United States

Virtual laboratories that enable novice scientists to construct, evaluate and revise models of complex systems heavily involve parameter estimation tasks. We seek to understand novice strategies for parameter estimation in model exploration to design better cognitive supports for them. We conducted a study of 50 college students for a parameter estimation task in exploring an ecological model. We identified three types of behavioral patterns and their underlying cognitive strategies. Specifically, the students used systematic search, problem decomposition and reduction, and global search followed by local search as their cognitive strategies.

CogSci 2021

Cognitive Strategies for Parameter Estimation in Model Exploration

<center>Louis Stokes Regional Center of Excellence for the Study of STEM Interventions<center>

<CENTER>Translating Critical, Strengths-Based Conceptual Frameworks to STEM Programming Design & Dissemination<CENTER>


 **Sponsored in Partnership by:**
 National Science Foundation, Understanding Interventions, University of Michigan and University of Missouri
 
 
 		
	
Dear Colleagues,

Welcome to the inaugural Louis Stokes Regional Center of Excellence for the Study of STEM Interventions ‎‎(LSCRE ‎‎‎‎SOSI) Summer Institute. SOSI is an NSF funded initiative that reflects a collaboration of STEM and STEM Education Scholars ‎and Program Administrators from the University of Missouri-Columbia, Understanding ‎Interventions, Inc., and the University of Michigan.‎ We offer training and development for STEM program administrators, faculty, and other ‎stakeholders ‎in critical, strengths-based theories and evidence-based practices. Key to our work is providing the tools and avenues for our participants to inform their practice and contribute to the body of translational research that facilitate ‎underrepresented students’ success in STEM. Beyond the Summer Institutes, SOSI, through the Understanding Interventions Index, supports resources for access to the STEM research-to-practice translational literature, a database of journals that publish these works, relevant published measures, and the Understanding Interventions Journal as an outlet for dissemination. This work also extends to webinars and workshops held throughout the year as an integral part of the collaboration between SOSI and its partners. 

This 2021 Summer Institute is designed to address participant needs across a range of experience in our concurrent sessions. Concurrent Sessions 1 are tailored for those of us who have professional training outside the social and behavioral sciences but are looking to develop proficiency, scholarship, and utilize methodologies that test theories like Community Cultural Wealth (CCW), Phenomenological Variant of Ecological Systems Theory (PVEST) and Social Cognitive Career Theory (SCCT). Concurrent Sessions 2 are tailored to those with some experience in these areas and are looking to engage more deeply. Some sessions like those that address measures, implementation, and outcomes will be joint. For the current topic sessions, participants will not be limited a specific concurrent session but will have the flexibility to attend the session that they feel most appropriate to their level of experience. 

We look forward to an exciting and productive institute as we all leverage our varied experiences, perspectives, and ideas to this growing community of practice. We would also like to extend our deep gratitude to the National Science Foundation for its generous support of this work.


Best,

SOSI Leadership Team

SOSI Virtual Summer Institute 2021

poster

### 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,**
![](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**

<iframe width="560" height="315" src="https://www.youtube.com/embed/464h1m3iU68" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

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.

Bayesian experimental design (BED) is a methodology to identify designs that are expected to yield informative data. Recent work in cognitive science considered BED for cognitive models with tractable and known likelihood functions. However, as cognitive models have become more complex and richer, their likelihood functions are often intractable. In this work, we leverage recent advances in BED for intractable models and demonstrate their application on a set of multi-armed bandit tasks. We further propose a generalized latent state model that unifies two previously proposed models. Our experiments show that data gathered using optimal designs results in improved model discrimination and parameter estimation, as compared to naive designs. Furthermore, we find that increasing the number of bandit arms increases the expected information gain in experiments.

Bayesian Experimental Design for Intractable Models of Cognition

Executive function and metacognition are two core concepts in cognitive psychology, yet research linking EF and metacognition directly remains rare. It has been suggested that error monitoring can be used as a window to study the association between EF and metacognition (Roebers, 2018). Error monitoring is indexed by post-error slowing, a phenomenon describing a delayed response in actions after error commissions. The present study uses hierarchical multiple regression to investigate whether EF and metacognition can both predict PES. Individual constructs of EF are measured by four computerised-based tasks. Metacognition is assessed by a simplified version of the Self-Regulated Learning Questionnaire (Dowson & McInerney, 2004). A total of 456 participants (M age of 11.9, SD = 0.92) are included in the final analysis. Results indicate that two EF constructs: inhibition and planning, predict corresponding PES in inhibition and planning tasks. Only the regulation component of metacognition predicts PES in the planning task.

Bridging Executive Function and Metacognition through Post-Error Slowing

The role of the Sense of Embodiment (SoE) in teleoperation is becoming prominent. The SoE affects both the operator's experience and the task performance. In this study we investigate how the individual level of kinaesthetic intelligence affects the SoE during an embodiment illusion experience (EIE). We identify the experimental group in dancers and gymnasts who practice at a competitive level. We hypothesise that individuals with high kinaesthetic intelligence are more resilient to the EIE, due to their awareness of the joints position in the space. Moreover, we designed an active EIE to better assess the sense of agency and self-location. Usually, EIEs propose static tasks which are appropriate to assess the sense of ownership, but cannot clearly assess the other two components of the SoE. Finally, for the first time, to the knowledge of the authors, the variation of the pupil dilation was used as psycho-physiological measure of the SoE.

Individuals with High Kinesthetic Intelligence Experience an Active Embodiment Illusion Assessed with Pupil Dilation

A mental model is an internal representation that explains how something works. Mental model construction is facilitated by self-explanation, the active generation of explanations for oneself. The overarching goal of this research is to empirically investigate the utility of self-explanation for developing mental models when learning to program. Programming is notoriously challenging and, despite evidence of the importance of mental models for learning, little work has focused on mental models of students learning how to program. They need correct mental models of the notional machine, an abstraction of the steps taken by a computer as it processes a program. Because students do not spontaneously self-explain, we are using a user-centered approach to design a computer tutor to prompt for self-explanation about the notional machine. Here, we present qualitative results on students’ interactions with an initial version of the tutor, including the form of their self-explanations and corresponding mental models.

Investigating the Utility of Prompting Novice Programmers for Self-Explanations to Improve Mental Models

Mapping words to referents is important for language acquisition. Learning a word relies heavily on memory because it requires forming associations between labels and referents, integrating examples across time, and retrieving words. Memory supports, such as variability, can be added to word learning events to help children learn words. This study examined the effect of emotional variability on word learning. Four-year-olds learned eight novel words presented four times in a storybook organized in one of three conditions: no variability, low variability, or high variability. Words with no variability were presented in the same emotional context (i.e., happy, sad, afraid, or angry). Words with low variability were presented with two emotions (e.g., happy and sad), and words with high variability were presented with all four emotions. After hearing the book, preschoolers participated in a generalization test. This study informs our understanding of the role of social contextual variability in word learning.

Preschoolers' Learning of Words with Emotional Variability in Shared Book Reading

Humans navigate daily decision-making by flexibly choosing appropriate approximations of what ought to be done. Which mental algorithms do people use, and when? We use behavioural experiments and modelling to investigate three computational principles known to influence decision making: noisy utility maximization, discounting, and the probability warping principle of Prospect Theory.
While these principles have been shown to separately influence human behaviour in simple laboratory tasks, such as bandits and gambles, we evaluate their combined use in the context of a naturalistic spatial search that required sequential decision-making. We found that while aggregate human behaviour can be reasonably well explained by an optimal planner with noisy utility maximization, individual-level behaviour exhibits consistent irregularities, that deviate from expected utility theory.
We show that model-based prediction of individual-level behaviours in our experiment is significantly improved by combining the three computational principles, and benefits particularly strongly from probability warping. Furthermore, our results suggest that probability warping may be a common factor of human decision making, that generalizes beyond the gambles explored in Prospect Theory, to natural human behaviours such as spatial search and navigation.

Unpacking the computations of human spatial search under uncertainty: noisy utility maximization, discounting, and probability warping

Distinguishing spurious correlations from unconfounded causal evidence is a challenge in every day reasoning. Adapting the classic “blicket detector” paradigm (Sobel et al., 2014), we investigate whether 15-17-month-old infants’ neural activity (theta oscillations), indicates that infants recognize when the information they are expecting or observing is confounded or not. By concurrently tracking infants’ eye-movements, we investigate also whether infants correctly infer the functionality of unknown objects (when such an inference is possible, i.e. when data is unconfounded) and predict future events based on these causal inferences. Data collection is on-going (current N = 29) and preliminary analysis suggests infants show increased theta activity in anticipation of receiving un-confounded (as opposed to confounded) information. By relating the neural measures of information expectation to the accuracy of infants’ subsequent predictions, we hope to offer a unique insight into the mechanisms, development, and individual differences in early causal reasoning.

Mechanisms of early causal reasoning: Investigating infants' sensitivity to confounded information in a causal reasoning task, using EEG and eyetracking

We study human learning & decision-making in tasks with probabilistic rewards. Recent studies in a 2-armed bandit task find that a modification of classical Q-learning algorithms, with outcome-dependent learning rates, better explains behavior compared to constant learning rates. We propose a simple alternative: humans directly track the decision variable underlying choice in the task. Under this policy learning perspective, asymmetric learning can be reinterpreted as an increasing confidence in the preferred choice. We provide specific update rules for incorporating partial feedback (outcomes on chosen arms) and complete feedback (outcome on chosen & unchosen arms), and show that our model consistently outperforms previously proposed models on a range of datasets. Our model and update rules also add nuance to previous findings of perseverative behavior in bandit tasks; we show evidence of outcome-dependent choice perseveration, i.e., that humans persevere in their choices unless contradictory evidence is presented.

Tracking what matters: A decision-variable account of human behavior in bandit tasks

`Embodied cognition' suggests that our motor experiences shape our cognitive and perceptual capabilities broadly, but often considers tasks that directly relate to or manipulate the body. Here we study how a history of natural embodied experience affects abstract physical problem-solving in a virtual, disembodied physical reasoning task. 
We compare how groups with different embodied experience -- congenitally limb-different versus two-handed children and adults -- perform on this task, and find that while there is no difference in overall performance, limb-different participants solved problems using fewer actions, and spent a longer time thinking before acting. This suggests that differences in embodied experience drive the acquisition of different meta-strategies for balancing acting with thinking, even on tasks that are designed to equalize differences in embodiment.

Meta-strategy learning in physical problem-solving: the effect of embodied experience

Our social media activity data is a valuable source of information about our preferences, psychological and social processes. However, collecting such private data, including messages, for scientific research is at an early stage (Ueberwasser & Stark, 2017), which is natural given privacy issues involved (Bemmann & Buschek, 2020). Our study is geared towards: i) making the process of sharing personal data more ethical, consensual, informed and comfortable; ii) identifying profiles of participants willing to share these data. 293 students of both technical and non-technical background completed an online questionnaire designed to identify the relationship between willingness to share the data and factors such as: 1) kinds of data; 2) method of data processing; 3) purpose of data gathering and use; 4) demographics of participants. Qualitative and quantitative analyses revealed the categories of participants’ concerns and preferences regarding the form of anonymization conditional on the subjects’ profile and their technical skills.

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Bayesian Experimental Design for Intractable Models of Cognition

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