Effects of machine learning errors on human decision-making: manipulations of model accuracy, error types, and error importance.

This study addressed the cognitive impacts of providing correct and incorrect machine learning (ML) outputs in support of an object detection task. The study consisted of five experiments that manipulated the accuracy and importance of mock ML outputs. In each of the experiments, participants were given the T and L task with T-shaped targets and L-shaped distractors.

Examining Limits of Small Multiples: Frame Quantity Impacts Judgments with Line Graphs.

Small multiples are a popular visualization method, displaying different views of a dataset using multiple frames, often with the same scale and axes. However, there is a need to address their potential constraints, especially in the context of human cognitive capacity limits. These limits dictate the maximum information our mind can process at once.

Numerical and Visual Representations of Uncertainty Lead to Different Patterns of Decision Making.

Although visualizations are a useful tool for helping people to understand information, they can also have unintended effects on human cognition. This is especially true for uncertain information, which is difficult for people to understand. Prior work has found that different methods of visualizing uncertain information can produce different patterns of decision making from users.

Data Visualization Saliency Model: A Tool for Evaluating Abstract Data Visualizations.

Evaluating the effectiveness of data visualizations is a challenging undertaking and often relies on one-off studies that test a visualization in the context of one specific task. Researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles that could be applied to assessing the effectiveness of data visualizations in a more rapid and generalizable manner. One possibility for such a tool is a model of visual saliency for data visualizations.

Retrieval speeds context fluctuation: why semantic generation enhances later learning but hinders prior learning.

In recent work, retrieval has been shown to enhance memory for events following that retrieval. In this set of experiments, we examined the effects of interleaved semantic retrieval on both previous and future learning within a multilist learning paradigm. Interleaved retrieval led to enhanced memory for lists learned following retrieval.