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A quantum walk model is developed for emotion transmission in serial reproduction of narratives. The readers&#39; emotions are represented by density operators, and the influences of the narratives on the readers&#39; emotions are modeled by applying the controlled unitary operators to the density operators. The performance of the quantum model is evaluated on a large corpus of narratives, compared to that of the Bayesian Markov chain model. The quantum model not only outperforms the Bayesian model for all five emotion transmissions presented in the corpus but can also account for order effects in serial reproductions. These results suggest a promising first step towards extending quantum-like models to explain group-level cognition.

CogSci 2022

A Quantum Walk Model For Emotion Transmission In Serial Reproduction of Narratives

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. 
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).

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CogSci 2023

The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

A quantum walk model is developed for emotion transmission in serial reproduction of narratives. The readers' emotions are represented by density operators, and the influences of the narratives on the readers' emotions are modeled by applying the controlled unitary operators to the density operators. The performance of the quantum model is evaluated on a large corpus of narratives, compared to that of the Bayesian Markov chain model. The quantum model not only outperforms the Bayesian model for all five emotion transmissions presented in the corpus but can also account for order effects in serial reproductions. These results suggest a promising first step towards extending quantum-like models to explain group-level cognition.

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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.
 
 [**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. 

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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. 





Virtual Reality (VR) technology has been widely used in researching situations that require high ecological validity but are difficult to copy. In this paper, we compare the effect of 360-degree videos in VR head-mounted display and 2D computer screen on evoking four types of emotional states, and innovatively assess the effect of emotion evoking using the Go/No-go attention paradigm. Based on the eye movement data collected from a considerable number of participants (N=48), the result reaches marginally significance in the effect of immersion level of evoking (p=0.075), emotion’s level of arousal (p=0.019), and the interaction between them (p=0.037) under the condition of positive evoking. We find that immersive device can better evoke positive emotions with high arousal, while non-immersive device cannot manipulate the intensity of evoked emotions. Our study empirically demonstrate that high-arousal positive emotion evoking is more effective in VR than on a 2D monitor based on computational affection.

High-arousal positive emotion evoking is more effective in VR than on a 2D monitor based on computational affection

Spatial deployment of visual attention in humans is crucial for selecting and prioritizing task-relevant visual information for efficiently navigating natural visual environments in daily life. As prominent landmarks of social environments, human faces conveying salient emotion information, have been found to influence attention. We investigated if facial emotions also modulate the spatial distribution of visual attention and whether any such effect associates with individual differences in internal affective states, e.g. anxiety. Participants (n = 28) discriminated the orientation of target Gabor patches co-presented with distractors, speedily and accurately. The key manipulation was randomly presenting a task-irrelevant, face emotion prime briefly (50 ms) at unexpected time points, conveying either Neutral/Disgust/Scrambled (null) emotion signal 150 ms before
the target patches. Disgust signal modulated the gradient of attention (change in negative inverse attentional efficiency with unit change in distance from the source of emotion signal) in significant association with trait-anxiety scores, such that the direction of attention gradient flipped (spatial attentional shift) with increasing severity of trait anxiety. Neutral signal yielded attention gradients comparable to Scrambled with no clear association with anxiety, implying the presence of no anticipated effect. Altogether, the results suggest that individual trait-anxiety levels condition the effect of negative and
physiologically arousing emotion signal (e.g., Disgust) on spatial distribution of visual attention. The findings may help furthering the understanding of visual distortions underlying affective states and disorders.

Trait anxiety modulates negative affect-cued distribution of visuo-spatial attention.

COVID-19 implied social distancing, forced behavioural changes, and the economic downturn has been associated with poor mental health and wellbeing. Depression and suicide are the highly predicted psychosocial risks caused by the pandemic crises. Early studies evaluating the effect of COVID-19 on psychiatric health have succeeded in developing screening measures. However, they have been limited in understanding its relations with individual psychological flexibility. An individual's psychological flexibility determines the ability to fight against such adversities on an immediate time scale and the future psychotherapeutic treatment. We conducted an online study to examine the relationship between psychological flexibility and risks to depression and their relationship with COVID-19 peritraumatic distress. We used Multidimensional Psychological Flexibility Inventory (MPFI), Beck's Depression Inventory-II (BDI-II) and COVID-19 Peritraumatic Distress Index (CPDI) to measure the above psychological factors. The results are discussed in light of individual psychological flexibility and its association with BDI-II and CPDI outcomes.

Psychological Flexibility Determines COVID-19 Peritraumatic Distress and Severity of Depression

Can people learn causal relationships about the world from someone’s emotions? We present a computational model integrating observational causal learning with emotional information, which uses emotional displays to disambiguate the beliefs, desires, and knowledge of other agents, in turn allowing causal inferences about the world. We compared our model predictions to human causal judgements on two observational learning tasks involving multiple possible causes or multiple possible outcomes. Across three studies (N = 129,127,125), emotional displays (compared to actions alone) led people to interpret agents’ beliefs differently, which in some contexts resulted in different causal inferences. Our model closely reflected these patterns of belief and causal inference and revealed new insights on how people learn causal relationships from others’ emotions.

Modeling Causal Inference from Emotional Displays

How and why are analogies spontaneously generated? Despite the prominence of analogy in learning and reasoning, there is little research on whether and how analogy is spontaneously generated in everyday settings. Here we fill this gap by gathering parents' answers to children's real questions, and examining analogy use in parental explanations. Study 1 found that parents used analogy spontaneously in their explanations, despite no prompt nor mention of analogy in the instruction. Study 2 found that these analogical explanations were rated highly by parents, schoolteachers, and university students alike. In Study 3, six-year-olds also rated good analogical explanations highly, but unlike their parents, did not rate them higher than causal, non-analogical explanations. We discuss what makes an analogy a good explanation, and how theories from both explanation and analogy research explain one’s motivation for spontaneously generating analogies.

Analogy Use in Parental Explanation

Causal learning is shaped by people’s prior beliefs, including their expectations. In this paper, we specifically examine expectations of determinism: do they vary with perceptual features of physical causal events, and how do they influence subsequent causal learning from data? We show that perceptual features lead adults to different expectations of determinism for different causes of launching (Exps. 1A & 1B). Those expectations lead to significant differences in responses to causal “failures”; that is, we show a difference in violation-of-expectation effect after a failed launch (Exp. 2). Actual data can reduce or eliminate the impact of these expectations, but they do not override the effect of perceptual features (Exp. 3). Overall, spatiotemporal contiguity cues and expectation of determinism have similar effects on causal learning outcomes, but neither is fully reducible to the other.

Expectations of Causal Determinism in Causal Learning

The present paper reports an experiment (N=254) testing two views of how reasoners learn and generalize potentially complex causal knowledge. Previous work has focused on reasoners’ ability to learn rules describing how pre-defined candidate causes combine, potentially interactively, to produce an outcome in a domain. This empirical-function learning view predicts that reasoners would generalize an acquired combination rule based on similarity to stimuli they experienced in the domain. An alternative causal-invariance view goes beyond empirical learning: it allows for the possibility that one’s current representation may not yield useable (i.e., invariant) causal knowledge –– knowledge that holds true when applied. Accordingly, because useable causal knowledge is the evident aspiration of causal induction, this view posits that deviation from causal invariance is a criterion for knowledge revision. The criterion shapes the empirical functions learned and retained. A discriminating test is whether reasoners would re-represent interacting causes as a whole cause that does not interact with other causes, even when in their relevant experience all (pre-defined) causes in the domain interact. Our results favor the causal-invariance view.

Causal invariance guides inference of empirical integration rules

The paper aims to examine participants’ open-text ‘think aloud’ explanations of their reasoning while making a judgement about an ambiguous scenario. It aims to consider this data in light of frameworks such as causal modelling, intuitive theories, coherence and the story model. Consistent with these frameworks, we find that participants bring in a large amount of world knowledge to connect ambiguous evidence to unobserved, inferred variables and, via these, to the target judgement. We attempt to represent these chains of inferences using causal diagrams and find that participants interpretations of the scenario can be lumped into one of two distinct causal models, each presenting an internally coherent ‘image’ of the ambiguous scenario. Furthermore, participants’ judgement predicts which of those two models they adhere to. We discuss the limitations and merits of this methodological approach for investigating these types of frameworks.

Exploring the Richness of Human Causal Reasoning with Think Aloud Data

Human collaboration often involves a decision to pursue one out of multiple comparable goals, in which case it is challenging to remain committed to the same goal collectively. Philosophical theories as well as empirical evidence from developmental psychology suggest that humans, having shared intentionality as an underlying cognitive structure, may be able to form joint commitment in pursuing a collective goal without communication. By conducting experiments in a real-time cooperative hunting game that heavily relies on visual perception, we demonstrated that humans established and maintained robust cooperation with high-quality hunting, even with a large number of potential targets. Additionally, we showed that a Bayesian imagined “We” (IW) model within a joint commitment framework, could capture humans’ robustness in resisting alternative targets with relatively high quality of hunting. This poses a contrast with a Reward Sharing (RS) model that, despite performing proficiently in pursuing a single goal, mostly exhibited low-quality hunting and whose teaming fell apart as available targets increased. In a hybrid team simulation experiment, the IW model could better mimic the intentions of human hunters compared to the RS model. Together, the success of the persevered group commitment in humans suggests that shared intentionality is a pivotal element in human cooperation. Moreover, the similarity between the performance of humans and the IW model sheds light on the computational formulation of shared intentionality and further advances our understanding of the nature of cooperation.

Exploring an Imagined “We” in Human Collective Hunting: Joint Commitment within Shared Intentionality

Online communities rely on their members to understand and follow community norms, which they learn by observing others and the consequences of their behavior, seeing codes of conduct, and receiving feedback via moderation. Here, to determine the contribution of each source of learning to the preservation of a social norm, we extend cultural consensus theory, a mathematical framework for identifying the cultural consensus in a community. In particular, we extend the model to include learning from experience, centralized moderation, and decentralized moderation, three features commonly found in online communities. We then apply the extended model to data from an online community dedicated to preserving a norm related to the psychophysical scaling of intersubjective notions of beauty derived from facial aesthetics. We find that users' perceptual alignment with the norm before enculturation predicts involvement in the community and that experience in the community is an important indicator for group perceptual learning.

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High-arousal positive emotion evoking is more effective in VR than on a 2D monitor based on computational affection

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