The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling

Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensory-motor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialogue between two fictional characters: Ernest, the �experimenter�, and Mary, the �computational modeller�. The dialogue consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modelling

Is visual lexical decision a dynamic and competitive process? no, if we look at reaction times. yes, if we study how it unfolds in time

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\u27 hand movements toward one of two items alternatives by recording the streaming x,y coordinates of the computer mouse. Participants categorized as \u27lexical\u27 or \u27non-lexical\u27 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\u27 complexity and trajectories\u27 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

Tracking Second Thoughts: Continuous and Discrete Revision Processes during Visual Lexical Decision

We studied the dynamics of lexical decisions by asking participants to categorize lexical and nonlexical stimuli and recording their mouse movements toward response buttons during the choice. In a previous report we revealed greater trajectory curvature and attraction to competitors for Low Frequency words and Pseudowords. This analysis did not clarify whether the trajectory curvature in the two conditions was due to a continuous dynamic competition between the response alternatives or if a discrete revision process (a "change of mind") took place during the choice from an initially selected response to the opposite one. To disentangle these two possibilities, here we analyse the velocity and acceleration profiles of mouse movements during the choice. Pseudowords\u27 peak movement velocity occurred with 100ms delay with respect to words and Letters Strings. Acceleration profile for High and Low Frequency words and Letters Strings exhibited a butterfly plot with one acceleration peak at 400ms and one deceleration peak at 650ms. Differently, Pseudowords\u27 acceleration profile had double positive peaks (at 400 and 600ms) followed by movement deceleration, in correspondence with changes in the decision from lexical to nonlexical response buttons. These results speak to different online processes during the categorization of Low Frequency words and Pseudowords, with a continuous competition process for the former and a discrete revision process for the latter

The Value of Foresight: How Prospection Affects Decision-Making

Traditional theories of decision-making assume that utilities are based on the intrinsic value of outcomes; in turn, these values depend on associations between expected outcomes and the current motivational state of the decision-maker. This view disregards the fact that humans (and possibly other animals) have prospection abilities, which permit anticipating future mental processes and motivational and emotional states. For instance, we can evaluate future outcomes in light of the motivational state we expect to have when the outcome is collected, not (only) when we make a decision. Consequently, we can plan for the future and choose to store food to be consumed when we expect to be hungry, not immediately. Furthermore, similarly to any expected outcome, we can assign a value to our anticipated mental processes and emotions. It has been reported that (in some circumstances) human subjects prefer to receive an unavoidable punishment immediately, probably because they are anticipating the dread associated with the time spent waiting for the punishment. This article offers a formal framework to guide neuroeconomic research on how prospection affects decision-making. The model has two characteristics. First, it uses model-based Bayesian inference to describe anticipation of cognitive and motivational processes. Second, the utility-maximization process considers these anticipations in two ways: to evaluate outcomes (e.g., the pleasure of eating a pie is evaluated differently at the beginning of a dinner, when one is hungry, and at the end of the dinner, when one is satiated), and as outcomes having a value themselves (e.g., the case of dread as a cost of waiting for punishment). By explicitly accounting for the relationship between prospection and value, our model provides a framework to reconcile the utility-maximization approach with psychological phenomena such as planning for the future and dread

Reading as Active Sensing: A Computational Model of Gaze Planning in Word Recognition

We offer a computational model of gaze planning during reading that consists of two main components: a lexical representation network, acquiring lexical representations from input texts (a subset of the Italian CHILDES database), and a gaze planner, designed to recognize written words by mapping strings of characters onto lexical representations. The model implements an active sensing strategy that selects which characters of the input string are to be fixated, depending on the predictions dynamically made by the lexical representation network. We analyze the developmental trajectory of the system in performing the word recognition task as a function of both increasing lexical competence, and correspondingly increasing lexical prediction ability. We conclude by discussing how our approach can be scaled up in the context of an active sensing strategy applied to a robotic setting

Embodied Choice:How Action Influences Perceptual Decision Making

Embodied Choice considers action performance as a proper part of the decision making process rather than merely as a means to report the decision. The central statement of embodied choice is the existence of bidirectional influences between action and decisions. This implies that for a decision expressed by an action, the action dynamics and its constraints (e.g. current trajectory and kinematics) influence the decision making process. Here we use a perceptual decision making task to compare three types of model: a serial decision-then-action model, a parallel decision-and-action model, and an embodied choice model where the action feeds back into the decision making. The embodied model incorporates two key mechanisms that together are lacking in the other models: action preparation and commitment. First, action preparation strategies alleviate delays in enacting a choice but also modify decision termination. Second, action dynamics change the prospects and create a commitment effect to the initially preferred choice. Our results show that these two mechanisms make embodied choice models better suited to combine decision and action appropriately to achieve suitably fast and accurate responses, as usually required in ecologically valid situations. Moreover, embodied choice models with these mechanisms give a better account of trajectory tracking experiments during decision making. In conclusion, the embodied choice framework offers a combined theory of decision and action that gives a clear case that embodied phenomena such as the dynamics of actions can have a causal influence on central cognition

Planning in view of future needs: a bayesian model of anticipated motivation

Traditional neuroeconomic theories of decision-making assume that utilities are based on intrinsic values of outcomes and that those values depend on how salient are outcomes in relation to the current motivational state. The fact that humans, and possibly also other animals, are able to plan in view of future motivations is not accounted by this view. So far, it is not clear which are the structures and the computational mechanisms employed by the brain during these processes. In this article, we present a Bayesian computational model that describes how the brain considers future motivations and assigns value to outcomes in relation to this information. We compare our model of anticipated motivation with a model that implements the standard perspective in decision-making and assigns value only based on the animal\u27s current motivations. The results of our simulations indicate an advantage of the model of anticipated motivation in volatile environments. Finally we connect our computational proposal to animal and human studies on prospection and foresight abilities and to neurophysiological investigations on their neural underpinnings

The Cat Is On the Mat. Or Is It a Dog? Dynamic Competition in Perceptual Decision Making

Recent neurobiological findings suggest that the brain solves simple perceptual decision-making tasks by means of a dynamic competition in which evidence is accumulated in favor of the alternatives. However, it is unclear if and how the same process applies in more complex, real-world tasks, such as the categorization of ambiguous visual scenes and what elements are considered as evidence in this case. Furthermore, dynamic decision models typically consider evidence accumulation as a passive process disregarding the role of active perception strategies. In this paper, we adopt the principles of dynamic competition and active vision for the realization of a biologically- motivated computational model, which we test in a visual catego- rization task. Moreover, our system uses predictive power of the features as the main dimension for both evidence accumulation and the guidance of active vision. Comparison of human and synthetic data in a common experimental setup suggests that the proposed model captures essential aspects of how the brain solves perceptual ambiguities in time. Our results point to the importance of the proposed principles of dynamic competi- tion, parallel specification, and selection of multiple alternatives through prediction, as well as active guidance of perceptual strategies for perceptual decision-making and the resolution of perceptual ambiguities. These principles could apply to both the simple perceptual decision problems studied in neuroscience and the more complex ones addressed by vision research.Peer reviewe