Mechanisms for sensitivity

We have seen how signal detection theory proposes that the presentation of a stimulus, such as a random-dot kinematogram, is assumed to evoke an 'internal response' that, when combined with the observer's criterion, forms the basis of a perceptual decision. The mechanisms that produce the 'internal response' are not required to be specified — that is, we do not need to know how the physical stimulus is transformed into the 'internal response' in order to use signal detection theory. However, an understanding of such mechanisms can be informative of how the visual system goes about processing the physical stimulation.

In this section, we will consider a potential way in which the visual system might process visual stimulation to permit sensitivity to motion.

Requirements

First, we need to think about what sort of properties we would like our motion detection system to possess. We can gain some insight by again looking at the space-time representation, such as of the 100% coherence rightwards global motion stimulus depicted in the figure below.

Space-time plot of a rightwards moving dot stimulus
Space-time representation of the rightwards moving dot stimulus.

We can then compare this with the space-time representation of a 'noise' (0% coherence) stimulus shown in the figure below:

Space-time plot of a dot stimulus with 0% coherence
Space-time representation of a moving dot stimulus with 0% coherence.

The main difference between the two is that the 'signal' space-time representation contains many instances of patterns that look like . These 'streaks' are caused by the coherent rightwards motion in the stimulus. In contrast, the 'noise' space-time representation contains lots of blobs and lines pointing in different directions.

Hence, it is the presence of the 'streaks' that primarily distinguishes the 'signal' and 'noise' space-time representations. We would therefore want the response of our motion detector to be sensitive to the 'streaks' evident in the space-time representation of the 100% coherence stimulus.

Put back into the language of the physical stimulus, that means that we would want the response of our motion detector to depend on:

The motion direction.
For our example, it should give a large response to rightwards motion and smaller responses to other directions.

We will also impose two additional requirements that any good motion detector should satisfy:

The motion speed.
It should give a large response to the speed of our stimulus (indicated by the slope of the 'streaks') and a smaller response to slower or faster speeds.
Whether the stimulus is actually moving.
It should not give a large response to a stationary input.

A simplified scenario

To simplify the investigation of our motion detection circuit, we will use a moving bar of light rather than a random-dot kinematogram.

With this simplified stimulus, we would expect a strong response to the space-time representation shown in the figure below:

Space-time plot of a rightwards-moving bar of light
Space-time representation of a rightwards-moving bar of light.

We would not expect a strong response to each of the following:

Space-time plot of a leftwards-moving bar of light

What does the above space-time representation show?

Choose the best answer from these options
Space-time plot of a slowly rightwards-moving bar of light

What does the above space-time representation show?

Choose the best answer from these options
Space-time plot of a rapidly rightwards-moving bar of light

What does the above space-time representation show?

Choose the best answer from these options
Space-time plot of a large static bar of light

What does the above space-time representation show?

Choose the best answer from these options

Motion detection circuit components

We will now think about what components we will need to use to implement our motion detection circuit. We will call these 'neurons', in acknowledgement that the motion detection system that we are considering would actually be implemented by networks of neurons in the brain. However, this labelling is rather loose — we will not be considering any of the biophysical properties of actual neurons, or even any of the principles of neural responses that you will learn about in this course.

For our motion detection circuit, we will use the following types of 'neurons':

Intensity neuron
This neuron signals the intensity of the visual input in a particular location in space (its receptive field).
Delay neuron
This neuron receives input from an Intensity neuron and stores it for a short period of time before retransmitting. It also adapts, and will only retransmit its input if it has not recently transmitted.
Comparator neuron
This neuron receives multiple inputs and performs a multiplicative operation. That is, if any of the inputs are weak (~0) then the neuron does not output a signal — all the inputs are required to be active (~1).
Summation neuron
This neuron receives input from other neurons and sums them together.

Circuit structure

To implement our rightwards motion detector, we organise the components in the way shown in below. This creates a form of 'delay-and-compare' circuit.

Circuit for a rightwards motion detector
Circuit for a rightwards motion detector.

How does this work?

Circuit operation

Why does this circuit structure meet our requirements? Let's talk through how it would process the space-time representation of rightwards motion that we introduced earlier.

First, here is the motion that we are seeking to detect:

Space-time plot of a rightwards-moving bar of light.
Space-time plot of a rightwards-moving bar of light.

Now, let's depict the spatial locations that our Intensity neurons will be sensitive to. As shown below, the left Intensity neuron responds to intensities in a small region to the left of centre and the right Intensity neuron responds to intensities in a small region to the right of centre.

Space-time representation of a rightwards-moving bar of light, with superimposed receptive field locations of two Intensity neurons.
Space-time representation of a rightwards-moving bar of light, with superimposed receptive field locations of two Intensity neurons.

The left Intensity neuron will thus be responsive at around 0.75 seconds into the video and the right Intensity neuron will be responsive at around 1.25 seconds into the video. To put it another way, the right Intensity neuron responds 0.5 seconds after the left Intensity neuron.

This different response timing is critical for our motion detector, because the Comparator neuron requires both inputs to be active at the same time. That is where the Delay neuron comes in — if we ask it to 'hold on to' the signals coming from the left Intensity neuron for 0.5 seconds, then the active input to the Comparator neuron will arrive simultaneously and produce a strong response in the Summation neuron.

To summarise the operation of the circuit:

Does this circuit meet our other requirements? We will discuss further in the "low-level motion" lecture.

Summary

In this section, we wanted to gain some insight into the mechanisms that might underpin the 'internal response' in signal detection theory. By defining a few key 'neural' components, which affect the processing of intensity signals in different ways, we can organise a circuit that satisfies our requirements for a rightwards motion detector at the speed of our stimulus. This provides a potential mechanism by which the biological visual system might go about producing an 'internal response' to a stimulus.