Is the Mind Massively Modular?

Articl

ETHICA EX MACHINA. Exploring artificial moral agency or the possibility of computable ethics

Since the automation revolution of our technological era, diverse machines or robots have gradually begun to reconfigure our lives. With this expansion, it seems that those machines are now faced with a new challenge: more autonomous decision-making involving life or death consequences. This paper explores the philosophical possibility of artificial moral agency through the following question: could a machine obtain the cognitive capacities needed to be a moral agent? In this regard, I propose to expose, under a normative-cognitive perspective, the minimum criteria through which we could recognize an artificial entity as a genuine moral entity. Although my proposal should be considered from a reasonable level of abstraction, I will critically analyze and identify how an artificial agent could integrate those cognitive features. Finally, I intend to discuss their limitations or possibilities

How Society Can Maintain Human-Centric Artificial Intelligence

Although not a goal universally held, maintaining human-centric artificial intelligence is necessary for society's long-term stability. Fortunately, the legal and technological problems of maintaining control are actually fairly well understood and amenable to engineering. The real problem is establishing the social and political will for assigning and maintaining accountability for artifacts when these artefacts are generated or used. In this chapter we review the necessity and tractability of maintaining human control, and the mechanisms by which such control can be achieved. What makes the problem both most interesting and most threatening is that achieving consensus around any human-centred approach requires at least some measure of agreement on broad existential concerns

Forced Moves or Good Tricks in Design Space? Landmarks in the Evolution of Neural Mechanisms for Action Selection

This review considers some important landmarks in animal evolution, asking to what extent specialized action-selection mechanisms play a role in the functional architecture of different nervous system plans, and looking for “forced moves” or “good tricks” (see Dennett, D., 1995, Darwin’s Dangerous Idea, Penguin Books, London) that could possibly transfer to the design of robot control systems. A key conclusion is that while cnidarians (e.g. jellyfish) appear to have discovered some good tricks for the design of behavior-based control systems—largely lacking specialized selection mechanisms—the emergence of bilaterians may have forced the evolution of a central ganglion, or “archaic brain”, whose main function is to resolve conflicts between peripheral systems. Whilst vertebrates have many interesting selection substrates it is likely that here too the evolution of centralized structures such as the medial reticular formation and the basal ganglia may have been a forced move because of the need to limit connection costs as brains increased in size

Introduction. Modelling natural action selection

Action selection is the task of resolving conflicts between competing behavioural alternatives. This theme issue is dedicated to advancing our understanding of the behavioural patterns and neural substrates supporting action selection in animals, including humans. The scope of problems investigated includes: (i) whether biological action selection is optimal (and, if so, what is optimized), (ii) the neural substrates for action selection in the vertebrate brain, (iii) the role of perceptual selection in decision-making, and (iv) the interaction of group and individual action selection. A second aim of this issue is to advance methodological practice with respect to modelling natural action section. A wide variety of computational modelling techniques are therefore employed ranging from formal mathematical approaches through to computational neuroscience, connectionism and agent-based modelling. The research described has broad implications for both natural and artificial sciences. One example, highlighted here, is its application to medical science where models of the neural substrates for action selection are contributing to the understanding of brain disorders such as Parkinson's disease, schizophrenia and attention deficit/hyperactivity disorder. Action selection is the task of resolving conflicts between competing behavioural alternatives, or, more simply put, of deciding ‘what to do next’. As a general problem facing all autonomous beings—animals and artificial agents—it has exercised the minds of scientists from many disciplines: those concerned with understanding the biological bases of behaviour (ethology, neurobiology and psychology) and those concerned with building artefacts, real or simulated, that behave appropriately in complex worlds (artificial intelligence, artificial life and robotics). Work in these different domains has established a wide variety of methodologies that address the same underlying problems from different perspectives. One approach to characterizing this multiplicity of methods is to distinguish between the analytical and the synthetic branches of the behavioural and brain sciences (Braitenberg 1986). From the perspective of analytical science, an important goal is to describe transitions in behaviour; these can occur at many different temporal scales, and can be considered as instances of ‘behavioural switching’ or, more anthropomorphically, as ‘choice points’. Analytical approaches also seek to identify the biological substrates that give rise to such transitions, for instance, by probing in the nervous system to find critical components—candidate action-selection mechanisms—on which effective and appropriate switching may depend. Beyond such descriptions, of course, a central goal of behavioural science is to explain why any observed transition (or sequence of transitions) occurs in a given context, perhaps referencing such explanation to normative concepts such as ‘utility’ or ‘fitness’. These explanations may also make use of mechanistic accounts that explain how underlying neural control systems operate to generate observed behavioural outcomes. It is at the confluence of these mechanistic and normative approaches that the synthetic approach in science is coming to have an increasing influence. The experimentalist seeks the help of the mathematician or engineer and asks ‘what would it take to build a system that acts in this way?’ Modelling—the synthesis of artificial systems that mimic natural ones—has always played an important role in biology; however, the last few decades have seen a dramatic expansion in the range of modelling methodologies that have been employed. Formal, mathematical models with provable properties continue to be of great importance (e.g. Bogacz et al. 2007; Houston et al. 2007). Now, added to these, there is a burgeoning interest in larger-scale simulations that allow the investigation of systems for which formal mathematical solutions are, as a result of their complexity, either intractable or simply unknown. However, synthetic models, once built, may often be elucidated by analytical techniques; thus synthetic and analytical approaches are best pursued jointly. Analysis of a formally intractable simulation often consists of observing the system's behaviour then measuring and describing it using many of the same tools as traditional experimental science (Bryson et al. 2007). Such an analysis can serve to uncover heuristics for the interpretation of empirical data as well as to generate novel hypotheses to be tested experimentally. The questions to be addressed in considering models of action selection include: is the model sufficiently constrained by biological data that its functioning can capture interesting properties of the natural system of interest? Do manipulations of the model, intended to mirror scientific procedures or observed natural processes, result in similar outcomes to those seen in real life? Does the model make predictions? Is the model more complex than it needs to be in order to describe a phenomenon, or is it too simple to engage with empirical data? A potential pitfall of more detailed computational models is that they may trade the sophistication with which they match biological detail with comprehensibility. The scientist is then left with two systems, one natural and the other synthesized, neither of which is well understood. Hence, the best models hit upon a good trade-off between accurately mimicking key properties of a target biological system at the same time as remaining understandable to the extent that new insights into the natural world are generated. In this theme issue, we present a selection of some of the most promising contemporary approaches to modelling action selection in natural systems. The range of methodologies is broad—from formal mathematical models, through to models of artificial animals, here called agents, embedded in simulated worlds (often containing other agents). We also consider mechanistic accounts of the neural processes underlying action selection through a variety of computational neuroscience and connectionist approaches. In this article, we summarize the main substantive areas of this theme issue and the contributions of each article and then return briefly to a discussion of the modelling techniques

A robot model of the basal ganglia: Behavior and intrinsic processing

The existence of multiple parallel loops connecting sensorimotor systems to the basal ganglia has given rise to proposals that these nuclei serve as a selection mechanism resolving competitions between the alternative actions available in a given context. A strong test of this hypothesis is to require a computational model of the basal ganglia to generate integrated selection sequences in an autonomous agent, we therefore describe a robot architecture into which such a model is embedded, and require it to control action selection in a robotic task inspired by animal observations. Our results demonstrate effective action selection by the embedded model under a wide range of sensory and motivational conditions. When confronted with multiple, high salience alternatives, the robot also exhibits forms of behavioral disintegration that show similarities to animal behavior in conflict situations. The model is shown to cast light on recent neurobiological findings concerning behavioral switching and sequencing

Decision-Making From the Animal Perspective: Bridging Ecology and Subjective Cognition

Organisms have evolved to trade priorities across various needs, such as growth, survival, and reproduction. In naturally complex environments this incurs high computational costs. Models exist for several types of decisions, e.g., optimal foraging or life history theory. However, most models ignore proximate complexities and infer simple rules specific to each context. They try to deduce what the organism must do, but do not provide a mechanistic explanation of how it implements decisions. We posit that the underlying cognitive machinery cannot be ignored. From the point of view of the animal, the fundamental problems are what are the best contexts to choose and which stimuli require a response to achieve a specific goal (e.g., homeostasis, survival, reproduction). This requires a cognitive machinery enabling the organism to make predictions about the future and behave autonomously. Our simulation framework includes three essential aspects: (a) the focus on the autonomous individual, (b) the need to limit and integrate information from the environment, and (c) the importance of goal-directed rather than purely stimulus-driven cognitive and behavioral control. The resulting models integrate cognition, decision-making, and behavior in the whole phenotype that may include the genome, physiology, hormonal system, perception, emotions, motivation, and cognition. We conclude that the fundamental state is the global organismic state that includes both physiology and the animal's subjective “mind”. The approach provides an avenue for evolutionary understanding of subjective phenomena and self-awareness as evolved mechanisms for adaptive decision-making in natural environments