Canada

Knowledge of repeating patterns is foundational for early mathematical thinking. While interventions that rely on direct instruction help children to master specific patterns, they often struggle to transfer this knowledge to new patterns. This paper investigates exploration as a learning strategy for abstract patterning and improving knowledge transfer. In a yoked, between-subjects design, 5- and 6-year-old children (n = 90) were tasked with finding up to three hidden stars in a repeating shape pattern (ABB). Exploration participants (n=45) explored pattern materials themselves, while demonstration participants (n=45) observed the experimenter revealing the contents of each shape in the same order as the exploration counterpart. In a transfer task, children saw the same repeating pattern with different stimuli. We found that 6-year-olds significantly outperformed 5-year-olds in the exploration condition and used more sophisticated patterning strategies. These findings suggest that exploration may support and extend older children’s emerging understanding of underlying repeating pattern units.

CogSci 2022

Exploration as a Learning Strategy to Support Children&#39;s Pattern Learning

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) 


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#### **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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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.

Exploration as a Learning Strategy to Support Children's Pattern Learning

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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.
 
 [**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. 





Research suggests the processes involved in analogical transfer are not unlike those of insightful problem solving. This study tests whether encoding cross-domain analogies leads to more feelings of insight and higher rates of far transfer. On each trial in the present study, subjects induced a conceptual relationship from a set of three verbal analogy pairs (30 trials). Half of the trials had a semantically distant (i.e. expansive) final pair, and half of the trials had a semantically close (i.e. consistent) final pair. Subsequently, subjects attempted to complete a novel far analogy for each of the previously learned relations. Far transfer performance on new analogies was predicted by the semantic vector distance of the originally encoded relation. Subjects also reported significantly stronger aha moments at the moment of transfer for accurate transfer responses compared to inaccurate responses, which suggests that the aha response may provide metacognitive information about solution accuracy.

Far Analogical Transfer Is Associated with Aha Moments

The Aha-Accuracy effect refers to the finding that experiencing an Aha! moment is associated with reaching correct solutions on insight problems. Because this effect has generally been demonstrated with verbal problems, this study tested for this effect on spatial problems (matchstick arithmetic). In addition, this study also explored the effect of hints on the Aha! experience and the Aha-Accuracy effect. Overall, there was no Aha-Accuracy effect in the no-hint control condition. There was an Aha-Accuracy effect in the hint condition, but it was limited to problems with solutions that were not directly cued by the hint. When the hint was directly relevant for solution, then many participants were able to reach a correct solution without an Aha! experience. These findings provide evidence that providing hints may not simply increase the likelihood of reaching a solution, but it may also alter the Aha! experience.

Hints and the Aha-Accuracy Effect in Insight Problem Solving

Machine learning models that automatically assess reasoning quality are trained on human-annotated written products. These “gold-standard” corpora are typically created by prompting annotators to choose, using a forced choice design, which of two products presented side by side is the most convincing, contains the strongest evidence or would be adopted by more people. Despite the increase in popularity of using a forced choice design for assessing quality of reasoning (QoR), no study to date has established the validity and reliability of such a method. In two studies, we simultaneously presented two products of reasoning to participants and asked them to identify which product was ‘better justified’ through a forced choice design. We investigated the criterion validity and inter-rater reliability of the forced choice protocol by assessing the relationship between QoR, measured using the forced choice protocol, and accuracy in objectively answerable problems using naive raters sampled from MTurk (Study 1) and experts (Study 2), respectively. In both studies products that were closer to the correct answer and products generated by larger teams were consistently preferred. Experts were substantially better at picking the reasoning products that corresponded to accurate answers. Perhaps the most surprising finding was just how rapidly raters made judgements regarding reasoning: On average, both novices and experts made reliable decisions in under 15 seconds. We conclude that forced choice is a valid and reliable method of assessing QoR.

Measuring Quality of General Reasoning

The study presents the experimentally developed technique to determine how and where semantic categories are represented in the human brain. For this purpose, the combination of linguistic annotation of oral texts in Russian with the registration of BOLD signal in functional MRI subjects’ testing was applied. The goal is to correlate groups of words in input texts and patterns of brain activation. The algorithm of the texts’ markup consisted of four sequential procedures: (1) time annotation, (2) lemmatization, (3) vectorization, (4) indexation of words-attributes.
Regarding the issue of brain activity allocation, the corresponding activation distributed nature can be noted. It proves our hypothesis that conceptual information during text comprehension is processed in different (however specific) areas of the brain and is not compactly localized.
The main result is that the allocated lexical clusters are composed of paradigmatically close words (synonyms, hyponyms) and situationally related words.

Russian-language semantic categories and their brain activity correspondence

In the study of human language comprehension, the N400 brain potential is often used as an index of on-line processing of meaning. However, a more precise account of its underlying computational process is still debated. Previous neural network modelling work simulated N400 amplitudes as change in a probabilistic representation corresponding to an internal implicit prediction error or Bayesian surprise at the level of meaning (Rabovsky, Hansen, & McClelland, 2018). Here we further test this account using a Bayesian learner model to simulate semantic processing of nouns from different semantic categories presented in an oddball like roving paradigm. We show that Bayesian surprise at the level of meaning significantly predicts N400 amplitudes and does so to a significantly higher degree than a non-Bayesian baseline. This sets the N400 in relation to ERP mismatch negativities in perceptual oddball paradigms, which have featured prominently in Bayesian accounts of brain function.

N400 amplitudes as Bayesian surprise at the level of meaning

Several popular sequence-based and pretrained language models have been found to be successful for text-driven prediction of brain activations. However, these models still lack long-term cognitive plausibility as well as insights on the underlying neural substrate mechanisms. This paper studies the influence of context representations of different language models such as sequence-based models: Long short-term memory networks (LSTMs) and ELMo, and popular pretrained Transformer language model (Longformer). In particular, we study how the internal hidden representations align with the brain activity observed via fMRI when the subjects listen to several narrative stories. We use brain imaging recordings of subjects listening to narrative stories to interpret word and sequence embeddings. We further investigate how the representations of language models layers reveal better semantic context during listening. Experiments across all language model representations provide the following cognitive insights: (i) the representations of LSTM cell states are better aligned with brain recordings than LSTM (hidden state), the cell state activity can represent more long-term information, (ii) the representations of ELMo and Longformer display a good predictive performance across brain regions for listening stimuli; (iii) Posterior Medial Cortex (PMC), Temporo-Parieto-Occipital junction (TPOJ), and Dorsal Frontal Lobe (DFL) have higher correlation versus Early Auditory (EAC) and Auditory Association Cortex (AAC).

Long-Term Plausibility of Language Models and Neural Dynamics during Narrative Listening

Sequential learning in artificial neural networks is known to trigger catastrophic interference (CI), where previously learned skills are forgotten after learning new skills. This is in direct contrast to humans’ ability to learn increasingly complex skills across the lifespan without major instances of CI. The present work builds on techniques for mitigating CI that have been proposed in prior work. Anderson and Schooler (1991) first documented that the memory environment has a lawful structure. Following from their observation, we constructed a training environment where previously mastered tasks (Boolean functions) decrease in frequency over time according to a power law. It was predicted that training in this environment would (1) mitigate CI, (2) replicate human performance in learning curves following a power law of practice, and (3) promote positive transfer of training to new skills, all without the need to posit additional mechanisms. The present results support all three predictions.

Catastrophic interference in neural network models is mitigated when the training data reflect a power-law environmental structure

Research shows the existence of a small-number system (~1-3) invoking parallel individuation, and a large-number system (~4+) based on Weberian magnitude estimation. A 128-channel EEG system was used to investigate ERPs while participants detected numerical changes in dot arrays during an oddball task.
During “No Change” trials, the N170 over the parietal-occipital-temporal areas showed distinct waveforms to the habituation of small (but not large) numbers, indicating that more items encoded in working memory leads to stronger N170 amplitudes. We also observed decreased accuracy for increasing vs. decreasing changes in magnitude.
Converging over the mid-parietal area (Pz), lower P3b amplitudes were observed for harder, numerically-larger conditions, while Increasing-Large changes showed longer reaction times compared to Decreasing-Large.
Our findings suggest a neurobehavioral differentiation in perceiving small vs. large numbers and increasing vs. decreasing change at early stages of processing, and a later stage that involves higher-order numerical processing linked to context-updating.

The Neurobehavioral Basis of Parallel Individuation and Numerical Approximation

Research shows that participants predict their memory performance to be lower when they experience disfluencies during encoding, even though encoding disfluency does not always affect actual memory performance. Bizarre statements are typically encoded slower than common statements, which constitutes an example of an encoding disfluency. The current study investigated how disfluencies during encoding for bizarre and common statements affect actual and predicted memory performance from a metacognitive perspective. Across two experiments under intentional learning instructions, participants made either memory predictions or vividness ratings for bizarre and common statements, followed by a free recall task. Participants predicted to remember common statements more than bizarre statements for both Experiment 1 (self-paced encoding) and Experiment 2 (experimenter-paced encoding), even though the actual memory performance was higher for bizarre than common statements. This demonstrates a metacognitive illusion for the bizarreness effect, similar to other manipulations of encoding disfluency.

Bizarreness Effect and Its Relation to Memory and Metamemory

The new Sans Forgetica (SF) typeface was designed to promote desirable difficulty. Here, we investigate whether SF improves memory for words with within-subject designs. Participants studied words in Arial and SF (Exp 1 and 2) and completed old-new recognition tests where words retained their study fonts (Exp 1) or were in either Arial or SF (Exp 2). They had significantly better recognition (hit rate) in SF than in Arial (Exp 1) and significantly higher sensitivity indexes (d’) when words were tested in SF than in Arial (Exp 2). While encouraging, further examination of these results (e.g., response bias) suggest a less straightforward interpretation. Thus, we have reservations for the effectiveness and use of SF for improving word memory.

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Far Analogical Transfer Is Associated with Aha Moments

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