Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

Emmanuele Chersoni, Cassandra Jacobs, Yohei Oseki, Laurent Prévot, Enrico Santus (Editors)


Anthology ID:
2020.cmcl-1
Month:
November
Year:
2020
Address:
Online
Venues:
CMCL | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
URL:
https://aclanthology.org/2020.cmcl-1
DOI:
Bib Export formats:
BibTeX MODS XML EndNote

pdf bib
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
Emmanuele Chersoni | Cassandra Jacobs | Yohei Oseki | Laurent Prévot | Enrico Santus

pdf bib
Images and Imagination : Automated Analysis of Priming Effects Related to Autism Spectrum Disorder and Developmental Language Disorder
Michaela Regneri | Diane King | Fahreen Walji | Olympia Palikara

Different aspects of language processing have been shown to be sensitive to priming but the findings of studies examining priming effects in adolescents with Autism Spectrum Disorder (ASD) and Developmental Language Disorder (DLD) have been inconclusive. We present a study analysing visual and implicit semantic priming in adolescents with ASD and DLD. Based on a dataset of fictional and script-like narratives, we evaluate how often and how extensively, content of two different priming sources is used by the participants. The first priming source was visual, consisting of images shown to the participants to assist them with their storytelling. The second priming source originated from commonsense knowledge, using crowdsourced data containing prototypical script elements. Our results show that individuals with ASD are less sensitive to both types of priming, but show typical usage of primed cues when they use them at all. In contrast, children with DLD show mostly average priming sensitivity, but exhibit an over-proportional use of the priming cues.

pdf bib
Production-based Cognitive Models as a Test Suite for Reinforcement Learning Algorithms
Adrian Brasoveanu | Jakub Dotlacil

We introduce a framework in which production-rule based computational cognitive modeling and Reinforcement Learning can systematically interact and inform each other. We focus on linguistic applications because the sophisticated rule-based cognitive models needed to capture linguistic behavioral data promise to provide a stringent test suite for RL algorithms, connecting RL algorithms to both accuracy and reaction-time experimental data. Thus, we open a path towards assembling an experimentally rigorous and cognitively realistic benchmark for RL algorithms. We extend our previous work on lexical decision tasks and tabular RL algorithms (Brasoveanu and Dotlail, 2020b) with a discussion of neural-network based approaches, and a discussion of how parsing can be formalized as an RL problem.