Differential neural mechanisms for early and late prediction error detection

Emerging evidence indicates that prediction, instantiated at different perceptual levels, facilitate visual processing and enable prompt and appropriate reactions. Until now, the mechanisms underlying the effect of predictive coding at different stages of visual processing have still remained unclear. Here, we aimed to investigate early and late processing of spatial prediction violation by performing combined recordings of saccadic eye movements and fast event-related fMRI during a continuous visual detection task. Psychophysical reverse correlation analysis revealed that the degree of mismatch between current perceptual input and prior expectations is mainly processed at late rather than early stage, which is instead responsible for fast but general prediction error detection. Furthermore, our results suggest that conscious late detection of deviant stimuli is elicited by the assessment of prediction error’s extent more than by prediction error per se. Functional MRI and functional connectivity data analyses indicated that higher-level brain systems interactions modulate conscious detection of prediction error through top-down processes for the analysis of its representational content, and possibly regulate subsequent adaptation of predictivemodels. Overall, our experimental paradigm allowed to dissect explicit from implicit behavioral and neural responses to deviant stimuli in terms of their reliance on predictive models

Generic Neuromorphic Principles of Cognition and Attention for Ants, Humans and Real-world Artefacts: a Comparative Computational Approach

Es considera que la cognició biològica fa servir mecanismes com la predicció, l'anticipació i l'atenció per resoldre tasques complexes. S'ha suggerit que aquests mecanismes es materialitzen en els mamífers a través d'interaccions entre les capes corticals, mentre que la seva manifestació en cervells relativament més simples, como el dels invertebrats, és encara poc clara. En la cognició artificial, la naturalesa i la interacció dels mecanismes mencionats roman, en gran mesura, no quantificada. Aquí proposem un enfoc filogènic i basat en models per descobrir com interactuen aquests mecanismes cognitius. Comencem amb el model simple del cervell d'un insecte i demostrem la necessitat dels anomenats forward models per explicar el comportament d'un insecte a escenaris dinàmics. Llavors proposem el marc PASAR per integrar i quantificar la interacció dels mencionats components de la cognició. Validem el PASAR en tasques robòtiques i en un experiment psicofísic humà, demostrant que el PASAR és una eina valuosa per modelar i avaluar la cognició biològica i per construir sistemes cognitius artificials.Biological cognition is thought to employ mechanisms like prediction, anticipation and attention for solving complex tasks. These mechanisms are suggested to be materialized through inter-layer cortical interactions in mammals, whereas their manifestation in relatively simpler brains, like the invertebrate brain, remains unclear. In artificial cognition, the nature and interplay of the above mechanisms remain largely unquantified. Here we propose a phylogenic, model-based approach to answer how these cognitive mechanisms interplay. We start with a simple model of the insect brain and demonstrate the necessity of the so-called forward models to account for insect behavior in dynamic scenarios. We then propose the PASAR framework to integrate and quantify the interplay of the above components of cognition. We validate PASAR in robotic tasks and in a human psychophysical experiment, proving PASAR as a valuable tool to model and evaluate biological cognition and to construct artificial cognitive systems

PASAR: an integrated model of prediction, anticipation, sensation, attention and response for artificial sensorimotor systems

A wide range of neuroscientific studies suggest the existence of cognitive mechanisms like attention, prediction, anticipation and strong vertical interactions between different hierarchical layers of the brain while performing complex tasks. Despite advances in both cognitive brain research and in the development of brain-inspired artificial cognitive systems, the interplay of these key ingredients of cognition remain largely elusive and unquantified in complex real-world tasks. Furthermore, it has not yet been demonstrated how a self-contained hierarchical cognitive system acting under limited resource constraints can quantifiably benefit from the incorporation of top–down and bottom–up attentional mechanisms. In this context, an open fundamental question is how a data association mechanism can integrate bottom–up sensory information and top–down knowledge. Here, building on the Distributed Adaptive Control (DAC) architecture, we propose a single framework for integrating these different components of cognition and demonstrate the framework’s performance in solving real-world and simulated robot tasks. Using the model we quantify the interactions between prediction, anticipation, attention and memory. Our results support the strength of a complete system that incorporates attention, prediction and anticipation mechanisms compared to incomplete systems for real-world and complex tasks. We unveil the relevance of transient memory that underlines the utility of the above mechanisms for intelligent knowledge management in artificial sensorimotor systems. These findings provide concrete predictions for physiological and psychophysical experiments to validate our model in biological cognitive systems.info:eu-repo/semantics/publishedVersio

Visual anticipation biases conscious decision making but not bottom-up visual processing

Prediction plays a key role in control of attention but it is not clear which aspects of prediction are most prominent in conscious experience. An evolving view on the brain is that it can be seen as a prediction machine that optimizes its ability to predict states of the world and the self through the top-down propagation of predictions and the bottom-up presentation of prediction errors. There are competing views though on whether prediction or prediction errors dominate the formation of conscious experience. Yet, the dynamic effects of prediction on perception, decision making and consciousness have been difficult to assess and to model. We propose a novel mathematical framework and a psychophysical paradigm that allows us to assess both the hierarchical structuring of perceptual consciousness, its content and the impact of predictions and/or errors on conscious experience, attention and decision-making. Using a displacement detection task combined with reverse correlation, we reveal signatures of the usage of prediction at three different levels of perceptual processing: bottom-up fast saccades, top-down driven slow saccades and consciousnes decisions. Our results suggest that the brain employs multiple parallel mechanism at different levels of perceptual processing in order to shape effective sensory consciousness within a predicted perceptual scene. We further observe that bottom-up sensory and top-down predictive processes can be dissociated through cognitive load. We propose a probabilistic data association model from dynamical systems theory to model the predictive multi-scale bias in perceptual processing that we observe and its role in the formation of conscious experience. We propose that these results support the hypothesis that consciousness provides a time-delayed description of a task that is used to prospectively optimize real time control structures, rather than being engaged in the real-time control of behavior itselfThis work was carried out as part of the CEEDS project; an EU funded Integrated Project under the Seventh Framework Programme (ICT-258749) and ERC grant cDAC (ERC-341196)

Visual anticipation biases conscious decision making but not bottom-up visual processing

Prediction plays a key role in control of attention but it is not clear which aspects of prediction are most prominent in conscious experience. An evolving view on the brain is that it can be seen as a prediction machine that optimizes its ability to predict states of the world and the self through the top-down propagation of predictions and the bottom-up presentation of prediction errors. There are competing views though on whether prediction or prediction errors dominate the formation of conscious experience. Yet, the dynamic effects of prediction on perception, decision making and consciousness have been difficult to assess and to model. We propose a novel mathematical framework and a psychophysical paradigm that allows us to assess both the hierarchical structuring of perceptual consciousness, its content and the impact of predictions and/or errors on conscious experience, attention and decision-making. Using a displacement detection task combined with reverse correlation, we reveal signatures of the usage of prediction at three different levels of perceptual processing: bottom-up fast saccades, top-down driven slow saccades and consciousnes decisions. Our results suggest that the brain employs multiple parallel mechanism at different levels of perceptual processing in order to shape effective sensory consciousness within a predicted perceptual scene. We further observe that bottom-up sensory and top-down predictive processes can be dissociated through cognitive load. We propose a probabilistic data association model from dynamical systems theory to model the predictive multi-scale bias in perceptual processing that we observe and its role in the formation of conscious experience. We propose that these results support the hypothesis that consciousness provides a time-delayed description of a task that is used to prospectively optimize real time control structures, rather than being engaged in the real-time control of behavior itselfThis work was carried out as part of the CEEDS project; an EU funded Integrated Project under the Seventh Framework Programme (ICT-258749) and ERC grant cDAC (ERC-341196)

A Novel Brain-Based Approach for Multi-Modal Multi- Target Tracking in a Mixed Reality Space

We propose an architecture for multi-modal multi-target tracking, for the integration of multi-sensory input and its top-down modulation through active deployment of sensors and effectors based on Bayesian inference. The implementation of our architecture, which is inspired by the Superior Colliculus (SC), engages a method for dynamical allocation of sensors and effectors to enhance tracking and to resolve conflicting multi-modal data. We suggest to use the joint probabilistic data association method as the basis for facilitating the topdown modulation of bottom-up sensory data. A world model, which is automatically created, provides high level knowledge of the current state and interaction scenarios, which is then used for modulation of bottom-up sensory data. We test our SC-based framework for multi-sensory data fusion to tackle in real-time a multi-person tracking problem in a human accessible mixed reality environment called XIM (eXperience Induction Machine)

An overview of disease-free buffalo breeding projects with reference to the different systems used in South Africa

Laubscher, L. & Hoffman, L. 2012. An overview of disease-free buffalo breeding projects with reference to the different systems used in South Africa. Sustainability, 4(11), 3124-3140, doi:10.3390/su4113124.The original publication is available at http://www.mdpi.com/2071-1050/4/11/3124This paper describes the successful national program initiated by the South African government to produce disease-free African buffalo so as to ensure the sustainability of this species due to threats from diseases. Buffalo are known carriers of foot-and-mouth disease, bovine tuberculosis, Corridor disease and brucellosis. A long-term program involving multiphase testing and a breeding scheme for buffalo is described where, after 10 years, a sustainable number of buffalo herds are now available that are free of these four diseases. A large portion of the success was attributable to the use of dairy cows as foster parents with the five-stage quarantine process proving highly effective in maintaining the “disease-free” status of both the calves and the foster cows. The projects proved the successfulness of breeding with African buffalo in a commercial system that was unique to African buffalo and maintained the “wildness” of the animals so that they could effectively be released back into the wild with minimal, if any, behavioral problems.Publishers' versio

The synthetic moth: a neuromorphic approach toward artificial olfaction in robots

info:eu-repo/semantics/publishedVersio

Chapter Moth-Like Chemo-Source Localization and Classification on an Indoor Autonomous Robot

Medical genetic