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People often think about counterfactual possibilities to an event and imagine how it could have been otherwise. The study of how this occurs is central to many areas of cognitive science, including decision making, social cognition, and causal judgment; however, modeling the memory processes at play in naturalistic counterfactual retrieval has been difficult. We use established memory models to evaluate and compare multiple mechanisms that could be involved in counterfactual retrieval. Our models are able to capture nuanced dynamics of retrieval (e.g. how retrieved counterfactuals cue subsequent counterfactuals), and can predict the effects of retrieval on evaluations and decisions. In doing so, we show how existing theories of counterfactual thinking can be combined with quantitative models of memory search to provide new insights about the formation and consequences of counterfactual thought.

CogSci 2021

Dynamics of Counterfactual Retrieval

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
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so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

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### Looking for Cogsci 2023? Please [click here](https://underline.io/events/405/reception) to switch to 2023 edition.


**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

We hope that you enjoy the conference this year! 

**Your Program Chairs,**
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**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

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To review the proceedings for the conference or review the searchable program, please visit the [website](https://cognitivesciencesociety.org/program/).

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

The present study explores moral judgment in COVID-19 related moral dilemma situations involving allocation of ventilators with conflicting allocation principles. Utilitarian triage criteria like the chance of recovery or longer life expectancy are opposed to egalitarian procedures like random allocation and ‘first come, first served’. In the first part of the experiment, participants are presented with three hypothetical situations in which there are two patients admitted to a hospital in a critical state needing a ventilator but only one is available. The conditions about the patients are described and several triage procedures are suggested and rated by participants. Separately, participants rated their agreement with several triage principles. The result shows a clear preference for utilitarian allocation principles. The random allocation principle receives the lowest ratings. The ‘first come, first served’ correlates with the belief in fate score hinting that the egalitarian nature of this principle is questionable.

Moral Judgments and Triage Principles related to COVID-19 Pandemic

Mechanistic explanations reveal the rich causal structure of the world we inhabit. For instance, an explanation like “A clock ticks because an internal motor turns a gear which moves the hands” explains a feature of the clock (i.e., the fact that it ticks) by describing the parts and actions that cause it. People often seek out such explanations, as they may be particularly valuable to understanding the world. However, are mechanistic explanations truly a single class of explanation? Here, we distinguish between two subtypes of mechanism: constitutive and etiological. We argue that this distinction, long made by philosophers of science, has cognitive consequences: People treat these two kinds of explanation differently and prefer one kind over the other. We discuss implications for understanding mechanism and for explanation preferences more broadly.

What is a 'mechanism'? A distinction between two sub-types of mechanistic explanations

Across 3 studies, we investigated children's ability to consider hypothetical interventions to complex and dynamic causal systems. Five- to 7-year-olds learned about novel food chains and were asked about the effects of the removal of one species on others in the food chain. In Studies 1 (n = 72) and 2 (n = 72), 6- and 7-year-olds made correct inferences about the effects on remaining species, but performed better when reasoning about direct predators or prey than indirectly-connected species. Five-year-olds' performance was at chance across all question types. In Study 3 (n = 65, target n = 72), we are currently investigating whether 5-7-year-old children’s performance improves when given more background information on the causal dynamics of the food chains. The results indicate that hypothetical thinking about dynamic causal systems develops between 5 and 7 years. This ability may be leveraged for teaching science concepts.

Children’s reasoning about hypothetical interventions to complex and dynamic causal systems

Although interacting with visual displays of websites is a central part of our everyday online practices, little is known about the impact of geometric principles on users’ perceptions.

In an exploratory study, canonical interface layout structures were extracted from websites for different purposes, such as education, medicine or finance, amongst others. Preference for the obtained prototypes was then rated in a quasi-experiment (n=65) following aesthetic and usability criteria (Lavie & Tractinsky, 2004). 

Our results indicate that vertical and horizontal alignment properties, such as different degrees of symmetry and complexity, shape the rating of expressive and creative aesthetics, as well as perceived usability. 

This contribution aims at shedding light on geometric constraints in UX analysis and giving an outlook on potential impacts of preference on content comprehension in an ecological setting.

The impact of interface alignment structure on aesthetic appreciation and usability rating

We examine when, why, and how people use category-based knowledge in determining how much they will like an object based on memories. We find that people rely on category-based knowledge when making liking evaluations of items from memory and rely on such knowledge more when items are typical of a category. We suggest that people rely on categories to fill in information that memories leave out and use typicality as a cue for the likelihood that category-based knowledge will be a good substitute for knowledge about an item. In 2 studies, using products and color patches as stimuli, we find that people rely on category liking evaluations in forming liking evaluations of specific items from memory and that they do so more when an items is typical (vs atypical) of that category. This work shows that category-based knowledge can play an important part in preference formation.

The Role of Categories in the Formation of Liking Evaluations

This paper presents a study of the selection task based on Cognitive Argumentation (CA), a computational framework for dialectic argumentation-based reasoning. CA is built from a theoretical framework of argumentation in AI which is then grounded via cognitive principles from Cognitive Science. The aim is to understand the selection task variations by studying how argumentative reasoning is suitably flexible to uniformly capture the differences among the individuals' selections, the canonical groups and the shifts within the different contexts in which the experiment is carried out. Our approach is assessed with respect to the developed criteria within the meta-analysis of Ragni and Johnson-Laird in 2018.

Cognitive Argumentation and the Selection Task

Congenital amusia of pitch (tone-deafness), which affects ~1.5% of the population, involves a deficit in pitch processing affecting the perception of musical melody and some speech contrasts. Lay-knowledge of tone-deafness considers the phenomenon to be categorical, as does prior work contrasting ‘amusics’ to ‘controls’, designated as such by thresholds on diagnostic tests. Is amusia a qualitative break from normal pitch discrimination, or does it represent the extreme end of a distributed skill? Large-scale datasets, combined with theoretically motivated tools for extracting latent measures of ability, can answer this question. We studied individual differences in pitch discrimination in 700,000 people using Bayesian hierarachical diffusion models. We found no evidence for a categorical deficit: pitch perception ability was normally and continuously distributed. We additionally report preliminary findings on pitch perception ability as a function of age, gender, native language, musical experience, and self-assessments of tone-deafness.

Am I tone-deaf? Assessing pitch discrimination in 700,000 people

The study analyzed the effect of 2-year systematic chess training on the IQ of schoolchildren. A pretest–posttest with control group design was used with randomly selected children studying in four city schools (grades 3–9), of both genders. The experimental group (N = 80) underwent weekly chess training for 2 years, while the control group (N = 77) was involved in extracurricular activities offered in school such as cricket, football, and hockey. Both the groups were involved in these activities after school hours. Intelligence was measured by Wechsler Intelligence Scale for Children (WISC-IV INDIA). Assessment was carried out prior to the chess training, after 1 and 2 years of training. ANCOVA revealed significant increase in both first- and second-year scores by about 12 points across both the years. When systematic in-school chess training program is offered, one could expect a significant increase in IQ.

MALLEABILITY OF INTELLIGENCE THROUGH CHESS TRAINING-A TWO YEAR EMPIRICAL STUDY

A complete and holistic understanding of human cognitive function using a cognitive model should be able to incorporate both the idiographic biological parameters from behavioral data and interactions between connected brain networks identified by neuroanatomical techniques. Here, we argue that first, computational modeling can be used to extract biological parameters, and second, the biological parameter should be identifiable through the corresponding brain functional networks. We tested this empirically with the long-term memory’s decay rate (rate of forgetting α) measured from a Swahili-English vocabulary learning task, and resting-state fMRI (rs-fMRI) data, both collected from 33 participants. α was estimated based on a rational model of episodic memory. rs-fMRI data from each brain was processed into a 264 x 264 connectivity matrix, and further selected and grouped into memory-related network matrices using group lasso and cross-validation techniques. We were able to (1) predict the observed α using these connectivity matrices, and (2) show that forgetting can be related to specific brain networks.

Distributed Brain Connectivity Predicts Individual Differences in Forgetting: A Neurocomputational Analysis of resting-state fMRI

A deep understanding of any phenomenon requires knowing how its causal elements are related to one another. Here, we examine whether children recognize similar causal structures across superficially distinct events. We presented 4- to 7-year-olds with three-variable narratives in which story events unfold according to a causal chain or a common effect structure. We then asked children to make judgments about which stories are the most similar. Results indicate that the ability to recognize and use abstract causal structure as a metric of similarity develops gradually between the ages of 4 and 7: While we find no evidence that 4-year-olds recognize the common causal structure between events, 7-year-olds have a relatively mature understanding of causal system categories when making similarity judgements. Five- and 6-year-olds show mixed success. We discuss these findings in light of children’s developing causal and abstract reasoning and propose directions for future work.

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Moral Judgments and Triage Principles related to COVID-19 Pandemic

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