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Studies of bilingual language processing typically assign participants to groups based on their language proficiency and average across participants in order to compare the two groups. This approach loses much of the nuance and individual differences that could be important for furthering theories of bilingual language comprehension. In this study, we present a novel use of machine learning (ML) to develop a predictive model of language proficiency based on behavioral data collected in a priming task. The model achieved 75% accuracy in predicting which participants were proficient in both Spanish and English. Our results indicate that ML can be a useful tool for characterizing and studying individual differences.

CogSci 2021

Using Machine Learning to Predict Bilingual Language Proficiency from Reaction Time Priming Data

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COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

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### Looking for Cogsci 2023? Please [click here](https://underline.io/events/405/reception) to switch to 2023 edition.


**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

We hope that you enjoy the conference this year!<br><br>

**Your Program Chairs,**
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**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

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To review the proceedings for the conference or review the searchable program, please visit the [website](https://cognitivesciencesociety.org/program/).


The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

Anaphora, a ubiquitous feature of natural language, poses a particular challenge to young children as they first learn language due to its referential ambiguity. In spite of this, parents and caregivers use anaphora frequently in child-directed speech, potentially presenting a risk to effective communication if children do not yet have the linguistic capabilities of resolving anaphora successfully. Through an eye-tracking study in a naturalistic free-play context, we examine the strategies that parents employ to calibrate their use of anaphora to their child's linguistic development level. We show that, in this way, parents are able to intuitively scaffold the complexity of their speech such that greater referential ambiguity does not hurt overall communication success.

Parents Adaptively Use Anaphora During Parent-child Social Interaction

We investigated racial bias in emotion inference by having participants describe the emotion of the featured person in the image, including Asian, Black, and White. We collected 4,197 sentences (63,900 tokens) and used the data to train Word2Vec, a neural network-based word embedding model. We calculated the cosine distance between emotion words and words indicating the target for each racial group in order to measure the strength of association. Although all images portrayed neutral emotions, the results show that negative emotion words were close to Asian and Black, whereas neutral words were close to White. This result indicates that stereotypes contribute to the racial bias in Artificial Intelligence as crowdsourcing workers can generate annotations depending on the featured person's race. Based on the present study, we suggest employing various de-biasing methods such as data augmentation or removing bias components in word embeddings before using Artificial Intelligence in the real world.

Racial Bias in Emotion Inference: An Experimental Study Using a Word Embedding Method

While lexical norm databases are being developed with a renewed emphasis on perceptual psycholinguistic features, verbs are often neglected. Research on nouns and adjectives utilizes vertical spatial localization ratings, providing an analogue for the inclusion of verbs via vertical directionality ratings. This study demonstrates the feasibility of collecting such ratings for 32 English verbs, as well as the possibility of assessing directionality ratings in other spatial dimensions. Further, ratings were analyzed using distributional semantic models. Results indicate that language statistics are strongly associated with human ratings, providing convergent validity for vertical directionality as a useful psycholinguistic measure. Additionally, a comparison of the predictive performance of LSA and Web 1T 5-gram models on human ratings revealed that the first-order 5-gram model accounted for significant unique variance in the norms, while LSA did not. It may be concluded that spatial verb associations are encoded linguistically by proximal, syntactic dependencies.

Vertical directionality ratings as lexical norms for English verbs

During language comprehension, words are easier to process when predictable based on local sentence context. It is unclear how information available from the global discourse interacts with this local predictive processing. To test this, we conducted an online self-paced reading study (n=100), manipulating the predictability of a critical word in a target sentence while varying the coherency of the target sentence with a preamble context that preceded it. We found that people processed the critical word faster when it was highly predictable from local context, replicating a common finding. We also found people were slower to process the critical word when the target sentence was presented in an incoherent context. However, no interaction was observed. Results indicate that subtler semantic changes, such as topic shifts, slow language processing but do not reduce the benefit of a highly predictable, local context.

Effects of global discourse coherence on local contextual predictions

We introduce the LARC-ID, a pilot corpus of eye-movements obtained from subjects reading texts from a range of genres. Materials were presented in multiple paragraphs on the screen to more closely match naturalistic reading environments. Readers were encouraged to read for comprehension and enjoyment, engaging in various kinds of comprehension questions, including an open-ended reflection at the end of each text. Subjects also participated in a battery of individual difference measures, including those known to predict reading behavior in controlled experimental contexts, e.g., Rapid Automatized Naming (RAN; Denckla & Rudel, 1976), the author recognition (ART; Stanovich & West, 1989), and reading span (RSpan; Daneman & Carpenter, 1980) tasks. In addition to describing the central properties of the text and relationships between tasks in the battery, we present a sample analysis exploring how intrinsic lexical characteristics (length, frequency, and morphological complexity) interact with selected individual difference measures. The analysis provides the very first glimpse into what we hope will become a useful resource for reading researchers and educators. The entire corpus is freely available for unrestricted use.

Los Angeles Reading Corpus of Individual Differences: Pilot distribution and analysis

In bilinguals, certain concepts across languages come to be represented similarly—a semantic convergence effect that reflects interactivity between languages. The causal factors that affect semantic convergence are not fully understood; this gap may be due to limitations of the correlative methods used in extant work, which assesses the representations of real-world bilinguals. Here, we utilize an artificial language learning paradigm—inspired by the study of category learning—to elucidate causal influences on semantic convergence. We contrast simulated simultaneous bilingual learners with simulated sequential bilingual learners before assessing the representations of both. Bilingual groups are additionally compared to simulated monolingual controls from each language. We report on the pattern of semantic convergence and conclude with implications for theories of bilingual representation.

Now or Later: Representational Convergence in Simulated Simultaneous and Sequential Bilingual Learning Contexts

The paper addresses both issues of first language (L1) attrition and second language (L2) acquisition in the context of two mutually intelligible languages from a psycholinguistic perspective. It summarises results of a study examining how native speakers of Slovak living long-term in Czechia process and produce Slovak and Czech cognates and noncognates. Two experimental sessions consisting of lexical decision task and picture naming task were conducted once with Slovak stimuli and once with Czech stimuli. The results showed that Slovak noncognates were processed and produced more slowly by Slovaks who use their L1 less, and their L2 more. Analogically, this subgroup processed and produced Czech noncognates relatively faster. Thus, the experiments give evidence of (a) L1 attrition (online processing difficulties), and (b) the shift towards L2 patterns depending on the amount of L1 and L2 use even in speakers of two very close and mutually intelligible languages.

Slovaks in Czechia: L1 Attrition and L2 Acquisition in Two Mutually Intelligible Languages

Listeners adapt to variability in language use by updating their expectations over variants, often in speaker-specific ways. We propose that adaptation of this sort contributes to satiation, the phenomenon whereby the acceptability of unacceptable sentences increases after repeated exposure. We provide support for an adaptation account of satiation by showing that the satiation of purportedly unacceptable island-violating constructions demonstrates speaker-specificity, a key property of adaptation.

Syntactic satiation is driven by speaker-specific adaptation

This study investigated whether bilingual experience would influence young adults’ integration of multiple cues to infer a speaker’s intention. Using a cue-integration task coupled with eye-tracking, we examined the effects of balanced language usage on young bilingual adults’ ability to integrate multiple cues in determining a speaker’s referential intent. Behavioral and eye-tracking findings indicated that balanced bilinguals were better able than unbalanced bilinguals in identifying a target object in the three-cue condition (i.e., contextual, semantic and gaze cues were shown). However, there were no group differences in the two-cue condition (i.e., only contextual and semantic cues were shown). Our results suggest that the extent of bilingualism could modulate the sensitivity to and integration of multiple cues in the intention-inference process. We argue that balanced bilinguals’ greater exposure to complex communicative situations could enhance their ability in utilizing multiple cues to understand a speaker’s intention.

Extent of bilingual experience in modulating young adults’ processing of social-communicative cues in a cue integration task: An eye-tracking study

Experimental investigation is fundamental to theory-building in cognitive science, but its value depends on the linking assumptions made by researchers about the mapping between empirical measurements and theoretical constructs. We argue that sufficient clarity and justification are often lacking for linking assumptions made in visual world eye-tracking, a widely used experimental method in psycholinguistic research. We test what we term the Referential Belief linking assumption: that the proportion of looks to a referent in a time window reflects participants’ degree of belief that the referent is the in- tended target in that time window. We do so by comparing eye-tracking data against explicit beliefs collected in an incremental decision task (Exp. 1), which replicates a scalar implicature processing study (Exp. 3 of Sun & Breheny, 2020). In Exp. 2, we replicate Sun and Breheny (2020) in a web-based eye-tracking paradigm using WebGazer.js. The results provide support for the Referential Belief link and cautious optimism for the prospect of conducting web-based eye-tracking. We discuss limitations on both fronts.

Using Machine Learning to Predict Bilingual Language Proficiency from Reaction Time Priming Data

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Parents Adaptively Use Anaphora During Parent-child Social Interaction

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