LECTURE 7 Flashcards
Explain “representational geometry” and why it is useful to compare information processing between a biological visual system and artificial intelligence models such as deep convolutional neural networks.
Representational geometry refers to how different stimuli or information are encoded and organized in patterns of neural activity. comparing information processing between a biological visual system and artificial intelligence models, such as deep convolutional neural networks, from a psychological perspective is useful because it allows us to gain a better understanding of the underlying cognitive processes involved in perception and cognition. By examining similarities and differences in how these systems process information, we can uncover fundamental principles of human cognition and potentially improve our models and theories of psychological processes. Additionally, it provides an opportunity to test psychological theories using computational models and vice versa, leading to a more comprehensive understanding of the human mind.
Describe the design and findings of the brain imaging study of Bracci et al. (2016) that investigates the representation of shape and category in human visual cortex. To what extent are similar representations found in deep neural networks?
To compare the contribution of shape and category information within the two visual pathways, They implemented a two-factorial event-related fMRI design where shape and category membership are manipulated independently. This design allowed them to separate object shape and object category and investigate the contribution of the two factors.
They found evidence for the following: (1) category representations with different properties in the ventral and the dorsal stream; (2) shape representations of varying complexity; and (3) association between category and shape representations.
