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People are capable of learning diverse functional relationships from data; nevertheless, they are most accurate when learning linear relationships, and deviate further from estimating the true relationship when presented with non-linear functions. We investigate whether, when given the opportunity to learn actively, people choose samples in an efficient fashion, and whether better sampling policies improve their ability to learn linear and non-linear functions. We find that, across multiple different function families, people make informative sampling choices consistent with a simple, low-effort policy that minimizes uncertainty at extreme values without requiring adaptation to evidence. While participants were most accurate at learning linear functions, those who more closely adhered to the simple sampling strategy also made better predictions across all non-linear functions. We discuss how the use of this heuristic might reflect rational allocation of limited cognitive resources.

CogSci 2021

Sampling Heuristics for Active Function Learning

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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.

poster

Recent theories suggest that emotion words may facilitate the development of emotion concepts. However, most research investigating this relation in early childhood has been correlational. To assess whether emotion words causally influence emotion concept development, we conducted a pre-test post-test study examining which facial configurations 3-year-olds associate with complex emotional scenarios (annoyed, disgusted, and nervous). Between pre- and post-test, children were randomly assigned to one of three conditions. Children observed a facial configuration paired with a scenario while presented with either an explicit emotion label, a vague emotion label, or irrelevant information. Data from 54 children (36 female, mean age= 3.53) revealed that children’s average change in number of correct responses from pre-test to post-test by condition were as follows: Explicit=1.00 (SD=1.68); Vague=0.11 (SD=1.45); Irrelevant=-0.28, (SD=1.71). These results hold implications for how specific emotion words may casually influence children’s ability to learn new emotion concepts.

Emotion Words May Connect Complex Emotional Events and Facial Expressions in Early Childhood

The testing effect was used to teach the testing effect to increase feelings of self-efficacy in test taking and reduce test anxiety in 10-to11- year-olds. The impact of this intervention was measured with a new Self-efficacy in Test Taking measure and an adapted thoughts' subscale from the Children’s Test Anxiety Scale in pre-test, post-test conditions and with a control group. The intervention was designed to target the self-knowledge beliefs ‘layer’ from the S-REF (Self-referent Executive Function) model of test anxiety. The intervention was delivered primary classrooms, over six weeks and aimed to teach children to believe in test taking abilities, that testing routes were ‘well-oiled’, to improve feelings of self-efficacy about taking high stakes’ tests. There was a significant increase in self-efficacy in test taking for students, who were high in test anxiety, η²p = 0.07 when measured with a subscale from the Self-efficacy in Test Taking new measure.

Can Retrieval Practice of The Testing Effect Increase Self-efficacy in Tests and Reduce Test Anxiety, in 10- to 11-Year-olds?

Improving the magnitude processing during early childhood is essential for further developing numerical cognition and mathematical skills. In this regard, there is an intense debate about whether numbers are processed using a number-specific system or a general magnitude processing system. Additionally, the evidence available focuses on the magnitude’s interference but not on the translation and integration process. This study aims to analyze the ability to map between non-symbolic and symbolic numerical information and integrate numbers and space. For this purpose, we designed an observational between-subjects study using two-cross format comparison tasks involving the integration and interconversion between magnitudes and formats.
Improving the magnitude processing during early childhood is essential for further developing numerical cognition and mathematical skills. In this regard, there is an intense debate about whether numbers are processed using a number-specific system or a general magnitude processing system. Additionally, the evidence available focuses on the magnitude’s interference but not on the translation and integration process. This study aims to analyze the ability to map between non-symbolic and symbolic numerical information and integrate numbers and space. For this purpose, we designed an
observational between-subjects study using two-cross format comparison tasks involving the integration and interconversion between magnitudes and formats. We will assess for each task the relation between ratio and performance, and the discrimination thresholds in 5-, 10- and 15-years old Colombian children to explore the developmental trajectory of these numerical cognition processes.

Mapping between numerical and non-numerical magnitude information: An observational study of the integration and interconversion between magnitudes and formats in Colombian children

Recently, several behavioral studies have demonstrated 4-10 Hz rhythmic fluctuations in attention. So far, this attentional sampling has only been demonstrated with regards to external stimuli. Attention, however, is often directed towards internal working memory representations. We conducted a human behavioral dense-sampling experiment on whether simultaneously held representations of two distinct feature dimensions (color and orientation) also exhibit a rhythmic temporal profile. We found an oscillatory component at 9.4 Hz in the joint time-courses of both representations, presumably reflecting a common early perceptual sampling process in the alpha-frequency range. Further, we observed an oscillatory component at 3.5 Hz with a significant phase-difference between feature dimensions. This likely corresponds to a later attentional sampling process, indicating that internal representations of distinct features are activated in alteration. In summary, we demonstrate the cyclic reactivation of internal representations, as well as the co-occurrence of perceptual- and attentional rhythmic fluctuations at distinct frequencies.

Cyclic reactivation of internal working memory representations of distinct feature dimensions

Computational models are greatly useful in cognitive science in revealing the mechanisms of learning and decision making. However, it is hard to know whether all meaningful variance in behavior has been account for by the best-fit model selected through model comparison. In this work, we propose to use recurrent neural networks (RNNs) to assess the limits of predictability afforded by a model of behavior, and reveal what (if anything) is missing in the cognitive models. We apply this approach in a complex reward-learning task with a large choice space and rich individual variability. The RNN models outperform the best known cognitive model through the entire learning phase. By analyzing and comparing model predictions, we show that the RNN models are more accurate at capturing the temporal dependency between subsequent choices, and better at identifying the subspace in the space of choices where participants' behavior is more likely to reside. The RNNs can also capture individual differences across participants by utilizing an embedding. The usefulness of this approach suggests promising applications of using RNNs to predict human behavior in complex cognitive tasks, in order to reveal cognitive mechanisms and their variability.

Using Recurrent Neural Networks to Understand Human Reward Learning

Children vary widely in their lexical development. These differences have been shown to carry through later ages and can even be found in adults. One parameter often found as a predictor of varying vocabulary size is speed of processing, measuring the reaction time in familiar word recognition tasks. However, the underlying nature of speed of processing is still unclear: Is it a purely linguistic phenomenon or is it tied to the general cognitive abilities of the child. Our study aims to shed light on the nature of speed of processing, by testing 17-month-olds in a word learning experiment and assessing their reaction time during word recognition for both newly acquired and familiar words as well as visual reaction times. Our results can thus disentangle how broad or narrow lexical speed of processing is and help us better understand the origin of its link with language skills across the lifespan.

Investigating the nature of infants' lexical speed of processing

According to theories of event-predictive cognition, neural processing focuses on the next relevant interaction targets. Evidence for this notion comes from the anticipatory crossmodal congruency effect (aCCE), which implies that spatial body representations are mapped onto future goal locations in advance of a goal-directed action. Here we present a free energy based normative process model that accounts for the aCCE quantitatively by applying crossmodal mappings between vision and touch as well as active inference. A comparison with a diffusion model shows that our model accounts for the response time distributions and the aCCE with a sparser set of parameters. However, the temporal dynamics of the model require further fine tuning to account for all aspects of the aCCE. The model shows how the free energy framework can be used to account for behavioral data in general and how to implement theories of event-predictive cognition in a normative cognitive process model.

Modeling the Anticipatory Remapping of Spatial Body Representations: A Free Energy Approach

Spacing is a highly effective encoding strategy that has been shown to benefit memory in a variety of domains. Recent work has emphasized the evaluation of spaced practice under conditions that more closely reflect daily life. This work found that spacing repetitions over days, weeks, or months is effective over retention intervals as long as one year. One aspect of spaced study that has received less attention, however, is the relationship between the number of acquisition sessions and final retention. That is, if a student preparing for an exam plans to allocate 10 hours to preparing for that exam, is there an optimal, or perhaps minimal, number of study sessions that they should engage in to best leverage the benefits of spacing? In the present experiment we had participants complete 16 practice tests of Japanese-English pairs (e.g., boushi – hat). These practice tests were either all completed in one session, or distributed across two, three, or four sessions. These sessions were spaced either 1, 7, 30, or 90 days apart. Participants completed four test trials following a retention interval of 90 days, 180 days, or some variable length. Our results suggest that the number of acquisition sessions monotonically enhanced first-trial test performance as well as relearning, though evidence for enhanced relearning between one and two sessions was ambiguous. Unexpectedly, these monotonic trends were stable across practice lags and retention intervals. These findings suggest that, in addition to the temporal lag between practice episodes, the number of sessions over which one elects to distribute those episodes also has ramifications for long-term retention, and that each additional session yields meaningful benefits.

Additional acquisition sessions monotonically benefit retention and relearning

The physical world is rich with social information that people readily detect and extract, such as inferring that someone was present when we encounter a stack of rocks in the woods. How do people recognize that a physical scene contains social information? Research in developmental psychology has argued that this capacity is supported by a sensitivity to violations of randomness. Here we present a computational model of this idea and test its explanatory power in a quantitative manner. Our model infers agency by estimating the likelihood that a scene would arise naturally, as determined by human intuitive physics instantiated as a physics engine. Our results suggest that people's ability to detect agency in a physical scene is sensitive not only to the superficial visual properties, but also to the underlying physical generative process. Our results highlight how people use intuitive physics to decide when to engage in nuanced social reasoning.

Detecting the involvement of agents through physical reasoning

Young infants reason about the goals, preferences, and actions of others. State of the art computational models, however, still fail in such reasoning. The Baby Intuitions Benchmark (BIB) was designed to test agency reasoning in AI using an infant behavioral paradigm. While BIB’s presentation of simple animations makes it particularly suitable for testing AI, such vignettes have yet to be validated with infants.

In this pilot, 11-month-old infants watched two sets of animations from BIB, one on agents’ consistent preferences and the other on agents’ efficient actions. Infants looked longer towards violations in agents’ behavior in both the preference (N = 24, β = 3.24 p = .040) and efficiency task (N = 24, β = 4.50 p = .016).

These preliminary results suggest that infants’ agency reasoning is abstract enough to be elicited by simple animations and validate BIB as a test of agency reasoning for humans and AIs.

Sampling Heuristics for Active Function Learning

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