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Neural Correlates of Degraded Picture Perception. Tom Busey, Rob Goldstone and Bethany Knapp. General Method. Record brain activity during perception of degraded pictures. Change knowledge by sometimes showing undegraded picture. Always record during presentation of degraded picture.
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Neural Correlates of Degraded Picture Perception Tom Busey, Rob Goldstone and Bethany Knapp
General Method • Record brain activity during perception of degraded pictures. • Change knowledge by sometimes showing undegraded picture. • Always record during presentation of degraded picture. • Also change experience by showing primes.
What was the picture? 1 Raccoon 2 Baseball player 3 Chairs 4 Two women talking 5 Bird facing right 6 Trees 7 Monkey 8 Don’t know (no clue)
Experimental Design One Trial Pre-exposure Post-exposure Flipping with undegraded picture 4 Repetitions degraded pict 4 Repetitions degraded pict Questions Flipping but blank screen instead of undegraded picture Process repeated 60 times 30 Pictures in each condition. Each condition is replicated 120 times per subject x 3 subjects.
What was the picture? 1 Dog 2 Woman with hands 3 Camel facing left 4 Horse 5 Raccoon 6 Bird facing right 7 Baseball player 8 Don’t know (no clue)
Experimental Design One Trial Pre-exposure Post-exposure Flipping with undegraded picture 4 Repetitions degraded pict 4 Repetitions degraded pict Questions Flipping but blank screen instead of undegraded picture Process repeated 60 times 30 Pictures in each condition. Each condition is replicated 120 times per subject x 3 subjects.
ERP Data From Experiment 1 Front of Head Left Side Right Side Back of Head
ERP Data From Experiment 1 Cz (Middle of Head) Pz (Back Middle of Head) Front of Head Left Side Right Side Back of Head
ICA Decomposition • Goal: Recover a set of independent signals (components) that were mixed together in the EEG electrodes. • Recovered components can be considered as latent variables or factors like those in Factor Analysis. • Not the same as dipole analysis, but dipoles can be fit to ICA components.
ICA Decomposition • Independence: • Knowing something about one component tells you nothing about the state of the other components. • Joint density equals the product of the marginal densities: p(y1,y2)= p(y1)p(y2) • Independent components are uncorrelated, but uncorrelated factors (from PCA) need not be independent. • Decomposition is an interative process • Knows nothing about time or conditions • Adjusts weight values assigned to each electrode to find components with distributions that are as independent as possible. • Decomposition is not unique, but we see good convergence over repeated simulations.
ICA Decomposition • Visualization • Compute components (which are defined by their weights) from concatenated individual subject data. • Visualize using grand average data. • Look for components that differentiate between the conditions • Back project each component to voltage, which simulates what we would have recorded if this was the only active component. • Statistical Issues • Statistical analysis of ICA components is still relatively new. • Stress replication across experiments over hypothesis testing.
Experiment 1 Conclusions • Experience with the real image produces large centrally-located changes in the ERP beginning around 400 ms. • Also see an ICA component that separates out this condition in perceptual regions as early as 250 ms. • Knowledge of the picture's gist but not its interpretation produces an ICA component that separates at 400 ms and is localized to the occipital portion of the head.
Experiment 2 • How does prior knowledge about the content of the picture help you interpret the degraded image? • Precede the degraded image with a text description of the contents, called a prime. • How are the components identified by ICA affected by the prime?
Experiment 2 Design Changes • Pre-expose half of the pictures at the start of the experiment, along with their primes and degraded versions.
Prior to experiment bird facing right
Prior to experiment bird facing right
Experiment 2 Design Changes • Pre-expose half of the pictures at the start of the experiment, along with their primes and degraded versions. • The other half of the pictures are shown in their degraded form only.
Prior to experiment baseball player
Prior to experiment baseball player
Experimental Design One trial Prime Correct Description Delay Degraded Picture Incorrect Description (comes from other pictures) 1000 ms 1000 ms 30 Pictures in each condition Each condition is replicated 120 times per subject x 4 subjects. 1000 ms
(One Trial) baseball player
Experimental Design-End Allow flipping with description (prime) Ask subject: How well did you figure this picture out?
End of experiment baseball player
End of experiment baseball player
How well did you interpret this picture? • No Clue: I never figured it out. • Partial: I got some of the details, or I figured it out midway through the experiment. • Knew: I figured this picture out almost immediately (or it was shown to me at the beginning of the experiment). Exclude data from pictures in the No-Knowledge condition that subjects figure out.
Experimental Design Prior Knowledge? Prime veracity Knowledge No-Knowledge Correct Blue Green Incorrect Red Cyan Incorrect primes come from other Knowledge or No-Knowledge pictures. No information in the prime as to whether a Knowledge or No-Knowledge picture would appear. But, some No-Knowledge pictures will have primes associated with known pictures.
Bias Model • Prime makes it more likely that picture will be interpreted in a manner consistent with the prime. • Bias or preference effect. Verbal/semantic in nature. • Doesn't involve recall of image from memory. • Look at processing of prime to see if get differences that may reflect recall of memory.
