United States

Educational technologies with AI are designed to personalize students’ learning, but also to alleviate teacher’s workload. However, acceptability of such technologies among teachers may be impacted by factors such as fear of replacement or ethical concerns. The purpose of the current study is to investigate attitudes of teachers towards educational tools with or without AI. The main hypothesis is that technologies with AI would be more negatively judged than technologies without AI, and thus intention to use would be weaker for technologies with AI. Results show that teachers seem to accurately perceive the potential benefit of AI technologies for reduction of workload, without feeling threatened by a replacement in the future. Ethical concerns are higher for AI technologies, but intention to use is pretty much similar. Differences between primary and secondary teachers are discussed.

CogSci 2023

Teachers’ attitudes towards AI: what is the difference with non-AI technologies?

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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.

Recent progress in artificial intelligence has renewed interest in
building machines that learn like animals. Almost all of the
work comparing learning across biological and artificial systems
comes from studies where animals and machines received
different training data, obscuring whether differences between
animals and machines emerged from differences in learning
mechanisms versus training data. We present an experimental
approach—a “newborn embodied Turing Test”—that allows
newborn animals and machines to be raised in the same
environments and tested with the same tasks, permitting direct
comparison of their learning abilities. To make this platform, we
first collected controlled-rearing data from newborn chicks, then
performed “digital twin” experiments in which machines were
raised in virtual environments that mimicked the rearing
conditions of the chicks. We found that 1) machines (deep
reinforcement learning agents with intrinsic motivation) can
spontaneously develop visually guided preference behavior, akin
to imprinting in newborn chicks, and 2) machines are still far
from newborn-level performance on object recognition tasks.
Almost all of the chicks developed view-invariant object
recognition, whereas the machines tended to develop
view-dependent recognition. Moreover, the learning outcomes
(developmental pathways) were far more constrained in the
chicks versus machines. Ultimately, we anticipate that this
approach will help researchers develop embodied AI systems
that learn like newborn animals.




A newborn embodied Turing test for view-invariant object recognition

What are the computational foundations of social grouping?
Traditional approaches to this question have focused on verbal
reasoning or simple (low-dimensional) quantitative models. In
the real world, however, social preferences emerge when
high-dimensional learning systems (brains and bodies) interact
with high-dimensional sensory inputs during an animal’s
embodied interactions with the world. A deep understanding of
social grouping will therefore require embodied models that
learn directly from sensory inputs using high-dimensional
learning mechanisms. To this end, we built artificial neural
networks (ANNs), embodied those ANNs in virtual fish bodies,
and raised the artificial fish in virtual fish tanks that mimicked
the rearing conditions of real fish. We then compared the social
preferences that emerged in real fish versus artificial fish. We
found that when artificial fish had two core learning
mechanisms (reinforcement learning and curiosity-driven
learning), artificial fish developed fish-like social preferences.
Like real fish, the artificial fish spontaneously learned to prefer
members of their own group over members of other groups. The
artificial fish also spontaneously learned to self-segregate with
their in-group, akin to self-segregation behavior seen in nature.
Our results suggest that social grouping can emerge in embodied
agents from three ingredients: (1) reinforcement learning, (2)
intrinsic motivation, and (3) early social experiences with
in-group members. This approach lays a foundation for reverse
engineering animal-like social behavior with image-computable
models, bridging the divide between high-dimensional sensory
inputs and social preferences.

Parallel development of social preferences in fish and machines

Recent years have seen remarkable advances in the development and use of Artificial Intelligence (AI) in image classification, driving cars, and writing scientific articles. Although AI can outperform humans in many tasks, there remain domains where humans and AI working together can outperform either working alone. For humans and AI to work together effectively, the human must trust the AI bot to the right degree (calibrated). If the human does not trust the bot sufficiently, or conversely trusts the bot more than is warranted, the human-bot team will not perform as well as they could. We report three experiments examining trust in human-AI teaming. While existing studies typically collect binary responses (to trust, or not to trust), we present a novel paradigm that quantifies trust in a bot-recommendation in a continuous fashion. These data allow better precision, and in the future the development of more refined models of human-bot trust.

Trust in Human-bot Teaming: Applications of the Judge Advisor System

With AIs becoming prevalent in our daily lives, it is still controversial whether they are a reliable conversational assistant. We, therefore, developed a chatbot, a Friendly-Bot, where users can share interpersonal experiences (e.g., friendship and romantic relationships). We then manipulated chatbot appearances into two types: robot and human-looking. We found that chatbot appearance predicted users' trust. Participants preferred the robot-looking chatbot over the human-looking one. Participants also showed higher trust in robot-looking chatbots when conversing about positive interpersonal relationships. Participants showed a different pattern of trust depending on the appearance condition: positive experience led to higher trust in robot-looking condition, whereas the opposite was observed in human-looking condition. Our findings show how chatbot appearance influences rapport-building in AI-assisted interactions (e.g., counseling).

Friendly-Bot: The Impact of Chatbot Appearance and Relationship Style on User Trust

People can understand images that vary in visual abstraction---from detailed illustrations to schematic icons. 
To what degree are current vision algorithms sensitive to such variation when encoding their meaning? 
We first obtained >80K human-generated sketches produced under different time limits (4s, 8s, 16s, 32s; N=5,563 participants) and AI-generated sketches (Vinker et al., 2022) produced under different ink limits (8, 16, 32 strokes) of 2,048 real-world objects spanning 128 categories from the THINGS dataset (Hebart et al.,2019).
We then evaluated how 13 state-of-the-art vision algorithms varying in architecture and training represented the meaning of these sketches, both at the category and exemplar levels, and without additional training.
We found that these models were generally sensitive to variation in visual abstraction, although some models were substantially more sensitive than others.
Together, these findings suggest promising avenues for building better models of human visual abstraction.

Evaluating machine comprehension of sketch meaning at different levels of abstraction

Although object detection AI plays an important role in many critical systems, corresponding Explainable AI (XAI) methods remain very limited. Here we first developed FullGrad-CAM and FullGrad-CAM++ by extending traditional gradient-based methods to generate object-specific explanations with higher plausibility. Since human attention may reflect features more in-terpretable to humans, we explored the possibility to use it as guidance to learn how to combine the explanatory information in the detector model to best present as an XAI saliency map that is interpretable (plausible) to humans. Interestingly, we found that human attention maps had higher faithfulness for explaining the detector model than existing saliency-based XAI methods. By using trainable activation functions and smoothing kernels to maximize the XAI saliency map similarity to human attention maps, the generated map had higher faithfulness and plausibility than both existing XAI methods and human atten-tion maps. The learned functions were model-specific, well generalizable to other databases.

Human Attention-Guided Explainable AI for Object Detection

For the last decade, convolutional neural networks (CNNs) have vastly superseded their predecessors in nearly all vision tasks in artificial intelligence, including object recognition. However, despite abundant advancements, they continue to pale in comparison to biological vision. This chasm has prompted the development of biologically-inspired models that have attempted to mimic the human visual system, primarily at a neural level, which is evaluated using standard dataset benchmarks. However, more work is needed to understand how these models perceive the visual world. This article proposes a state-of-the-art procedure that generates a new metric, Psychophysical-Score, which is grounded in visual psychophysics and is capable of reliably estimating perceptual responses across numerous models --- representing a large range in complexity and biological inspiration. We perform the procedure on twelve models that vary in degree of biological inspiration and complexity, we compare the results against the aggregated results of 2,390 Amazon Mechanical Turk workers who together provided $\sim2.7$ million perceptual responses. Each model's Psychophysical-Score is compared against the state-of-the-art neural activity-based metric, Brain-Score. Our study indicates that models with a high correlation to human perceptual behavior also have a high correlation with the corresponding neural activity.

Psychophysical-Score: A Behavioral Measure for Assessing the Biological Plausibility of Visual Recognition Models

Relational ability—the ability to compare situations or ideas and discover common relations – is a key process in higher-order cognition that underlies transfer in learning and creative problem solving. For this reason, it has generated intense interest both among developmentalist and in cross-species comparative studies. The gold standard for evaluating relational ability is the Relational-Match-to-Sample (RMTS) task (Premack, 1983). Current work in cognitive development has produced inconsistent results as to when children are able to pass the RMTS, with Christie and Gentner (2014) finding earlier success than do Hochmann et al. (2017). In this research, we attempt to resolve this issue. We first describe two studies that bear out and extend the Christie and Gentner (2014) findings. We then discuss factors that might explain the discrepancy between the findings.

When do Children Pass the Relational-Match-To-Sample Task?

Abstract reasoning in early childhood is often described as following a "relational shift," over which children become increasingly sensitive to relations. However, recent work has challenged the generality of this account, showing that children in the US and China follow distinct trajectories in a relational match-to-sample task (Carstensen et al., 2019). This difference aligns with multiple cultural and linguistic factors implicated in relational reasoning, in which English speakers in the US and Mandarin speakers in China appear at opposite ends of a continuum spanning from a focus on objects (US) to relations (CN). We explore early relational reasoning in a context that represents a cultural middle ground with a key linguistic similarity (noun spurts) to the US: Korean-learning children in South Korea. In two experiments with 262 Korean children, we document relational reasoning in this novel cultural context, revealing similarities and differences to developmental trajectories in the US and China.

Relational abstraction in early childhood: Three cultures and three trajectories

Analogy is considered a core cognitive capacity that enables relational reasoning. It has been commonly believed that relational reasoning can be tested by the four-term analogy task of type “what is to c as b is to a?” or “a:b::c:?” in short. In this study, we challenge this common belief by investigating an alternative hypothesis that this classic task is solvable only by attributional reasoning on attributional similarities, rather than relational reasoning on the relation of the two relations. A word vector model was employed to demonstrate how these two types of reasoning were performed in the classic three-stem analogy task “a:b::c:?” and the two-stem analogy task “a:b::?:?”. The results showed that the two-stem analogy task was solvable only using relational reasoning, while the three-stem analogy task was solvable using both types of reasoning. It suggests that the two-stem analogy task is a better test for relational reasoning.

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A newborn embodied Turing test for view-invariant object recognition