Linguistic focus guides attention during the encoding and refreshing of Working Memory content

Káldi, Tamás; Babarczy, Anna

doi:10.1016/j.jml.2020.104187

Cited by 13 publications

(19 citation statements)

References 89 publications

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“…This is well exemplified by the subject focus conditions, which displayed a subject preference from the beginning of the analysis window, with an additional steep slope increase in subject preference beginning from around 400 ms onward. Foraker and McElree (2007 ; also see Káldi and Babarczy, 2021 ) showed that linguistic focus, specifically clefting, renders the referent in focus more available (facilitating its memory representation), but not more accessible (not affecting the speed of processing). That is, the pronoun is not merely matched to the content in focal attention.…”

Section: Discussionmentioning

confidence: 98%

“…Still, focus marking is a primary function of clefts, as well as of prosody (which usually seems to accompany clefts and help them perform this function, see Arnhold, 2021 ). In turn, the focused parts of utterances are boosted in terms of attention and memory ( Cutler and Foss, 1977 ; Cutler and Fodor, 1979 ; Birch and Garnsey, 1995 ; Foraker and McElree, 2007 ; Kember et al, 2019 ; Káldi and Babarczy, 2021 ); in other words they become more activated and central in the listener’s mind. It is commonly assumed that pronouns are interpreted as referring to the entities that are most activated and central at a given moment ( Gundel et al, 1993 ; Zwaan and Radvansky, 1998 ; Hartshorne et al, 2015 ).…”

Section: Discussionmentioning

confidence: 99%

“…In other words, clefts and prosodic focus marking are employed by speakers to guide listeners’ attention and to explicitly signal how the speaker expects the listener to update their discourse model, i.e., their mental representation of the events and referents under discussion ( Johnson-Laird, 1983 ; Zwaan and Radvansky, 1998 ). In particular, they mark the focus of an utterance, i.e., the new or contrastive information, separating it from the background or presupposed information ( Krifka, 2007 , for a discussion of more formal definitions), which has long been known to enhance processing speed and memory of the focused words ( Cutler and Foss, 1977 ; Cutler and Fodor, 1979 ; Birch and Garnsey, 1995 ; Kember et al, 2019 ; Káldi and Babarczy, 2021 ). In turn, this memory advantage for the focussed element should serve pronoun interpretation because it is more available and therefore easier to establish as the pronoun referent.…”

Section: Introductionmentioning

confidence: 99%

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The Influence of Focus Marking on Pronoun Resolution in Dialogue Context

et al. 2021

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Using visual world eye-tracking, we examined whether adults (N = 58) and children (N = 37; 3;1–6;3) use linguistic focussing devices to help resolve ambiguous pronouns. Participants listened to English dialogues about potential referents of an ambiguous pronoun he. Four conditions provided prosodic focus marking to the grammatical subject or to the object, which were either additionally it-clefted or not. A reference condition focussed neither the subject nor object. Adult online data revealed that linguistic focussing via prosodic marking enhanced subject preference, and overrode it in the case of object focus, regardless of the presence of clefts. Children’s processing was also influenced by prosodic marking; however, their performance across conditions showed some differences from adults, as well as a complex interaction with both their memory and language skills. Offline interpretations showed no effects of focus in either group, suggesting that while multiple cues are processed, subjecthood and first mention dominate the final interpretation in cases of conflict.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Influence of Focus Marking on Pronoun Resolution in Dialogue Context

et al. 2021

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“…The most important part of a sentence, oral or written, is the focus. Recent articles have demonstrated the importance of marking elements as a guide for better information exchange [26] between speakers and listeners. In particular, these studies argue that focus marking captures the listener's attention to what the speaker considers the most relevant part of the message.…”

Section: Related Workmentioning

confidence: 99%

SENECA: An Attention Support Tool for Context-related Content Learning

Auriemma¹

2021

JVLC

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In this paper, we suggest SENECA, a tool that attempts to assist students who follow remote classes in maintaining/capturing attention, allowing them to focus on context-driven learning. Distance education has a number of disadvantages, including a lack of physical interaction between students and teachers, emotional and motivational isolation as a result of this strategy, and a reduction in active engagement. All of these things have an impact on student learning abilities. The largest distractions at home are considered among these disadvantages of distant education, particularly for subjects with low awareness. These distractions cause a movement of the student's attention from the current lesson to disturbing events. For this reason, there is a need to experiment with new solutions also linked to Information Technology (IT) to improve the focused learning during distance education. Our tool's technical idea is to create a real-time summary of the topic treated by the teacher. The system captures the text every five minutes, generates outlines, and browses them to eliminate repetitive portions after each survey. We looked at two different sorts of filters, semantic and summary, to see if the first could distinguish between topics and the second could evaluate the topic's highlights. Natural Language Processing algorithms are used to extract categories and keywords from the general generated summary. The latter will emphasize the most important points of the speech, while the keywords will be utilized to extract the candidate literature about the discussed topics.

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“…The most important part of a sentence, oral or written, is the focus. Recent articles have demonstrated the importance of marking elements as a guide for better information exchange [24] between speakers and listeners. In particular, these studies argue that focus marking captures the listener's attention to what the speaker considers the most relevant part of the message.…”

Section: Introductionmentioning

confidence: 99%

SENECA: An Attention Support Tool for Context-related Content Learning

Auriemma¹

2021

International Conferences on Distributed Multimedia Systems

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In this work, we proposed a tool named SENECA that aims to help the students who follow remote lessons to maintain/capture attention, allowing them to focus on learning led by the context. Among the disadvantages of distance education, especially for subjects who lack awareness, the greatest distractions at home are counted. These distractions cause a movement of the student's attention from the current lesson to disturbing events. For this reason, there is a need to experiment with new solutions also linked to Information Technology (IT) to improve the focused learning during distance education. Our tool's technical idea is to create a real-time summary of the topic treated by the teacher. The system captures the text every five minutes, generates outlines, and scratches them and browses them to eliminate repetitive portions after each survey. On the general generated summary, Natural Language Processing techniques are applied to extract categories and keywords. The latter will show the highlights of the speech.

show abstract

Linguistic focus guides attention during the encoding and refreshing of Working Memory content

Cited by 13 publications

References 89 publications

The Influence of Focus Marking on Pronoun Resolution in Dialogue Context

The Influence of Focus Marking on Pronoun Resolution in Dialogue Context

SENECA: An Attention Support Tool for Context-related Content Learning

SENECA: An Attention Support Tool for Context-related Content Learning

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