2018
DOI: 10.31234/osf.io/hvnym
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Spatial narrative context modulates semantic (but not visual) competition during discourse processing

Abstract: Recent research highlights the influence of (e.g., task) context on conceptual retrieval. In order to assess whether conceptual representations are context-dependent rather than static, we investigated the influence of spatial narrative context on accessibility for lexical-semantic information by exploring competition effects. In two visual world experiments, participants listened to narratives describing semantically related (piano-trumpet; Experiment 1) or visually similar (bat-cigarette; Experiment 2) objec… Show more

Help me understand this report
View published versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

1
2
0

Year Published

2019
2019
2023
2023

Publication Types

Select...
3

Relationship

1
2

Authors

Journals

citations
Cited by 3 publications
(3 citation statements)
references
References 0 publications
1
2
0
Order By: Relevance
“…Beyond the question of pronoun interpretation, the results are compatible with findings showing that listeners and speakers spontaneously update mental representations based on past events, and that referential expressions are interpreted relative to these dynamic representations (see, e.g., Altmann & Kamide, 2007;Chambers & San Juan, 2008;Ibarra & Tanenhaus, 2016;Kukona et al, 2014;Williams, Kukona, & Kamide, 2019). It is also interesting to note the connection between these (and our) findings and work in visual cognition, where the indexical pointers used for tracking entities (e.g., Pylyshyn, 1989) may be separable from the information associated with these entities in working memory (Thyer et al, 2022).…”
Section: Discussionsupporting
confidence: 85%
“…Beyond the question of pronoun interpretation, the results are compatible with findings showing that listeners and speakers spontaneously update mental representations based on past events, and that referential expressions are interpreted relative to these dynamic representations (see, e.g., Altmann & Kamide, 2007;Chambers & San Juan, 2008;Ibarra & Tanenhaus, 2016;Kukona et al, 2014;Williams, Kukona, & Kamide, 2019). It is also interesting to note the connection between these (and our) findings and work in visual cognition, where the indexical pointers used for tracking entities (e.g., Pylyshyn, 1989) may be separable from the information associated with these entities in working memory (Thyer et al, 2022).…”
Section: Discussionsupporting
confidence: 85%
“…Relatedly, while the ratings of associated sounds were counterbalanced across competitors and distractors, suggesting that competitors were not fixated simply because they were more likely to have associated sounds than distractors, the current results reflect a (e.g., task) context in which non-targets often had associated sounds. Thus, another potential issue for future research is to consider whether participants can strategically suppress semantic competition effects in contexts in which non-targets never have associated sounds, paralleling other context-based effects in the linguistic domain (e.g., Williams, Kukona, & Kamide, 2019;Yee & Thompson-Schill, 2016). In summary, in much the same way that the growing body of psycholinguistic research on semantic competition effects has yielded a wide range of insights, the current results lay the foundation for exploring a wide range of issues with environmental sounds.…”
Section: Discussionmentioning
confidence: 99%
“…We conducted statistical analyses during the time window spanning from the onset of a critical time window in the linguistic stimulus (e.g., onion) +200 ms until its offset +200 ms. We selected the time window of a critical word +200 ms since previous studies have demonstrated that the competition effects of related objects were observed around 200-300 ms after the onset of the target word (e.g., Huettig and Altmann, 2005;Yee and Sedivy, 2006). We transformed the proportion of fixations for each time window using the arcsine square root transformation to account for the bounded nature of binomial responses (e.g., Williams et al, 2019). We then fit linear mixed models for data of each time window using the lmer function in the lme4 package (Bates et al, 2015) of R (R Core Team, 2020).…”
Section: Data Processingmentioning
confidence: 99%