United States

Two very different kinds of views exist of how infants learn about physics: on the one hand, through domain-specific rules, on the other, through general purpose simulation.
We attempt to reconcile these two views through a model that uses simulation to bootstrap rule learning. This model makes a variety of predictions about rapid concept acquisition in young infants which are consistent with experiments performed by developmental cognitive scientists. Consistent with the developmental literature, our model learns physical rules from just a few examples, but only when those examples are consistent with general physical principles. A model without simulation shows no such biases. Our approach provides a general mechanism for explaining how simulation and rule learning might bootstrap off of each other throughout development, and opens up a number of new questions about how children learn physical representations.

CogSci 2021

Combining rules and simulation to explain infant physical learning

<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

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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.

Perceiving 3D structure in natural images is an immense computational challenge for the visual system. While many previous studies focused on rigid 3D objects, we applied a novel method on a common set of non-rigid objects—static images of the human body in the natural world. We investigated to what extent human ability to interpret 3D poses in natural images depends on pose typicality and viewpoint informativeness. We tested subjects on matching natural pose images with synthetic body images of the same poses given viewpoint changes. We found that performance for typical poses was measurably better than atypical poses; however, we found no significant difference between informative and less informative viewpoints. Results suggested that human ability to interpret 3D poses depends on pose typicality but not viewpoint informativeness. Further comparisons of 2D and 3D pose matching models suggested that humans probably use prior knowledge of 3D pose structures.

Three-dimensional pose discrimination in natural images of humans

Building on research arguing for the possibility of conceptual and categorical knowledge acquisition through statistics contained in language, we evaluate predictive language models (LMs)---informed solely by textual input---on a prevalent phenomenon in cognitive science: typicality. Inspired by experiments that involve language processing and show robust typicality effects in humans, we propose two tests for LMs. Our first test targets whether typicality modulates LM probabilities in assigning taxonomic category memberships to items. The second test investigates sensitivities to typicality in LMs' probabilities when extending new information about items to their categories. Both tests show modest---but not completely absent---correspondence between LMs and humans, suggesting that text-based exposure alone is insufficient to acquire typicality knowledge.

Do language models learn typicality judgments from text?

Categorizing spatial relations is central to the development of visual understanding and spatial cognition, with roots in the first few months of life. Quinn (2003) reviews two findings in infant relation categorization: categorizing one object as above/below another precedes categorizing an object as between other objects, and categorizing relations over specific objects predates abstract relations over varying objects. We model these phenomena with deep neural networks, including contemporary architectures specialized for relational learning and vision models pretrained on baby headcam footage (Sullivan et al., 2020). Across two computational experiments, we can account for most of the developmental findings, suggesting these neural network models are useful for studying the computational mechanisms of infant categorization.

Examining Infant Relation Categorization Through Deep Neural Networks

Two assumptions of psycholinguistic research are that text corpora can be used as a proxy of the language that people have been exposed to and that the reaction time with which people recognize words decreases with the probability (or frequency) of the words in a corpus. We propose a method that produces topic-specific word probabilities from a text corpus using latent Dirichlet allocation, then combines them to fit lexical decision reaction times and re-estimates word probabilities. We evaluated how well independent lexical decision reaction times could be predicted from re-estimated word probabilities compared to original probabilities, using independent lexical decision data. In an experiment designed to prove the concept, the re-estimated word frequency model explained up to 9.6% of additional variability in reaction times on group level and up to 2.9% on level of individual participants.

Word Probability Re-Estimation Using Topic Modeling and Lexical Decision Data

Improving the accuracy of medical image interpretation is critical to improving the diagnosis of many diseases. Using both novices (undergraduates) and experts (medical professionals), we investigate methods for improving the accuracy of a single decision maker by aggregating repeated decisions from an individual in different ways. Our participants made classification decisions (cancerous versus non-cancerous) and confidence judgments on a series of cell images, viewing and classifying each image twice. We first applied the maximum confidence slating algorithm (Koriat, 2012), which leverages metacognitive ability by using the most confident response for an image as the `final response'. We also examined algorithms that aggregated decisions based on image similarity, leveraging neural network models to determine similarity. We found maximum confidence slating improves classification accuracy for both novices and experts. However, aggregating responses on similar images improves classification accuracy for novices and not experts, suggesting differences in the decision mechanisms of novices and experts.

Improving Medical Image Decision Making by Leveraging Metacognitive Processes and Representational Similarity

Estimating parameters of cognitive models is crucial to be able to accurately describe cognitive processing of individuals, under varying circumstances. To ensure that individual parameter estimates represent individual cognitive processes, it is important to consider model identification and specific influence. Model identification refers to whether a unique set of parameter estimates is associated with a particular pattern in the data; Specific influence means that certain experimental manipulations affect only specific cognitive processes, reflected in changes in only those parameters that represent those processes, and not others. These two general concepts also apply to cognitive models of more applied tasks and settings, such as driving. In the current work, we specifically test whether these two requirements hold in a commonly used cognitive model of visual control of steering behavior. For this model, we test the identifiability, and then estimate parameters of two experiments to understand how cognitive load and driving speed specifically influence parameter estimates of the model. The results indicate that the two-point visual control of steering model is identified, and that cognitive load and driving speed are related to different parameters.

Identifiability and Specificity of the Two-Point Visual Control Model of Steering

Studies on similarity-based interference in subject-verb number agreement dependencies have found a consistent facilitatory effect in ungrammatical sentences but no conclusive effect in grammatical sentences. Existing models propose that interference is caused either by a faulty representation of the input (encoding-based models) or by difficulty in retrieving the subject based on cues at the verb (retrieval-based models). Neither class of model captures the observed patterns in human reading time data. We propose a new model that integrates a feature encoding mechanism into an existing cue-based retrieval model. Our model outperforms the cue-based retrieval model in explaining interference effect data from both grammatical and ungrammatical sentences. These modeling results yield a new insight into sentence processing, encoding modulates retrieval. Nouns stored in memory undergo feature distortion, which in turn affects how retrieval unfolds during dependency completion.

Feature encoding modulates cue-based retrieval: Modeling interference effects in both grammatical and ungrammatical sentences

Computational model simulations have been very fruitful for gaining insight into how the systematic structure we observe in the world's natural languages could have emerged through cultural evolution. However, these model simulations operate on a toy scale compared to the size of actual human vocabularies, due to the prohibitive computational resource demands that simulations with larger lexicons would pose. Using computational complexity analysis, we show that this is not an implementational artifact, but instead it reflects a deeper theoretical issue: these models are (in their current formulation) computationally intractable. This has important theoretical implications, because it means that there is no way of knowing whether or not the properties and regularities observed for the toy models would scale up. All is not lost however, because awareness of intractability allows us to face the issue of scaling head-on, and can guide the development of our theories.

Why is scaling up models of language evolution hard?

Collecting new information about the outside world is a key aspect of brain function. In the context of vision, we move our eyes multiple times per second to accumulate evidence about a scene. Prior studies have suggested that this process is goal-directed and close to optimal. Here, we show that this process of seeking new information suffers from a confirmation bias similar to what has been observed in a wide range of other contexts. We present data from a new gaze-contingent task that allows us to both estimate a participant's current belief, and compare that to their subsequent eye-movements. We find that these eye-movements are biased in a confirmatory way. Finally, we show that these empirical results can be parsimoniously explained under the assumption that the brain performs approximate, not exact, inference, with computations being more approximate in decision-making compared to sensory areas.

A confirmation bias due to approximate active inference

Decision-making is often hierarchical and approximate in nature: decisions are not being made based on actual observations, but on intermediate variables that themselves have to be inferred. Recently, we showed that during sequential perceptual decision-making, those conditions induce characteristic temporal biases that depend on the balance of sensory and category information present in the stimulus. Here, we show that the same model makes predictions for when observers will be over-confident and when they will be under-confident with respect to a Bayesian observer. We tested these predictions by collecting new data in a dual-report decision-making task. We found that for most participants the bias in confidence judgments changed in the predicted direction for stimulus changes that led them from over-weighting early evidence to equal weighting of evidence or over-weighting of late evidence. Our results suggest that approximate hierarchical inference might provide the computational basis for biases beyond low-level perceptual decision-making, including those affecting higher level cognitive functions like confidence judgements.

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