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Children’s production of mental state verbs can reveal evidence of their theory of mind and general cognitive development. Children produce a certain class of mental state verbs, namely desire verbs such as want, wish, and hope, early in development. Among these desire verbs, they produce want the most frequently. We report on a corpus study of 450+ instances of want as gathered from children’s dialogues with caretakers in the CHILDES database. We developed a novel coding scheme to measure children’s use and understanding of want utterances: i.e., we sought to track the contents of their desires and the agents children predicated desires about. We report on the frequencies of these features across the ages of 2- 4, and highlight noteworthy trends in the way children learn to use want. Children appear to talk about their own desires most often; they primarily use questions to talk about second person desires; and they desire more complex objects as they mature. We describe how these patterns of linguistic competency may serve as an index of a developing theory of mind.

CogSci 2022

How children talk about their desires: A corpus study of ‘want’

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**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.

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CogSci 2021

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.

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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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<br>[**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)
<br>[**The Detailed Program can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/08/DeArmond-CogSci2022-program-v113-final.pdf)
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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. 





Rule-based learning is an important aspect of language acquisition. Yang (2005,2016) proposed the Tolerance Principle (TP) to predict when a rule will be formed by the language learner. We present the derivation of the TP as originally proposed and test it on both hypothetical data and corpus data from 8 children. Results for the hypothetical data contradict the TP’s predictions, as do the data from 7 of the 8 children. We conclude that the original form of the TP does not explain rule-learning.

Intolerant Data: Testing The Tolerance Principle

Learning to establish joint reference is an important milestone of communicative and linguistic development. Pointing is one of the first entry points into this process, since gestures often precede verbal communication. During early development, as well as later language use, pointing and linguistic utterances interact in many ways, complementing each other. However, little is known about the development of this relationship during development. In this paper, we focus on the development of the co-occurrence of finger pointing and accompanying utterances in two different cultures: Russia and Chintang (Sino-Tibetan, Eastern Nepal). We show that despite the differences in environment, the development of finger pointing and accompanying language use show substantial similarities. Early on, a larger proportion of points is not accompanied by language. As the children's linguistic abilities develop, children first use language to specify what is being pointed at, and later elaborate on some aspect of the referent.

How Do Children Combine Pointing and Language in the Earliest Stages of Development? A Case Study of Russian and Chintang

It has been conjectured that verb learning is hard because verb meanings are not readily "packaged'' from the physical world. To provide new empirical evidence on this account, we analyzed egocentric video collected from natural toy-play interaction and focused on the naming events when action verbs were uttered in parent speech. Using the Human Simulation Paradigm, we showed egocentric videos of those naming events to adult observers and asked them to guess the target verb in parent speech. We found that adult observers used many different verbs to describe the same visual event, and only one of them matched with the verb in parent speech. We analyzed mismatched verbs and identified several sources of mismatch, and found that all of the mismatched verbs are relevant to the target verb, but they capture different properties (temporal, semantic, etc) of the visual event. We also found that different naming events for the same verb also differ in terms of the degree of ambiguity. Taken together, the results in the present paper provided new evidence from the child’s view, showing that verb learning is hard not only because multiple possible meanings are embedded in each learning situation, but also because these candidate meanings expand across multiple dimensions of the physical world, overlap with each other, and relate to the target meaning in many different ways.

Grounding Action Verbs in Egocentric Visual Perception

Even simple, ambiguous signals can have a rich interpretation when viewed in the context of an interaction in a shared environment. We create a model called Paternalistic Communication by combining an existing model of overloaded language -- Rational Speech Acts (RSA) -- with a full agent model of Theory of Mind (ToM). This integration allows signals to be processed in conjunction with common ground in a principled manner dependent on task-dependent action utilities. This modeling perspective treats communication as a way to coordinate diverging perspectives in a cooperative setting. Under Paternalistic Communication, a speaker decides what to say by predicting their partner’s reaction based on the information in common ground and then evaluates those reactions using their own mind which may contain additional information. We demonstrate the flexibility and performance of Paternalistic Communication in a case study with ambiguous signaling through a set of simulations.

Overloaded Communication as Paternalistic Helping

Literature has suggested that self-faces are processed differently at various stages of information processing. Although mechanisms like familiarity, implicit positive attitude, emotional arousal, dual-coding, and dopamine reward pathway have been theorized to explain this effect, it may share a fundamental basis in the attentional mechanism resulting in perceptual prioritization for self-face. In this study, we have assessed the attentional bias resulting from the self-face (over other familiar and unfamiliar faces), by using face pairs as cues before a dot-probe task. We looked at reaction time and its underlying latent variables as a function of face pairs used as cues. We find that both self-face and familiar face result in a faster reaction time for subsequent stimuli at cued locations. Though self-face shows this advantage for both short and long cue-time, a familiar face shows the advantage only for longer cue-time. We also found that drift rate bias is found for the location where self-face is presented. Familiar faces show a prior bias (z) as the reason for underlying advantage. We conclude that although, self-face, as well as familiar faces, might bias the processing of subsequent stimuli the underlying latent factor might differ.

Attentional Bias for Self-Face: Investigation using Dri Diffusion Modelling

People detect painful expressions more easily in members of their racial ingroup than outgroup. Here, we wanted to investigate this racial bias with a machine learning model trained to detect activations of different action units of painful facial expressions. We examined whether the model detected higher action unit activation for European than African faces when trained on datasets with mostly White faces. To control for confounding variables, pictures of faces were generated with the FaceGen Modeller. Results revealed that there exist differences in the visual detectability of some facial muscle activations due to skin color or other race-dependent facial features. Despite the bias towards European looking faces in the training data, some activations were more easily detectable in African faces. Thus, neither the perceptual detectability, nor the larger exposure to own-race faces seems to solely explain the racial bias in pain detection.

Exploring the Racial Bias in Pain Detection with a Computer Vision Model

Categorical perception involves our perceptual system creating sharp boundaries along an objectively continuous stimulus property, such as the discrete colors of the rainbow being perceived despite continuous change in wavelength. The same mechanism is thought to take place in facial emotion perception. But how are emotions at these boundaries perceived? We presented participants with morphed emotional faces made by blending different emotional expressions in equal proportions. Next, we asked participants to respond freely to these ambiguous face morphs and examined these responses via natural language processing methods. The results showed that participants used many more labels than those related to the categories which went into the morphs. These results can inform theories on categorical facial perception as well as the mental representation of facial expressions.

Categorizing Ambiguous Facial Expressions

Inferences of other’s emotion states are influenced by multiple sources including target cues such as facial movements and the situational context. Our understanding of how information from these cues is integrated is limited, however. We examined whether people integrate information from faces and situations to infer emotions as predicted by an existing model of affective cognition. We applied a Bayesian cue-integration model to a dataset that includes a variety of complex social situations that reflect the heterogeneity of emotion contexts in social lives. Results indicate that when viewing both faces and situations, situation information alone predicted people’s inference about emotions better than Bayesian cue-integration model. However, there was some variability in this pattern across emotion categories as the Bayesian cue-integration model best predicted inferences for emotion categories of amusement and happiness. These findings better our understanding of the interplay between facial and situational cues in informing emotion inferences.

Modeling Cue-integration in Emotion Inferences

An examination of emotion recognition and response to music can isolate perception and experience of emotion from the potentially confounding effects of other social cues (e.g., faces). Participants aged 5-6-years-old listened to clips of calm, scary, and sad music and either identified the emotional content of the music or reported on the feelings elicited by the music clip. Children correctly identified the emotions and reported feeling the emotions conveyed in music above chance. Accurately recognizing and resonating with the emotion conveyed were correlated, although the relationship varied as a function of child characteristics. Specifically, children whose parents reported them as showing more prosocial behavior had significantly greater alignment between emotion recognition and resonation. Results provide new insights into emotion perception in the absence of direct social signals and provide evidence that children’s ability to perceive and resonate with the emotion conveyed through music differs depending on key socioemotional characteristics.

Alignment of Knowing Versus Feeling the Emotion in Music During Middle-Childhood

Children increasingly rely on the internet for information. In this study, children ages 7-10 (n=80) indicated whether a human source or Google could answer questions involving past, present, or future events, and which informant would be better able to do so. Children indicated that Google could accurately answer questions more frequently than the human could, and they were least likely to indicate that either informant could answer questions about the future. Children selected Google as the better informant across all question types, but they did so most frequently in the future condition. Children’s responses also varied such that as the age of the participants increased, they judged the person as less able to answer questions about current events and Google as better able to do so. Children believe that search engines can accurately answer questions more often than a person can, perhaps reflecting their exposure to digital learning environments.

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Intolerant Data: Testing The Tolerance Principle

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Learning how to use the verb ‘want’: A corpus study