Canada

Traditional expectancy disconfirmation theories have focused on the discrepancy between expectations and perceptions. Here, we propose a new Bayesian framework model that takes into account human responses triggered by disconfirmation. It aims to explain, for example, the differences of responses to waiting time at a hospital reception, where the wait time discrepancy is ideally expected to be zero, by introducing the sensitivity of individuals which corresponds to the decision threshold for responses when coping with disconfirmation. The model was applied to a real-world clinical appointment dataset. The dataset contains multiple appointments with expected and actual times and individual response patterns depending on the disconfirmation. Individual sensitivity was estimated from the dataset. The sensitivity was correlated with the response patterns such as sticking to expectation. In addition, with agent-based simulations, results suggest that the introduction of sensitivity contributes to identifying different groups of responses and predicting negative responses such as &quot;No-Show&quot;.

CogSci 2022

Sensitivity to time discrepancy: A computational model of human response to expectancy disconfirmation

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
The opportunity to present at NAACL should not depend on a researcher’s travel budget, or their family status. This is why it is so important to make virtual participation at NAACL as good an experience as possible – but we want to also provide a good experience for in-person participants. As a community, we are still working out the best way to do that. This year at NAACL, we are trying out a big virtual poster session ahead of the conference, with the hope that this will make make for a lively and interactive experience. At the same time, we are reducing virtual oral presentations, which seem to be particularly tricky to make to work well. A big thanks to the NAACL program chairs and to Luciana Benotti for all their ideas and work to improve the virtual experience. And participants, virtual as well as in-person: Please let us know what worked for you and what didn’t, so we can continue to improve hybrid conferences. 
 
I have been lucky to work with many amazing people. Without their insight, dedication and patience, and without the many hours of work they put in, NAACL would not have been possible. A huge thank you to the program chairs Helena Gomez, Kevin Duh, and Steve Bethard – you are the best! 
 
Finally, I would like to thank all authors, invited speakers and panelists, area chairs and reviewers, the volunteers organizing and chairing sessions, and all attendees, in-person and virtual. Thank you for helping us make NAACL 2024 come to life. 
 
Welcome and hope you all enjoy the conference! 
Katrin Erk 
The University of Texas at Austin 
NAACL-2024 General Chair 
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NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Traditional expectancy disconfirmation theories have focused on the discrepancy between expectations and perceptions. Here, we propose a new Bayesian framework model that takes into account human responses triggered by disconfirmation. It aims to explain, for example, the differences of responses to waiting time at a hospital reception, where the wait time discrepancy is ideally expected to be zero, by introducing the sensitivity of individuals which corresponds to the decision threshold for responses when coping with disconfirmation. The model was applied to a real-world clinical appointment dataset. The dataset contains multiple appointments with expected and actual times and individual response patterns depending on the disconfirmation. Individual sensitivity was estimated from the dataset. The sensitivity was correlated with the response patterns such as sticking to expectation. In addition, with agent-based simulations, results suggest that the introduction of sensitivity contributes to identifying different groups of responses and predicting negative responses such as "No-Show".

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.
 
 [**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/07/Cogsci-2022-PAAG-Detailed-July-12final-1.pdf)
 [**The Detailed Program can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/08/DeArmond-CogSci2022-program-v113-final.pdf)

This conference requires a registration to attend. Please use the link below to secure your ticket. The website will be available to all registered attendees on July 25th. 

[Register to Attend](https://cognitivesciencesociety.org/registration/)

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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. 





Smartphones are now the most widely used devices in the world, and their usage monitoring applications have become a general interest topic. However, few experimental studies investigate the reflexive effects of this monitoring on users. To address this point, this paper presents a longitudinal experiment on the effects of monitoring on various variables (e.g. screen time, types of uses). Objective and subjective data from 60 participants, divided into treatment and control groups, were collected over a 3 weeks period. Both groups had to estimate their daily usages, but the treatment group subsequently had access to their real data. Results have shown a normalizing influence of monitoring on smartphone usage, by improving estimation of screen time, reducing time spent on some underestimated applications and increasing use of others overestimated applications. This research paves the way for public policies promoting mastery of its own technological uses and responsible digital usage.


Self-report vs. objective data. What impact of monitoring on smartphone use regulation?

Given considerable advancements in automated driving systems, the day when autonomous vehicles will be regularly present in our everyday life is impending. It is, therefore, very significant to put emphasis on the effect that giving up autonomy might have on an individual. We take into consideration an experimental data set regarding participants' reported discomfort levels to tackle the following questions: How can we represent a discomfort measurement in a meaningful way? Using this representation, can future discomfort reactions be predicted? We identify key features, identify baseline models, and develop a new approach based on the k-nearest neighbor model to considerably improve the prediction of individual user's discomfort measurements. A discussion of limits and potentials concludes the paper.

Predicting Individual Discomfort in Autonomous Driving

We explore whether younger children (4- and 5-year-olds) and older children (9- and 10-year-olds) expect a costly signaler (someone who engages in a costly action) to be a more committed group member than someone who engages in a comparatively less costly action. In Experiment 1 (N=173), older children and adults—but not younger children—expect a costly signaler wants to be in a group more than a control, and they give more positive evaluations of the costly signaler than the control. In Experiment 2 (N=84; ongoing), employing a different manipulation of cost both younger and older children infer that a costly signaler wants their goal more than the control, but they make different evaluations of the costly signaler depending on whether they exerted effort on behalf of a group versus an individual. Future research may be needed to rule out alternative explanations.

Do children interpret costs as signals of commitment to groups?

Learning frequently forces us to rely on the good judgment and epistemic vigilance of sources with no more firsthand knowledge of a topic than ourselves, but who may have more second or third- hand knowledge. Yet, being forced to rely on their judgment doesn’t prevent us from evaluating their judgment: one might trust information because it was passed on to you by someone whose epistemic vigilance you trust, but reject it from someone whom you believe lacks good judgment. We present two experiments suggest that by integrating affective cues like anger and surprise along with perceptual access and consensus, children infer what others believe and what the correct answer to a question is. We discuss implications for consensus-based social learning strategies.

“He only changed his answer because they shouted at him”: children use affective cues to distinguish between genuine and forced consensus

To figure out what happened in the past, we often rely on others’ testimony. One challenge is that people can disagree in their interpretations of what happened. We will investigate children’s use of disagreement as evidence for what happened, specifically, inferring that the event itself was ambiguous and could generate multiple interpretations. Children (N = 50; 7 to 11 years) will hear two observers’ testimony, in which the observers either agreed about another speaker’s desires (e.g., both observers agreed that the speaker wanted an intervention) or disagreed (one observer was sure the speaker wanted the intervention, while the other observer was sure the speaker did not want it). Children will then be asked to infer which of three events happened: the speaker uttered an unambiguous request (should be inferred more in agreement trials), an ambiguous request (should be inferred more in disagreement trials), or a random statement (neither trial type).

Inferences from Disagreement

In the first few years of life, much of what children learn about language they learn at home. Steeped in quotidian talk, children at home are folded into the program of everyday routines and are close witnesses to the interactions and activities taking place around them. Who addresses them, when, and why shapes children's linguistic debut onto this daily landscape of activity. By investigating how children's language environments are structured by their daily lives, we can better understand both the material and the aims by which children build their language knowledge. In this talk I will review recent work analyzing young children's linguistic and object-centric activities over the course of whole waking days at home in a set of urban and rural communities. While the findings I present will only give a initial glimpse into children's home language use, they suggest where we can expect to find robust cross-cultural similarities—as well as robust differences—in children's early language experiences. I discuss how we might usefully engage with both similarities and differences in refining our theories of language learning, which are otherwise primarily focused on research from a handful of populations and language features.

Children's everyday language experiences across urban and rural contexts

Understanding how humans use facial expressions to communicate social messages has remained a central question for over a century. However, this is empirically challenging due to their complexity, and the use of traditional, theory-driven approaches and Western-centric biases that have restricted understanding. New data-driven methods and advances in information-theoretic analysis tools now alleviate these constraints, giving real traction and delivering novel insights. Here, I will showcase one such approach that can objectively and precisely model dynamic facial expressions within and across cultures and characterize the specific social information they transmit. We show that four, not six, core expressive patterns are cross-cultural, that facial expressions transmit distinct social information over time in an evolving, broad-to-specific hierarchical structure, and can multiplex complex information. Our work challenges longstanding beliefs of cultural universality and forms the basis of a new theoretical framework. Finally, we show direct transference of our dynamic facial expression models to the digital economy including generative signalling models for digital agents.

Understanding dynamic facial expression communication across cultures using data-driven methods

coming by end of the month

Comparing Impact of Time Lag and Item Lag in Relative Judgment of Recency

Many memory models suggest self-terminating backward scanning along a memory representation. In these models, time to retrieve a particular item from memory could depend on how far in the past the item was presented or on the number of items presented since that item. To investigate which of these two types of memory representation is more likely, we designed a relative Judgment of Recency (JOR) task with variable presentation rates. The variable presentation rate deconfounded the age of memory and the number of intervening items. Our results favor the hypothesis that memory representation is temporally organized. This result is important for advancing memory models and for building stronger ties between cognitive and neural models of memory.

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Self-report vs. objective data. What impact of monitoring on smartphone use regulation?

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