Lecture 14 Flashcards
The ‘‘Where’’ Pathway (Dorsal Stream)
-Processes locations and shapes of objects
-Does not encode object names or functions
-Extends from the occipital lobe to the parietal lobe
The ‘‘What’’ Pathway (Ventral Stream)
-Processes object identity (names) and functions, independent of location
-Extends from the occipital lobe to the temporal lobe
Area V4
Neural responses to polar, hyperbolic, and cartesian gratings
Posterior IT
Key transitional area
Lateral Occipital Complex
-Important for high-level vision`
-Bridges mid-level feature processing (V4, PIT) with high-level object recognition (IT Cortex, FFA, PPA)
-Supports invariant object recognition
-Provides whole-object representations, making it a key step in the ventral visual stream
-Major hub for object recognition
-Understanding how brain transforms raw visual input into meaningful objects
Fusiform Face Area
-Located in the fusiform gyrus of the ventral temporal lobe
-Category selectivity: highly tuned to faces, but it also responds to expert-level recognition
-Invariant face recognition: FFA helps recognize faces across different angles, lighting conditions, expressions, suggesting a view-invariant representation
-Damage to this area is linked to prosopagnosia
-Debate : awareness or identity of face?
Parahippocampal Place Area
-A cortical representation of the local visual environment
-Dedicated scene-processing region
-Challenges the idea that object recognition alone explains scene perception-instead, spatial layout is key
-Provides a functional link between vision and spatial cognition
The Role of Context in Object Recognition
Our visual systems make assumptions depending on context + cues
Viewpoint and Scale Invariance
Many IT neurons demonstrate invariance- meaning they continue to respond to an object regardless of its size, position, or viewpoint. This suggests that IT neurons encode more abstract representations of objects rather than raw sensory features
Decoding Methods
-Collect fMRI scans of a participant while they view images from multiple known categories
-Train a computer model to recognize the brain activity patterns associated with each category
-Test the model to see if it can correctly identify an unseen image based on learned brain activity patterns
Encoding Method
-Collect fMRI scans of a participant while they view images from multiple known categories
-Define a feature space
-Fit weights that show how each feature contributes to the neural signal at each voxel
-Onced trained, encoding models can predict responses to new, unseen data
Identifying Natural Images from human brain activity
Stage 1. Model Estimation
Stage 2. Image Identification
Stage 1. Model Estimation
Estimate a receptive-field model for each voxel
Stage 2. Image Identification
(1) Measure brain activity for an image
(2) Predict brain activity for a set of images using receptive-field models
(2) Select the image whose predicted brain activity is most similar to the measured brain activity
Second Order Isomorphism (Roger Shepard)
Similar objects in the world must have similar representations in the mind
