Modelling dataset bias in machine-learned theories of economic decision-making.

Normative and descriptive models have long vied to explain and predict human risky choices, such as those between goods or gambles. A recent study reported the discovery of a new, more accurate model of human decision-making by training neural networks on a new online large-scale dataset, choices13k. Here we systematically analyse the relationships between several models and datasets using machine-learning methods and find evidence for dataset bias.

Naturalistic embodied interactions elicit intuitive physical behaviour in accordance with Newtonian physics.

The success of visuomotor interactions in everyday activities such as grasping or sliding a cup is inescapably governed by the laws of physics. Research on intuitive physics has predominantly investigated reasoning about objects' behaviour involving binary forced choice responses. We investigated how the type of visuomotor response influences participants' beliefs about physical quantities and their lawful relationship implicit in their active behaviour.

Intuitive physical reasoning about objects' masses transfers to a visuomotor decision task consistent with Newtonian physics.

While interacting with objects during every-day activities, e.g. when sliding a glass on a counter top, people obtain constant feedback whether they are acting in accordance with physical laws.