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When Arabic speakers write in their dialect, they have the choice of using either the standard Arabic script or the non-standard Roman script. Arabizi writing is a new emerging writing system that Arabic speakers use to type their dialects utilizing Roman characters. Although Arabizi is not standardized, people have developed an efficient way to communicate through it. One phenomenon that emerged with this new system is vowel dropping. In this paper, we approach this phenomenon from the perspective of communicative efficiency. We study the informativity of short and long spellings of words and investigate whether the predictability of the word in certain contexts impacts whether the vowel is dropped in that word.

CogSci 2022

The efficiency of dropping vowels in Romanised Arabic script

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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

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.

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The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





Different domains exhibit different degrees of lexical precision. Existing work has suggested that communicative need may modulate the precision of word meaning in individual domains. We extend this proposal across domains by asking why languages have more precise vocabulary in some domains than others. We hypothesize that lexical precision for a domain reflects how frequently speakers need to refer to it. We test this proposal using a cross-linguistic dataset of word-concept mappings for nine diverse domains from seven languages, and word frequencies from independent corpora. We find that the more frequent domains (except for kinship) tend to be more precise in every language, supporting a domain-level account of efficient communication on the precision of the lexicon.

Communicative need modulates lexical precision across semantic domains: A domain-level account of efficient communication

We quantitatively study the emergence of symbolic communication in humans with a communication game that attempts to recapitulate an essential step in the development of human language: the emergence of shared signs. In our experiment, a teacher must communicate a first order logic formula to a student through a narrow channel deprived of common shared signs: subjects cannot communicate with each other with the sole exception of car motions in a computer game. Subjects spontaneously develop a shared vocabulary of car motions including indices, icons, and symbols, spanning both task-specific and task-agnostic concepts such as "square'' and "understand''. We characterize the conditions under which indices, icons, and symbols arise, finding that symbols are harder to establish than icons and indices. We observe the dominant sign category being developed transitions from indices to icons to symbols, and identify communicating in ambiguous game environments as a pressure for icon and symbol development.

Quantifying the Emergence of Symbolic Communication

Graphical representations such as scatterplots have become the elective tool to transmit quantitative information in an efficient manner. However, little is known about the way humans extract statistical information from graphs, and if these abilities correlate with higher level cognition, such as numerical skills. We proposed a new assessment tool of intuitive statistics: the judgment of trends in noisy scatterplots with varying slope, noise and number of points. We found that human performance is beautifully predicted by the t-value associated with the regression of the scatterplot; our findings reveal a remarkable human ability at performing visual statistics tasks fast and accurately, close to an optimal observer model. Crucially, we found that these "graphicacy" skills are present in both educated (n=4000) and uneducated adults living in non-industrialized societies (n=50), and even in preschoolers (n=23); furthermore, they correlate with numerical skills.

Graphicacy skills across ages and cultures: a new assessment tool of intuitive statistics' abilities

New analyses of pseudo-homophone RTs (e.g., BRANE) from two published lexical decision studies clarify lexical involvement in pseudo-homophone processing and challenge widespread assumptions about word frequency effects. First, RTs increased along with increases in the proportion of base- word letters that appeared in the pseudo-homophone (e.g., WHELT-WELT slower than PHAWT – FOUGHT) suggesting that “No” decision-making is slowed by mutually reinforcing activation in phonological and orthographic representations of base word knowledge. Second, effects of base-word frequency were either extremely weak or nonexistent among pseudo-homophones that contained most or all the letters that make up their base word. In contrast, among pseudo-homophones that shared fewer letters with their base word (e.g., “PHAWT”), RTs for items derived from high-frequency base words were faster than RTs for items derived from low-frequency base words. These findings (i) challenge the ubiquitous assumption that lexical representations are frequency sensitive and (ii) suggest that lexical decision involves a spell-check.

New Analyses of Lexical Influences on the Processing of Pseudo-homophones in the Lexical Decision Task: Still More Challenges for Models of Visual Word Recognition

Rational inference over a noisy channel can potentially explain readers’ occasional misreading. We tested if the prior probability of an intended message modulates the rate of misreading a transposed-word sentence as grammatical. In Experiment 1 we manipulated the cloze probability of a word given its full context (Because my socks had holes, I bought a new pair/pack) but found no reliable effect on the rate of noticing word transpositions (pair new vs. pack new). In Experiment 2 we manipulated the 4-gram frequency of the sequence ending with the transposed words and again found no effect (I always know what mean they vs. love they). We conclude readers do not effectively exploit full-context information to derive nonliteral messages. Despite the results of Experiment 2, comparison of error rates across conditions in several experiments suggests a role for local ngram statistics, though perhaps only in a restricted range of ngram frequency.

Readers do not strongly rely on full-context information, but might utilize local word statistics, when ‘correcting’ word transposition errors in text

Listeners adapt to specific speakers' speech cue distributions and generalize the adaptation to the perception of a different speaker. It remains unclear whether listeners track and generalize the distributional statistics of raw, un-normalized cues or normalized cue distributions relative to the speaker's acoustic space. To address this question, we adopted a perceptual generalization paradigm to investigate whether manipulating context properties of a training speaker (Female A)'s speech would lead to different categorization results of critical phonemes in a test speaker (Female B)'s speech. Experiment 1 showed that learning Female A's speech containing the same set of sibilants but shifted vowel formants would lead to different categorization of Female B's sibilants: listeners exposed to raised vowel formants were more likely to identify an "s" and those exposed to lowered vowel formants were more likely to identify "sh" in Female B's speech, compared with participants exposed to unaltered vowel contexts. Experiment 2 showed that learning of Female A's speech containing the same set of stops but manipulated context duration would lead to different categorization results of Female B's stops: listeners temporally exposed to expanded context were more likely to identify a "t" and those exposed to compressed contexts were less likely to identify a "t" in Female B's speech, compared to participants exposed to unaltered temporal cues. These results suggest that listeners keep track of normalized cue distributions relative to the speaker's acoustic space and generalize those distributions to guide their speech perception behaviors.

The perceptual generalization of normalized cue distributions across speakers

Cue-based retrieval theories of sentence processing assume that subject-verb dependencies are resolved through a content-addressable search in memory. The model assumes that multiple nouns with similar syntactic or semantic features increase dependency completion difficulty. English eyetracking data (reading) are consistent with model predictions; interestingly, a similar experiment with German --a language marking case overtly-- suggests that only syntactic features affect dependency completion difficulty. Why would German show different behavior than English? Using a computational implementation of the cue-based retrieval model and model comparison using Bayes factors, we show that the reason is systematic variation at the individual-participant level: German participants overwhelmingly give higher weighting to syntactic cues over semantic cues, whereas English participants mostly give equal weighting to syntactic and semantic cues. The richer morphosyntax of German leads to syntactic cues being favoured; if such cues are largely absent (as in English) the parser relies on both cue types equally.

Individuals differ cross-linguistically in cue weighting: A computational evaluation of cue-based retrieval in sentence processing

This study aimed to characterize the developmental trajectories of different cognitive component processes underlying planning decisions. Participants (ages 8-25 years) completed a planning task called Four-in-a-row. We used computational modeling to distinguish between three cognitive component processes of planning: planning depth, heuristic quality, and attentional oversights, each of which three contributed to better playing strength, but differed in their developmental trajectories. Specifically, from early to mid-adolescence, heuristic quality rapidly improved and contributed to better playing strength. From mid to late-adolescence, planning depth increased and supported better playing strength. Fewer attentional oversights were associated with better playing strength and this relation did not show age differences. Together, these results reveal sequential development of the cognitive component processes underlying planning, with early refinement of heuristic strategies, and gradual increases into young adulthood in the number of considered future actions, states, and outcomes. These findings provide a more complete account of the development of planning and its component processes.

Distinct Developmental Trajectories In The Cognitive Components Of Complex Planning

As the history of science has documented, there is an important role for thought experiments in scientific progress. Yet, there is very little empirical research about whether and how children learn from thought experiments. Here, we asked that question in the context of 6-year-olds’ developing theory of matter. At the outset of the study, over half of the children claimed that small pieces of matter weigh nothing at all. Children were randomly assigned to a Real (RE) and a Thought Experiment (TE) condition. The goal of each condition was to show – via demonstration in the RE and via mental simulation in the TE – that the weight of a single grain of rice can cause a card resting on a fulcrum to topple. We found that children simulated accurately in the TE, and they changed their judgments and justifications concerning the weight of small pieces equally from the TE and RE.

Young children learn equally from real and thought experiments

Research has documented children’s understanding that a choice made when constrained to a single option is a poor indicator of another person’s preference. However, when constraints are constant over time—as they often are in social contexts—they may lose their salience. We examined whether children (N = 133, 5- to 12-year-olds) were more likely to refrain from inferring that a constrained actor prefers their choice if they first observe unconstrained actors (Alternatives condition) compared to if they only observe constrained actors (Constant condition). Presence of alternatives was crossed with constraint type: either the second option was hard to access or there was no other option. In line with our predictions, results indicated that observing alternative situations with greater choice increased children’s subsequent attention to constraints. Effects were stronger for the hard to access constraint and for older children.

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Communicative need modulates lexical precision across semantic domains: A domain-level account of efficient communication

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A Unified Model for Arabizi Detection and Transliteration using Sequence-to-Sequence Models