Merge pull request #670 from pupil-labs/al_aois_sam

Initial AL SAM article

alpha-lab/.vitepress/config.mts

@@ -17,7 +17,7 @@ let theme_config_additions = {
     {
       text: "Reference Image Mapper",
       items: [
-        { text: "Define Areas of Interest and Gaze Metrics", link: "/gaze-metrics-in-aois/" },
+        { text: "Automate AOI Masking in Pupil Cloud", link: "/gaze-metrics-in-aois/" },
         { text: "Map Gaze Throughout an Entire Room", link: "/multiple-rim/" },
         { text: "Generate Scanpath Visualisations", link: "/scanpath-rim/" },
         { text: "Uncover Gaze Behaviour on Phones", link: "/phone-neon/" },

alpha-lab/cards.json

@@ -17,15 +17,6 @@
         },
         "image": "/alpha-lab/neon-capture.webp"
     },
-    {
-        "title": "Define Areas of Interest and Gaze Metrics",
-        "details": "Define areas of interest and compute gaze metrics, such as dwell time and time to first fixation, with data downloaded from Pupil Cloud's Reference Image Mapper.",
-        "link": {
-            "text": "View",
-            "href": "/alpha-lab/gaze-metrics-in-aois/#define-aois-and-calculate-gaze-metrics"
-        },
-        "image": "/alpha-lab/aoi-demo.webp"
-    },
     {
         "title": "Map Gaze Onto Dynamic Screen Content",
         "details": "Map and visualise gaze onto a screen with dynamic content, e.g. a video, web browsing, or other, using Pupil Cloud's Reference Image Mapper and screen recording software.",
@@ -106,5 +97,14 @@
             "href": "/alpha-lab/gpt4-eyes/"
         },
         "image": "/alpha-lab/gpt4-eyes.webp"
+    },
+    {
+        "title": "Automate AOI Masking in Pupil Cloud",
+        "details": "Extend the capabilities of Pupil Cloud’s AOI tool by automatically segmenting and drawing masks using natural language.",
+        "link": {
+            "text": "View",
+            "href": "/alpha-lab/gaze-metrics-in-aois/"
+        },
+        "image": "/alpha-lab/aoi-demo.webp"
     }
 ]

alpha-lab/phone-neon/index.md

@@ -29,7 +29,7 @@ import TagLinks from '@components/TagLinks.vue'
 <Youtube src="gp5O1uskDME"/>
 
 ::: tip
-What catches your attention and how do you visually navigate through the interface of a mobile app? In this article, we use Neon, our wearable eye tracker, and all the other tools you need to address these questions. 
+What catches your attention and how do you visually navigate through the interface of a mobile app? In this article, we use Neon, our wearable eye tracker, and all the other tools you need to address these questions.
 :::
 
 ## Gaze Behaviour During Mobile App Engagement
@@ -79,7 +79,7 @@ Scanpaths depict the sequence of eye movements and fixations during visual explo
 
 Examining eye tracking data provides valuable insights into user behaviour, but relying solely on visualisations like heatmaps and scanpaths might not always reveal the complete picture. To gain a more comprehensive understanding, we conducted a quantitative analysis of our data. This involved calculating gaze metrics such as dwell time and time to first contact. These metrics offer tangible and quantitative outcomes about the salience of each AOI.
 
-Dwell time signifies the total duration of fixation on a specific AOI, serving as an indicator of attentional allocation. Longer dwell times suggest prolonged focus on an AOI. On the other hand, the time to first contact measures how quickly an AOI captures a user's attention, indicating its prominence. 
+Dwell time signifies the total duration of fixation on a specific AOI, serving as an indicator of attentional allocation. Longer dwell times suggest prolonged focus on an AOI. On the other hand, the time to first contact measures how quickly an AOI captures a user's attention, indicating its prominence.
 
 As shown in this example, there are clear differences between the areas of interest in both attentional allocation and salience that became evident thanks to the quantitative analysis that focused on the measures mentioned earlier. In line with the heatmap and scanpath visualizations, it is evident that dwell time is higher in AOI-1 compared to the other two areas of interest. Additionally, the time to first contact metric revealed that users directed their attention more swiftly towards AOI-0.
 

default_config.mts

@@ -68,7 +68,7 @@ export const theme_config: ThemeConfigProps = {
       icon: {
         svg: '<svg xmlns="http://www.w3.org/2000/svg" height="24" viewBox="0 -960 960 960" width="24"><path d="m240-240-92 92q-19 19-43.5 8.5T80-177v-623q0-33 23.5-56.5T160-880h640q33 0 56.5 23.5T880-800v480q0 33-23.5 56.5T800-240H240Zm240-120q17 0 28.5-11.5T520-400q0-17-11.5-28.5T480-440q-17 0-28.5 11.5T440-400q0 17 11.5 28.5T480-360Zm0-160q17 0 28.5-11.5T520-560v-160q0-17-11.5-28.5T480-760q-17 0-28.5 11.5T440-720v160q0 17 11.5 28.5T480-520Z"/></svg>',
       },
-      link: "https://feedback.pupil-labs.com/",
+      link: "https://discord.com/channels/285728493612957698/1203965124759519252",
     },
     {
       icon: {