220 research outputs found
LexFindR: A fast, simple, and extensible R package for finding similar words in a lexicon
Published 30 September 2021Language scientists often need to generate lists of related words, such as potential competitors. Theymay do this for purposes
of experimental control (e.g., selecting items matched on lexical neighborhood but varying in word frequency), or to test
theoretical predictions (e.g., hypothesizing that a novel type of competitor may impact word recognition). Several online
tools are available, but most are constrained to a fixed lexicon and fixed sets of competitor definitions, and may not give the
user full access to or control of source data. We present LexFindR, an open-source R package that can be easily modified
to include additional, novel competitor types. LexFindR is easy to use. Because it can leverage multiple CPU cores and
uses vectorized code when possible, it is also extremely fast. In this article, we present an overview of LexFindR usage,
illustrated with examples.We also explain the details of how we implemented several standard lexical competitor types used
in spoken word recognition research (e.g., cohorts, neighbors, embeddings, rhymes), and show how “lexical dimensions”
(e.g., word frequency, word length, uniqueness point) can be integrated into LexFindR workflows (for example, to calculate
“frequency-weighted competitor probabilities”), for both spoken and visual word recognition research.This work was supported in part by U.S. National
Science Foundation grants PAC 1754284 (JM, PI) and IGE NRT
1747486 (JM, PI). The authors are solely responsible for the content
of this article. This work was also supported in part by the Basque
Government through the BERC 2018-2021 program, and by the
Agencia Estatal de Investigaci´on through BCBL Severo Ochoa
excellence accreditation SEV-2015-0490
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Watching Spoken Language Perception: Using Eye-movements to Track Lexical Access
Listener expectations and the perceptual accommodation of talker variability: A pre-registered replication
Published: 04 May 2021Researchers have hypothesized that in order to accommodate variability in how talkers produce their speech sounds, listeners
must perform a process of talker normalization. Consistent with this proposal, several studies have shown that spoken word
recognition is slowed when speech is produced by multiple talkers compared with when all speech is produced by one talker (a
multitalker processing cost). Nusbaum and colleagues have argued that talker normalization is modulated by attention (e.g.,
Nusbaum & Morin, 1992, Speech Perception, Production and Linguistic Structure, pp. 113–134). Some of the strongest
evidence for this claim is from a speeded monitoring study where a group of participants who expected to hear two talkers
showed a multitalker processing cost, but a separate group who expected one talker did not (Magnuson & Nusbaum, 2007,
Journal of Experimental Psychology, 33[2], 391–409). In that study, however, the sample size was small and the crucial
interaction was not significant. In this registered report, we present the results of a well-powered attempt to replicate those
findings. In contrast to the previous study, we did not observe multitalker processing costs in either of our groups. To rule out the
possibility that the null result was due to task constraints, we conducted a second experiment using a speeded classification task.
As in Experiment 1, we found no influence of expectations on talker normalization, with no multitalker processing cost observed
in either group. Our data suggest that the previous findings of Magnuson and Nusbaum (2007) be regarded with skepticism and
that talker normalization may not be permeable to high-level expectations.This research was supported by NSF 1754284, NSF
IGERT 1144399 & NSF NRT 1747486 (PI: JSM) and NSF BCS
1554810 & NIH R01 DC013064 (PI: EBM). This research was also
supported in part by the Basque Government through the BERC 2018-
2021 program and by the Agencia Estatal de Investigación through
BCBL Severo Ochoa excellence accreditation SEV-2015-0490. SL was
supported by an NSF Graduate Research Fellowshi
Does signal reduction imply predictive coding in models of spoken word recognition?
Published online: 14 April 2021Pervasive behavioral and neural evidence for predictive processing has led to claims that language processing depends upon
predictive coding. Formally, predictive coding is a computational mechanism where only deviations from top-down expectations
are passed between levels of representation. In many cognitive neuroscience studies, a reduction of signal for expected inputs is
taken as being diagnostic of predictive coding. In the present work, we show that despite not explicitly implementing prediction,
the TRACE model of speech perception exhibits this putative hallmark of predictive coding, with reductions in total lexical
activation, total lexical feedback, and total phoneme activation when the input conforms to expectations. These findings may
indicate that interactive activation is functionally equivalent or approximant to predictive coding or that caution is warranted in
interpreting neural signal reduction as diagnostic of predictive coding.This researchwas supported by NSF 1754284, NSF IGERT
1144399, and NSF NRT 1747486 (PI: J.S.M.). This research was also
supported in part by the Basque Government through the BERC 2018-
2021program, and by the Agencia Estatal de Investigación through
BCBL Severo Ochoa excellenceaccreditation SEV-2015-0490. S.L.
was supported by an NSF Graduate Research Fellowship
Robust Lexically Mediated Compensation for Coarticulation: Christmash Time Is Here Again
First published: 20 April 2021A long-standing question in cognitive science is how high-level knowledge is integrated with sensory
input. For example, listeners can leverage lexical knowledge to interpret an ambiguous speech
sound, but do such effects reflect direct top-down influences on perception or merely postperceptual
biases? A critical test case in the domain of spoken word recognition is lexically mediated compensation
for coarticulation (LCfC). Previous LCfC studies have shown that a lexically restored context
phoneme (e.g., /s/ in Christma#) can alter the perceived place of articulation of a subsequent target
phoneme (e.g., the initial phoneme of a stimulus from a tapes-capes continuum), consistent with the
influence of an unambiguous context phoneme in the same position. Because this phoneme-to-phoneme
compensation for coarticulation is considered sublexical, scientists agree that evidence for LCfC would
constitute strong support for top–down interaction. However, results from previous LCfC studies have
been inconsistent, and positive effects have often been small. Here, we conducted extensive piloting of
stimuli prior to testing for LCfC. Specifically, we ensured that context items elicited robust phoneme
restoration (e.g., that the final phoneme of Christma# was reliably identified as /s/) and that unambiguous
context-final segments (e.g., a clear /s/ at the end of Christmas) drove reliable compensation for
coarticulation for a subsequent target phoneme.We observed robust LCfC in a well-powered, preregistered
experiment with these pretested items (N = 40) as well as in a direct replication study (N = 40).
These results provide strong evidence in favor of computational models of spoken word recognition
that include top–down feedback
Using TMS to evaluate a causal role for right posterior temporal cortex in talker-specific phonetic processing
Available online 21 April 2023Theories suggest that speech perception is informed by listeners’ beliefs of what phonetic variation is typical of a talker. A previous fMRI study found right middle temporal gyrus (RMTG) sensitivity to whether a phonetic variant was typical of a talker, consistent with literature suggesting that the right hemisphere may play a key role in conditioning phonetic identity on talker information. The current work used transcranial magnetic stimulation (TMS) to test whether the RMTG plays a causal role in processing talker-specific phonetic variation. Listeners were exposed to talkers who differed in how they produced voiceless stop consonants while TMS was applied to RMTG, left MTG, or scalp vertex. Listeners subsequently showed near-ceiling performance in indicating which of two variants was typical of a trained talker, regardless of previous stimulation site. Thus, even though the RMTG is recruited for talker-specific phonetic processing, modulation of its function may have only modest consequences.This research was supported by NSF 1554810 (PI: EBM), NIH NIDCD
2R01 DC013064 (PI: EBM) and NSF NRT 1747486 (PI: JSM). This
research was supported in part by the Basque Government through the
BERC 2022–2025 program, by the Spanish State Research Agency
through BCBL Severo Ochoa excellence accreditation CEX2020-001010-
S and award PID2020-119131 GB-I000
Universal Features in Phonological Neighbor Networks
Human speech perception involves transforming a countinuous acoustic signal into discrete linguistically meaningful units (phonemes) while simultaneously causing a listener to activate words that are similar to the spoken utterance and to each other. The Neighborhood Activation Model posits that phonological neighbors (two forms [words] that differ by one phoneme) compete significantly for recognition as a spoken word is heard. This definition of phonological similarity can be extended to an entire corpus of forms to produce a phonological neighbor network (PNN). We study PNNs for five languages: English, Spanish, French, Dutch, and German. Consistent with previous work, we find that the PNNs share a consistent set of topological features. Using an approach that generates random lexicons with increasing levels of phonological realism, we show that even random forms with minimal relationship to any real language, combined with only the empirical distribution of language-specific phonological form lengths, are sufficient to produce the topological properties observed in the real language PNNs. The resulting pseudo-PNNs are insensitive to the level of lingustic realism in the random lexicons but quite sensitive to the shape of the form length distribution. We therefore conclude that “universal” features seen across multiple languages are really string universals, not language universals, and arise primarily due to limitations in the kinds of networks generated by the one-step neighbor definition. Taken together, our results indicate that caution is warranted when linking the dynamics of human spoken word recognition to the topological properties of PNNs, and that the investigation of alternative similarity metrics for phonological forms should be a priorit
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EARSHOT:A minimal network model of human speech recognition that operates on real speech
Despite the lack of invariance problem (the many-to-manymapping between acoustics and percepts), we experiencephonetic constancy and typically perceive what a speakerintends. Models of human speech recognition have side-stepped this problem, working with abstract, idealized inputsand deferring the challenge of working with real speech. Incontrast, automatic speech recognition powered by deeplearning networks have allowed robust, real-world speechrecognition. However, the complexities of deep learningarchitectures and training regimens make it difficult to usethem to provide direct insights into mechanisms that maysupport human speech recognition. We developed a simplenetwork that borrows one element from automatic speechrecognition (long short-term memory nodes, which providedynamic memory for short and long spans). This allows thenetwork to learn to map real speech from multiple talkers tosemantic targets with high accuracy. Internal representationsemerge that resemble phonetically-organized responses inhuman superior temporal gyrus, suggesting that the modeldevelops a distributed phonological code despite no explicittraining on phonetic or phonemic targets. The ability to workwith real speech is a major advance for cognitive models ofhuman speech recognition
The role of pre-school quality in promoting resilience in the cognitive development of young children
The study reported here investigates the role of pre-school education as a protective factor in the development of children who are at risk due to environmental and individual factors. This investigation builds upon earlier research by examining different kinds of 'quality' in early education and tests the hypothesis that pre-schools of high quality can moderate the impacts of risks upon cognitive development. Cognitive development was measured in 2857 English pre-schoolers at 36 and 58 months of age, together with 22 individual risks to children's development, and assessments were made of the quality of their pre-school provision. Multilevel Structural Equation Modelling revealed that: the global quality of pre-school can moderate the effects of familial risk (such as poverty); the relationships between staff and children can moderate the effects of child level risk (such as low birth weight); and the specific quality of curricular provision can moderate the effects of both. Policy makers need to take quality into account in their efforts to promote resilience in young 'at risk' children through early childhood services
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