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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Sun, Zhewei Xu, Yang Department of Computer Science University of Toronto Toronto Canada Cognitive Science Program University of Toronto Toronto Canada

The meaning of a slang term can vary in different communities. However, slang semantic variation is not well understood and under-explored in the natural language processing of slang. One existing view argues that slang semantic variation is driven by culture-dependent communicative needs. An alternative view focuses on slang's social functions suggesting that the desire to foster semantic distinction may have led to the historical emergence of community-specific slang senses. We explore these theories using computational models and test them against historical slang dictionary entries, with a focus on characterizing regularity in the geographical variation of slang usages attested in the US and the UK over the past two centuries. We show that our models are able to predict the regional identity of emerging slang word meanings from historical slang records. We offer empirical evidence that both communicative need and semantic distinction play a role in the variation of slang meaning yet their relative importance fluctuates over the course of history. Our work offers an opportunity for incorporating historical cultural elements into the natural language processing of slang. © 2022 Association for Computational Linguistics.

关键词： Semantics

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Diffusion Decision Modeling of Retrieval Following the Temporal Selection of Behaviorally Relevant Moments

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Computational Brain and Behavior 2022年第3期5卷 302-325页

作者： Turker, Hamid B. Swallow, Khena M. Department of Psychology & Cognitive Science Program Cornell University 211 Uris Hall Ithaca 14853 NY United States

When targets appear in a detection task, unrelated but concurrent stimuli benefit from an encoding enhancement (the attentional boost effect). For instance, visual scenes paired with target cues (e.g., female, but not male, faces) are subsequently better recognized than those paired with distractors. Recent work suggests that this also benefits incidental relational memory for all features of the visual display, such as which cue appeared with a given scene, and not just the scene itself. However, it remains unclear whether this can be accounted for by response bias or uninstructed memorization of those concurrent items. This study used diffusion decision modeling to investigate the contributions of evidence accumulation rates (indicative of richer or more easily accessed representations) and response bias to recognition memory. Furthermore, two new experiments investigated the effects of intentionality during encoding. Across all five experiments, target-paired information was subsequently recognized with a higher drift rate than distractor-paired information. While there was evidence of response bias in some cases, keeping bias fixed produced better model fits. Moreover, the new experiments replicated earlier findings, extended them to spatial features, and demonstrated that instructing participants to divide attention across three aspects of a trial (scene, face gender, and face identity) rather than two (scene and face gender) increased, rather than reduced, the magnitude of the attentional boost effect for both the background scenes and relational memory. This study strengthens the claim that temporal selection boosts intentional and incidental encoding of the relationships between co-occurring stimuli. © 2022, Society for Mathematical Psychology.

关键词： Attentional boost effect Diffusion decision modeling Incidental memory Relational memory Temporal selection

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Adaptive Robot-Mediated Assessment using LLM for Enhanced Survey Quality in Older Adults Care programs 20

Adaptive Robot-Mediated Assessment using LLM for Enhanced Su...

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20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Park, Cheonshu Cho, Miyoung Shin, Minjung Ryu, Jeh-Kwang Jang, Minsu Social Robotics Research Lab Daejeon Korea Republic of Seoul National University Interdisciplinary Program in Cognitive Science Seoul Korea Republic of Dongguk University Department of Physical Education Seoul Korea Republic of

ISBN: (纸本)9798350378931

This study presents an adaptive human-robot interaction (HRI) system that evaluates older adult participants' satisfaction with personalized health care programs. By integrating the CLOi robot with a large language model (LLM), the system conducts satisfaction surveys that adapt in real-time to participant responses. The system was applied to evaluate healthcare programs that include physical health measurements, exercise assessments, and virtual reality (VR) experiences. The system utilizes the CLOi robot and Claude API to analyze response clarity in real-time, automatically generating contextually appropriate follow-up questions when responses are deemed ambiguous. This adaptive questioning strategy ensures comprehensive response quality before proceeding to subsequent survey items. We conducted a preliminary feasibility study with five older adult participants to evaluate our approach. The system leverages LLM prompts to analyze gaps between question intent and participant responses, generating targeted follow-up questions as needed. Results demonstrate that our LLM-enhanced robotic interview system effectively reduced response ambiguity through dynamic follow-up questioning, achieving an 85% response resolution rate. This adaptive approach improved the clarity and specificity of healthcare satisfaction assessments for older adults. © 2025 IEEE.

关键词： Industrial robots

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Visual moral inference and communication

arXiv

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arXiv 2025年

作者： Zhu, Warren Ramezani, Aida Xu, Yang Department of Computer Science University of Toronto Canada Department of Computer Science Cognitive Science Program University of Toronto Canada

Humans can make moral inferences from multiple sources of input. In contrast, automated moral inference in artificial intelligence typically relies on language models with textual input. However, morality is conveyed through modalities beyond language. We present a computational framework that supports moral inference from natural images, demonstrated in two related tasks: 1) inferring human moral judgment toward visual images and 2) analyzing patterns in moral content communicated via images from public news. We find that models based on text alone cannot capture the fine-grained human moral judgment toward visual stimuli, but language-vision fusion models offer better precision in visual moral inference. Furthermore, applications of our framework to news data reveal implicit biases in news categories and geopolitical discussions. Our work creates avenues for automating visual moral inference and discovering patterns of visual moral communication in public media. © 2025, CC BY-NC-SA.

关键词： Computer vision

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Studying mind perception in social robotics implicitly: The need for validation and norming 23

Studying mind perception in social robotics implicitly: The ...

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18th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2023

作者： Pekçetin, Tuǧçe Nur Barinal, Badel Tunç, Jana Acarturk, Cengiz Urgen, Burcu A. Department of Cognitive Science Middle East Technical University Ankara Turkey Bilkent University Ankara Turkey Department of Psychology Middle East Technical University Ankara Turkey Department of Cognitive Science Jagiellonian University Krakow Poland Department of Psychology Neuroscience Program Bilkent University Umram Ankara Turkey

ISBN: (纸本)9781450399647

The recent shift towards incorporating implicit measurements into the mind perception studies in social robotics has come along with its promises and challenges. The implicit tasks can go beyond the limited scope of the explicit tasks and increase the robustness of empirical investigations in human-robot interaction (HRI). However, designing valid and reliable implicit tasks requires norming and validating all stimuli to ensure no confounding factors interfere with the experimental manipulations. We conducted a lexical norming study to systematically explore the concepts suitable for an implicit task that measures mind perception induced by social robots. Two-hundred seventy-four participants rated an expanded and strictly selected list of forty mental capacities in two categories: Agency and Experience, and in two levels of capacities: High and Low. We used the partitioning around medoids algorithm as an objective way of revealing the clusters. We discussed the diferent clustering solutions in light of the previous fndings. We consulted on frequency-based natural language processing (NLP) on the answers to the open-ended questions. The NLP analyses verifed the signifcance of clear instructions and the presence of some common conceptualizations across *** proposed a systematic approach that encourages validation and norming studies, which will further improve the reliability and reproducibility of HRI studies. © 2023 Copyright held by the owner/author(s).

关键词： Human robot interaction

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What does functional connectivity tell us about the behaviorally-functional connectivity of a multifunctional neural circuit?

What does functional connectivity tell us about the behavior...

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2022 Conference on Artificial Life, ALIFE 2022

作者： Izquierdo, Eduardo J. Candadai, Madhavun Cognitive Science Program Indiana University Luddy School of Informatics Computing and Engineering Indiana University United States

What insights can statistical analysis of the time series recordings of neurons and brain regions during behavior give about the neural basis of behavior? With the increasing amount of whole-brain imaging data becoming available, the importance of addressing this unanswered theoretical challenge has become increasingly urgent. We propose a computational neuroethology approach to begin to address this challenge. We evolve dynamical recurrent neural networks to be capable of performing multiple tasks. We then analyze the neural activity using popular network neuroscience tools, specifically functional connectivity using Pearson’s correlation, mutual information, and transfer entropy. We compare the results from these tools against a series of informational lesions, as a way to reveal their degree of approximation to the ground-truth. Our initial analysis reveals an overwhelming large gap between the insights gained from statistical inference of the functionality of the circuits based on neural activity and the actual functionality of the circuits as revealed by mechanistic interventions. © Artificial Life Conference Proceedings 2022.

关键词： Recurrent neural networks

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What comes to mind? Samples from relevance-based feature spaces 44

What comes to mind? Samples from relevance-based feature spa...

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44th Annual Meeting of the cognitive science Society: cognitive Diversity, CogSci 2022

作者： Mills, Tracey Phillips, Jonathan Program in Cognitive Science Dartmouth College 23 N Main St. HanoverNH03755 United States

Recent work in judgment and decision making has focused on which actions people consider when solving open-ended problems and found that the actions that come to mind tend to have particular features, such as having a high historical value. Here, we pursue the idea that the process of generating actions for decision-making tasks may actually reflect more general mechanisms for generating kinds of things. We provide evidence that what comes to mind may simply be a reflection of participants sampling from the most relevant part of the representational space they use to encode the type of thing they are generating. In this paper, we (1) introduce an approach for empirically describing a category in terms of the features that people use to represent category members, and for locating category members within that feature space, (2) show that certain locations in a category's feature space predict an item's likelihood of coming to mind, (3) introduce an approach for understanding the relevance of various features to people's representations of category members, and (4) show that features which are most involved in people's representations of category members are also predictors of what comes to mind within a category. We close by proposing that features that are most relevant to our representations of category members and predict coming to mind are those for which it has been historically useful to have information about during past experiences with the category in question. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Decision making

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Consent and the Doctrine of Double Effect 44

Consent and the Doctrine of Double Effect

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44th Annual Meeting of the cognitive science Society: cognitive Diversity, CogSci 2022

作者： Ocampo, Joshua Phillips, Jonathan Program in Cognitive Science Dartmouth College 23 N Main St. HanoverNH03755 United States

The doctrine of double effect (DDE) explains that it may be permissible to cause harm as a foreseen side-effect of an action that brings about a good result but impermissible to cause harm as a means of bringing about the same good result. The DDE is commonly illustrated with the Trolley Problem, which along with similarly structured examples, have become widely popular as a tool for studying moral psychology and have been taken to demonstrate a universal feature of moral judgment. Across two studies, we investigate how consenting to being harmed interacts with the Doctrine of Double Effect. Specifically, we ask whether (1) harming someone as a means becomes morally acceptable when that person consents to being used as a means, and (2) whether the distinction between harming as a means vs. side-effect persists even when the person being harmed consents. We find that consent significantly interacts with the DDE. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： consent doctrine of double effect moral psychology

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The shape of option generation in open-ended decision problems 44

The shape of option generation in open-ended decision proble...

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44th Annual Meeting of the cognitive science Society: cognitive Diversity, CogSci 2022

作者： Srinivasan, Gokul Acierno, Jane Phillips, Jonathan Program in Cognitive Science Dartmouth College 23 N Main St. HanoverNH03755 United States

There has been a small but now growing interest in studying decision making in real-world contexts where part of the problem faced by decision makers is to generate candidate options they will actually decide between. While some of this work has employed large decision spaces where options are discrete and valuation is computationally tractable (e.g., chess), very little work has focused on genuinely open-ended decision contexts that more closely mirror mundane real-world decisions. This paper leverages large language models to investigate how people generate options when facing genuinely open-ended problems. Across three experiments, we apply semantic similarity and sentiment analyses to the options that participants sequentially generate for real-world decision problems. We find that the first options generated tend to be sampled from a relatively local region of semantic space and are typically of high value. As additional options are generated, they become increasingly dissimilar and are of lower value. These patterns held both at the level of individual option generation trajectories within a given participant and at the level of individual differences across participants. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Sentiment analysis

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Culture, communicative need, and the efficiency of semantic categories 44

Culture, communicative need, and the efficiency of semantic ...

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44th Annual Meeting of the cognitive science Society: cognitive Diversity, CogSci 2022

作者： Gao, Shan Regier, Terry Zhejiang Ningbo China Department of Linguistics Cognitive Science Program UC Berkeley BerkeleyCA94720 United States

It has been proposed that a drive for efficient communication shapes systems of semantic categories across languages. Recent work in this vein has increasingly emphasized communicative need: how often a particular object or idea will need to be referenced. Many studies assume for simplicity that the distribution of need across referents is the same for different cultures, and that this need distribution can be reliably inferred from corpora. In contrast, we elicited culture-specific estimates of communicative need from native speakers of English and Chinese. We compared those need distributions to each other and to a corpus-based need distribution, and we assessed the efficiency of the English and Chinese naming systems for the semantic domain of household containers under different need distributions. Our results suggest that languages reflect culture-specific need patterns, and that subjective estimates are sometimes superior to corpus data as a measure of need. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Semantics

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