Knowledge-based and signal-based cues are weighted flexibly during spoken language comprehension.

During spoken language comprehension, listeners make use of both knowledge-based and signal-based sources of information, but little is known about how cues from these distinct levels of representational hierarchy are weighted and integrated online. In an eye-tracking experiment using the visual world paradigm, we investigated the flexible weighting and integration of morphosyntactic gender marking (a knowledge-based cue) and contextual speech rate (a signal-based cue). We observed that participants used the morphosyntactic cue immediately to make predictions about upcoming referents, even in the presence of uncertainty about the cue's reliability. Moreover, we found speech rate normalization effects in participants' gaze patterns even in the presence of preceding morphosyntactic information. These results demonstrate that cues are weighted and integrated flexibly online, rather than adhering to a strict hierarchy. We further found rate normalization effects in the looking behavior of participants who showed a strong behavioral preference for the morphosyntactic gender cue. This indicates that rate normalization effects are robust and potentially automatic. We discuss these results in light of theories of cue integration and the two-stage model of acoustic context effects. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

[1]  Laura C. Dilley,et al.  Not just a function of function words: Distal speech rate influences perception of prosodically weak syllables , 2018, Attention, Perception, & Psychophysics.

[2]  B. McMurray,et al.  What information is necessary for speech categorization? Harnessing variability in the speech signal by integrating cues computed relative to expectations. , 2011, Psychological review.

[3]  W. Marslen-Wilson Functional parallelism in spoken word-recognition , 1987, Cognition.

[4]  K. Boff,et al.  Saccadic overhead: Information-processing time with and without saccades , 1993, Perception & psychophysics.

[5]  Andrea E. Martin,et al.  Language Processing as Cue Integration: Grounding the Psychology of Language in Perception and Neurophysiology , 2016, Front. Psychol..

[6]  Laura C. Dilley,et al.  Altering Context Speech Rate Can Cause Words to Appear or Disappear , 2010, Psychological science.

[7]  Atsuko Takashima,et al.  Neural Entrainment Determines the Words We Hear , 2017, Current Biology.

[8]  Eva Reinisch,et al.  The uptake of spectral and temporal cues in vowel perception is rapidly influenced by context , 2013, J. Phonetics.

[9]  Matthias J. Sjerps,et al.  Cognitive load makes speech sound fast, but does not modulate acoustic context effects , 2017 .

[10]  Bob McMurray,et al.  The time-course of speaking rate compensation: effects of sentential rate and vowel length on voicing judgments , 2015, Language, cognition and neuroscience.

[11]  A. Cutler,et al.  Use of Syntax in Perceptual Compensation for Phonological Reduction , 2014, Language and speech.

[12]  M. Kutas,et al.  Anticipating Words and Their Gender: An Event-related Brain Potential Study of Semantic Integration, Gender Expectancy, and Gender Agreement in Spanish Sentence Reading , 2004, Journal of Cognitive Neuroscience.

[13]  Mante S. Nieuwland,et al.  Large-scale replication study reveals a limit on probabilistic prediction in language comprehension , 2018, eLife.

[14]  Monique Flecken,et al.  Lexical prediction in language comprehension: a replication study of grammatical gender effects in Dutch , 2018, Language, Cognition and Neuroscience.

[15]  Holger Mitterer,et al.  The singleton-geminate distinction can be rate dependent: Evidence from Maltese , 2018 .

[16]  Hans Rutger Bosker,et al.  How the Tracking of Habitual Rate Influences Speech Perception , 2019, Journal of experimental psychology. Learning, memory, and cognition.

[17]  J. McQueen Segmentation of Continuous Speech Using Phonotactics , 1998 .

[18]  Gregory C. DeAngelis,et al.  Bridging the gap between theories of sensory cue integration and the physiology of multisensory neurons , 2013, Nature Reviews Neuroscience.

[19]  Marta Kutas,et al.  Potato not Pope: human brain potentials to gender expectation and agreement in Spanish spoken sentences , 2003, Neuroscience Letters.

[20]  Interactions between distal speech rate, linguistic knowledge, and speech environment , 2015, Psychonomic bulletin & review.

[21]  Marc Brysbaert,et al.  MultiPic: A standardized set of 750 drawings with norms for six European languages , 2018, Quarterly journal of experimental psychology.

[22]  H. Bülthoff,et al.  Merging the senses into a robust percept , 2004, Trends in Cognitive Sciences.

[23]  James L. McClelland,et al.  The TRACE model of speech perception , 1986, Cognitive Psychology.

[24]  Laurence White,et al.  Effects of syntactic expectations on speech segmentation. , 2007, Journal of experimental psychology. Human perception and performance.

[25]  L. Lisker,et al.  Some Effects of Context On Voice Onset Time in English Stops , 1967, Language and speech.

[26]  Martha E. Pollack,et al.  Incremental Interpretation , 1991, Artif. Intell..

[27]  Eva Reinisch,et al.  Foreign Languages Sound Fast: Evidence from Implicit Rate Normalization , 2017, Front. Psychol..

[28]  Colin M. Brown,et al.  Anticipating upcoming words in discourse: evidence from ERPs and reading times. , 2005, Journal of experimental psychology. Learning, memory, and cognition.

[29]  D. Norris,et al.  Shortlist B: a Bayesian model of continuous speech recognition. , 2008, Psychological review.

[30]  P C Gordon,et al.  Induction of rate-dependent processing by coarse-grained aspects of speech , 1988, Perception & psychophysics.

[31]  Laurence White,et al.  Integration of multiple speech segmentation cues: a hierarchical framework. , 2005, Journal of experimental psychology. General.

[32]  H. Bosker,et al.  Linguistic expectation management in online discourse processing: An investigation of Dutch inderdaad 'indeed' and eigenlijk 'actually' , 2018, Journal of Memory and Language.

[33]  Philip J. Monahan,et al.  Prediction of Agreement and Phonetic Overlap Shape Sublexical Identification , 2017, Language and speech.

[34]  Titia Benders,et al.  Native, non-native and L2 perceptual cue weighting for Dutch vowels: The case of Dutch, German, and Spanish listeners , 2009, J. Phonetics.

[35]  Laura C. Dilley,et al.  Age-Related Differences in Speech Rate Perception Do Not Necessarily Entail Age-Related Differences in Speech Rate Use. , 2015, Journal of speech, language, and hearing research : JSLHR.

[36]  Falk Huettig,et al.  Effects of speech rate, preview time of visual context, and participant instructions reveal strong limits on prediction in language processing , 2019, Brain Research.

[37]  L. Holt,et al.  Perceptual effects of preceding nonspeech rate on temporal properties of speech categories , 2005, Perception & psychophysics.

[38]  G. Altmann,et al.  Incremental interpretation at verbs: restricting the domain of subsequent reference , 1999, Cognition.

[39]  M. Pickering,et al.  Architectures and Mechanisms for Language Processing , 1999 .

[40]  Oded Ghitza,et al.  Entrained theta oscillations guide perception of subsequent speech: behavioural evidence from rate normalisation , 2018 .

[41]  H. Bosker Accounting for rate-dependent category boundary shifts in speech perception , 2016, Attention, Perception, & Psychophysics.

[42]  Paul Boersma,et al.  Praat: doing phonetics by computer , 2003 .

[43]  Falk Huettig,et al.  Individual differences in working memory and processing speed predict anticipatory spoken language processing in the visual world , 2016 .

[44]  H. Schriefers,et al.  Prediction in language comprehension beyond specific words: An ERP study on sentence comprehension in Polish , 2013 .

[45]  J. Towse,et al.  Individual differences in working memory , 2006, Neuroscience.

[46]  P. Boersma Praat : doing phonetics by computer (version 5.1.05) , 2009 .

[47]  J. McQueen,et al.  Speaking rate from proximal and distal contexts is used during word segmentation. , 2011, Journal of experimental psychology. Human perception and performance.

[48]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[49]  T. Jaeger,et al.  Categorical Data Analysis: Away from ANOVAs (transformation or not) and towards Logit Mixed Models. , 2008, Journal of memory and language.