Computational Psychology

Shimon Edelman, <se37@cornell.edu>

Unit 4: Perception I, Representation spaces

measurements and representation spaces


Perception IN THE SERVICE OF BEHAVIOR involves the brain performing many measurements on the outside world.

Think of this is as intelligence-gathering for the sake of the command-and-control processes that reside in the War Room. No generals, though!

perceptual measurements and representations


Perception involves the brain performing many measurements on the outside world.

The MEASUREMENTS are STRUCTURED in space and time, and they carry information about the space-time structure of the world.

The use of ITD in sound localization was one example; another one is shown here on the right; yet another one will come up later in this lecture.

The resulting REPRESENTATION SPACES are also STRUCTURED. For this too there will be examples.


How many measurements?

A lot!

dimensionality of representation space = # of measurements PER POINT


The measurements populate a REPRESENTATION SPACE.

It's a topological SPACE (not merely a set) because intermediate points in it make sense too (think morphing).

In this example, the "fruit space" has 2 dimensions, which means 2 numbers per point.

Each point represents a single kind of item (apples, bananas, etc.).

how many dimensions?


Consider a skier tumbling down a hill...

To represent this process, one must represent a function that maps

time
[the domain of the function].

to

the state of the skier
[the range of the function].

How many dimensions does this function's range possess?

[an abstract high-dimensional representation space]

[For a scientific debunking of such approaches to matchmaking, see this article.]

[If you both think "foreign movies" is a coherent category, you deserve each other.]

the state space of a 3-neuron brain

The diagram shows the trajectory — state plotted against time — of a three-neuron dynamical system through the space of its possible states.

NOMINAL and EFFECTIVE dimensionality




How many dimensions are there in the data that the eye sends to the brain?

About 1,000,000.

Luckily, throughout cognition, EFFECTIVE dimensionality << NOMINAL dimensionality.

a visual task that illustrates the importance of spatially structured measurements: acuity


Dimensionality is about the number of measurements.

The spatial structure of the measurements is very important (as is their temporal structure).


On the right: two types of stimuli, illustrating two-dot and vernier acuity tasks —

the measurement device


On the right: a magnified image of the retinal mosaic —

This is the fovea, hence no rods — only cones.


hyperacuity


The smallest discernible vernier, as it projects onto the retinal mosaic —

Note that the vernier displacement is much smaller than photoreceptor size.

This is an example of hyperacuity-level performance.

Right: a cross-section of the receptive fields of three adjacent receptors.

"tabletop" receptive field (RF) coding isn't very good


This measurement device is too insensitive: two close-by dots will likely fall under the same RF and their locations will be perceived as the same.

"high-resolution" coding isn't very good either, in another way


This measurement device is also suboptimal: dot locations get "digitized", but some information is still lost.

overlapping "tabletop" coding is better


To have many overlapping RFs is a better idea. Can you tell why?

overlapping, graded RF coding is the thing!


Can you tell why many overlapping and graded RFs would do an even better job?

here's why broad, overlapping, graded receptive fields are so effective


Even small lateral displacements of the dot will not go unnoticed: they get TRANSDUCED into measurable changes in the outputs of the RFs.

here's why broad, overlapping, graded receptive fields are so effective


Can you tell why the performance for the vernier (two-line) stimulus is so much better than for the two-dot stimulus?


Summary: hyperacuity-level performance is possible because

  1. the RFs are graded, and
  2. the RFs are broad and overlapping in space.

the structure of representation spaces

The plan for the rest of today:
  1. experimental evidence for the representation space being a SPACE
  2. a powerful tool for studying representation spaces

On the right: A schematic diagram of a (high-dimensional*) face space, illustrating the following concepts:


* Questions to ponder: WHY and HOW high-dimensional?

face space and view spaces


In the plane:
the face [shape] space.

Perpendicular to it:
the view [orientation] spaces of some of the faces.

caricature as deviation from the mean in the face space

Caricature Generator: The Dynamic Exaggeration of Faces by Computer, Susan E. Brennan, Leonardo 18:170-178 (1985) Caricatures and face morphing

caricature as deviation from the mean

Richard M. "Shifty Dick" Nixon George W. "Dubya" Bush as
Alfred E. "What, me worry?" Newman

perceptual adaptation: a demo that reveals a general principle of representation in the brain


On the right: a quick demo of visual color adaptation. Stare at the parrot for at least 30 seconds, then look at the center of the cage. What do you see?

Adaptation, to which all perceptual modalities are susceptible, reveals an important characteristic of neural coding: the brain uses DISTRIBUTED REPRESENTATIONS, so that when some of the units that participate in representing a particular stimulus get fatigued and respond less vigorously, the ones that have not been active "take over" and make the entire representation more like their own preferred features of the stimulus.

Re distributed representations: recall the Haxby et al. (2001) paper from Unit 2.

A useful analogy: the Tug-of-War game.

[EXTRA] using morphing to study face space in the brain (Jiang, Blanz, and O'Toole, 2006)

(a) The face space used in Experiment 1 of Jiang, Blanz, and O'Toole (2006). Morphing along the line from the average to an original face corresponds to anticaricatures, where the identity strength is lower relative to the original. Increasing identity strength beyond the original creates caricatures. The antiface of an original is located on the other side of the average.

(b) The four original scans that were used in the experiment and their antifaces.

(c) The identity strengths from the experiment, scaled in units of the distance of the original (1.0) from the average (0.0). Using antifaces with identity strengths equal to -0.75 avoided some morphing artifacts that tend to occur at more extreme values.

[EXTRA] experiment 1 (Jiang at al., 2006)

(a) An example trial: across-viewpoint, neutral condition with 5 sec antiface adaptation. (b) Left: an antiface adapting stimulus as presented in the within-viewpoint condition; Middle: the corresponding antiface adapting stimulus in the view-changed condition; Right: the corresponding warped antiface. (c) The proportion of trials in the within-viewpoint, across-viewpoint, and warped conditions on which the test face was identified as the match to the adapting antiface stimulus, as a function of the identity strength of the test face.

Findings:

mapping representation spaces from behavioral data and measurements of neural activity

How can we visualize the face space as it is represented by the subject, from

PROBLEM: Unlike brain scans, behavioral data concerning internal representations do not include the coordinates for each point — only relational information.

PROBLEM: Brain data typically reside in a high-dimensional space, which cannot be easily visualized (In this example, there are 10 dimensions).

A particularly useful and theoretically interesting computational technique that can do that is multidimensional scaling (MDS). For both behavioral and neural cases, MDS can embed the data (1) faithfully and (2) in a space of much lower dimensionality.

multidimensional scaling (MDS)


Multidimensional scaling (MDS) is a procedure that takes


Why must a mapping procedure preserve at least relative distances?

INSIGHT: two complementary conceptions of multidimensional scaling (MDS)

The conceptual underpinnings of MDS serve both the scientists who study the brain and the brain itself:

neuroscience cognition
obtaining relational ("2nd-order") insight into a system The scientist needs to know how brain representations are related to each other. The brain needs to know how external objects and events are related to each other.
dimensionality reduction The scientist needs to map the high-dimensional brain activity space into a few meaningful dimensions. The brain needs to map the high-dimensional sensory measurement space into a few meaningful dimensions.


MDS is uniquely suitable for bridging the gap between the brain and the world — in either direction — because it relates configurations of corresponding items in two spaces that are otherwise in principle absolutely unrelated to each other. (Note that this is not the case for projection-based methods such as Principal Components Analysis or PCA.)

The key idea here is REPRESENTATION OF SIMILARITY.
For a book-length detailed mathematical treatment and empirical tests, see Representation and Recognition in Vision, S. Edelman, MIT Press (1999).