We propose a view of embodied representations that is alternative to both symbolic/linguistic approaches and purely sensorimotor views of cognition, and can account for procedural and declarative knowledge manipulation. In accordance with recent evidence in cognitive neuroscience and psychology, we argue that anticipatory and simulative mechanisms, which arose during evolution for action control and not for cognition, determined the first form of representational content and were exapted for increasingly sophisticated cognitive uses. In particular, procedural and declarative forms of knowledge can (...) be explained, respectively, in terms of on-line sensorimotor anticipation and off-line simulations of potential actions, which can give access to tacit knowledge and make it explicit. That is, mechanisms that evolved for the on-line prediction of the consequences of one's own actions (i.e. forward models) determine a (procedural) form of representation, and became exapted for off-line use. They can therefore be used to produce (declarative) knowledge of the world, by running a simulation of the action that would produce the relevant information. We conclude by discussing how embodied representations afford a form of internal manipulation that can be described as internalized situated action. (shrink)

Mind reading (i.e. the ability to infer the mental state of another agent) is taken to be the main cognitive ability required to share an intention and to collaborate. In this paper, I argue that another cognitive ability is also necessary to collaborate: representing others’ and ones’ own goals from a third-person perspective (other-centred or allocentric representation of goals). I argue that allocentric mind reading enables the cognitive ability of goal adoption, i.e. having the goal that another agent’s achieve p (...) because and as long as another agent has that goal that p. Having clarified the relevance of mutual goal adoption for acting jointly, I argue that when an intention is shared between several agents, each individual has an intention in favour of the joint action and one in favour of a joint mode of reasoning. This mode of reasoning is allocentric reasoning. Finally, I elaborate on the consequences of this view for the scientific study of human collaboration. (shrink)

In recent works, Kind has argued that imagination is a skill, since it possesses the two hallmarks of skill: improvability by practice, and control. I agree with Kind that and are indeed hallmarks of skill, and I also endorse her claim that imagination is a skill in virtue of possessing these two features. However, in this paper, I argue that Kind’s case for imagination’s being a skill is unsatisfactory, since it lacks robust empirical evidence. Here, I will provide evidence for (...) by considering data from mental rotation experiments and for by considering data from developmental experiments. I conclude that imagination is a skill, but there is a further pressing question of how the cognitive architecture of imagination has to be structured to make this possible. I begin by considering how can be implemented sub-personally. I argue that this can be accounted for by positing a selection mechanism which selects content from memory representations to be recombined into imaginings, using Bayesian generation. I then show that such an account can also explain. On this basis, I hold that not only is imagination a skill, but that it is also plausibly implemented sub-personally by a Bayesian selection mechanism. (shrink)

What is the function of cognition? On one influential account, cognition evolved to co-ordinate behaviour with environmental change or complexity. Liberal interpretations of this view ascribe cognition to an extraordinarily broad set of biological systems—even bacteria, which modulate their activity in response to salient external cues, would seem to qualify as cognitive agents. However, equating cognition with adaptive flexibility per se glosses over important distinctions in the way biological organisms deal with environmental complexity. Drawing on contemporary advances in theoretical biology (...) and computational neuroscience, we cash these distinctions out in terms of different kinds of generative models, and the representational and uncertainty-resolving capacities they afford. This analysis leads us to propose a formal criterion for delineating cognition from other, more pervasive forms of adaptive plasticity. On this view, biological cognition is rooted in a particular kind of functional organisation; namely, that which enables the agent to detach from the present and engage in counterfactual inference. (shrink)

(2013). Distributed intentionality: A model of intentional behavior in humans. Philosophical Psychology: Vol. 26, No. 2, pp. 267-290. doi: 10.1080/09515089.2011.641743.

This commentary proposes that “cognitive control” is neither componential nor emergent, but a fundamental feature of behavior. The term “control” requires an operational definition. This is best provided by the negative feedback loop that utilizes behavior to control perception; it does not control behavior per se. In order to model complex cognitive control, Perceptual Control Theory proposes that loops are organized into a dissociable hierarchical network (PCT; Powers, Clark, & McFarland, 1960; Powers, 1973a, 2008). In this way, behavior is dynamically (...) adaptive to environmental disturbances, rather than being formed by, or superimposed upon, learned associations between stimulus and response. (shrink)

This paper presents a systematic analysis of the various steps of goal-processing and intention creation, as the final outcome of goal-driven action generation. Intention theory has to be founded on goal theory: intentions require means-end reasoning and planning, conflict resolution, coherence. The process of intention formation and intentional action execution is strictly based on specific sets of beliefs (predictions, evaluations, calculation of costs, responsibility beliefs, competence, etc.). The origin of an intention is not necessarily a “desire” (which is just a (...) kind of goal). Intention is a two-layered goal-structure: the intended action(s) to be executed, and the intended outcome motivating that action—with two distinct kinds of “failure”. This belief-goal perspective also allows to examine two stages/types of intention, and the relations and differences between intention “in agenda” (future directed) and intention under execution (intention in action). I will argue that the will is much more than the intention driving an intentional action. I will also claim that intentions are not there just for motivating and regulating intentional actions (from the motor level to more complex behaviors), but that they play also several other important roles. (shrink)

Why is interaction so simple? This article presents a theory of interaction based on the use of shared representations as “coordination tools” (e.g., roundabouts that facilitate coordination of drivers). By aligning their representations (intentionally or unintentionally), interacting agents help one another to solve interaction problems in that they remain predictable, and offer cues for action selection and goal monitoring. We illustrate how this strategy works in a joint task (building together a tower of bricks) and discuss its requirements from a (...) computational viewpoint. (shrink)

Cognitive control has long been one of the most active areas of computational modeling work in cognitive science. The focus on computational models as a medium for specifying and developing theory predates the PDP books, and cognitive control was not one of the areas on which they focused. However, the framework they provided has injected work on cognitive control with new energy and new ideas. On the occasion of the books' anniversary, we review computational modeling in the study of cognitive (...) control, with a focus on the influence that the PDP approach has brought to bear in this area. Rather than providing a comprehensive review, we offer a framework for thinking about past and future modeling efforts in this domain. We define control in terms of the optimal parameterization of task processing. From this vantage point, the development of control systems in the brain can be seen as responding to the structure of naturalistic tasks, through the filter of the brain systems with which control directly interfaces. This perspective lays open a set of fascinating but difficult research questions, which together define an important frontier for future computational research. (shrink)