Perception of inflation: polynomial not exponential

On getting it right by being wrong: A case study of how flawed research may become self-fulfilling at last

Understanding how humans process time series data is more pressing now than ever amid a progressing pandemic. Current research draws on some fifty years of empirical evidence on laypeople’s (in-)ability to extrapolate exponential growth. Yet even canonized evidence ought not to be trusted blindly. As a case in point, I review a seminal study that is still highly (even increasingly) cited, although seriously flawed. This case serves as both a reminder of how readily even experienced, well-meaning researchers underestimate exponential dynamics, and an admonition for subsequent researchers to critically read and evaluate the research they cite in order to catch and correct errors quickly rather than carry them forward over decades.

Scientists prominently argue that the COVID-19 pandemic stems not least from people’s inability to understand exponential growth. They increasingly cite evidence from a classic psychological experiment published some 45 years prior to the first case of COVID-19. Despite—or precisely because of—becoming such a canonical study (more often cited than read), its critical design flaws went completely unnoticed. They are discussed here as a cautionary tale against uncritically enshrining unsound research in the “lore” of a field of research. In hindsight, this is a unique case study of researchers falling prey to just the cognitive bias they set out to study—undermining an experiment’s methodology while, ironically, still supporting its conclusion.

Perception of Linear and Nonlinear Trends: Using Slope and Curvature Information to Make Trend Discriminations

This study investigated several factors influencing the perception of nonlinear relationships in time series graphs. To model real-world data, the graphed data represented different underlying trends and included different sample sizes and amounts of variability. Six trends (increasing and decreasing linear, exponential, asymptotic) were presented on four graph types (histogram, line graph, scatterplot, suspended bar graph). The experiment assessed how these factors affect trend discrimination, with the overall goal of judging what types of graphs lead to better discrimination. Six participants (two psychology professors, four psychology graduate students) viewed graphs on a computer screen and identified the underlying trend. All participants were familiar with the types of trends presented and were aware of the purpose of the experiment. Analysis indicated higher accuracy when variability was lower and sample size was higher. Choice accuracy was higher for nonlinear trends and was highest when line graphs were used.