Handbook of Psychology, Volume 4: Experimental Psychology

296 Action Selection

RESPONSE EXECUTION RESPONSE EXECUTION

STIMULUS STIMULUS

PROCESS 1

PROCESS 2

PROCESS 3

PROCESS 4

0 TIME RT

DISCRETE STAGES

0 TIME RT

PROCESSES IN CASCADE

PROCESS 1

PROCESS 2

PROCESS 3

PROCESS 4

Figure 11.2 Illustration of discrete stage model (left) and cascade model (right). Source:From McClelland (1979).

Discrete and Continuous Models of
Information Processing

Sternberg’s (1969) additive factors method is based on a view
of human information processing that assumes that the pro-
cessing sequence between stimulus and response consists of
a series of discrete stages, with each stage completing its pro-
cessing before the next stage begins (see Figure 11.2, left
side). Other models allow for parallel or overlapping opera-
tion of the different processing stages. McClelland (1979)
proposed the cascade model of information processing in
which partial information at one subprocess, or stage, is
transferred to the next (see Figure 11.2, right side). The
model assumes that each stage is continuously active and its
output is a continuous value that is always available to the
next stage. As in the discrete stage model, it is also assumed
that each stage operates only on the output from the preced-
ing stage. The output of the final stage indicates which of the
alternative responses to execute.
In the cascade model, an experimental manipulation may
affect a stage by altering the rate of activation or the as-
ymptotic level of activation. The asymptotic level is equiva-
lent to the stage output in the discrete stage model, which is
assumed to be constant, and the activation rate determines the
speed at which the final output is attained. Although the as-
sumptions of the cascade model are different from those of
the discrete stage model from which the additive factors

method was derived, the patterns of interactivity and additiv-

ity can be interpreted similarly. For the cascade model, if two

variables affect the rate parameter of the same stage, their ef-

fects on RT will be interactive; if each variable affects the

rate parameter of a different stage, their effects on RT will be

additive. In sum, as long as it is assumed that the final output

of a stage does not vary as a function of the manipulations,

then use of the additive factors logic to interpret the RT pat-

terns does not require an assumption of discrete stages.

Miller (1988) argued that the discrete versus continuous

categorization should not be viewed as dichotomous but

as extremes on a quantitative dimension called grain size. In

his words, “a variable is more continuous to the extent that

it has a small grain size and more discrete to the extent that it

has a large one” (p. 195). Miller suggested that there are three

different senses in which models of human information pro-

cessing can be characterized as discrete or continuous: repre-

sentation, transformation, and transmission.

Representation refers to the discrete/continuous nature of

the input and output codes for the processing stage. For exam-

ple, if the locations of stimuli and responses in two-choice

spatial reaction tasks are coded as left or right in terms of

relative position, as is often assumed, the spatial codes are dis-

crete. However, if the locations are represented in terms of ab-

solute positions in physical space, then the representations are

continuous. Transformation refers to the nature of the opera-

tion that the processing stage performs. The transformation of