Merge pull request #396 from neuromatch/prepod-day-2

explanation t2

tutorials/W1D2_ComparingTasks/W1D2_Tutorial2.ipynb

@@ -1602,13 +1602,16 @@
    "source": [
     "# to_remove explanation\n",
     "\"\"\"\n",
-    "Konkle and Alvarez (2022) speculate that perceptual judgements of visual similarity might be\n",
-    "driven by a process of contrastive learning not unlike that in AI. Different views of the same\n",
-    "scene, like the augmentations used in contrastive learning, can be generated by eye movements,\n",
-    "body movements and the passage of time. The hippocampus could hold these views in working memory\n",
-    "to implement a form of contrastive learning in neocortex. These ideas are closely related to\n",
-    "long-standing ideas about predictive coding in the brain. This remains an active and fascinating\n",
-    "area of research.\n",
+    "Our brain's ability to perform contrastive learning is linked to the function of the ventral visual stream.\n",
+    "This area of the brain processes visual information and has been shown to develop hierarchical features that\n",
+    "capture the structure of visual input through self-supervised learning mechanisms. Evidence suggests that anterior\n",
+    "regions of the ventral visual stream, particularly the anterior occipito-temporal cortex (aOTC), encode substantial\n",
+    "information about object categories without requiring explicit category-level supervision (Konkle and Alvarez, 2022).\n",
+    "Instead, these representations emerge through domain-general learning from natural image structures, where the brain\n",
+    "differentiates between individual views and categories based on the inherent statistical properties of visual input\n",
+    "(Livingstone et al., 2019; Arcaro and Livingstone, 2021). This capability supports the notion that the brain's visual\n",
+    "system can form complex object representations and categorization using self-supervised learning frameworks similar to\n",
+    "those in artificial neural networks.\n",
     "\"\"\""
    ]
   },

tutorials/W1D2_ComparingTasks/instructor/W1D2_Tutorial2.ipynb

@@ -1606,13 +1606,16 @@
    "source": [
     "# to_remove explanation\n",
     "\"\"\"\n",
-    "Konkle and Alvarez (2022) speculate that perceptual judgements of visual similarity might be\n",
-    "driven by a process of contrastive learning not unlike that in AI. Different views of the same\n",
-    "scene, like the augmentations used in contrastive learning, can be generated by eye movements,\n",
-    "body movements and the passage of time. The hippocampus could hold these views in working memory\n",
-    "to implement a form of contrastive learning in neocortex. These ideas are closely related to\n",
-    "long-standing ideas about predictive coding in the brain. This remains an active and fascinating\n",
-    "area of research.\n",
+    "Our brain's ability to perform contrastive learning is linked to the function of the ventral visual stream.\n",
+    "This area of the brain processes visual information and has been shown to develop hierarchical features that\n",
+    "capture the structure of visual input through self-supervised learning mechanisms. Evidence suggests that anterior\n",
+    "regions of the ventral visual stream, particularly the anterior occipito-temporal cortex (aOTC), encode substantial\n",
+    "information about object categories without requiring explicit category-level supervision (Konkle and Alvarez, 2022).\n",
+    "Instead, these representations emerge through domain-general learning from natural image structures, where the brain\n",
+    "differentiates between individual views and categories based on the inherent statistical properties of visual input\n",
+    "(Livingstone et al., 2019; Arcaro and Livingstone, 2021). This capability supports the notion that the brain's visual\n",
+    "system can form complex object representations and categorization using self-supervised learning frameworks similar to\n",
+    "those in artificial neural networks.\n",
     "\"\"\""
    ]
   },

tutorials/W1D2_ComparingTasks/solutions/W1D2_Tutorial2_Solution_0cd5a0dc.py

@@ -0,0 +1,12 @@
+"""
+Our brain's ability to perform contrastive learning is linked to the function of the ventral visual stream.
+This area of the brain processes visual information and has been shown to develop hierarchical features that
+capture the structure of visual input through self-supervised learning mechanisms. Evidence suggests that anterior
+regions of the ventral visual stream, particularly the anterior occipito-temporal cortex (aOTC), encode substantial
+information about object categories without requiring explicit category-level supervision (Konkle and Alvarez, 2022).
+Instead, these representations emerge through domain-general learning from natural image structures, where the brain
+differentiates between individual views and categories based on the inherent statistical properties of visual input
+(Livingstone et al., 2019; Arcaro and Livingstone, 2021). This capability supports the notion that the brain's visual
+system can form complex object representations and categorization using self-supervised learning frameworks similar to
+those in artificial neural networks.
+"""

tutorials/W1D2_ComparingTasks/student/W1D2_Tutorial2.ipynb

@@ -1413,7 +1413,7 @@
     "execution": {}
    },
    "source": [
-    "[*Click for solution*](https://github.com/neuromatch/NeuroAI_Course/tree/main/tutorials/W1D2_ComparingTasks/solutions/W1D2_Tutorial2_Solution_81063c05.py)\n",
+    "[*Click for solution*](https://github.com/neuromatch/NeuroAI_Course/tree/main/tutorials/W1D2_ComparingTasks/solutions/W1D2_Tutorial2_Solution_0cd5a0dc.py)\n",
     "\n"
    ]
   },