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Women are twice as likely as men to suffer from generalized anxiety disorder. Inhibition is the most studied executive function in anxiety disorders and research has shown that elevated levels of anxiety are related to slower response times on tasks assessing inhibition. The aim of the present study was to compare inhibitory control toward emotional words, including those eliciting anxiety, in naturally cycling (NC) women and women taking different kinds of oral contraceptives (OC), androgenic and anti-androgenic OC. We present two phases of one study: a cross-sectional phase with 240 women and a daily diary phase with 89 women. Inhibitory control was measured by an Emotional Stroop task in the two phases. In both phases, there was no difference in anxiety levels between groups. However, the cross-sectional study showed that in women taking anti-androgenic OC, the higher the level of anxiety, the longer the reaction time on the Stroop task. The daily diary study showed that anti-androgenic OC users had slower response times on all the categories of words, compared to androgenic OC users. Further studies should focus on the link between different kinds of OC and inhibitory control as it can be linked to anxiety symptoms in women.

CogSci 2023

Impaired Inhibitory Control in Women Taking Anti-Androgenic Oral Contraceptives – Implications for Anxiety

The University Big Data Challenge (IUBDC) successfully concluded its latest edition, bringing together national health industry leaders, policymakers, and dignitaries for a conference centered on mental health research and innovation. Participants had the opportunity to showcase their projects in a poster session, engaging with representatives from industry and academia. The event provided a rich network of industry and academic professionals, offering pathways for scholarly publication, access to curated datasets, learning materials materials, and workshops.

Held at the Microsoft Headquarters in Toronto on July 26, 2024, the challenge featured hands-on data science and AI research projects focused on mental health. For over seven years, IUBDC has fostered collaboration between data science and health professionals and motivated university students. This year’s projects, which spanned topics from dementia to addictions, aimed to enhance our understanding of mental health, improve patient lifestyles, and reduce disease risk. Abstracts from teams were published in the NRC Research Press STEM Fellowship Journal, with all team's works featured by Underline publications.

Leveraging Open Data Analytics and Machine Learning to Improve Mental Health Research and Innovation

2024 Inter-University Big Data And AI Challenge -

Thee challenge featured hands-on data science and AI research projects focused on mental health. 

poster

The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

Join us!

<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
<br>[**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
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The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

This paper proposes and tests a method to study people’s blame attributions displayed in ‘Tweets’. Artificial intelligence (AI) incidents were chosen as they are a contemporary topic that is often talked about on Twitter, and are a “problem of many hands”, where multiple different agents bring about an outcome. The benefit of the method is that it was able to replicate and contextualize findings from experimental settings. For example, AIs get blamed more for negative outcomes, whilst humans get blamed for their intention. Another benefit of the approach was that it was able to identify novel factors that haven't been previously studied experimentally. For example, people were willing to directly blame data for being biased and blamed AI for replacing humans. The proposed method can serve as a way of avoiding entrenched measurement bias within a specific field by allowing researchers to update their frameworks with novel factors.


Blame attribution in human-AI and human-only systems: Crowdsourcing judgments from Twitter

In this study, we present a stimulus set that contains 80 action videos depicting 40 action exemplars (communicative and non-communicative), each performed by a social robot and a female actor with the same manner, timing, and number of repetitions. We describe the creation of the video stimuli, the normative tests conducted to validate the stimulus set (N=438), and the data annotation process. We compute an H score for each action based on entropy, which takes into account the homogeneity of the intended labels for the observers of the actions. We also report the confidence level of the participants and the frequencies of the action classifications. We discuss the implications of the results of this unique and standardized source that is suitable to use in various research areas in cognitive science, not limited to human-robot interaction but also social cognition and cognitive neuroscience.

A Set of Communicative and Noncommunicative Action Video Stimuli for Human-Robot Interaction Research

This study investigated the empathy of humans toward non-biological agents like robots and compared it to their empathy toward their conspecifics. 90 participants watched a human and a robot in painful scenarios (32 short videos created with animation techniques) and rated the intensity of pain experienced by the agent. The scenarios were created by manipulating the type of agent experiencing pain (human or robot), the source of pain (object or person), and the type of pain (physical or emotional). Results showed that humans were perceived to feel more pain than robots. In addition, the perceived pain scores were higher for scenarios where the source of pain was an object compared to a person, however, this depended on the type of pain. Emotional pain scores showed significant differences between the source of pain being an object versus a person, but physical pain scores did not depend on the source of pain.

Perceived pain of humans and robots: An exploration of the effect of agent, pain source, and pain type in pain attribution to biological and non-biological agents

In early stages of language acquisition, children often make inflectional errors on regular verbs. For example, Spanish-speaking children produce –a (present-tense 3rd person singular) when other inflections are expected. Most previous models of morphology learning have focused on later stages of learning relating to production of irregular verbs. We propose a computational model of Spanish inflection learning to examine the earlier stages of learning and present a novel data set of gold-standard inflectional annotations for Spanish verbs. Our model replicates data from Spanish-learning children, capturing the acquisition order of different inflections and correctly predicting the substitution errors they make. Analyses show that the learning trajectory can be explained as a result of the gradual acquisition of inflection-meaning associations. Ours is the first computational model to provide an explanation for this acquisition trajectory in Spanish, and represents a theoretical advance more generally in explaining substitution errors in early morphology learning.

Modeling Substitution Errors in Spanish Morphology Learning

Understanding how children process ambiguous words is a challenge because sense disambiguation depends on sentence context bottom-up and top-down aspects. Here, we seek insight into this phenomenon by investigating how such a competence might arise in large distributional learners (Transformers) that purport to acquire sense representations from language input in a largely unsupervised fashion. We investigated how sense disambiguation might be achieved using model representations derived from naturalistic child-directed speech. We tested a large pool of Transformer models, varying in their pretraining input size/nature as well as the size of their parameter space. Tested across three behavioral experiments from the developmental literature, we found that these models capture some essential properties of child sense disambiguation, although most still struggle in the more challenging tasks with contrastive cues. We discuss implications for both theories of word learning and for using Transformers to capture child language processing.

Modeling Children's Word Sense Disambiguation with Large Language Models: Insights and Challenges

Artificial grammar learning (AGL) experiments are commonly used to assess implicit learning. However, in these tasks participants may acquire explicit rather than implicit knowledge. We combined an AGL task with measures of metacognition to assess whether participants acquired explicit knowledge of the grammar. Participants were initially exposed to sequences of visual symbols generated by an artificial grammar, followed by a testing phase in which they classified sequences as either grammatical or ungrammatical. On a subset of trials (choice trials) participants were given the option to either take or skip this classification. If participants had explicit knowledge of the grammar, they should be more likely to take trials when they are confident and skip when they are not. Participants learned the grammar, and performed better in choice trials than in forced trials, demonstrating explicit knowledge of the grammar. These results suggest that AGL paradigms might elicit explicit rather than implicit learning.

Using measures of metacognition to assess the acquisition of implicit and explicit knowledge in artificial grammar learning tasks

Morphosyntactic rules are central to compositional structure in language, and therefore crucial in language acquisition. Previous research on L1 and L2 acquisition suggests that active production leads learners to notice linguistic generalisations more than passive learning does. Here, we explore whether production helps learners to access less obvious generalisations when more than one analysis is possible. We taught participants an artificial language compatible with two grammatical analyses: one that marks semantic roles using word order, the other using case suffixes. After training, half of the participants do a comprehension task, and the other half do a production task. We predict that the comprehension group will adopt the English-like word order analysis, while the production group will be more likely to learn the recurring case suffixes and thus adopt the alternative analysis. Overall, our results shed light on the role of retrieval and production for the acquisition of morphosyntactic generalisations.

Does production facilitate learning morphosyntactic generalisations?

Previous studies suggest that speech categorization is established during the first year of life (Werker & Tees, 1984). However, recent work suggests protracted development through adolescence (McMurray et al., 2018). While the early development has been thought to be driven by the statistical properties of the input, it is unclear if this late phase is also input driven. We asked whether being exposed to diverse linguistic input leads to different patterns of speech categorization. Visual Analogue Scaling task and social network questionnaires were used to measure speech categorization and the effects of linguistic diversity in children’s and adults’ social environments. Results suggest that the development of speech perception is not finished during the first year of life. It continues to be shaped by the developmental changes across the lifespan (McMurray et al., 2018). Importantly, diverse linguistic experience makes both children and adults more flexible in their speech categorization.

Network Diversity Leads to More Flexibility in Speech Categorization

Language learning emerges from dynamic communicative exchanges characterized by two key dimensions. First, children are not simply passive observers, but instead actively seek new information. Second, caregivers scaffold the learning environment. We will present results from a preregistered study (target N=90; 36-72 mos; preregistration: https://osf.io/s24eb/?view_only=7cbed0ef12354103b5fe7a897d13f694; current sample size: N=51) investigating how caregiver scaffolding and children’s information-seeking mutually contribute to word learning. We introduce a novel word learning paradigm that independently varies whether children and/or their caregivers can structure input in a joint Learning Phase and measure children’s word learning in a pre-post test design. We predict that (1) children and caregivers will actively structure learning events and (2) children will benefit when they themselves and their caregivers can structure the Learning Phase. Our results will provide insight into how children’s information-seeking strategies interact with caregivers’ scaffolding to create informative word learning moments.

The dynamics of child-driven active information-seeking and caregiver scaffolding during word learning

The early moments of compound and pseudocompound visual word recognition were investigated by probing their “constituent” concepts (e.g., BED in bedroom and CAP in capsize). This was achieved by concomitantly presenting target words in one visual field (left or right, projected to the right or left hemisphere, respectively) and a picture representing the referent of either the first or second “constituent” in the opposing visual field. The stimuli were presented for 133 ms followed by a backward mask and participants judged whether the word and picture were related to each other. The experimental manipulations consisted of target word type (compound or pseudocompound), word complexity (whole word or “constituent”), probed constituent position (first or second “constituent”), and word projection (left or right hemisphere). Our results suggest that the “constituents” of compounds and pseudocompounds are conceptually accessed. We discuss the implications of our findings for the nature of the visual word recognition system.

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Blame attribution in human-AI and human-only systems: Crowdsourcing judgments from Twitter

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