Because the Gumbel-min model implies a unique behavioural principle that is beautifully confirmed by the data: invariance to choice set-size expansion for a detect new task, while simultaneously predicting increased accuracy with choice set size for a detect old task. bsky.app/profile/sing... - ThreadSky

singmann.bsky.social • 55 days ago

Because the Gumbel-min model implies a unique behavioural principle that is beautifully confirmed by the data: invariance to choice set-size expansion for a detect new task, while simultaneously predicting increased accuracy with choice set size for a detect old task. https://bsky.app/profile/singmann.bsky.social/post/3lnsil3ex4m2m

Reposted from Henrik Singmann

The Gumbel-min model implies a behavioural principle: the probability of choosing a new item remains constant as choice sets grow. An experiment confirms this principle with constant accuracy for new item detection (2M-min). For old-item detection (2M-max), accuracy increase with choice set.

Comments

singmann.bsky.social•55 days ago

The Gaussian model can only predict invariance for both tasks or increase for both tasks, but not the differential behaviour that is shown in the data.

achetverikov.bsky.social•54 days ago

Thanks for clarifying! I guess I'm just not used to seeing the word principled when ppl talk about descriptive models, even though they can predict data. One more thing I wonder about: if this is how the signal distributions look like, should we see them directly with continuous adjustment tasks?

singmann.bsky.social•54 days ago

Not sure how such a task would even look like in the context of recognition-memory as covered in the paper.

achetverikov.bsky.social•54 days ago

Well, hypothetically memory is not based on the task, so whether you do old-new on, say, colors, or ask ppl to indicate the color shown on a color wheel, the memory signals are the same. And with Gaussians, you can easily draw a link between the performance on mAFC, yes-no and adjustment.

achetverikov.bsky.social•54 days ago

Sorry, I'm used to the Bayesian observer perspective and just try to wrap my head around what it means to have this kind of distributions instead of Gaussians.

singmann.bsky.social•54 days ago

I agree there should be some commonality across tasks, and our tests do compare yes-no, forced-choice, and ranking. But they target multi-feature stimuli like words or faces. Single-feature tasks like colour recall may well align with Gaussian models--it's just not our focus (yet).

mzloteanu.bsky.social•53 days ago

Do you have an example of linking tasks in such a way? sounds v interesting.

achetverikov.bsky.social•53 days ago

I'd refer you to the wonderful book on Bayesian observers:
https://www.cns.nyu.edu/malab/bayesianbook.html
Basically, the SD of errors in a continuous task is the same as the SD (sigma) for a signal distribution in the mAFC tasks / yes-no task.

singmann.bsky.social•53 days ago

We reanalyse several data sets that combine multiple tasks, specifically yes/no with either m-AFC or ranking tasks. See section "Predictive Benchmarking" (pp 43 to 47). The relevant papers are: Jang et al. (2009), Smith & Duncan (2004), Kellen et al. (2012) and Kellen et al. (2021)

Comments

Posting Rules

Reply