Saliency prediction in the coherence theory of attention

AbstractIn the coherence theory of attention, introduced by Rensink, O'Regan, and Clark (2000), a coherence field is defined by a hierarchy of structures supporting the activities taking place across the different stages of visual attention. At the interface between low level and mid-level attention processing stages are the proto-objects; these are generated in parallel and collect features of the scene at specific location and time. These structures fade away if the region is no further attended by attention. We introduce a method to computationally model these structures. Our model is based experimentally on data collected in dynamic 3D environments via the Gaze Machine, a gaze measurement framework. This framework allows to record pupil motion at the required speed and projects the point of regard in the 3D space (Pirri, Pizzoli, & Rudi, 2011; Pizzoli, Rigato, Shabani, & Pirri, 2011). To generate proto-objects the model is extended to vibrating circular membranes whose initial displacement is generated by the features that have been selected by classification. The energy of the vibrating membranes is used to predict saliency in visual search tasks

Dynamic visual attention model in image sequences

A new computational architecture of dynamic visual attention is introduced in this paper. Our approach defines a model for the generation of an active attention focus on a dynamic scene captured from a still or moving camera. The aim is to obtain the objects that keep the observer?s attention in accordance with a set of predefined features, including color, motion and shape. The solution proposed to the selective visual attention problem consists in decomposing the input images of an indefinite sequence of images into its moving objects, by defining which of these elements are of the user?s interest, and by keeping attention on those elements through time. Thus, the three tasks involved in the attention model are introduced. The Feature-Extraction task obtains those features (color, motion and shape features) necessary to perform object segmentation. The Attention-Capture task applies the criteria established by the user (values provided through parameters) to the extracted features and obtains the different parts of the objects of potential interest. Lastly, the Attention-Reinforcement task maintains attention on certain elements (or objects) of the image sequence that are of real interest

Executive Attention, Action Selection and Attention-Based Learning in Neurally Controlled Autonomous Agents

I describe the design and implementation of an integrated neural architecture, modelled on human executive attention, which is used to control both automatic (reactive) and willed action selection in a simulated robot. The model, based upon Norman and Shallice's supervisory attention system, incorporates important features of human attentional control: selection of an intended task over a more salient automatic task; priming of future tasks that are anticipated; and appropriate levels of persistence of focus of attention. Recognising that attention-based learning, mediated by the limbic system, and the hippocampus in particular, plays an important role in adaptive learning, I extend the Norman and Shallice model, introducing an intrinsic, attention-based learning mechanism that enhances the automaticity of willed actions and reduces future need for attentional effort required for dealing with distractions. These enhanced features support a new level of attentional autonomy in the operation of the simulated robot. Some properties of the model are explored using lesion studies, leading to the identification of a correspondence between the behavioural pathologies of the simulated robot and those seen in human patients suffering dysfunction of executive attention

Functional imaging of response selection

The functions of the prefrontal cortex remain controversial. Electrophysio- logical and lesion studies in monkeys have emphasised a role in working memory. In contrast, human functional neuroimaging studies and neuropsychology have emphasised a role in executive processes and volition. An alternative interpretation of the role of the prefrontal cortex is proposed in this thesis: that the prefrontal cortex mediates the attentional selection of sensory, mnemonic and motor representations in non-prefrontal cortex. This hypothesis is tested in a series of functional imaging experiments. In the first two experiments (chapters 4 and 5), event-related functional magnetic resonance imaging (fMRI) was used to re-examine the role of the prefrontal cortex in spatial and spatio-temporal working memory. Maintenance of information in memory was associated with activation of posterior prefrontal cortex (area 8). In contrast, the selection of an item from several remembered items was associated with activation of the middle and anterior parts of the prefrontal cortex (including area 46). To test the generalisation of 'selection' as a function of prefrontal cortex, experiment three (chapter 6) required subjects to select either a finger to move, or a colour from a multicolour display. Free selection was associated with activation of the prefrontal cortex (area 46) bilaterally, regardless of sensory or motor modality. The selection of voluntary actions has been proposed to depend on top-down modulation of motor regions by prefrontal cortex. The fourth and fifth experiments used structural equation modelling of fMRI time -series to measure the effective connectivity among prefrontal, premotor and parietal cortex. In young (chapter 7) and old (chapter 8) normal subjects, attention to action specifically enhanced coupling between prefrontal and premotor regions. This effect was not seen in patients with Parkinson's disease (chapter 8). Lastly, positron emission tomography was used to study planning in the Tower of London task, a common clinical measure of prefrontal function. Several variants of the task were developed, to distinguish the neural basis of the task's multiple cognitive components (chapter 9). The prefrontal cortex was activated in association with generation, selection or memory for moves, rather than planning towards a specified goal. The results support a generalised role in attentional selection of neuronal representations, whether stimuli, actions, or remembered items. The hypothesised attentional selection of responses is consistent with the activation of prefrontal cortex in working memory tasks and during attention to voluntary action. This role is compatible with the neurophysiological properties of individual neurons in the prefrontal cortex and the results of neuroimaging and lesion studies

Towards the Development of a Model of Vision: An Investigation into the Architectures and Mechanisms of Visual Perception

A conceptual model of visual perception has been developed using a multidisciplinary approach which combines both top-down and bottom-up descriptions of vision. Top-down psychological theories of visual perception have been investigated resulting in the development of a theory of perception which combines the best of existing accounts. Perception is defined in terms of a combination of "data driven" and "concept driven" explanations. Bottom-up neurophysiological descriptions have also been investigated to provide possible descriptions of structure and function for the development of a conceptual model based upon the theory. An attempt is made to provide a "complete" account of visual perception through the development of both the theory and conceptual model. Further it is envisaged that the development of such a model will provide new insight into the development of artificial vision systems and new algorithms for perceptual function in such systems

International Summerschool Computer Science 2014: Proceedings of Summerschool 7.7. - 13.7.2014

Proceedings of International Summerschool Computer Science 201

Interactive and life-long learning for identification and categorization tasks

Abstract (engl.) This thesis focuses on life-long and interactive learning for recognition tasks. To achieve these targets the separation into a short-term memory (STM) and a long-term memory (LTM) is proposed. For the incremental build up of the STM a similarity-based one-shot learning method was developed. Furthermore two consolidation algorithms were proposed enabling the incremental learning of LTM representations. Based on the Learning Vector Quantization (LVQ) network architecture an error-based node insertion rule and a node dependent learning rate are proposed to enable life-long learning. For learning of categories additionally a forward-feature selection method was introduced to separate co-occurring categories. In experiments the performance of these learning methods could be shown for difficult visual recognition problems