equanimitivity

>The ASCERTAIN dataset comprises a diverse range of physiological signals, including ECG recordings, collected from 58 participants exposed to video stimuli (36 videos) categorized in different categories based on valence and arousal levels. In particular, there are four subcategories of these 36 video clips. Clip 1 to 9 is categorized into High Arousal and High Valance (HAHV), Clip 10 to 18 Low Arousal and High Valance (LAHV), Clips 19 to 27 Low Arousal and Low Valance (LALV), and 28 to 36 High Arousal and Low Valance (HALV) clips [10]. ECG signals from the right and left arm were recorded at a sampling rate of 256Hz… our model outperformed the closest rival by a wide margin (0.56), achieving an accuracy of 0.94 for the extraversion trait. Similar trends are seen for agreeableness (0.92 versus 0.55), conscientiousness (0.92 versus 0.60), emotional stability (0.93 versus 0.53), and openness (0.93 versus 0.48).

Predicts Big 5 based on heart beat response to various stimuli. I wonder if EKG’s really contain more information beyond heart rate as the conduction within the heart is pretty consistent normally, regardless of rate. Neural nets seem to work great at finding any signal- too bad it isn’t so clear what those signals are.

Thanks for posting this link! Marginal land use, green water use, and inedible feed being standard really puts into perspective what stopping beer consumption would actually do. I found it a surprising perspective.

This makes some sense: to maximize the illusion, both leading and tailing edges of the bus should be going through the light and dark strips at the same time.

“It’s only acausal if you thought that awareness is what causes movement”

Exactly, people believed that the thoughts they are aware of when making a decision to move were actually how they decided to move.

P-hacking can be done by analyzing the data in different ways until a significant effect is found, and then failing to report all of the analyses done. There is no universal way to analyze data (especially what collected data to include); there are choices that are made and this leads to a “garden of forking paths” where if you look around the garden enough noise alone will give you ‘signicance’. Preregistration makes this impossible by declaring your analysis before collecting data.

Neither are special