# Something’s different: elaboration’s transferrable role for false alarm reduction

## TL;DR

This study shows that adding detailed explanations about non-errors helps people avoid false alarms when assessing complex visual displays.

## Contribution

The paper introduces the transferrable role of elaboration in reducing false alarms across different models.

## Key findings

- Participants who received elaboration performed better at rejecting non-error differences.
- Elaboration on one model improved performance on a second, similar model without elaboration.
- Elaboration aids in distinguishing errors from non-errors in complex visual assessments.

## Abstract

False alarming, or detecting an error when there is not one, is a pervasive problem across numerous industries. The present study investigated the role of elaboration, or additional information about non-error differences in complex visual displays, for mitigating false error responding. In Experiment 1, learners studied errors and non-error differences about a virtual LEGO® model. Half of the participants received information about the error (location, omission, orientation) and difference (color, addition) categorization and identification (i.e., what constituted the error or difference). The other half of participants received the same information plus further elaboration about (1) the potential consequences of errors and (2) why differences would not pose potential problems. Receiving additional elaboration about errors and differences aided learners’ ability to accurately reject non-error differences at test. Experiment 2 replicated these results with a new stimulus model and extended findings by testing whether receiving elaboration on the first model transferred to support learning in a second, similar model that did not provide elaborations. Our results replicated and extended findings from Experiment 1, such that learners who received elaboration while learning the first model also performed better at correctly rejecting non-error differences at test on the second model. Taken together, our findings provide insight on the transferrable role of feature elaboration to reduce false alarm rates during complex visual display assessments.