Publications

Abstract

A recurrently connected attractor neural network with a Hebbian learning rule is currently our best ANN analogy for a piece cortex. Functionally biological memory operates on a spectrum of time scales with regard to induction and retention, and it is modulated in complex ways by sub-cortical neuromodulatory systems. Moreover, biological memory networks are commonly believed to be highly distributed and engage many co-operating cortical areas. Here we focus on the temporal aspects of induction and retention of memory in a connectionist type attractor memory model of a piece of cortex. A continuous time, forgetful Bayesian-Hebbian learning rule is described and compared to the characteristics of LTP and LTD seen experimentally. More generally, an attractor network implementing this learning rule can operate as a long-term, intermediate-term, or short-term memory. Modulation of the print-now signal of the learning rule replicates some experimental memory phenomena, like e.g. the von Restorff effect.

Abstract

Linguistic skills are primarily tuned to the proper conduct of conversation. The innate ability to converse has provided species with a capacity to share moods, attitudes, and information of almost any kind, to assemble knowledge and skills, to plan coordinated action, to educate its offspring, in short, to create and transmit culture. In conversation the interlocutors are involved in negotiating meaning. Speaking is most complex cognitive-motor skill. It involves the conception of an intention, the selection of information whose expression will make that intention recognizable, the selection of appropriate words, the construction of a syntactic framework, the retrieval of the words’ sound forms, and the computation of an articulatory plan for each word and for the utterance as a whole. The question where communicative intentions come from is a psychodynamic question rather than a psycholinguistic one. Speaking is a form of social action, and it is in the context of action that intentions, goals, and subgoals develop.

Abstract

It is argued that the dominantly successive nature of language is largely mode-independent and holds equally for sign and for spoken language. A preliminary distinction is made between what is simultaneous or successive in the signal, and what is in the process; these need not coincide, and it is the successiveness of the process that is at stake. It is then discussed extensively for the word/sign level, and in a more preliminary fashion for the clause and discourse level that online processes are parallel in that they can simultaneously draw on various sources of knowledge (syntactic, semantic, pragmatic), but successive in that they can work at the interpretation of only one unit at a time. This seems to hold for both sign and spoken language. In the final section, conjectures are made about possible evolutionary explanations for these properties of language processing.

Abstract

Presentation of the official opening of the Jerome Bruner Library, January 8th, 2020

Abstract

Languages differ in the degree of semantic flexibility of their syntactic roles. For example, Eng-
lish and Indonesian are considered more flexible with regard to the semantics of subjects,
whereas German and Japanese are less flexible. In Hawkins’ classification, more flexible lan-
guages are said to have a loose fit, and less flexible ones are those that have a tight fit. This
classification has been based on manual inspection of example sentences. The present paper
proposes a new, quantitative approach to deriving the measures of looseness and tightness from
corpora. We use corpora of online news from the Leipzig Corpora Collection in thirty typolog-
ically and genealogically diverse languages and parse them syntactically with the help of the
Universal Dependencies annotation software. Next, we compute Mutual Information scores for
each language using the matrices of lexical lemmas and four syntactic dependencies (intransi-
tive subjects, transitive subject, objects and obliques). The new approach allows us not only to
reproduce the results of previous investigations, but also to extend the typology to new lan-
guages. We also demonstrate that verb-final languages tend to have a tighter relationship be-
tween lexemes and syntactic roles, which helps language users to recognize thematic roles early
during comprehension.

Abstract

Children learn language from the speech in their home environment. Recent work shows that more infant-directed speech
(IDS) leads to stronger lexical development. But what makes IDS a particularly useful learning signal? Here, we expand on an attention-based account first proposed by Räsänen et al. (2018): that prosodic modifications make IDS less predictable, and thus more interesting. First, we reproduce the critical finding from Räsänen et al.: that lab-recorded IDS pitch is less predictable compared to adult-directed speech (ADS). Next, we show that this result generalizes to the home language environment, finding that IDS in daylong recordings is also less predictable than ADS but that this pattern is much less robust than for IDS recorded in the lab. These results link experimental work on attention and prosodic modifications of IDS to real-world language-learning environments, highlighting some challenges of scaling up analyses of IDS to larger datasets that better capture children’s actual input.

Abstract

Lexical stress is realised similarly in English, German, and
Dutch. On a suprasegmental level, stressed syllables tend to be
longer and more acoustically salient than unstressed syllables;
segmentally, vowels in unstressed syllables are often reduced.
The frequency of unreduced unstressed syllables (where only
the suprasegmental cues indicate lack of stress) however,
differs across the languages. The present studies test whether
listener behaviour is affected by these vocabulary differences,
by investigating German listeners’ use of suprasegmental cues
to lexical stress in German and English word recognition. In a
forced-choice identification task, German listeners correctly
assigned single-syllable fragments (e.g., Kon-) to one of two
words differing in stress (KONto, konZEPT). Thus, German
listeners can exploit suprasegmental information for
identifying words. German listeners also performed above
chance in a similar task in English (with, e.g., DIver, diVERT),
i.e., their sensitivity to these cues also transferred to a nonnative
language. An English listener group, in contrast, failed
in the English fragment task. These findings mirror vocabulary
patterns: German has more words with unreduced unstressed
syllables than English does.

Abstract

This chapter outlines a Bayesian model of spoken-word recognition and reviews how
prosody is part of that model. The review focuses on the information that assists the lis­
tener in recognizing the prosodic structure of an utterance and on how spoken-word
recognition is also constrained by prior knowledge about prosodic structure. Recognition
is argued to be a process of perceptual inference that ensures that listening is robust to
variability in the speech signal. In essence, the listener makes inferences about the seg­
mental content of each utterance, about its prosodic structure (simultaneously at differ­
ent levels in the prosodic hierarchy), and about the words it contains, and uses these in­
ferences to form an utterance interpretation. Four characteristics of the proposed
prosody-enriched recognition model are discussed: parallel uptake of different informa­
tion types, high contextual dependency, adaptive processing, and phonological abstrac­
tion. The next steps that should be taken to develop the model are also discussed.

Abstract

The results of a word-spotting experiment are presented in which Dutch listeners tried to spot different types of bisyllabic Dutch words embedded in different types of nonsense contexts. Embedded verbs were not reliably harder to spot than embedded nouns; this suggests that nouns and verbs are recognised via the same basic processes. Iambic words were no harder to spot than trochaic words, suggesting that trochaic words are not in principle easier to recognise than iambic words. Words were harder to spot in consonantal contexts (i.e., contexts which themselves could not be words) than in longer contexts which contained at least one vowel (i.e., contexts which, though not words, were possible words of Dutch). A control experiment showed that this difference was not due to acoustic differences between the words in each context. The results support the claim that spoken-word recognition is sensitive to the viability of sound sequences as possible words.

Abstract

Lexical knowledge influences how human listeners make decisions about speech sounds. Positive lexical effects (faster responses to target sounds in words than in nonwords) are robust across several laboratory tasks, while negative effects (slower responses to targets in more word-like nonwords than in less word-like nonwords) have been found in phonetic decision tasks but not phoneme monitoring tasks. The present experiments tested whether negative lexical effects are therefore a task-specific consequence of the forced choice required in phonetic decision. We compared phoneme monitoring and phonetic decision performance using the same Dutch materials in each task. In both experiments there were positive lexical effects, but no negative lexical effects. We observe that in all studies showing negative lexical effects, the materials were made by cross-splicing, which meant that they contained perceptual evidence supporting the lexically-consistent phonemes. Lexical knowledge seems to influence phonemic decision-making only when there is evidence for the lexically-consistent phoneme in the speech signal.

Abstract

We briefly describe the Merge model of phonemic decision-making, and, in the light of general arguments about the possible role of feedback in spoken-word recognition, defend Merge's feedforward structure. Merge not only accounts adequately for the data, without invoking feedback connections, but does so in a parsimonious manner.

Abstract

The human language processor is conceived as a system that consists of several interrelated subsystems. Each subsystem performs a specific task in the complex process of language comprehension and production. A subsystem receives a particular input, performs certain specific operations on this input and yields a particular output. The subsystems can be characterized in terms of the transformations that relate the input representations to the output representations. An important issue in describing the language processing system is to identify the subsystems and to specify the relations between the subsystems. These relations can be conceived in two different ways. In one conception the subsystems are autonomous. They are related to each other only by the input-output channels. The operations in one subsystem are not affected by another system. The subsystems are modular, that is they are independent. In the other conception, the different subsystems influence each other. A subsystem affects the processes in another subsystem. In this conception there is an interaction between the subsystems.

Abstract

This paper describes recent experimental evidence which shows that models of spoken word recognition must incorporate both inhibition between competing lexical candidates and a sensitivity to metrical cues to lexical segmentation. A new version of the Shortlist [1][2] model incorporating the Metrical Segmentation Strategy [3] provides a detailed simulation of the data.

Abstract

Two word-spotting experiments are reported that examine whether the Possible-Word Constraint (PWC) [1] is a language-specific or language-universal strategy for the segmentation of continuous speech. The PWC disfavours parses which leave an impossible residue between the end of a candidate word and a known boundary. The experiments examined cases where the residue was either a CV syllable with a lax vowel, or a CVC syllable with a schwa. Although neither syllable context is a possible word in English, word-spotting in both contexts was easier than with a context consisting of a single consonant. The PWC appears to be language-universal rather than language-specific.

Abstract

Simulations explored the inability of the TRACE model of spoken-word recognition to model the effects on human listening of subcategorical mismatch in word forms. The source of TRACE's failure lay not in interactive connectivity, not in the presence of inter-word competition, and not in the use of phonemic representations, but in the need for continuously optimised interpretation of the input. When an analogue of TRACE was allowed to cycle to asymptote on every slice of input, an acceptable simulation of the subcategorical mismatch data was achieved. Even then, however, the simulation was not as close as that produced by the Merge model, which has inter-word competition, phonemic representations and continuous optimisation (but no interactive connectivity).

Abstract

A database is presented of relative familiarity ratings for 24 sets of Japanese words, each set comprising words overlapping in the initial portions. These ratings are useful for the generation of material sets for research in the recognition of spoken words.

Abstract

Automating robot behavior in a specific situation is an active area of research. There are several approaches available in the literature of robotics to cater for the automatic behavior of a robot. However, when it comes to humanoids or human-robot interaction in general, the area has been less explored. In this paper, a pseudo-randomization approach has been introduced to automatize the gestures and facial expressions of an interactive humanoid robot called ROBIN based on its mental state. A significant number of gestures and facial expressions have been implemented to allow the robot more options to perform a relevant action or reaction based on visual stimuli. There is a display of noticeable differences in the behaviour of the robot for the same stimuli perceived from an interaction partner. This slight autonomous behavioural change in the robot clearly shows a notion of automation in behaviour. The results from experimental scenarios and human-centered evaluation of the system help validate the approach.

Abstract

This work focuses on efficient use of the training material by selecting the optimal set of model topologies. We do this by training multiple word models of each word class, based on a subclassification according to a priori knowledge of the training material. We will examine classification criteria with respect to duration of the word, gender of the speaker, position of the word in the utterance, pauses in the vicinity of the word, and combinations of these. Comparative experiments were carried out on a corpus consisting of Dutch spoken connected digit strings and isolated digits, which are recorded in a wide variety of acoustic conditions. The results show, that classification based on gender of the speaker, position of the digit in the string, pauses in the vicinity of the training tokens, and models based on a combination of these criteria perform significantly better than the set with single models per digit.

Abstract

Due to its numerous bibliometric entries of scholarly articles and connected information Wikidata can serve as an open and rich
source for deep scientometrical analyses. However, there are currently certain limitations: While 31.5% of all Wikidata entries represent scientific articles, only 8.9% are entries describing a person and the number
of entries researcher is accordingly even lower. Another issue is the frequent absence of established relations between the scholarly article item and the author item although the author is already listed in Wikidata.
To fill this gap and to improve the content of Wikidata in general, we established a workflow for matching authors and scholarly publications by integrating data from the ORCID (Open Researcher and Contributor ID) database. By this approach we were able to extend Wikidata by more than 12k author-publication relations and the method can be
transferred to other enrichments based on ORCID data. This is extension is beneficial for Wikidata users performing bibliometrical analyses or using such metadata for other purposes.