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Curiosity relates to learning and recent work shows robust associations between curiosity and recall of information in adults and children over age 10. The current study tested a similar association in younger children and explored children’s information-seeking behaviors. Children (n=90, 4-10-year-olds) played a free-exploration game in which they clicked shapes to learn different facts about different topics. They then recalled what they remembered learning on the task (recall) and asked questions about what they were curious to know more about (curiosity). We observed a positive association between children’s curiosity and recall of information, even when controlling for amount of information seeking and children’s age. This association was specifically seen for recall without memory cues. There was no association between children’s curiosity and information seeking behavior, and children showed a strong tendency of breadth exploration over exploring in depth with some indication of children beginning to explore depth more with age.


CogSci 2023

Exploring to learn: Curiosity, breadth and depth of exploration, and recall in young children

technical paper

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>
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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.

Modeling human navigation and route selection in complex environments has used path planning in the past and more recently, dynamical models. The current study modeled the navigational movement trajectories of human herder agents tasked with corralling an autonomous target agent into a specified containment zone. Analyses of trajectories of participants completing a first-person herding game revealed that their movement entailed sequential (i) approach and then (ii) corral phases that were invariant to the initial location of the herder and target agents. More importantly, the trajectories of human herders could be captured using a low-dimensional environmentally coupled dynamical model. The novelty of this work is in extending the previous research in modeling navigational behaviors of humans to include moving targets that need to be manipulated into a desired position. Implications of the findings for the development of artificial agents for human-machine herding, as well as more general human-machine teaming are discussed.

Modeling Human Navigation in First-Person Herding Tasks

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.

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

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

Categories simplify the vast number of entities we encounter into equivalence classes, serving as a fundamental block of our cognition. This simplification of information supports our ability to reason about and interact with members of each category. In adulthood, selective attention helps us form categories efficiently by focusing on relevant attributes while filtering out irrelevant information. However, young children tend to encode more attributes than necessary, and this developmental difference is partly due to a prolonged development of selective attention. In the current study, we proposed another important contributor to the development. As shown in three experiments, children prefer information gathering to efficiency, regardless of its value. Experiment 1 revealed that children sample more information than adults even when filtering demands are low. Experiment 2 further supports children's preference for information. And Experiment 3 eliminates alternative explanations, demonstrating that children enjoy gathering information rather than motor actions associated with it.

Driven by Information: Children's Exploration Shapes Their Distributed Attention in Category Learning

Young children acquire a wide range of linguistic and cognitive skills in the first three years of life. Decades of experimental work have established a solid empirical foundation for our understanding of cognitive development. But most experimental studies are limited in statistical power and focus on specific psychological constructs, thus making them unsuitable for describing developmental growth at scale. Here, we turned to meta-analyses of experimental research. We conducted a meta-meta-analysis to consolidate and integrate 23 meta-analyses compiled on MetaLab, a community-augmented meta-analysis platform. We found that most datasets can not meaningfully distinguish different functional forms for developmental change, but in those that could, there is great diversity in the best-fitting functional forms of the age model. We also evaluated the impact of a range of methodological factors. Overall, our work sheds light on the heterogeneous nature of developmental trajectories and the subtle interactions between research methods and experimental outcomes.

A synthesis of early cognitive and language development using (meta-)meta-analysis

Quantitative models of behavior are a fundamental tool in cognitive science. Typically, models are hand-crafted to implement specific cognitive mechanisms. Such "classic" models are interpretable by design, but may provide poor fit to experimental data. Artificial neural networks (ANNs), on the contrary, can fit arbitrary datasets at the cost of opaque mechanisms. Here, we adopt a hybrid approach, combining the predictive power of ANNs with the interpretability of classic models.
We apply this approach to Reinforcement Learning (RL), beginning with classic RL models and replacing their components one-by-one with ANNs. We find that hybrid models can provide similar fit to fully-general ANNs, while retaining the interpretability of classic cognitive models: 
They reveal reward-based learning mechanisms in humans that are strikingly similar to classic RL. They also reveal mechanisms not contained in classic models, including separate reward-blind mechanisms, and the specific memory contents relevant to reward-based and reward-blind mechanisms.

Predictive and Interpretable: Combining Artificial Neural Networks and Classic Cognitive Models to Understand Human Learning and Decision Making

Dependency tree is efficient for relation extraction model to exploit relations between words. Recent approaches have achieved promising performance while still suffering from inherent limitations. 
In this paper, we propose a novel relation extraction framework called Progressive Graph Learning over pruned dependency tree (PGLNet). PGLNet constructs a set of graphs by progressively adapting to input sentence. Specially, we implement the model to construct progressive weighted adjacency matrices by learning the relations among graph nodes with self-attention mechanism. Then, the model takes the learned weights as reference to prune dependency tree. Next, progressive convolution module is designed to encode the relations of entities and followed by relations classification. We evaluate our proposed model using public real-world datasets, experimental results demonstrate that the proposed model achieves state-of-the-art performance with consistency in all datasets. We conclude that the ability of PGLNet to progressively adapt to input data and enable the model with robustness.


Progressive Graph Learning over Pruned Dependency Trees For Relation Extraction

In this work, we develop convolutional neural generative coding (Conv-NGC), a generalization of predictive coding to the case of convolution/deconvolution-based computation. Specifically, we concretely implement a flexible neurobiologically-motivated algorithm that progressively refines latent state feature maps in order to dynamically form a more accurate internal representation/reconstruction model of natural images. The performance of the resulting sensory processing system is evaluated on complex datasets such as Color-MNIST, CIFAR-10, and Street House View Numbers (SVHN). We study the effectiveness of our brain-inspired model on the tasks of reconstruction and image denoising and find that it is competitive with convolutional auto-encoding systems trained by backpropagation of errors and outperforms them with respect to out-of-distribution reconstruction (including the full 90k CINIC-10 test set).


Convolutional Neural Generative Coding: Scaling Predictive Coding to Natural Images

Punishment is a cost imposed on a target, in response to an undesirable action. Yet choosing to punish also reveals information about the authority’s own motives and values. 
We propose that observers jointly infer the wrongness of the action and the authority's motivations. Using hypothetical scenarios in unfamiliar societies, we experimentally manipulated observers' prior beliefs and measured human observers' inferences after observing punishment. These inferences were recapitulated in a formal model that inverts an intuitive causal model of authorities who make rational choices about punishment by weighing its costs and benefits (i.e. utilities). An essential component of this model, driving these inferences, is that legitimate authorities consider the utility of a proportional response to harmful actions, which depends on the balance between the wrongness of the act and the severity of the punishment.

What people learn from punishment: joint inference of wrongness and punisher's motivations from observation of punitive choices

Political polarization is driven by many factors, but the role of moral values as both a signal of political identity and a source of internal conflict is understudied. We report an agent-based computational model of polarization that fills this gap. Agents seek to differentiate in- and outgroup neighbors with a slight preference for the former. However, they must do so by inferring neighbors’ identities from visible but transient moral signals. Moreover, agents experience conflicts within their own values, and if difficult to resolve internally, can copy the values of their ingroup or disengage (i.e., act immorally). Results show that liberals form larger, more homogeneous clusters, are happier, and experience less moral conflict than conservatives. Conservatives experience more and higher levels of conflict, and morally disengage significantly more often than liberals.


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Exploring to learn: Curiosity, breadth and depth of exploration, and recall in young children

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Modeling Human Navigation in First-Person Herding Tasks