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Conversational AI devices are increasingly present in our daily lives and used by both adults and children to ask questions, play and learn. However, their appearance oddly blurs the line between agents and objects—they both look and behave like inanimate objects yet engage in polite helpful conversations—raising questions about how humans conceptualize such novel entities. In this work we explore adult and children&#39;s attributions of the AI&#39;s &quot;mental states&quot; through a variant of a false belief task. We establish that, while adults successfully differentiate the individual minds of people from the shared &quot;minds&quot; of conversational AI agents, children through age 8 reason about AI agent minds much as they do human minds: as separate entities that do not share information. These findings suggest conversational AI agents are rather unintuitive to young children, (i.e. understanding develops sometime after age 8) and we discuss implications of this for childhood AI education.


CogSci 2023

Theory of AI Minds: Children&#39;s reasoning about the &quot;mental states&quot; of conversational AI agents

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Conversational AI devices are increasingly present in our daily lives and used by both adults and children to ask questions, play and learn. However, their appearance oddly blurs the line between agents and objects—they both look and behave like inanimate objects yet engage in polite helpful conversations—raising questions about how humans conceptualize such novel entities. In this work we explore adult and children's attributions of the AI's "mental states" through a variant of a false belief task. We establish that, while adults successfully differentiate the individual minds of people from the shared "minds" of conversational AI agents, children through age 8 reason about AI agent minds much as they do human minds: as separate entities that do not share information. These findings suggest conversational AI agents are rather unintuitive to young children, (i.e. understanding develops sometime after age 8) and we discuss implications of this for childhood AI education.


Theory of AI Minds: Children's reasoning about the "mental states" of conversational AI agents

poster

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).

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<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
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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.

Explicit similarity judgments tend to emphasize relational information more than do difference judgments. We propose and test the hypothesis that this asymmetry arises because human reasoners represent the relation different as the negation of the relation same, so that processing difference is more cognitively demanding than processing similarity. For both verbal comparisons between word pairs, and visual comparisons between sets of geometric shapes, we asked participants to select which of two options was either more similar to or more different from a standard. On unambiguous trials, one option was unambiguously more similar to the standard; on ambiguous trials, one option was more featurally similar to the standard, whereas the other was more relationally similar. Given the higher cognitive complexity of assessing relational similarity, we predicted that detecting relational difference would be particularly demanding. We found that participants (1) had more difficulty accurately detecting relational difference than they did relational similarity on unambiguous trials, and (2) tended to emphasize relational information more when judging similarity than when judging difference on ambiguous trials. The latter finding was captured by a computational model of comparison that weights relational information more heavily for similarity than for difference judgments. Our results provide convergent evidence for a representational asymmetry between the relations same and different.


Asymmetry in similarity and difference judgments results from asymmetry in the complexity of the relations same and different

Preschoolers have difficulties in relational reasoning tasks, which are usually attributed to their object focus. However, studies directly testing children’s object focus yielded inconsistent results. Indirect investigations supported the role of object focus, yet the differences in study procedures might hinder a clear conclusion. To address these issues, in two experiments, we manipulated task characteristics that accommodated the previous studies and tested whether children’s use of object property words (e.g., color, shape) was directly associated with their performance in the relational match-to-sample task (RMTS). Our results showed that stimuli with basic shapes enhanced preschoolers’ performance in RMTS, and children’s use of object property words dampened their performance. These findings underline considering task- and child-related factors when investigating children’s relational reasoning in particular and cognitive performance in general.


Object Focus and Preschoolers’ Relational Reasoning

Past work has found that dogs perceive and respond to certain characteristics of items, specifically the object's familiarity and value when making choices. However, in the real world, these characteristics don't exist in isolation, and understanding the interaction between familiarity and valuation as they relate to object choice can provide insight into how dogs make decisions. We aimed to explore how item familiarity and value intersect to form dogs’ preferences for one food over another in a two-alternative forced choice task. We found that dogs choices were driven only by the value of the food item, and the familiarity of the item did not impact choice behavior. Determining what motivates dogs and contributes to their preferences has implications for understanding decision-making at large, as well as for advancing canine science.


The Impact of Value and Familiarity on Dogs' Food Preferences

In humans, simple 2D visual displays of launching events (“Michottean launches”) can evoke the impression of causality. Direct launching events are regarded as causal, which are contrasted with similar non-causal events where a temporal and/or spatial gap is added between the movements of the two objects. This ability to distinguish between causal and non-causal events is perceptual in nature and develops early and preverbally in infancy. In the present study we investigated the evolutionary origins of this phenomenon and tested whether Guinea baboons (Papio papio) perceive causality in launching events. We used a discrimination and categorisation task of causal and non-causal events. Our results indicate that Guinea baboons discriminate between different Michottean events, but we did not find a learning advantage for a categorisation based on causality. This implies that they focused on the spatio- and temporal gap to achieve accurate categorisation, but not on causality per se. Currently we cannot rule out that Guinea baboons have causal representations of Michottean events, but our findings point to a feature-based discrimination strategy in a sorting task.


Causal perception in Guinea Baboons (Papio papio)

Philosophy drawing on studies of animal behavior has paved a middle-way path regarding natural and non-natural meaning (Bar-On, 2021; Bar-On & Moore, 2017; Green, 2019; Moore, 2018). Elaborating the question of communicative intention goes hand-in-hand with growing appreciation of the reaches of non-human animal mindedness (Andrews & Beck, 2018; Merritt, 2021), pushing the plausible bounds of higher-order cognitive capacities beyond the singular province of homo sapiens. The question of relating human languaging to non-human languaging is enriched through a dialogue between enactive philosophy of social cognition and post-Gricean discourse. Speaker meaning presupposes coherent, stable, individual, internal, and prior intention as a cognitive or mental state. The enactive theory of social cognition, participatory sense-making (De Jaegher & Di Paolo, 2007), calls all of these premises into serious question. Prioritizing the role of interactions in cognition brings attention to the multitude of possible partners humans had and have in communication and sense-making engagements.


Intentions as interactional and interspecies achievements: enactivist contributions to language evolution

Comparative sequence learning experiments allow cognitive processes that are required for language in humans to be studied in nonhuman animals. We investigated Rhesus macaques’ ability to detect abstract regularities in visual sequences, and their capacity to generalize to novel stimuli. On each trial, two colors were randomly generated and arranged into three-element long sequences based on three rules (‘AAB’, ‘ABA’, ‘BAA’). Monkeys were able to learn the rules and select appropriate sequences of colored squares, and generalized this learning to the novel color combinations. To further assess generalization, we presented probe trials using sequences comprised of randomly generated white shapes. Monkeys showed no evidence of spontaneous generalization between stimulus categories. Our results demonstrate that monkeys are able to learn abstract rules governing visual sequences and that they generalize these to novel sequences within a stimulus category, but provides no evidence of generalization between stimulus categories.


Rhesus macaques generalize abstract rules within but not between stimulus categories

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.


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

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.


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Asymmetry in similarity and difference judgments results from asymmetry in the complexity of the relations same and different

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