Decision researchers frequently analyze attention to individual objects to test hypotheses about underlying cognitive processes. Generally, fixations are assigned to objects using a method known as area of interest (AOI). Ideally, an AOI includes all fixations belonging to an object while fixations to other objects are excluded. Unfortunately, due to measurement inaccuracy and insufficient distance between objects, the distributions of fixations to objects may overlap, resulting in a signal detection problem. If the AOI is to include all fixations to an object, it will also likely include fixations belonging to other objects (false positives). In a survey, we find that many researchers report testing multiple AOI sizes when performing analyses, presumably trying to balance the proportion of true and false positive fixations. To test whether AOI size influences the measurement of object attention and conclusions drawn about cognitive processes, we reanalyze four published studies and conduct a fifth tailored to our purpose. We find that in studies in which we expected overlapping fixation distributions, analyses benefited from smaller AOI sizes (0° visual angle margin). In studies where we expected no overlap, analyses benefited from larger AOI sizes (>.5° visual angle margins). We conclude with a guideline for the use of AOIs in behavioral eye‐tracking research. Copyright © 2015 John Wiley & Sons, Ltd.

Recent research investigating decisions from experience suggests that not all information is treated equally in the decision process, with more recently encountered information having a greater impact. We report 2 studies investigating how this differential treatment of sequentially encountered information affects subjective valuations of risky prospects when observations of past outcomes must be used to estimate the prospect's payoff distribution, and examine how individual differences in cognitive capacities influence information usage. In Study 1 we found that a sliding window of information model that averages a subset of (only) the most recently encountered outcomes (samples) fit the subjective valuation data for a portion of individuals better than models that integrate all observed outcomes. This pattern of results is replicated in Study 2, in which we also found that the amount of information used to form valuations varies greatly between individuals, and that individual difference in memory span explains a portion of this variation. Combined, these results suggest a process in which information usage is in part reliant on cognitive capacity, and where information aggregation appears to be memory based rather than online, providing new insight into the processes involved in the construction of valuation in experiential decisions.

Objective This study aimed to examine the impact of the coronavirus disease (COVID‐19) pandemic on patronage to unhealthy eating establishments in populations with obesity. Methods Anonymized movement data accounting for roughly 10% of devices in the United States at 138,989 unhealthy eating locations from December 1, 2019, through April 2020 and the percentage of adults with obesity, the poverty rate, and the food environment index in 65% of United States counties were collected and merged. A cluster corrected Poisson spline regression was performed predicting patronage by day, the percentage of adults with obesity in the establishment’s county, the county’s poverty rate, and its food environment index, as well as their interactions. Results Patronage to unhealthy eating establishments was higher where there was a higher percentage of the adult population with obesity. A similar pattern was observed for counties with a lower food environment index. These disparities appear to have increased as the COVID‐19 pandemic spread. Conclusions These results suggest unhealthy eating patterns during the COVID‐19 pandemic are higher in already at‐risk populations. Policy makers can use these findings to motivate interventions and programs aimed at increasing healthy food intake in at‐risk communities during crises.

Recent research makes increasing use of eye-tracking methodologies to generate and test process models. Overall, such research suggests that attention, generally indexed by fixations (gaze duration), plays a critical role in the construction of preference, although the methods used to support this supposition differ substantially. In 2 studies we empirically test prototypical versions of prominent processing assumptions against 1 another and several base models. We find that general evidence accumulation processes provide a good fit to the data. An accumulation process that assumes leakage and temporal variability in evidence weighting (i.e., a primacy effect) fits the aggregate data, both in terms of choices and decision times, and does so across varying types of choices (e.g., charitable giving and hedonic consumption) and numbers of options well. However, when comparing models on the level of the individual, for a majority of participants simpler models capture choice data better. The theoretical and practical implications of these findings are discussed. (PsycINFO Database Record