United States

The world’s languages differ in how they order adjectives and nouns relative to each other. We ask whether cross-linguistic variation and systematicity in adjective-noun order can be explained by the iterated pressure for pragmatic referential communication. To this end, we formalize cooperative pressures on referential communication in the Rational Speech Act framework in an iterated learning setting. First, we show that the less informative adjectives are relative to nouns, the more likely they are to occur post-nominally. This is the case when informativeness is manipulated via the composition of the lexical space (i.e., changing the relative number of adjectives vs. nouns that are available for reference), and via the inherent referential utility of adjectives vs. nouns. Secondly, we show that under the assumption that nouns are on average more informative than adjectives, the model predicts a cross-linguistic distribution of ordering preferences that qualitatively resembles the empirical one, with these biases becoming further entrenched with iterated language use. Taken together, these results suggest a possible pathway for syntactic preferences to be calcified over time as the result of pragmatic communicative pressures on language.


CogSci 2023

The cross-linguistic order of adjectives and nouns may be the result of iterated pragmatic pressures on referential communication

### Looking for Cogsci 2023? Please [click here](https://underline.io/events/405/reception) to switch to 2023 edition.


**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.

We hope that you enjoy the conference this year!<br><br>

**Your Program Chairs,**
![](https://assets.underline.io/uploads/markdown_image/1/image/99984908caba5e6453e5fc5ba34f3507.jpg)

**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

<iframe width="560" height="315" src="https://www.youtube.com/embed/464h1m3iU68" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

To review the proceedings for the conference or review the searchable program, please visit the [website](https://cognitivesciencesociety.org/program/).


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.

technical paper

The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

Join us!

<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
<br>[**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
<br><br>

#### **CogSci 2023 Program Book**
<iframe style="width:700px;height:400px" src="https://online.fliphtml5.com/ebtyf/tzgk/"  seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe>
<br>

The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

The best-performing artificial intelligence systems for object recognition are deep neural networks (DNNs). Engineers and neuroscientists have claimed that DNNs show human-level performance in object recognition. However, Barbu et al. (2019) reported accuracies of around 30% for state-of-the-art object recognition systems when testing on their better-controlled image set – ObjectNet. How do humans perform on ObjectNet? In our experiment, we tested 25 undergraduates on their ability to classify the ten categories of objects in ObjectNet that the Deep Convolutional Neural Networks (DCNNs) found easiest, moderate and hardest. Although humans and DCNNs had similar levels of overall accuracy, there were some everyday, basic-level categories for which machine performance was much lower than humans. The pattern of errors generated by the DCNNs were about as similar to human error patterns as the individual human error patterns were to each other. Implications of these results and plans for future work are discussed.

A Comparison of Human and Machine Performance in Object Recognition Using The ObjectNet Image Set

Neural network models of language have long been used as a tool for developing hypotheses about conceptual representation in the mind and brain. For many years, such use involved extracting vector-space representations of words and using distances among these to predict or understand human behavior in various semantic tasks. In contemporary language AIs, however, it is possible to interrogate the latent structure of conceptual representations using methods nearly identical to those commonly used with human participants. The current work uses two common techniques borrowed from cognitive
psychology to estimate and compare lexical-semantic structure in both humans and a well-known AI, the DaVinci variant of GPT-3. In humans, we show that conceptual structure is robust to differences in culture, language, and method of estimation. Structures estimated from AI behavior, while individually fairly consistent with those estimated from human behavior, depend much more upon the particular task used to generate behavior responses–responses generated by the very same model in the two tasks yield estimates of conceptual structure that cohere less with one another than do human structure estimates. The results suggest one important way that knowledge inhering in contemporary AIs can differ from human cognition.

Behavioral estimates of conceptual structure are robust across tasks in humans but not large language models

We investigated the potential and limitations of generative artificial intelligence (AI) in reflecting the authors' cognitive processes through creative expression. The focus is on the AI-generated artwork's ability to understand human intent (alignment) and visually represent emotions based on criteria such as creativity, aesthetic, novelty, amusement, and depth. Results show a preference for images based on the descriptions of the authors' emotions over the main events. We also found that images that overrepresent specific elements or stereotypes negatively impact AI alignment. Our findings suggest that AI could facilitate creativity and the self-expression of emotions. Our research framework with generative AIs can help design AI-based interventions in related fields (e.g., mental health education, therapy, and counseling).

A Portrait of Emotion: Empowering Self-Expression through AI-Generated Art

Categorical learning plays a foundational role in language development. By reviewing comparative studies on categorical learning in humans and nonhuman animals, we show that categorical learning displays evolutionary continuity across invertebrates and vertebrates. Great apes and parrots can be trained to produce categories of (proto-)language-like symbols in different modalities. From the neurological perspective, we show that as a conserved brain structure, the basal ganglia are involved in categorical learning across species, and language processing in humans. This raises the possibility that categorical learning is one of the crucial cognitive foundations for language evolution.

Categorical Learning and the Cognitive Foundations of Language Evolution and Development

Alcohol binge drinking is ubiquitous among young adults. While the neurotoxic effects of general alcohol consumption are well known, binge drinking has unique effects on the brain through the intoxication and withdrawal cycle. In particular, the research regarding binge drinking and attention is mixed; this study investigates this relationship in young adults. University students (N=105) were recruited. Socio-demographic and alcohol use information was collected, then participants completed cognitive tasks based on the Attention Network Theory: alerting, orienting, and executive control. Binge drinking, sex, and age of first binge did not predict attention impairment, nor did sex moderate the relationship. The tasks measuring attention did not relate in the expected manner. While there were no differences in attention performance between those who binge drink and controls in this study, the relationship between binge drinking and attention impairments in young adults may be more nuanced and future research directions are suggested.

The Effects of Binge Drinking on Attention in Young Adults.

Traditionally considered random noise, variability in behaviour in fact contains meaningful structure. Healthy functioning can be characterised by behavioural variability that is statistically self-similar (i.e., exhibits 1/f scaling), while disease or injury can result in deviations from this pattern. To date this work has focused predominantly on physical conditions. Little is known about the link between mental health and 1/f scaling. To address this gap, in the present study participants (n = 61) completed a simple walking task during which their gait variability was captured. Cognitive load, a factor known to impact 1/f scaling, was manipulated and symptoms of social anxiety assessed. As expected, being distracted by a cognitive task led to deviations in gait structure away from 1/f scaling. Notably, this effect was amplified by symptoms of social anxiety. This provides initial evidence that difference in mental health status impacts the structure of behaviour.

Exploring the impact of social anxiety on behavioural variability

The current study investigates whether a running man's pictogram on emergency signage is as effective as an arrow in conveying direction, especially in emergencies. We conducted two experiments to address the following questions: first, comparing the ease of reading the two signages, and second, presenting an incomplete image of a running man (RM) signage design pictogram to investigate important components of running man that contribute in conveying direction. In both conditions, the participant's task is to identify the orientation/direction and choose the keys corresponding to the right/left orientation/direction.

Investigating design of running man in emergency signage

Why are some words more frequent than others? Some reasons are self-evident. We have far more reasons to use a word like ”eat” than a word like ”diagonalize”. But robust differences in frequency are also observed for words with seemingly equal communicative usefulness. For example, hot and cold seem equally important for communicating temperature yet hot in English is more frequent than cold. We focused on antonym pairs such as these and sought to predict differences in frequency from the connection patterns of these words in a semantic network while controlling for predictors like the number of word senses. Two network properties predicted word frequency especially well: the number of connections the word and its surrounding words have, and the ability of the
word to connect less interconnected words. These two network properties not only predicted present word frequency, but also predicted future frequency changes suggesting a potential causality relationship between network properties and word frequency. Overall, this study offers new insights into the underlying causes of differences in word frequency and highlights the importance of considering a network perspective when examining how word frequency evolves.


Understanding the Frequency of a Word by its Associates: A Network Perspective

Sampling assumptions --- the assumptions people make about how an example of a category or concept has been chosen --- help us learn from examples efficiently. One context where sampling assumptions are particularly important is in social contexts, where a learner needs to infer the knowledge and intentions of the information provider and vice-versa. The pedagogical sampling assumptions model describes a Bayesian account of how learners and providers should behave given different assumptions they have about the other (e.g., is the provider trying to deceive or help me? Does the learner trust me?). In this study, we tested how well this model could describe learning behaviour in the rectangle game, where a fictional information provider revealed clues about the structure of a rectangle that the learner (a participant) needed to guess. Participants received clues from either a helpful information provider, a provider who was randomly sampling clues, or one of two kinds of unhelpful providers (who could mislead but could not lie). We found that people learned efficiently and in line with model predictions when the provider was helpful and that this was the case even when no cover story was provided. However, although participants could identify that unhelpful providers were not being helpful, they struggled to learn the strategy those providers were using.


Premium content

Downloads

Next from CogSci 2023

A Comparison of Human and Machine Performance in Object Recognition Using The ObjectNet Image Set

Similar lecture

Word order affects the frequency of adjective use across languages