Computational models of intrinsic motivation for curiosity and creativity

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Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

How the 'what', 'where', and 'when' of past experiences are stored in episodic memories and retrieved for suitable decisions remains unclear. In an effort to address these questions, the authors present computational models of neural networks that behave l ...

Context: Introducing Computer Science (CS) into formal education can be challenging, notably when considering the numerous stakeholders involved which include the students, teachers, schools, and policy makers. We believe these perspectives should be con ...

Handwriting is an important skill in children development as well as in education that take years to be mastered. While keyboards and similar technological devices increasingly become more popular, new tools and engaging approaches are also required to kee ...

Information derived from experiences is incorporated into the brain as changes to ensembles of cells, termed engram cells, which allow memory storage and recall. The mechanism by which those changes hold specific information is unclear. Here, we test the h ...

Understanding user’s perception of service variability is essential to discern their overall perception of any type of (transport) service. We study the perception of waiting time variability for ride-hailing services. We carried out a stated preference su ...

Over the course of a lifetime, the human brain acquires an astonishing amount of semantic knowledge and autobiographical memories, often with an imprinting strong enough to allow detailed information to be recalled many years after the initial learning exp ...

Aversively-motivated associative learning allows animals to avoid harm and thus ensures survival. Aversive learning can be studied by the fear learning paradigm, in which an innocuous sensory stimulus like a tone (conditioned stimulus, CS), acquires a nega ...

ML-based edge devices may face memory and computational errors that affect applications' reliability and performance. These errors can be the result of particular working conditions (e.g., radiation areas in physical experiments or avionics) or could be th ...

Learning how to reach a reward over long series of actions is a remarkable capability of humans, and potentially guided by multiple parallel learning modules. Current brain imaging of learning modules is limited by (i) simple experimental paradigms, (ii) e ...

Computational thinking (CT) skills are becoming increasingly relevant for future professionals across all domains, beyond computer science (CS). As such, an increasing number of bachelor and masters programs outside of the computer science discipline integ ...

Learning journals are increasingly used in vocational education to foster self-regulated learning and reflective learning practices. However, for many apprentices, documenting working experiences is a difficult task. In this paper, we profile apprentices' ...

A self-consistent model is presented for the simulation of a multi-component plasma in the tokamak boundary. A deuterium plasma is considered, with the plasma species that include electrons, deuterium atomic ions and deuterium molecular ions, while the deu ...

The increasing prevalence of personal devices motivates the design of algorithms that can leverage their computing power, together with the data they generate, in order to build privacy-preserving and effective machine learning models. However, traditional ...

Humans have an incredible sense of self-preservation that is both instilled, and also learned through experience. One system which contributes to this is the pain and reflex system which both minimizes damage through involuntary reflex ac- tions and also s ...

In everyday life, we constantly act and interact with objects and with others' people through our body. To properly perform actions, the representations of the dimension of body-parts (metric body representation, BR) and of the space surrounding the body ( ...

In reinforcement learning (RL), an agent makes sequential decisions to maximise the reward it can obtain from an environment. During learning, the actual and expected outcomes are compared to tell whether a decision was good or bad. The difference between ...

Substantial evidence implicates the nucleus accumbens in motivated performance, but very little is known about the neurochemical underpinnings of individual differences in motivation. Here, we applied 1H magnetic resonance spectroscopy (1H-MRS) at ultra-hi ...

The changes introduced by both ball milling and the addition of small amounts of TiF3 in the kinetics of the hydrogen desorption of three different Ca(BH4)2 polymorphs (α, β and γ) have been systematically investigated. The samples with different polymorph ...

Subword modeling for zero-resource languages aims to learn low-level representations of speech audio without using transcriptions or other resources from the target language (such as text corpora or pronunciation dictionaries). A good representation should ...

Computational models of intrinsic motivation for curiosity and creativity

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Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Computational models of episodic-like memory in food-caching birds

A Research-Practice Partnership to Introduce Computer Science in Secondary School: Lessons from a Pilot Program

The iReCheck project: using tablets and robots for personalised handwriting practice

Engram cell connectivity as a mechanism for information encoding and memory function

An instance-based learning approach for evaluating the perception of ride-hailing waiting time variability

A theory of memory consolidation and synaptic pruning in cortical circuits

Plasticity of sensory representations in the posterior insular cortex during fear learning

Hardware-Software co-design Methodologies for Edge AI Optimization

Brain signals of a Surprise-Actor-Critic model: Evidence for multiple learning modules in human decision making

Promoting Computational Thinking Skills in Non-Computer Science Students Gamifying Computational Notebooks to Increase Student Engagement

Evolutionary Clustering of Apprentices' Self- Regulated Learning Behavior in Learning Journals

A self-consistent multi-component model of plasma turbulence and kinetic neutral dynamics for the simulation of the tokamak boundary

Democratizing Machine Learning

Bio-inspired Reflex System for Learning Visual Information for Resilient Robotic Manipulation

Effect of tool-use observation on metric body representation and peripersonal space

Neural Correlates of Reinforcement Learning: eligibility trace, reward prediction error, novelty and surprise

Glutamine-to-glutamate ratio in the nucleus accumbens predicts effort-based motivated performance in humans

Effects of Ball Milling and TiF3 Addition on the Dehydrogenation Temperature of Ca(BH4)2 Polymorphs

Multilingual and Unsupervised Subword Modeling for Zero-Resource Languages