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Group disparities, such as the gender wage or racial achievement gap, pervade our society. Unfortunately, from a young age, we often attribute these disparities to inherent features of groups, leading to problematic outcomes (e.g. overgeneralizing, justifying group disparities as the norm). Structural thinking, which attributes disparities to a stable external structure, could be an alternative causal framework, although little work has explored its outcomes. Could structural thinking lead to more positive social outcomes than inherent thinking? Adults who learned about the structural (versus biological or cultural) origins of an occupational disparity were more context-sensitive in their generalizations, less likely to endorse the disparity, and provided more structural interventions. 7- to 9-year-olds showed hints of the former two as well. Cultural thinking showed an intermediate pattern between biological and structural thinking. Overall, structural thinking could be a more socially fruitful way of thinking that mobilizes progress on social disparities.

CogSci 2023

Let&#39;s talk structure: the positive outcomes of structural thinking

Let's talk structure: the positive outcomes of structural thinking

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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#### **CogSci 2023 Program Book**
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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.

Prior knowledge can affect causal judgments by inducing expectations in learners. One important type of prior knowledge is about domains; for example, physical systems are typically believed to be more deterministic than psychological ones. We examine the role of these types of determinism expectations in shaping subsequent causal judgments, and we argue that they mediate effects of abstract domain-wide expectations. Study 1 shows that expectations of determinism—in particular, of sufficiency—positively predict judgments of strength and sufficiency ratings, even when holding statistical data constant. Study 2 shows that abstract expectations at the domain level have an effect on causal judgments, but these effects are mediated (differently between conditions) by vignette-level expectations of determinism. These results jointly suggest a productive way to conceptualize domain effects on causal judgments, namely as effects of expectations about the causal relations being investigated.

Expectations of Determinism Underlie Domain Effects on Adult Causal Learning

In contrast to rationalist models, people do not always have singular goals or even explain other people's behaviour as goal pursuit. Elsewhere, counterfactual accounts determine ways an event may be modified to measure the effects of different causes. We explore how people explain surprising behaviour in two online experiments, with the help of computational modelling. First, 90 UK-based adults rated the likelihood of various outcomes given various situations. Then 49 others saw each situation and outcome, and verbally gave their best explanations for what happened. People generate a range of explanations for even incongruous behaviour. We present an expanded version of a counterfactual effect size model which uses innovative features (crowdsourced parameters and free text responses) that not only can generalise to human situations and handle a range of surprising behaviours, but also performs better than the existing model it is based on.

Extending counterfactual reasoning models to capture unconstrained social explanations

We investigate the role of causal stability in guiding children’s exploration. To study this, we developed a computerized version of an information-search paradigm to study how children dynamically explore different agents and backgrounds to learn more about their causal stability. We presented 5- to 7- year old children (n = 60) with stable and unstable causes and assessed children’s causal attributions of outcomes and their exploratory behavior as they tried out previously observed and novel causes across previously observed and novel backgrounds. We find that children acknowledge causal instability in their causal attributions. They become increasingly adept at tracking causal efficacy across multiple factors simultaneously, but this does not translate into a blanket preference for exploring stable or unstable causes. We suggest a possibility that causal (in)stability guides exploration in more subtle and indirect ways and discuss the implications of our findings for the development of active exploration.

The Role of Causal Stability in Children's Active Exploration

There are at least three ways of learning how the world works: learning from observations, from interventions, and from explanations. Prior work on causal inference focused on how people learn the underlying causal structure of a model through observation and intervention. Our study is the first to look at what people can learn from explanations in the context of a causal structure learning paradigm. We develop a normative inference model that learns from observations and explanations, and compare the model's predictions to participants' performance. The task is to infer the causal connections in 3-node graphs, based on information about their co-activation, and explanations of the kind "B activated because A activated". We find that participants learn better from explanations than from observations. However, while the normative model benefits from having observations in addition to explanations, participants did not.

Show and tell: Learning causal structures from observations and explanations

Children exhibit a sophisticated explanatory judgment: they expect, generate, and judge explanations. Do children also exhibit a metaphysical explanatory judgment, such that everything must have an explanation? If they do, this would conform to what philosophers have called the Principle of Sufficient Reason (PSR). In this study, 6-9-year-old children (N = 55, Mage = 8.07, SDage = 1.32) were shown a number of statements across domains (Psychology, Biology, Nature, Physics, Religion, and Supernatural) and were asked if they agree or disagree that each statement has to have an explanation. As a comparison, children were also asked about coincidences, which, metaphysically, do not have an explanation. We found that children conform to the PSR: of the statements that children thought were true, children of all ages believed that the statements must have an explanation, except for coincidences. This is the first step at uncovering the development of our metaphysical explanatory judgment.

Must there be an explanation? Children and the Principle of Sufficient Reason

In science, we use common graphical representations to indicate changes in events over time, independent of domain. Are children also sensitive to abstract patterns in the ways events change over time? In a series of four experiments, we show that young children (range: 48-84 months) distinguish different function families (Exp 1). Children can also distinguish specific function types within function families (e.g., between linear and sigmoid monotonic functions; Exp 2); nonetheless, they will group different function types within a family together, rather than with functions in a different family (Exp 3). Finally, we show that children’s sensitivity to functions is abstract, allowing them to match observable causes to verbal descriptions of their effects (Exp 4). These results suggest that although some aspects of function understanding, like learning how to interpret graphs, requires formal education, the ability to identify abstract functional relationships is intuitive and early emerging.

Representations of Abstract Relations in Early Childhood

Previous work has shown that even young infants can “add” or “subtract” quantities of objects using the non-symbolic object tracking system. We asked how these non-symbolic addition skills may change as children learn to count. Thirty 3-year-old children completed a non-symbolic exact addition task, in which children saw an animal collect two sets of objects inside a container, and children were asked to choose the summed total from two alternatives. We also measured children’s knowledge of the count list and their working memory capacity. We found that children with higher count knowledge performed better in solving non-symbolic addition problems with small sets of objects (<3). However, even strong counters struggled when one of the operands exceeded working memory capacity (>=3). Our results suggested that non-symbolic addition skills in 3-year-old’s may be influenced by both their emerging number knowledge and the object tracking system.

How count-list knowledge relates to 3-year-olds’ exact non-symbolic addition abilities

Motion quality can differentiate experts from novices in fields like surgery (Ershad et al., 2018). We extend approaches used by researchers in that field to examine the relationship between motion and skill in a children’s block-building task. We ask whether the relationship between these two variables is detected equally well by humans and machines—in this case, motion sensors embedded in the blocks. We investigate whether adults’ judgments about motion quality and children’s overall building skill reflect children’s actual construction ability, and whether data from embedded motion sensors predict children’s skill as well as adult judgments do. We find that human raters outperform the motion sensor data. Our findings raise questions about how people form such intuitive judgments of expertise, and how automated judgments of skill can be enhanced to more accurately predict expertise in block building and other similar cognitive tasks.

Naive human judges can accurately predict expertise in children's block building. Can embedded motion sensors do just as well?

Brain function connectivity, derived from functional magnetic resonance imaging (fMRI), has enjoyed high popularity in studies of Autism Spectrum Disorder (ASD) diagnosis. Albeit rapid progress has been made, most studies still suffer from several knotty issues: (1) the hardship of effectively modeling the sophisticated brain neuronal connectivity; 
(2) the dimensionality explosion resulted from excessive voxels in each fMRI sample; (3) the poor interpretability giving rise to unpersuasive diagnosis. To ameliorate these issues, we propose a GCN-based model, namely ConResNet. Specifically, a convolutional restructuring attention is designed for efficiently ROI-interaction feature extraction. Besides, we embed a salient graph pooling method to sift salient nodes. Extensive experiments conducted on ABIDE dataset demonstrate ConResNet achieves state-of-the-art performance. Moreover, the most salient brain regions predicted by ConResNet are closely consistent with theoretical knowledge in the medical domain, providing potential biomarkers for ASD clinical diagnosis. Codes have been uploaded to Github.

GCN-based Autism Spectrum Disorder Diagnosis via Convolutional Restructuring Attention

Most models in cognitive and computational neuroscience trained on one subject don’t generalize to other subjects due to individual differences. An ideal individual-to-individual neural converter is expected to generate real neural signals of one subject from those of another one, which can overcome the problem of individual differences for cognitive and computational models. In this study, we propose a novel individual-to-individual EEG converter, called EEG2EEG, inspired by generative models in computer vision. We applied THINGS EEG2 dataset to train and test 72 independent EEG2EEG models corresponding to 72 pairs across 9 subjects. Our results demonstrate that EEG2EEG is able to effectively learn the mapping of neural representations in EEG signals from one subject to another and achieve high conversion performance. Additionally, the generated EEG signals contain clearer representations of visual information than that can be obtained from real data. This method establishes a novel and state-of-the-art framework for neural conversion of EEG signals, which can realize a flexible and high-performance mapping from individual to individual and provide insight for both neural engineering and cognitive neuroscience.

Next from CogSci 2023

Expectations of Determinism Underlie Domain Effects on Adult Causal Learning

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