Knowing one's place: a free-energy approach to pattern regulation.

Understanding how organisms establish their form during embryogenesis and regeneration represents a major knowledge gap in biological pattern formation. It has been recently suggested that morphogenesis could be understood in terms of cellular information processing and the ability of cell groups to model shape. Here, we offer a proof of principle that self-assembly is an emergent property of cells that share a common (genetic and epigenetic) model of organismal form. This behaviour is formulated in terms of variational free-energy minimization-of the sort that has been used to explain action and perception in neuroscience. In brief, casting the minimization of thermodynamic free energy in terms of variational free energy allows one to interpret (the dynamics of) a system as inferring the causes of its inputs-and acting to resolve uncertainty about those causes. This novel perspective on the coordination of migration and differentiation of cells suggests an interpretation of genetic codes as parametrizing a generative model-predicting the signals sensed by cells in the target morphology-and epigenetic processes as the subsequent inversion of that model. This theoretical formulation may complement bottom-up strategies-that currently focus on molecular pathways-with (constructivist) top-down approaches that have proved themselves in neuroscience and cybernetics

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

Adolescent childbearing experiences in Kenya: geographical and socioeconomic determinants

Sub-Saharan Africa has one of the highest level of teenage pregnancies in the world. Some studies on this topic highlight the presence of unmet reproductive health needs of adolescent in different regions. Improving maternal health has been established as a key development priority among the Millennium Development Goals, and upgrading reproductive and maternal health is usually associated with the eradication of inequality and poverty and with the presence of health care programs and services devoted to girls’ education. We attempt to investigate the geographical and socioeconomic determinants of both teenage pregnancies and maternal health behaviours among adolescent women in Kenya. We ascertain the influence of the availability of health care facilities mainly oriented to the specific needs of reproductive health. Main data are represented by 2003 Kenyan Demographic and Health Survey. In addition, the DHS data set collects Global Positioning System locators for each of the primary sampling units included in the samples that enable a deep geographical analysis. We perform a multivariate multilevel analysis to estimate the influence that individual, household, and community-level factors have on the risk of adolescent childbearing. Additionally, a spatial component allows for the presence and proximity of maternal health services. We expect that the availability of reproductive health facilities acts together with levels of socio-economic development, individual and household characteristics and community fertility norms, in influencing individual reproductive behavior at very young ages.Kenya, gravidanze adolescenziali, salute materna, strutture sanitarie, modelli multilivello Kenya, teenage pregnancy, maternal health, health facilities, multilevel modelling, millennium development goals

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

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

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

How active perception and attractor dynamics shape perceptual categorization: A computational model

We propose a computational model of perceptual categorization that fuses elements of grounded and sensorimotor theories of cognition with dynamic models of decision-making. We assume that category information consists in anticipated patterns of agent–environment interactions that can be elicited through overt or covert (simulated) eye movements, object manipulation, etc. This information is firstly encoded when category information is acquired, and then re-enacted during perceptual categorization. The perceptual categorization consists in a dynamic competition between attractors that encode the sensorimotor patterns typical of each category; action prediction success counts as ‘‘evidence’’ for a given category and contributes to falling into the corresponding attractor. The evidence accumulation process is guided by an active perception loop, and the active exploration of objects (e.g., visual exploration) aims at eliciting expected sensorimotor patterns that count as evidence for the object category. We present a computational model incorporating these elements and describing action prediction, active perception, and attractor dynamics as key elements of perceptual categorizations. We test the model in three simulated perceptual categorization tasks, and we discuss its relevance for grounded and sensorimotor theories of cognition.Peer reviewe

How can bottom-up information shape learning of top-down attention-control skills?

How does bottom-up information affect the development of top-down attentional control skills during the learning of visuomotor tasks? Why is the eye fovea so small? Strong evidence supports the idea that in humans foveation is mainly guided by task-specific skills, but how these are learned is still an important open problem. We designed and implemented a simulated neural eye-arm coordination model to study the development of attention control in a search-and-reach task involving simple coloured stimuli. The model is endowed with a hard-wired bottom-up attention saliency map and a top-down attention component which acquires task-specific knowledge on potential gaze targets and their spatial relations. This architecture achieves high performance very fast. To explain this result, we argue that: (a) the interaction between bottom-up and top-down mechanisms supports the development of task-specific attention control skills by allowing an efficient exploration of potentially useful gaze targets; (b) bottom-up mechanisms boast the exploitation of the initial limited task-specific knowledge by actively selecting areas where it can be suitably applied; (c) bottom-up processes shape objects representation, their value, and their roles (these can change during learning, e.g. distractors can become useful attentional cues); (d) increasing the size of the fovea alleviates perceptual aliasing, but at the same time increases input processing costs and the number of trials required to learn. Overall, the results indicate that bottom-up attention mechanisms can play a relevant role in attention control, especially during the acquisition of new task-specific skills, but also during task performance

The Mixed Instrumental Controller: Using Value of Information to Combine Habitual Choice and Mental Simulation

Instrumental behavior depends on both goal-directed and habitual mechanisms of choice. Normative views cast these mechanisms in terms of model-free and model-based methods of reinforcement learning, respectively. An influential proposal hypothesizes that model-free and model-based mechanisms coexist and compete in the brain according to their relative uncertainty. In this paper we propose a novel view in which a single Mixed Instrumental Controller produces both goal-directed and habitual behavior by flexibly balancing and combining model-based and model-free computations. The Mixed Instrumental Controller performs a cost-benefits analysis to decide whether to chose an action immediately based on the available “cached” value of actions (linked to model-free mechanisms) or to improve value estimation by mentally simulating the expected outcome values (linked to model-based mechanisms). Since mental simulation entails cognitive effort and increases the reward delay, it is activated only when the associated “Value of Information” exceeds its costs. The model proposes a method to compute the Value of Information, based on the uncertainty of action values and on the distance of alternative cached action values. Overall, the model by default chooses on the basis of lighter model-free estimates, and integrates them with costly model-based predictions only when useful. Mental simulation uses a sampling method to produce reward expectancies, which are used to update the cached value of one or more actions; in turn, this updated value is used for the choice. The key predictions of the model are tested in different settings of a double T-maze scenario. Results are discussed in relation with neurobiological evidence on the hippocampus – ventral striatum circuit in rodents, which has been linked to goal-directed spatial navigation

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