William Kaye Estes 1919 -
Stimulus Sampling Theory (SST)
Biography

William Kaye Estes was born in 1919. He earned his Ph.D. in 1943 from the University of Minnesota, and taught at Indiana University, Stanford, Rockefeller University, and Harvard. He published from 1950 - 1978.

Theory

In the late 1980's there was a trend to move away from the broad comprehensive theories of learning and toward miniature systems which were deep instead of broad. Estes' early work was influenced by Edwin Guthrie. Estes' lab work on learning was based on several assumptions that formed the foundation of his Stimulus Sampling Theory:

1. Learning stimuli are a large but finite number of stimulus elements. (S) = the entire possible range of stimulus elements in a trial.

2. The desired or correct response after a stimulus was called the A1 response. All other responses were incorrect and were labeled A2 responses. When an A1 response is elicited, the experiment is terminated.

3. All elements in (S) are attached to either A1 or A2.

4. The experimental subject does not experience all (S) on any given trial, but only a small proportion of them. Theta is the proportion of (S) experienced.

5. If an A1 response terminates a trial, then Theta proportion of S will become conditioned to A1 response by contiguity. That is, an association is formed between the set of stimuli and the A1 response that followed them. Thus when A1 terminates a learning trial, the number of stimulus elements conditioned to A1 goes up. Also, the tendency of an A1 response to be elicited at the onset of the trial also goes up.

6. Gradually the attachment of S elements to A1 and A2 changes. Estes calls this gradual change "learning."

Estes' theory is a statistical learning theory. In all statistical learning theories, the dependent variables are the probability of various responses occurring. Probabilities are attached to the various outcomes. When all the (S) are condition to A1, the desired response behavior, no further learning occurs and the probability of A1 is 1.0.

Estes' later work incorporated cognitive elements. The theory was still mechanistic but became more complex with the inclusion of memory as a mediating factor. Rather than stimuli (S) leading directly to responses, the later version of the theory stated that these (S) elicit memories of previous experiences, and the interaction of the current stimuli and memory is what produces behavior.

The "Scanning Model of Decision Making" proposed that the individual scans a situation to determine what responses are possible and recalls their probable outcomes from memory. Then the individual can choose a response that yields the most valuable outcome.

Estes was never a reinforcement (S - R) theorist. He saw reinforcement as preventing the un-learning of associations between stimuli (S) and responses (R). He believed the reinforcements were learned also, as an outcome of some response. In this view, reinforcement and punishment are performance variables since they determine how material already learned will manifest itself in behavior. (Response - Outcome, R - O theory). This line of thinking makes a clear distinction between "learning" and "performance".

Estes theories were mechanistic, and he viewed learning and behavior as machine-like. He did however, believe from his laboratory observations that humans become increasingly effective at solving problems as more problems are solved. He called this process "Learning to Learn".

Estes theories are aligned with Tolman and Bandura in that (1) they retain an informational interpretation of reinforcement, (2) they distinguish between learning and performance, and (3) they maintain that goals influence behavior outcomes. There is also a kinship to information processing theory.

Current Views:

Mathematical models of learning have helped make psychological research more "scientific" and moved psychology toward a more cognitive orientation, but have provided little new understanding about the process of learning.
 
Learning Theory Bibliography
Hergenhahn (1988)

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