Canada

According to Rozenblit &amp; Keil (2002), attempting a mechanistic explanation of a concrete phenomenon results in reduced self-reported knowledge, suggesting that the latter is normally overestimated (Illusion of Explanatory Depth or IoED). Fernbach and Sloman (2013) demonstrated the effect in sociopolitical issues, finding that causal explanation results in short-term moderation of belief extremity. While the effect of explanation on knowledge self-estimation has been robustly replicated, debates about the impact on extremity continue. Here, we replicate both effects in an online sample of Iranians (N=100 and p&lt;.05). Given IoED’s reliance on mechanistic framing, we further hypothesized that the more concrete construal afforded national hot-button issues would produce a more robust effect compared to international ones. Our experiment indeed found stronger IoED effects on both knowledge estimation (p&lt;.005) and belief extremity (p&lt;.003) across national issues. We discuss how this moderation intervention can be helpful in extremism context.

CogSci 2022

The moderation effect of Illusion of Explanatory Depth of Knowledge towards National and International Issues

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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*- Colin Cherry, Google, NAACL 2025 General Chair* 

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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

According to Rozenblit & Keil (2002), attempting a mechanistic explanation of a concrete phenomenon results in reduced self-reported knowledge, suggesting that the latter is normally overestimated (Illusion of Explanatory Depth or IoED). Fernbach and Sloman (2013) demonstrated the effect in sociopolitical issues, finding that causal explanation results in short-term moderation of belief extremity. While the effect of explanation on knowledge self-estimation has been robustly replicated, debates about the impact on extremity continue. Here, we replicate both effects in an online sample of Iranians (N=100 and p<.05). Given IoED’s reliance on mechanistic framing, we further hypothesized that the more concrete construal afforded national hot-button issues would produce a more robust effect compared to international ones. Our experiment indeed found stronger IoED effects on both knowledge estimation (p<.005) and belief extremity (p<.003) across national issues. We discuss how this moderation intervention can be helpful in extremism context.

poster

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
 
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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. 





People expect others to take efficient paths toward goals. Inefficiency changes how we categorize actions, leading us to see actions as play (Chu & Schulz, 2020), or as movements performed for their own intrinsic value (Schachner & Carey, 2013). Here we find that performing actions for their own value (e.g., aesthetic value), versus for instrumental purposes, provides social information about others. In a pre-registered experiment (N=360), participants judged which character in a pair was more compassionate, or more selfish/manipulative. For one key pair (among distractors), both characters performed the same activity (music, painting, eating, exercising, math, being in nature), and we manipulated why: Either for its own aesthetic value, or as a means-to-an-end (instrumental value). Across all activities, aesthetically-motivated characters were judged as more compassionate and less selfish/manipulative than instrumentally-motivated characters (p’s<0.01). Aesthetically-motivated behavior may signal others’ emotionality moreso than instrumentally-motivated activities, driving inferences about prosociality.

People think of others as more prosocial when they are motivated by aesthetic goals vs. instrumental goals

We present proof-of-concept modeling results which demonstrate a novel way to evaluate theories of the structure of the phonological grammar: We generate predictions of how the behavior of constraint-based grammars and analogical models is likely to change under processing load. Previous results (Moore-Cantwell and Kush, 2019) show systematic differences in participant behavior when generating novel forms under working memory load, vs not. Under load, participants observed fewer generalizations of the language, produced less intra-speaker variation, and differed more participant-to-participant. We modeled memory load in constraint-based grammar (MaxEnt, Goldwater and Johnson, 2003) via probabilistic ignoring of constraints. Memory load in analogy (TiMBL, Daelemans et al., 2018) was modeled as a restricting of the search space for an analogical set, based on word-frequency. Our constraint-based model predicted a more systematic breakdown, more in line with previous experimental findings, while our analogical model predicted more word-to-word variation.

Modeling grammatical breakdown under processing load

In conversation, interlocutors rapidly exchange well-timed turns-at-talk, generally starting to plan their contributions already during the incoming turns. Previous research showed that while this early-planning-strategy supports fast turn-taking, it comes with increased planning difficulty compared to planning after the incoming turn. The present study investigates whether concurrent planning is also detrimental to speech input comprehension, targeting the level of phonological processing. 60 subjects conducted a dual-task experiment, verbally responding to auditorily presented quiz-questions while simultaneously monitoring them for pre-specified target phonemes (60% present), pressing the space-bar upon encounter. Subjects’ monitoring performance for phonemes encountered during response planning was significantly worse than for phonemes encountered when response planning was not yet possible. Additionally, phoneme detection latencies were significantly longer when concurrently planning a verbal response than in a single-task phoneme-detection control experiment. Thus, results indicate that parallel speech planning, while common, leads to decreased input processing, possibly due to interference effects.

Reduced phonological processing during speech-planning

First language learners use various linguistic cues to construct their language(s)’ grammar. I study how phonological and morphological structures interact in the minds of two young learners of Russian (1;6 - 2;11 years of age) and their caretakers. While phonology is traditionally recognized to develop ahead of morphology, my data show a ‘backward’ directionality for the younger child only: the grasp of consonant cluster inventory depends on the rising morphological complexity, measured as a number of morphemes per word.
This interaction is first backed by consonant clustering, being a natural result of phonological alternations at the morpheme boundary; and second, by language pathology data, an aphasic patient attempting to improve the coda sonority in inflected words but not monomorphemic ones during speech production.
If this result is validated on a larger sample, the finding is meaningful both theoretically and practically for the speech therapy procedures.

Morphological Strengthening of Phonological Performance in Child Speech

A challenge in learning phonological grammars is learning how phonological processes interact. It has been argued that some process interactions are easier to learn than others. One basis for this argument is asymmetries observed in experimental settings: artificial languages generated from certain process interactions are more likely to be successfully reproduced by participants than others. In this paper, we argue that asymmetries in production do not necessarily provide direct support that some phonological interactions are easier to learn. Rather, we show that these asymmetries can instead emerge due to differences in the number of consistent or nearly-consistent grammars each pattern has. We present a noisy channel model of morpho-phonological learning and apply it to a recent behavioral study examining the learnability of phonological process interactions. We find that, due to the relative difference in the number of grammars that can exactly match or nearly match the observed data, the model achieves the same qualitative results as those observed in experimental settings.

Assessing the learnability of process interactions using grammatical spaces

Previous research suggests that a verb’s meaning is learned partly through the aggregated profile of syntactic frames associated with it. For example, “turn” occurs with transitive and intransitive frames in causative alternation (“He turned the car”/“The car turned”), indicating it is a causal verb. Some evidence demonstrates that young children combine multiple frames to map verbs to appropriate events. However, previous work always presented these frames together, in a single dialogue. What remains unknown is how verb learning occurs when the frames are separated, uttered in different referential contexts, as is likely in children’s everyday life. In a series of cross-situational word-learning experiments, we show that adult learners update their hypothesis about a novel verb's meaning when they encounter the verb again in a new frame, integrating their previous hypothesis about the verb’s meaning with the new frame. These results shed light on the cross-situational mechanisms of syntactic bootstrapping.

​​Learners Integrate Syntactic Frames and Semantic Hypotheses in Cross-situational Verb Learning

A central question for language development is whether very early telegraphic speech, ungrammatical but intelligible, reflects adult-like abstract linguistic competence, or instead is the result of statistical learning without the reliance on the abstract knowledge. In this paper, we develop a simulation paradigm to evaluate the predictive fit of different accounts of three-word utterances children (<20 months) produced. The simulation subjects are a) human adults with full linguistic competence, b) a statistical model that captures local statistical regularities of language input, and c) a deep neural model that learns and processes language input with global context-aware learning mechanisms. The statistical language model predicted child three-word utterances, both grammatical and ungrammatical ones, better than the neural models and even better (but not statistically) than human adults. The findings suggest simple local statistical regularities underlying child early telegraphic speech.

Key Open Door: Simulating Children’s Early Three-word Utterances Reveals Simple Statistical Regularities Underlying Telegraphic Speech

Elicited imitation is a widely used method for testing a child's knowledge of a language for scientific or clinical purposes. A child hears an utterance and is asked to repeat what they have heard. While it is assumed that their fluency or speed in doing so is contingent on their linguistic competence, little is known about the cognitive mechanisms and/or representations involved. To explore this, we train an encoder-decoder model, consisting of recurrent neural networks, to encode and reproduce a corpus of child-directed speech and then test its performance on the experimental task of Bannard and Matthews (2008). In that study pre-school children were asked to repeat high- and low-frequency four-word sequences in which the first three words were identical (e.g., sit in your chair and sit in your truck) and the final words and bigrams were closely matched for frequency. We find that like those children our model makes more errors on the initial three words when they are part of a low-frequency than a high-frequency sequence, despite the fact that the words being repeated are identical. We explore why this might be and pinpoint some possible similarities between the model and child language processing.

Modelling Children's Sentence Recall using an Encoder-Decoder Network

Query-Based Memory (QBM) models are heavily used in machine learning, though their relevance to human cognition is unclear. In this paper, we explore QBM models through both formal exploration and a simulation study to address this question. We found that QBM models are theoretically motivated, as they approximate rational induction with neurally-plausible mechanisms. Additionally, a simple implementation of the model could readily reproduce four benchmark findings in infant statistical learning. These results provide an encouraging starting point for further research using these formal tools to understand cognition across development.

Query-Based Memory Approximates Rational Induction: Applications to Infant Statistical Learning

Zipf’s law asserts that words form a power law distribution: word frequency is inversely proportional to rank. Relatively recent cognitive and usage-based linguistics argue that speech differs structurally from writing. Except for a few older analyses performed on tiny corpora, studies of Zipf’s law prior to 2021 have been done on written corpora and use informal methods to determine Zipfianness. 
We argue that recent work indicating that transcribed speech forms a Zipfian distribution can be extended to the speech of traditionally developing children. Further, we show that the transcribed speech of children with a clinical diagnosis of autism spectrum disorder is non-Zipfian. These judgements are made using formal statistical techniques developed in Clauset (2009). They include the Kolmogorov-Smirnov statistic for goodness-of-fit and likelihood ratio to rule out other distributions.

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People think of others as more prosocial when they are motivated by aesthetic goals vs. instrumental goals

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