Hierarchical and sequential processing of language

Frank, Stefan

doi:10.1080/23273798.2018.1424347

Cited by 48 publications

(23 citation statements)

References 30 publications

(40 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The finding that surprisal computed by marginalizing over syntactic structures (PCFG Surprisal) modulates the LANG response independently of surface-level n-gram surprisal is evidence that participants are indeed computing such structures during incremental sentence processing (Hale, 2001;Levy, 2008;Fossum & Levy, 2012;Rasmussen & Schuler, 2018) and is inconsistent with previous arguments that the human sentence processing response is largely insensitive to such structures (Frank & Bod, 2011;Frank et al, 2012;Frank & Christiansen, 2018). At the same time, the finding that 5-gram Surprisal modulates the LANG response independently of PCFG Surprisal is evidence that the human sentence processing mechanism is sensitive to word co-occurrence patterns in ways that are not well captured by a strictly context-free parser.…”

Section: Discussioncontrasting

confidence: 66%

“…This position is widely supported by behavioral and electrophysiological experiments using constructed stimuli (see Lewis & Collins, 2015 for review) and by some behavioral (Roark et al, 2009;Fossum & Levy, 2012;van Schijndel & Schuler, 2015;Shain et al, 2016), electrophysiological (Brennan & Hale, 2019) and neuroimaging experiments using naturalistic stimuli. However, other naturalistic studies reported null or negative syntactic effects (Frank & Bod, 2011;Shain & Schuler, 2018contra Shain et al, 2016, or mixed syntactic results within the same set of experiments (Demberg & Keller, 2008;Henderson et al, 2016), leading some to argue that the representations used for language comprehension (in the absence of task artifacts from constructed stimuli) contain little hierarchical structure (Frank & Christiansen, 2018). Furthermore, the few naturalistic fMRI studies that have explored structural prediction effects have yielded incongruent localizations for these effects.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

Shain

Blank

Schijndel

et al. 2019

Preprint

View full text Add to dashboard Cite

Much research in cognitive neuroscience supports prediction as a canonical computation of cognition in many domains. Is such predictive coding implemented by feedback from higher-order domain-general circuits, or is it locally implemented in domain-specific circuits? What information sources are used to generate these predictions? This study addresses these two questions in the context of language processing. We present fMRI evidence from a naturalistic comprehension paradigm (1) that predictive coding in the brain's response to language is domain-specific, and (2) that these predictions are sensitive both to local word co-occurrence patterns and to hierarchical structure. Using a recently developed deconvolutional time series regression technique that supports data-driven hemodynamic response function discovery from continuous BOLD signal fluctuations in response to naturalistic stimuli, we found we found effects of prediction measures in the language network but not in the domain-general, multiple-demand network. Moreover, within the language network, surface-level and structural prediction effects were separable. The predictability effects in the language network were substantial, with the model capturing over 37% of explainable variance on held-out data. These findings indicate that human sentence processing mechanisms generate predictions about upcoming words using cognitive processes that are sensitive to hierarchical structure and specialized for language processing, rather than via feedback from high-level executive control mechanisms. 4 2016; Lopopolo et al., 2017; see Table 1 for summary), a well-established predictor of behavioral measures in naturalistic language comprehension (These previous naturalistic studies of linguistic prediction effects in the brainusing estimates of prediction effort such as surprisal, the negative log probability of a word given its context, or entropy, an information-theoretic measure of the degree of constraint placed by the context on upcoming words (Hale, 2001)have yielded mixed results on the existence, type, and functional location of such effects. For example, of the lexicalized and unlexicalized (part-of-speech) bigram and trigram models of word surprisal explored in Brennan et al. (2016), only part-of-speech bigrams positively modulated neural responses in most regions of the functionally localized language network. Lexicalized bi-and trigrams and part-of-speech trigrams yielded generally null or negative results (16 out of 18 comparisons). By contrast, Willems et al. (2015) found lexicalized trigram effects in regions typically associated with language processing (e.g., anterior and posterior temporal lobe). In addition, Willems et al. (2015) and Lopopolo et al. (2017) found prediction effects in regions that are unlikely to be specialized for language processing, including (aggregating across both studies) the brain stem, amygdala, putamen, and hippocampus, as well as in superior frontal areas more typically associated with domain-general executive functions like...

show abstract

Section: Discussioncontrasting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

Shain

Blank

Schijndel

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Another limitation of constructing manipulations along dimensions derived from traditional generative linguistic approaches is that it does not adequately consider either approaches that are usage-based ( Goldberg, 2003 , Ibbotson, 2013 ) or theoretical approaches for explaining language that are essentially syntax free, of the sort we describe in Section 5 . As recently reviewed by Frank and Christiansen (2018) , their is substantial recent work suggesting that the presence of hierarchical syntactic structures should not be assumed a priori, and that sequential explanations may suffice (for recent neurobiological work, see Lopopolo, Frank, Van den Bosch, & Willems, 2017 ). Furthermore, as suggested recently ( Frégnac, 2017 ) rather than choosing stimuli that can produce strong firing rates, presenting a larger spectrum of input statistics and examining the system’s response to those may be more appropriate.…”

Section: Taking Context Seriouslymentioning

confidence: 99%

Grounding the neurobiology of language in first principles: The necessity of non-language-centric explanations for language comprehension

et al. 2018

View full text Add to dashboard Cite

Recent decades have ushered in tremendous progress in understanding the neural basis of language. Most of our current knowledge on language and the brain, however, is derived from lab-based experiments that are far removed from everyday language use, and that are inspired by questions originating in linguistic and psycholinguistic contexts. In this paper we argue that in order to make progress, the field needs to shift its focus to understanding the neurobiology of naturalistic language comprehension. We present here a new conceptual framework for understanding the neurobiological organization of language comprehension. This framework is non-language-centered in the computational/neurobiological constructs it identifies, and focuses strongly on context. Our core arguments address three general issues: (i) the difficulty in extending language-centric explanations to discourse; (ii) the necessity of taking context as a serious topic of study, modeling it formally and acknowledging the limitations on external validity when studying language comprehension outside context; and (iii) the tenuous status of the language network as an explanatory construct. We argue that adopting this framework means that neurobiological studies of language will be less focused on identifying correlations between brain activity patterns and mechanisms postulated by psycholinguistic theories. Instead, they will be less self-referential and increasingly more inclined towards integration of language with other cognitive systems, ultimately doing more justice to the neurobiological organization of language and how it supports language as it is used in everyday life.

show abstract

“…Results from the frequency-tagging paradigm have generally been taken as evidence in favour of hierarchical views of language syntax (e.g., [40]). However, these results could alternatively be compatible with a sequential model of language [62], which considers syntactic structure to be composed of sequences of words based on linear rather than nested, that is, hierarchical, relations (for a discussion see [63,64]). In particular, it has been shown that computational models based on either hierarchical [65] or sequential representations [62] can similarly reproduce human data using materials from Ding and colleagues [40].…”

Section: The Link Between Delta-band Oscillations and Structure-basedmentioning

confidence: 80%

Sentence processing: How words generate syntactic structures in the brain

Martorell¹,

Morucci²,

Mancini³

et al. 2020

Preprint

View full text Add to dashboard Cite

Language comprehension relies on the fundamental ability to create meaningful syntactic structures from single words during on-line processing. Time-resolved neuroimaging techniques can be used to measure electrophysiological activity revealing the neural dynamics underpinning these combinatorial processes in the brain. In the present chapter, we review and critically evaluate studies that have specifically investigated the electrophysiological bases of sentence-level syntactic processing using different types of analyses and paradigms. Besides reviewing event-related potential (ERP) studies, we focus on recent investigations of neural oscillations, highlighting their relevance and implications for our understanding of syntactic processing. Furthermore, we discuss critical issues related to the interpretation of current electrophysiological findings, including open questions such as the relationship between syntax and semantics and the link between ERPs and oscillations.

show abstract

Hierarchical and sequential processing of language

Cited by 48 publications

References 30 publications

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

Grounding the neurobiology of language in first principles: The necessity of non-language-centric explanations for language comprehension

Sentence processing: How words generate syntactic structures in the brain

Contact Info

Product

Resources

About