1. Visual Computation
and Learning Lab
A multichannel model of face processing based on self-organizing principles
Guy Wallis
Centre for Sensorimotor Neuroscience
University of Queensland, Australia

2. Faces are special…different from other objects
Behavioural Holistic processing, configural effects
Developmental Preferential looking in babies
Imaging EEG visually evoked potentials, fMRI hotspots
Neuropsychology Prosopagnosia
Thompson (1980) Perception

3. Complex feature analysers
Apart from face cells (which may represent as little as 5% of all IT neurons in macaque), other cells respond to sub-features of complex natural stimuli.

4. The prototype effect Novel vs prototype (level I effect) AND
Exposure to parts of objects can lead to a false sense of familiarity of those parts in novel combination
Novel vs prototype (level I effect)
AND
Familiar vs prototype (level II effect)
Solso & McCarthy (1981) British J. Exp. Psych.
Later studies failed to replicate level II effect
Bruce et al. (1991) Cognition.
Cabeza & Kato. (2000) Psychological Science.
Cabeza et al. (1999) Memory and Cognition.
More recent study replicated the level II effect
Wallis et al. (2008) Journal of Vision.

5. Reconciling holistic sensitivity with feature-based recognition
A simple competitive network, suitable for all forms of object recognition, can explain a number of phenomena regarded as ‘special’ about faces including holistic processing.
Precise details of the implementation are not especially important, but at its core lie three crucial ingredients:
A rule for synaptic adaptation, based on simple Hebbian principles
A form of competition between neural classifiers within an inhibitory pool
A limited resources model of synaptic weights

6. Reconciling holistic sensitivity with feature-based recognition
Unidimensional
Multidimensional = Holistic
Small numbers of classifiers = low dimensional representation
Small numbers of classifiers = high dimensional representation

7. Reconciling holistic sensitivity with feature-based recognition
Other race effect
This accords with Michel et al. (2006) Psychological Science
(NB Works for Caucasian but not Asian subjects !?)

8. Caricature effect

9. Full face recognition model

10. Composite task, holistic processing and the other race effect
Manipulate same and other race faces seen by the network
The composite task affects same race neural processing but doesn’t impact other-race processing

11. Patches of similarly tuned neurons in cortex
Layers
Surface
As with V1, temporal lobe cortex appears to be functionally organised. Cells with similar selectivity cluster together.

12. Feature organisation in IT Cortex using short-range lateral excitation
Without lateral excitation
With lateral excitation

13. Conclusions: Malleable represents of faces and objects
The ventral stream operates as a feed-forward, competitive network
Objects and face are represented on the basis of multiple, reusable features
Expertise produces ever more selective and (necessarily) holistic tuning in neurons in the anterior areas of IT cortex
Abstract features (multi-channel models) can explain
Caricature effect
Configural processing
Conjunction effect
Prototype effect
Adaptation after effects (see Ross et al. (2011) Journal of Vision)
Local lateral excitation produces feature maps (clustered selectivity) in IT