This source preferred by Jan Wiener

Authors: Wiener, J.M., Lafon, M. and Berthoz, A.

Journal: Memory and Cognition

Volume: 36

Issue: 3

Pages: 495-504

DOI: 10.3758/MC.36.3.495

This data was imported from PubMed:

Authors: Wiener, J.M., Lafon, M. and Berthoz, A.

Journal: Mem Cognit

Volume: 36

Issue: 3

Pages: 495-504

ISSN: 0090-502X

DOI: 10.3758/mc.36.3.495

In this article, we present experiments studying path planning under spatial uncertainties. In the main experiment, the participants' task was to navigate the shortest possible path to find an object hidden in one of four places and to bring it to the final destination. The probability of finding the object (probability matrix) was different for each of the four places and varied between conditions. Givensuch uncertainties about the object's location, planning a single path is not sufficient. Participants had to generate multiple consecutive plans (metaplans)--for example: If the object is found in A, proceed to the destination; if the object is not found, proceed to B; and so on. The optimal solution depends on the specific probability matrix. In each condition, participants learned a different probability matrix and were then asked to report the optimal metaplan. Results demonstrate effective integration of the probabilistic information about the object's location during planning. We present a hierarchical planning scheme that could account for participants' behavior, as well as for systematic errors and differences between conditions.

This data was imported from Scopus:

Authors: Wiener, J.M., Lafon, M. and Berthoz, A.

Journal: Memory and Cognition

Volume: 36

Issue: 3

Pages: 495-504

ISSN: 0090-502X

DOI: 10.3758/MC.36.3.495

In this article, we present experiments studying path planning under spatial uncertainties. In the main experiment, the participants' task was to navigate the shortest possible path to find an object hidden in one of four places and to bring it to the final destination. The probability of finding the object (probability matrix) was different for each of the four places and varied between conditions. Given such uncertainties about the object's location, planning a single path is not sufficient. Participants had to generate multiple consecutive plans (metaplans) - for example: If the object is found in A, proceed to the destination; if the object is not found, proceed to B; and so on. The optimal solution depends on the specific probability matrix. In each condition, participants learned a different probability matrix and were then asked to report the optimal metaplan. Results demonstrate effective integration of the probabilistic information about the object's location during planning. We present a hierarchical planning scheme that could account for participants' behavior, as well as for systematic errors and differences between conditions. Copyright 2008 Psychonomic Society, Inc.

This data was imported from Web of Science (Lite):

Authors: Wiener, J.M., Lafon, M. and Berthoz, A.

Journal: MEMORY & COGNITION

Volume: 36

Issue: 3

Pages: 495-504

eISSN: 1532-5946

ISSN: 0090-502X

DOI: 10.3758/MC.36.3.495

This data was imported from Europe PubMed Central:

Authors: Wiener, J.M., Lafon, M. and Berthoz, A.

Journal: Memory & cognition

Volume: 36

Issue: 3

Pages: 495-504

eISSN: 1532-5946

ISSN: 0090-502X

In this article, we present experiments studying path planning under spatial uncertainties. In the main experiment, the participants' task was to navigate the shortest possible path to find an object hidden in one of four places and to bring it to the final destination. The probability of finding the object (probability matrix) was different for each of the four places and varied between conditions. Givensuch uncertainties about the object's location, planning a single path is not sufficient. Participants had to generate multiple consecutive plans (metaplans)--for example: If the object is found in A, proceed to the destination; if the object is not found, proceed to B; and so on. The optimal solution depends on the specific probability matrix. In each condition, participants learned a different probability matrix and were then asked to report the optimal metaplan. Results demonstrate effective integration of the probabilistic information about the object's location during planning. We present a hierarchical planning scheme that could account for participants' behavior, as well as for systematic errors and differences between conditions.