High Performance on a Pragmatic Task May Not Be the Result of Successful Reasoning: On the Importance of Eliciting Participants’ Reasoning Strategies

Author:

Mayn Alexandra1,Demberg Vera12

Affiliation:

1. Department of Language of Science and Technology, Saarland University

2. Department of Computer Science, Saarland University

Abstract

Abstract Formal probabilistic models, such as the Rational Speech Act model, are widely used for formalizing the reasoning involved in various pragmatic phenomena, and when a model achieves good fit to experimental data, that is interpreted as evidence that the model successfully captures some of the underlying processes. Yet how can we be sure that participants’ performance on the task is the result of successful reasoning and not of some feature of experimental setup? In this study, we carefully manipulate the properties of the stimuli that have been used in several pragmatics studies and elicit participants’ reasoning strategies. We show that certain biases in experimental design inflate participants’ performance on the task. We then repeat the experiment with a new version of stimuli which is less susceptible to the identified biases, obtaining a somewhat smaller effect size and more reliable estimates of individual-level performance.

Funder

European Union’s Horizon 2020 Research and Innovation Programme

Publisher

MIT Press

Subject

Cognitive Neuroscience,Linguistics and Language,Developmental and Educational Psychology,Experimental and Cognitive Psychology

Reference15 articles.

1. Advanced bayesian multilevel modeling with the R package brms;Bürkner;arXiv:1705.11123,2017

2. Rationalization is rational;Cushman;Behavioral and Brain Sciences,2020

3. Optimal reasoning about referential expressions;Degen,2012

4. Cost-based pragmatic inference about referential expressions;Degen;Proceedings of the Annual Meeting of the Cognitive Science Society,2013

5. Reflections on reflection: the nature and function of type 2 processes in dual-process theories of reasoning;Evans;Thinking & Reasoning,2019

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3