Jonas Dora @jonasdora.bsky.social · 06/10/2026Big changes were rarely missed (sensitivity .77–.87), and people usually got the direction right. But they often reported change when none was reliable. Specificity was about .5, and only about 1 in 4 "I feel different" reports matched a reliable change. 120
Jonas Dora @jonasdora.bsky.social · 06/10/2026The MIC was 1 point, the smallest change the scale can show. Even when the rating didn't move at all, people said they felt different 35–45% of the time. So the bar for feeling a change (1 point) sits below the bar for measuring one (2 points): Felt, but not detected. 120
Jonas Dora @jonasdora.bsky.social · 06/10/2026The SDC was 1.56–1.92 points on a 0–4 scale. Since ratings move in whole points, that means only a jump of 2+ points counts as reliable. About three-quarters of all consecutive changes fell within the noise. 120
Jonas Dora @jonasdora.bsky.social · 06/10/2026Very cool new paper by @aleksakaurin.bsky.social, Mario Wenzel, @paulaphilippi.bsky.social using some of my K99 data: osf.io/preprints/ps... EMA studies often ask people how they feel several times a day. But when is a change bigger than measurement noise? And does the person actually feel change? 43613
Jonas Dora @jonasdora.bsky.social · 02/09/2026I'm super excited to share a new preprint in which @kevinmking.bsky.social and I propose an update to the affect regulation theory of alcohol use: "A mathematical and computational formalization of the affect regulation hypothesis of alcohol use" doi.org/10.31234/osf... 65325
Jonas Dora @jonasdora.bsky.social · 14/06/2026I'm really excited to share a new preprint! Lab studies have consistently shown that stress and negative emotions make people drink alcohol, but studies of daily life (EMA) keep failing to find it. What does that mean for the shared theoretical prediction both designs test? osf.io/preprints/ps... 38226
Jonas Dora @jonasdora.bsky.social · 03/06/2026Congratulations to brilliant RADLab student Megan Schultz who successfully defended her dissertation today titled: **How the short-term influence of drinking consequences shapes the development of expectancies: An event-level study** 🥳 270
Jonas Dora @jonasdora.bsky.social · 25/05/2026Looking forward to see many of you at #APS26BCN this week! I'm extra excited to hear about the amazing research of Kendra Wyant (Wisconsin), Paula Philippi (Wuppertal), and @eikofried.bsky.social (Leiden) in our symposium on Saturday at 10:30 in Room 123; their talks are going to be ✨spectacular✨ 1227
Jonas Dora @jonasdora.bsky.social · 11/04/2026Somehow the images didn't upload earlier, but here is the effect of goal conflict on the probability to attempt to resist the desire across desire domains: 010
Jonas Dora @jonasdora.bsky.social · 12/01/2026New Stage 2 RR published in Clinical Psych Science: doi.org/10.1177/2167... contrary to what some claim, urgency DOES NOT moderate associations between affect and alcohol/cannabis use in EMA data. Take a look for a comprehensive test of this idea and let us know what you think! 1197
Jonas Dora @jonasdora.bsky.social · 16/05/2025Our computational model revealed that people tended to make worse decisions when intoxicated and possibly also when in a bad mood not because they were insensitive to punishments but because they were more sensitive to rewards. 110
Jonas Dora @jonasdora.bsky.social · 16/05/2025New preprint with @hollysully.bsky.social & @kevinmking.bsky.social: Alcohol intoxication and negative mood similarly affect reward learning but not punishment learning in the Iowa Gambling Task doi.org/10.31219/osf... 2219
Jonas Dora @jonasdora.bsky.social · 14/01/2025Bonus: This is the bar laboratory in which we conducted this study 190
Jonas Dora @jonasdora.bsky.social · 14/01/2025Stress moderately increased choices for alcohol (only in sober participants), but the effect on the bias parameter was much stronger. This is interesting, because in our paradigm stress had to shift the relative value of alcohol enough to overcome a preference for the alternative. 120
Jonas Dora @jonasdora.bsky.social · 14/01/2025Using drift diffusion modeling, we decomposed choices and response times into latent decision-making mechanisms - how carefully participants make decisions, how much they relied on their baseline drink preferences, and whether they developed a bias toward alcohol regardless of preference. 120
Jonas Dora @jonasdora.bsky.social · 14/01/2025We tested this with a 2 (stress/no stress) x 2 (intoxicated /sober) design: Participants (N = 160) rated drinks and made repeated choices between alcoholic and non-alcoholic drinks before and after manipulations of stress and alcohol intoxication (BrAC=.06%). 110
Jonas Dora @jonasdora.bsky.social · 07/11/2024Instead of summing over various consequences (e.g., having a hangover, drunk driving), Megan modeled random slopes nested in pps & consequences. She found that, irrelevant of its average valence (neg/pos), the more often a consequence is experienced the higher its expected likelihood and valence. 110