Canada

The effect of prioritization on information in working memory
has primarily been examined in tasks containing a single type
of stimulus and with one item that is prioritized. However,
many theories of working memory posit different types of
components for the maintenance of verbal or visuospatial
information. This study examined differences between
prioritized and nonprioritized items as well as word and image
stimuli. Participants completed an association learning task in
which working memory demands were varied along with the
number of items to be prioritized. Following a short delay,
retention was tested. Prioritization effects were identified
during both the learning and testing phases of the experiment,
and the impact of prioritization was moderated by working
memory demands of the task. Significant differences in
accuracy between word and image stimuli were only observed
in the testing phase, with accuracy for verbal information being
worse. While prioritization improved accuracy and response
times during learning, it led to decreases in the testing phase.

CogSci 2022

Examining Prioritization in Working Memory for Verbal and Visual Stimuli

The 46th Annual Meeting of the Cognitive Science Society was an in-person meeting held in Rotterdam, The Netherlands at the Postillion Hotel & Conference Centre.

**ON-DEMAND PROGRAM ACCESS AFTER THE CONFERENCE** 
Recordings of the invited program are now available. You can access them by clicking on the 'Schedule' icon on the left. Select view by week and navigate to the day and time slot of the recording you wish to view. Click on the time slot to access the recording.

Recordings available (click on each recording to view):

**Thursday, July 25** 
0900: [Keynote Speaker Morgan Barense on Enhancing real-world event memory](https://underline.io/events/465/sessions/17997/lecture/99084-dynamics-between-minds-and-the-environment) 
1000: [Gleitman Award Winner Isabelle Dautriche on Language Foundations: Insights from acquisition, communication, cognition, and more](https://underline.io/events/465/sessions/18000/lecture/99085-gleitman-talk) 
1415: [Invited Symposium: Dynamics between minds and the environment](https://underline.io/events/465/sessions?eventSessionId=18016&searchGroup=lecture) 
1700: [Rumelhart Prize Presentation Speaker Alison Gopnik on Exploit, explore, empower: Three ages and three intelligences](https://underline.io/events/465/sessions/18028/lecture/99161-rp1-rumelhart-prize-presentation) 

**Friday, July 26** 
0900: [C.L. de Carvalho-Heineken Prize Keynote Speaker Kia Nobre on Focusing in memory](https://underline.io/events/465/sessions/18035/lecture/99162-hp1-c-l-de-carvalho-heineken-prize-keynote-address) 
1030: [Rumelhart Symposium: Childhood as exploration](https://underline.io/events/465/sessions/18038/lecture/100346-childhood-as-exploration) 
1415: [Elman Prize Symposium](https://underline.io/events/465/sessions?eventSessionId=18053&searchGroup=lecture) 
1600: [Invited Symposium: Dynamics between minds](https://underline.io/events/465/sessions?eventSessionId=18065&searchGroup=lecture) 
1745: [Keynote Speaker Andrea E. Martin on Neural dynamics encode the structure and statistics of language](https://underline.io/events/465/sessions/18077/lecture/99271-neural-dynamics-encode-the-structure-and-statistics-of-language) 

**Saturday, July 27** 
0900: [Keynote Speaker Gregor Schöner on How higher cognition emerges from the dynamics of strongly interacting neural populations](https://underline.io/events/465/sessions/18083/lecture/99273-dynamics-within-the-mind) 
1000: [Glushko Talks](https://underline.io/events/465/sessions?eventSessionId=18086&searchGroup=lecture) 
1500: [Invited Symposium: Dynamics within minds](https://underline.io/events/465/sessions?eventSessionId=18100&searchGroup=lecture) 


#### **CogSci 2024 Program Book**
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**The Program Book can be downloaded [here](https://drive.google.com/file/d/1yRVp1cBqRnAbdYi4GXWV_pZeyWNsRquv/view?usp=sharing).**

#### **Letter from the President**

Welcome to the 2024 Meeting of the Cognitive Science Society in Rotterdam! CogSci 2024 brings together a large community of cognitive scientists who have traveled here from around the world, as well as a smaller group of remote presenters. I would like to extend an especially warm welcome to our first-time CogSci meeting attendees, and hope that 
their experience inspires them to join our future meetings for many years to come.

I want to thank this year’s conference co-chairs, Larissa K Samuelson, Stefan Frank, Mariya Toneva, Allyson Mackey, and Eliot Hazeltine, for putting together an amazing series of invited talks and symposia around the theme of Dynamics of Cognition. The co-chairs also deserve credit for handling a record-breaking number of submissions and producing a truly exciting conference program 

Beyond the invited talks and symposia dedicated to this year’s conference theme, I would like to highlight the presentations and events honoring the 
Rumelhart, Elman, Gleitman and C .L . de Carvalho Heineken prize winners, including the Rumelhart reception on Thursday. As you may know, we are recording all of the invited talks and symposia, and all prize-winners’ talks, and will be making this content available after the conference. 

The Cognitive Science Society relies on the volunteer efforts of our Governing Board members who work on matters related to our membership; conference policies; diversity and inclusion, international, and outreach initiatives; prizes and much more. We welcome interest from Society members who would like to become more involved in what we do. The 
Society is deeply grateful to Rick Dale and Andrea Bender, who serve as editors of the two Society journals, Cognitive Science and Topics in Cognitive Science respectively. We especially acknowledge our editors’ generosity in donating their stipends each year to D&I and outreach efforts. 

This year’s Annual Meeting depends critically on the support of Marischal De Armond and his team at Podium Conference Specialists: Sharon Zwack, Cendrine De Vis, Sarah-Kate Burke, and Rachel Waller worked tirelessly to secure the venue and handled a multitude of logistical and organizational issues, from arranging coffee breaks and receptions to making sure that a huge number of posters fits comfortably into the available space. Finally, and importantly, I would like to acknowledge the Cognitive Science Society Executive Officer, Erica Wojcik, for managing the complex and ever expanding sphere of our Society’s activities.

I hope you enjoy this year’s meeting and the many different opportunities to engage with the vibrant community of cognitive scientists gathered here. I also hope you get a chance to explore the great city of Rotterdam with its many attractions, museums, restaurants and scenic views. If you’d like to talk more about our Society, or simply want to say hello, please stop by!

![](https://assets.underline.io/markdown_image/1/image/3c66d0e21fff30e204df1138ffd15173.jpeg)

**Anna Papafragou** 
President, Cognitive Science 
Society 2023-2024

**CODE OF CONDUCT** 
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**ABOUT THE COGNITIVE SCIENCE SOCIETY** 
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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CogSci 2024

The 46th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in 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.
 
 [**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/)

Registration Is Required

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. 





Inspired by notions of intrinsic motivation (Schmidhuber, 2010) and play as proposing and solving arbitrary problems (Chu and Schulz, 2020b), we report initial progress toward computational modeling of playful goal generation. We create an embodied, 3D environment resembling a child's room, and ask study participants to play in the environment and then create a scorable game. We propose to model games using a domain-specific language, which represents each game as a computer program. These programs act as reward-generating functions, mapping states visited by an agent as they play a game to the score they should receive. We then analyze our corpus of program representations to highlight four key aspects of human games that would contribute to constructing effective computational models of game generation: creativity, compositionality, common sense, and context sensitivity.

Creativity, Compositionality, and Common Sense in Human Goal Generation

As voice-AI technology becomes commonplace in today’s world, speech synthesis technology is rapidly becoming more naturalistic. While previous studies investigated the intelligibility of synthetic speech, it is not clear how the naturalness of a synthetic voice affects listeners’ memory of the meaning content of a spoken message. The present study investigates how listeners’ memory of semantic gist is affected when participants are exposed to a naturalistic synthetic or a roboticized synthetic voice. Participants completed a Deese-Roediger-McDermott (DRM) task to assess recognition accuracy for semantically related word lists. The naturalistic and robotic synthetic exposure voices showed similar levels of recognition accuracy across conditions. However, both synthetic voices resulted in worse recognition accuracy compared to previous research on DRM tasks when the lists were read by human talkers. These findings inform the development of synthetic voices used in information delivery contexts and point to future directions for memory research with synthetic voices.

Investigating the Effect of Synthetic Voice Naturalness on Gist Memory

Temporal Binding (TB) is standardly regarded as an implicit measure of the sense of agency (Haggard, 2017). Though the TB effect is robust, an underlying mechanism has not been agreed upon (Hoerl et al., 2020). Here we propose a memory process as an explanation for the observed error in two publicly available datasets. We first replotted the data and found that on average, across both experiments, participants overestimate the length of the shortest timing interval and underestimate the longest interval, a classic regression to the mean pattern. Summary statistics extracted from the data from each experiment were then used as parameters in a simple Bayesian model of memory. Model simulations reproduced the behavioral data for almost all timing intervals and experimental trial-types across two experiments. Adjusting one of the parameters in the model (prior mean for actions) resulted in an improved qualitative fit. We suggest that other more likely sources of error, apart from experienced agency, may account for this result.

Bayesian rational memory model simulates temporal binding effect

Why are some people able to explore a problem space more effectively? We used a simple problem-solving task in which problems can be solved quickly within five moves but with a problem space containing 40-60 distinct states. Problem-solving in this task consists of many attempts to solve the problem, but only one solution works. Across these attempts, participants sometimes repeat an attempt from earlier. Exploratory analyses were conducted on data from 70 problems and six individual difference measures for each of 147 participants. We hypothesized that working memory capacity (WMC) would be associated with a higher proportion of unique attempts. WMC was calculated from two complex span tasks. Higher WMC was associated with a higher proportion of unique attempts and faster solution times. Higher WMC was also associated with more self-terminated solution attempts as opposed to being forced to restart the problem after seven moves by the system.


Working Memory Capacity Predicts More Effective Problem Space Exploration

Memory is limited in capacity, which means that we must choose what information to prioritize for storage. Part of knowing what to prioritize is predicting future needs. For example, if you view a 3D object, later on you may wish to recall exactly how it was oriented. Alternatively, you might need to remember its shape, independent of viewpoint. Given this kind of uncertainty, a good strategy would be to store multiple kinds of information about the objects we observe, and then decode in a task-dependent manner. We tested whether people apply these strategies in the specific domain of short-term memory for novel faces. To test whether people store various kinds of information about a face, and then decode in a task-dependent manner, we modeled their responses in a memory task using features (extracted from deep neural networks) that varied in how much 3D information they carried. We found strong evidence for a mixed-storage strategy, which did not vary in response to task demands. Our results suggest that in order to fully understand resource allocation and retrieval strategies in human memory, it may be critical to consider not just the distribution over tasks in people's natural environments, but also task uncertainty at the time of encoding.

Coding Strategies in Memory for 3D Objects: The Influence of Task Uncertainty

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.

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

Testing the testing effect with featural and relational categories
Theoreticians have recently proposed that the benefits of testing depend on the extent to which the to-be-learned concepts are interconnected, with testing effects emerging from low element interconnectivity and becoming weaker with greater element interconnectivity. We tested this idea by using a classification task and manipulating the amount of element interconnectivity in the categories that subjects learned. Some subjects learned featural categories, which lack element interconnectivity, whereas others learned relational categories, which are defined by how its elements are interconnected. This factor was crossed with type of training, wherein some subjects learned through classification (i.e., testing) and others through observation (i.e., studying). For featural categories, it was predicted that classification training would produce better learning than observational training, whereas this benefit should become weaker or disappear for relational categories. However, preliminary results show that both types of training lead to similar learning for both category types.

Testing the testing effect with featural and relational categories

We investigated neural networks’ ability to generalize during visual object recognition. In three experiments, we show that while basic multilayer neural networks easily learn to classify the objects on which they are trained, they show serious difficulties transferring that knowledge to novel items. However, our experiments also show that when the previously trained networks are then trained on the novel items, they learn to respond correctly to the novel items much faster than untrained networks. This shows that these networks are learning abstract representations that go beyond the simple items on which they were trained. We argue that this demonstrates that regarding abstract rule learning, the problem with neural networks is not their inability to learn abstractions, but their ability to apply that knowledge when classifying new objects.

Generalization and Transfer Learning in Neural Networks Performing Shape, Size, and Color Classification

Holistic and analytic thinking styles are well-documented in cultural psychology. However, recent studies suggest that language potentially mediates the influence of culture on thinking styles. The overarching goal of this study is to examine how verbal labels impact people’s thinking styles. Study 1 sought to examine whether thinking styles in a classic triad task could depend on verbal or pictorial formats. Although we observed a significant correlation between performance in verbal and picture triad tasks, more participants were classified as holistic thinkers with a verbal compared to a picture triad task. In Study 2, we examined whether participants could shift their thinking styles in the verbal triad task after being primed to focus on categorical associations. We found that females were influenced by this prime and displayed more analytic thinking. Our results suggest that language can influence thinking styles and that thinking styles are context-dependent.


Verbal Labels Affect Holistic and Analytic Thinking Styles in Native English Speakers

Basic category learning mechanisms are largely thought to be universal (Shepard, 1987). However, research has shown differences in processing styles between Eastern and Western cultures (Nisbett et al., 2001; Norenzayan et al., 2002), though this has not always been replicated (Murphy et al., 2017). To explore category learning styles and effects of culture, this online study paired the Shepard, Hovland, and Jenkins tasks (Shepard et al., 1961) with the Analysis-Holism scale (Choi et al., 2007). These tasks vary in complexity and optimal strategy to test the reliance on single feature rules, disjunctive rules, and family resemblance. Cultural differences for holistic approaches in non-Western samples were expected when categories could be acquired in more ways than one. The results show expected learning trends for the SHJ tasks, signifying reliable data collection. Additionally, of the six tasks, higher holistic thinking was significantly correlated with the family resemblance task (Type IV).

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Creativity, Compositionality, and Common Sense in Human Goal Generation

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