Semantic Memory 449network or the lexical network. For instance, an individual
could try to think of exemplars of bird,which involves acti-
vating the conceptual network, or think of words that sound
likebird,which involves activating the lexical network. An-
other assumption is that semantic decisions, such as verifica-
tions of member-category and property statements (e.g., A
robin is an animalandA robin has feathers,respectively), are
made by accumulating positive and negative evidence until a
positive or a negative criterion is reached. The evidence con-
sists of various kinds of connections that are found during the
memory search. For example, for a member-category state-
ment, such as A robin is an animal,the superordinate con-
nections from robintobirdand from birdtoanimalwould
count as positive evidence. These evidence accumulation
processes are very similar to the processing assumptions of
the feature comparison theory later described.
One of the longest lasting impacts of Collins and Loftus’s
(1975) model came from its ability to provide an elegant
explanation of semantic priming; indeed, this model became
the canonical model of semantic priming. According to this
model, processing of a prime word causes activation to
spread from the prime throughout the conceptual network.
More activation will accumulate at concepts close to the
prime than at concepts far from the prime. This residual acti-
vation then facilitates the semantic decision on the target
word. For example, because birdandrobinare closer in
memory than are dogandrobin,more activation accumulates
atrobinwhenbirdis the prime than when dogis the prime,
and decision times are correspondingly faster.
Feature-Comparison Theory
The feature-comparison theory (e.g., E. E. Smith et al., 1974)
has two major sets of assumptions, those concerning the rep-
resentation of word meaning and those concerning the pro-
cessing of word meaning.
The meaning of a word is represented by a set of semantic
attributes or features. The features vary continuously on a
scale of “definingness”: At one end of the scale are features
that are essential to the word’s meaning; at the other end of
the scale are features that are only characteristic of the con-
cept. For example, the concept mammalmight include as
defining features the facts that mammals are animate, have
mammary glands, and nurse their young, and as characteris-
tic features the facts that mammals give birth to live young,
have four limbs, and live on land.
It is assumed in the model that verification of a statement,
such as A dog is an animal,involves a two-stage process.
In the first stage, a global index of meaning similarity is
computed by matching all of the features in the subject and
the predicate. If this index of similarity exceeds an upper
criterion (e.g., A dog is an animal), a rapid truedecision is
made, and if it falls below a lower criterion (e.g., A dog is
furniture), a rapid falsedecision is made. However, if the
similarity index is intermediate in value (e.g., A dog is a
quadruped), the defining features of the predicate are com-
pared to those of the subject. If all match, the statement is
true, whereas if any mismatch, the statement is false.
The basic predictions of the model rely on the assumption
that response latencies are faster for statements that can be
verified by the first stage than for statements that require both
stages. For true statements, the model predicts that statements
will be verified faster, on the average, if the subject and the
predicate are highly semantically related than if they are not
highly related. The reason is that the global index of meaning
similarity is more likely to exceed the upper criterion for se-
mantically related subjects and predicates, and therefore pro-
cessing of the statement is more likely to engage only the first
stage. E. E. Smith et al. (1974) assumed that typicality ratings
and association norms were reflections of featural similarity
between concepts. Hence, the model predicts, in particular,
that true statements will be verified faster if the subject is a
typical exemplar than if it is an atypical exemplar of the pred-
icate category (e.g.,A robin is a birdvs.A penguin is a bird).
For false statements, the more similar the subject and the
predicate, the less likely the statement is to fall below the
lower criterion. Therefore, similar false statements (e.g., A
bat is a bird) should be more likely to engage the second
stage of processing, and so take longer to reject, than dissim-
ilar false statements (e.g., A robin is furniture). Although this
prediction has been confirmed (e.g., E. E. Smith et al., 1974),
it has also been disconfirmed for certain types of false state-
ments (Holyoak & Glass, 1975), as discussed below.Major Issues and FindingsCollins and Loftus’s (1975) spreading activation theory is
sufficiently complex that it is probably unfalsifiable (but see
the section below on semantic priming). In contrast, both the
hierarchical network model and the feature comparison
model made strong assumptions about how meanings were
represented and processed and, therefore, made testable pre-
dictions about performance in semantic decision tasks. In the
following paragraphs, we summarize two lines of research
that were influential in testing these models.Associative Strength and TypicalityCollins and Quillian’s (1969) hierarchical network model
made two crucial assumptions: First, noun concepts were