Disfluency effects on lexical selection

Medimorec, Srdan; Young, Torin; Risko, Evan F.

doi:10.1016/j.cognition.2016.10.008

Cited by 7 publications

(9 citation statements)

References 24 publications

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“…As with the original TAACO, we foresee TAACO 2.0 being used in a number of prediction tasks that go beyond text coherence and speaking judgments. For instance, TAACO 1.0 has been used in a number of published studies already, in domains such as creativity (Skalicky, Crossley, McNamara, & Muldner, 2017), transcription disfluency (Medimorec, Young, & Risko, 2017), literary studies (Jacobs, Schuster, Xue, & Lüdtke, 2017), formative writing assessment (Wilson, Roscoe, & Ahmed, 2017), predicting math performance (Crossley, Liu, & McNamara, 2017a), self-regulated learning (Piotrkowicz et al, 2017), and medical discourse (Schillinger et al, 2017). We presume that researchers will continue to find innovative and disciplinespecific applications of TAACO 2.0 in future research, especially considering the addition of the new semantic similarity metrics and source overlap scores.…”

Section: Resultsmentioning

confidence: 99%

The Tool for the Automatic Analysis of Cohesion 2.0: Integrating semantic similarity and text overlap

2018

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This article introduces the second version of the Tool for the Automatic Analysis of Cohesion (TAACO 2.0). Like its predecessor, TAACO 2.0 is a freely available text analysis tool that works on the Windows, Mac, and Linux operating systems; is housed on a user's hard drive; is easy to use; and allows for batch processing of text files. TAACO 2.0 includes all the original indices reported for TAACO 1.0, but it adds a number of new indices related to local and global cohesion at the semantic level, reported by latent semantic analysis, latent Dirichlet allocation, and word2vec. The tool also includes a source overlap feature, which calculates lexical and semantic overlap between a source and a response text (i.e., cohesion between the two texts based measures of text relatedness). In the first study in this article, we examined the effects that cohesion features, prompt, essay elaboration, and enhanced cohesion had on expert ratings of text coherence, finding that global semantic similarity as reported by word2vec was an important predictor of coherence ratings. A second study was conducted to examine the source and response indices. In this study we examined whether source overlap between the speaking samples found in the TOEFL-iBT integrated speaking tasks and the responses produced by test-takers was predictive of human ratings of speaking proficiency. The results indicated that the percentage of keywords found in both the source and response and the similarity between the source document and the response, as reported by word2vec, were significant predictors of speaking quality. Combined, these findings help validate the new indices reported for TAACO 2.0. Keywords Cohesion. Coherence. Natural language processing. Essay quality. Speaking proficiency Cohesion generally refers to text elements that help readers facilitate comprehension (Graesser, McNamara, & Louwerse, 2003). These elements can include argument overlap across sentences and paragraphs, the use of connectives to link sentence and paragraphs, and casual relationships within a text. Cohesion can be categorized at a base level as either local or global, where local cohesion refers to textual links between sentences, and global cohesion identifies links between larger text segments such as paragraphs (Givón, 1995; Kintsch, 1995). Measuring text cohesion is important because it can help identify text that is more difficult to comprehend, text genres, and writing quality

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Section: Resultsmentioning

confidence: 99%

The Tool for the Automatic Analysis of Cohesion 2.0: Integrating semantic similarity and text overlap

2018

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“…However for text production lags on different levels of activation might be associated with different disfluency magnitudes and might be cumulative. If the size of the disfluency is assumed to depend on the inhibited process upstream or combination of processes, this can be implemented as additional mixture component(s) (similar to Baaijen et al, 2012; see also Almond et al, 2012) to address different types of disfluencies (Medimorec et al, 2017;Medimorec & Risko, 2016;Wengelin, 2001). In other words extensions of mixture models allow us to test different hypotheses about the cascade of processes involved in writing and language production.…”

Section: Discussionmentioning

confidence: 99%

“…Keystroke-logging captures this information. From these logs, researchers calculate different process measures including measures of writing fluency (Chukharev-Hudilainen et al, 2019;Medimorec et al, 2017;Medimorec & Risko, 2016;Van Waes & Leijten, 2015), means, medians and standard deviations of inter-keystroke intervals (the latency between two consecutive keystrokes), and writing hesitations such as the number of pauses or pause duration (for an overview of frequently used keystroke measures see Conijn et al, 2019a). Indeed, Conijn et al (2019a) suggested that these aggregates are sensitive to processing difficulty that arises on different levels of mental representation.…”

Section: Introductionmentioning

confidence: 99%

Modelling typing disfluencies as finite mixture process

et al. 2021

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To writing anything on a keyboard at all requires us to know first what to type, then to activate motor programmes for finger movements, and execute these. An interruption in the information flow at any of these stages leads to disfluencies. To capture this combination of fluent typing and typing hesitations, researchers calculate different measures from keystroke-latency data—such as mean inter-keystroke interval and pause frequencies. There are two fundamental problems with this: first, summary statistics ignore important information in the data and frequently result in biased estimates; second, pauses and pause-related measures are defined using threshold values which are, in principle, arbitrary. We implemented a series of Bayesian models that aimed to address both issues while providing reliable estimates for individual typing speed and statistically inferred process disfluencies. We tested these models on a random sample of 250 copy-task recordings. Our results illustrate that we can model copy typing as a mixture process of fluent and disfluent key transitions. We conclude that mixture models (1) map onto the information cascade that generate keystrokes, and (2) provide a principled approach to detect disfluencies in keyboard typing.

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“…Harris & Coltheart, 1986, p. 214). Medimorec & Risko (2016) and Medimorec, Young & Risko (2017) conclude that, for some typists, decreasing typing speed may yield positive effects on cognitive processing and text quality.…”

Section: Typingmentioning

confidence: 99%

A task segment framework to study keylogged translation processes

Martín

Apfelthaler

2022

Trans-Int

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The Task Segment Framework (TSF) is a tool to analyze full typing flows of translation tasks as keylogged with timestamps recorded for keydown, keyup, mouse clicks and moves, and actions performed in other applications. The TSF assumes that intentional pauses flag stretches where subjects concentrate on unrecorded cognitive processes such as planning and assessment. The interspersed typing stretches are task segments, with or without text, where basic subtasks may be observed, mainly adding new text, changing existing text, and searching for information. Accumulated experience and planning allow translators to lump strategically similar activities together, in order to spare efforts and task switching costs while maximizing efficiency. Hence, task segments may contain activities of just one such subtask or many. Translation fluency is a key notion of the TSF, operationalized through many indicators such as typing speed, prior pause length, TS (task segment) length in events, text length as full words, number of typos and respites (=mid inter-keystroke intervals), subtask(s), and the like. The approach seems particularly sensitive to translation expertise levels and may be applied with variations to other multilectal mediated communication tasks. This article lays down the conceptual basis of the TSF and summarizes its basic notions and constructs.

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Disfluency effects on lexical selection

Cited by 7 publications

References 24 publications

The Tool for the Automatic Analysis of Cohesion 2.0: Integrating semantic similarity and text overlap

The Tool for the Automatic Analysis of Cohesion 2.0: Integrating semantic similarity and text overlap

Modelling typing disfluencies as finite mixture process

A task segment framework to study keylogged translation processes

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