Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Behavioural data analysis results #13

Open
skjerns opened this issue Jun 4, 2024 · 1 comment
Open

Behavioural data analysis results #13

skjerns opened this issue Jun 4, 2024 · 1 comment

Comments

@skjerns
Copy link
Collaborator

skjerns commented Jun 4, 2024

I've added the behavioural results

It seems like the manipulation for valence worked very well, not that good for arousal. There are some participants for which the correlation of valence is very low, which means that they rated negative GIFs positive and vice versa. Might be an idea to exclude these participants or pay extra attention to them (eg ERP19). Also they rated low arousal GIFs as high arousal and vice versa (e.g. ERP05)

image

image

Same shown in this plot, valence ratings nicely separate between positive and negative. For arousal, it seems like negative GIFs show much higher arousal. But in general the arousal of mean 3+ is quite okay I would say, so we succeeded in eliciting arousal in participants.

image

@skjerns
Copy link
Collaborator Author

skjerns commented Jun 4, 2024

Button presses

Buttons were more often pressed for negative emotions. This is potentially important, as the button presses should be decodable in the MEG, e.g. we could potentially just decode a motor response if we include all trials and have as a target valence.

image

per participant we see that most participants are quite consistent and either press or do not press the button (e.g. ERP19 almost never pressed the button)

image

Some participants have bias and press more on negative than on positive buttons. However the effect is not massive

image

Buttons are also more often pressed when subjective arousal is high and when valence is either very high or very low. That is great!! What we would expect.

image

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

1 participant