A Common Representation of Serial Position in Language and Memory

Fischer‐Baum, Simon

doi:10.1016/bs.plm.2018.08.002

Cited by 12 publications

(12 citation statements)

References 47 publications

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“…In that case, phoneme X would have a different representation in the context AXB and VXY. Finally, generalisability is inconsistent with position-specific encoding accounts, such as edge-based schemes [29,30], which would posit that X is encoded differently in ABX and XBC. While it is possible that multiple representational systems co-exist, our results support that at least one of those encoding schemes is context-independent, which encodes content regardless of lexical edges.…”

Section: Discussionmentioning

confidence: 99%

Neural dynamics of phoneme sequences: Position-invariant code for content and order

Gwilliams

Marantz

et al. 2020

Preprint

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Listeners experience speech as a sequence of discrete words. However, the real input is a continuously varying acoustic signal that blends words and phonemes into one another. Here we recorded two-hour magnetoencephalograms from 21 subjects listening to stories, in order to investigate how the brain concurrently solves three competing demands: 1) processing overlapping acoustic-phonetic information while 2) keeping track of the relative order of phonemic units and 3) maintaining individuated phonetic information until successful word recognition. We show that the human brain transforms speech input, roughly at the rate of phoneme duration, along a temporally-defined representational trajectory. These representations, absent from the acoustic signal, are active earlier when phonemes are predictable than when they are surprising, and are sustained until lexical ambiguity is resolved. The results reveal how phoneme sequences in natural speech are represented and how they interface with stored lexical items. One sentence summaryThe human brain keeps track of the relative order of speech sound sequences by jointly encoding content and elapsed processing time Speech comprehension involves mapping non-stationary, highly variable and continuous 1 acoustic signals onto discrete linguistic representations [1]. Although the human experience is 2 typically one of effortless understanding, the computational infrastructure underpinning speech 3 processing remains a major challenge for neuroscience [2] and artificial intelligence systems [3] 4 alike. 5Existing cognitive models primarily serve to explain the recognition of words in isolation 6 [4, 5, 6]. Predictions of these models have gained empirical support in terms of neural encoding 7 of phonetic features [7, 8, 9, 10], and interactions between phonetic and (sub)lexical units of 8 representation [11, 12, 13, 14, 15]. What is not well understood, and what such models largely 9 ignore, however, is how sequences of acoustic-phonetic signals (e.g. the phonemes k-a-t) are 10 mapped to lexical items (e.g. cat) during comprehension of naturalistic continuous speech. 11One substantial challenge is that naturalistic language does not come pre-parsed: there are, 12 e.g. no reliable cues for word boundaries, and adjacent speech sounds (phonemes) acoustically 13 overlap both within and across words due to co-articulation [1]. In addition, the same sequence 14 of phonemes can form completely different words (e.g. pets versus pest), so preserving phoneme 15 order is critical. Furthermore, phonemes elicit a cascade of neural responses, which long surpass 16 the duration of the phonemes themselves [16, 17, 9]). This means, concretely, that a given 17 phoneme i is still present in both the acoustic and neural signals while subsequent phonemes 18 stimulate the cochlea. Such signal complexity presents serious challenges for the key goals of 19 achieving invariance and perceptual constancy in spoken language comprehension. 20Based on decoding analyses of acoustic and neural data we show how t...

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Section: Discussionmentioning

confidence: 99%

Neural dynamics of phoneme sequences: Position-invariant code for content and order

Gwilliams

Marantz

et al. 2020

Preprint

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“…Thus, while a participant's overall goals in a conversation and a VWM task are very different, it should be clear that the task demands of both activities overlap, including simultaneously encoding input while developing and maintaining plans to generate a response. Researchers are actively investigating the attention and cognitive control demands of language planning in advance of speaking, including serial ordering and monitoring of utterance plans (for review, see Novick, 2017 andFischer-Baum, 2018 for potential implications for VWM tasks). Some methods manipulating selective attention to individual words in a list could prove to be useful for new studies of both VWM tasks and more typical language production (e.g., Nozari and Dell, 2012;Nozari and Thompson-Schill, 2013).…”

Section: Implications For Attention Task Subcomponents and Domain Gmentioning

confidence: 99%

Verbal Working Memory as Emergent from Language Comprehension and Production

Schwering

MacDonald

2020

Front. Hum. Neurosci.

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This article reviews current models of verbal working memory and considers the role of language comprehension and long-term memory in the ability to maintain and order verbal information for short periods of time. While all models of verbal working memory posit some interaction with long-term memory, few have considered the character of these long-term representations or how they might affect performance on verbal working memory tasks. Similarly, few models have considered how comprehension processes and production processes might affect performance in verbal working memory tasks. Modern theories of comprehension emphasize that people learn a vast web of correlated information about the language and the world and must activate that information from long-term memory to cope with the demands of language input. To date, there has been little consideration in theories of verbal working memory for how this rich input from comprehension would affect the nature of temporary memory. There has also been relatively little attention to the degree to which language production processes naturally manage serial order of verbal information. The authors argue for an emergent model of verbal working memory supported by a rich, distributed long-term memory for language. On this view, comprehension processes provide encoding in verbal working memory tasks, and production processes maintenance, serial ordering, and recall. Moreover, the computational capacity to maintain and order information varies with language experience. Implications for theories of working memory, comprehension, and production are considered.

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“…An important question remains, however, regarding whether the two parietal regions involved in phonological and orthographic WM can be differentiated within the same study. In alphabetic writing systems, there is a close association between phonological and orthographic codes and the same mechanism has been postulated to represent both the order of speech sounds and orthographic codes in WM (37), lending credence to the possibility that perhaps a single WM system supports maintenance in the two domains. Moreover, as discussed earlier, embedded processes approaches assume an attentional spotlight that holds information in the focus of Rapp and Dufor (28).…”

Section: Introductionmentioning

confidence: 82%

Distinct Neural Substrates Support Phonological and Orthographic Working Memory: Implications for Theories of Working Memory

2021

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Prior behavioral and neuroimaging evidence supports a separation between working memory capacities in the phonological and orthographic domains. Although these data indicate distinct buffers for orthographic and phonological information, prior neural evidence does indicate that nearby left inferior parietal regions support both of these working memory capacities. Given that no study has directly compared their neural substrates based on data from the same individuals, it is possible that there is a common left inferior parietal region shared by both working memory capacities. In fact, those endorsing an embedded processes account of working memory might suggest that parietal involvement reflects a domain-general attentional system that directs attention to long-term memory representations in the two domains, implying that the same neural region supports the two capacities. Thus, in this work, a multivariate lesion-symptom mapping approach was used to assess the neural basis of phonological and orthographic working memory using behavioral and lesion data from the same set of 37 individuals. The results showed a separation of the neural substrates, with regions in the angular gyrus supporting orthographic working memory and with regions primarily in the supramarginal gyrus supporting phonological working memory. The results thus argue against the parietal involvement as supporting a domain-general attentional mechanism and support a domain-specific buffer account of working memory.

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A Common Representation of Serial Position in Language and Memory

Cited by 12 publications

References 47 publications

Neural dynamics of phoneme sequences: Position-invariant code for content and order

Neural dynamics of phoneme sequences: Position-invariant code for content and order

Verbal Working Memory as Emergent from Language Comprehension and Production

Distinct Neural Substrates Support Phonological and Orthographic Working Memory: Implications for Theories of Working Memory

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