MST 2 Alternative First Choice Implementation - #15

Open
amaar-mc wants to merge 3 commits into
brainiak:mainfrom
amaar-mc:mst-2afc
Open

MST 2 Alternative First Choice Implementation#15
amaar-mc wants to merge 3 commits into
brainiak:mainfrom
amaar-mc:mst-2afc

Conversation

@amaar-mc

@amaar-mcamaar-mc commented Oct 1, 2025

Copy link
Copy Markdown

Compares the brain's clip voxel embedding data collected from ses-03 to the embeddings of 31 pairs of MST images (62 images) to determine which MST image pairmate the clip voxel embedding data is closer to via cosine similarity.

Because the 31 pairs of MST images are shown twice, the MST 2AFC Score is calculated twice for both showings of each set of pairmates, as reflected by subset0 and subset1, thus we have mst_2afc_subset0 and mst_2afc_subset1.

The details regarding the 2AFC process are printed in the mindeye.ipynb notebook in the format as follows (example):

 image 1: pair_46_w_pool2.jpg
image 2: pair_46_w_pool1.jpg
img 1 brain embedding chose: pair_46_w_pool2.jpg Correct
img 2 brain embedding chose: pair_46_w_pool1.jpg Correct

Cosine similarity is performed on the img 1's brain embeddings against the two images, and the img 2's brain embedding data. The labeling of whether or not the decision was correct is labeled on the right.

The net MST 2AFC Score is provided after the detailed breakdown above, as in this format:

mst 2afc score: 0.7903 (79.03%)

@review-notebook-app

review-notebook-appBot commented Oct 1, 2025

Copy link
Copy Markdown

Check out this pull request on ReviewNB

See visual diffs & provide feedback on Jupyter Notebooks.

Powered by ReviewNB

@amaar-mc
amaar-mc marked this pull request as ready for review October 1, 2025 07:46
Sign up for freeto join this conversation on GitHub. Already have an account? Sign in to comment

Reviewers

No reviews

Assignees

No one assigned

Labels

None yet

Projects

None yet

Milestone

No milestone

Development

Successfully merging this pull request may close these issues.

1 participant

@amaar-mc
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
MST 2 Alternative First Choice Implementation - #15
Open
amaar-mc wants to merge 3 commits into
brainiak:mainfrom 
amaar-mc:mst-2afc
Open
MST 2 Alternative First Choice Implementation#15
amaar-mc wants to merge 3 commits into
brainiak:mainfrom 
amaar-mc:mst-2afc
Conversation
@amaar-mc
@amaar-mcamaar-mc
commented
Oct 1, 2025

Copy link


Copy Markdown

Compares the brain's clip voxel embedding data collected from ses-03 to the embeddings of 31 pairs of MST images (62 images) to determine which MST image pairmate the clip voxel embedding data is closer to via cosine similarity.
Because the 31 pairs of MST images are shown twice, the MST 2AFC Score is calculated twice for both showings of each set of pairmates, as reflected by subset0 and subset1, thus we have mst_2afc_subset0 and mst_2afc_subset1.
The details regarding the 2AFC process are printed in the mindeye.ipynb notebook in the format as follows (example):
 image 1: pair_46_w_pool2.jpg
image 2: pair_46_w_pool1.jpg
img 1 brain embedding chose: pair_46_w_pool2.jpg Correct
img 2 brain embedding chose: pair_46_w_pool1.jpg Correct

Cosine similarity is performed on the img 1's brain embeddings against the two images, and the img 2's brain embedding data. The labeling of whether or not the decision was correct is labeled on the right.
The net MST 2AFC Score is provided after the detailed breakdown above, as in this format:
mst 2afc score: 0.7903 (79.03%)

@review-notebook-app
review-notebook-appBot
commented
Oct 1, 2025

Copy link


Copy Markdown

Check out this pull request on ReviewNB
See visual diffs & provide feedback on Jupyter Notebooks.
Powered by ReviewNB
@amaar-mc
amaar-mc
marked this pull request as ready for review
October 1, 2025 07:46
Sign up for freeto join this conversation on GitHub.
Already have an account?
Sign in to comment

Reviewers


No reviews


Assignees


No one assigned


Labels


None yet


Projects


None yet


Milestone


No milestone


Development

Successfully merging this pull request may close these issues.

1 participant

@amaar-mc

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
MST 2 Alternative First Choice Implementation - #15
Open
amaar-mc wants to merge 3 commits into
brainiak:mainfrom 
amaar-mc:mst-2afc
Open
MST 2 Alternative First Choice Implementation#15
amaar-mc wants to merge 3 commits into
brainiak:mainfrom 
amaar-mc:mst-2afc
Conversation
@amaar-mc
@amaar-mcamaar-mc
commented
Oct 1, 2025

Copy link


Copy Markdown

Compares the brain's clip voxel embedding data collected from ses-03 to the embeddings of 31 pairs of MST images (62 images) to determine which MST image pairmate the clip voxel embedding data is closer to via cosine similarity.
Because the 31 pairs of MST images are shown twice, the MST 2AFC Score is calculated twice for both showings of each set of pairmates, as reflected by subset0 and subset1, thus we have mst_2afc_subset0 and mst_2afc_subset1.
The details regarding the 2AFC process are printed in the mindeye.ipynb notebook in the format as follows (example):
 image 1: pair_46_w_pool2.jpg
image 2: pair_46_w_pool1.jpg
img 1 brain embedding chose: pair_46_w_pool2.jpg Correct
img 2 brain embedding chose: pair_46_w_pool1.jpg Correct

Cosine similarity is performed on the img 1's brain embeddings against the two images, and the img 2's brain embedding data. The labeling of whether or not the decision was correct is labeled on the right.
The net MST 2AFC Score is provided after the detailed breakdown above, as in this format:
mst 2afc score: 0.7903 (79.03%)

@review-notebook-app
review-notebook-appBot
commented
Oct 1, 2025

Copy link


Copy Markdown

Check out this pull request on ReviewNB
See visual diffs & provide feedback on Jupyter Notebooks.
Powered by ReviewNB
@amaar-mc
amaar-mc
marked this pull request as ready for review
October 1, 2025 07:46
Sign up for freeto join this conversation on GitHub.
Already have an account?
Sign in to comment

Reviewers


No reviews


Assignees


No one assigned


Labels


None yet


Projects


None yet


Milestone


No milestone


Development

Successfully merging this pull request may close these issues.

1 participant

@amaar-mc

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
MST 2 Alternative First Choice Implementation - #15
Open
amaar-mc wants to merge 3 commits into
brainiak:mainfrom 
amaar-mc:mst-2afc
Open
MST 2 Alternative First Choice Implementation#15
amaar-mc wants to merge 3 commits into
brainiak:mainfrom 
amaar-mc:mst-2afc
Conversation
@amaar-mc
@amaar-mcamaar-mc
commented
Oct 1, 2025

Copy link


Copy Markdown

Compares the brain's clip voxel embedding data collected from ses-03 to the embeddings of 31 pairs of MST images (62 images) to determine which MST image pairmate the clip voxel embedding data is closer to via cosine similarity.
Because the 31 pairs of MST images are shown twice, the MST 2AFC Score is calculated twice for both showings of each set of pairmates, as reflected by subset0 and subset1, thus we have mst_2afc_subset0 and mst_2afc_subset1.
The details regarding the 2AFC process are printed in the mindeye.ipynb notebook in the format as follows (example):
 image 1: pair_46_w_pool2.jpg
image 2: pair_46_w_pool1.jpg
img 1 brain embedding chose: pair_46_w_pool2.jpg Correct
img 2 brain embedding chose: pair_46_w_pool1.jpg Correct

Cosine similarity is performed on the img 1's brain embeddings against the two images, and the img 2's brain embedding data. The labeling of whether or not the decision was correct is labeled on the right.
The net MST 2AFC Score is provided after the detailed breakdown above, as in this format:
mst 2afc score: 0.7903 (79.03%)

@review-notebook-app
review-notebook-appBot
commented
Oct 1, 2025

Copy link


Copy Markdown

Check out this pull request on ReviewNB
See visual diffs & provide feedback on Jupyter Notebooks.
Powered by ReviewNB
@amaar-mc
amaar-mc
marked this pull request as ready for review
October 1, 2025 07:46
Sign up for freeto join this conversation on GitHub.
Already have an account?
Sign in to comment

Reviewers


No reviews


Assignees


No one assigned


Labels


None yet


Projects


None yet


Milestone


No milestone


Development

Successfully merging this pull request may close these issues.

1 participant

@amaar-mc

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
MST 2 Alternative First Choice Implementation - #15
Open
amaar-mc wants to merge 3 commits into
brainiak:mainfrom 
amaar-mc:mst-2afc
Open
MST 2 Alternative First Choice Implementation#15
amaar-mc wants to merge 3 commits into
brainiak:mainfrom 
amaar-mc:mst-2afc
Conversation
@amaar-mc
@amaar-mcamaar-mc
commented
Oct 1, 2025

Copy link


Copy Markdown

Compares the brain's clip voxel embedding data collected from ses-03 to the embeddings of 31 pairs of MST images (62 images) to determine which MST image pairmate the clip voxel embedding data is closer to via cosine similarity.
Because the 31 pairs of MST images are shown twice, the MST 2AFC Score is calculated twice for both showings of each set of pairmates, as reflected by subset0 and subset1, thus we have mst_2afc_subset0 and mst_2afc_subset1.
The details regarding the 2AFC process are printed in the mindeye.ipynb notebook in the format as follows (example):
 image 1: pair_46_w_pool2.jpg
image 2: pair_46_w_pool1.jpg
img 1 brain embedding chose: pair_46_w_pool2.jpg Correct
img 2 brain embedding chose: pair_46_w_pool1.jpg Correct

Cosine similarity is performed on the img 1's brain embeddings against the two images, and the img 2's brain embedding data. The labeling of whether or not the decision was correct is labeled on the right.
The net MST 2AFC Score is provided after the detailed breakdown above, as in this format:
mst 2afc score: 0.7903 (79.03%)

@review-notebook-app
review-notebook-appBot
commented
Oct 1, 2025

Copy link


Copy Markdown

Check out this pull request on ReviewNB
See visual diffs & provide feedback on Jupyter Notebooks.
Powered by ReviewNB
@amaar-mc
amaar-mc
marked this pull request as ready for review
October 1, 2025 07:46
Sign up for freeto join this conversation on GitHub.
Already have an account?
Sign in to comment

Reviewers


No reviews


Assignees


No one assigned


Labels


None yet


Projects


None yet


Milestone


No milestone


Development

Successfully merging this pull request may close these issues.

1 participant

@amaar-mc

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
MST 2 Alternative First Choice Implementation - #15
Open
amaar-mc wants to merge 3 commits into
brainiak:mainfrom 
amaar-mc:mst-2afc
Open
MST 2 Alternative First Choice Implementation#15
amaar-mc wants to merge 3 commits into
brainiak:mainfrom 
amaar-mc:mst-2afc
Conversation
@amaar-mc
@amaar-mcamaar-mc
commented
Oct 1, 2025

Copy link


Copy Markdown

Compares the brain's clip voxel embedding data collected from ses-03 to the embeddings of 31 pairs of MST images (62 images) to determine which MST image pairmate the clip voxel embedding data is closer to via cosine similarity.
Because the 31 pairs of MST images are shown twice, the MST 2AFC Score is calculated twice for both showings of each set of pairmates, as reflected by subset0 and subset1, thus we have mst_2afc_subset0 and mst_2afc_subset1.
The details regarding the 2AFC process are printed in the mindeye.ipynb notebook in the format as follows (example):
 image 1: pair_46_w_pool2.jpg
image 2: pair_46_w_pool1.jpg
img 1 brain embedding chose: pair_46_w_pool2.jpg Correct
img 2 brain embedding chose: pair_46_w_pool1.jpg Correct

Cosine similarity is performed on the img 1's brain embeddings against the two images, and the img 2's brain embedding data. The labeling of whether or not the decision was correct is labeled on the right.
The net MST 2AFC Score is provided after the detailed breakdown above, as in this format:
mst 2afc score: 0.7903 (79.03%)

@review-notebook-app
review-notebook-appBot
commented
Oct 1, 2025

Copy link


Copy Markdown

Check out this pull request on ReviewNB
See visual diffs & provide feedback on Jupyter Notebooks.
Powered by ReviewNB
@amaar-mc
amaar-mc
marked this pull request as ready for review
October 1, 2025 07:46
Sign up for freeto join this conversation on GitHub.
Already have an account?
Sign in to comment

Reviewers


No reviews


Assignees


No one assigned


Labels


None yet


Projects


None yet


Milestone


No milestone


Development

Successfully merging this pull request may close these issues.

1 participant

@amaar-mc

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
MST 2 Alternative First Choice Implementation - #15
Open
amaar-mc wants to merge 3 commits into
brainiak:mainfrom 
amaar-mc:mst-2afc
Open
MST 2 Alternative First Choice Implementation#15
amaar-mc wants to merge 3 commits into
brainiak:mainfrom 
amaar-mc:mst-2afc
Conversation
@amaar-mc
@amaar-mcamaar-mc
commented
Oct 1, 2025

Copy link


Copy Markdown

Compares the brain's clip voxel embedding data collected from ses-03 to the embeddings of 31 pairs of MST images (62 images) to determine which MST image pairmate the clip voxel embedding data is closer to via cosine similarity.
Because the 31 pairs of MST images are shown twice, the MST 2AFC Score is calculated twice for both showings of each set of pairmates, as reflected by subset0 and subset1, thus we have mst_2afc_subset0 and mst_2afc_subset1.
The details regarding the 2AFC process are printed in the mindeye.ipynb notebook in the format as follows (example):
 image 1: pair_46_w_pool2.jpg
image 2: pair_46_w_pool1.jpg
img 1 brain embedding chose: pair_46_w_pool2.jpg Correct
img 2 brain embedding chose: pair_46_w_pool1.jpg Correct

Cosine similarity is performed on the img 1's brain embeddings against the two images, and the img 2's brain embedding data. The labeling of whether or not the decision was correct is labeled on the right.
The net MST 2AFC Score is provided after the detailed breakdown above, as in this format:
mst 2afc score: 0.7903 (79.03%)

@review-notebook-app
review-notebook-appBot
commented
Oct 1, 2025

Copy link


Copy Markdown

Check out this pull request on ReviewNB
See visual diffs & provide feedback on Jupyter Notebooks.
Powered by ReviewNB
@amaar-mc
amaar-mc
marked this pull request as ready for review
October 1, 2025 07:46
Sign up for freeto join this conversation on GitHub.
Already have an account?
Sign in to comment

Reviewers


No reviews


Assignees


No one assigned


Labels


None yet


Projects


None yet


Milestone


No milestone


Development

Successfully merging this pull request may close these issues.

1 participant

@amaar-mc

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
MST 2 Alternative First Choice Implementation - #15
Open
amaar-mc wants to merge 3 commits into
brainiak:mainfrom 
amaar-mc:mst-2afc
Open
MST 2 Alternative First Choice Implementation#15
amaar-mc wants to merge 3 commits into
brainiak:mainfrom 
amaar-mc:mst-2afc
Conversation
@amaar-mc
@amaar-mcamaar-mc
commented
Oct 1, 2025

Copy link


Copy Markdown

Compares the brain's clip voxel embedding data collected from ses-03 to the embeddings of 31 pairs of MST images (62 images) to determine which MST image pairmate the clip voxel embedding data is closer to via cosine similarity.
Because the 31 pairs of MST images are shown twice, the MST 2AFC Score is calculated twice for both showings of each set of pairmates, as reflected by subset0 and subset1, thus we have mst_2afc_subset0 and mst_2afc_subset1.
The details regarding the 2AFC process are printed in the mindeye.ipynb notebook in the format as follows (example):
 image 1: pair_46_w_pool2.jpg
image 2: pair_46_w_pool1.jpg
img 1 brain embedding chose: pair_46_w_pool2.jpg Correct
img 2 brain embedding chose: pair_46_w_pool1.jpg Correct

Cosine similarity is performed on the img 1's brain embeddings against the two images, and the img 2's brain embedding data. The labeling of whether or not the decision was correct is labeled on the right.
The net MST 2AFC Score is provided after the detailed breakdown above, as in this format:
mst 2afc score: 0.7903 (79.03%)

@review-notebook-app
review-notebook-appBot
commented
Oct 1, 2025

Copy link


Copy Markdown

Check out this pull request on ReviewNB
See visual diffs & provide feedback on Jupyter Notebooks.
Powered by ReviewNB
@amaar-mc
amaar-mc
marked this pull request as ready for review
October 1, 2025 07:46
Sign up for freeto join this conversation on GitHub.
Already have an account?
Sign in to comment

Reviewers


No reviews


Assignees


No one assigned


Labels


None yet


Projects


None yet


Milestone


No milestone


Development

Successfully merging this pull request may close these issues.

1 participant

@amaar-mc