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\begin{titlepage}
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\begin{center}
{\LARGE Priming Effects:
\medskip \\
Priming and Lexical Decision}
\bigskip\bigskip \\
{\Large (9.63 Laboratory in Cognitive Science)}
\bigskip\bigskip \\
{\large Juliet C. Bourne
\bigskip\bigskip\bigskip\bigskip\bigskip \\
April 22, 1992}
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\begin{quotation}
{\footnotesize In this experiment, the well-established semantic priming
effect is duplicated. Somewhat similar to the first experiment described
in Meyer and Schvaneveldt (1971), this experiment involves a lexical
decision task for the second string of letters in a pair. The first is
always a word, but the second can be a non-word. For pairs of real
words, the association between the first and the second is varied
systematically.  The results show a definite effect of the strength of
this association: the stronger the association, the faster the response
time in the lexical decision task.  Two possible models of lexical
access which could explain this effect of association are proposed and
future experiments to distinguish between these models are suggested.}
\end{quotation}
\vspace*{.4in}

In 1971, Meyer and Schvaneveldt established the semantic priming effect
in which access of one word affects the access of a word presented soon
after. If the words are strongly related, the access time for the second
word is faster than if the words were unrelated. In their experiments,
they showed this effect through two different tasks. The first was a
simple lexical decision task, similar to the one used in this
experiment. The other was one in which subjects had to tell whether the
two strings of letters were the same in terms of lexical status. For
both of these tasks, the response time for two strongly associated words
was significantly faster than for unassociated words. Meyer and
Schvaneveldt concluded from these results that the way words are
retrieved from memory involves a dependence between immediately
successive lexical decision tasks. 

In this experiment, a simple lexical decision task is used to
demonstrate the semantic priming effect. The first string of letters
(the prime) is always a word. The second string of letters can be a word
or a non-word. When both are real words, the association between them
can either be strong, weak, or none. The lexical decision task, unlike
that in Meyer and Schvaneveldt (1971), only involves the second string
of letters. When the words are strongly associated, response time in the
lexical decision task is expected to be faster then when the words are
weakly associated or not associated. The results show this effect as
expected.

Multiple explanations or models of retrieval can be proposed to explain
this effect. Meyer and Schvaneveldt proposed two different models:
location-shifting and spreading-excitation. Each involves some change in
the retrieval of the second word after the first lexical decision task.
In our task, there is no lexical decision on the first string of
letters. Subjects are merely asked to read it and then make a lexical
decision on the second string of letters. Thus, the assumption is that
merely processing a word is effective to prime associated words and make
a lexical decision on those words quicker than on unassociated words.
However, the two models proposed can still be used to explain this
priming effect with the assumption that any sort of processing of a word
is enough to prime associated words. Further experiments are needed to
distinguish between these two models of lexical retrieval.

\sect{Method}

\subsect{Subjects}

The subjects for this experiment consisted of fifteen MIT students,
mainly undergraduates, enrolled in the Laboratory for Cognitive Science.

\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 only one independent variable
which is the relatedness of the prime and the target (the subject of the
lexical decision task). There are 151 pairs of primes and targets. In
all cases, the prime is a real word, but the target is a real word in
only 96 of these pairs. For 48 of the pairs, the prime and target were
unrelated. In the other 48, the prime and target were related, split
evenly between weakly related and strongly related\footnote{See the
{\bf Materials} appendix for the 96 pairs, divided into unrelated,
weakly related, and strongly related.}.

\sect{Results}

Only trials in which both strings of letters were real words are used in
the analysis. Further, only trials in which the subject gave the correct
response are used. The mean response time for each relatedness condition
is given in Figure 1, along with the error rate for each
condition\footnote{Error rates were calculated by dividing the number of
trials that subjects responded correctly to by the total number of
trials with real word pairs in each condition.}. There does not seem to
be a speed-accuracy tradeoff, since the fastest mean response time also
has the lowest error rate. Nor does there seem to be a correlation
between speed and accuracy.

\begin{figure}
\epsfbox{results.ps}
\caption{Error rates and mean response times for trials in which both
the prime and target are real words.}
\end{figure}

The analysis of the data shows a main effect of relatedness, $F(2,28) =
10.25, \,\, p < .01$. As shown in Figure 1, the direction of this effect
is that unrelated response times are bigger than strongly related
response times. Thus, the conclusion can be made, within 1\% of being
wrong, that there is a semantic priming effect which makes response time
in a lexical decision task quicker when a strongly associated prime is
presented before the target. 

Two contrasts were conducted, comparing weakly related to strongly
related and unrelated to related (collapsing over weakly and strongly
related). Assuming that they are meaningful a priori, each was
statistically significant, $F(1,14) = 9.92, \,\, p < .01$ and $F(1,14) =
11.28, \,\, p < .01$, respectively. Thus it could be concluded that
response times in strongly related trials are significantly faster than
in weakly related trials, within 1\% of being wrong, and that response
times for related pairs are significantly faster than for unrelated
pairs, again within 1\% of being wrong. Assuming that these contrasts
are done post hoc using the Scheff\'e procedure, each was statistically
significant, $F(2,28) = 9.92, \,\, critF_{Scheffe} = 8.44, \,\, p <
.025$ and $F(2,28) = 11.28, \,\, critF_{Scheffe} = 10.90, \,\, p < .01$,
respectively. Thus it could again be concluded that this comparisons are
significant. 

However, these contrasts are meaningful a priori, since it is expected
that strongly related pairs would have faster response times than weakly
related pairs and related pairs would have response times than unrelated
pairs. Therefore, the first set of F- and p-values are the relevant
values. And since these contrasts are independent\footnote{These
contrasts are independent since they are orthogonal. They are orthogonal
since the dot product of the vectors of weights associated with them
equals zero. For the first, $(s,w,u) = (-1,1,0)$ where $s$ stands for
strongly related, $w$ is weakly related, and $u$ is unrelated. For the
second, $(s,w,u) =(-1,-1,2)$. Taking the dot product: $(1,-1,0) \cdot
(-1,-1,2) = 1(-1) + -1(-1) + 0(2) = 0$.}, each of the contrasts
contributes new information to the analysis. Thus, it makes sense to say
that the lexical decision task for strongly related pairs is
significantly faster than for weakly related pairs and that it is
significantly faster for related pairs than for unrelated
pairs\footnote{A contrast that would be interesting to do is comparing
unrelated to weakly related pairs. The data from this experiment shows
only about a 4ms advantage of weakly related over unrelated.}.

\sect{Discussion}

As expected, the results show a definite effect of word association on
the lexical decision task: the stronger the association between the
prime and the target, the faster the response time for the lexical
decision task on the target. Even the difference between weakly related
pairs and strongly related pairs is statistically significant. This
semantic priming effect has been well-established by Meyer and
Schvaneveldt (1971) and others.

Meyer and Schvaneveldt (1971) proposed two models of lexical retrieval
which could explain the semantic priming effect. In the first, the
lexical decision task on the first string of letters moves ``readout''
to a certain location in memory. If the second string of letters is
semantically associated with the first, and given the assumption that
memory is organized into semantically associated sectors, then the
lexical decision task for the second string of letters involves less of
a ``shift'' of the readout to the location for the second word than if
the second word was unassociated to the first. In the
spreading-excitation model, the lexical decision task on the first
string of letters excites paths to associated words and thus the lexical
decision task for the second one is quicker if it associated with the
first and therefore can used the already excited pathways.

With the location-shifting model, one would expect that once a new prime
is presented the readout is moved to the new location and thus a lexical
decision on a word associated with the old prime should no longer take
significantly less time. In the spreading-excitation model, however, one
would expect that pathways to associated words would still be excited
(if a little less so) and thus presenting a new prime would not
completely negate the priming effect of the old prime. Given this
difference between the two models, one can construct experiments which
will show whether the semantic priming effect disappears immediately or
merely slowly dissipates with intervening words. These experiments would
involve lexical decision tasks like the ones in this experiment, but
with the associated primes being at various distances before the target.
In this way, the timing of the semantic priming effect can be
determined. If it is found that there is still a priming effect after
intervening words and tasks, then the spreading-excitation model would
seem to be more accurate than the location-shifting model\footnote{I
believe Molly Potter's lab has done experiments similar to the ones
suggested.}. 

\sect{References}

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}

\begin{center}
\begin{tabular}{|ll|ll|ll|ll|}\hline
\multicolumn{8}{|c|}{{\bf Unrelated}: prime target} \\ \hline
ape & file & apple & hatchet & axe & jelly & bacon & walnut \\ \hline
banjo & cake & beef & berry & bicep & piano & bread & cat \\ \hline
cashew & hammer & chest & almond & chest & elk & cornet & eggs \\ \hline
cow & trumpet & desk & deer & dessert & wrench & dill & pork \\ \hline
dove & butter & drill & steak & drum & salad & elm & ham \\ \hline
fig & monkey & finch & pie & galoshes & pig & grapefruit & cream \\ \hline
jam & dog & jumper & flute & kidney & melon & lemon & knife \\ \hline
leopard & plum & mallet & gravy & milk & cello & mustard & pliers \\ \hline
oriole & fox & osprey & squirrel & peach & guitar & pecan & horse \\ \hline
rat & orange & ribbon & grape & rutabega & rabbit & sauce & goat \\ \hline
screwdriver & lime & sheep & peanuts & skull & cheese & thrush & apricot \\ \hline
violin & mouse & watch & cherry & wolf & pear & woodpecker & tuba \\ \hline
\end{tabular}
\end{center}

\vspace*{.2in}

\begin{center}
\begin{tabular}{|ll|ll|ll|ll|}\hline
\multicolumn{8}{|c|}{{\bf Weakly Related}: prime target} \\ \hline
artichoke & broccoli & asparagus & celery & avocado & turnip & boots &
gloves \\ \hline
cap & socks & cauliflower & potato & eye & hair & falcon & vulture \\ \hline
hat & shoes & hip & toe & jaw & ear & lark & cardinal \\ \hline
lip & face & palm & finger & parrot & crow & parsnip & mushroom \\ \hline
pelican & ostrich & penguin & duck & sandals & mittens & seagull &
pigeon \\ \hline
stockings & shorts & throat & chin & trousers & coat & yams & tomatos \\ \hline
\end{tabular}
\end{center}

\vspace*{.2in}

\begin{center}
\begin{tabular}{|ll|ll|ll|ll|}\hline
\multicolumn{8}{|c|}{{\bf Strongly Related}: prime target} \\ \hline
arm & leg & beans & peas & blouse & sweater & chicken & turkey \\ \hline
crane & stork & eagle & hawk & elbow & knee & garlic & onion \\ \hline
hand & foot & lettuce & spinach & mouth & nose & neck & head \\ \hline
parakeet & canary & peppers & corn & pumpkin & squash & radish & carrot
\\ \hline
robin & sparrow & scarf & tie & shirt & pants & skirt & dress \\ \hline
suspenders & belt & swan & goose & vest & suit & wrist & ankle \\ \hline
\end{tabular}
\end{center}

\newpage
\sect{Appendix: Statistics}

Calculations: $MS = \frac{SS}{df}$, $F = \frac{MS_Y}{MS_{Y x SUBJ}}$,
and $p$ values were obtained from tables of F distributions in {\em The
Essence of Statistics}, except in the case of $p < .001$.

\vspace*{.2in}
\begin{tabular}{lrrrrl}
\multicolumn{6}{c}{ANOVA: Unrelated, Weakly Related, Strongly Related} \\
Source & SS & df & MS & F & p \\ \hline
mean & 17546266.3175 & 1 & 17546266.3175 & 444.9273 & 0.001 \\
related & 16168.7163 & 2 & 8084.3582 & 10.2435 & 0.01 \\
related x subj & 22098.2111 & 28 & 789.2218 & \mbox{} & \mbox{} \\
subjects & 552107.5902 & 14 & 39436.2560 & \mbox{} & \mbox{} \\
\end{tabular}

\vspace*{.2in}
\begin{tabular}{lrrrrl}
\multicolumn{6}{c}{ANOVA: Weakly Related vs. Strongly Related} \\
Source & SS & df & MS & F & p \\ \hline
mean & 11413644.9856 & 1 & 114113644.9856 & 4222.4992 & 0.001 \\
related & 10938.5873 & 1 & 10938.5873 & 9.9254 & 0.01 \\
related x subj & 15429.1176 & 14 & 1102.0798 & \mbox{} & \mbox{} \\
subjects & 378352.0040 & 14 & 27025.1431 & \mbox{} & \mbox{} \\
\end{tabular}

\vspace*{.2in}
\begin{tabular}{lrrrrl}
\multicolumn{6}{c}{ANOVA: Unrelated vs. Related} \\
Source & SS & df & MS & F & p \\ \hline
mean & 11832298.4967 & 1 & 11832298.4967 & 455.1637 & 0.001 \\
related & 4077.9845 & 1 & 4077.9845 & 11.2806 & 0.01 \\
related x subj & 5061.0668 & 14 & 361.5048 & \mbox{} & \mbox{} \\
subjects & 363939.8014 & 14 & 25995.7001 & \mbox{} & \mbox{} \\
\end{tabular}

\end{document}

