TY - JOUR

T1 - Predicting pragmatic cue integration in adults’ and children’s inferences about novel word meanings.

AU - Bohn, Manuel

AU - Tessler, Michael Henry

AU - Merrick, Megan

AU - Frank, Michael C.

N1 - Funding Information: Manuel Bohn received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant 749229. Michael Henry Tessler was supported by a National Science Foundation SBE Postdoctoral Research Fellowship Grant 1911790. Michael C. Frank was supported by a Jacobs Foundation Advanced Research Fellowship and the Zhou Fund for Language and Cognition. Parts of Study 1 appeared in the proceedings of the 41st Annual Meeting of the Cognitive Science Society, Montreal, Canada, 2019. All experiments and analyses were preregistered (https://osf.io/u7kxe/;Bohn &Frank, 2018). Experiments, data, and analysis code are available in a public repository (https://github.com/manuelbohn/mcc). The authors declare no conflict of interest Publisher Copyright: © 2022 American Psychological Association

PY - 2022/4/7

Y1 - 2022/4/7

N2 - Language is learned in complex social settings where listeners must reconstruct speakers’ intended meanings from context. To navigate this challenge, children can use pragmatic reasoning to learn the meaning of unfamiliar words. A critical challenge for pragmatic reasoning is that it requires integrating multiple information sources, which have typically been studied separately. Here we study this integration process. First, we experimentally isolate two sources of pragmatic information: expectations about informative communication and common ground. Next, we use a probabilistic model of conversational reasoning to formalize how these information sources should be combined and how this process might develop. We use this model to generate quantitative predictions, which we test against new experimental data from 3 to 5-year-old children (N = 243) and adults (N = 694). Results show close alignment between model predictions and data. Furthermore, the model provided a better explanation of the data compared with simpler alternative models assuming that participants selectively ignore one information source.

AB - Language is learned in complex social settings where listeners must reconstruct speakers’ intended meanings from context. To navigate this challenge, children can use pragmatic reasoning to learn the meaning of unfamiliar words. A critical challenge for pragmatic reasoning is that it requires integrating multiple information sources, which have typically been studied separately. Here we study this integration process. First, we experimentally isolate two sources of pragmatic information: expectations about informative communication and common ground. Next, we use a probabilistic model of conversational reasoning to formalize how these information sources should be combined and how this process might develop. We use this model to generate quantitative predictions, which we test against new experimental data from 3 to 5-year-old children (N = 243) and adults (N = 694). Results show close alignment between model predictions and data. Furthermore, the model provided a better explanation of the data compared with simpler alternative models assuming that participants selectively ignore one information source.

KW - Bayesian modeling

KW - Common ground

KW - Language acquisition

KW - Pragmatics

KW - Social cognition

UR - http://www.scopus.com/inward/record.url?scp=85130595964&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/f149adc1-6288-31bb-b6ba-446a0f13aa72/

U2 - 10.1037/xge0001216

DO - 10.1037/xge0001216

M3 - Journal articles

C2 - 35389743

AN - SCOPUS:85130595964

VL - 151

SP - 2927

EP - 2942

JO - Journal of Experimental Psychology: General

JF - Journal of Experimental Psychology: General

SN - 0096-3445

IS - 11

ER -