Publication

Cognitive Flexibility in Cognitive Architecture: Simulating using Contextual Learning in PRIMs

Ji, Y., van Rij, J. & Taatgen, N., 2020, Poster session presented at 18th International Conference on Cognitive Modeling.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

APA

Ji, Y., van Rij, J., & Taatgen, N. (2020). Cognitive Flexibility in Cognitive Architecture: Simulating using Contextual Learning in PRIMs. In Poster session presented at 18th International Conference on Cognitive Modeling

Author

Ji, Yang ; van Rij, Jacolien ; Taatgen, Niels. / Cognitive Flexibility in Cognitive Architecture : Simulating using Contextual Learning in PRIMs. Poster session presented at 18th International Conference on Cognitive Modeling. 2020.

Harvard

Ji, Y, van Rij, J & Taatgen, N 2020, Cognitive Flexibility in Cognitive Architecture: Simulating using Contextual Learning in PRIMs. in Poster session presented at 18th International Conference on Cognitive Modeling. The 18th Annual Meeting of the International Conference on Cognitive Modelling, Toronto, Canada, 22/07/2020.

Standard

Cognitive Flexibility in Cognitive Architecture : Simulating using Contextual Learning in PRIMs. / Ji, Yang; van Rij, Jacolien; Taatgen, Niels.

Poster session presented at 18th International Conference on Cognitive Modeling. 2020.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Vancouver

Ji Y, van Rij J, Taatgen N. Cognitive Flexibility in Cognitive Architecture: Simulating using Contextual Learning in PRIMs. In Poster session presented at 18th International Conference on Cognitive Modeling. 2020


BibTeX

@inproceedings{c348d5638d7046c28a5e4946801a1b4f,
title = "Cognitive Flexibility in Cognitive Architecture: Simulating using Contextual Learning in PRIMs",
abstract = "The universal flexibility of biological systems needs to be reflected in cognitive architecture. In PRIMs, we attempt to achieve flexibility through a bottom-up approach. Using contextual learning, randomly firing of a set of instantiated primitive operators are gradually organized into context-sensitive operator firing sequences (i.e., primordial “skills”). Based on this implementation, the preliminary results of the model simulated the averaged single-pattern processing latency that is consistent with infants{\textquoteright} differential focusing time in three theoretically controversial artificial language studies, namely Saffran, Aslin, and Newport (1996), Marcus, Vijayan, Rao, and Vishton (1999), and Gomez (2002). In our ongoing work, we are analyzing (a) whether the model can arrive at primordial “skills” adaptive to the trained tasks, and (b) whether the learned chunks mirror the trained patterns.",
author = "Yang Ji and {van Rij}, Jacolien and Niels Taatgen",
year = "2020",
language = "English",
booktitle = "Poster session presented at 18th International Conference on Cognitive Modeling",
note = "The 18th Annual Meeting of the International Conference on Cognitive Modelling ; Conference date: 22-07-2020 Through 31-07-2020",

}

RIS

TY - GEN

T1 - Cognitive Flexibility in Cognitive Architecture

T2 - The 18th Annual Meeting of the International Conference on Cognitive Modelling

AU - Ji, Yang

AU - van Rij, Jacolien

AU - Taatgen, Niels

PY - 2020

Y1 - 2020

N2 - The universal flexibility of biological systems needs to be reflected in cognitive architecture. In PRIMs, we attempt to achieve flexibility through a bottom-up approach. Using contextual learning, randomly firing of a set of instantiated primitive operators are gradually organized into context-sensitive operator firing sequences (i.e., primordial “skills”). Based on this implementation, the preliminary results of the model simulated the averaged single-pattern processing latency that is consistent with infants’ differential focusing time in three theoretically controversial artificial language studies, namely Saffran, Aslin, and Newport (1996), Marcus, Vijayan, Rao, and Vishton (1999), and Gomez (2002). In our ongoing work, we are analyzing (a) whether the model can arrive at primordial “skills” adaptive to the trained tasks, and (b) whether the learned chunks mirror the trained patterns.

AB - The universal flexibility of biological systems needs to be reflected in cognitive architecture. In PRIMs, we attempt to achieve flexibility through a bottom-up approach. Using contextual learning, randomly firing of a set of instantiated primitive operators are gradually organized into context-sensitive operator firing sequences (i.e., primordial “skills”). Based on this implementation, the preliminary results of the model simulated the averaged single-pattern processing latency that is consistent with infants’ differential focusing time in three theoretically controversial artificial language studies, namely Saffran, Aslin, and Newport (1996), Marcus, Vijayan, Rao, and Vishton (1999), and Gomez (2002). In our ongoing work, we are analyzing (a) whether the model can arrive at primordial “skills” adaptive to the trained tasks, and (b) whether the learned chunks mirror the trained patterns.

M3 - Conference contribution

BT - Poster session presented at 18th International Conference on Cognitive Modeling

Y2 - 22 July 2020 through 31 July 2020

ER -

ID: 130100099