Bar charts are common visual tools used to convey statistical information. Even though bar charts are effective in making abstract concepts more accessible, poorly-designed bar charts - whether designed intentionally or unintentionally - can easily mislead the viewer. For example, a poorly-designed bar chart may only present part of the effective range on the vertical axis. This exaggerates the contrast among bars, leading an unsuspecting graph viewer to wrong conclusions. More broadly, misrepresentation in data visualization is becoming an increasing societal problem contributing to daily misinformation. This paper presents a computational and cognitive solution to this problem. Our idea is to train viewers by showing them a few dozen carefully designed bar charts that are misleading, together with guidance on why these bar charts are misleading. We then test whether the viewers identify similarly misleading bar charts in the future. Importantly, we use neural networks and cognitive models to optimize the training (i.e., the design of those few dozen bar charts). Our experiment shows that perceptual trainings can help viewers not be fooled by similar misleading graphs in the future.

In STEM domains, students are expected to acquire domainknowledge from visual representations that they may not yetbe able to interpret. Such learning requires perceptual flu-ency, or the ability to intuitively and rapidly see the underlyingconcepts in visuals and to translate between them. Perceptualfluency is acquired via nonverbal, implicit learning processes.Thus far, we have lacked a principled approach for identify-ing a sequence of perceptual fluency problems that promoterobust learning. Here, we describe how a novel machine learn-ing technique can generate an optimal sequence of perceptualfluency problems. In a human experiment, we show that amachine-generated sequence outperforms both a random se-quence and a sequence generated by a human domain expert.Interestingly, the machine-generated sequence resulted in sig-nificantly lower accuracy during training, but higher posttestaccuracy. This suggests that the machine-generated sequenceinduced desirable difficulties. To our knowledge, our study isthe first to show that machine learning can yield desirable dif-ficulties for perceptual learning

Following the increasing use of graphs to communicate statistical information in social media and news platforms, the occurrence of poorly designed misleading graphs has also risen. Thus, previous research has identified common misleading visual features of such graphs. Our study extends this research by empirically comparing the deceptive impact of 14 distinct misleading graph types on viewers' understanding of the depicted data. We investigated the deceptive nature of these misleading graph types to identify those with the biggest potential to mislead viewers. Our findings indicate that misleading graphs significantly decreased viewers' accuracy in interpreting data. While certain misleading graphs (e.g., graphs with inverted y-axis or manipulated time intervals) significantly impeded viewers' accurate graph comprehension, other graphs (e.g., graphs using pictorial bars or graphs with compressed y-axis) had little misleading impact. By identifying misleading graphs that strongly affect viewers' understanding about depicted data, we suggest that these misleading graphs should be the focus of educational interventions.