Advanced

From focused thought to reveries : A memory system for a conscious robot

Balkenius, Christian LU ; Tjøstheim, Trond A. LU ; Johansson, Birger LU and Gärdenfors, Peter LU (2018) In Frontiers Robotics AI 5(APR).
Abstract

We introduce a memory model for robots that can account for many aspects of an inner world, ranging from object permanence, episodic memory, and planning to imagination and reveries. It is modeled after neurophysiological data and includes parts of the cerebral cortex together with models of arousal systems that are relevant for consciousness. The three central components are an identification network, a localization network, and a working memory network. Attention serves as the interface between the inner and the external world. It directs the flow of information from sensory organs to memory, as well as controlling top-down influences on perception. It also compares external sensations to internal top-down expectations. The model is... (More)

We introduce a memory model for robots that can account for many aspects of an inner world, ranging from object permanence, episodic memory, and planning to imagination and reveries. It is modeled after neurophysiological data and includes parts of the cerebral cortex together with models of arousal systems that are relevant for consciousness. The three central components are an identification network, a localization network, and a working memory network. Attention serves as the interface between the inner and the external world. It directs the flow of information from sensory organs to memory, as well as controlling top-down influences on perception. It also compares external sensations to internal top-down expectations. The model is tested in a number of computer simulations that illustrate how it can operate as a component in various cognitive tasks including perception, the A-not-B test, delayed matching to sample, episodic recall, and vicarious trial and error.

(Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Computational model, Consciousness, Episodic memory, Semantic memory, Working memory
in
Frontiers Robotics AI
volume
5
issue
APR
external identifiers
  • scopus:85050113666
DOI
10.3389/frobt.2018.00029
language
English
LU publication?
yes
id
7009d3d4-c89b-47cc-a743-4369bdc5e13e
date added to LUP
2018-08-02 14:30:38
date last changed
2018-08-02 14:30:38
@article{7009d3d4-c89b-47cc-a743-4369bdc5e13e,
  abstract     = {<p>We introduce a memory model for robots that can account for many aspects of an inner world, ranging from object permanence, episodic memory, and planning to imagination and reveries. It is modeled after neurophysiological data and includes parts of the cerebral cortex together with models of arousal systems that are relevant for consciousness. The three central components are an identification network, a localization network, and a working memory network. Attention serves as the interface between the inner and the external world. It directs the flow of information from sensory organs to memory, as well as controlling top-down influences on perception. It also compares external sensations to internal top-down expectations. The model is tested in a number of computer simulations that illustrate how it can operate as a component in various cognitive tasks including perception, the A-not-B test, delayed matching to sample, episodic recall, and vicarious trial and error.</p>},
  articleno    = {29},
  author       = {Balkenius, Christian and Tjøstheim, Trond A. and Johansson, Birger and Gärdenfors, Peter},
  keyword      = {Computational model,Consciousness,Episodic memory,Semantic memory,Working memory},
  language     = {eng},
  month        = {01},
  number       = {APR},
  series       = {Frontiers Robotics  AI},
  title        = {From focused thought to reveries : A memory system for a conscious robot},
  url          = {http://dx.doi.org/10.3389/frobt.2018.00029},
  volume       = {5},
  year         = {2018},
}