Controlled Exploration of Alternative Mechanisms in Cognitive Modeling

Overt cognitive behavior arises through a complex interaction between internal, not directly observable, cognitive mechanisms. As there ma

Mental Image Reinterpretation in the Intersection of Conceptual and Visual Constraints

Introduction Mental imagery in general, and mental image reinterpretation in particular, has attracted much attention in the field of cognitive science as it involves a highly debated phenomenon, namely that of seeing an image "in the mind's eye". Alternative accounts for this mental experience range from the `descriptive view' that mental images are non-visual and non-functional (Pylyshyn, 1978, 1981), and the claim that mental images are "overspecified" and therefore unambiguous (Reisberg and Chambers, 1991), to the `depictive view' stating that mental images constitute rich repositories of visually represented information which could support alternative interpretations (Kosslyn, 1978, 1994; Farah, 1988; Peterson et al, 1992). These views take opposite sides in what is called the `imagery debate', and offer different answers to the question of how mental images are represented internally, and whether they constitute a pregnant "sounding board" for non-visua

Emergence of Attention Focus in a Biologically-Based Bidirectionally-Connected Hierarchical Network

We present a computational model for visual processing where attentional focus emerges fundamental mechanisms inherent to human vision. Through detailed analysis of activation development in the network we demonstrate how normal interaction between top-down and bottom-up processing and intrinsic mutual competition within processing units can give rise to attentional focus. The model includes both spatial and object-based attention, which are computed simultaneously, and can mutually reinforce each other. We show how a non-salient location and a corresponding non-salient feature set that are at first weakly activated by visual input can be reinforced by top-down feedback signals (centrally controlled attention), and instigate a change in attentional focus to the weak object. One application of this model is highlight a task-relevant object in a cluttered visual environment, even when this object is nonsalient (non-conspicuous)

Multi - step - ahead cyclone intensity prediction with Bayesian neural networks

The chaotic nature of cyclones makes track and wind-intensity prediction a challenging task. The complexity in attaining robust and accurate prediction increases with an increase of the prediction horizon. There is lack of robust uncertainty quantification in models that have been used for cyclone prediction problems. Bayesian inference provide a principled approach for quantifying uncertainties that arise from model and data, which is essential for prediction, particularly in the case of cyclones. In this paper, Bayesian neural networks are used for multi-step ahead time series prediction for cyclones in the South Pacific region. The results show promising prediction accuracy with uncertainty quantification for shorter prediction horizon; however, the challenge lies in higher prediction horizons