1. Perception as an inference process
Bias, prime duration, and the ROUSE model (Huber, Shiffrin, Lyle, & Ruys, 2001)
Reformulating ROUSE as a generative model
Adding dynamics (synaptic depression = temporal discounting?)

3. Perceptual Identification
doctor
Prime: ms
NURSE
Target: ~ 50 ms
Presentation
Sequence
Mask: ~ 500 ms
What word did you see?

4. Forced Choice Perceptual Identification

5. Passive Repetition Priming

6. Active Repetition Priming (both-primed condition)

7. Active Repetition Priming

8. Responding Optimally with Unknown Sources of Evidence (ROUSE)
Source Confusion results in a preference for primed words and both-primed deficits
Responding Optimally
Discounting primed features results in the preference/bias change

11. Repetition Priming and ROUSE

12. a priori ROUSE predictions Huber, Shiffrin, Lyle, & Quach (2002)
discounting is an expected probability assuming infinite sampling
Implications of feature based similarity and lower bound to discounting
Discounting efficacy relies upon N, b, and r
High similarity between choice words
GRANT versus GIANT
Very short target flash durations
Low similarity priming
SHINY priming THINK

13. ˆ a ˆ b ˆ a ˆ b a b a b Reformulating ROUSE as a Generative Model A AP T P T ˆ a ˆ b ˆ a ˆ b F F F F F F 1 2 3 4 5 6
choice A (target)
choice B (foil)
feature activation
feature activation F F F F F F 1 2 3 4 5 6 a b a b A A B B P T P T

16. Forced Choice Perceptual Identification

17. too little or too much discounting
Reformulating ROUSE as a Generative Model A A B B P T P T ˆ a ˆ b ˆ a ˆ b F F F F F F 1 2 3 4 5 6
choice A (target)
choice B (foil)
feature activation
feature activation F F F F F F 1 2 3 4 5 6 a b a b A A B B P T P T

18. temporal discounting through synaptic depression

19. Network model w/ synaptic depression (Huber & O’Reilly, 2003)

20. Real-valued prime and feature activation

21. Relating ROUSE to synaptic depression

22. ˆ d ˆ d ˆ b ˆ b ˆ d ˆ d ˆ d ˆ d ˆ a ˆ b ˆ a ˆ b
Hidden Markov Model (Mozer, Cologrosso, & Huber, 2004)
(dynamic activation through priors and association strengths) ˆ d ˆ d T o T 1 T 2 ˆ b ˆ b F 1 F 2
Factorial Hidden Markov Model
(dynamic activation and explaining away) ˆ d ˆ d T o T 1 T 2 ˆ d ˆ d P o P 1 P 2 ˆ a ˆ b ˆ a ˆ b F 1 F 2

23. Conclusions Cognition: Inferring causes from observations
Observations of familiarity (REM)
Infer whether an item exists in episodic memory
Observations of perception (ROUSE)
Infer which item was seen most recently
Our original formulation of ROUSE can be reworked as a true generative model
More specific independence assumptions
Low probability of prime (previous input), rather than under-estimation
ROUSE can mimic synaptic depression
Synaptic depression reduces persistent activation from previous inputs