552 Reading
letters, even in nonpronounceable nonwords, are processed
in parallel. Third, subsequent experiments (e.g., Baron &
Thurston, 1973; Hawkins, Reicher, Rogers, & Peterson,
1976) showed that the word superiorityeffect extends to
pseudowords(i.e., orthographically legal and pronounceable
nonwords like mard): that is, letters in pseudowords are also
identified more accurately than are letters in isolation. (In
fact, many experiments found virtually no difference be-
tween words and pseudowords in this task.) Because it is
extremely implausible that people have templates for pseudo-
words, they cannot merely have visual templates of words
unconnected to the component letters. Instead, it seems
highly likely that all short strings of letters are processed in
parallel and that for words or wordlike strings, there is mutual
facilitation in the encoding process.
Although the above explanation in terms of so-called
mutual facilitation may seem a bit vague, several successful
and precise quantitative models of word encoding have ac-
counted very nicely for the data in this paradigm. The two
original ones were by McClelland and Rumelhart (1981) and
Paap, Newsome, McDonald, and Schwaneveldt (1982). In
both of these models, there are both word detectors and letter
detectors. In the McClelland and Rumelhart model, there is
explicit feedback from words to letters, so that if a stimulus is
a word, partial detection of the letters will excite the word de-
tector, which in turn feeds back to the letter detectors to help
activate them further. In the Paap et al. model, there is no
explicit feedback; instead, a decision stage effectively incor-
porates a similar feedback process. Moreover, both of the
models successfully explain the superiority of pseudowords
over isolated letters. That is, even though a pseudoword like
mardhas no marddetector, it has quite a bit of letter overlap
with several words (e.g., card, mark, maid). Thus, its compo-
nent letters will get feedback from all of these word detectors,
which for the most part will succeed in activating the detec-
tors for the component letters in mard. Although this verbal
explanation might seem to indicate that the facilitation would
be significantly less for pseudowords than for words because
there is no direct match with a single word detector, both
models in fact quantitatively gave a good account of the data.
To summarize, the aforementioned experiments (and
many related ones) all point to the conclusion that words
(short words, at least) are processed in parallel, but through a
process in which the component letters are identified and feed
into the word identification process. Above, we have been
vague about what letter detectormeans. Are the letter detec-
tors that feed into words abstract letter detectors(i.e., case-
and font-independent) or specific to the visual form that is
seen? (Needless to say, if there are abstract letter detectors,
they would have to be fed by case-specific letter detectors, as
it is unlikely that a single template or set of features would be
able to recognize aandAas the same thing.) As we have
mentioned, the word superiority experiments chiefly used all
uppercase letters, and it seems implausible that there would
be prearranged hook-ups between the uppercase letters and
the word detectors. Other experiments using a variety of
techniques (e.g., Besner, Coltheart, & Davelaar, 1984; Evett
& Humphreys, 1981; Rayner, McConkie, & Zola, 1980) also
indicate that the hook-up is almost certainly between abstract
letter detectors and the word detectors. One type of experi-
ment had participants either identify individual words or read
text that was in MiXeD cAsE, like this. Even though such
text looks strange, after a little practice, people can read it
almost as fast as they read normal text (Smith, Lott, &
Cronnell, 1969). Among other things, this research indicates
that word shape (i.e., the visual pattern of the word) plays lit-
tle or no part in word identification.
These word superiority effect experiments, besides show-
ing that letters in words are processed in parallel, suggest that
word recognition is quite rapid. The exposure durations in
these experiments that achieved about 75% correct recogni-
tion was typically about 30 ms, and if the duration is in-
creased to 50 ms, word identification is virtually perfect. This
does not necessarily mean, however, that word identification
only takes 50 ms—it merely shows that some initial visual
encoding stages are completed in something like 50 ms.
However, after 50 ms or so, it may just be that the visual in-
formation is held in a short-term memory buffer, but it has
not yet been fully processed. In fact, most estimates of the
time to recognize a word are significantly longer than that
(Rayner & Pollatsek, 1989). As we have previously noted, it
takes about 500 ms to begin to name a word out loud, but that
is clearly an upper estimate because it also includes motor
programming and execution time. Skilled readers read about
300 words per minute or about 5 words per second, which
would suggest that one fifth of a second or 200 ms might not
be a bad guess for how long it takes to identify a word. Of
course in connected discourse, some words are predictable
and can be identified to the right of fixation in parafoveal vi-
sion, so that not all words need to be fixated. On the other
hand, readers have to do more than identify words to under-
stand the meaning of text. However, most data point to some-
thing like 150–200 ms as a ballpark estimate of the time to
encode a word.Automaticity of Word EncodingOne surprising result from the word encoding literature is that
encoding of words seems to be automatic; that is, people can’t
help encoding words. The easiest demonstration of this is
called theStroop effect(Stroop, 1935; see MacLeod, 1991 for
a comprehensive review). There is actually some controversy