Canada

The direction of our attention can influence our performance on a variety of tasks. For example, reading from left to right relates to people associating small numbers on the left and large numbers on the right. In contrast, reading from right to left relates to people associating small numbers on the right and large numbers on the left. The current study tests if this type of “attentional momentum” can be induced by storytelling based on pictures and whether it affects college students’ reaction time on an arithmetic verification task with equations in a traditional (e.g., 2+2=4) or non-traditional (e.g., 4=2+2) direction. Our results show that students were faster at verifying simple traditional math problems after telling stories based on pictures arranged from left to right, but faster at verifying simple non-traditional math problems after telling stories based on pictures arranged from right to left.


CogSci 2022

Attentional Momentum Effects on Addition Verification

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

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#### About

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.

#### Our History 

* **Society Creation**<br>
The Society was incorporated as a 501(c)(3) non-profit professional organization in Massachusetts in 1979. The organizing committee included Roger Schank, Allan Collins, Donald Norman, and a number of other scholars from psychology, linguistics, computer science, and philosophy. 
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* **Conference Creation**<br>
The first conference on cognitive science was held at La Jolla, California in August, 1979, and has occurred annually since then. The proceedings of each conference are published, and those from most years are available through Lawrence Erlbaum Associates, Inc. The annual proceedings of the Cognitive Science Conference represent a major source of information on new work and new ideas in the scientific study of thinking. In 1990, the Society, with help from an anonymous donor, established the David Marr Prize for the best student paper at each annual meeting.
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* **Journal Creation**<br>
The Journal, Cognitive Science, began publication in 1976, and is now published by Wiley-Blackwell. The Executive Editor is currently Richard P. Cooper of Birkbeck, University of London, and there are 18 Associate Editors and a 30-member editorial board. It serves as the premier outlet for research reports that intersect two or more disciplines. Copyrights for articles published in the journal are held by the Society. The Governing Board of the Cognitive Science Society voted in late 2006 to found a new journal, Topics in Cognitive Science (topiCS). The Editor in Chief is Wayne Gray, Cognitive Science Department, Rensselaer Polytechnic Institute. The journal seeks to fill a niche not occupied by Cognitive Science Journal or other cognitive science journals. Membership in the Society includes a subscription to Cognitive Science and TopiCS. Copyrights for articles published in the journal are held by the Society.
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#### Code of Conduct

By attending the CogSci 2025 Conference, you are required to adhere to the society’s **[Code of Conduct](https://drive.google.com/file/d/1ChPuihLy6jE_BWqfO7J2KKgX35JW2zsM/view?usp=sharing)**.
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CogSci 2025

The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

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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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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. 





The debate of whether bilingualism provides a cognitive and or linguistic advantage is a lasting one. Underlying this debate is the idea that an additional language shapes cognition and linguistic processing. The current research analyzes a behavioral dataset containing individuals’ performance in different general cognitive and linguistic tests using a machine learning approach to classify individuals as bilinguals or multilinguals based on their performance. Using an extreme gradient boosting model, we were able to achieve a balanced accuracy of 77%. High scores on a prescriptive grammar test, a verbal fluency test, and a picture naming test were predictive for multilingualism. The implications of the reported results for the field and future research are discussed.

Can you tell them apart? Using machine learning to classify bilinguals’ and multilinguals’ cognitive and linguistic performance

In this experiment we set out to investigate how predictive processing may modulate alignment between interlocutors in a dyadic setting (Pickering & Garrod, 2013). The experiment presents a novel interactional task where participants are involved in a partially controlled association game where speaker A names a picture and speaker B responds with a semantically related word. Importantly, the predictability for the upcoming object is manipulated. Data has been collected from 20 dyads, and the results show a prediction effect with a mean difference of 400 ms between predictable and non-predictable conditions. Crucially, this prediction effect was not only present in speaker A who had to name the predictable or unpredictable object, but also for the interlocutor. To our knowledge, this is the first study to demonstrate speaker-listener prediction effects in a dyadic interaction. This will be further tested in a dual-EEG setting to explore this question at the neural level.

The effects of prediction in conversational alignment.

Does the human mind contain a task-general ‘randomization machine’? Stable biases of randomization have been identified that span multiple domains and modalities, in both lower-level perceptual tasks and in higher-level cognitive tasks. The stability of such biases indicates that the mind may rely on a stable set of properties to create and perceive randomness. But what computational principles support randomization? Here, we approach this question by building a computational model of human randomization that generalizes across spatial and numerical tasks. We show that simple computational heuristics capture higher-order properties of human-generated random sequences, in both numerical and spatial randomization tasks each with many possible options. Furthermore, we show that human behavior in both types of tasks can be approximated by the same low-dimensional model, implying that a domain-general set of computational principles may underlie randomization behavior in general.


A task-general model of human randomization

This paper explores the value of skepticism towards the Approximate Number System (ANS). I sketch some of the main arguments levied against ANS-based interpretations of numerical cognition data and argue that there are empirical and conceptual reasons to reject wholesale replacement of the ANS with an Analog Magnitude System (AMS). To simplify the discussion, I focus for the most part on a recent critical review representative of this new wave of revisionist skepticism (Leibovich, T., Katzin, N., Harel, M., & Henik, A., 2017). I start with a brief review of some of the reasons offered to deny that experiments studying our numerical abilities reveal the presence of a system dedicated to representing quantities of discrete objects, before turning briefly to empirical responses to these worries. I then offer a few reflections on why even if the empirical rebuttal were to fail, there are conceptual reasons to doubt that we are only equipped with an AMS. While some of these reasons involve methodological implications of AMS-based theories, other conceptual reasons to doubt AMS skepticism revolve around how ANS-skepticism seems to go against the history of the relation between the continuous and the discrete, and how one cannot be derived from the other. I then end with a potential reply to my worries involving an appeal to the Object-File System (OFS) as a source of discrete content in our numerical abilities and find it wanting.

The Discrete, the Continuous, and the Approximate Number System

Formal mathematical reasoning is unique in its precision: any valid conclusion can be justified by a sequence of base axioms. But human-written proofs or solutions rarely operate at that level. Instead, obvious steps are skipped to provide a simple, lucid argument. This is especially important in an educational setting, where too many details in an example solution, or too few, can confuse a student. What are the key steps for humans in a given formal solution? We investigate several computational hypotheses in the context of equation solving. Specifically, we take a reinforcement learning agent that solves equations using low-level axioms, and propose a series of methods for abstracting its solutions by selecting key steps. We consider methods based on the semantic distance between subsequent steps, based on the steps with the highest uncertainty for the agent, and based on transitions between latent "high-level skills" learned from a large number of agent-produced solutions. In a human evaluation we find that skill-base simplifications were judged most useful. These results suggest new directions for understanding human mathematical reasoning.

Le to the Reader: Abstracting Solutions in Mathematical Reasoning

We investigated individuals’ representation of composite numbers (i.e., those with factors other than 1 and themselves). Some numbers are “more composite” (have more factors) than others (e.g., 72 vs. 74). We hypothesized that people mentally represent numbers in factor neighborhoods. This should result in a compositeness penalty: performance on factoring problems (e.g., x * 9 = 72) should be slower for more composite products, because retrieving the corresponding factor requires inhibiting neighboring factor pairs. We measured 50 undergraduates’ time to correctly solve factoring problems for composite products (6 to 100). We found that for well-practiced facts (i.e., the 12x12 multiplication table), performance slowed as compositeness increased, even after controlling for problem size and factor size. However, there was no compositeness penalty for “non-table” facts. These findings support our factor neighborhood hypothesis for well-practiced facts and suggest that people's representations of composite numbers are influenced by mathematics instruction (i.e., “multiplication tables”).

Neighborhood effects for composite (i.e., non-prime) numbers

Decision-making is commonly impaired in those with cognitive decline. I compared individuals with Mild Cognitive Impairment (MCI; n = 45) and cognitively healthy older adults (n = 45) in their ability to improve their decision-making in the Iowa Gambling Task over time. On each trial, participants chose from one of four decks of cards and received a net gain or loss. The four decks differed in their expected value (EV). They continued choosing among the decks for one hundred trials, trying to maximize their winnings. Results indicate that both groups learned to choose from the higher EV decks over time, but those with MCI learned more slowly. The data were fit to a reinforcement learning model that indicated those with MCI performed worse in part because of task strategy: those with MCI were less likely to perseverate on wins and updated their beliefs more slowly to losses.

Individuals with Mild Cognitive Impairment are impaired in a gambling task

Procrastination is universal but not monolithic; a second dimension, the Intention–Behavior Gap (IBG), has been suggested to be relevant. The IBG is the difference between one’s intentions and one’s behavior. So far, the IBG has not been directly measured within a behavioral experiment. We assigned subjects a week to work on a lengthy online reading task. Before the task started, subjects reported their plan by the number of reading units for each day. We found great variability in their plans, actual time course and the difference between the two (IBG). The time course of work ranged from completing earlier than planned to not finishing. We calculated correlations between the mean unit completion day, the IBG, the Causal Dimension Scale, indices of regret and satisfaction, and scales for procrastination (General, Active and Irrational). Besides the correlations expected based on definitions, we did not find any significant correlations.

Procrastination and the Intention-Behavior Gap

The propensity for people to avoid mentally demanding tasks in the absence of reward is well documented. As a result, humans are often described as cognitive misers. This characterisation, while consistent with the psychological literature, contradicts everyday instances of effort being sought: reading, board games, and brain-teasing puzzles. Such examples however are markedly different from the types of tasks typically used in the mental effort literature (e.g., working memory tasks, demand selection tasks). The current set of experiments assessed whether the type of task (i.e., N-Back, Number Sequence Problems [NSP], or Anagrams) affects people’s aversion to, or desire for, increased effort. On average, across 3 experiments, participants showed an aversion to effort regardless of whether the effort required was more attentional (N-Back) or cognitive (NSP and anagrams) in nature, and were willing to forgo financial reward in order to avoid more difficult tasks. A minority of participants, however, sought more effortful tasks for equal or lesser reward.

Does Mental Effort Avoidance Depend on the ‘Type of Effort’?

To maintain performance in perceptual-motor tasks involving interactions with the external environment, appropriate regulation of the arousal level is essential. Both internal (learning and saturation) and external (stimuli) factors can be assumed to affect such arousal levels. We investigated these factors by using a line-following task, in which participants had to follow a scrolling line with a circle. Participants recruited through crowdsourcing engaged in this task for 30 minutes. They responded to periodical probes during the task to indicate their level of concentration thereon. Experiment 1 tested the effect of external stimuli designed to decrease/increase arousal levels in this task. Based on the results of this basic experiment, Experiment 2 switched the stimulus pattern from high to low arousal / low to high arousal during the task. As a result, we found differences between task conditions, suggesting the effect of adaptive stimulus presentation to maintain the arousal level.

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Can you tell them apart? Using machine learning to classify bilinguals’ and multilinguals’ cognitive and linguistic performance

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