Imagination and the generation of new ideas

By Rachel Magid, Mark Sheskin, Laura Schulz. 2015

43 citations recorded by the source corpus.

Subfields: Developmental and Educational Psychology, Education, Statistics and Probability, Cognitive Science & Psychology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Developmental and Educational Psychology

1. Pedro Tsividis
2. Laura Schulz
3. Josh Tenenbaum
4. Tomer D. Ullman

In Education

1. Pedro Tsividis
2. Laura Schulz
3. Josh Tenenbaum

In Statistics and Probability

1. Pedro Tsividis
2. David Bau
3. Laura Schulz
4. Leilani H. Gilpin

In Cognitive Science & Psychology

1. Pedro Tsividis
2. Laura Schulz
3. Josh Tenenbaum

Citations in the corpus

• Explaining Explanations: An Overview of Interpretability of Machine Learning (2018, background): “This is also a well-studied field in brain and cognitive science by filling in the gaps of knowledge by imagining new ideas [85] or using statistical approaches [86].”
• Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning (2018, background): “This is also a well-studied field in brain and cognitive science by filling in the gaps of knowledge by imagining new ideas[85] or using a statistical approaches [86].”
• Constraints on Hypothesis Selection in Causal Learning (2015, background): “In a series of experiments, children successfully mapped discrete causes to discrete effects and continuous causes to continuous effects (Magid et al., 2014).”