What corpus-based Cognitive Linguistics can and cannot expect from neurolinguistics

Blumenthal-Dramé, Alice

doi:10.1515/cog-2016-0062

Cited by 43 publications

(9 citation statements)

References 74 publications

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“…This and similar research (for a review see Blumenthal-Dramé, 2016) has shown that corpus-derived metrics can be tested against processing cost at different levels of language description, from orthography up to syntax. This makes it possible to adjudicate between competing metrics so as to identify the cognitively most pertinent and thus meaningful metrics for a given language.…”

Section: Complexity Metrics and Cognitive Researchmentioning

confidence: 85%

Meaning and Measures: Interpreting and Evaluating Complexity Metrics

et al. 2021

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Research on language complexity has been abundant and manifold in the past two decades. Within typology, it has to a very large extent been motivated by the question of whether all languages are equally complex, and if not, which language-external factors affect the distribution of complexity across languages. To address this and other questions, a plethora of different metrics and approaches has been put forward to measure the complexity of languages and language varieties. Against this backdrop we address three major gaps in the literature by discussing statistical, theoretical, and methodological problems related to the interpretation of complexity measures. First, we explore core statistical concepts to assess the meaningfulness of measured differences and distributions in complexity based on two case studies. In other words, we assess whether observed measurements are neither random nor negligible. Second, we discuss the common mismatch between measures and their intended meaning, namely, the fact that absolute complexity measures are often used to address hypotheses on relative complexity. Third, in the absence of a gold standard for complexity metrics, we suggest that existing measures be evaluated by drawing on cognitive methods and relating them to real-world cognitive phenomena. We conclude by highlighting the theoretical and methodological implications for future complexity research.

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Section: Complexity Metrics and Cognitive Researchmentioning

confidence: 85%

Meaning and Measures: Interpreting and Evaluating Complexity Metrics

et al. 2021

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“…Chunk status does not only influence the production of sequences in different modalities; it also modulates how observers and comprehenders segment the incoming sensory signal. For example, high-frequency chunks are both more difficult to segment and more expected than lower-frequency sequences (Blumenthal-Dramé, 2012, 2016a, 2016bBlumenthal-Dramé et al, 2017).…”

Section: Effects Of Repetition Across Domainsmentioning

confidence: 99%

“…Surprisal and entropy reduction have both been shown to be good predictors of processing cost across different levels of language description, from morphology via the levels of lexical combinatorics up to syntax (Blumenthal-Dramé, 2016b;Blumenthal-Dramé et al, 2017;McConnell & Blumenthal-Dramé, 2018).…”

Section: Information Transfer Measures In Communicationmentioning

confidence: 99%

“…A further cue to prediction is statistical knowledge extracted from prior experience. Humans are known to be very proficient at extracting and storing statistical regularities from their motor and linguistic environment, and at exploiting mental schemas representing these regularities to make predictions as to upcoming material (Blumenthal-Dramé, 2016b). Thus, a study on predictive gaze fixations showed that after passive observation, toddlers as young as 8-11 months were able to predict upcoming actions based on statistical relationships extracted from the input, at least when the observed action sequences led up to salient sensory changes (Monroy et al, 2017).…”

Section: Cues For Predictive Processing Across Scalesmentioning

confidence: 99%

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Shared neural and cognitive mechanisms in action and language: The multiscale information transfer framework

Blumenthal-Dramé

Malaia

2018

WIRES Cognitive Science

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This review compares how humans process action and language sequences produced by other humans. On the one hand, we identify commonalities between action and language processing in terms of cognitive mechanisms (e.g., perceptual segmentation, predictive processing, integration across multiple temporal scales), neural resources (e.g., the left inferior frontal cortex), and processing algorithms (e.g., comprehension based on changes in signal entropy). On the other hand, drawing on sign language with its particularly strong motor component, we also highlight what differentiates (both oral and signed) linguistic communication from nonlinguistic action sequences. We propose the multiscale information transfer framework (MSIT) as a way of integrating these insights and highlight directions into which future empirical research inspired by the MSIT framework might fruitfully evolve. This article is categorized under: Psychology > Language Linguistics > Language in Mind and Brain Psychology > Motor Skill and Performance Psychology > Prediction

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“…But a stimulus may also cause surprise by virtue of deviating from a cognitive ground, e.g., when violating social or probabilistic expectations (Clark, 2013 ). This has prompted researchers to examine the relationship between expectations and the perceptual salience of linguistic stimuli in new ways (Hanulíková et al, 2012 ; Rácz, 2012 ; Hanulíková and Carreiras, 2015 ; Blumenthal-Dramé, 2016a , b ; Roller, 2016 ; Blumenthal-Dramé et al, 2017 ), and inspired us to organize a workshop devoted to this particular area.…”

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confidence: 99%