Feeling *gitty* about this fall with two exciting workshops on Version Control with Git for Scientists coming up @mpib-berlin.bsky.social and @rtg2660.bsky.social @uni-wuerzburg.de! ✨

Sounds interesting? Check out my website, where I share course materials: lennartwittkuhn.com 💫

#OpenScience #Git

Taken together, we provide novel insights into how attribute translations lead to behavior change and how behaviorally effective translations in the form of ratings differ from less effective numeric translations.

For all who use Bayesian hierarchical models, have a look at our new preprint, out now together with @linushof.bsky.social @nunobusch.bsky.social and @thorstenpachur.bsky.social

osf.io/preprints/ps...

In contrast, we found only response deliberation to increase for behaviorally less effective numeric translations (carbon emissions in kg).

Our modeling results suggest that a translation in form of an evaluative rating caused participants to make more ecological choices as a result of a shift in attribute weights in favor of the translated attribute, a decreased attentional bias on the attended option, and increased deliberation.

To fill this gap, we model the interplay of attention and evidence accumulation using the maaDDM for a pre-existing data set from an online process tracing study in which participants completed a consumer choice task w/ and w/o the translation of the items' energy and water consumption.

Attribute translations promote behavior change by translating decision-relevant information into more meaningful units and have been widely adopted by policy makers (e.g., EU energy label). However, little is known about the computational mechanisms that underlie their effects on behavior.

Excited to share joint work with Ulf Hahnel and @sgluth.bsky.social on investigating how attribute translations - a widely implemented behavior intervention - lead to more ecological consumer choices. Main results are below, but check out our preprint 👇
www.researchsquare.com/article/rs-7...

Our modeling results suggest that a translation in form of an evaluative rating caused participants to make more ecological choices as a result of a shift in attribute weights in favor of the translated attribute, a decreased attentional bias on the attended option, and increased deliberation.

To fill this gap, we model the interplay of attention and evidence accumulation using the maaDDM for a pre-existing data set from an online process tracing study in which participants completed a consumer choice task w/ and w/o the translation of the items' energy and water consumption.

Attribute translations promote behavior change by translating decision-relevant information into more meaningful units and have been widely adopted by policy makers (e.g., EU energy label). However, little is known about the computational mechanisms that underlie their effects on behavior.