for the integration of sensory-motor contingencies in rules and plans: a prefrontal cortex based extension of the Distributed Adaptive Control architecture

Intelligence depends on the ability of the brain to acquire and apply rules and representations. At the neuronal level these properties have been shown to critically depend on the prefrontal cortex. Here we present, in the context of the Distributed Adaptive Control architecture (DAC), a biologically based model for flexible control and planning based on key physiological properties of the prefrontal cortex, i.e. reward modulated sustained activity and plasticity of lateral connectivity. We test the model in a series of pertinent tasks, including multiple T-mazes and the Tower of London that are standard experimental tasks to assess flexible control and planning. We show that the model is both able to acquire and express rules that capture the properties of the task and to quickly adapt to changes. Further, we demonstrate that this biomimetic self-contained cognitive architecture generalizes to planning. In addition, we analyze the extended DAC architecture, called DAC 6, as a model that can be applied for the creation of intelligent and psychologically believable synthetic agents.