Publications

Abstract

Thirty years ago, Pim Levelt published Speaking. During the 10th International Workshop on Language Production held at the Max Planck Institute for Psycholinguistics in Nijmegen in July 2018, researchers reflected on the impact of the book in the field, developments since its publication, and current research trends. The contributions in this Special Issue are closely related to the presentations given at the workshop. In this editorial, we sketch the research agenda set by Speaking, review how different aspects of this agenda are taken up in the papers in this volume and outline directions for further research.

Abstract

In simultaneous interpreting, speech comprehension and production processes have to be coordinated in close temporal proximity. To examine the coordination, Dutch-English bilingual participants were presented with narrative fragments recorded in English at speech rates varying from 100 to 200 words per minute and they were asked to translate the fragments into Dutch (interpreting) or repeat them in English (shadowing). Interpreting yielded more errors than shadowing at every speech rate, and increasing speech rate had a stronger negative effect on interpreting than on shadowing. To understand the differential effect of speech rate, a computational model was created of sub-lexical and lexical processes in comprehension and production. Computer simulations revealed that the empirical findings could be captured by assuming a bottleneck preventing simultaneous lexical selection in production and comprehension. To conclude, our empirical and modelling results suggest the existence of a lexical bottleneck that limits the translation of narratives at high speed.

Abstract

Three experiments are reported that examined whether stem complexity plays a role in inflecting polymorphemic words in language production. Experiment 1 showed that preparation effects for words with polymorphemic stems are larger when they are produced among words with constant inflectional structures compared to words with variable inflectional structures and simple stems. This replicates earlier findings for words with monomorphemic stems (Janssen et al., 2002). Experiments 2 and 3 showed that when inflectional structure is held constant, the preparation effects are equally large with simple and compound stems, and with compound and complex adjectival stems. These results indicate that inflectional encoding is blind to the complexity of the stem, which suggests that specific inflectional rather than generic morphological frames guide the generation of inflected forms in speaking words.

Abstract

How can one conceive of the neuronal implementation of the processing model we proposed in our target article? In his commentary (Pulvermüller 1999, reprinted here in this issue), Pulvermüller makes various proposals concerning the underlying neural mechanisms and their potential localizations in the brain. These proposals demonstrate the compatibility of our processing model and current neuroscience. We add further evidence on details of localization based on a recent meta-analysis of neuroimaging studies of word production (Indefrey & Levelt 2000). We also express some minor disagreements with respect to Pulvermüller’s interpretation of the “lemma” notion, and concerning his neural modeling of phonological code retrieval. Branigan & Pickering discuss important aspects of syntactic encoding, which was not the topic of the target article. We discuss their well-taken proposal that multiple syntactic frames for a single verb lemma are represented as independent nodes, which can be shared with other verbs, such as accounting for syntactic priming in speech production. We also discuss how, in principle, the alternative multiple-frame-multiplelemma account can be tested empirically. The available evidence does not seem to support that account.

Abstract

This study investigates how speakers of Dutch compute and produce relative time expressions. Naming digital clocks (e.g., 2:45, say ‘‘quarter to three’’) requires conceptual operations on the minute and hour information for the correct relative time expression. The interplay of these conceptual operations was investigated using a repetition priming paradigm. Participants named analog clocks (the primes) directly before naming digital clocks (the targets). The targets referred to the hour (e.g., 2:00), half past the hour (e.g., 2:30), or the coming hour (e.g., 2:45). The primes differed from the target in one or two hour and in five or ten minutes. Digital clock naming latencies were shorter with a five- than with a ten-min difference between prime and target, but the difference in hour had no effect. Moreover, the distance in minutes had only an effect for half past the hour and the coming hour, but not for the hour. These findings suggest that conceptual facilitation occurs when conceptual transformations are shared between prime and target in telling time.

Abstract

There is a remarkable lack of research bringing together the literatures on oral reading and speaking.
As concerns phonological encoding, both models of reading and speaking assume a process of segmental
spellout for words, which is followed by serial prosodification in models of speaking (e.g., Levelt,
Roelofs, & Meyer, 1999). Thus, a natural place to merge models of reading and speaking would be
at the level of segmental spellout. This view predicts similar seriality effects in reading and object naming.
Experiment 1 showed that the seriality of encoding inside a syllable revealed in previous studies
of speaking is observed for both naming objects and reading their names. Experiment 2 showed that
both object naming and reading exhibit the seriality of the encoding of successive syllables previously
observed for speaking. Experiment 3 showed that the seriality is also observed when object naming and
reading trials are mixed rather than tested separately, as in the first two experiments. These results suggest
that a serial phonological encoding mechanism is shared between naming objects and reading
their names.

Abstract

Although humans are the ultimate “natural language generators”, the area of psycholinguistic modeling has been somewhat underrepresented in recent approaches to Natural Language Generation in computer science. To draw attention to the area and illustrate its potential relevance to Natural Language Generation, I provide an overview of recent work on psycholinguistic modeling of language production together with some key empirical findings, state-of-the-art experimental techniques, and their historical roots. The techniques include analyses of speech-error corpora, chronometric analyses, eyetracking, and neuroimaging.
The overview is built around the issue of cognitive control in natural language generation, concentrating on the production of single words, which is an essential ingredient of the generation of larger utterances. Most of the work exploited the fact that human speakers are good but not perfect at resisting temptation, which has provided some critical clues about the nature of the underlying system.

Abstract

B. Rapp and M. Goldrick (2000) claimed that the lexical and mixed error biases in picture naming by
aphasic and nonaphasic speakers argue against models that assume a feedforward-only relationship
between lexical items and their sounds in spoken word production. The author contests this claim by
showing that a feedforward-only model like WEAVER ++ (W. J. M. Levelt, A. Roelofs, & A. S. Meyer,
1999b) exhibits the error biases in word planning and self-monitoring. Furthermore, it is argued that
extant feedback accounts of the error biases and relevant chronometric effects are incompatible.
WEAVER ++ simulations with self-monitoring revealed that this model accounts for the chronometric
data, the error biases, and the influence of the impairment locus in aphasic speakers.

Abstract

WEAVER++ has no backward links in its form-production network and yet is able to explain the lexical
and mixed error biases and the mixed distractor latency effect. This refutes the claim of B. Rapp and M.
Goldrick (2000) that these findings specifically support production-internal feedback. Whether their restricted interaction account model can also provide a unified account of the error biases and latency effect remains to be shown.