# Publications

Displaying 1 - 98 of 98
• Alhama, R. G., Siegelman, N., Frost, R., & Armstrong, B. C. (2019). The role of information in visual word recognition: A perceptually-constrained connectionist account. In A. Goel, C. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Meeting of the Cognitive Science Society (CogSci 2019) (pp. 83-89). Austin, TX: Cognitive Science Society.

## Abstract

Proficient readers typically fixate near the center of a word, with a slight bias towards word onset. We explore a novel account of this phenomenon based on combining information-theory with visual perceptual constraints in a connectionist model of visual word recognition. This account posits that the amount of information-content available for word identification varies across fixation locations and across languages, thereby explaining the overall fixation location bias in different languages, making the novel prediction that certain words are more readily identified when fixating at an atypical fixation location, and predicting specific cross-linguistic differences. We tested these predictions across several simulations in English and Hebrew, and in a pilot behavioral experiment. Results confirmed that the bias to fixate closer to word onset aligns with maximizing information in the visual signal, that some words are more readily identified at atypical fixation locations, and that these effects vary to some degree across languages.

https://mindmodeling.org/cogsci2019/papers/0036/0036.pdf
• Bentum, M., Ten Bosch, L., Van den Bosch, A., & Ernestus, M. (2019). Listening with great expectations: An investigation of word form anticipations in naturalistic speech. In Proceedings of Interspeech 2019 (pp. 2265-2269). doi:10.21437/Interspeech.2019-2741.

## Abstract

The event-related potential (ERP) component named phonological mismatch negativity (PMN) arises when listeners hear an unexpected word form in a spoken sentence [1]. The PMN is thought to reflect the mismatch between expected and perceived auditory speech input. In this paper, we use the PMN to test a central premise in the predictive coding framework [2], namely that the mismatch between prior expectations and sensory input is an important mechanism of perception. We test this with natural speech materials containing approximately 50,000 word tokens. The corresponding EEG-signal was recorded while participants (n = 48) listened to these materials. Following [3], we quantify the mismatch with two word probability distributions (WPD): a WPD based on preceding context, and a WPD that is additionally updated based on the incoming audio of the current word. We use the between-WPD cross entropy for each word in the utterances and show that a higher cross entropy correlates with a more negative PMN. Our results show that listeners anticipate auditory input while processing each word in naturalistic speech. Moreover, complementing previous research, we show that predictive language processing occurs across the whole probability spectrum.
• Bentum, M., Ten Bosch, L., Van den Bosch, A., & Ernestus, M. (2019). Quantifying expectation modulation in human speech processing. In Proceedings of Interspeech 2019 (pp. 2270-2274). doi:10.21437/Interspeech.2019-2685.

## Abstract

The mismatch between top-down predicted and bottom-up perceptual input is an important mechanism of perception according to the predictive coding framework (Friston, [1]). In this paper we develop and validate a new information-theoretic measure that quantifies the mismatch between expected and observed auditory input during speech processing. We argue that such a mismatch measure is useful for the study of speech processing. To compute the mismatch measure, we use naturalistic speech materials containing approximately 50,000 word tokens. For each word token we first estimate the prior word probability distribution with the aid of statistical language modelling, and next use automatic speech recognition to update this word probability distribution based on the unfolding speech signal. We validate the mismatch measure with multiple analyses, and show that the auditory-based update improves the probability of the correct word and lowers the uncertainty of the word probability distribution. Based on these results, we argue that it is possible to explicitly estimate the mismatch between predicted and perceived speech input with the cross entropy between word expectations computed before and after an auditory update.
• Bentz, C., Dediu, D., Verkerk, A., & Jäger, G. (2018). Language family trees reflect geography and demography beyond neutral drift. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 38-40). Toruń, Poland: NCU Press. doi:10.12775/3991-1.006.
• Brand, J., Monaghan, P., & Walker, P. (2018). Changing Signs: Testing How Sound-Symbolism Supports Early Word Learning. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 1398-1403). Austin, TX: Cognitive Science Society.

## Abstract

Learning a language involves learning how to map specific forms onto their associated meanings. Such mappings can utilise arbitrariness and non-arbitrariness, yet, our understanding of how these two systems operate at different stages of vocabulary development is still not fully understood. The Sound-Symbolism Bootstrapping Hypothesis (SSBH) proposes that sound-symbolism is essential for word learning to commence, but empirical evidence of exactly how sound-symbolism influences language learning is still sparse. It may be the case that sound-symbolism supports acquisition of categories of meaning, or that it enables acquisition of individualized word meanings. In two Experiments where participants learned form-meaning mappings from either sound-symbolic or arbitrary languages, we demonstrate the changing roles of sound-symbolism and arbitrariness for different vocabulary sizes, showing that sound-symbolism provides an advantage for learning of broad categories, which may then transfer to support learning individual words, whereas an arbitrary language impedes acquisition of categories of sound to meaning.
• Brehm, L., Jackson, C. N., & Miller, K. L. (2019). Incremental interpretation in the first and second language. In M. Brown, & B. Dailey (Eds.), BUCLD 43: Proceedings of the 43rd annual Boston University Conference on Language Development (pp. 109-122). Sommerville, MA: Cascadilla Press.
• Bruggeman, L., & Cutler, A. (2019). The dynamics of lexical activation and competition in bilinguals’ first versus second language. In S. Calhoun, P. Escudero, M. Tabain, & P. Warren (Eds.), Proceedings of the 19th International Congress of Phonetic Sciences (ICPhS 20195) (pp. 1342-1346). Canberra, Australia: Australasian Speech Science and Technology Association Inc.

## Abstract

Speech input causes listeners to activate multiple
candidate words which then compete with one
another. These include onset competitors, that share a
beginning (bumper, butter), but also, counterintuitively,
rhyme competitors, sharing an ending
(bumper, jumper). In L1, competition is typically
stronger for onset than for rhyme. In L2, onset
competition has been attested but rhyme competition
has heretofore remained largely unexamined. We
assessed L1 (Dutch) and L2 (English) word
recognition by the same late-bilingual individuals. In
each language, eye gaze was recorded as listeners
heard sentences and viewed sets of drawings: three
unrelated, one depicting an onset or rhyme competitor
of a word in the input. Activation patterns revealed
substantial onset competition but no significant
rhyme competition in either L1 or L2. Rhyme
competition may thus be a “luxury” feature of
maximally efficient listening, to be abandoned when
resources are scarcer, as in listening by late
bilinguals, in either language.
• Byun, K.-S., De Vos, C., Roberts, S. G., & Levinson, S. C. (2018). Interactive sequences modulate the selection of expressive forms in cross-signing. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 67-69). Toruń, Poland: NCU Press. doi:10.12775/3991-1.012.
• Cristia, A., Ganesh, S., Casillas, M., & Ganapathy, S. (2018). Talker diarization in the wild: The case of child-centered daylong audio-recordings. In Proceedings of Interspeech 2018 (pp. 2583-2587). doi:10.21437/Interspeech.2018-2078.

## Abstract

Speaker diarization (answering 'who spoke when') is a widely researched subject within speech technology. Numerous experiments have been run on datasets built from broadcast news, meeting data, and call centers—the task sometimes appears close to being solved. Much less work has begun to tackle the hardest diarization task of all: spontaneous conversations in real-world settings. Such diarization would be particularly useful for studies of language acquisition, where researchers investigate the speech children produce and hear in their daily lives. In this paper, we study audio gathered with a recorder worn by small children as they went about their normal days. As a result, each child was exposed to different acoustic environments with a multitude of background noises and a varying number of adults and peers. The inconsistency of speech and noise within and across samples poses a challenging task for speaker diarization systems, which we tackled via retraining and data augmentation techniques. We further studied sources of structured variation across raw audio files, including the impact of speaker type distribution, proportion of speech from children, and child age on diarization performance. We discuss the extent to which these findings might generalize to other samples of speech in the wild.
• Cutler, A., Burchfield, L. A., & Antoniou, M. (2018). Factors affecting talker adaptation in a second language. In J. Epps, J. Wolfe, J. Smith, & C. Jones (Eds.), Proceedings of the 17th Australasian International Conference on Speech Science and Technology (pp. 33-36).

## Abstract

Listeners adapt rapidly to previously unheard talkers by
adjusting phoneme categories using lexical knowledge, in a
process termed lexically-guided perceptual learning. Although
this is firmly established for listening in the native language
(L1), perceptual flexibility in second languages (L2) is as yet
less well understood. We report two experiments examining L1
and L2 perceptual learning, the first in Mandarin-English late
bilinguals, the second in Australian learners of Mandarin. Both
studies showed stronger learning in L1; in L2, however,
learning appeared for the English-L1 group but not for the
Mandarin-L1 group. Phonological mapping differences from
the L1 to the L2 are suggested as the reason for this result.
• Ip, M. H. K., & Cutler, A. (2018). Cue equivalence in prosodic entrainment for focus detection. In J. Epps, J. Wolfe, J. Smith, & C. Jones (Eds.), Proceedings of the 17th Australasian International Conference on Speech Science and Technology (pp. 153-156).

## Abstract

Using a phoneme detection task, the present series of
experiments examines whether listeners can entrain to
different combinations of prosodic cues to predict where focus
will fall in an utterance. The stimuli were recorded by four
female native speakers of Australian English who happened to
have used different prosodic cues to produce sentences with
prosodic focus: a combination of duration cues, mean and
maximum F0, F0 range, and longer pre-target interval before
the focused word onset, only mean F0 cues, only pre-target
interval, and only duration cues. Results revealed that listeners
can entrain in almost every condition except for where
duration was the only reliable cue. Our findings suggest that
listeners are flexible in the cues they use for focus processing.
• Cutler, A. (1987). Components of prosodic effects in speech recognition. In Proceedings of the Eleventh International Congress of Phonetic Sciences: Vol. 1 (pp. 84-87). Tallinn: Academy of Sciences of the Estonian SSR, Institute of Language and Literature.

## Abstract

Previous research has shown that listeners use the prosodic structure of utterances in a predictive fashion in sentence comprehension, to direct attention to accented words. Acoustically identical words spliced into sentence contexts arc responded to differently if the prosodic structure of the context is \ aricd: when the preceding prosody indicates that the word will he accented, responses are faster than when the preceding prosodv is inconsistent with accent occurring on that word. In the present series of experiments speech hybridisation techniques were first used to interchange the timing patterns within pairs of prosodic variants of utterances, independently of the pitch and intensity contours. The time-adjusted utterances could then serve as a basis lor the orthogonal manipulation of the three prosodic dimensions of pilch, intensity and rhythm. The overall pattern of results showed that when listeners use prosody to predict accent location, they do not simply rely on a single prosodic dimension, hut exploit the interaction between pitch, intensity and rhythm.
• Ip, M. H. K., & Cutler, A. (2018). Asymmetric efficiency of juncture perception in L1 and L2. In K. Klessa, J. Bachan, A. Wagner, M. Karpiński, & D. Śledziński (Eds.), Proceedings of Speech Prosody 2018 (pp. 289-296). Baixas, France: ISCA. doi:10.21437/SpeechProsody.2018-59.

## Abstract

In two experiments, Mandarin listeners resolved potential syntactic ambiguities in spoken utterances in (a) their native language (L1) and (b) English which they had learned as a second language (L2). A new disambiguation task was used, requiring speeded responses to select the correct meaning for structurally ambiguous sentences. Importantly, the ambiguities used in the study are identical in Mandarin and in English, and production data show that prosodic disambiguation of this type of ambiguity is also realised very similarly in the two languages. The perceptual results here showed however that listeners’ response patterns differed for L1 and L2, although there was a significant increase in similarity between the two response patterns with increasing exposure to the L2. Thus identical ambiguity and comparable disambiguation patterns in L1 and L2 do not lead to immediate application of the appropriate L1 listening strategy to L2; instead, it appears that such a strategy may have to be learned anew for the L2.
• Cutler, A., & Butterfield, S. (1989). Natural speech cues to word segmentation under difﬁcult listening conditions. In J. Tubach, & J. Mariani (Eds.), Proceedings of Eurospeech 89: European Conference on Speech Communication and Technology: Vol. 2 (pp. 372-375). Edinburgh: CEP Consultants.

## Abstract

One of a listener's major tasks in understanding continuous speech is segmenting the speech signal into separate words. When listening conditions are difficult, speakers can help listeners by deliberately speaking more clearly. In three experiments, we examined how word boundaries are produced in deliberately clear speech. We found that speakers do indeed attempt to mark word boundaries; moreover, they differentiate between word boundaries in a way which suggests they are sensitive to listener needs. Application of heuristic segmentation strategies makes word boundaries before strong syllables easiest for listeners to perceive; but under difficult listening conditions speakers pay more attention to marking word boundaries before weak syllables, i.e. they mark those boundaries which are otherwise particularly hard to perceive.
• Cutler, A., & Carter, D. (1987). The prosodic structure of initial syllables in English. In J. Laver, & M. Jack (Eds.), Proceedings of the European Conference on Speech Technology: Vol. 1 (pp. 207-210). Edinburgh: IEE.
• Cutler, A., Burchfield, A., & Antoniou, M. (2019). A criterial interlocutor tally for successful talker adaptation? In S. Calhoun, P. Escudero, M. Tabain, & P. Warren (Eds.), Proceedings of the 19th International Congress of Phonetic Sciences (ICPhS 20195) (pp. 1485-1489). Canberra, Australia: Australasian Speech Science and Technology Association Inc.

## Abstract

Part of the remarkable efficiency of listening is
accommodation to unfamiliar talkers’ specific
pronunciations by retuning of phonemic intercategory
boundaries. Such retuning occurs in second
(L2) as well as first language (L1); however, recent
research with emigrés revealed successful adaptation
in the environmental L2 but, unprecedentedly, not in
L1 despite continuing L1 use. A possible explanation
involving relative exposure to novel talkers is here
tested in heritage language users with Mandarin as
family L1 and English as environmental language. In
English, exposure to an ambiguous sound in
disambiguating word contexts prompted the expected
adjustment of phonemic boundaries in subsequent
categorisation. However, no adjustment occurred in
Mandarin, again despite regular use. Participants
reported highly asymmetric interlocutor counts in the
two languages. We conclude that successful retuning
ability requires regular exposure to novel talkers in
the language in question, a criterion not met for the
emigrés’ or for these heritage users’ L1.
• Delgado, T., Ravignani, A., Verhoef, T., Thompson, B., Grossi, T., & Kirby, S. (2018). Cultural transmission of melodic and rhythmic universals: Four experiments and a model. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 89-91). Toruń, Poland: NCU Press. doi:10.12775/3991-1.019.
• Dideriksen, C., Fusaroli, R., Tylén, K., Dingemanse, M., & Christiansen, M. H. (2019). Contextualizing Conversational Strategies: Backchannel, Repair and Linguistic Alignment in Spontaneous and Task-Oriented Conversations. In A. K. Goel, C. M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society (CogSci 2019) (pp. 261-267). Montreal, QB: Cognitive Science Society.

## Abstract

Do interlocutors adjust their conversational strategies to the specific contextual demands of a given situation? Prior studies have yielded conflicting results, making it unclear how strategies vary with demands. We combine insights from qualitative and quantitative approaches in a within-participant experimental design involving two different contexts: spontaneously occurring conversations (SOC) and task-oriented conversations (TOC). We systematically assess backchanneling, other-repair and linguistic alignment. We find that SOC exhibit a higher number of backchannels, a reduced and more generic repair format and higher rates of lexical and syntactic alignment. TOC are characterized by a high number of specific repairs and a lower rate of lexical and syntactic alignment. However, when alignment occurs, more linguistic forms are aligned. The findings show that conversational strategies adapt to specific contextual demands.
• Dieuleveut, A., Van Dooren, A., Cournane, A., & Hacquard, V. (2019). Acquiring the force of modals: Sig you guess what sig means? In M. Brown, & B. Dailey (Eds.), BUCLD 43: Proceedings of the 43rd annual Boston University Conference on Language Development (pp. 189-202). Sommerville, MA: Cascadilla Press.
• Duarte, R., Uhlmann, M., Van den Broek, D., Fitz, H., Petersson, K. M., & Morrison, A. (2018). Encoding symbolic sequences with spiking neural reservoirs. In Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN). doi:10.1109/IJCNN.2018.8489114.

## Abstract

Biologically inspired spiking networks are an important tool to study the nature of computation and cognition in neural systems. In this work, we investigate the representational capacity of spiking networks engaged in an identity mapping task. We compare two schemes for encoding symbolic input, one in which input is injected as a direct current and one where input is delivered as a spatio-temporal spike pattern. We test the ability of networks to discriminate their input as a function of the number of distinct input symbols. We also compare performance using either membrane potentials or filtered spike trains as state variable. Furthermore, we investigate how the circuit behavior depends on the balance between excitation and inhibition, and the degree of synchrony and regularity in its internal dynamics. Finally, we compare different linear methods of decoding population activity onto desired target labels. Overall, our results suggest that even this simple mapping task is strongly influenced by design choices on input encoding, state-variables, circuit characteristics and decoding methods, and these factors can interact in complex ways. This work highlights the importance of constraining computational network models of behavior by available neurobiological evidence.
• Ergin, R., Senghas, A., Jackendoff, R., & Gleitman, L. (2018). Structural cues for symmetry, asymmetry, and non-symmetry in Central Taurus Sign Language. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 104-106). Toruń, Poland: NCU Press. doi:10.12775/3991-1.025.
• Felker, E. R., Ernestus, M., & Broersma, M. (2019). Evaluating dictation task measures for the study of speech perception. In S. Calhoun, P. Escudero, M. Tabain, & P. Warren (Eds.), Proceedings of the 19th International Congress of Phonetic Sciences (ICPhS 2019) (pp. 383-387). Canberra, Australia: Australasian Speech Science and Technology Association Inc.

## Abstract

This paper shows that the dictation task, a well-
known testing instrument in language education, has
untapped potential as a research tool for studying
speech perception. We describe how transcriptions
can be scored on measures of lexical, orthographic,
phonological, and semantic similarity to target
phrases to provide comprehensive information about
accuracy at different processing levels. The former
three measures are automatically extractable,
increasing objectivity, and the middle two are
traditionally used. We evaluate the measures in an
English dictation task featuring phonetically reduced
continuous speech. Whereas the lexical and
orthographic measures emphasize listeners’ word
identification difficulties, the phonological measure
demonstrates that listeners can often still recover
phonological features, and the semantic measure
captures their ability to get the gist of the utterances.
Correlational analyses and a discussion of practical
and theoretical considerations show that combining
multiple measures improves the dictation task’s
utility as a research tool.
• Felker, E. R., Ernestus, M., & Broersma, M. (2019). Lexically guided perceptual learning of a vowel shift in an interactive L2 listening context. In Proceedings of Interspeech 2019 (pp. 3123-3127). doi:10.21437/Interspeech.2019-1414.

## Abstract

Lexically guided perceptual learning has traditionally been studied with ambiguous consonant sounds to which native listeners are exposed in a purely receptive listening context. To extend previous research, we investigate whether lexically guided learning applies to a vowel shift encountered by non-native listeners in an interactive dialogue. Dutch participants played a two-player game in English in either a control condition, which contained no evidence for a vowel shift, or a lexically constraining condition, in which onscreen lexical information required them to re-interpret their interlocutor’s /ɪ/ pronunciations as representing /ε/. A phonetic categorization pre-test and post-test were used to assess whether the game shifted listeners’ phonemic boundaries such that more of the /ε/-/ɪ/ continuum came to be perceived as /ε/. Both listener groups showed an overall post-test shift toward /ɪ/, suggesting that vowel perception may be sensitive to directional biases related to properties of the speaker’s vowel space. Importantly, listeners in the lexically constraining condition made relatively more post-test /ε/ responses than the control group, thereby exhibiting an effect of lexically guided adaptation. The results thus demonstrate that non-native listeners can adjust their phonemic boundaries on the basis of lexical information to accommodate a vowel shift learned in interactive conversation.
• Fisher, S. E., & Tilot, A. K. (Eds.). (2019). Bridging senses: Novel insights from synaesthesia [Special Issue]. Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences, 374.
• Friederici, A., & Levelt, W. J. M. (1987). Spatial description in microgravity: Aspects of cognitive adaptation. In P. R. Sahm, R. Jansen, & M. Keller (Eds.), Proceedings of the Norderney Symposium on Scientific Results of the German Spacelab Mission D1 (pp. 518-524). Köln, Germany: Wissenschaftliche Projektführung DI c/o DFVLR.
• Frost, R. L. A., Isbilen, E. S., Christiansen, M. H., & Monaghan, P. (2019). Testing the limits of non-adjacent dependency learning: Statistical segmentation and generalisation across domains. In A. K. Goel, C. M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Meeting of the Cognitive Science Society (CogSci 2019) (pp. 1787-1793). Montreal, QB: Cognitive Science Society.

## Abstract

Achieving linguistic proficiency requires identifying words from speech, and discovering the constraints that govern the way those words are used. In a recent study of non-adjacent dependency learning, Frost and Monaghan (2016) demonstrated that learners may perform these tasks together, using similar statistical processes - contrary to prior suggestions. However, in their study, non-adjacent dependencies were marked by phonological cues (plosive-continuant-plosive structure), which may have influenced learning. Here, we test the necessity of these cues by comparing learning across three conditions; fixed phonology, which contains these cues, varied phonology, which omits them, and shapes, which uses visual shape sequences to assess the generality of statistical processing for these tasks. Participants segmented the sequences and generalized the structure in both auditory conditions, but learning was best when phonological cues were present. Learning was around chance on both tasks for the visual shapes group, indicating statistical processing may critically differ across domains.
• Galke, L., Gerstenkorn, G., & Scherp, A. (2018). A case study of closed-domain response suggestion with limited training data. In M. Elloumi, M. Granitzer, A. Hameurlain, C. Seifert, B. Stein, A. Min Tjoa, & R. Wagner (Eds.), Database and Expert Systems Applications: DEXA 2018 International Workshops, BDMICS, BIOKDD, and TIR, Regensburg, Germany, September 3–6, 2018, Proceedings (pp. 218-229). Cham, Switzerland: Springer.

## Abstract

We analyze the problem of response suggestion in a closed domain along a real-world scenario of a digital library. We present a text-processing pipeline to generate question-answer pairs from chat transcripts. On this limited amount of training data, we compare retrieval-based, conditioned-generation, and dedicated representation learning approaches for response suggestion. Our results show that retrieval-based methods that strive to find similar, known contexts are preferable over parametric approaches from the conditioned-generation family, when the training data is limited. We, however, identify a specific representation learning approach that is competitive to the retrieval-based approaches despite the training data limitation.
• Galke, L., Vagliano, I., & Scherp, A. (2019). Can graph neural networks go „online“? An analysis of pretraining and inference. In Proceedings of the Representation Learning on Graphs and Manifolds: ICLR2019 Workshop.

## Abstract

Large-scale graph data in real-world applications is often not static but dynamic,
i. e., new nodes and edges appear over time. Current graph convolution approaches
are promising, especially, when all the graph’s nodes and edges are available dur-
ing training. When unseen nodes and edges are inserted after training, it is not
yet evaluated whether up-training or re-training from scratch is preferable. We
construct an experimental setup, in which we insert previously unseen nodes and
edges after training and conduct a limited amount of inference epochs. In this
setup, we compare adapting pretrained graph neural networks against retraining
from scratch. Our results show that pretrained models yield high accuracy scores
on the unseen nodes and that pretraining is preferable over retraining from scratch.
Our experiments represent a ﬁrst step to evaluate and develop truly online variants
of graph neural networks.
• Galke, L., Melnychuk, T., Seidlmayer, E., Trog, S., Foerstner, K., Schultz, C., & Tochtermann, K. (2019). Inductive learning of concept representations from library-scale bibliographic corpora. In K. David, K. Geihs, M. Lange, & G. Stumme (Eds.), Informatik 2019: 50 Jahre Gesellschaft für Informatik - Informatik für Gesellschaft (pp. 219-232). Bonn: Gesellschaft für Informatik e.V. doi:10.18420/inf2019_26.
• Galke, L., Mai, F., & Vagliano, I. (2018). Multi-modal adversarial autoencoders for recommendations of citations and subject labels. In T. Mitrovic, J. Zhang, L. Chen, & D. Chin (Eds.), UMAP '18: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (pp. 197-205). New York: ACM. doi:10.1145/3209219.3209236.

## Abstract

We present multi-modal adversarial autoencoders for recommendation and evaluate them on two different tasks: citation recommendation and subject label recommendation. We analyze the effects of adversarial regularization, sparsity, and different input modalities. By conducting 408 experiments, we show that adversarial regularization consistently improves the performance of autoencoders for recommendation. We demonstrate, however, that the two tasks differ in the semantics of item co-occurrence in the sense that item co-occurrence resembles relatedness in case of citations, yet implies diversity in case of subject labels. Our results reveal that supplying the partial item set as input is only helpful, when item co-occurrence resembles relatedness. When facing a new recommendation task it is therefore crucial to consider the semantics of item co-occurrence for the choice of an appropriate model.
• Goldrick, M., Brehm, L., Pyeong Whan, C., & Smolensky, P. (2019). Transient blend states and discrete agreement-driven errors in sentence production. In G. J. Snover, M. Nelson, B. O'Connor, & J. Pater (Eds.), Proceedings of the Society for Computation in Linguistics (SCiL 2019) (pp. 375-376). doi:10.7275/n0b2-5305.
• Hahn, L. E., Ten Buuren, M., De Nijs, M., Snijders, T. M., & Fikkert, P. (2019). Acquiring novel words in a second language through mutual play with child songs - The Noplica Energy Center. In L. Nijs, H. Van Regenmortel, & C. Arculus (Eds.), MERYC19 Counterpoints of the senses: Bodily experiences in musical learning (pp. 78-87). Ghent, Belgium: EuNet MERYC 2019.

## Abstract

Child songs are a great source for linguistic learning. Here we explore whether children can acquire novel words in a second language by playing a game featuring child songs in a playhouse. We present data from three studies that serve as scientific proof for the functionality of one game of the playhouse: the Energy Center. For this game, three hand-bikes were mounted on a panel. When children start moving the hand-bikes, child songs start playing simultaneously. Once the children produce enough energy with the hand-bikes, the songs are additionally accompanied with the sounds of musical instruments. In our studies, children executed a picture-selection task to evaluate whether they acquired new vocabulary from the songs presented during the game. Two of our studies were run in the field, one at a Dutch and one at an Indian pre-school. The third study features data from a more controlled laboratory setting. Our results partly confirm that the Energy Center is a successful means to support vocabulary acquisition in a second language. More research with larger sample sizes and longer access to the Energy Center is needed to evaluate the overall functionality of the game. Based on informal observations at our test sites, however, we are certain that children do pick up linguistic content from the songs during play, as many of the children repeat words and phrases from songs they heard. We will pick up upon these promising observations during future studies
• Heilbron, M., Ehinger, B., Hagoort, P., & De Lange, F. P. (2019). Tracking naturalistic linguistic predictions with deep neural language models. In Proceedings of the 2019 Conference on Cognitive Computational Neuroscience (pp. 424-427). doi:10.32470/CCN.2019.1096-0.

## Abstract

Prediction in language has traditionally been studied using
simple designs in which neural responses to expected
and unexpected words are compared in a categorical
fashion. However, these designs have been contested
as being ‘prediction encouraging’, potentially exaggerating
the importance of prediction in language understanding.
A few recent studies have begun to address
these worries by using model-based approaches to probe
the effects of linguistic predictability in naturalistic stimuli
(e.g. continuous narrative). However, these studies
so far only looked at very local forms of prediction, using
models that take no more than the prior two words into
account when computing a word’s predictability. Here,
we extend this approach using a state-of-the-art neural
language model that can take roughly 500 times longer
linguistic contexts into account. Predictability estimates
fromthe neural network offer amuch better fit to EEG data
from subjects listening to naturalistic narrative than simpler
models, and reveal strong surprise responses akin to
the P200 and N400. These results show that predictability
effects in language are not a side-effect of simple designs,
and demonstrate the practical use of recent advances
in AI for the cognitive neuroscience of language.
• Hopman, E., Thompson, B., Austerweil, J., & Lupyan, G. (2018). Predictors of L2 word learning accuracy: A big data investigation. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 513-518). Austin, TX: Cognitive Science Society.

## Abstract

What makes some words harder to learn than others in a second language? Although some robust factors have been identified based on small scale experimental studies, many relevant factors are difficult to study in such experiments due to the amount of data necessary to test them. Here, we investigate what factors affect the ease of learning of a word in a second language using a large data set of users learning English as a second language through the Duolingo mobile app. In a regression analysis, we test and confirm the well-studied effect of cognate status on word learning accuracy. Furthermore, we find significant effects for both cross-linguistic semantic alignment and English semantic density, two novel predictors derived from large scale distributional models of lexical semantics. Finally, we provide data on several other psycholinguistically plausible word level predictors. We conclude with a discussion of the limits, benefits and future research potential of using big data for investigating second language learning.
• Huettig, F., Kolinsky, R., & Lachmann, T. (Eds.). (2018). The effects of literacy on cognition and brain functioning [Special Issue]. Language, Cognition and Neuroscience, 33(3).
• Isbilen, E., Frost, R. L. A., Monaghan, P., & Christiansen, M. (2018). Bridging artificial and natural language learning: Comparing processing- and reflection-based measures of learning. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 1856-1861). Austin, TX: Cognitive Science Society.

## Abstract

A common assumption in the cognitive sciences is that artificial and natural language learning rely on shared mechanisms. However, attempts to bridge the two have yielded ambiguous results. We suggest that an empirical disconnect between the computations employed during learning and the methods employed at test may explain these mixed results. Further, we propose statistically-based chunking as a potential computational link between artificial and natural language learning. We compare the acquisition of non-adjacent dependencies to that of natural language structure using two types of tasks: reflection-based 2AFC measures, and processing-based recall measures, the latter being more computationally analogous to the processes used during language acquisition. Our results demonstrate that task-type significantly influences the correlations observed between artificial and natural language acquisition, with reflection-based and processing-based measures correlating within – but not across – task-type. These findings have fundamental implications for artificial-to-natural language comparisons, both methodologically and theoretically.

http://mindmodeling.org/cogsci2018/papers/0358/0358.pdf
• Janssen, R., Moisik, S. R., & Dediu, D. (2018). Agent model reveals the influence of vocal tract anatomy on speech during ontogeny and glossogeny. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 171-174). Toruń, Poland: NCU Press. doi:10.12775/3991-1.042.
• Joo, H., Jang, J., Kim, S., Cho, T., & Cutler, A. (2019). Prosodic structural effects on coarticulatory vowel nasalization in Australian English in comparison to American English. In S. Calhoun, P. Escudero, M. Tabain, & P. Warren (Eds.), Proceedings of the 19th International Congress of Phonetic Sciences (ICPhS 20195) (pp. 835-839). Canberra, Australia: Australasian Speech Science and Technology Association Inc.

## Abstract

This study investigates effects of prosodic factors (prominence, boundary) on coarticulatory Vnasalization in Australian English (AusE) in CVN and NVC in comparison to those in American English
(AmE). As in AmE, prominence was found to
lengthen N, but to reduce V-nasalization, enhancing N’s nasality and V’s orality, respectively (paradigmatic contrast enhancement). But the prominence effect in CVN was more robust than that in AmE. Again similar to findings in AmE, boundary
induced a reduction of N-duration and V-nasalization phrase-initially (syntagmatic contrast enhancement), and increased the nasality of both C and V phrasefinally.
But AusE showed some differences in terms
of the magnitude of V nasalization and N duration. The results suggest that the linguistic contrast enhancements underlie prosodic-structure modulation of coarticulatory V-nasalization in
comparable ways across dialects, while the fine phonetic detail indicates that the phonetics-prosody interplay is internalized in the individual dialect’s phonetic grammar.
• Kanero, J., Franko, I., Oranç, C., Uluşahin, O., Koskulu, S., Adigüzel, Z., Küntay, A. C., & Göksun, T. (2018). Who can benefit from robots? Effects of individual differences in robot-assisted language learning. In Proceedings of the 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (pp. 212-217). Piscataway, NJ, USA: IEEE.

## Abstract

It has been suggested that some individuals may benefit more from social robots than do others. Using second
language (L2) as an example, the present study examined how individual differences in attitudes toward robots and personality
traits may be related to learning outcomes. Preliminary results with 24 Turkish-speaking adults suggest that negative attitudes
toward robots, more specifically thoughts and anxiety about the negative social impact that robots may have on the society,
predicted how well adults learned L2 words from a social robot. The possible implications of the findings as well as future directions are also discussed
• Klein, W. (). (1989). Kindersprache [Special Issue]. Zeitschrift für Literaturwissenschaft und Linguistik, (73).
• Klein, W. (). (1984). Textverständlichkeit - Textverstehen [Special Issue]. Zeitschrift für Literaturwissenschaft und Linguistik, (55).
• Klein, W. (). (1979). Sprache und Kontext [Special Issue]. Zeitschrift für Literaturwissenschaft und Linguistik, (33).
• Klein, W. (). (1987). Sprache und Ritual [Special Issue]. Zeitschrift für Literaturwissenschaft und Linguistik, (65).
• Lattenkamp, E. Z., Vernes, S. C., & Wiegrebe, L. (2018). Mammalian models for the study of vocal learning: A new paradigm in bats. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 235-237). Toruń, Poland: NCU Press. doi:10.12775/3991-1.056.
• Lauscher, A., Eckert, K., Galke, L., Scherp, A., Rizvi, S. T. R., Ahmed, S., Dengel, A., Zumstein, P., & Klein, A. (2018). Linked open citation database: Enabling libraries to contribute to an open and interconnected citation graph. In J. Chen, M. A. Gonçalves, J. M. Allen, E. A. Fox, M.-Y. Kan, & V. Petras (Eds.), JCDL '18: Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries (pp. 109-118). New York: ACM. doi:10.1145/3197026.3197050.

## Abstract

Citations play a crucial role in the scientific discourse, in information retrieval, and in bibliometrics. Many initiatives are currently promoting the idea of having free and open citation data. Creation of citation data, however, is not part of the cataloging workflow in libraries nowadays.
In this paper, we present our project Linked Open Citation Database, in which we design distributed processes and a system infrastructure based on linked data technology. The goal is to show that efficiently cataloging citations in libraries using a semi-automatic approach is possible. We specifically describe the current state of the workflow and its implementation. We show that we could significantly improve the automatic reference extraction that is crucial for the subsequent data curation. We further give insights on the curation and linking process and provide evaluation results that not only direct the further development of the project, but also allow us to discuss its overall feasibility.
• Lefever, E., Hendrickx, I., Croijmans, I., Van den Bosch, A., & Majid, A. (2018). Discovering the language of wine reviews: A text mining account. In N. Calzolari, K. Choukri, C. Cieri, T. Declerck, S. Goggi, K. Hasida, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, S. Piperidis, & T. Tokunaga (Eds.), Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) (pp. 3297-3302). Paris: LREC.

## Abstract

It is widely held that smells and flavors are impossible to put into words. In this paper we test this claim by seeking predictive patterns in wine reviews, which ostensibly aim to provide guides to perceptual content. Wine reviews have previously been critiqued as random and meaningless. We collected an English corpus of wine reviews with their structured metadata, and applied machine learning techniques to automatically predict the wine's color, grape variety, and country of origin. To train the three supervised classifiers, three different information sources were incorporated: lexical bag-of-words features, domain-specific terminology features, and semantic word embedding features. In addition, using regression analysis we investigated basic review properties, i.e., review length, average word length, and their relationship to the scalar values of price and review score. Our results show that wine experts do share a common vocabulary to describe wines and they use this in a consistent way, which makes it possible to automatically predict wine characteristics based on the review text alone. This means that odors and flavors may be more expressible in language than typically acknowledged.

http://www.lrec-conf.org/proceedings/lrec2018/pdf/496.pdf
• Levelt, W. J. M., & Plomp, R. (1962). Musical consonance and critical bandwidth. In Proceedings of the 4th International Congress Acoustics (pp. 55-55).
• Levelt, W. J. M. (1984). Spontaneous self-repairs in speech: Processes and representations. In M. P. R. Van den Broecke, & A. Cohen (Eds.), Proceedings of the 10th International Congress of Phonetic Sciences (pp. 105-117). Dordrecht: Foris.
• Levelt, W. J. M., & Schriefers, H. (1987). Stages of lexical access. In G. A. Kempen (), Natural language generation: new results in artificial intelligence, psychology and linguistics (pp. 395-404). Dordrecht: Nijhoff.
• Levinson, S. C. (1987). Minimization and conversational inference. In M. Bertuccelli Papi, & J. Verschueren (Eds.), The pragmatic perspective: Selected papers from the 1985 International Pragmatics Conference (pp. 61-129). Benjamins.
• Levinson, S. C. (1979). Pragmatics and social deixis: Reclaiming the notion of conventional implicature. In C. Chiarello (), Proceedings of the Fifth Annual Meeting of the Berkeley Linguistics Society (pp. 206-223).
• Liu, S., & Zhang, Y. (2019). Why some verbs are harder to learn than others – A micro-level analysis of everyday learning contexts for early verb learning. In A. K. Goel, C. M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Meeting of the Cognitive Science Society (CogSci 2019) (pp. 2173-2178). Montreal, QB: Cognitive Science Society.

## Abstract

Verb learning is important for young children. While most
previous research has focused on linguistic and conceptual
challenges in early verb learning (e.g. Gentner, 1982, 2006),
the present paper examined early verb learning at the
attentional level and quantified the input for early verb learning
by measuring verb-action co-occurrence statistics in parent-
child interaction from the learner’s perspective. To do so, we
used head-mounted eye tracking to record fine-grained
multimodal behaviors during parent-infant joint play, and
analyzed parent speech, parent and infant action, and infant
attention at the moments when parents produced verb labels.
Our results show great variability across different action verbs,
in terms of frequency of verb utterances, frequency of
corresponding actions related to verb meanings, and infants’
attention to verbs and actions, which provide new insights on
why some verbs are harder to learn than others.
• Lopopolo, A., Frank, S. L., Van den Bosch, A., Nijhof, A., & Willems, R. M. (2018). The Narrative Brain Dataset (NBD), an fMRI dataset for the study of natural language processing in the brain. In B. Devereux, E. Shutova, & C.-R. Huang (Eds.), Proceedings of LREC 2018 Workshop "Linguistic and Neuro-Cognitive Resources (LiNCR) (pp. 8-11). Paris: LREC.

## Abstract

We present the Narrative Brain Dataset, an fMRI dataset that was collected during spoken presentation of short excerpts of three
stories in Dutch. Together with the brain imaging data, the dataset contains the written versions of the stimulation texts. The texts are
accompanied with stochastic (perplexity and entropy) and semantic computational linguistic measures. The richness and unconstrained
nature of the data allows the study of language processing in the brain in a more naturalistic setting than is common for fMRI studies.
We hope that by making NBD available we serve the double purpose of providing useful neural data to researchers interested in natural
language processing in the brain and to further stimulate data sharing in the field of neuroscience of language.
• Lupyan, G., Wendorf, A., Berscia, L. M., & Paul, J. (2018). Core knowledge or language-augmented cognition? The case of geometric reasoning. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 252-254). Toruń, Poland: NCU Press. doi:10.12775/3991-1.062.
• Mai, F., Galke, L., & Scherp, A. (2019). CBOW is not all you need: Combining CBOW with the compositional matrix space model. In Proceedings of the Seventh International Conference on Learning Representations (ICLR 2019). OpenReview.net.

## Abstract

Continuous Bag of Words (CBOW) is a powerful text embedding method. Due to its strong capabilities to encode word content, CBOW embeddings perform well on a wide range of downstream tasks while being efficient to compute. However, CBOW is not capable of capturing the word order. The reason is that the computation of CBOW's word embeddings is commutative, i.e., embeddings of XYZ and ZYX are the same. In order to address this shortcoming, we propose a
learning algorithm for the Continuous Matrix Space Model, which we call Continual Multiplication of Words (CMOW). Our algorithm is an adaptation of word2vec, so that it can be trained on large quantities of unlabeled text. We empirically show that CMOW better captures linguistic properties, but it is inferior to CBOW in memorizing word content. Motivated by these findings, we propose a hybrid model that combines the strengths of CBOW and CMOW. Our results show that the hybrid CBOW-CMOW-model retains CBOW's strong ability to memorize word content while at the same time substantially improving its ability to encode other linguistic information by 8%. As a result, the hybrid also performs better on 8 out of 11 supervised downstream tasks with an average improvement of 1.2%.
• Mai, F., Galke, L., & Scherp, A. (2018). Using deep learning for title-based semantic subject indexing to reach competitive performance to full-text. In J. Chen, M. A. Gonçalves, J. M. Allen, E. A. Fox, M.-Y. Kan, & V. Petras (Eds.), JCDL '18: Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries (pp. 169-178). New York: ACM.

## Abstract

For (semi-)automated subject indexing systems in digital libraries, it is often more practical to use metadata such as the title of a publication instead of the full-text or the abstract. Therefore, it is desirable to have good text mining and text classification algorithms that operate well already on the title of a publication. So far, the classification performance on titles is not competitive with the performance on the full-texts if the same number of training samples is used for training. However, it is much easier to obtain title data in large quantities and to use it for training than full-text data. In this paper, we investigate the question how models obtained from training on increasing amounts of title training data compare to models from training on a constant number of full-texts. We evaluate this question on a large-scale dataset from the medical domain (PubMed) and from economics (EconBiz). In these datasets, the titles and annotations of millions of publications are available, and they outnumber the available full-texts by a factor of 20 and 15, respectively. To exploit these large amounts of data to their full potential, we develop three strong deep learning classifiers and evaluate their performance on the two datasets. The results are promising. On the EconBiz dataset, all three classifiers outperform their full-text counterparts by a large margin. The best title-based classifier outperforms the best full-text method by 9.4%. On the PubMed dataset, the best title-based method almost reaches the performance of the best full-text classifier, with a difference of only 2.9%.
• Mamus, E., Rissman, L., Majid, A., & Ozyurek, A. (2019). Effects of blindfolding on verbal and gestural expression of path in auditory motion events. In A. K. Goel, C. M. Seifert, & C. C. Freksa (Eds.), Proceedings of the 41st Annual Meeting of the Cognitive Science Society (CogSci 2019) (pp. 2275-2281). Montreal, QB: Cognitive Science Society.

## Abstract

Studies have claimed that blind people’s spatial representations are different from sighted people, and blind people display superior auditory processing. Due to the nature of auditory and haptic information, it has been proposed that blind people have spatial representations that are more sequential than sighted people. Even the temporary loss of sight—such as through blindfolding—can affect spatial representations, but not much research has been done on this topic. We compared blindfolded and sighted people’s linguistic spatial expressions and non-linguistic localization accuracy to test how blindfolding affects the representation of path in auditory motion events. We found that blindfolded people were as good as sighted people when localizing simple sounds, but they outperformed sighted people when localizing auditory motion events. Blindfolded people’s path related speech also included more sequential, and less holistic elements. Our results indicate that even temporary loss of sight influences spatial representations of auditory motion events

https://mindmodeling.org/cogsci2019/papers/0395/index.html
• Marcoux, K., & Ernestus, M. (2019). Differences between native and non-native Lombard speech in terms of pitch range. In M. Ochmann, M. Vorländer, & J. Fels (Eds.), Proceedings of the ICA 2019 and EAA Euroregio. 23rd International Congress on Acoustics, integrating 4th EAA Euroregio 2019 (pp. 5713-5720). Berlin: Deutsche Gesellschaft für Akustik.

## Abstract

Lombard speech, speech produced in noise, is acoustically different from speech produced in quiet (plain speech) in several ways, including having a higher and wider F0 range (pitch). Extensive research on native Lombard speech does not consider that non-natives experience a higher cognitive load while producing
speech and that the native language may influence the non-native speech. We investigated pitch range in plain and Lombard speech in native and non-natives.
Dutch and American-English speakers read contrastive question-answer pairs in quiet and in noise in English, while the Dutch also read Dutch sentence pairs. We found that Lombard speech is characterized by a wider pitch range than plain speech, for all speakers (native English, non-native English, and native Dutch).
This shows that non-natives also widen their pitch range in Lombard speech. In sentences with early-focus, we see the same increase in pitch range when going from plain to Lombard speech in native and non-native English, but a smaller increase in native Dutch. In sentences with late-focus, we see the biggest increase for the native English, followed by non-native English and then native Dutch. Together these results indicate an effect of the native language on non-native Lombard speech.
• Marcoux, K., & Ernestus, M. (2019). Pitch in native and non-native Lombard speech. In S. Calhoun, P. Escudero, M. Tabain, & P. Warren (Eds.), Proceedings of the 19th International Congress of Phonetic Sciences (ICPhS 2019) (pp. 2605-2609). Canberra, Australia: Australasian Speech Science and Technology Association Inc.

## Abstract

Lombard speech, speech produced in noise, is
typically produced with a higher fundamental
frequency (F0, pitch) compared to speech in quiet. This paper examined the potential differences in native and non-native Lombard speech by analyzing median pitch in sentences with early- or late-focus produced in quiet and noise. We found an increase in pitch in late-focus sentences in noise for Dutch speakers in both English and Dutch, and for American-English speakers in English. These results
show that non-native speakers produce Lombard speech, despite their higher cognitive load. For the early-focus sentences, we found a difference between the Dutch and the American-English speakers. Whereas the Dutch showed an increased F0 in noise
in English and Dutch, the American-English speakers did not in English. Together, these results suggest that some acoustic characteristics of Lombard speech, such as pitch, may be language-specific, potentially
resulting in the native language influencing the non-native Lombard speech.
• Merkx, D., Frank, S., & Ernestus, M. (2019). Language learning using speech to image retrieval. In Proceedings of Interspeech 2019 (pp. 1841-1845). doi:10.21437/Interspeech.2019-3067.

## Abstract

Humans learn language by interaction with their environment and listening to other humans. It should also be possible for computational models to learn language directly from speech but so far most approaches require text. We improve on existing neural network approaches to create visually grounded embeddings for spoken utterances. Using a combination of a multi-layer GRU, importance sampling, cyclic learning rates, ensembling and vectorial self-attention our results show a remarkable increase in image-caption retrieval performance over previous work. Furthermore, we investigate which layers in the model learn to recognise words in the input. We find that deeper network layers are better at encoding word presence, although the final layer has slightly lower performance. This shows that our visually grounded sentence encoder learns to recognise words from the input even though it is not explicitly trained for word recognition.
• Merkx, D., & Scharenborg, O. (2018). Articulatory feature classification using convolutional neural networks. In Proceedings of Interspeech 2018 (pp. 2142-2146). doi:10.21437/Interspeech.2018-2275.

## Abstract

The ultimate goal of our research is to improve an existing speech-based computational model of human speech recognition on the task of simulating the role of fine-grained phonetic information in human speech processing. As part of this work we are investigating articulatory feature classifiers that are able to create reliable and accurate transcriptions of the articulatory behaviour encoded in the acoustic speech signal. Articulatory feature (AF) modelling of speech has received a considerable amount of attention in automatic speech recognition research. Different approaches have been used to build AF classifiers, most notably multi-layer perceptrons. Recently, deep neural networks have been applied to the task of AF classification. This paper aims to improve AF classification by investigating two different approaches: 1) investigating the usefulness of a deep Convolutional neural network (CNN) for AF classification; 2) integrating the Mel filtering operation into the CNN architecture. The results showed a remarkable improvement in classification accuracy of the CNNs over state-of-the-art AF classification results for Dutch, most notably in the minority classes. Integrating the Mel filtering operation into the CNN architecture did not further improve classification performance.
• Micklos, A., Macuch Silva, V., & Fay, N. (2018). The prevalence of repair in studies of language evolution. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 316-318). Toruń, Poland: NCU Press. doi:10.12775/3991-1.075.
• Moisik, S. R., Zhi Yun, D. P., & Dediu, D. (2019). Active adjustment of the cervical spine during pitch production compensates for shape: The ArtiVarK study. In S. Calhoun, P. Escudero, M. Tabain, & P. Warren (Eds.), Proceedings of the 19th International Congress of Phonetic Sciences (ICPhS 20195) (pp. 864-868). Canberra, Australia: Australasian Speech Science and Technology Association Inc.

## Abstract

The anterior lordosis of the cervical spine is thought
to contribute to pitch (fo) production by influencing
cricoid rotation as a function of larynx height. This
study examines the matter of inter-individual
variation in cervical spine shape and whether this has
an influence on how fo is produced along increasing
or decreasing scales, using the ArtiVarK dataset,
which contains real-time MRI pitch production data.
We find that the cervical spine actively participates in
fo production, but the amount of displacement
depends on individual shape. In general, anterior
spine motion (tending toward cervical lordosis)
occurs for low fo, while posterior movement (tending
towards cervical kyphosis) occurs for high fo.
• Mulder, K., Ten Bosch, L., & Boves, L. (2018). Analyzing EEG Signals in Auditory Speech Comprehension Using Temporal Response Functions and Generalized Additive Models. In Proceedings of Interspeech 2018 (pp. 1452-1456). doi:10.21437/Interspeech.2018-1676.

## Abstract

Analyzing EEG signals recorded while participants are listening to continuous speech with the purpose of testing linguistic hypotheses is complicated by the fact that the signals simultaneously reflect exogenous acoustic excitation and endogenous linguistic processing. This makes it difficult to trace subtle differences that occur in mid-sentence position. We apply an analysis based on multivariate temporal response functions to uncover subtle mid-sentence effects. This approach is based on a per-stimulus estimate of the response of the neural system to speech input. Analyzing EEG signals predicted on the basis of the response functions might then bring to light conditionspecific differences in the filtered signals. We validate this approach by means of an analysis of EEG signals recorded with isolated word stimuli. Then, we apply the validated method to the analysis of the responses to the same words in the middle of meaningful sentences.
• Nijveld, A., Ten Bosch, L., & Ernestus, M. (2019). ERP signal analysis with temporal resolution using a time window bank. In Proceedings of Interspeech 2019 (pp. 1208-1212). doi:10.21437/Interspeech.2019-2729.

## Abstract

In order to study the cognitive processes underlying speech comprehension, neuro-physiological measures (e.g., EEG and MEG), or behavioural measures (e.g., reaction times and response accuracy) can be applied. Compared to behavioural measures, EEG signals can provide a more fine-grained and complementary view of the processes that take place during the unfolding of an auditory stimulus.

EEG signals are often analysed after having chosen specific time windows, which are usually based on the temporal structure of ERP components expected to be sensitive to the experimental manipulation. However, as the timing of ERP components may vary between experiments, trials, and participants, such a-priori defined analysis time windows may significantly hamper the exploratory power of the analysis of components of interest. In this paper, we explore a wide-window analysis method applied to EEG signals collected in an auditory repetition priming experiment.

This approach is based on a bank of temporal filters arranged along the time axis in combination with linear mixed effects modelling. Crucially, it permits a temporal decomposition of effects in a single comprehensive statistical model which captures the entire EEG trace.
• Parhammer*, S. I., Ebersberg*, M., Tippmann*, J., Stärk*, K., Opitz, A., Hinger, B., & Rossi, S. (2019). The influence of distraction on speech processing: How selective is selective attention? In Proceedings of Interspeech 2019 (pp. 3093-3097). doi:10.21437/Interspeech.2019-2699.

## Abstract

-* indicates shared first authorship -
The present study investigated the effects of selective attention on the processing of morphosyntactic errors in unattended parts of speech. Two groups of German native (L1) speakers participated in the present study. Participants listened to sentences in which irregular verbs were manipulated in three different conditions (correct, incorrect but attested ablaut pattern, incorrect and crosslinguistically unattested ablaut pattern). In order to track fast dynamic neural reactions to the stimuli, electroencephalography was used. After each sentence, participants in Experiment 1 performed a semantic judgement task, which deliberately distracted the participants from the syntactic manipulations and directed their attention to the semantic content of the sentence. In Experiment 2, participants carried out a syntactic judgement task, which put their attention on the critical stimuli. The use of two different attentional tasks allowed for investigating the impact of selective attention on speech processing and whether morphosyntactic processing steps are performed automatically. In Experiment 2, the incorrect attested condition elicited a larger N400 component compared to the correct condition, whereas in Experiment 1 no differences between conditions were found. These results suggest that the processing of morphosyntactic violations in irregular verbs is not entirely automatic but seems to be strongly affected by selective attention.
• Pouw, W., Paxton, A., Harrison, S. J., & Dixon, J. A. (2019). Acoustic specification of upper limb movement in voicing. In A. Grimminger (), Proceedings of the 6th Gesture and Speech in Interaction – GESPIN 6 (pp. 68-74). Paderborn: Universitaetsbibliothek Paderborn. doi:10.17619/UNIPB/1-812.

https://osf.io/9843h/
• Pouw, W., & Dixon, J. A. (2019). Quantifying gesture-speech synchrony. In A. Grimminger (), Proceedings of the 6th Gesture and Speech in Interaction – GESPIN 6 (pp. 75-80). Paderborn: Universitaetsbibliothek Paderborn. doi:10.17619/UNIPB/1-812.

## Abstract

Spontaneously occurring speech is often seamlessly accompanied by hand gestures. Detailed
observations of video data suggest that speech and gesture are tightly synchronized in time,
consistent with a dynamic interplay between body and mind. However, spontaneous gesturespeech
synchrony has rarely been objectively quantified beyond analyses of video data, which
do not allow for identification of kinematic properties of gestures. Consequently, the point in
gesture which is held to couple with speech, the so-called moment of “maximum effort”, has
been variably equated with the peak velocity, peak acceleration, peak deceleration, or the onset
of the gesture. In the current exploratory report, we provide novel evidence from motiontracking
and acoustic data that peak velocity is closely aligned, and shortly leads, the peak pitch
(F0) of speech

https://osf.io/9843h/
• Räsänen, O., Seshadri, S., & Casillas, M. (2018). Comparison of syllabification algorithms and training strategies for robust word count estimation across different languages and recording conditions. In Proceedings of Interspeech 2018 (pp. 1200-1204). doi:10.21437/Interspeech.2018-1047.

## Abstract

Word count estimation (WCE) from audio recordings has a number of applications, including quantifying the amount of speech that language-learning infants hear in their natural environments, as captured by daylong recordings made with devices worn by infants. To be applicable in a wide range of scenarios and also low-resource domains, WCE tools should be extremely robust against varying signal conditions and require minimal access to labeled training data in the target domain. For this purpose, earlier work has used automatic syllabification of speech, followed by a least-squares-mapping of syllables to word counts. This paper compares a number of previously proposed syllabifiers in the WCE task, including a supervised bi-directional long short-term memory (BLSTM) network that is trained on a language for which high quality syllable annotations are available (a “high resource language”), and reports how the alternative methods compare on different languages and signal conditions. We also explore additive noise and varying-channel data augmentation strategies for BLSTM training, and show how they improve performance in both matching and mismatching languages. Intriguingly, we also find that even though the BLSTM works on languages beyond its training data, the unsupervised algorithms can still outperform it in challenging signal conditions on novel languages.
• Ravignani, A., Garcia, M., Gross, S., de Reus, K., Hoeksema, N., Rubio-Garcia, A., & de Boer, B. (2018). Pinnipeds have something to say about speech and rhythm. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 399-401). Toruń, Poland: NCU Press. doi:10.12775/3991-1.095.
• Raviv, L., Meyer, A. S., & Lev-Ari, S. (2018). The role of community size in the emergence of linguistic structure. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 402-404). Toruń, Poland: NCU Press. doi:10.12775/3991-1.096.
• Rissman, L., & Majid, A. (2019). Agency drives category structure in instrumental events. In A. K. Goel, C. M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Meeting of the Cognitive Science Society (CogSci 2019) (pp. 2661-2667). Montreal, QB: Cognitive Science Society.

## Abstract

Thematic roles such as Agent and Instrument have a long-standing place in theories of event representation. Nonetheless, the structure of these categories has been difficult to determine. We investigated how instrumental events, such as someone slicing bread with a knife, are categorized in English. Speakers described a variety of typical and atypical instrumental events, and we determined the similarity structure of their descriptions using correspondence analysis. We found that events where the instrument is an extension of an intentional agent were most likely to elicit similar language, highlighting the importance of agency in structuring instrumental categories.

https://mindmodeling.org/cogsci2019/papers/0455/0455.pdf
• Saleh, A., Beck, T., Galke, L., & Scherp, A. (2018). Performance comparison of ad-hoc retrieval models over full-text vs. titles of documents. In M. Dobreva, A. Hinze, & M. Žumer (Eds.), Maturity and Innovation in Digital Libraries: 20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018, Hamilton, New Zealand, November 19-22, 2018, Proceedings (pp. 290-303). Cham, Switzerland: Springer.

## Abstract

While there are many studies on information retrieval models using full-text, there are presently no comparison studies of full-text retrieval vs. retrieval only over the titles of documents. On the one hand, the full-text of documents like scientific papers is not always available due to, e.g., copyright policies of academic publishers. On the other hand, conducting a search based on titles alone has strong limitations. Titles are short and therefore may not contain enough information to yield satisfactory search results. In this paper, we compare different retrieval models regarding their search performance on the full-text vs. only titles of documents. We use different datasets, including the three digital library datasets: EconBiz, IREON, and PubMed. The results show that it is possible to build effective title-based retrieval models that provide competitive results comparable to full-text retrieval. The difference between the average evaluation results of the best title-based retrieval models is only 3% less than those of the best full-text-based retrieval models.
• Scharenborg, O., & Merkx, D. (2018). The role of articulatory feature representation quality in a computational model of human spoken-word recognition. In Proceedings of the Machine Learning in Speech and Language Processing Workshop (MLSLP 2018).

## Abstract

Fine-Tracker is a speech-based model of human speech
recognition. While previous work has shown that Fine-Tracker
is successful at modelling aspects of human spoken-word
recognition, its speech recognition performance is not
comparable to that of human performance, possibly due to
suboptimal intermediate articulatory feature (AF)
representations. This study investigates the effect of improved
AF representations, obtained using a state-of-the-art deep
convolutional network, on Fine-Tracker’s simulation and
recognition performance: Although the improved AF quality
resulted in improved speech recognition; it, surprisingly, did
not lead to an improvement in Fine-Tracker’s simulation power.
• Schoenmakers, G.-J., & De Swart, P. (2019). Adverbial hurdles in Dutch scrambling. In A. Gattnar, R. Hörnig, M. Störzer, & S. Featherston (Eds.), Proceedings of Linguistic Evidence 2018: Experimental Data Drives Linguistic Theory (pp. 124-145). Tübingen: University of Tübingen.

## Abstract

This paper addresses the role of the adverb in Dutch direct object scrambling constructions. We report four experiments in which we investigate whether the structural position and the scope sensitivity of the adverb affect acceptability judgments of scrambling constructions and native speakers' tendency to scramble definite objects. We conclude that the type of adverb plays a key role in Dutch word ordering preferences.
• Schuerman, W. L., McQueen, J. M., & Meyer, A. S. (2019). Speaker statistical averageness modulates word recognition in adverse listening conditions. In S. Calhoun, P. Escudero, M. Tabain, & P. Warren (Eds.), Proceedings of the 19th International Congress of Phonetic Sciences (ICPhS 20195) (pp. 1203-1207). Canberra, Australia: Australasian Speech Science and Technology Association Inc.

## Abstract

We tested whether statistical averageness (SA) at the level of the individual speaker could predict a speaker’s intelligibility. 28 female and 21 male speakers of Dutch were recorded producing 336 sentences,
each containing two target nouns. Recordings were compared to those of all other same-sex speakers using dynamic time warping (DTW). For each sentence, the DTW distance constituted a metric
of phonetic distance from one speaker to all other speakers. SA comprised the average of these distances. Later, the same participants performed a word recognition task on the target nouns in the same sentences, under three degraded listening conditions. In all three conditions, accuracy increased with SA. This held even when participants listened to their own utterances. These findings suggest that listeners process speech with respect to the statistical
properties of the language spoken in their community, rather than using their own speech as a reference
• Seidlmayer, E., Galke, L., Melnychuk, T., Schultz, C., Tochtermann, K., & Förstner, K. U. (2019). Take it personally - A Python library for data enrichment for infometrical applications. In M. Alam, R. Usbeck, T. Pellegrini, H. Sack, & Y. Sure-Vetter (Eds.), Proceedings of the Posters and Demo Track of the 15th International Conference on Semantic Systems co-located with 15th International Conference on Semantic Systems (SEMANTiCS 2019).

## Abstract

Like every other social sphere, science is inﬂuenced by individual characteristics of researchers. However, for investigations on scientiﬁc networks, only little data about the social background of researchers, e.g. social origin, gender, aﬃliation etc., is available.
This paper introduces ”Take it personally - TIP”, a conceptual model and library currently under development, which aims to support the
semantic enrichment of publication databases with semantically related background information which resides elsewhere in the (semantic) web, such as Wikidata.
The supplementary information enriches the original information in the publication databases and thus facilitates the creation of complex scientiﬁc knowledge graphs. Such enrichment helps to improve the scientometric analysis of scientiﬁc publications as they can also take social backgrounds of researchers into account and to understand social structure in research communities.
• Seijdel, N., Sakmakidis, N., De Haan, E. H. F., Bohte, S. M., & Scholte, H. S. (2019). Implicit scene segmentation in deeper convolutional neural networks. In Proceedings of the 2019 Conference on Cognitive Computational Neuroscience (pp. 1059-1062). doi:10.32470/CCN.2019.1149-0.

## Abstract

Feedforward deep convolutional neural networks (DCNNs) are matching and even surpassing human performance on object recognition. This performance suggests that activation of a loose collection of image
features could support the recognition of natural object categories, without dedicated systems to solve specific visual subtasks. Recent findings in humans however, suggest that while feedforward activity may suffice for
sparse scenes with isolated objects, additional visual operations ('routines') that aid the recognition process (e.g. segmentation or grouping) are needed for more complex scenes. Linking human visual processing to
performance of DCNNs with increasing depth, we here explored if, how, and when object information is differentiated from the backgrounds they appear on. To this end, we controlled the information in both objects
and backgrounds, as well as the relationship between them by adding noise, manipulating background congruence and systematically occluding parts of the image. Results indicated less distinction between object- and background features for more shallow networks. For those networks, we observed a benefit of training on segmented objects (as compared to unsegmented objects). Overall, deeper networks trained on natural
(unsegmented) scenes seem to perform implicit 'segmentation' of the objects from their background, possibly by improved selection of relevant features.
• Seuren, P. A. M. (1984). Logic and truth-values in language. In F. Landman, & F. Veltman (Eds.), Varieties of formal semantics: Proceedings of the fourth Amsterdam colloquium (pp. 343-364). Dordrecht: Foris.
• Shen, C., & Janse, E. (2019). Articulatory control in speech production. In S. Calhoun, P. Escudero, M. Tabain, & P. Warren (Eds.), Proceedings of the 19th International Congress of Phonetic Sciences (ICPhS 2019) (pp. 2533-2537). Canberra, Australia: Australasian Speech Science and Technology Association Inc.
• Shen, C., Cooke, M., & Janse, E. (2019). Individual articulatory control in speech enrichment. In M. Ochmann, M. Vorländer, & J. Fels (Eds.), Proceedings of the 23rd International Congress on Acoustics (pp. 5726-5730). Berlin: Deutsche Gesellschaft für Akustik.

## Abstract

ndividual talkers may use various strategies to enrich their speech while speaking in noise (i.e., Lombard speech) to improve their intelligibility. The resulting acoustic-phonetic changes in Lombard speech vary amongst different speakers, but it is unclear what causes these talker differences, and what impact these differences have on intelligibility. This study investigates the potential role of articulatory control in talkers’ Lombard speech enrichment success. Seventy-eight speakers read out sentences in both their habitual style and in a condition where they were instructed to speak clearly while hearing loud speech-shaped noise. A diadochokinetic (DDK) speech task that requires speakers to repetitively produce word or non-word sequences as accurately and as rapidly as possible, was used to quantify their articulatory control. Individuals’ predicted intelligibility in both speaking styles (presented at -5 dB SNR) was measured using an acoustic glimpse-based metric: the High-Energy Glimpse Proportion (HEGP). Speakers’ HEGP scores show a clear effect of speaking condition (better HEGP scores in the Lombard than habitual condition), but no simple effect of articulatory control on HEGP, nor an interaction between speaking condition and articulatory control. This indicates that individuals’ speech enrichment success as measured by the HEGP metric was not predicted by DDK performance.
• Speed, L., & Majid, A. (2018). Music and odor in harmony: A case of music-odor synaesthesia. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 2527-2532). Austin, TX: Cognitive Science Society.

## Abstract

We report an individual with music-odor synaesthesia who experiences automatic and vivid odor sensations when she hears music. S’s odor associations were recorded on two days, and compared with those of two control participants. Overall, S produced longer descriptions, and her associations were of multiple odors at once, in comparison to controls who typically reported a single odor. Although odor associations were qualitatively different between S and controls, ratings of the consistency of their descriptions did not differ. This demonstrates that crossmodal associations between music and odor exist in non-synaesthetes too. We also found that S is better at discriminating between odors than control participants, and is more likely to experience emotion, memories and evaluations triggered by odors, demonstrating the broader impact of her synaesthesia.

• Ten Bosch, L., Ernestus, M., & Boves, L. (2018). Analyzing reaction time sequences from human participants in auditory experiments. In Proceedings of Interspeech 2018 (pp. 971-975). doi:10.21437/Interspeech.2018-1728.

## Abstract

Sequences of reaction times (RT) produced by participants in an experiment are not only influenced by the stimuli, but by many other factors as well, including fatigue, attention, experience, IQ, handedness, etc. These confounding factors result in longterm effects (such as a participant’s overall reaction capability) and in short- and medium-time fluctuations in RTs (often referred to as ‘local speed effects’). Because stimuli are usually presented in a random sequence different for each participant, local speed effects affect the underlying ‘true’ RTs of specific trials in different ways across participants. To be able to focus statistical analysis on the effects of the cognitive process under study, it is necessary to reduce the effect of confounding factors as much as possible. In this paper we propose and compare techniques and criteria for doing so, with focus on reducing (‘filtering’) the local speed effects. We show that filtering matters substantially for the significance analyses of predictors in linear mixed effect regression models. The performance of filtering is assessed by the average between-participant correlation between filtered RT sequences and by Akaike’s Information Criterion, an important measure of the goodness-of-fit of linear mixed effect regression models.
• Ten Bosch, L., & Boves, L. (2018). Information encoding by deep neural networks: what can we learn? In Proceedings of Interspeech 2018 (pp. 1457-1461). doi:10.21437/Interspeech.2018-1896.

## Abstract

The recent advent of deep learning techniques in speech tech-nology and in particular in automatic speech recognition hasyielded substantial performance improvements. This suggeststhat deep neural networks (DNNs) are able to capture structurein speech data that older methods for acoustic modeling, suchas Gaussian Mixture Models and shallow neural networks failto uncover. In image recognition it is possible to link repre-sentations on the first couple of layers in DNNs to structuralproperties of images, and to representations on early layers inthe visual cortex. This raises the question whether it is possi-ble to accomplish a similar feat with representations on DNNlayers when processing speech input. In this paper we presentthree different experiments in which we attempt to untanglehow DNNs encode speech signals, and to relate these repre-sentations to phonetic knowledge, with the aim to advance con-ventional phonetic concepts and to choose the topology of aDNNs more efficiently. Two experiments investigate represen-tations formed by auto-encoders. A third experiment investi-gates representations on convolutional layers that treat speechspectrograms as if they were images. The results lay the basisfor future experiments with recursive networks.
• Ten Bosch, L., Mulder, K., & Boves, L. (2019). Phase synchronization between EEG signals as a function of differences between stimuli characteristics. In Proceedings of Interspeech 2019 (pp. 1213-1217). doi:10.21437/Interspeech.2019-2443.

## Abstract

The neural processing of speech leads to specific patterns in the brain which can be measured as, e.g., EEG signals. When properly aligned with the speech input and averaged over many tokens, the Event Related Potential (ERP) signal is able to differentiate specific contrasts between speech signals. Well-known effects relate to the difference between expected and unexpected words, in particular in the N400, while effects in N100 and P200 are related to attention and acoustic onset effects. Most EEG studies deal with the amplitude of EEG signals over time, sidestepping the effect of phase and phase synchronization. This paper investigates the relation between phase in the EEG signals measured in an auditory lexical decision task by Dutch participants listening to full and reduced English word forms. We show that phase synchronization takes place across stimulus conditions, and that the so-called circular variance is narrowly related to the type of contrast between stimuli.
• Ter Bekke, M., Ozyurek, A., & Ünal, E. (2019). Speaking but not gesturing predicts motion event memory within and across languages. In A. Goel, C. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Meeting of the Cognitive Science Society (CogSci 2019) (pp. 2940-2946). Montreal, QB: Cognitive Science Society.

## Abstract

In everyday life, people see, describe and remember motion events. We tested whether the type of motion event information (path or manner) encoded in speech and gesture predicts which information is remembered and if this varies across speakers of typologically different languages. We focus on intransitive motion events (e.g., a woman running to a tree) that are described differently in speech and co-speech gesture across languages, based on how these languages typologically encode manner and path information (Kita & Özyürek, 2003; Talmy, 1985). Speakers of Dutch (n = 19) and Turkish (n = 22) watched and described motion events. With a surprise (i.e. unexpected) recognition memory task, memory for manner and path components of these events was measured. Neither Dutch nor Turkish speakers’ memory for manner went above chance levels. However, we found a positive relation between path speech and path change detection: participants who described the path during encoding were more accurate at detecting changes to the path of an event during the memory task. In addition, the relation between path speech and path memory changed with native language: for Dutch speakers encoding path in speech was related to improved path memory, but for Turkish speakers no such relation existed. For both languages, co-speech gesture did not predict memory speakers. We discuss the implications of these findings for our understanding of the relations between speech, gesture, type of encoding in language and memory.

https://mindmodeling.org/cogsci2019/papers/0496/0496.pdf
• Thompson, B., & Lupyan, G. (2018). Automatic estimation of lexical concreteness in 77 languages. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 1122-1127). Austin, TX: Cognitive Science Society.

## Abstract

We estimate lexical Concreteness for millions of words across 77 languages. Using a simple regression framework, we combine vector-based models of lexical semantics with experimental norms of Concreteness in English and Dutch. By applying techniques to align vector-based semantics across distinct languages, we compute and release Concreteness estimates at scale in numerous languages for which experimental norms are not currently available. This paper lays out the technique and its efficacy. Although this is a difficult dataset to evaluate immediately, Concreteness estimates computed from English correlate with Dutch experimental norms at $\rho$ = .75 in the vocabulary at large, increasing to $\rho$ = .8 among Nouns. Our predictions also recapitulate attested relationships with word frequency. The approach we describe can be readily applied to numerous lexical measures beyond Concreteness
• Thompson, B., Roberts, S., & Lupyan, G. (2018). Quantifying semantic similarity across languages. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 2551-2556). Austin, TX: Cognitive Science Society.

## Abstract

Do all languages convey semantic knowledge in the same way? If language simply mirrors the structure of the world, the answer should be a qualified “yes”. If, however, languages impose structure as much as reflecting it, then even ostensibly the “same” word in different languages may mean quite different things. We provide a first pass at a large-scale quantification of cross-linguistic semantic alignment of approximately 1000 meanings in 55 languages. We find that the translation equivalents in some domains (e.g., Time, Quantity, and Kinship) exhibit high alignment across languages while the structure of other domains (e.g., Politics, Food, Emotions, and Animals) exhibits substantial cross-linguistic variability. Our measure of semantic alignment correlates with known phylogenetic distances between languages: more phylogenetically distant languages have less semantic alignment. We also find semantic alignment to correlate with cultural distances between societies speaking the languages, suggesting a rich co-adaptation of language and culture even in domains of experience that appear most constrained by the natural world
• Tourtouri, E. N., Delogu, F., & Crocker, M. W. (2018). Specificity and entropy reduction in situated referential processing. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (CogSci 2017) (pp. 3356-3361). Austin: Cognitive Science Society.

## Abstract

In situated communication, reference to an entity in the shared visual context can be established using eitheranexpression that conveys precise (minimally specified) or redundant (over-specified) information. There is, however, along-lasting debate in psycholinguistics concerningwhether the latter hinders referential processing. We present evidence from an eyetrackingexperiment recordingfixations as well asthe Index of Cognitive Activity –a novel measure of cognitive workload –supporting the view that over-specifications facilitate processing. We further present originalevidence that, above and beyond the effect of specificity,referring expressions thatuniformly reduce referential entropyalso benefitprocessing
• Troncoso Ruiz, A., Ernestus, M., & Broersma, M. (2019). Learning to produce difficult L2 vowels: The effects of awareness-rasing, exposure and feedback. In S. Calhoun, P. Escudero, M. Tabain, & P. Warren (Eds.), Proceedings of the 19th International Congress of Phonetic Sciences (ICPhS 2019) (pp. 1094-1098). Canberra, Australia: Australasian Speech Science and Technology Association Inc.
• Vagliano, I., Galke, L., Mai, F., & Scherp, A. (2018). Using adversarial autoencoders for multi-modal automatic playlist continuation. In C.-W. Chen, P. Lamere, M. Schedl, & H. Zamani (Eds.), RecSys Challenge '18: Proceedings of the ACM Recommender Systems Challenge 2018 (pp. 5.1-5.6). New York: ACM. doi:10.1145/3267471.3267476.

## Abstract

The task of automatic playlist continuation is generating a list of recommended tracks that can be added to an existing playlist. By suggesting appropriate tracks, i. e., songs to add to a playlist, a recommender system can increase the user engagement by making playlist creation easier, as well as extending listening beyond the end of current playlist. The ACM Recommender Systems Challenge 2018 focuses on such task. Spotify released a dataset of playlists, which includes a large number of playlists and associated track listings. Given a set of playlists from which a number of tracks have been withheld, the goal is predicting the missing tracks in those playlists. We participated in the challenge as the team Unconscious Bias and, in this paper, we present our approach. We extend adversarial autoencoders to the problem of automatic playlist continuation. We show how multiple input modalities, such as the playlist titles as well as track titles, artists and albums, can be incorporated in the playlist continuation task.
• Van Valin Jr., R. D. (1987). Aspects of the interaction of syntax and pragmatics: Discourse coreference mechanisms and the typology of grammatical systems. In M. Bertuccelli Papi, & J. Verschueren (Eds.), The pragmatic perspective: Selected papers from the 1985 International Pragmatics Conference (pp. 513-531). Amsterdam: Benjamins.
• Van Valin Jr., R. D. (1987). Pragmatics, island phenomena, and linguistic competence. In A. M. Farley, P. T. Farley, & K.-E. McCullough (Eds.), CLS 22. Papers from the parasession on pragmatics and grammatical theory (pp. 223-233). Chicago Linguistic Society.
• Van Dooren, A., Tulling, M., Cournane, A., & Hacquard, V. (2019). Discovering modal polysemy: Lexical aspect might help. In M. Brown, & B. Dailey (Eds.), BUCLD 43: Proceedings of the 43rd annual Boston University Conference on Language Development (pp. 203-216). Sommerville, MA: Cascadilla Press.
• Vernes, S. C. (2018). Vocal learning in bats: From genes to behaviour. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 516-518). Toruń, Poland: NCU Press. doi:10.12775/3991-1.128.
• Von Holzen, K., & Bergmann, C. (2018). A Meta-Analysis of Infants’ Mispronunciation Sensitivity Development. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 1159-1164). Austin, TX: Cognitive Science Society.

## Abstract

Before infants become mature speakers of their native language, they must acquire a robust word-recognition system which allows them to strike the balance between allowing some variation (mood, voice, accent) and recognizing variability that potentially changes meaning (e.g. cat vs hat). The current meta-analysis quantifies how the latter, termed mispronunciation sensitivity, changes over infants’ first three years, testing competing predictions of mainstream language acquisition theories. Our results show that infants were sensitive to mispronunciations, but accepted them as labels for target objects. Interestingly, and in contrast to predictions of mainstream theories, mispronunciation sensitivity was not modulated by infant age, suggesting that a sufficiently flexible understanding of native language phonology is in place at a young age.

http://mindmodeling.org/cogsci2018/papers/0229/0229.pdf
• Wagner, M. A., Broersma, M., McQueen, J. M., & Lemhöfer, K. (2019). Imitating speech in an unfamiliar language and an unfamiliar non-native accent in the native language. In S. Calhoun, P. Escudero, M. Tabain, & P. Warren (Eds.), Proceedings of the 19th International Congress of Phonetic Sciences (ICPhS 20195) (pp. 1362-1366). Canberra, Australia: Australasian Speech Science and Technology Association Inc.

## Abstract

This study concerns individual differences in speech imitation ability and the role that lexical representations play in imitation. We examined 1) whether imitation of sounds in an unfamiliar language (L0) is related to imitation of sounds in an unfamiliar
non-native accent in the speaker’s native language (L1) and 2) whether it is easier or harder to imitate speech when you know the words to be imitated. Fifty-nine native Dutch speakers imitated words with target vowels in Basque (/a/ and /e/) and Greekaccented
Dutch (/i/ and /u/). Spectral and durational
analyses of the target vowels revealed no relationship between the success of L0 and L1 imitation and no difference in performance between tasks (i.e., L1
imitation was neither aided nor blocked by lexical knowledge about the correct pronunciation). The results suggest instead that the relationship of the vowels to native phonological categories plays a bigger role in imitation
• Wolf, M. C., Smith, A. C., Meyer, A. S., & Rowland, C. F. (2019). Modality effects in vocabulary acquisition. In A. K. Goel, C. M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Meeting of the Cognitive Science Society (CogSci 2019) (pp. 1212-1218). Montreal, QB: Cognitive Science Society.

## Abstract

It is unknown whether modality affects the efficiency with which humans learn novel word forms and their meanings, with previous studies reporting both written and auditory advantages. The current study implements controls whose absence in previous work likely offers explanation for such contradictory findings. In two novel word learning experiments, participants were trained and tested on pseudoword - novel object pairs, with controls on: modality of test, modality of meaning, duration of exposure and transparency of word form. In both experiments word forms were presented in either their written or spoken form, each paired with a pictorial meaning (novel object). Following a 20-minute filler task, participants were tested on their ability to identify the picture-word form pairs on which they were trained. A between subjects design generated four participant groups per experiment 1) written training, written test; 2) written training, spoken test; 3) spoken training, written test; 4) spoken training, spoken test. In Experiment 1 the written stimulus was presented for a time period equal to the duration of the spoken form. Results showed that when the duration of exposure was equal, participants displayed a written training benefit. Given words can be read faster than the time taken for the spoken form to unfold, in Experiment 2 the written form was presented for 300 ms, sufficient time to read the word yet 65% shorter than the duration of the spoken form. No modality effect was observed under these conditions, when exposure to the word form was equivalent. These results demonstrate, at least for proficient readers, that when exposure to the word form is controlled across modalities the efficiency with which word form-meaning associations are learnt does not differ. Our results therefore suggest that, although we typically begin as aural-only word learners, we ultimately converge on developing learning mechanisms that learn equally efficiently from both written and spoken materials.