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International audience; Numerous studies indicate that lexical decision is a dynamic process in which various sources of information (e.g., orthographic, phonological, lexical) need to be integrated over time and that mismatches between these sources produce uncertainty during decision-making. However, the specific contribution of these different sources of information to the decision process, and the specific ways they interact over time, are still incompletely understood. Here we report two experiments conducted to investigate the role of a key characteristic of language-orthographic consistency-in modulating reading strategies in a lexical decision task. We aimed to test different theories of how exactly orthographic consistency influences decisions, and the ways lexical and su-blexical representations are matched within it. For this, we exploited a key characteristic of the French language: the fact that retrieving spelling from phonology is hard, whereas the reverse is not the case (i.e., French is feedback-inconsistent but feedforward-consistent). Our results indicate a feedback inconsistency effect: uncertainty in our lexical decision task originates from the process of retrieving spelling from phonology, not when retrieving phonology from spelling. These results support the idea of a verification mechanism that compares orthographic sublexical information with lexical information during lexical decisions.

Controlling the body – given its huge number of degrees of freedom – poses severe computational challenges. Mounting evidence suggests that the brain alleviates this problem by exploiting “synergies”, or patterns of muscle activities (and/or movement dynamics and kinematics) that can be combined to control action, rather than controlling individual muscles of joints [1–10]. D’Ausilio et al. [11] explain how this view of motor organization based on synergies can profoundly change the way we interpret studies of action recognition in humans and monkeys, and in particular the controversy on the “granularity” of the mirror neuron system (MNs): whether it encodes either (lower) kinematic aspects of movements, or (higher) goal representations, or both but at different hierarchical levels [12]. Here we offer a complementary, computational perspective on the role of synergies for action recognition and the MNs. In computational modeling and robotics, it is widely assumed that a control scheme using synergies simplifies movement planning and execution. This scheme permits to use elemental behaviors or primitives as “building blocks” to be composed (e.g., combined linearly, sequenced) to produce more complex behaviors, thus controlling relatively few degrees of freedom [13–15]. Do synergies yield equivalent benefits for action recognition? To answer this question from a computational viewpoint, we frame the concept of synergies within generative architectures of action execution and recognition [16–20]. According to two leading theories of motor control, optimal feedback control [21] and active inference [22], the motor system can be conceptualized as a (hierarchical) generative model, which encodes a (probabilistic) mapping between “task goals” specified at a higher level (e.g., grasping a cup) and states of the “plant” to be controlled (i.e., (Less)

How do humans and other animals face novel problems for which predefined solutions are not available? Human problem solving links to flexible reasoning and inference rather than to slow trial-and-error learning. It has received considerable attention since the early days of cognitive science, giving rise to well known cognitive architectures such as SOAR and ACT-R, but its computational and brain mechanisms remain incompletely known. Furthermore, it is still unclear whether problem solving is a “specialized” domain or module of cognition, in the sense that it requires computations that are fundamentally different from those supporting perception and action systems. Here we advance a novel view of human problem solving as probabilistic inference with subgoaling. In this perspective, key insights from cognitive architectures are retained such as the importance of using subgoals to split problems into subproblems. However, here the underlying computations use probabilistic inference methods analogous to those that are increasingly popular in the study of perception and action systems. To test our model we focus on the widely used Tower of Hanoi (ToH) task, and show that our proposed method can reproduce characteristic idiosyncrasies of human problem solvers: their sensitivity to the “community structure” of the ToH and their difficulties in executing so-called “counterintuitive” movements. Our analysis reveals that subgoals have two key roles in probabilistic inference and problem solving. First, prior beliefs on (likely) useful subgoals carve the problem space and define an implicit metric for the problem at hand—a metric to which humans are sensitive. Second, subgoals are used as waypoints in the probabilistic problem solving inference and permit to find effective solutions that, when unavailable, lead to problem solving deficits. Our study thus suggests that a probabilistic inference scheme enhanced with subgoals provides a comprehensive framework to study problem solving and its deficits., Author Summary How humans solve challenging problems such as the Tower of Hanoi (ToH) or related puzzles is still largely unknown. Here we advance a computational model that uses the same probabilistic inference methods as those that are increasingly popular in the study of perception and action systems, thus making the point that problem solving does not need to be a specialized module or domain of cognition, but it can use the same computations underlying sensorimotor behavior. Crucially, we augment the probabilistic inference methods with subgoaling mechanisms that essentially permit to split the problem space into more manageable subparts, which are easier to solve. We show that our computational model can correctly reproduce important characteristics (and pitfalls) of human problem solving, including the sensitivity to the “community structure” of the ToH and the difficulty of executing so-called “counterintuitive” movements that require to (temporarily) move away from the final goal to successively achieve it.

Visual lexical decision is a classical paradigm in Psycholinguistic, and numerous studies have assessed a so-called "lexicality effect" (i.e., better performance with lexical over non-lexical stimuli). Far less is know relative to the dynamics of choice, as many studies measure overal reaction times which are not informative of the underlying processes. To unfold visual lexical decision in time, we measured participants' hand movements toward one of two items alternatives by recording the streaming x,y coordinates of the computer mouse. Participants categorized as 'lexical' or 'non-lexical' four kinds of stimuli: high and low frequency words, pseudowords, and letter strings. Spatial attraction toward the opposite category was present for low frequency words and pseudowords. Increasing stimuli ambiguity lead to enhcanced movements' complexity and trajectories' attraction to competitors, as no such effect was present for high frequency words and letter strings. Results fit well with dynamic models of perceptual decision-making describing the process as a competition between alternatives guided by the continuous accumulation of evidence, as well as with a recent neural model of visual word recognition that highlights the role of top-down influences and predictions on perceptual processes. More broadly, our results point to a key role of statistical decision theory to study linguistic processing in terms of dynamic and non-modular mechanisms. Finally, we discuss two aspects that make our set-up challenging for current dynamical models of decision-making: 1) not all information (e.g. ortographic, phonological and semantic) is available at the same time, therefore the accumulation process is nonstationary; 2) the choice is not completed at the action onset, but can be revised at any time during the movement.

During joint actions, humans continuously exchange coordination signals and use nonverbal, sensorimotor forms of communication. Here we discuss a specific example of sensorimotor communication-"signaling"-which consists in the intentional modification of one's own action plan (e.g., a plan for reaching a glass of wine) to make it more predictable or discriminable from alternative action plans that are contextually plausible (e.g., a plan for reaching another glass on the same table). We first review the existing evidence on signaling in human-human interactions, discussing under which conditions humans use signaling. Successively, we distill these insights into a computational theory of signaling during online interactions. Central to our approach are the following ideas: 1) signaling endows pragmatic plans with communicative goals; 2) signaling can be understood within a cost-benefit scheme, balancing the costs for the signaling agent against its benefits for interaction success; and 3) signaling may be part of an interactive strategy that optimizes success when joint goals are uncertain. Finally, we exemplify the benefits of signaling in a series of simulations and discuss how endowing robots with signaling abilities can increase the quality of human-robot interactions by making their behavior more predictable and "legible" for humans.

Distributed and hierarchical models of control are nowadays popular in computational modeling and robotics. In the artificial neural network literature, complex behaviors can be produced by composing elementary building blocks or motor primitives, possibly organized in a layered structure. However, it is still unknown how the brain learns and encodes multiple motor primitives, and how it rapidly reassembles, sequences and switches them by exerting cognitive control. In this paper we advance a novel proposal, a hierarchical programmable neural network architecture, based on the notion ofprogrammabilityand an interpreter-programmer computational scheme. In this approach, complex (and novel) behaviors can be acquired by embedding multiple modules (motor primitives) in a single,multi-purposeneural network. This is supported by recent theories of brain functioning in which skilled behaviors can be generated by combining functional different primitives embedded in “reusable” areas of “recycled” neurons. Such neuronal substrate supports flexible cognitive control, too. Modules are seen asinterpretersof behaviors having controlling input parameters, orprogramsthat encode structures of networks to be interpreted. Flexible cognitive control can be exerted by aprogrammermodule feeding the interpreters with appropriate input parameters, without modifying connectivity. Our results in a multipleT-maze robotic scenario show how this computational framework provides a robust, scalable and flexible scheme that can be iterated at different hierarchical layers permitting to learn, encode and control multiple qualitatively different behaviors.

Converging evidence shows that hand-actions are controlled at the level of synergies and not single muscles. One intriguing aspect of synergy-based action-representation is that it may be intrinsically sparse and the same synergies can be shared across several distinct types of hand-actions. Here, adopting a normative angle, we consider three hypotheses for hand-action optimal-control: sparse-combination hypothesis (SC) – sparsity in the mapping between synergies and actions - i.e., actions implemented using a sparse combination of synergies; sparse-elements hypothesis (SE) – sparsity in synergy representation – i.e., the mapping between degrees-of-freedom (DoF) and synergies is sparse; double-sparsity hypothesis (DS) – a novel view combining both SC and SE – i.e., both the mapping between DoF and synergies and between synergies and actions are sparse, each action implementing a sparse combination of synergies (as in SC), each using a limited set of DoFs (as in SE). We evaluate these hypotheses using hand kinematic data from six human subjects performing nine different types of reach-to-grasp actions. Our results support DS, suggesting that the best action representation is based on a relatively large set of synergies, each involving a reduced number of degrees-of-freedom, and that distinct sets of synergies may be involved in distinct tasks.

Humans excel at recognizing (or inferring) another's distal intentions, and recent experiments suggest that this may be possible using only subtle kinematic cues elicited during early phases of movement. Still, the cognitive and computational mechanisms underlying the recognition of intentional (sequential) actions are incompletely known and it is unclear whether kinematic cues alone are sufficient for this task, or if it instead requires additional mechanisms (e.g., prior information) that may be more difficult to fully characterize in empirical studies. Here we present a computationally-guided analysis of the execution and recognition of intentional actions that is rooted in theories of motor control and the coarticulation of sequential actions. In our simulations, when a performer agent coarticulates two successive actions in an action sequence (e.g., "reach-to-grasp" a bottle and "grasp-to-pour"), he automatically produces kinematic cues that an observer agent can reliably use to recognize the performer's intention early on, during the execution of the first part of the sequence. This analysis lends computational-level support for the idea that kinematic cues may be sufficiently informative for early intention recognition. Furthermore, it suggests that the social benefits of coarticulation may be a byproduct of a fundamental imperative to optimize sequential actions. Finally, we discuss possible ways a performer agent may combine automatic (coarticulation) and strategic (signaling) ways to facilitate, or hinder, an observer's action recognition processes.

Turn-taking is a preverbal skill whose mastering constitutes an important precondition for many social interactions and joint actions. However, the cognitive mechanisms supporting turn-taking abilities are still poorly understood. Here, we propose a computational analysis of turn-taking in terms of two general mechanisms supporting joint actions: action prediction (e.g., recognizing the interlocutor's message and predicting the end of turn) and signaling (e.g., modifying one's own speech to make it more predictable and discriminable). We test the hypothesis that in a simulated conversational scenario dyads using these two mechanisms can recognize the utterances of their co-actors faster, which in turn permits them to give and take turns more efficiently. Furthermore, we discuss how turn-taking dynamics depend on the fact that agents cannot simultaneously use their internal models for both action (or messages) prediction and production, as these have different requirements--or, in other words, they cannot speak and listen at the same time with the same level of accuracy. Our results provide a computational-level characterization of turn-taking in terms of cognitive mechanisms of action prediction and signaling that are shared across various interaction and joint action domains.

There is wide consensus that the prefrontal cortex (PFC) is able to exert cognitive control on behavior by biasing processing toward task-relevant information and by modulating response selection. This idea is typically framed in terms of top-down influences within a cortical control hierarchy, where prefrontal-basal ganglia loops gate multiple input–output channels, which in turn can activate or sequence motor primitives expressed in (pre-)motor cortices. Here we advance a new hypothesis, based on the notion of programmability and an interpreter–programmer computational scheme, on how the PFC can flexibly bias the selection of sensorimotor patterns depending on internal goal and task contexts. In this approach, multiple elementary behaviors representing motor primitives are expressed by a single multi-purpose neural network, which is seen as a reusable area of "recycled" neurons (interpreter). The PFC thus acts as a "programmer" that, without modifying the network connectivity, feeds the interpreter networks with specific input parameters encoding the programs (corresponding to network structures) to be interpreted by the (pre-)motor areas. Our architecture is validated in a standard test for executive function: the 1-2-AX task. Our results show that this computational framework provides a robust, scalable and flexible scheme that can be iterated at different hierarchical layers, supporting the realization of multiple goals. We discuss the plausibility of the "programmer–interpreter" scheme to explain the functioning of prefrontal-(pre)motor cortical hierarchies.