Presentations

Abstract

Listeners are known to use contextual speech rate in processing temporally ambiguous speech sounds. For instance, a fast adjacent speech context makes a vowel sound relatively long, whereas a slow context makes it sound relatively short (Reinisch & Sjerps, 2013). Besides the local contextual speech rate, listeners also track talker-specific habitual speech rates (Reinisch, 2016; Maslowski et al., in press). However, effects of one’s own speech rate on the perception of another talker’s speech are yet unexplored. Such effects are potentially important, given that, in dialogue, a listener’s own speech often constitutes the context for the interlocutor’s speech. Three experiments tested the contribution of self-produced speech on perception of the habitual speech rate of another talker. In Experiment 1, one group of participants was instructed to speak fast (high-rate group), whereas another group had to speak slowly (low-rate group; 16 participants per group). The two groups were compared on their perception of ambiguous Dutch /A/-/a:/ vowels embedded in neutral rate speech from another talker. In Experiment 2, the same participants listened to playback of their own speech, whilst evaluating target vowels in neutral rate speech as before. Neither of these experiments provided support for the involvement of self-produced speech in perception of another talker's speech rate. Experiment 3 repeated Experiment 2 with a new participant sample, who did not know the participants from the previous two experiments. Here, a group effect was found on perception of the neutral rate talker. This result replicates the finding of Maslowski et al. that habitual speech rates are perceived relative to each other (i.e., neutral rate sounds fast in the presence of a slower talker and vice versa), with naturally produced speech. Taken together, the findings show that self-produced speech is processed differently from speech produced by others. They carry implications for our understanding of the perceptual and cognitive mechanisms involved in rate-dependent speech perception and the link between production and perception in dialogue settings.

Abstract

Interlocutors perceive phonemic category boundaries relative to talkers’ produced speech rates. For instance, a temporally ambiguous vowel between Dutch short /A/ and long /a:/ sounds short (i.e., as /A/) in a slow speech context, but long in a fast context. Besides the local contextual speech rate, listeners also track talker-specific habitual speech rates (Maslowski et al., in press). However, it is yet unclear whether self-produced speech rate modulates perception of another talker’s habitual rate. Such effects are potentially important, given that, in dialogue, a listener’s own speech often constitutes the context for the interlocutor’s speech. Three experiments addressed this question. In Experiment 1, one group of participants was instructed to speak fast, whereas another group had to speak slowly (16 participants per group). The two groups were then compared on their perception of ambiguous Dutch /A/-/a:/ vowels embedded in neutral rate speech from another talker. In Experiment 2, the same participants listened to playback of their own speech, whilst evaluating target vowels in neutral rate speech as before. Neither of these experiments provided support for the involvement of self-produced speech in perception of another talker's speech rate. Experiment 3 repeated Experiment 2 with a new participant sample, who were unfamiliar with the participants from the previous two experiments. Here, a group effect was found on perception of the neutral rate talker. This result replicates the finding of Maslowski et al. that habitual speech rates are perceived relative to each other (i.e., neutral rate sounds fast in the presence of a slower talker and vice versa), with naturally produced speech. Taken together, the findings show that self-produced speech is processed differently from speech produced by others. They carry implications for our understanding of the link between production and perception in dialogue.

Abstract

That speakers can vary their speaking rate is evident, but how they accomplish this has
hardly been studied. The effortful experience of deviating from one's preferred speaking rate
might result from shifting between different regimes (system configurations) of the speech
planning system. This study investigates control over speech rate through simulations of a
new connectionist computational model of the cognitive process of speech production, derived
from Dell, Burger and Svec’s (1997) model to fit the temporal characteristics of observed
speech. We draw an analogy from human movement: the selection of walking and running
gaits to achieve different movement speeds. Are the regimes of the speech production system
arranged into multiple ‘gaits’ that resemble walking and running?
During training of the model, different parameter settings are identified for different speech
rates, which can be conflated with the regimes of the speech production system. The
parameters can be considered to be dimensions of a high-dimensional ‘regime space’, in
which different regimes occupy different parts of the space.
In a single gait system, the regimes are qualitatively similar, but quantitatively different.
They are arranged along a straight line through regime space. Different points along this axis
correspond directly to different speaking rates. In a multiple gait system, the arrangement of
the regimes is more disperse, with no obvious relationship between the regions associated
with each gait.
After training, the model achieved good fits in all three speaking rates, and the parameter
settings associated with each speaking rate were different. The broad arrangement of the
parameter settings for the different speaking rates in regime space was non-axial, suggesting
that ‘gaits’ may be present in the speech planning system.

Abstract

In recent years, the theory that prediction is an important part of language processing has gained considerable attention (see Huettig, 2015, for overview). There is a large body of empirical evidence which suggests that language users’ ability to anticipate interlocutors’ upcoming utterances is one of the reasons why interactive speech can be so effortless, smooth, and efficient in nature (e.g. Wicha et al., 2004; van Berkum et al., 2005). The present study aimed to investigate whether the language production module is affected by prediction of another individual’s utterances using a joint language production task designed to establish whether simulation of an interlocutor’s utterance occurs automatically, even if this hinders one’s own speech production. The experiment aimed to replicate the finding of an interference effect in a joint naming task (Gambi et al., 2015), and investigate whether the same patterns could be found within a clearer social context where a partner was co-present in the same room. Participants named pictures of objects while their partners concurrently named or categorised congruent or incongruent stimuli. Analyses of naming onset latencies indicate that individuals may partially co-represent their partner’s utterances and that this shared representation influences language production. Congruency in task and stimuli display facilitated naming compared to incongruent trials which showed a tendency to impede production latencies. This finding of a social effect in a setting where simulation of language content is not necessary may suggest that some kind of anticipatory processing is an underlying feature of comprehension.

Abstract

Previous work on memory phenomena shows that pictures and words lead to a production effect, i.e. better memory for aloud than silent items, and that this interacts with the picture superiority effect, i.e. better memory for pictures than words (Fawcett, Quinlan and Taylor, 2012). We investigated the role of the generation effect, i.e. improved memory for generated words, in picture naming. As picture naming requires participants to think of an appropriate label, a generation effect might be elicited for pictures but not words. Forty-two participants named pictures silently or aloud and were given the correct picture name or an unreadable label; all conditions included pictures to control for the picture superiority effect. Memory was then tested using a yes/no recognition task. We found a production effect (p < 0.001) showing the role of articulation in memory, a generation effect (p < 0.001) showing the role of lexical access in memory, and an interaction (p <0.05) between the two suggesting the non-independence of the effects. Ongoing work further tests the role of label reliability in eliciting these effects. This research demonstrates a role for the generation effect in picture naming, with implications for memory asymmetries at different stages in language production.

Abstract

Subject-verb agreement is one of the most common
grammatical encoding operations in language
production. In many languages, morphological
inflection on verbs code for the number of the head
noun of a subject phrase (e.g., The key to the cabinets
is rusty). Despite the relative ease with which subjectverb
agreement is accomplished, people sometimes
make agreement errors (e.g., The key to the cabinets
are rusty). Such errors offer a window into the early
stages of production planning. Agreement errors are
influenced by both syntactic and semantic factors, and
are more likely to occur when a sentence contains either
conceptual or syntactic number mismatches. Little
is known about the timecourse of these influences,
however, and some controversy exists as to whether
they are independent. The current study was designed
to address these two issues using EEG. Semantic and
syntactic factors influencing number mismatch were
factorially-manipulated in a forced-choice sentence
completion paradigm. To avoid EEG artifact associated
with speaking, participants (N=20) were presented with
a noun-phrase, and pressed a button to indicate which
version of the verb ‘to be’ (is/are) should continue
the sentence. Semantic number was manipulated
using preambles that were semantically-integrated or
unintegrated. Semantic integration refers to the semantic
relationship between nouns in a noun-phrase, with
integrated items promoting conceptual-singularity.
The syntactic manipulation was the number (singular/
plural) of the local noun preceding the decision. This
led to preambles such as “The pizza with the yummy
topping(s)... “ (integated) vs. “The pizza with the tasty
bevarage(s)...” (unintegrated). Behavioral results showed
effects of both Local Noun Number and Semantic
Integration, with more errors and longer reaction times
occurring in the mismatching conditions (i.e., plural
local nouns; unintegrated subject phrases). Classic ERP
analyses locked to the local noun (0-700 ms) and to the
time preceding the response (-600 to 0 ms) showed no
systematic differences between conditions. Despite this
result, we assessed whether difference might emerge
using multivariate pattern analysis (MVPA). Using the
same epochs as above, support-vector machines with a
radial basis function were trained on the single-trial level
to classify the difference between Local Noun Number
and Semantic Integration conditions across time and
channels. Results revealed that both conditions could
be reliably classified at the single subject level, and
that classification accuracy was strongest in the epoch
preceding the response. Classification accuracy was
at chance when a classifier trained to dissociate Local
Noun Number was used to predict Semantic Integration
(and vice versa), providing some evidence of the
independence of the two effects. Significant inter-subject
variability was present in the channels and time-points
that were critical for classification, but earlier timepoints
were more often important for classifying Local Noun
Number than Semantic Integration. One result of this
variability is classification performed across subjects was
at chance, which may explain the failure to find standard
ERP effects. This study thus provides an important first
test of semantic and syntactic influences on subject-verb
agreement with EEG, and demonstrates that where
classic ERP analyses fail, MVPA can reliably distinguish
differences at the neurophysiological level.

Abstract

Prominent theories of predictive language processing assume that language production processes are used to anticipate upcoming linguistic input during comprehension (Dell & Chang, 2014; Pickering & Garrod, 2013). Here, we explore the converse case: Does a task set including production in addition to comprehension encourage prediction, compared to a task only including comprehension? To test this hypothesis, we conducted a cross-modal naming experiment (Experiment 1) including an object naming task and a self-paced reading experiment (Experiment 2) that did not include overt production. We used the same predictable (N = 40) and non-predictable (N = 40) sentences in both experiments. The sentences consisted of a fixed agent, a transitive verb and a predictable or non-predictable target word (The man drinks a beer vs. The man buys a beer). Most of the empirical work on prediction used sentences in which the target words were highly predictable (often with a mean cloze probability > .8) and thus it is little surprising that participants engaged in predictive language processing very easily. In the current sentences, the mean cloze probability in the predictable sentences was .39 (ranging from .06 to .8; zero in the non-predictable sentences). If comprehenders are more likely to engage in predictive processing when the task set involves production, we should observe more pronounced effects of prediction in Experiment 1 as compared to Experiment 2. If production does not enhance prediction, we should observe similar effects of prediction in both experiments. In Experiment 1, participants (N = 54) listened to recordings of the sentences which ended right before the spoken target word. Coinciding with the end of the playback, a picture of the target word was shown which the participants were asked to name as fast as possible. Analyses of their naming latencies revealed a statistically significant naming advantage of 106 ms on predictable over non-predictable trials. Moreover, we found that the objects’ naming advantage was predicted by the target words’ cloze probability in the sentences (r = .411, p = .016). In Experiment 2, the same sentences were used in a self-paced reading experiment. To allow for testing of potential spill-over effects, we added a neutral prepositional phrase (buys a beer from the bar keeper/drinks a beer from the shop) to each sentence. Participants (N = 54) read the sentences word-by-word, advancing by pushing the space bar. On 30% of the trials, comprehension questions were used to keep up participants' focus on comprehending the sentences. Analyses of participants’ target and post-target reading times revealed numerical advantages of 6 ms and 20 ms, respectively, in the predictable as compared to the non-predictable condition. However, in both cases, this difference was not statistically reliable (t = .757, t = 1.43) and the significant positive correlation between an item’s naming advantage and its cloze probability as seen in Experiment 1 was absent (r = .037, p = .822). Importantly, the analysis of participants' responses to the comprehension questions, showed that they understood the sentences (mean accuracy = 93%). To conclude, although both experiments used the same sentences, we observed effects of prediction only when the task included production. In Experiment 2, no evidence for anticipation was found although participants clearly understood the sentences and the method has previously been shown to be sensitive to measure prediction effects (Van Berkum et al., 2005). Our results fit with a recent study by Gollan et al. (2011) who found only a small processing advantage of predictive over non-predictive sentences in reading (using highly predictable sentences with a cloze probability > . 87) but a strong prediction effect when participants read the same sentences and carried out an additional object naming task (see also Griffin & Bock, 1998). Taken together, the studies suggest that the comprehenders' task set exerts a powerful influence on the likelihood and magnitude of predictive language processing. When the task set involves language production, as is often the case in natural conversation, comprehenders might engage in prediction to a stronger degree than in pure comprehension tasks. Being able to predict words another person is about to say might optimize the comprehension process and enable smooth turn-taking.