454 Semantic Memory and Priming
concept to another. This mechanism is used to explain the ef-
fects of hierarchical network distance on verification time.
ACT, in contrast, assumes that activation spreads extremely
quickly, reaching asymptote in as little as 50 ms. Effects of net-
work distance are attributed to differences in asymptotic activa-
tion levels. Another difference is that Collins and Loftus’s
model assumes that activation continues to spread (for a while)
even when a concept is no longer being processed. In ACT,
however, activation decays very rapidly, within 500 ms, when a
concept ceases to be a source of activation. Finally, the Collins
and Loftus model assumes that only one concept can be a
source of activation at a time, whereas ACT assumes that the
number of possible sources is limited only by the capacity of
attention.
The accounts of semantic priming in the two models are
really quite different. In the Collins and Loftus model, the
prime sends activation to the target, and the target can be in
a preactivated state even though the prime is no longer being
processed. In ACT, however, both the prime and the target
must be sources of activation—both must be objects of atten-
tion—for the association between them to produce height-
ened activation of the target. Priming occurs in ACT
because the prime is still a source of activation when the
target appears.
Two lines of evidence are problematic for the Collins and
Loftus (1975) model. Ratcliff and McKoon (1981) showed
that priming in item recognition was statistically reliable
when the stimulus onset asynchrony (SOA) between the
prime and the target was as short as 100 ms (no priming oc-
curred at an SOA of 50 ms). This finding suggests that acti-
vation spreads very rapidly. In addition, the magnitude of
priming at an SOA of 100 ms was the same for prime-target
pairs close in network distance and pairs far in network
distance. The effects of network distance appeared in the
sizes of priming effects at the longer SOAs: More priming
eventually occurred for close pairs than for far pairs. In
another line of research, Ratcliff and McKoon (1988) showed
that the decay of priming could be very rapid, within 500 ms
in some circumstances. These findings contradict basic as-
sumptions of the Collins and Loftus (1975) model, but they
are quite consistent with Anderson’s (1983) ACT model.
Compound-Cue Models
Compound-cue models of priming were proposed indepen-
dently by Ratcliff and McKoon (1988) and by Dosher and
Rosedale (1989). The compound-cue model is simply a state-
ment about the contents of retrieval cues. The claim is that
the cue to memory contains the target item and elements
of the surrounding context. In a lexical decision task, for
example, this context could include the prime, or even words
occurring before the prime.
The compound-cue model must be combined with a
model of memory to make predictions about performance in
a task. Models that have figured prominently are the search
of associative memory (SAM, Gillund & Shiffrin, 1984),
the theory of distributed associative memory (TODAM,
Murdock, 1982), and MINERVA 2 (Hintzman, 1986). In all
of these models, the familiarity of a cue containing two as-
sociated words will be higher than the familiarity of a cue
containing two unassociated words. Hence, in a lexical de-
cision task, if the cue contains the target and the prime, fa-
miliarity will be higher for a target related to its prime than
for a target unrelated to its prime (e.g.,lion-tigervs.table-
tiger,respectively). If familiarity is inversely related to re-
sponse time, basic priming effects can be explained (e.g.,
Ratcliff & McKoon, 1988).Distributed Network ModelsRelatively recently, several distributed network models of se-
mantic priming have been proposed. These models fall into
two broad categories:
In one category of models, which we refer to as proximity
models,priming is caused because related primes and targets
are closer to each other in a high-dimensional semantic space
than are unrelated primes and targets (e.g., Masson, 1995;
McRae, de Sa, & Seidenberg, 1997; Moss, Hare, Day, &
Tyler, 1994; Plaut & Booth, 2000; Sharkey & Sharkey, 1992).
A fundamental assumption in these models is that concepts
are represented by patterns of activity over a large number of
interconnected units. Related concepts have similar patterns
of activity. Semantic priming occurs because in processing a
target word the network begins from the pattern created by
processing of the prime; this pattern is more similar to the tar-
get’s representation when the prime is related than when it is
unrelated to the target. In effect, the network gets a head start
in processing the target when it is preceded by a related
prime. A few of these models (e.g., Moss et al., 1994; Plaut &
Booth, 2000) are able to distinguish semantic priming, which
is attributed to overlapping semantic features, from associa-
tive priming. Associative priming occurs in these models be-
cause the network learns to make efficient transitions from
primes to targets that co-occur frequently during training.
The other category of distributed models, which we refer to
aslearning models,attributes semantic priming to learning that
occurs when a word is recognized or is the object of a deci-
sion of some kind (e.g., S. Becker, Moscovitch, Behrmann, &
Joordens, 1997; Joordens & Becker, 1997). These models also
assume that concepts are represented by patterns of activity