A neural computational model for bottom-up attention with invariant and overcomplete representation

If they are to learn and interact with humans, robots need to understand actions and make use of language in social interactions. Hirsh-Pasek and Golinkoff (1996) have emphasized the use of language to learn actions when introducing the idea of acoustic packaging in human development. This idea suggests that acoustic information, typically in the form of narration, overlaps with action sequences, thereby providing infants with a bottom-up guide to attend to relevant parts and to find structure within them. The authors developed a computational model of the multimodal interplay of action and language in tutoring situations. This chapter presents the results of applying this model to multimodal parent-infant interaction data. Results are twofold and indicate that (a) infant-directed interaction is more structured than adult-directed interaction in that it contains more packages, and these packages have fewer motion segments; and (b) the synchronous structure within infant-directed packages contains redundant information making it possible to solve the reference problem when tying color adjectives to a moving object.

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Mechanistic insights into actin-driven polarity site movement in yeast

Molecular Biology of the Cell ◽

10.1091/mbc.e20-01-0040 ◽

2020 ◽

Vol 31 (10) ◽

pp. 1085-1102 ◽

Cited By ~ 3

Author(s):

Debraj Ghose ◽

Daniel Lew

Keyword(s):

Computational Model ◽

Bottom Up ◽

Vesicle Traffic

Cells dynamically orient their direction of growth or movement by moving a polarity site that defines the front. A bottom-up computational model is used to explore the mechanism of movement. Assumptions inspired by findings in the yeast system show that vesicle traffic directed to the polarity site would suffice to produce realistic movement.

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Massive Effects of Saliency on Information Processing in Visual Working Memory

Psychological Science ◽

10.1177/0956797620975785 ◽

2021 ◽

pp. 095679762097578

Author(s):

Martin Constant ◽

Heinrich R. Liesefeld

Keyword(s):

Working Memory ◽

Information Processing ◽

Computational Model ◽

Visual Working Memory ◽

Visual Cognition ◽

Weighted Sum ◽

Direct Control ◽

Top Down ◽

Bottom Up

Limitations in the ability to temporarily represent information in visual working memory (VWM) are crucial for visual cognition. Whether VWM processing is dependent on an object’s saliency (i.e., how much it stands out) has been neglected in VWM research. Therefore, we developed a novel VWM task that allows direct control over saliency. In three experiments with this task (on 10, 31, and 60 adults, respectively), we consistently found that VWM performance is strongly and parametrically influenced by saliency and that both an object’s relative saliency (compared with concurrently presented objects) and absolute saliency influence VWM processing. We also demonstrated that this effect is indeed due to bottom-up saliency rather than differential fit between each object and the top-down attentional template. A simple computational model assuming that VWM performance is determined by the weighted sum of absolute and relative saliency accounts well for the observed data patterns.

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Computational Model for Machine Vision Inspection Based on Vision Attention

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.383-390.2398 ◽

2011 ◽

Vol 383-390 ◽

pp. 2398-2403

Author(s):

Jin Fang Shi ◽

Zhen Wei Su ◽

Guo Hui Li

Keyword(s):

Machine Vision ◽

Computational Model ◽

Vision System ◽

Visual Selective Attention ◽

Human Vision ◽

Human Vision System ◽

Top Down ◽

Bottom Up ◽

Vision Inspection ◽

Machine Vision Inspection

Human vision system exploits this fact by visual selective attention mechanisms towards important and informative regions. A computational model of combination both bottom-up and top-down simulating human vision system for machine vision inspection is proposed. In this model, top-down knowledge-based information is highlighted to integrate into bottom-up stimulus-based process of vision attention. The model is tested on inspecting contaminants in cotton images. Experiment result shows that the proposed model is feasible and effective in visual inspection. And it is available and quasi-equivalent to human vision attention.

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Chemotactic movement of a polarity site enables yeast cells to find their mates

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2025445118 ◽

2021 ◽

Vol 118 (22) ◽

pp. e2025445118

Author(s):

Debraj Ghose ◽

Katherine Jacobs ◽

Samuel Ramirez ◽

Timothy Elston ◽

Daniel Lew

Keyword(s):

Saccharomyces Cerevisiae ◽

Computational Model ◽

Initial Orientation ◽

Yeast Cells ◽

Eukaryotic Cells ◽

Stochastic Effects ◽

Bottom Up ◽

Complex Chemical ◽

Chemical Gradients

How small eukaryotic cells can interpret dynamic, noisy, and spatially complex chemical gradients to orient growth or movement is poorly understood. We address this question using Saccharomyces cerevisiae, where cells orient polarity up pheromone gradients during mating. Initial orientation is often incorrect, but polarity sites then move around the cortex in a search for partners. We find that this movement is biased by local pheromone gradients across the polarity site: that is, movement of the polarity site is chemotactic. A bottom-up computational model recapitulates this biased movement. The model reveals how even though pheromone-bound receptors do not mimic the shape of external pheromone gradients, nonlinear and stochastic effects combine to generate effective gradient tracking. This mechanism for gradient tracking may be applicable to any cell that searches for a target in a complex chemical landscape.

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