United States

This study examined the influence of metacognition on declarative and reinforcement learning (RL) mechanisms. We collected data from 218 undergraduates using a within-subjects metacognitive manipulation of a stimulus-response (S-R) learning task created by Collins (2018). Contributions of declarative and RL mechanisms are assessed by differences in learning rate for blocks of 3 items versus 6 items, and by the rate of forgetting with an incidental post-test. If metacognition differentially affects declarative and RL, we expect a three-way interaction between the task phase (learning/post-test), block type (long/short), and metacognition (before/during). Our results showed significant main effects of phase (F(1,217) =143.18, p=9.18e-32), length (F(1,217)=541.11, p=2.06e-104) and metacognition (F(1,217) = 19.78, p = 9.22e-06), with better performance during the learning phase, short blocks, and metacognitive manipulation. A significant phase by metacognition interaction (F(1,217) =8.11, 4.45e-03) suggested that metacognition monitoring improved test performance while having little effect on learning performance.

CogSci 2021

Investigating the Impact of Metacognition on Working Memory and Procedural Learning Mechanisms

### 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!<br><br>

**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.

poster

In order to learn from metaphors, children must not only be able to understand metaphors, but also appreciate their relative informativeness. Although functional metaphors based on abstract commonalities (e.g. “Eyes are windows”) allow for more learning than perceptual metaphors based on superficial commonalities (e.g. “Eyes are buttons”), previous research shows that preschoolers prefer perceptual metaphors over functional metaphors. In the present studies, we ask whether providing additional context can shift metaphor preferences in preschoolers and adults. Experiment 1 finds that pedagogical context increases preferences for functional metaphors in adults, but not preschoolers. Experiment 2 finds that providing explanations for conceptual similarities in a metaphor increases preschoolers’ preferences for functional metaphors. These findings suggest that providing explanations allows even preschoolers to appreciate the informativeness of functional metaphors.

Providing explanations shifts preschoolers’ metaphor preferences

In a memory task, focusing on to-be-remembered information while concurrently engaging in a secondary task may result in impaired memory, possibly due to limited cognitive resources. However, previous research has demonstrated circumstances where interleaving a secondary task can impair immediate recall but enhance long-term retention. This suggests that the type and difficulty of the secondary task also affected memory. The present study explores the effect of processing a secondary task on recall. In Experiment 1, increasing complexity of the secondary task resulted in a detrimental effect on delayed free recall. Experiment 2 and 3 examine the effect of increasing cognitive load with faster stimuli presentation on delayed free recall and serial recall. These findings have implications for theories advocating the domain-general nature of cognitive resources.

Impact of Performing A Secondary Task on Recall

A stereotype is a generalization about people within a certain category––and category information is often used to make probabilistic predictions about people within a particular group. The current work examines whether stereotypes can be understood in terms of conditional probabilities as per Bayesian reasoning. For instance, the stereotype of Germans as efficient can be understood as the conditional probability of someone being efficient given that they are German. Whether such representations follow Bayes’ rule was tested in a replication and extension of McCauley and Stitt’s (1978) original studies. Across two experiments, we found that people's judgements of eight different social groups were appropriately Bayesian i.e., their direct posterior predictions were in line with what Bayes' rule suggests they should be, given subjects’ priors and likelihood ratios. For any given social group, it was also the case that traits with a high calculated diagnostic ratio distinguished stereotypic from non-stereotypic traits.

Stereotypes as Bayesian Judgements of Social Groups

The law of least mental effort suggests that humans seek to minimize cognitive effort exertion. It is thought that we do so because effort is inherently aversive, playing the cost function in a cost-benefit analysis. However, this is not always the case: Some human activities are valued precisely because they are effortful. This dual nature of effort as valued and costly is known as the Effort Paradox. The question is therefore: what features differentiate an aversively effortful task from a valued one? In the current study, we explore how perceived progress might be one of these features. Across two experiments, we demonstrate that people willfully choose to engage in more demanding cognitive tasks when doing so yields telegraphed progress information. These results suggest that perceived progress may play a moderating role in cognitive effort aversion and hints at the possibility that progress itself may be an inherently valuable stimulus.

Making Progress on the Effort Paradox: Progress Information Moderates Cognitive Demand Avoidance

Current theories of procrastination argue that people put things off into the future with the expectation that they will be better able to do them later. In this paper, we rationalize such expectations within the framework of evidence accumulation models of the choice process. Specifically, we show that it is rational for observers to adopt lower decision thresholds for choices with weak evidence for any alternative, and that observers learning to estimate optimal decision thresholds for tasks that involve decisions will find it reasonable to put the tasks off until the threshold has been sufficiently lowered by time-varying urgency. We designed a computational model and an experiment to differentiate our theory from more general expectancy based temporal motivation accounts. Both simulation and experimental results support our proposal, indicating a large role for choice difficulty in people's self-assessed estimates for how likely they are to procrastinate any given task.

Modeling procrastination as rational metareasoning about task effort

Are mental additions of single-digit numbers solved through direct retrieval from long-term memory or through persistent use of an automatized counting procedure along a mental line? In this paper, we present an experiment based on small additions along an artificial mental line, which tends to show that for very small addends (+2 to +4), counting may still be used at the end of a 3-week training. To investigate this issue, we developed the AutoCoP computational model, which describes how small additions could be solved, based on attention, working memory and experience. The simulations of AutoCoP based on this experiment showed that the effects detected at the behavioral level are reproduced and consistent with the theory, which assumes the use of a counting procedure in experts.

A computational model of counting along a mental line

The use of algorithmic decision-making is steadily increasing, but people may have misgivings about machines making moral decisions. In two experiments (N = 551), we examined whether people expect machines to weigh information differently than humans in making moral decisions. We found that people expected that a computer judge would be more likely to convict than a human judge, and that both judge types would be more likely to convict based on individuating information than on base-rate information. While our main hypotheses were not supported, these findings suggest that people might anticipate machines will commit to decisions based on less evidence than a human would require, providing a possible explanation for why people are averse to machines making moral decisions.

The computer judge: Expectations about algorithmic decision-making

We study active human visuo-locomotive experience in everyday navigation from the viewpoints of environmental familiarity, embodied reorientation, and (sensorimotor) spatial update. Following a naturalistic, in situ, embodied multimodal behaviour analysis method, we conclude that familiar users rely on environmental cues as a navigation-aid and exhibit proactive decision-making, whereas unfamiliar users rely on manifest cues, are late in decision-making, and show no sign of sensorimotor spatial update. Qualitative analysis reveals that both groups are able to sketch-map their route and consider path integration: i.e., conscious spatial representation updating was possible but not preferred during active navigation. Overall, the experimental task did not trigger automatic or reflexlike spatial updating, as subjects preferred strategies involving memory of perceptual cues and available manifest cues instead of relying on motor simulation and continuous spatial update. Rooted in the behavioural outcomes, we also position applications in computational modelling of navigation within cognitive technologies for architectural design synthesis.

Visuo-Locomotive Update in the Wild: The Role of (Un)Familiarity in Choice of Navigation Strategy, and its Application in Computational Spatial Design

Brainstorming suggests that generating ideas in groups can direct one's thoughts into areas that might be difficult to reach or consider, resulting in better performance than individuals working separately. However, while intuitively appealing, studies have repeatedly found no evidence for such synergy.
Here, we reinvestigate group synergy in a task with simulated agents. We use state-of-the-art language embeddings to generate close or far-off semantic associates to people's ideas. This design allows us to adopt a sampling for inference perspective, where individuals produce sample ideas from a generative process (Sanborn & Chater, 2016). We hypothesize that suggestions can allow participants to create samples from previously not considered concepts, and additionally, knowledge about the diversity of solutions can spur more broad idea generation. Our work helps inform broader questions about when and why groups can expect to produce better performance than individuals.

Sampling Associations with (Un)related Suggestions

The distinctly human ability to both represent number exactly and develop symbolic number systems has raised the question of whether such number concepts are culturally constructed through symbolic systems. Although previous work with innumerate and semi-numerate groups has provided some evidence that understanding exact equality is related to numeracy, it is possible that previous failures were driven by pragmatic factors, rather than the absence of conceptual knowledge. Here, we test whether such factors affect performance on a test of exact equality in 3- to 5-year-old children by modifying previous methods to draw children’s attention to number. We find no effect of highlighting exact equality, either through framing the task as a “Number” game or as a “Sharing” game. Instead, we replicate previous findings showing a link between numeracy and an understanding of exact equality, strengthening the proposal that exact number concepts are facilitated by the acquisition of symbolic number systems.

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Providing explanations shifts preschoolers’ metaphor preferences

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