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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.

CogSci 2021

What is a &#39;mechanism&#39;? A distinction between two sub-types of mechanistic explanations

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.
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<br>[**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)
<br>[**The Detailed Program can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/08/DeArmond-CogSci2022-program-v113-final.pdf)
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CogSci 2022

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. 





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

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**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!<br><br>

**Your Program Chairs,**
![](https://assets.underline.io/uploads/markdown_image/1/image/99984908caba5e6453e5fc5ba34f3507.jpg)

**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.

Representations held in working memory (WM) are crucial in guiding human attention in a goal-directed fashion. Currently, it is debated whether only a single or several of these representations can be active and bias behaviour at any given moment. In our present study, 25 university students performed a behavioural dense-sampling experiment to produce an estimate of the temporal activation patterns of two simultaneously held visual templates. We report two key novel results. First, the performance related to both representations was not continuous, but fluctuated rhythmically at 6 Hz. This corresponds to neural oscillations in the theta-band, whose functional importance in WM is well established. Second, our findings suggest that two concurrently held representations may be prioritized in alternation, not simultaneously. Our data extend recent research on rhythmic sampling of external information by demonstrating an analogous mechanism in the cyclic activation of internal WM representations.

Multiple items in working memory are cyclically activated at a theta-rhythm

The neural mechanisms supporting flexible relational inferences, especially in novel situations, are a major focus of current research. In the complementary learning systems framework, pattern separation in the hippocampus allows rapid learning in novel environments, while slower learning in neocortex accumulates small weight changes to extract systematic structure from well-learned environments. In this work, we adapt this framework to a task from a recent fMRI experiment where novel transitive inferences must be made according to implicit relational structure. We show that computational models capturing the basic cognitive properties of these two systems can explain relational transitive inferences in both familiar and novel environments, and reproduce key phenomena observed in the fMRI experiment.

Complementary Structure-Learning Neural Networks for Relational Reasoning

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

Localizing function in the brain has been an elusive long-term goal in the study of cognition. Prior studies have utilized four reference abilities (RAs) to capture cognition (Salthouse, 2009). Full-brain cortical networks have been tied to these abilities using common multi-voxel patterns across subjects in distinct age groups. Using voxel searchlights the current study explores purely local cortical representations of cognition, less commonly explored. This work analyzes 240 subjects’ responses to cognitive tasks from the four RAs. The current study further employs representational similarity analysis (RSA, Kriegeskorte, Mur, & Bandettini, 2008) to the similarity of brain activities from tasks within the same RA; RSA can capture representational consistencies within each subject even when exact voxel pattern may vary across subjects. We found distinct topographical localizations for each RA that were mostly consistent across age and suggested refinements of broader functional divisions of the brain from prior literature.

Identifying local cognitive representations in the brain across age spans through voxel searchlights and representational similarity analysis

Embodied cognition theory predicts that semantic processing shares processing resources with sensorimotor systems. The present study aimed to reveal mechanisms for impact of motor simulation on semantic processing of objects, which are manipulated by hands. We measured activation of inferior parietal lobule (IPL) by functional near-infrared spectroscopy (fNIRS) to examine the effect of constraint on hand movement. Participants were faced with two words representing name of objects that can be manipulated by hand (e.g. cup) or objects that cannot be manipulated by hand (e.g. windmill), and answered which object was larger. We analyzed effect of two factors on IPL activity: hand constraint and hand manipulability of the objects represented by the words. We found that (1) IPL activity for hand-manipulable objects was significantly higher than non-manipulable objects under control condition, and that (2) the difference in IPL activity between hand manipulable and non-manipulable objects was significantly reduced under hand-constraint condition.

Hand constraint affects semantic processing of hand-manipulable objects: An fNIRS study

The surprising efficiency of triple conjunction search has created a puzzle for modelers who link visual feature binding to selective attention, igniting an ongoing debate on whether features are bound with or without attention. Nordfang and Wolfe (2014) identified feature sharing and grouping as important factors in solving the puzzle and thereby established new constraints for models of visual search. Here we extend our neural dynamic model of scene perception and visual search (Grieben et al., 2020) to account for these constraints without the need for preattentive binding. By demonstrating that visual search is not only guided top-down, but that its efficiency is affected by bottom-up salience, we address a major theoretical weakness of models of conjunctive visual search (Proulx, 2007). We show how these complex interactions emerge naturally from the underlying neural dynamics.

A neural dynamic process model of combined bottom-up and top-down guidance in triple conjunction visual search

Our decisions are accompanied by a sense of confidence, a metacognitive assessment of how likely those decisions are to be correct, but the mechanisms that underlie this capacity remain poorly understood. A number of recent behavioral and neural data have suggested that decisions are made in accord with an optimal `balance-of-evidence' rule, whereas confidence is estimated using a heuristic `response-congruent-evidence' rule. We developed a deep neural network model optimized to classify images and predict its own likelihood of being correct, and found that this model naturally accounts for some of the key behavioral dissociations between decisions and confidence ratings. Further investigation revealed that neither the `balance-of-evidence' rule nor the `response-congruent-evidence' rule fully characterized the strategy that the model learned. We argue instead that the model learns to flexibly approximate the distribution of its training data, and, analogously, that apparently suboptimal features of human confidence ratings may arise from optimization for the statistics of naturalistic settings.

A Task-Optimized Neural Network Model of Decision Confidence

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

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

Children born preterm are at increased risk for white matter injury, impaired cognitive development and lower academic achievement. Here, we examined the association between fractional anisotropy and volume of select white matter tracts at age 5 with mathematics skills at age 7 in children born preterm (<33 weeks gestational age, n=52) without severe neurological complications and children born full-term (38-41 weeks gestational age, n=34). The preterm group had significantly lower mathematics scores and lower volume in several white matter tracts. Using multiple linear regression models, we examined white matter tracts that have previously been associated with mathematical cognition. We found a significant interaction with term status: fractional anisotropy of the corticospinal tract, and volume of corticospinal tract and parietal superior longitudinal fasciculus were significantly associated with mathematics skills in children born full-term, but not in children born preterm. These findings indicate white matter plasticity following preterm birth.

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Multiple items in working memory are cyclically activated at a theta-rhythm

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