Cognitive Processing 

About: Cognitive Processing is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Cognition & Medicine. It has an ISSN identifier of 1612-4782. Over the lifetime, 1097 publications have been published receiving 21812 citations.

The novel object recognition memory: neurobiology, test procedure, and its modifications.

Abstract: Animal models of memory have been considered as the subject of many scientific publications at least since the beginning of the twentieth century. In humans, memory is often accessed through spoken or written language, while in animals, cognitive functions must be accessed through different kind of behaviors in many specific, experimental models of memory and learning. Among them, the novel object recognition test can be evaluated by the differences in the exploration time of novel and familiar objects. Its application is not limited to a field of research and enables that various issues can be studied, such as the memory and learning, the preference for novelty, the influence of different brain regions in the process of recognition, and even the study of different drugs and their effects. This paper describes the novel object recognition paradigms in animals, as a valuable measure of cognition. The purpose of this work was to review the neurobiology and methodological modifications of the test commonly used in behavioral pharmacology.

Predictive coding: an account of the mirror neuron system.

Abstract: Is it possible to understand the intentions of other people by simply observing their actions? Many believe that this ability is made possible by the brain’s mirror neuron system through its direct link between action and observation. However, precisely how intentions can be inferred through action observation has provoked much debate. Here we suggest that the function of the mirror system can be understood within a predictive coding framework that appeals to the statistical approach known as empirical Bayes. Within this scheme the most likely cause of an observed action can be inferred by minimizing the prediction error at all levels of the cortical hierarchy that are engaged during action observation. This account identifies a precise role for the mirror system in our ability to infer intentions from actions and provides the outline of the underlying computational mechanisms.

The body schema and multisensory representation(s) of peripersonal space

Abstract: To guide the movement of the body through space, the brain must constantly monitor the position and movement of the body in relation to nearby objects. The effective piloting of the body to avoid or manipulate objects in pursuit of behavioural goals requires an integrated neural representation of the body (the ‘body schema’) and of the space around the body (‘peripersonal space’). In the review that follows, we describe and evaluate recent results from neurophysiology, neuropsychology, and psychophysics in both human and non-human primates that support the existence of an integrated representation of visual, somatosensory, and auditory peripersonal space. Such a representation involves primarily visual, somatosensory, and proprioceptive modalities, operates in body-part-centred reference frames, and demonstrates significant plasticity. Recent research shows that the use of tools, the viewing of one’s body or body parts in mirrors, and in video monitors, may also modulate the visuotactile representation of peripersonal space.

Model learning for robot control: a survey

Abstract: Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot’s own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.

Active inference, sensory attenuation and illusions

Abstract: Active inference provides a simple and neurobiologically plausible account of how action and perception are coupled in producing (Bayes) optimal behaviour. This can be seen most easily as minimising prediction error: we can either change our predictions to explain sensory input through perception. Alternatively, we can actively change sensory input to fulfil our predictions. In active inference, this action is mediated by classical reflex arcs that minimise proprioceptive prediction error created by descending proprioceptive predictions. However, this creates a conflict between action and perception; in that, self-generated movements require predictions to override the sensory evidence that one is not actually moving. However, ignoring sensory evidence means that externally generated sensations will not be perceived. Conversely, attending to (proprioceptive and somatosensory) sensations enables the detection of externally generated events but precludes generation of actions. This conflict can be resolved by attenuating the precision of sensory evidence during movement or, equivalently, attending away from the consequences of self-made acts. We propose that this Bayes optimal withdrawal of precise sensory evidence during movement is the cause of psychophysical sensory attenuation. Furthermore, it explains the force-matching illusion and reproduces empirical results almost exactly. Finally, if attenuation is removed, the force-matching illusion disappears and false (delusional) inferences about agency emerge. This is important, given the negative correlation between sensory attenuation and delusional beliefs in normal subjects--and the reduction in the magnitude of the illusion in schizophrenia. Active inference therefore links the neuromodulatory optimisation of precision to sensory attenuation and illusory phenomena during the attribution of agency in normal subjects. It also provides a functional account of deficits in syndromes characterised by false inference and impaired movement--like schizophrenia and Parkinsonism--syndromes that implicate abnormal modulatory neurotransmission.