Models Linking Production and Comprehension

Gambi, Chiara; Pickering, Martin J.

doi:10.1002/9781118829516.ch7

Cited by 26 publications

(21 citation statements)

References 136 publications

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“…In contrast, here we have shown that convergence occurs within individuals when comparing their activation profile as a speaker and as a listener. Our results can be explained by models of language processing which assume that semantic representations are shared between comprehension and production (Gambi & Pickering, 2017;Hagoort, 2013;Kintsch & van Dijk, 1978;Levelt, Roelofs, & Meyer, 1999). This shared representation may allow the production system to make forward predictions that aid comprehension (Dell & Chang, 2014;Pickering & Garrod, 2007) and help interlocutors to align their situation models during conversation .…”

Section: Discussionmentioning

confidence: 77%

Similar neural networks respond to coherence during comprehension and production of discourse

Morales

Patel

Tamm

et al. 2021

Preprint

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When comprehending discourse, listeners engage default mode regions associated with integrative semantic processing to construct a situation model of its content. We investigated how similar networks are engaged when we produce, as well as comprehend, discourse. During fMRI, participants spoke about a series of specific topics and listened to discourse on other topics. We tested how activation was predicted by natural fluctuations in the global coherence of the discourse, i.e., the degree to which utterances conformed to the expected topic. The neural correlates of coherence were similar across speaking and listening, particularly in default mode regions. This network showed greater activation when less coherent speech was heard or produced, reflecting updating of mental representations when discourse did not conform to the expected topic. In contrast, regions that exert control over semantic activation showed task-specific effects, correlating negatively with coherence during listening but not during production. Participants who showed greater activation in left inferior prefrontal cortex also produced more coherent discourse, suggesting a specific role for this region in goal-directed regulation of speech content. Results suggest strong alignment of discourse representations during speaking and listening. However, they indicate that the semantic control network plays different roles in comprehension and production.

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Section: Discussionmentioning

confidence: 77%

Similar neural networks respond to coherence during comprehension and production of discourse

Morales

Patel

Tamm

et al. 2021

Preprint

Self Cite

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“…Speaker-oriented and listeneroriented accounts are not mutually exclusive. Hypotheses and interpretations found in psycholinguistic literature fall on a continuum between the two views , especially if seen as a consequence of the constraints imposed by communication (Gambi and Pickering, 2017), i.e., the tension between production ease and robust message transmission (Zipf, 1935;Lindblom, 1990;Levy and Jaeger, 2007;Jaeger and Buz, 2016).…”

Section: Accounting For Probabilistic Effectsmentioning

confidence: 99%

Dimensions of Segmental Variability: Interaction of Prosody and Surprisal in Six Languages

et al. 2018

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Contextual predictability variation affects phonological and phonetic structure. Reduction and expansion of acoustic-phonetic features is also characteristic of prosodic variability. In this study, we assess the impact of surprisal and prosodic structure on phonetic encoding, both independently of each other and in interaction. We model segmental duration, vowel space size and spectral characteristics of vowels and consonants as a function of surprisal as well as of syllable prominence, phrase boundary, and speech rate. Correlates of phonetic encoding density are extracted from a subset of the BonnTempo corpus for six languages: American English, Czech, Finnish, French, German, and Polish. Surprisal is estimated from segmental n-gram language models trained on large text corpora. Our findings are generally compatible with a weak version of Aylett and Turk's Smooth Signal Redundancy hypothesis, suggesting that prosodic structure mediates between the requirements of efficient communication and the speech signal. However, this mediation is not perfect, as we found evidence for additional, direct effects of changes in surprisal on the phonetic structure of utterances. These effects appear to be stable across different speech rates.

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“…The reafference principle has indeed been suggested as a theoretical frame for perceptual processes in general, and has been suggested to explain “pre‐emptive perception” (Bodis‐Wollner, 2008), “proactive perception” (Rimmele, Morillon, Poeppel, & Arnal, 2018), the discontinuity of thinking in schizophrenia (Feinberg, 1978), and speech production (Tian & Poeppel, 2010, 2013; Tian, Zarate, & Poeppel, 2016). Although the reafference principle has been invited into the field of language perception (Gambi & Pickering, 2017; Pickering & Clark, 2014; Pickering & Gambi, 2018; Pickering & Garrod, 2013), the reasoning is basically on the computational and algorithmic level (Marr, 1982). Neurobiological features of the principle refer mainly to perception–production connections (Dick & Andric, 2013; Pickering & Garrod, 2013) rather than to the basis of the two (or three, as we propose) operations in the reafference principle.…”

Section: A Three‐stage Cycling Model In Language Processingmentioning

confidence: 99%

The three second time window in poems and language processing in general: Complementarity of discrete timing and temporal continuity

Bao

2020

PsyCh Journal

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The existence of discrete time windows has triggered the search for permanence and continuity for artists (including poets) in multiple cultures throughout history. In this article, we argue that there exists a 3‐s window in the temporal structure of poems as well as in the aesthetic appreciation of poetry by reviewing previous literature on the temporal aspects of poems. This 3‐s window can also be considered to be a general temporal machinery underlying human behavior, including language production and perception in general. The reafference principle has provided us a unique frame for understanding cognitive processes. However, “time” was absent in the original two‐stage reafference principle. Therefore, we propose a three‐stage cycling model of language perception, taking into account time and time windows. We also inspect the possible neural implementations of the three stages: the generation, maintenance, and comparison of predictions (as well as the integration of predictions into the representational context). These three stages are embedded in a temporal window of ~3 s and are repeated in a cycling mode, resulting in the representational context being continuously updated. Thus, it is possible that “semantics” could be carried forward across different time windows, being a “glue” linking the discrete time windows and thus achieving the subjective feeling of temporal continuity. Candidates of such “semantic glue” could include semantic and syntactic structures as well as identity and emotion.

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Models Linking Production and Comprehension

Cited by 26 publications

References 136 publications

Similar neural networks respond to coherence during comprehension and production of discourse

Similar neural networks respond to coherence during comprehension and production of discourse

Dimensions of Segmental Variability: Interaction of Prosody and Surprisal in Six Languages

The three second time window in poems and language processing in general: Complementarity of discrete timing and temporal continuity

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