Canada

In a ground war, achieving peace is challenging even when both sides suffer. Here we demonstrate that when humans jointly viewed a battlefield as perceptually grouped by colors, a factor irrelevant to the rules of land-war games, they waged fewer wars and accumulated more wealth due to the perceptual border’s constraint on their aggression. This facilitation of peace arose from perceptual grouping that serves as a visual common sense shared by humans, as a mismatched perceptual grouping between players failed to limit war. Moreover, it relies on the geopolitical principles that make ground wars destructive and disappeared when these principles were violated. Together, we show that perceptual grouping limits human ground war as it is a mutually acknowledged “rallying point” where expectations of peace converge.

CogSci 2022

Perceptual Grouping for War and Peace

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. 





Quantifying the meaning of a word is a complex challenge. Humans can encode semantic information along a large and diverse range of semantic dimensions for any given word. Whilst a number of studies have applied a range of techniques to quantify word meaning along specific dimensions, little work has focussed on the socio-semantic dimensions of meaning. Here, we present data that quantifies the socio-semantic representations of 2,700 Czech words along the dimensions of gender, location, political, valence and age. We also demonstrate the utility of the data set by calculating an estimate of socio-semantic similarity between all words, which can be used to identify words that are either proximally close or distant in socio-semantic space.

Quantifying the Socio-semantic Representations of Words

As techniques for generating deepfake videos (in which a generative adversarial network is used to alter the human subject of a video) improve, so does the potential to use them for nefarious ends. It is therefore important that we identify those among us who are most susceptible to being fooled. Tidler and Catrambone (2021) presented results which suggest that one’s political orientation and affect detection ability are strong predictors of one’s ability to detect deepfakes. The current work is a re-analysis of their data, intended to reveal a more highly-resolved profile of a susceptible individual. The data (N = 169) were submitted to a k-means clustering algorithm which revealed a 2-cluster solution.

Younger, more progressive thinking individuals, with agnostic or atheistic religious beliefs, and greater adeptness at spotting deepfakes formed Cluster 1; and individuals with more conservative political leanings, greater religiosity, and less skill at detecting deepfakes formed Cluster 2.


Assessing Deepfake Video Detection Ability Using Unsupervised Machine Learning

Faces are an important source of perceptual and social information. Multiple cortical regions including the fusiform faces area (FFA), the superior temporal sulcus (STS), and medial prefrontal cortex (MPFC) respond more to dynamic faces than videos of toy objects, human bodies, and pastoral scenes. Do face-preferring regions in FFA, STS, and MPFC have different functions? To address this question, we re-analyzed functional magnetic resonance imaging (fMRI) data from seven different experiments that included a dynamic faces versus objects localizer. Each of the seven experiments tested different perceptual and social features using dynamic videos, point light displays, narratives, and animated cartoons. Using a functional region of interest approach, we observed a significant condition by region interaction in four of the seven experiments. Thus, although FFA, STS, and MPFC respond more to dynamic faces than objects, bodies, and scenes, these three regions differ from each other functionally.

Face-preferring regions in FFA, STS, and MPFC have different functions

The following studies investigated the perceptual processes that are the basis of the face inversion effect (FIE). We evaluated the effects of disrupting holistic information conveyed by the face contour/outline. In Experiment 1 (n=144) we blurred the contour of the faces and using an old/new recognition task we found that a robust inversion effect similar to that for normal faces remains for these new no-contour faces. However, a significant reduction in overall performance was found for no-contour vs normal faces. In Experiment 2 (n=74) instead of blurring we inserted a novel face contour to replace the normal one and found the same pattern of results as in Experiment 1. Our results suggest that the holistic information provided by the face contour does not on its own influence the FIE, however it plays a role in face recognition more generally.

Manipulating the face contour affects face recognition performance leaving the Face Inversion Effect unaltered

In virtually every activity we engage in — from analyzing economic trends, to predicting which of two football teams is more likely to win a game — our minds are tasked with separating signal from noise. Such computations benefit from the fact that our minds are highly attuned to the statistical structure of the world. But how quickly do we detect statistical structure — and to what extent is our sensitivity to structure rooted in perceptual processes? To address this, we asked observers to judge whether briefly presented visual stimuli were generated randomly or non-randomly. In as little as a tenth of a second, people exhibited the same stable biases of statistical perception that they exhibit in classic cognitive tasks (i.e., without time constraints). These results suggest that certain biases of subjective probability may arise not from how we think about randomness, but from how we perceive statistical information in the first place.

The speed of statistical perception

English is the dominant language in computer science. In addition to English-based academic papers, English is frequently the only language provided in introduction sections and manuals of command and software libraries, which are essential aspects of computer programming. Hence, English-as-a-second-language (ESL) learners may have difficulty studying computer science because they must learn this field while also learning English. Despite this problem, few studies have assessed the difficulty level of computer science texts for ESL learners.
Ideally, the difficulty levels of texts are assessed by having groups of ESL learners read them. However, owing to the excessive time and financial costs involved, such practices can be impractical.
Hence, using two highly accurate automatic readability assessors based on natural language processing (NLP) techniques, we assessed the readability of various computer-science-related texts for ESL learners. The first assessor is based on state-of-the-art deep transfer learning, and the second is based on classical machine learning and applied linguistics. For training the assessors, we used a standard corpus employed in NLP, which was annotated by professional English teachers to evaluate the readability of the texts for ESL learners. To conduct the experiments, we built a collection of computer science texts ranging from academic papers to software manuals (READMEs) crawled from a source-code hosting website, namely GitHub. The experimental results showed that intermediate ESL learners were able to read most of the computer science related texts.

Neural Language Model-based Readability Assessment of Computer Science Introductory Texts for English-as-a-Second Language Learners

This study examines the relations between parent–educator communication and preschoolers’ numeracy, literacy, and executive function (EF), and explores if these relations work through the home numeracy environment (HNE) and the home literacy environment (HLE). Children (51.33% female, M = 57.52 months, SD = 3.62) were enrolled in a state-funded prekindergarten program (n = 211) or in another community-based preschool program (n = 52). Analyses revealed a significant relation only between parent–educator communication and numeracy skills in the spring of preschool (β = -.14, p = .002). Unexpectedly, higher parent–educator communication scores in the spring of preschool were related to lower numeracy skills at that same time. Furthermore, the indirect effects for the HNE and HLE were not significant for all outcomes. The findings suggest that it may be beneficial for educators to engage with families early in the academic year to support children’s numeracy skill development.

The relations between parent–educator communication, the home environment, and children’s outcomes in preschool

The present study assessed both implicit and explicit math self-concept, math-gender stereotype, math achievement, and math anxiety in 260 Swiss children from 1st, 3rd, and 5th grade. In line with prior research, explicit math self-concepts were found to decrease as a function of participant age cohort. Furthermore, already children in the first grade were found to implicitly endorse the math-gender stereotype. In line with these findings, math anxiety was found to be higher in older cohorts than in younger, and it was non-trivial in all age groups. Lastly, as expected, math achievement was higher for older children. Taken together, the present study found patterns of stereotypes increasing as a function of age, with simultaneous increases in math anxiety, and simultaneous decreases in math self-concept. These findings underline the importance of studying math-related constructs in children. Understanding these patterns will contribute to sustaining STEM interests in young children and adolescents.


Math Self-Concept, Stereotypes, Achievement and Anxiety – A Cross-Sectional Study

Math anxiety (MA) is negatively related to math performance. One proposed intervention with potential to disrupt the MA-math performance link is expressive writing. The current study aimed to conceptually replicate Park and colleagues (2014). In that study, the authors concluded that expressive writing effectively boosted math anxious students’ performance. In our current sample of 168 college students, participants randomly assigned to the expressive writing condition were no more accurate at posttest than were other participants assigned to a math self-concept intervention, active control, or passive control. Additionally, participants in the math self-concept and active control conditions reported lower state MA immediately following the intervention; participants in the expressive writing and passive control conditions reported no differences between pretest and posttest state MA. The current study provides boundary conditions for the effectiveness of expressive writing interventions in ameliorating MA during difficult math tasks and illuminates potential mechanisms underlying MA.

Does Expressive Writing Blunt the Effects of Math Anxiety on Math Performance? A Conceptual Replication and Extension of Park et al. (2014)

Learning to write is characterized by bottom-up mimicking of characters and top-down writing from memory. We introduce a CNN-RNN model that implements both pathways: It can (i) directly write a letter by generating a motion trajectory given an image, (ii) first classify the character in the image and then determine its motion trajectory `from memory', or (iii) use a combination of both pathways. The results show that, in one-shot and few-shot learning, the model profits from different combinations of the pathways: The generation of different character variants works best when the top-down is supported by the bottom-up pathway. Refilling occluded images of efficiently learned characters works best when using the top-down pathway alone. Overall, the architecture implies that a weighted merge of bottom-up and top-down information into a latent, generative code fosters the development of compositional encodings, which can be reused in efficient learning tasks.

Premium content

Perceptual Grouping for War and Peace

Downloads

Next from CogSci 2022

Quantifying the Socio-semantic Representations of Words

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES