Processing Scatterplots: Impact of Outliers on Correlational and Causal Inferences

Scatterplot research has identified factors that impact people’s perception of correlation magnitudes, yet much less is known about how people reason about data represented in scatterplots. We investigated how people make correlational and causal inferences based on scatterplots with and without outliers. In Experiment 1 and 2, participants viewed scatterplots matched in overall correlational magnitude depicted, but half had an outlier. In Experiment 3, the scatterplots in the two conditions were matched in the correlation magnitude depicted by all the dots excluding the outlier. For each scatterplot, participants stated their endorsement for correlational (X and Y change together) and causal statements (X changes Y). Only when outliers further strengthened an already moderate to strong relationship, people endorsed related correlational statements more and showed a stronger causality bias. Altogether we demonstrate that the impact of outliers in scatterplots on visual reasoning depends on the strength of the relationship depicted

Nature of *representations in spatial working memory.

The goal of this dissertation is to study how multiple locations are represented in spatial working memory. In particular, the focus is on whether spatial locations are represented as part of larger configurations, or whether they are presented independently, as if in isolation, with only absolute positional information preserved. If there is evidence to indicate that representations contain configuration information above and beyond absolute location information, then representations are characterized as configural. The first series of experiments show that when asked to recall sequentially presented locations people preserve configuration information in their representations, only for the shorter sequences. Further evidence for people preserving configuration information formed by spatial sequences comes from increased confusability of sequences forming similar configurations. These configural representations are demonstrated to be orientation-specific, because configurally identical but rotated spatial sequences are easily discriminated. In the second set of studies, I investigate effects of sequence length and sequence path complexity on how well configurations are preserved and whether these influence the precision with which each location is represented. Increased sequence complexity negatively influences how well configuration information is preserved. While complexity does not influence the precision with which each location is represented, an inverse relationship between sequence length and precision suggests that people strategically code longer sequences more coarsely. The third series of experiments investigates how spatial representations change based on task demands and practice. People cannot easily ignore configuration information and represent locations independently, regardless of varying task demands or practice. However, practice at recalling sequentially presented locations does result in superior recall of spatial configurations. The final set of experiments presented in this dissertation extend the research on the nature of spatial representations and address how spatial configuration information is represented along with visual object information. People are shown to effortlessly encode information about spatial layout of simultaneously presented objects, and this in turn is shown to affect their judgments about the visual properties of the objects. This suggests that visual object and spatial configuration information are tightly bound. Overall, it is concluded that representations of multiple locations are configuration-based.Ph.D.Cognitive psychologyExperimental psychologyPsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/125304/2/3192584.pd

Judgmental Time Series Forecasting: A systematic analysis of graph format and trend type

In many areas like economics, finance, and health, people make judgmental forecasts looking at previous time series data. In such efforts, either tabular presentations or graphs are utilized, where graphs can be in different formats like bars, lines or points. Different presentations may cause certain biases stemming from bottom-up processing. To delineate such perceptually driven biases in judgmental forecasting, we investigated the effect of graph format (line, bar, point) and trend type (upwards, downwards, flat) on judgmental point forecasts when no domain information was provided. Bringing together perspectives from graph processing, visualization and forecasting literatures, our major goals were to determine which graph formats lead to more accurate forecasts and whether bar graphs lead to mean reversion bias or within-the-bar bias in forecasts. Additionally, we wanted to determine whether asymmetric damping observed in sales forecasts of downward vs. upward trended series were confounded by graph characteristics. We found that forecasts in line and point graphs were less biased than those in bar graphs; forecasts based on bar graphs depicting trended data exhibited mean reversion bias. We also observed a general positivity bias in forecasts for all trend types in line and point graphs. This implied trend following forecasts in upward trends and mean reverting forecasts in downward trends revealing an asymmetricity in the absence of context as well

Comparing the effect of single outliers and outlier clusters on trend estimation in scatterplots

Scatterplots are commonly used data visualizations to depict relationships between variables. There are inconsistent findings in the literature regarding how outliers in scatterplots affect trendline estimates. Correll & Heer (2017) found no difference for trendline estimations between the no-outlier and the outlier conditions consisting of a separate group of items creating an outlier cluster. However, Ciccione et al. (2022) showed that single outlier points might be included in trendline estimations. To investigate whether an outlier cluster was perceived as a salient and separate unit and thus excluded from the remaining data points, we directly compared the effects of single and multiple outliers on trendline estimations, controlling for correlation strength, outlier position and trend direction. Participants drew trendlines. We found that participants included single outliers more than they’ve included outlier clusters into the trendlines; this pattern was similar across all other control variables; suggesting grouping might play a role in this process

Variability leads to overestimation of mean summaries

Abstract Research on ensemble perception has shown that people can extract both mean and variance information, but much less is understand how these two different types of summaries interact with one another. Some research has argued that people are more erroneous in extracting the mean of displays that have greater variability. In all three experiments, we manipulated the variability in the displays. Participants reported the mean size of a set of circles (Experiment 1) and mean length of horizontally placed (Experiment 2a) and randomly oriented lines (Experiment 2b). In all experiments, we found that mean size estimations were more erroneous for higher than smaller variance displays. More critically, there was a tendency to overestimate the mean, driven by variance in both task-relevant and task-irrelevant features. We discuss these findings in relation to limitations in concurrent summarization ability and outlier discounting in ensemble perception