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simple discrim

From Pavlov onwards, learning theorists experimented with providing US (in classical conditioning) or reinforcement (in operant conditioning) in the presence of one stimulus (CS+, SD, in general S+), but not in the presence of another (CS-, SΔ, in general S-)

In general, differential responding can be obtained, and we say the animal can discriminate the two stimuli

In early experiments, the stimuli were normally simple, and differed on some obvious physical dimension, e.g. tones of different pitch, lights of different colour








present one of the stimuli and see how the animal responds



present two stimuli and see which the animal approaches – normally considered to be easier



reinforce different responses (or different stimulus-response associations) in the presence of different stimuli



Discrimination boxes (mazes with discriminative stimuli added)

Lashley's jumping stand

Harlow's Wisconsin General Test Apparatus (WGTA)

Skinner boxes in many variants

Use of colour slides, video and computer displays, and touch screens


key phenom


generalisation decrement

generalisation gradient

peak shift


transfer along a continuum (TAC)



some response occurs to stimuli that are physically similar to S+ but not identical to it


generalisation decrement

response to other stimuli is less than that to S+ itself


generalisation grad

a graph relating generalized responding to values on a stimulus dimension

sharpening of generalization gradients when an S- is introduced
- Train S+ on own = broader generalization gradient


peak shift

Responding may be greater to a stimulus other than S+ (S’), on the "other side" of S+ from S- on the stimulus dimension



If a discrimination between S+ and S- is trained, and then S’ is tested vs. S+, S’ may be chosen



Training an easy discrimination on a dimension can help the animal acquire a difficult one more than simply practicing that difficult discrimination


peak shift example

Here the effect is demonstrated with naturalistic stimuli, for pigeons with different wavelengths of light, Hanson (1959)

see notes


Spence's explanation of peak shift

Interacting excitatory and inhibitory generalisation gradients (shown right) produce the result - as long as their shape is chosen correctly.

The theory makes the prediction that peak shift works best with similar (near) S+ and S- (true) and that the shift is greatest in this case (also true).

A modern variant using Rescorla-Wagner has proven very successful

S+ and S- quite strong – take one from the other

see notes


No. of instances of icons B-I present in the stim of exp 1a

see notes

Wills and Mackintosh (1998) an artificial dimension is created by using different icons chosen systematically as shown according to the position on the ‘dimension

S+ and S- overlap

see notes

Their results showed that a good peak-shift could be obtained with an artificial dimension constructed in this way

Humans also show peak-shift, which could have consequences for choice behaviour

see notes


classic theoretical issue: absolute v relative discrim

In any discrimination, an animal learns to respond to one stimulus rather than another.  But what is the effective stimulus?  Is it absolute or relative?

E.g. does a rat learn to respond to black rather than white or to darker rather than lighter?

For not necessarily good reasons, these two possibilities became embroiled in two complete perspectives on discrimination learning - one derived from early behaviourism, the other from Gestalt ideas and more sympathetic to cognitive interpretations

The supposed crucial experiment: transposition of discrimination to different values on the stimulus dimension


transposition: Wills and Mackintosh (1999)

see notes

Successive – shown one at a time – should respond to darker

Simultaneously – shown at same time but in diff orders


transposition example

An explanation in terms of discrimination on the basis of absolute values

see notes

Hence the mere existence of transposition does not establish relational learning in animals - with the proviso that it must be expected to reverse at extreme values on the dimension.


TAC example

The effect...is that pre-training on an easy problem followed by a shift to a hard problem can be more effective than training on the hard problem only, even when total training times are equated.

This was first reported by Lawrence (1952) using rats.

Choose darker grey

Once shifted to hard problem, instantly better at it

Not due to practice as both had same number of trials

Advantage persists over acquisition

see notes

Training on the easy problem (E+ vs. E-) exploits the bigger difference between the curves for this problem.

Training on the hard problem gives hardly any difference between H+ and H- (lots of generalisation), that’s why it’s hard!

Humans also show a TAC effect, and this could have implications for training phoneme perception

Bigger differences = easier to discriminate

Differences between inhib and excite grads greater

see notes


continuity v non-continuity theory

Is learning a gradual process (continuity) or all-or-none (non-continuity)?

This question is made all the harder when it is realised that a continuity account can be made to look very like a non-continuity account - and vice-versa!

The motivation for the non-continuity account originally came from studying individual pigeons


the Hull-Spence continuity theory (Spence, 1936)

Discrimination of absolute stimuli

A continuity theory: learning occurs gradually

Assumes smooth generalization gradient around the stimuli to which training has actually occurred

Assumes excitatory generalization around S+ and inhibitory generalization around S-.  These are hypothetical, internal response tendencies

Observed response tendency is predicted from an (unspecified) monotonic transformation of the algebraic sum of excitatory and inhibitory generalised response tendencies - i.e. excitatory minus inhibitory

With appropriate choices for shapes of the two gradients, this theory can predict transposition, peak-shift and transfer along a continuum.


Krechevsky and Lashley's non-continuity theories

Discrimination of relative stimuli

Non-continuity theories: learning occurs suddenly

Krechevsky (1932): rats form hypotheses about what is to be discriminated; when they get the right hypothesis, the problem is solved instantly

Predicts position habits, no impact of pre-solution reversal, Transfer along a continuum (Lawrence, 1952)

Fits naturally into modern cognitive ideas about selective attention


compromise theories - combining continuity and non-continuity theory

Discrimination involves both learning what stimulus dimension to attend to, and what stimulus values on that dimension are correct
- Sutherland & Mackintosh (1971) specified that attentional learning is slower to reach asymptote than response learning; allowed attention to multiple stimuli, but assumes that attention is limited so that increased attention to one dimension means less to another.
- This theory predicts the overtraining reversal effect and the impact of overtraining on the relative ease of intradimensional shift and extradimensional shift


assessment of compromise theories

Necessarily weaker (in the Popperian falsificationist sense) than either of the simple theories
- More in a theory = harder to falsify
But advocates make a good case that multiple factors are involved in discrimination learning


complex discriminations

We’re now moving swiftly up the scale in terms of complexity, but ask yourselves if simple conditioning could explain these abilities.

Following a pioneering experiment of Herrnstein & Loveland (1964), much modern work has concentrated on experiments on discrimination between sets of stimuli

The stimulus sets are usually defined in terms of human concepts, e.g. person vs. non-person, fish vs. non-fish, or artificial concepts defined by specified multiple features

Such categorical discriminations are frequently learned quite quickly

Most discussion has centred on the question of whether animals need to possess concepts in order to perform categorical discriminations - and what it would mean for an animal to "possess a concept".  


2 kinds of abstraction

Perceptual categories – these are all cats: Abstraction = prototype?
- Animals can learn this

Logical categories – these are all fours: Abstraction = concept?
- Animals cannot learn this


diffs of bird visual systems from typical mammalian systems

Cone-rich retinas

Dense receptor matrix over a wide retinal area

Multiple foveas

Classes of cone differ by oil-droplets filtering light, not by visual pigment

More than 3 types of cone

Spectral brightness response and discrimination

High flicker fusion frequency

Ectostriatum rather than visual cortex


some special features of the pigeon visual system

Two foveas in each eye, one forward (binocular), one lateral

Two visual systems have different functions and psychophysical responses

Very wide range of view

U/V light detected, and affects colour matches

Plane of polarisation of light discriminated


perceptual categories

Herrnstein and Loveland (1964) “Higher order concept formation in the pigeon”

Pigeons learned to peck in the presence of a picture of a person, and withhold pecks in the presence of a picture with no person in it

Stimuli (holiday slides) varied greatly in number of people, posture, whole/part person, clothing, etc

After successful learning, transfer trials show correct response to new stimuli

Could just learn the whole picture rather than just looking for the people

But then when slides mixed still performed above chance – learn the concept of person