Judgments of affect through inferential processes: A test of the inferential-perceptual model

Initial and preliminary implementations of predictive processing and active inference models are presented. These include the baseline hierarchical predictive coding models of (Friston 2003, 2005), and dynamical predictive coding models using generalised coordinates (Friston 2008, 2010, Buckley 2017). Additionally, we re-implement and experiment with the active inference thermostat presented in (Buckley 2017) and also implement an active inference agent with a hierarchical predictive coding perceptual model on the more challenging cart-pole task from OpanAI gym. We discuss the initial performance, capabilities, and limitations of these models in their preliminary stages and consider how they might be further scaled up to tackle more challenging tasks.

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MULTI-AGENT SYSTEM PATH-PLANNING USING AGENT PERCEPTUAL MODEL AND DYNAMIC ARTIFICIAL POTENTIAL FIELDS

The International Conference on Applied Mechanics and Mechanical Engineering ◽

10.21608/amme.2010.37554 ◽

2010 ◽

Vol 14 (14) ◽

pp. 1-12

Author(s):

M. MABROUK

Keyword(s):

Path Planning ◽

Potential Fields ◽

Multi Agent System ◽

Agent System ◽

Perceptual Model ◽

Artificial Potential Fields ◽

Multi Agent

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1. The Experience of God: A Perceptual Model

Perceiving God ◽

10.7591/9780801471254-003 ◽

2017 ◽

pp. 9-67

Keyword(s):

Perceptual Model ◽

Experience Of God

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Aboriginal Cultures and Technocratic Culture

Essays in Philosophy ◽

10.5840/eip20056128 ◽

2005 ◽

Vol 6 (1) ◽

pp. 226-234

Author(s):

Humberto Ortega Villasenor ◽

Genaro Quinones Trujillo ◽

Keyword(s):

Mass Media ◽

Human Nature ◽

High Speed ◽

Modern Technology ◽

Perceptual Model ◽

Religious Dimension ◽

Western Model ◽

Cultural Paradigm ◽

Meaningful Communication

Threatened aboriginal cultures provide valuable criteria for fruitful criticism of the dominant Western cultural paradigm and perceptual model, which many take for granted as the inevitable path for humankind to follow. However, this Western model has proven itself to be imprecise and limiting. It obscures fundamental aspects of human nature, such as the mythical, religious dimension, and communication with the Cosmos. Modern technology, high-speed communication and mass media affect our ability to perceive reality and respond to it. Non-Western worldviews could help us to regain meaningful communication with Nature and to learn new ways of perceiving our world.

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Man's Triune Conscious Mind: Part III

Perceptual and Motor Skills ◽

10.1177/003151259508100220 ◽

1995 ◽

Vol 81 (2) ◽

pp. 463-466

Author(s):

Carl G. Aurell

Keyword(s):

Visual Cortex ◽

Primary Visual Cortex ◽

Conscious Mind ◽

Perceptual Model ◽

Cortex Area ◽

Layer 4

The perceptual model, discussed previously in Part II, is applied to the organization of the visual cortex in a search for “consciousness neurons,” i.e., sources of sensations, images, and percepts. It is hypothesized that these three conscious phenomena emerge in the primary visual cortex, Area VI, possibly from neurons in its Layer 4.

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Lane-level localization system using surround-view cameras adaptive to different driving conditions

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420921630 ◽

2020 ◽

Vol 17 (2) ◽

pp. 172988142092163

Author(s):

Tianyi Li ◽

Yuhan Qian ◽

Arnaud de La Fortelle ◽

Ching-Yao Chan ◽

Chunxiang Wang

Keyword(s):

Deep Learning ◽

Low Cost ◽

Lateral Position ◽

Measurement Unit ◽

Map Matching ◽

Matching Model ◽

Localization System ◽

Perceptual Model ◽

Road Map ◽

Driving Conditions

This article presents a lane-level localization system adaptive to different driving conditions, such as occlusions, complicated road structures, and lane-changing maneuvers. The system uses surround-view cameras, other low-cost sensors, and a lane-level road map which suits for mass deployment. A map-matching localizer is proposed to estimate the probabilistic lateral position. It consists of a sub-map extraction module, a perceptual model, and a matching model. A probabilistic lateral road feature is devised as a sub-map without limitations of road structures. The perceptual model is a deep learning network that processes raw images from surround-view cameras to extract a local probabilistic lateral road feature. Unlike conventional deep-learning-based methods, the perceptual model is trained by auto-generated labels from the lane-level map to reduce manual effort. The matching model computes the correlation between the sub-map and the local probabilistic lateral road feature to output the probabilistic lateral estimation. A particle-filter-based framework is developed to fuse the output of map-matching localizer with the measurements from wheel speed sensors and an inertial measurement unit. Experimental results demonstrate that the proposed system provides the localization results with submeter accuracy in different driving conditions.

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