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\begin{titlepage}
\vspace*{2in}
\begin{center}
{\LARGE The Timing of
\medskip \\
Semantic Priming}
\bigskip\bigskip \\
{\Large (9.63 Laboratory in Cognitive Science)}
\bigskip\bigskip \\
{\large Juliet C. Bourne
\bigskip\bigskip\bigskip\bigskip\bigskip \\
May 11, 1992}
\end{center}
\end{titlepage}

\begin{quotation}
{\footnotesize
This experiment is a followup to the previous semantic priming
experiment. The semantic priming effect is robust and can be duplicated
fairly easily. The purpose of this experiment is to determine the timing
of the priming effect, that is, how long it lasts. This assumes that
priming is manifested as spreading excitation on neural pathways to
associated words. An alternative explanation proposed by Meyer and
Schvaneveldt (1971) is that priming involves a location-shifting model.
Thus, determining the timing of the priming effect will also determine
whether the spreading-excitation model or the location-shifting model is
more accurate. The results show that the priming effect is still strong
even after six intervening prime and lexical decision tasks. No
difference is found between the different priming distances, indicating
that longer priming distances need to be explored. However, the fact
that the semantic priming effect survives through intervening priming
shows that the location-shifting model could not be an accurate
explanation of how priming works.
}
\end{quotation}

The semantic priming effect is a well-established robust effect in which
priming a word with a strongly related word decreases the response time
in the lexical decision task for that word. Meyer and Schvaneveldt
(1971) proposed two alternative models of lexical retrieval to explain
this effect. The first is the location-shifting model and the second is
the spreading-excitation model. In the location-shifting model, the
prime word causes the ``readout'' in the lexicon to point to a certain
area. Words which are associated with the prime word are in this area.
Thus, when the lexical decision task requires looking up another word,
the lookup time is less when the word is associated to the prime since
the ``readout'' does not have to move to a different area in the
lexicon. With this model, subsequent primes unrelated to the original
should move the ``readout'' to a different area of the lexicon and thus
nullify any priming effect for the original prime. In the
spreading-excitation model, the prime word causes neural excitation to
spread along the pathways to associated words. When a lexical decision
is made, the excited pathways enable quicker lookup for words associated
to the prime than for unrelated words. With this model, one would expect
that subsequent primes would not nullify the priming effect of the
original prime; rather, the priming effect would deteriorate with time,
as the neural excitation tapered off. 

The purpose of this experiment is to distinguish between these two
models while determining the timing of the semantic priming effect. To
do this, the primes for target words are placed at various distances
before the lexical decision tasks for the targets. If a prime still has
an effect on its target even though there are many intervening primes
and lexical decision tasks, then the location-shifting model of lexical
retrieval described above could not be an accurate model. Instead, the
spreading-excitation model would seem more likely. If there is a
difference in priming effect among the various priming distances used,
the timing of the priming effect can be determined. However, the results
show no difference between the various priming distances, all of which
prime their targets equally well. Thus, the conclusion is that the
spreading-excitation model is accurate and that the priming effect lasts
longer than six seconds.

\sect{Method}

\subsect{Subjects}

The subjects for this experiment consisted of twelve MIT students,
mainly undergraduates, enrolled in the Laboratory for Cognitive Science.
Data from two of the subjects were not used, due to the accuracy cutoff.

\subsect{Procedure}

Subjects were asked to fixate on a \verb=+= in the middle of the screen.
They were told that a string of letters would appear above the fixation
point followed shortly by the appearance of another string of letters
below the fixation point. The target stimulus appeared 400ms after the
prime stimulus. They were asked to read the first string of letters and
then decide if the second string of letters was a word. They were told
to press the \verb+x+ key if it was a word and the \verb+z+ if it was
not with their preferred hand.

\subsect{Design}

The design of this experiment involves three independent variables:
relatedness, word/non-word, and distance. The relatedness variable has
two values: related and unrelated. It refers to the relationship between
a target and its prime. The word/non-word variable indicates whether the
target is a real word or not. The distance variable refers to where the
prime for a target appears. The values are 0, 1, 3, and 6. A distance of
0 means that the related prime and target are in the same trial. A
distance of 1 means that the prime for a target appears in the the
previous trial. Distances of 3 and 6 mean that the prime for a target
appears three trials and six trials back, respectively.

There are 88 test trials. In 44 of them, the target is has a strongly
related prime. In the other 44, the target is not primed. In half of
the non-primed trials, the target is a non-word. The strongly associated
pairs of words were obtained from a compilation of highly associated
words made by Deborah Summa and Howard Kurtzman. The main source of
these pairs came from {\em Norms of Word Association}, which describes
many word association studies. The words for the unrelated pairs were
taken from this compilation, making sure that the words that were paired
up were not related. The non-words were made up and have little to no
resemblance to real English words, other than the fact that they are
pronounceable. See the {\bf Materials} appendix for a list of all the
words and non-words used in this experiment, in the order presented.
There were also eight practice trials.

The 44 trials in which the target had a related prime were divided
evenly and randomly among the four distance values. (For the trials with
non-primed targets, distance values were assigned arbitrarily since
priming distance is not applicable.)

\sect{Results}

Only data from 10 of the 12 subjects tested are used. The two subjects
whose data are not used had accuracy rates below 90\%. In addition, only
trials in which the target was a real word and in which subjects made
the correct response in the lexical decision task are considered for the
analysis. There does not appear to be a speed-accuracy tradeoff, nor
does there seem to be a correlation between the various distances and
their error rates. However, the error rate for non-primed targets (4\%) is
twice that of the error rate for primed targets (2\%). (See Figure 1.)

\begin{figure}
\center{\epsfbox{results.ps}}
\caption{Error rates and mean reaction times for the non-primed targets
and the primed targets broken into the priming distances. (The priming
distances of 0 and 3 have a 0\% error rate.)}
\end{figure}

The results show a statistically significant semantic priming effect, as
expected. The lexical decision task for primed words is significantly
faster than non-primed words, $F(1,9)=9.117, \,\,\, p<.025$. However, no
significant difference was found between the various prime distances,
$F(3,27)=1.225, \,\,\, p>.30$. Figure 1 shows the mean reaction times
for the various priming distances and the non-primed trials.

\sect{Discussion}

From Figure 1, it appears that the various priming distances produce
very similar effects. The slightly increased mean reaction time for the
priming distance of 1 is interesting, though it isn't statistically
significant. The difference between reaction times for primed and
non-primed targets, however, is significant, thus duplicating the
semantic priming effect as expected. Thus, it can be concluded that the
semantic priming effect is not nullified when intervening primes and
lexical decision tasks are present. So, the spreading-excitation model
seems to be more accurate than the location-shifting model of lexical
retrieval. The duration of the priming effect seems to be longer than
six seconds\footnote{Each trial takes about one second (maybe a bit
more), including the response time for the lexical decision.}, since the
priming effect for a priming distance of six and a priming distance of
zero are basically the same. In order to determine when the priming
effect disappears, an experiment with longer priming distances (possibly
several minutes) is needed\footnote{Also, since word frequency also
affects retrieval time, more attention should be paid to controlling the
word frequencies of the targets. In this experiment, word frequency was
not a consideration, which might account for some of the results. An
item analysis (which I don't have time for) might be useful in
determining the effects of word frequency in this experiment.}.

\sect{References}

Keppel, Geoffrey and Leo Postman. {\em Norms of Word Association}.
Academic Press, Inc: 1970. (As reported by Deborah Summa and Howard
Kurtzman in ``A Compilation of Highly Associated Words''.)

Meyer, David E. and Roger W. Schvaneveldt, ``Facilitation in Recognizing
Pairs of Words: Evidence of a Dependence between Retrieval Operations'',
{\em Journal of Experimental Psychology} (1971), Vol. 90, No. 2, 227-234.

\newpage
\sect{Appendix: Materials}

{\noindent\bf Ordered Trials}

\noindent Primed (Y/N), Word/Nonword (w/n), Distance\footnote{When the
target is not primed, distance is assigned arbitrarily.}, Prime, Target

{\footnotesize
\listing{trials}
}

\vspace*{.15in}
{\noindent\bf Related Pairs}

\vspace*{.15in}
\begin{tabular}{|l|l|l|l|}\hline
carpet rug & hammer nail & king queen & needle thread \\ \hline
spider web & leaf tree & question answer & mother father \\ \hline
tiger lion & town city & husband wife & cloak dagger \\ \hline
cider apple & beach sand & swamp mud & rose flower \\ \hline
goat milk & dentist teeth & yam potato & thunder lightning \\ \hline
fawn deer & sound noise & pupil student & strand hair \\ \hline
chimney smoke & hatchet axe & lawyer doctor & jacket coat \\ \hline
pilot plane & pony horse & shoulder arm & skillet pan \\ \hline
squall storm & steel iron & tuna fish & tobacco smoke \\ \hline
socks shoes & man woman & stork baby & beetle bug \\ \hline
wasp sting & plumber pipes & lettuce tomatoes & file cabinet \\ \hline
toll bridge & boy girl & calf cow & island water \\ \hline
\end{tabular}

\newpage
\sect{Appendix: Statistics}

\center{{\bf Related vs. Unrelated}}
\begin{verbatim}
SOURCE: grand mean
rel         n     N       MEAN         SD         SE
          648    20   518.2056    51.6470    11.5486

SOURCE: rel 
rel         n     N       MEAN         SD         SE
N         213    10   531.7828    45.9528    14.5315
Y         435    10   504.6284    55.7665    17.6349

FACTOR:       subj        rel         rt 
LEVELS:         10          2         20
TYPE  :     RANDOM     WITHIN       DATA

SOURCE             SS     df            MS         F      p
===========================================================
mean     5370741.4779     1   5370741.4779  1114.919  0.000 ***
s/         43354.4311     9      4817.1590

rel         3686.8099     1      3686.8099     9.117  0.014 *
rs/         3639.6117     9       404.4013
\end{verbatim}

\newpage
\center{{\bf Priming Distances}}
\begin{verbatim}
SOURCE: grand mean
dist        n     N       MEAN         SD         SE
          435    40   504.6255    57.0317     9.0175

SOURCE: dist 
dist        n     N       MEAN         SD         SE
1         107    10   513.2209    57.3928    18.1492
0         110    10   495.0455    61.8362    19.5543
6         108    10   501.6991    43.3035    13.6938
3         110    10   508.5364    69.9374    22.1162

FACTOR:       subj       dist         rt 
LEVELS:         10          4         40
TYPE  :     RANDOM     WITHIN       DATA

SOURCE             SS     df            MS         F     p
==========================================================
mean    10185873.8277     1  10185873.8277  823.163  0.000 ***
s/        111366.5991     9     12374.0666

dist       1895.1703  	  3       631.7234    1.255  0.309 
ds/       13590.1772     27       503.3399
\end{verbatim}

\end{document}
