1. What do object-sensitive regions show tuning to?
fMRI
Object classes
Haxby et al. (2001)
Grey-scale photographs; MVPA
Schwarzlose, Swisher, Dang & Kanwisher (2008)
Grey scale photographs; MVPA [tuning to class & location]
Shape
Grill-Spector et al (1999)
Grey-scale photographs; adaptation
Kim, Biederman, Lescroart & Hayworth (2009)
Line drawings of basic level objects; adaptation
Drucker & Aguirre (2009)
Synthetic 2d shapes; adaptation & MVPA
Op de Beeck, Torfs & Wagemans (2008)
Synthetic 3d shapes, MVPA
Haushofer et al. (2008a,b)
Synthetic 2d shapes, adaptation/MVPA

2. What do object-sensitive regions show tuning to?
Electrophysiology
Sparse response with tuning to a narrow range of stimuli
Monkeys - Rolls & Tovee (1995), Humans - Quiroga et al. (2005)
Waydo et al. (2006) [medial temporal] – 0.2-1% of stimuli
Computational modelling
Ullman, Vidal-Naquet & Sali (2002)
Sparse response tuned to object parts
Parts chosen for each class to be selective but invariant to within-class variation

3. Investigate tuning to many feature dimensions simultaneously
Aims
Investigate tuning to many feature dimensions simultaneously
New real-time MVPA imaging method
Allow for tuning to parts of real objects
Use rich sets of (colour) photographic stimuli
Kriegeskorte et al. (2008); Tyler & colleagues [Data from Naci]
No need to…
Assume pattern of selectivity vs. invariance across features is same for all objects
Pre-specify a set of conditions
Region-of-interest
LOC & posterior fusiform
From localiser scan (objects-scrambled objects, Naci PhD)

4. Dynamically Adaptive Imaging (DAI) Stimulus delivery Modify stimuli
Real time analysis
Pre-processing
MVPA
Scanner console

5. DAI similarity search Similarity search Kriegeskorte (2008)
Choose a referent object
Find the “neural neighbourhood” of items that evoke a similar pattern of response
Kriegeskorte (2008)
Iterative procedure
Present the items for this generation (initially, all of them)
Use real-time MVPA to compare the similarity of the pattern evoked by each object to that evoked by the referent
Choose the best items and repeat
Details
Generation sizes
Referent is repeated every 7 items
MVPA using correlation across voxels on beta values. Each object modelled separately.

6. Bonferroni corrected across features
(***)
Bonferroni corrected across features

8. (**)

9. Quantifying relationship between features and neural neighbourhood
Train LDA classifier to predict from their features which objects will be in the neural neighbourhood
Leave-one-out across subjects
Classification performance good
Tuning to different features defines the neural neighbourhoods of different referents

10. Assess number of features while remaining agnostic about what they are
Which features?
Assess number of features while remaining agnostic about what they are

11. Dissimilarity search
Reverse selection rule for search
Number of features encoded…
…small, easy to find opposite
…high, dissimilarity search will not converge because:
(1) Many ways for something to be dissimilar
(2) Exact opposite may not be in set (or even exist)
Feature space is not very low dimensional
But can we quantify the dimensionality?

12. Assessing dimensionality
Two or three DAI sessions in each subject
First generations of each all contain the same set of 92 objects
Use PCA to assess dimensionality of the response

13. Assessing dimensionality
Do dimensions reflect neural features, or might some reflect artefacts in the data?
Test by quantifying information content of pattern as a function of the number of components using MVPA
In each subject:
(1) Pick an object, train classifier to recognise activity pattern using all but one session
(2) Test on the remaining session
Repeat for all objects and subjects

15. Summary: Object processing
Object-selective cortex reflects both perceptual and semantic features
Pattern of tuning versus invariance to these features is different in the neighbourhood of different referents
Estimate of at least 25 distinct feature dimensions
Summary: Methodological results
Real-time MVPA with DAI Similarity Search can effectively search complex stimulus spaces
Neural response evoked by a single presentation can be reliably classified
Can classify single objects from a set of 92
Thanks to…
Michele Veldsman, Daniel Mitchell, Annika Linke & other lab. members
Lorina Naci, Niko Kriegeskorte & the CSL; the CBU radiographers