Interpretable machine learning aims at unveiling the reasons behind predictions returned by
uninterpretable classifiers. One of the most valuable types of explanation consists of …

Inhibitory control is thought to demonstrate heterotypic continuity, in other words, continuity
in its purpose or function but changes in its behavioral manifestation over time. This creates …

Post-hoc explanations of machine learning models are crucial for people to understand and
act on algorithmic predictions. An intriguing class of explanations is through counterfactuals …

Abstract Thinking about what might have been—counterfactual thinking—is a common
feature of the mental landscape. Key questions about counterfactual thinking center on why …

We argue for a theoretical link between the development of an extended period of immaturity
in human evolution and the emergence of powerful and wide-ranging causal learning …

Explaining deep learning model inferences is a promising venue for scientific
understanding, improving safety, uncovering hidden biases, evaluating fairness, and …

Episodic foresight is the future‐directed counterpart of episodic memory. It is a sophisticated,
potentially uniquely human capacity, with tremendous adaptive consequences. Here we …

The objective of this study was to describe the developmental progression of counterfactual
reasoning from childhood to adulthood. In contrast to the traditional view, it was recently …

A visual counterfactual explanation replaces image regions in a query image with regions
from a distractor image such that the system's decision on the transformed image changes to …

This study investigated at what point in development 3‐to 6‐year‐old children begin to
demonstrate counterfactual reasoning by controlling for fortuitously correct answers that …