A cognitive model of spatial path-planning

David Reitter, Christian Lebiere

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Planning a path to a destination, given a number of options and obstacles, is a common task. We suggest a two-component cognitive model that combines retrieval of knowledge about the environment with search guided by visual perception. In the first component, subsymbolic information, acquired during navigation, aids in the retrieval of declarative information representing possible paths to take. In the second component, visual information directs the search, which in turn creates knowledge for the first component. The model is implemented using the ACT-R cognitive architecture and makes realistic assumptions about memory access and shifts in visual attention. We present simulation results for memory-based high-level navigation in grid and tree structures, and visual navigation in mazes, varying relevant cognitive (retrieval noise and visual finsts) and environmental (maze and path size) parameters. The visual component is evaluated with data from a multi-robot control experiment, where subjects planned paths for robots to explore a building. We describe a method to compare trajectories without referring to aligned points in the itinerary. The evaluation shows that the model provides a good fit, but also that planning strategies may vary with task loads.

Original languageEnglish (US)
Pages (from-to)220-245
Number of pages26
JournalComputational and Mathematical Organization Theory
Volume16
Issue number3
DOIs
StatePublished - Sep 22 2010

Fingerprint

Cognitive Models
Path Planning
Motion planning
Navigation
Path
Retrieval
Robots
Data storage equipment
Planning
Cognitive Architecture
Visual Attention
Visual Perception
Multi-robot
Robot Control
Component Model
Tree Structure
Trajectories
Robot
Vary
Trajectory

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Computer Science(all)
  • Modeling and Simulation
  • Computational Mathematics
  • Applied Mathematics

Cite this

@article{402241780a9c4384a63b6fd55f622cd9,
title = "A cognitive model of spatial path-planning",
abstract = "Planning a path to a destination, given a number of options and obstacles, is a common task. We suggest a two-component cognitive model that combines retrieval of knowledge about the environment with search guided by visual perception. In the first component, subsymbolic information, acquired during navigation, aids in the retrieval of declarative information representing possible paths to take. In the second component, visual information directs the search, which in turn creates knowledge for the first component. The model is implemented using the ACT-R cognitive architecture and makes realistic assumptions about memory access and shifts in visual attention. We present simulation results for memory-based high-level navigation in grid and tree structures, and visual navigation in mazes, varying relevant cognitive (retrieval noise and visual finsts) and environmental (maze and path size) parameters. The visual component is evaluated with data from a multi-robot control experiment, where subjects planned paths for robots to explore a building. We describe a method to compare trajectories without referring to aligned points in the itinerary. The evaluation shows that the model provides a good fit, but also that planning strategies may vary with task loads.",
author = "David Reitter and Christian Lebiere",
year = "2010",
month = "9",
day = "22",
doi = "10.1007/s10588-010-9073-3",
language = "English (US)",
volume = "16",
pages = "220--245",
journal = "Computational and Mathematical Organization Theory",
issn = "1381-298X",
publisher = "Kluwer Academic Publishers",
number = "3",

}

A cognitive model of spatial path-planning. / Reitter, David; Lebiere, Christian.

In: Computational and Mathematical Organization Theory, Vol. 16, No. 3, 22.09.2010, p. 220-245.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A cognitive model of spatial path-planning

AU - Reitter, David

AU - Lebiere, Christian

PY - 2010/9/22

Y1 - 2010/9/22

N2 - Planning a path to a destination, given a number of options and obstacles, is a common task. We suggest a two-component cognitive model that combines retrieval of knowledge about the environment with search guided by visual perception. In the first component, subsymbolic information, acquired during navigation, aids in the retrieval of declarative information representing possible paths to take. In the second component, visual information directs the search, which in turn creates knowledge for the first component. The model is implemented using the ACT-R cognitive architecture and makes realistic assumptions about memory access and shifts in visual attention. We present simulation results for memory-based high-level navigation in grid and tree structures, and visual navigation in mazes, varying relevant cognitive (retrieval noise and visual finsts) and environmental (maze and path size) parameters. The visual component is evaluated with data from a multi-robot control experiment, where subjects planned paths for robots to explore a building. We describe a method to compare trajectories without referring to aligned points in the itinerary. The evaluation shows that the model provides a good fit, but also that planning strategies may vary with task loads.

AB - Planning a path to a destination, given a number of options and obstacles, is a common task. We suggest a two-component cognitive model that combines retrieval of knowledge about the environment with search guided by visual perception. In the first component, subsymbolic information, acquired during navigation, aids in the retrieval of declarative information representing possible paths to take. In the second component, visual information directs the search, which in turn creates knowledge for the first component. The model is implemented using the ACT-R cognitive architecture and makes realistic assumptions about memory access and shifts in visual attention. We present simulation results for memory-based high-level navigation in grid and tree structures, and visual navigation in mazes, varying relevant cognitive (retrieval noise and visual finsts) and environmental (maze and path size) parameters. The visual component is evaluated with data from a multi-robot control experiment, where subjects planned paths for robots to explore a building. We describe a method to compare trajectories without referring to aligned points in the itinerary. The evaluation shows that the model provides a good fit, but also that planning strategies may vary with task loads.

UR - http://www.scopus.com/inward/record.url?scp=77957314436&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77957314436&partnerID=8YFLogxK

U2 - 10.1007/s10588-010-9073-3

DO - 10.1007/s10588-010-9073-3

M3 - Article

AN - SCOPUS:77957314436

VL - 16

SP - 220

EP - 245

JO - Computational and Mathematical Organization Theory

JF - Computational and Mathematical Organization Theory

SN - 1381-298X

IS - 3

ER -