Introduction to psychometric curves and fitting a simple psychometric function

Code
GitHub: Matlab code and Python package

Introduction
An important aim of psychophysics is to quantify the relationship between stimulus parameters and an observer's subjective appreciation of what's going on. Signal detection theory approaches this problem by attributing perceptual sensitivity to thresholds that act at multiple levels in a system. In this context, a threshold could, for example, be how loud a sound needs to be to determine its location to a certain degree of accuracy, or how long a light needs to be on to determine its direction of movement. Above threshold, and the task is possible to a certain degree of accuracy. Below threshold and it's not possible to discriminate as accurately.

Experimentally, thresholds are often measured in two-alternative forced choice (2AFC) tasks or in go-no go paradigms (GNG). In 2AFC tasks a subject is asked to indicate one of two choices in response to a stimulus. For example, indicate white if a colour appears white, or black if it appears black in response to a shade of grey. Alternatively, GNG tasks require a subject to not respond ("no go") until a stimuli meets a certain criteria, then respond ("go").

In both of these types of task, a subjects performance can be measured as a function of the stimulus parameters - either as % correct, or similar, in 2AFC or d' in GNG, where d' is a function of hits, misses, and false alarms. Regardless of the exact metric used, the subjects performance can be described by two main parameters; bias (the "point of subject equality", PSE) and discrimination sensitivity (as in ability to discriminate between two conditions, such as white and black).

For example, imagine a subject performing a 2AFC task where they're presented with a shade of grey and they have to press one button to categorise the colour as white, or another button to categorise the colour as black. Intuitively, they're more likely to classify a grey stimulus as black the closer it is to black, and less likely the colour it is to white. So plotting their responses as a proportion of black responses (y) against the stimuli presented (x). We could use this sort of task to see how other factors, like the colour of a background, could affect perception of the shade of grey.

Alternative function: Net present value of a cash flow

See also the time value of money for other related Matlab functions.

Download
npvFlow.m

Introduction
The net present value (NPV) of a cash flow is how much a flow of cash is worth right now. The "net" part refers to the fact that the cash flow isn't necessarily fixed, and may even be negative at certain points in time. Matlab has two functions in the financial toolbox for calculating NPV for fixed and variable cash flows (pvfix and pvvar respectively), npvFlow.m is designed to emulate the basic function of both these functions and works for fixed and variable cash flows.

For example, consider a cash flow of £200 per year for the next 4 years, with an annual interest rate of 4%. What is the value of this cash flow right now, at this point in time? It's not £800, it must be less. To work it out, we need to calculate the present value of each individual chunk of cash. The NPV is simply the sum of these.

This is done with the PV calculation (pvLump,m) of each lump sum, which is

PV = FV / (1+r)^n,

where r = interest rate and n = number of time periods.

So,
End of period 1: £200 received = PV1 = 200/(1+0.04)^1 = 192
End of period 2: £200 received = PV2 = 200/(1+0.04)^2 = 185
End of period 3: £200 received = PV3 = 200/(1+0.04)^3 = 178
End of period 4: £200 received = PV4 = 200/(1+0.04)^4 = 171

The sum of PV1-4 = NPV = 192 + 185 + 178 + 171 = £725.

Alternative function: Present value of lump sum

See also the time value of money for other related Matlab functions.

Download
pvLump.m

Introduction
The present value of a lump sum of cash is the value of cash available in the future, right now.

But why should the value of something be now compared to in the future? This is a core concept of what is known as the "time value of money", which basically states that money is worth more now than it is if received in the future. The reason for this is interest - if you have money now, you have the opportunity to earn interest on it. If you only get the money later, you miss this opportunity.

This concept also applies to cash flows, but is slightly more complex as the interest needs to be applied at multiple points in time - see npvFlow.m.

For example, for a lump sum, assuming a positive interest rate; £1000 in 2 years time is worth £1000 in 2 years, whereas £1000 now is worth £1000+2 years interest in 2 years time. But what is £1000 in 2 years time worth to us right now? This value is the present value (PV).

So, for a lump sum, the equation is a rearranged form of the future value (FV) compound interest equation (see fvLump.m). Intuitively, for compounding interest, the FV is the PV plus the interest from each time point:

FV = PV * (1+r)^n,

where r = interest rate and n = number of time periods.

This means to calculate the PV, the equation rearranges to:

PV = FV / (1+r)^n.

This is the equation we will implement in a simple Matlab function, pvLump.m.

Alternative function: Future value of lump sum

See also the time value of money for other related Matlab functions.

Download
fvLump.m

Introduction
The future value (FV) of a single lump sum of cash is its present value (PV, see also pvLump.m and npvflow.m) plus interest earned over a period of time. Interest can be earned in two ways - simple interest or compound. Simple interest is linear and is a proportion of the original amount applied at the end of the time period. Compound interest, which is mostly commonly used, increases in an exponential fashion as it is added at each time period and adds to the amount on which the interest is applied in the next period. fvLump provides an alternative to fvdisc in the financial toolbox.

Simple interest
FV = PV * (1+r*t)

Compounding interest
FV = PV * (1+r)^t

Where r = interest rate and t = number of time periods.

These equations are available in fvLump. For calculating the future value of a cash flow, for example, when saving a regular amount of money and earning interest, see nfvFlow.m.

Alternative function: Moving average

Download
movAv2.m

This is a slightly more advanced version of movAv and provides an alternative to movavg and tsmovavg ("time-series moving average"). This version allows weighting of the mean and setting of the lag value (rather than just centring the average automatically). It also shows examples of basic management of the variables in a functions workspace, using switch and case for convenient string logic, and how to perform a weighted mean calculation. See also movAv for a couple of examples when moving averages may or may not be appropriate.

Description
Compared to movAv.m, this version adds two features:

• Lag - when performing a moving average with length n, n points in total are lost from the output. The lag value positions the output in a vector of the same length as in the input, by determining the number of NaNs at the start and end of the vector.

• Weights - Included are a few random weights as examples in the script as examples, although the only one that might be useful is 'simExp', which exponentially weights the values in the centre of the average window higher than the outer points. Weights can also be supplied as a vector the same length as the average window (n). The absolute values of the weights are unimportant, as they're normalised to relative values that sum to 1 in the script.