Truth or Square: Aspect Ratio Biases Recall of Position Encodings

Ceja, Cristina R.; McColeman, Caitlyn; Xiong, Cindy; Franconeri, Steven

doi:10.1109/tvcg.2020.3030422

Cited by 11 publications

(9 citation statements)

References 31 publications

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“…In other reproduction tasks, like the one used in the present study, surrounding values in a display created memory biases, such that recollections of a single relevant value were repulsed from the 0, .5, and 1.0 proportion of a second larger reference bar [52]. Memory bias has been shown even for values presented alone, such that tall bars with a high height:width ratio were underestimated, and wide bars with a low height:width ratio were overestimated [14].…”

Section: Context and Bias Effects On Visual Judgementsmentioning

confidence: 65%

“…Among these, bias bias is how the mean of the responses deviates from the actual value presented as the reference the reference. Think of bias as systematic error or the tendency to make mistakes in a certain direction, such as exhibiting a bias to overestimate wide bars [14]. Precision Precision is the consistency of participants' responses; they may consistently report the same value, regardless of the reference the reference value.…”

Section: Methodsmentioning

confidence: 99%

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Rethinking the ranks of visual channels

McColeman¹,

Yang²,

Brady³

et al. 2021

Preprint

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Data can be visually represented using visual channels like position, length or luminance. An existing ranking of these visual channels is based on how accurately participants could report the ratio between two depicted values. There is an assumption that this ranking should hold for different tasks and for different numbers of marks. However, there is surprisingly little existing work that tests this assumption, especially given that visually computing ratios is relatively unimportant in real-world visualizations, compared to seeing, remembering, and comparing trends and motifs, across displays that almost universally depict more than two values.To simulate the information extracted from a glance at a visualization, we instead asked participants to immediately reproduce a set of values from memory after they were shown the visualization. These values could be shown in a bar graph (position (bar)), line graph (position (line)), heat map (luminance), bubble chart (area), misaligned bar graph (length), or `wind map' (angle). With a Bayesian multilevel modeling approach, we show how the rank positions of visual channels shift across different numbers of marks (2, 4 or 8) and for bias, precision, and error measures. The ranking did not hold, even for reproductions of only 2 marks, and the new probabilistic ranking was highly inconsistent for reproductions of different numbers of marks. Other factors besides channel choice had an order of magnitude more influence on performance, such as the number of values in the series (e.g., more marks led to larger errors), or the value of each mark (e.g., small values were systematically overestimated). Every visual channel was worse for displays with 8 marks than 4, consistent with established limits on visual memory. These results point to the need for a body of empirical studies that move beyond two-value ratio judgments as a baseline for reliably ranking the quality of a visual channel, including testing new tasks (detection of trends or motifs), timescales (immediate computation, or later comparison), and the number of values (from a handful, to thousands)

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Section: Context and Bias Effects On Visual Judgementsmentioning

confidence: 65%

Section: Methodsmentioning

confidence: 99%

Rethinking the ranks of visual channels

McColeman¹,

Yang²,

Brady³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…If the rate of the lower bar was small, overestimation appeared in the direction of the cumulative bar, and if the ratio of the lower bar was large, underestimation was appeared in the direction of the lower bar. Consequently, in conjunction with the work of Ceja et al (2020), it can be concluded that in a single bar task, the ratio of the bar itself can affect the length judgment, and when doing a multiple bar task, the ratio of the cumulative and lower bar can affect the average judgment. Of course, it seems necessary to verify in future studies whether this result is limited to the cumulative bar or can be applied to other types of salient objects combined with the lower bar.…”

Section: Discussionmentioning

confidence: 95%

“…Recently, Ceja et al. (2020) conducted meaningful studies related to the perception of the single bar.…”

Section: Discussionmentioning

confidence: 99%

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Estimating Bar Graph Averages: Overcoming Within-the-Bar Bias

Kang

Yun

et al. 2021

i-Perception

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Although most people are not aware of it, bias can occur when interpreting graphs. Within-the-bar bias describes a misinterpretation of the distribution of data underlying bar graphs that indicate an average or where the average estimation point moves inside the bar when the average of several graphs is estimated. This study proposes and tests two methods based on information processing to reduce within-the-bar bias. The first method facilitates bottom-up processing by changing various graph features, such as presenting confidence intervals, placing boundaries around the graph, and showing cumulative bars with different tones. The second method facilitates top-down processing by instructing participants to estimate the mean based on a dot at the end of each bar. Testing of the first method showed that cumulative bars reduced bias, but the other methods did not. The second method was found to reduce bias. Overall, our results demonstrate that the accurate interpretation of bar graphs can be facilitated through the manipulation of specific graph features and instruction.

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More Than Meets the Eye: A Closer Look at Encodings in Visualization

Kosara

Rhyne²

2022

IEEE Comput. Grap. Appl.

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Truth or Square: Aspect Ratio Biases Recall of Position Encodings

Cited by 11 publications

References 31 publications

Rethinking the ranks of visual channels

Rethinking the ranks of visual channels

Estimating Bar Graph Averages: Overcoming Within-the-Bar Bias

More Than Meets the Eye: A Closer Look at Encodings in Visualization

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