United States

The present study explores moral judgment in COVID-19 dilemmas involving allocation of two types of resources – ventilators or beds. Utilitarian principles are opposed to random allocation and first-come, first-served. In triage dilemmas there are two patients in a critical state either needing a ventilator or a bed but only one is available. Participants also rated their agreement with a list of triage principles. Four clusters of participants are identified. The first has a uniform distribution of preferences over all principles; the second favors all utilitarian principles; the third - only one utilitarian principle (higher chances of recovery); and the fourth - the first-come, first-served principle. The results show different pattern of moral judgments depending on the type of the resource. If ventilators are allocated, the utilitarian principles are supported. If the limited resource is a bed, first-come, first-served is preferred thus casting doubt on the egalitarian nature of this principle.


CogSci 2023

Moral Judgments in COVID-19 Triage Dilemmas: Does the Type of Life-Saving Resource Matter?

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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.

Reddit's subforum ChangeMyView is a subreddit where users post about a specific view they hold and challenge other users to present arguments to change their view. Here, we investigated what distinguishes the most convincing participants on Reddit change my view (henceforth, top persuaders), as measured by the all-time deltaboard, from equally active users who are nonetheless less likely to persuade peoples on the forum. We investigated the most common features of top persuaders and compared them to their similarly active Reddit counterparts. We found that top persuaders were more likely to provide external evidence for their claims and use morality-based reasoning, made longer comments and engaged in more back-and-forth argumentation, were less likely to use informal language in their arguments, and were more likely to show some overlapping semantic content with an original poster's comment. Other content was comparatively less effective: including the use of causal language, and asking questions in an argument, among others. These findings replicate and expand on previously established effects and may highlight ways to develop more effective educational interventions.


Characteristics of persuasive deltaboard members on Reddit’s r/ChangeMyView

Although there is a great deal of interest in conditional prob-abilities in Bayesian cognitive science, there is still little understanding of how well human agents can learn them. In our study, subjects are confronted with a belief revision problem in which the new information concerns conditional probabilities. In our material, we distinguish between cases of vacuous learning, where the probabilistic information is not new, and belief revision is unwarranted, from conditions with truly new information that conflicts with the previous belief state. We investigate how well participants can distinguish these cases and how well they respect the probabilistic norm, thus adding new insights to the long-standing research question of the extent to which the human mind is adapted to probabilistic reasoning.


How Well Do Humans Learn Conditional Probabilities?

Prediction, explanation, and control are basic cognitive tasks. Here we show how they can arise from unstructured exploration-based learning. We present a new method to analyze how people make use of mental models learned under free exploration. Our method allows us to control for the relative difficulty of the task, and to measure the extent to which participants can leverage the evidence provided by the mental model---however strong or weak---for decision-making. The key result of our subsequent experimental work is that free exploration leads, in simple cases, to far better performance on prediction and control compared to explanation. As systems become more complex, performance declines, but people can sustain relatively better performance on control. In the presence of hidden variables, explanatory abilities rise relative to the other two, even though the task is harder.


Prediction, Explanation, and Control Under Free Exploration

We explore intervention policies chosen by participants when learning about causal structures in continuous systems. We used the continuous time and state space formalism of Ornstein-Uhlenbeck networks (Davis et al., 2020b). We find that participants' interventions can be understood as compositions of two basic actions: swipes, large movements generating large changes in values in a short time window, and holds, fixing variables for a few frames and observing the outcome. We further show that intervention policies depend on the underlying causal structure that participants are attempting to retrieve. Swipes were performed more often when there was no outgoing link, suggesting that rapid successive movements was useful in validating the absence of causal relations. On the other hand, interventions featuring longer holding times were favoured in cases where there was at least one outgoing link from the intervened node and appeared to serve the purpose of establishing the sign and strength of causal relationships. Overall, these findings indicate that causal inference in continuous contexts appears to be a two step process, where participants use different interventions for causal discovery than they do to qualify the nature of discovered links, hinting at a tendency to decompose inference to simplify it.

Swipe and hold: composing interventions in continuous time causal learning

Recent work suggests that decisions to detain defendants before trial increase the likelihood of conviction. One reason may be that knowledge of detainment makes jurors more likely to convict. Previous work has claimed this as an example of ‘bounded rationality’ i.e., due to a simple bias. We argue that this inference represents sophisticated causal reasoning e.g. about information hidden from jurors such as criminal history. We examine whether the effect of detainment knowledge on conviction depends on rational inference by presenting participants with a legal vignette in a 2x2 design: a defendant either has or has not been detained, and this detainment decision is either (1) not explained or (2) explained as due to an iron clad rule always used for this class of crimes. We find an effect of detainment when it is not explained, but no effect when explained, providing evidence against the ‘bounded rationality’ view. We provide qualitative extracts of participants’ reasoning, demonstrating sophisticated and nuanced inferences from detainment to hidden information when the decision is not explained.

How does knowledge of detainment affect juror reasoning?

The intentional binding (IB) phenomenon reflects a perceived attraction between a voluntary action and an intended consequence. Recent research has shown that during IB, intended outcomes expand in time. Although, this effect was significant only for shorter action-outcome delays. However, literature on IB suggests it can also exist for long delays. To address this, we implemented a temporal reproduction task to observe the expansion of an intended outcome. Results revealed the expansion of an intended outcome in the shorter and longer action-outcome delays. These results were discussed under the PIDI (proximal intent distal intent) framework since, using this methodology, both types of intention can be operationalized. Proximal for shorter delays under the motor action, and distal for longer delays as the inference made towards the objective duration to be reproduced. Our implementation allows a robust way to observe the temporal dynamics of an intended outcome, irrespective of the action-outcome delay.

Intended Outcomes Expand In Time: Evidence from the Temporal Reproduction Task

Recent work highlighted a cause’s scope, the number of distinct effects it generates, as important for changing perceived causal strength. The findings, however, have been inconsistent, with researchers offering effect valence and domain knowledge as mutually exclusive explanations for the presence of scope effects. Our pre-registered experiment (N=322) simultaneously controlled or manipulated the links between scope and causal strength to adjudicate between the two accounts. We also examined another structural feature as a potential moderator of a cause’s perceived strength: while prior work has focused on causal power in common cause networks, we compared them with causal chains, where mechanistic clarity may lead to higher causal power ratings. We found that causal strength is robustly higher in chains regardless of valence and domain (p=.009). However, we failed to replicate the prior findings on the scope effect. Our experiment casts doubt on previous authors’ interpretation of scope's impact on causal judgments.

Perceived Causal Strength in Chains vs. Common Causes

Information about an individual’s moral character can bias legal judgments even when it is incidental. The present study explored how evaluations of a defendant’s character can influence mock jurors’ judgments of blame, guilt, and punishment. Participants (N=143) were shown a trial transcript in which the defendant’s character was manipulated by introducing an irrelevant moral behavior observed before the alleged crime as well as prior conviction. Bad defendants were judged more harshly than good or neutral defendants, especially for punishment judgments, where differences were persistent. While character similarly influenced judgments of guilt, blame, and intentionality, the effect diminished as participants received more information about the case. Thematic analysis of judgment rationales also revealed four key themes in participants’ reasoning process: facts of the case, inference of the defendant’s mental states, counterfactual reasoning, and evaluation of the defendant. We discuss the implications of this study in moral and legal decision-making.

Defendant character influences mock juror judgments of blame, guilt, and punishment

This work attempts to bridge the divide between accounts of causal reasoning with respect to agents and objects. We begin by examining the influence of animacy. In a collision-based context, we vary the animacy status of an object using 3D animations. By holding the fine-grained kinematics of the actual and counterfactual outcomes fixed across animate and inanimate conditions, we find that animacy, itself, has no effect on causal attribution judgments. Next, we test if causal judgments for animate and inanimate objects differ as a function of the counterfactuals they respectively afford in a disjunctive causal structure. Here, we find that the effect of perceived animacy on causal attribution is mediated by differences in counterfactual judgments. Finally, we introduce the known effect of prescriptive norm violations to this paradigm. Our findings collectively highlight how normative expectations specify the counterfactual considerations that guide causal reasoning for both agents and objects.

Causal Reasoning Across Agents and Objects

Humans excel at causal reasoning, yet at the same time consistently fail to respect its basic axioms. They seemingly fail to recognize, for instance, that only the direct causes of an event can affect its probability (the Markov condition). How can one explain this paradox? Here we argue that standard normative analyses of causal reasoning mostly apply to the idealized case where the reasoner has perfect confidence in her knowledge of the underlying causal model. Given uncertainty about the correct representation of a causal system, it is not always rational for a reasoner to respect the Markov condition and other ‘normative’ principles. To test whether uncertainty can account for the apparent fallibility of human judgments, we formulate a simple computational model of a rational-but-uncertain causal reasoner. In a re-analysis of a recent causal reasoning study, the model fits the data significantly better than its standard normative counterpart.

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Moral Judgments in COVID-19 Triage Dilemmas: Does the Type of Life-Saving Resource Matter?

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Characteristics of persuasive deltaboard members on Reddit’s r/ChangeMyView

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