Canada

Over the last few decades, Causal Model Theory (CMT) has become a dominant framework for human causal-based reasoning, including categorization and inference. CMT prescribes how people should reason about probabilistic events in terms of causal models. In typical causal-based categorization experiments, subjects are provided with verbal descriptions of causally linked features, generally including probabilistic information. Another line of research focuses on perceived or experienced causal events, rather than on verbal descriptions. In this work we asked whether effects which are consistent with CMT, and that have been obtained with verbal descriptions, generalize to visually perceived events. In two experiments, we presented subjects with videos of a 3D A→B causal event rather than verbal descriptions. In Exp. 1, we found that subjects who saw the causal event did not show the coherence effect in categorization (i.e., subjects tend to rate the null ¬A¬B event as a category member). However, subjects who did see the null event during training did show the effect. In Exp. 2, we ruled out the possibility that Exp. 1’s results were simply an effect of how frequently events were experienced during training. We conclude that a one-shot perceived causal event is not sufficient for people to show causal-based reasoning as CMT predicts.


CogSci 2022

Categorizing perceived causal events

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

[![](https://assets.underline.io/markdown_image/1/image/b087f8a4dc5816d6e1a6514e59c59ac3.png)](https://drive.google.com/file/d/1T96GzPqObXrMln2BMByCSXTSizjTg69P/view?usp=sharing)

You need to log in with the email address you registered with. Access credentials have been sent to your email. 

Please be sure to check your spam and other email folders if you do not see an email confirmation right away.

Please log in to explore this event.

To access NAACL 2025 event page you are required to register. Please follow [**this link**](https://2025.naacl.org/registration/registration/) to register. Access will depend on your registration type.

Please register!

NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

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. 





Decades of sentence comprehension research have focused on the cognitive factors that determine the processing difficulty. In this work, we investigate how locality and interference effects interact with each other in Hindi, a head-final language with flexible word order and a rich case-marking system. Using linear regression, we examine the extent to which the dependency distance of a sentence, which is based on retrieving previously-stored elements in the working memory can be predicted by various other backward-looking measures of processing complexity. We evaluate how dependency distance is influenced by similarity-based interference, case density, information structure, and finally, a forward surprisal measure proposed recently to model planning processes in comprehension and production systems. Overall, our results indicate that similarity-based interference and case density are significant positive predictors of dependency length, lending credence to the view in the literature that the mechanisms underlying locality might be driven by interference.


Interference and Case Marker Effects in Dependency Locality: Insights from Hindi

Research on cognition in people with impairments is still insufficient to account for cognitive diversity. This study addresses the issue by making the first attempt to examine synthetic speech perception in congenitally blind versus sighted participants. With the rising prevalence of modern speech technologies, the study of synthetic speech perception across various groups becomes indispensable. Although recent research revealed certain advantages of blind listeners compared to sighted ones, these studies only addressed natural speech perception. Using a speeded AX discrimination task in a controlled experiment, we test how the two groups perceive signal distortions resulting from differing synthetic speech qualities, generated using neural networks. Results show that blind participants had significantly better discrimination, especially in the difficult condition involving small distortions. This suggests that blind participants were sensitive to deviations that remained imperceptible to sighted participants. Theoretical implications for speech perception and practical insights for Text-to-Speech technologies will be discussed.


Do we all perceive synthetic speech in the same way? Testing congenitally blind versus sighted listeners

In describing images, visual and linguistic processes coordinate with each other and they both proceed in a linear fashion. Three main theories about the nature of the relation between these processes and how they unfold over time have been proposed in the literature of 'linearization'. In this work, we investigate the hypotheses put forward by these theories utilizing a corpus of spoken image descriptions with speakers' eye-movement data. We explore the time alignment between the fixations on objects and the utterance of the corresponding nouns in the data. In contrast to previous studies, this dataset allows us to inspect unrestricted language production in the context of real scenes on a larger scale. We find both confirming and conflicting evidence for each of the theories in question, suggesting that the intricate relation between eye movements and language production may involve mechanisms proposed by all three theories.


Time Alignment between Gaze and Speech in Image Descriptions: Exploring Theories of Linearization

How do children within the autism spectrum (ASD) integrate linguistic and visual information during real-time processing of sentences and dynamic scenes? We employed a visual word paradigm with sentences containing different verb types (causatives such as 'crack'; perception/psychological verbs such as 'examine') and with different scene contexts (agent in the scene moving towards a target object or remaining neutral). Participants were a group of 10-year old ASD and typically developing children (TD). We reasoned that the two dominant theories about ASD cognitive processing style—the weak central coherence theory (WCC) and the enhanced perceptual function (EPF)—make different predictions on how and where information about linguistic and visual information may be integrated. WCC predicts that ASD children would have greater difficulty with sentence and scene semantics while EPF predicts an early, post-perceptual integration via verb-specific and object (but not full scene) representations. The results suggest that ASD children differ from TD children by computing the link between verb and object faster, suggesting an enhanced post-perceptual integration.


Enhanced Visual-Linguistic Interaction in Children within the Autism Spectrum: Evidence from the Visual-World Paradigm with Dynamic Scenes

During speech perception, humans constantly sense multiple sources of information. As so, the McGurk effect has been typically conceived as a prototypical example of language multi-modal integration, specifically, it has been used to study audio-visual
integration during speech. The McGurk effect arises when in-congruent audio-visual stimuli are paired and perceived as a different syllable ( auditory /ba/ +
visual /ga/ = percept “da” ).

We developed a hierarchical computer model, based on self-organizing maps and Hebbian learning to study speech multi-modal integration. Our architecture allows studying the McGurk effect purely on bottom-up processing of audio-visual information.
We trained several versions of the model and measured the activation similarity between
McGurk alike stimuli and congruent ones by means of mutual information. Our results suggest that the illusory percept arises from the best congruent representation that reduces uncertainty. Furthermore, the reliability of each sensory modality determines the best congruent representation.


Multimodal integration as predictions. An explanation of the McGurk effect

Correlation judgments are at the core of belief formation. In previous studies of correlation judgment from 2D scatterplots, observers underestimate correlations, and display stronger underestimation biases when the scatterplot is shown in a landscape view than in a portrait view. Yet, it is unclear how these biases arise. Here, we propose that observers are Bayesian learners who perform “mental regression” using the observed data points in graph. Accordingly, judgment errors can arise from biased visual information sampling. We test our model’s predictions with two eye-tracking experiments and find that the Bayesian learning model, applied to information obtained from visual fixation data, replicates classic behavioral findings. The model also predicts trial-level estimation biases at a high accuracy level. Our study shows how computational models trained on process-level data can shed light on the cognitive mechanisms underlying belief formation, and yield theory-driven practical implications for data visualization and statistical communication.

Information sampling explains Bayesian learners’ biases in correlation judgment

The traveling salesperson problem (TSP) is an NP-Hard problem that computers find difficult to solve. Humans are surprisingly good at solving the TSP, with solutions within 10% of optimal for problems with up to 100 points, constructed in time linear with the number of points. We propose that humans solve the TSP by initially clustering the points and then connecting them first within and then between clusters. In this study, 67 participants first clustered 40 stimuli and then solved them as TSPs. Strikingly, participants' TSP solutions perfectly followed their clusters for 52% of the stimuli. Further, participants' TSP solutions' were more congruent with their clusters for stimuli with statistically higher levels of clustered structure. This provides strong evidence for the clustering proposal. Random TSP solutions, however, showed no such congruence to cluster structure. These findings suggest that clustering might be a fundamental ability for reasoning about graph-theoretic algorithmic problems.

Use of clustering in human solutions of the traveling salesperson problem

Humans are uniquely adept at extracting structure from the world around them. It is well known that people often form hierarchical task representations during learning, even when a task does not explicitly necessitate a hierarchical representation. Still, how individuals capitalize on this structure to facilitate behavior is an open question. In the present study, we address this question by carefully examining patterns of response time switch costs in a hierarchically structured visuomotor association learning task, to adjudicate between multiple models of behavior. We find evidence that participants do appear to navigate through a hierarchical representation of stimulus-response associations as they prepare responses, rather than maintaining a non-hierarchical, flat model or being primarily affected by changes in stimulus features. These results establish the existence of hierarchical mental representations even for static visuomotor mappings, and imply that such representations are internally navigated in an orderly manner during action selection.

Climbing the Tree: Structured Hierarchical Representations in Visuomotor Maps

Three-dimensional objects pose a challenge for our visual system, since we can only view objects from a single limited perspective at a given moment. Previous work found that given a limited perspective, infants represent 3D objects as complete volumes. Our study replicated this finding in 4- to 7-year-olds and adults, using an explicit prediction measure rather than looking times. We also explored whether humans have a bias to represent visually limited 3D objects as symmetrical rather than asymmetrical across shape, size, texture, and color. Overall, there was an above-chance preference for full volumetric and symmetrical object completion that increased with age. Low-level perceptual similarity of choices did not predict participants’ choices. Moreover, we evaluated ResNet-50 neural networks on the same tasks: they represented objects as complete volumes, but did not show substantial preference for symmetrical 3D representations. This raises the possibility that incorporating human symmetry biases could improve computer vision.

Three-Dimensional Object Completion in Humans and Computational Models

Because there are many situations in our daily life in which the option space is not discrete but continuous, recently developed decision models have been able to examine the cognitive processes underlying choice in laboratory tasks with a continuous outcome space. One of the most important of these continuous models is the circular diffusion model (CDM) by Smith, which has been shown to account for continuous space data from a wide range of paradigms, including color identification, orientation, brightness, pricing. However, in addition to the inherent complexity of this model, it has become more complex in order to predict reliable data patterns, making it a tool only for experts. Here we propose a more easy version of the CDM, the EZ version, to fit the model on continuous scale data. The EZ-CDM for continuous choice space tasks can estimate the parameter values for the cognitive processes underlying without considering the response time distribution but only using traditionally favored summary statistics (i.e. the mean and variance of response time, and angular variance of accuracy.) by simple formulas that can be computed easily and needs neither theoretical knowledge of model fitting nor much programming skills. Here, we formulate the EZ method and show that, despite being easy and fast to calculate, it’s performance in recovering true parameters is acceptable.

Premium content

Downloads

Next from CogSci 2022

Interference and Case Marker Effects in Dependency Locality: Insights from Hindi

Similar lecture

ACCESS : A Benchmark for Abstract Causal Event Discovery and Reasoning