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This study identified a novel and robust reasoning error. Lay reasoners significantly deviate from the prediction of Bayesian inference by consistently underestimating the added probative value of corroborating testimonial reports. Most surprisingly, however, is that in certain contexts the sum of corroborating evidence is considered to be significantly less valuable than a single report. There is a selective devaluing of corroborating testimony when a highly reliable report is corroborated by less reliable, but credible, reports. This intuitive error is not explained by an inaccurate understanding of individual cues of reliability and number of reports, but specifically when it is required to integrate these both cues. Findings indicate the operation of alternative reasoning strategies, resulting in errors at individual and group level.

CogSci 2023

Evaluating testimony from multiple witnesses: consistent undervaluing and selective devaluing of corroborating reports

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The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

Join us!

<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
<br>[**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
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#### **CogSci 2023 Program Book**
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The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Theories can be designed to predict novel evidence or to accommodate known evidence. Despite the lively debate in philosophy of science whether prediction may hold a superior value over accommodation, people’s intuitions about this issue have not been empirically examined. Within a medical scenario, we assess individuals’ sensitivity to this dilemma. Overall, we find tentative evidence that people favour the predictive account and regard the predicting theorist (i.e., doctor) as more reliable in contrast to the accommodating counterpart. Strikingly, discrepant preference patterns emerged out of their verbal reasoning data echoing the distinct philosophical stances surprisingly well. Possible reasons why people’s reasoning systematically diverges despite the general preference for prediction are discussed.


Do people prefer prediction over accommodation? An empirical study

Intuitive conceptual systems impact how university students learn formal biological science. One such conceptual system--anthropocentric thinking--involves anchoring our understanding of the biological world in human terms. Prior research indicates that anthropocentric thinking, like other intuitive conceptual systems, can engender misconceptions, but also varies with richness of informal biological experience. To examine the impact of informal experience on anthropocentric thinking and formal biology learning, we assessed students from a rural institution (n=212) and an urban institution (n=188) on their use of anthropocentric reasoning and their agreement with biological misconceptions using both qualitative coding of free responses and quantitative analysis of close-ended measures. Our findings indicate linkages between anthropocentric thinking and misconceptions, as well as differences between urban and rural students. This work demonstrates context-based variability in anthropocentric thinking, highlights potential mechanisms driving this variability, and underlines the implications of intuitive thinking for STEM learning.

Relations Between Anthropocentric Thinking and Biological Misconceptions in Students attending Urban and Rural Universities

We investigate people’s use of strategies for sampling data in an active learning task. In the spirit of resource-rational analysis, we argue that people may often use effective heuristics to guide sampling in lieu of more computationally expensive optimization strategies, but that they do not simply continue relying heuristics that are shown to be ineffective, but instead flexibly shift to new strategies. By clustering participants’ sampling behaviour, we show that people engage in varied sampling strategies, shifting more often when encountering some function families. Although not all strategies improved participants’ performance on a subsequent prediction task, when the function family changed, participants quickly updated their beliefs about the likely function family on subsequent trials. Although sparse opportunities to learn may limit participants’ ability to fully learn the underlying functional relationships, people’s ability to dynamically shift their active learning behaviour may help them understand the abstract features of complex relationships.

Characterizing Shifts in Strategy in Active Function Learning

Theory of Mind enables us to attribute mental states to others. But we not only make inferences about mental states (like what someone believes or wants), but about mental processes (like if someone is distracted or whether they remember something). Here, we present a computational formalization of these kinds of inferences. We propose that inferences about mental processes are structured around a principle of rational mental effort: the expectation that other people allocate mental resources rationally so as to minimize thinking costs incurred while pursuing their goals. We develop this theory into a computational model in the context of the Rush Hour puzzle game. In two behavioral experiments testing different inferences about mental processing, we find that our model predicts participant judgments. This work advances our understanding of the richness of the human mind's ability to think about other minds, and even about thinking itself.

Thinking about Thinking as Rational Computation

Problem solving under time and material constraints assumes that humans face both their own internal computational limitations and constraints of the external task environment. To find optimal solutions under these constraints, the agent must adapt their behavior by using various strategies such as offloading (i.e., using external materials to aid their performance). We designed a novel tower building task to investigate adaptive use of strategies under constraints. The task worked as designed: Participants found the optimal solution most often on the least difficult scenario and least often on the most difficult scenario. Surprisingly, offloading led to no significant differences in performance. However, on the most difficult scenario, some participants found the optimal solution using a prospective, concurrent, or retrospective strategy based on experience with the constraints of the task environment. We conclude that optimality can be understood as a trend over time and investigated in studies that allow multiple attempts.

Adaptivity and optimization under constraints

Comprehensive datasets used for modeling endeavours in syllogistic reasoning
research usually contain only a single conclusion per task for each subject.
However, this means that no information about the other conclusions is provided,
preventing the distinction between conclusions that were rejected, conclusions
deemed to be valid but not the preferred conclusion and conclusions that were
not considered at all. In this work, we present a multiple choice dataset
containing all conclusions that participants considers valid. The data is
compared to datasets with other response designs, and an extensive evaluation
is performed to assess the impact of the response design on the predictive 
performance of cognitive models. Finally, our results are discussed and put
into perspective.


Effect of Response Format on Syllogistic Reasoning

A set of fundamental principles governs our reasoning about agents since infancy. Past research has shown that adults are surprised when they observe apparent violations of these principles, which might prime them to learn from the violations and update their beliefs. In the present experiments, we demonstrate that adults can revise their beliefs about these principles in a specific, virtual world when they observe multiple pieces of counterevidence, and generalize their revised beliefs to new agents in the same environment. These findings suggest important future directions to investigate whether adults can also revise these principles in the real world.

Can Adults Revise Their Core Beliefs about Agents?

In our global and technical world, there is an increasing need for humans to be able to solve problems that are complex, nontransparent and self-dynamic. There are vast differences between humans in terms of their capability to solve such problems that go beyond classical intelligence and having an analytical mind. Factors such as personality, knowledge, and the motivation to engage in effortful cognitive problems can contribute to success. Only a few studies investigated the relationship between these `soft' factors and complex problem-solving (CPS). In the following, we will investigate these factors in the CPS framework Tailorshop, a computer-based scenario with the goal of increasing the company value by manipulating several variables. Results indicate that personality traits and need for cognition are no successful predictors. Overall, the present study points out the tendency of relevant personality traits as CPS predictors.

The Impact of Personality for Solving Complex Problems

Vaccines are among the best tools to limit the spread of preventable diseases. yet, recent years have seen a rise in anti-vaccination sentiments for vaccines against COVID-19, the MMR, and more. It is critical to understand the factors that influence whether a person will accept or reject a vaccine for a given disease. 
This paper tests a Bayesian model to predict attitudes toward vaccines. with five factors (subjective beliefs concerning danger of illness, safety of the vaccine, prevalence of the disease, perceived social norm, and governmental recommendation). 
To parameterize the model, Study 1 elicits the full conditional probability table while Study 2 tests model predictions by eliciting people’s priors for COVID-19 and the common flu. We find a good fit between predictions and observations, accounting for 53% and 44% of the variance. This suggests the usefulness of a formal model to capture people’s beliefs about vaccination.


Estimating attitudes toward vaccination: A Bayesian framework

Both the logical form and content of assertions can have an impact on the conclusions humans derive. Despite this, research in that domain has often singled out one aspect: e.g., in syllogistic reasoning research about the logical form dominates (with some exceptions), while in conditional reasoning the role of different contents is more prominent. A core result in the latter domain focuses on the suppression effect, namely that alternatives or disablers can make a conclusion less likely. An effect that is very likely in everyday reasoning, where rarely a statement is exclusively true or false. So far, however, it has not been studied whether this is an effect specific to the conditional domain, or whether it transfers to the domain of syllogisms. This paper systematically investigates the impact of assertions that allows for disablers and alternatives in syllogistic reasoning. The response distribution for both conditions demonstrate on the aggregate level a shift for the case of non valid conclusions to preferred patterns. Using methods from machine learning we develop information based models that represent the gathered data with an accuracy of up to 83%. We discuss the need to bridge the gap between concepts and phenomena found in different reasoning domains.

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Do people prefer prediction over accommodation? An empirical study

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