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Followers
218
Account age
3 yrs
💰 Suggested sponsorship rate
$40 – $65 / sponsored post
Estimated from audience size & typical reach — every deal is negotiable and held in SocialDB escrow.
🎁 Free analysisfor Andrey Chetverikov
📊 Post engagement
3
Avg engagement / post
1.4%
Engagement vs followers
Nov 2022
On Mastodon since
🔥 Top post: There was a person from Taylor & Francis visiting the department · 5 likes + reposts
📊 Activity & format
Posting cadence
0.09 / week
A lower-frequency account — each post lands with more weight.
Content mix
Mostly text
Recent: 7 text · 5 image · 0 video.
Follower / following
0.8×
Follows 274 back. A more reciprocal / networked account.
Recent posts
View on Mastodon ↗
2 year position in math and cogsci at our department
https://www.jobbnorge.no/en/available-jobs/job/286799/research-scientist-position-in-mathematics-and-cognitive-science
Three days full of science in Frankfurt visiting @bledowski.bsky.social and @rademaker.bsky.social. Very happy to chat with the amazing students and colleagues in both labs! Now off to @ecvp.bsky.social in Mainz.
On my way from Aberdeen to Liverpool now. Huge thanks to the Aberdeen Psychology team @abdnpsych.bsky.social and especially @mauromanassi.bsky.social for making this visit possible. It was lovely to meet so many people doing fantastic rese…
And that’s how you know that VSS abstracts been published:) #visionscience
We found strong evidence for bimodality of observers' representations as predicted by the model both in behavior and in the brain. In the brain, this is evident from bimodality in the decoded representations. In behavior, we see it in surp…
Our paper on motion direction representation in the visual cortex has now been published in NatComm. We describe a Bayesian observer model combining information about velocity and orientation signals ('motion streaks') and test its predict…
Sure, there might be other algorithms to solve this problem (see also the supplement), but unless you abandon the idea of being accurate, you can't get rid of biases. That being said, predictions for empirical data are in the discussion an…
I'm obviously biased 🤠 but I like this model for two reasons: it's simple (no complex priors or likelihoods) and if you accept its premises (there is noise and the link between stimuli and sensory signals have to be inferred), it must be c…
Crucially, optimal (most accurate) solutions for this problem are often biased with bias patterns matching previous data. Without any extra components (priors, changes in the likelihood or cost functions) you can have attraction or repulsi…
The model proposed here goes in the opposite direction: it suggests that biases arises because observers try to separate ("demix") stimuli representations. This is because an observer can't know in advance which stimulus caused which sens…
Such biases have many names: tilt illusion, serial dependence, distractor repulsion, adaptive search templates, etc. Previous models proposed that biases arise because observers mix in the context - either it is added directly as a prior (…
Three days full of science in Frankfurt visiting @bledowski.bsky.social and @rademaker.bsky.social. Very happy to chat with the amazing students and colleagues in both labs! Now off to @ecvp.bsky.social in Mainz.
On my way from Aberdeen to Liverpool now. Huge thanks to the Aberdeen Psychology team @abdnpsych.bsky.social and especially @mauromanassi.bsky.social for making this visit possible. It was lovely to meet so many people doing fantastic rese…
And that’s how you know that VSS abstracts been published:) #visionscience
We found strong evidence for bimodality of observers' representations as predicted by the model both in behavior and in the brain. In the brain, this is evident from bimodality in the decoded representations. In behavior, we see it in surp…
Our paper on motion direction representation in the visual cortex has now been published in NatComm. We describe a Bayesian observer model combining information about velocity and orientation signals ('motion streaks') and test its predict…
Sure, there might be other algorithms to solve this problem (see also the supplement), but unless you abandon the idea of being accurate, you can't get rid of biases. That being said, predictions for empirical data are in the discussion an…
I'm obviously biased 🤠 but I like this model for two reasons: it's simple (no complex priors or likelihoods) and if you accept its premises (there is noise and the link between stimuli and sensory signals have to be inferred), it must be c…
Crucially, optimal (most accurate) solutions for this problem are often biased with bias patterns matching previous data. Without any extra components (priors, changes in the likelihood or cost functions) you can have attraction or repulsi…
The model proposed here goes in the opposite direction: it suggests that biases arises because observers try to separate ("demix") stimuli representations. This is because an observer can't know in advance which stimulus caused which sens…
Such biases have many names: tilt illusion, serial dependence, distractor repulsion, adaptive search templates, etc. Previous models proposed that biases arise because observers mix in the context - either it is added directly as a prior (…
🐘 Community & instance
Home server
fediscience.org
Their home server on the fediverse — the instance a creator picks signals the community they belong to.
On Mastodon since
Nov 2022
Joined in the Twitter-exodus wave of late 2022 — part of the migration that made Mastodon a real destination.
💡 Facts
🗓️Joined Mastodon in 2022 — 3 years ago.
👁️Averages 3 views per post.
📤Posts about 0.1× per week.
🕵️ Fake follower check
Estimated43/100
Fair
Credibility score
73%
Mixed
Real audience
Medium
Fake-follower risk
High
Data confidence
- Est. 73% real, active audience · Medium fake-follower risk.
- Low engagement (~1.4% of followers engage each post) — a sign of an inflated or inactive audience.
- Established account (3+ years old).
Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.
About
Cog/psy/neuroscientist dabbling in computational modelling. I’m interested in how we make sense of the world using visual perception as a model. How do we perceive things that have varying features, how do we decide which features belong to which object, and what do we do after we make such decisions?
📸 Gallery
🔀 Audience overlap
EstimatedEstimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.
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Frequently asked questions
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Andrey Chetverikov has 218 followers on Mastodon.
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