COGNITION AND EMOTION, 2000, 14 (2), 177± 191
The Adaptive Value of Lexical Connotation
in Speech Perception
Lee H. Wurm
Wayne State University, Detroit, USA
Douglas A. Vakoch
SETI Institute, Mountain View, CA, USA
Previous work (e.g. Bargh, Chaiken, Govender, & Pratto, 1992; Fazio,
Sanbonmatsu, Powell, & Kardes, 1986) has shown that objects are evaluated
on a goodness/badness dimension automatically and preconsciously. Vakoch
and Wurm (1997; Wurm & Vakoch, 1996) have found similar results using
different stimuli and tasks. They found that auditory lexical decision times
depend on dimensions of connotation (Evaluation, Potency, and Activity).
Reaction times (RTs) from these studies were interpreted in terms of the
evolutionary adaptiveness of different types of perceptual processing. The
current study introduces a new way to de® ne words, using two dimensions
(Danger and Usefulness) rather than three, that allows a direct test of the
adaptiveness model. RTs for nouns chosen for their adaptive signi® cance
were related to Usefulness and Danger, and to the interaction between
Usefulness and Danger. A database of dimension weights is included.
INTRODUCTION
Recent research has led to the notion that certain emotional or evaluative
processes are fast and automatic. For example, Murphy and Zajonc’s
(1993) affect primacy hypothesis states that affective information (e.g.
whether something is seen as positive or negative) is processed immediately by a nonconscious mental system. Bargh, Chaiken, Raymond, and
Hymes (1996) supported this hypothesis by showing that words with
similar valences primed each other in a pronunciation task, whereas words
with dissimilar valences did not. The word ¯ owers, for example, primed the
Requests for reprints should be sent to Lee H. Wurm, Department of Psychology, Wayne
State University, 71 West Warren Avenue, Detroit, MI 48202, USA; e-mail: lwurm@sun.
science.wayne.edu.
We would like to thank Annmarie Cano, Patricia Siple, and Rebecca Treiman for helpful
comments on an earlier version of this paper.
q 2000 Psychology Press Ltd
178
WURM AND VAKOCH
word knowledge, because both are judged to be positive words (see also
Bargh, 1990; Chartrand & Bargh, 1996; Fazio, 1990; Fazio, Sanbonmatsu,
Powel, & Kardes, 1986).
Bargh, Chaiken, Govender, and Pratto (1992) found that this type of
early evaluative process is general and widespread. It seems to occur for a
wide range of objects, and not only for objects that elicit a strong evaluative
attitude or for which social interactions are important. Furthermore,
although many studies have shown a link between a person’s mood or
psychiatric state on the one hand, and selective attention to mood-relevant
stimulus characteristics on the other (e.g. see Bower, 1987; MacLeod &
Mathews, 1991; Mogg, Mathews, Eysenck, & May, 1991), the Bargh et al.
(1992) study suggests that such a process occurs for everyone as a part of
normal perception. This evaluative assessment is extremely fast, occurring
automatically and preconsciously.
Findings such as these may re¯ ect the net result of the evolutionary
history of humans and human predecessors. At various times in the life of
an organism, survival depends on drawing on representations of the goodness or badness of something in the environment. How best, though, can
we represent these aspects of what various objects mean to an organism?
Anderson (1991) has highlighted the computational bene® ts of organising
information in terms of a small number of relevant dimensions, rather than
maintaining a large number of categories. Such dimensional coding would
allow a ® rst-pass, rough-grained analysis in perception that could be
extremely valuable for survival. We used this dimensional approach in
our previous two studies. One of the goals of the current study is to
investigate further what the relevant dimensions of meaning might be.
The idea that language evolved through the process of natural selection
has been argued since the late 19th century (e.g. Darwin, 1871). The
position taken in the current study is that the transfer of information is
adaptive insofar as it contributes to two goals: to gain bene® cial resources
and to avoid danger (see Darwin, 1859/1968). The importance of these
goals was central to Schneirla’s (1965) theory of biphasic approach/
withdrawal processes, and Davidson (1992, p. 259) has more recently
noted: ``To approach or to withdraw is the fundamental adaptive decision
in situations or conditions that have recurred during our evolutionary
past’ ’ .
The preceding arguments suggest that one might be able to measure
effects of survival-relevant dimensions in a range of perceptual tasks. In
one study of emotion words (Wurm & Vakoch, 1996) we examined the
relationship between lexical decision times and the dimensions of Evaluation, Potency, and Activity. These dimensions, identi® ed by Osgood (1969;
Osgood, May, & Miron, 1975; Osgood, Suci, & Tannenbaum, 1957), are
useful for understanding a range of judgement processes (e.g. Apple &
ADAPTIVE VALUE OF CONNOTATION
179
Hecht, 1982; Daly, Lancee, & Polivy, 1983; Green & Cliff, 1975). We found
that emotion words that connote bad (low Evaluation), strong (high
Potency), and fast (high Activity) had the quickest lexical decision times,
when word frequency was controlled. This pattern is consistent with the
adaptive goal of avoiding danger: Words with bad, strong, and fast
connotations may refer to objects in the environment that pose serious
threats to the organism. Clearly, these would be things to avoid if possible.
In another study (Vakoch & Wurm, 1997) we examined words in general
(rather than emotion words) and found that lexical decision times were
fastest for words rated high on both Evaluation and Activity, or high on
both Evaluation and Potency. This pattern is consistent with an adaptive
response strategy in which the goal is to gather valuable resources. Words
with these connotations (i.e. good plus fast, or good plus strong) may refer
to valuable objects that have to be seized quickly or lost. Thus, it appears
that there is differential processing of emotion words and words in general.
However, the dimensions of Evaluation, Potency, and Activity cannot
always be interpreted unambiguously. As a case in point, the low end of the
Evaluation dimension can describe a range of constructs that are each
evolutionarily important. Something can be bad and dangerous (e.g. lion
or cancer), or bad and harmless (e.g. skunk or mosquito); something can be
bad and useful (e.g. knife or syringe), or bad and useless (e.g. scorpion or
quicksand). The evolutionary signi® cance of words, de® ned using Osgood’s
dimensions, is always somewhat vague.
The current study therefore sought to test the dimensions of Danger and
Usefulness, which have more direct relevance to human survival than
Evaluation, Potency, and Activity do. The goals of this study are twofold. First, we wanted to disambiguate what is meant by the dimensions
of Evaluation, Potency, and Activity. Doing this should allow us to achieve
our second purpose, which is to examine perceptual behaviour when there
is con¯ ict or disagreement between the cues of Danger and Usefulness. An
object that is highly useful and not at all dangerous may be unambiguously
desirable, but what happens when the object is both useful and dangerous?
Before the main experiment could be run, stimuli needed to be selected
and normed on Danger and Usefulness.
PRELIMINARY RATING STUDY
Method
Participants. Participants were 32 undergraduate psychology students.
All were native speakers of English. They received extra credit in a
psychology course for their participation.
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WURM AND VAKOCH
Materials. A total of 200 nouns were generated by the authors and two
research assistants. We wanted nouns with referents that could be seen as
either dangerous or not dangerous, and either useful or not useful. We
attempted to choose words so that considering the entire set, we would be
likely to get participant judgements that spanned the full range from low to
high Danger, and low to high Usefulness.
One hundred of the resulting nouns were selected at random to be
changed into pseudowords. To accomplish this, the phoneme at each
word’s uniqueness point (UP) was changed to a different phoneme from
the same broad class (e.g. stop consonants replaced stop consonants, and
nasals replaced nasals). The UP is the point in the acoustic signal where the
word in question diverges from all other words in the language (see
Marslen-Wilson, 1984; Marslen-Wilson & Welsh, 1978). For example, the
word chemical was changed to the pseudoword *chemital. This is the same
method we used for creating pseudowords in our previous studies. A
complete list of the stimuli can be found in the Appendix.
Procedure. Rating packets were printed that contained the 100 words to
be used in the main experiment. Each rating packet was printed in one of six
different random orders. Participants were asked to rate each word on
Danger and on Usefulness, using an 8-point scale whose extreme points
were labeled ``not dangerous to human survival’’ (1) and ``dangerous to
human survival’’ (8); or ``useless for human survival’’ (1) and ``useful for
human survival’’ (8). The order in which ratings were given was randomised.
Results and Discussion
The mean rating on Danger for the 100 words was 3.47 (SEM 5 .22), and
on Usefulness it was 3.52 (SEM 5 .18). Items covered almost the entire
range from 1 to 8 on both dimensions, and the distributions of ratings were
similar for the two. The least dangerous item was judged to be constellation
(mean rating 5 1.07), whereas the most dangerous items were cancer and
plague (mean rating 5 7.81 for both). The least useful items were judged to
be mugger and plague (mean rating 5 1.09 for both), whereas the most
useful items were food and water (mean rating 5 7.97 for both). Mean
Danger and Usefulness ratings for the 100 words were moderately but
signi® cantly correlated with each other (r 5 2 .25, P < .05).
CALCULATION OF POTENTIAL
CONTROL VARIABLES
Many variables are known to affect word recognition performance. Given
the present research questions, it was not possible to equate stimuli on all
of these variables; doing so would have left too few stimuli to work with.
ADAPTIVE VALUE OF CONNOTATION
181
Instead, we controlled for these by including some of them as factors in our
regression model in the main reaction time experiment, along with Danger
and Usefulness.
Eight classes of such variables were identi® ed. The following paragraphs
list these classes of variables and describe how each was computed.
1. Word frequency was taken from Francis and KucË era (1982). Highfrequency words are generally identi® ed more quickly than low-frequency
words.
2. Word length was computed two ways for each word, in milliseconds and
in number of phonemes. Longer words tend to require more time to recognise.
3. Word onset characteristics can in¯ uence the ease (and thus the speed)
of lexical processing. Three different subclasses were used: vowel versus
consonant onset; place-of-articulation of the onset phoneme; and voicing
of the onset phoneme.
4. Bigram frequency is a measure of how frequently pairs of letters
occur, as a function of letter position and word length. This was calculated
for each word using the tables of Mayzner and Tresselt (1965). Pairs of
letters that occur together very frequently tend to be processed more
quickly than unusual pairs.
5. First-syllable stress was coded as either strong (1) or weak (0). Some
models of speech perception make a distinction between items with strong
® rst syllables and those with weak ® rst syllables (e.g. Cutler & Norris, 1988;
Grosjean & Gee, 1987), and stress has been shown to in¯ uence lexical
decision times (e.g. Wurm, 1997).
6. Neighbourhood density refers to the number of existing words that are
orthographically or phonetically similar. There is abundant evidence that
processing dif® culty for a given word depends on the number of neighbours
it has (e.g. Coltheart, Davelaar, Jonasson, & Besner, 1977; Goldinger, Luce,
& Pisoni, 1989; Luce, Pisoni, & Goldinger, 1990).
Four different measures of neighbourhood density were computed. One
of the measures used in the present study was Coltheart’s N (Coltheart et
al., 1977), which is the number of words that can be made from a given
target word by the substitution of one letter, preserving letter position. For
example, if the target word is pin, then pen, win, and pit (along with many
others) would be included in the list of neighbours.
The second approach is like Coltheart’s N, but also allows for the
addition or deletion of one letter at the beginning or end of a word. Using
the foregoing example, we would also add to the list of neighbours words
such as spin and pins.
For both of these measures, the summed frequency of all of a word’s
neighbours was also computed. This gives a second, frequency-weighted
version of each density measure.
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7. Familiarity, concreteness, imagery, and meaningfulness were taken
from the Oxford Psycholinguistic Database (Quinlan, 1987). This database
includes two different measures of meaningfulness, both of which were
used in the preliminary analyses. Words that are rated as more familiar,
more concrete, more imageable, or more meaningful tend to be recognised
more quickly.
8. Animate or inanimate referent was also included. It is possible that
words referring to animate objects would require less processing time than
words referring to inanimate objects.
These eight classes of variables contain a total of 18 variables, the effects
of which were assessed prior to the main regression analysis. Any of these
variables that were signi® cantly related to reaction time (RT) performance
were included as factors in the regression model, except in the cases where
there were multiple measures of the same construct. In such a case, if more
than one measure (e.g. of neighbourhood density) was signi® cant, the
measure that had the strongest association with RTs was retained.
LEXICAL DECISION STUDY
This experiment provided a direct test of the adaptiveness model we have
discussed above, de® ning word referents in terms of Danger and Usefulness
rather than Evaluation, Potency, and Activity. Rede® ning words in this way
should make the results more readily interpretable than those of our
previous studies.
The major questions addressed by this study are: Controlling for the
effects of frequency, length, and other important characteristics of words,
will the judged Danger and Usefulness of those words have any effects on
RTs?; and if so, will responses be characterised by a danger avoidance
motive or by a resource gathering motive?
Method
Participants. Participants were 70 undergraduate psychology students.
All were native speakers of English with normal hearing. They received
extra credit in a psychology course for their participation.
Materials. The words and pseudowords described in the previous
Materials subsection were used. Each stimulus was read by a male native
speaker of English who was unfamiliar with the purpose of the study,
digitised at a sampling rate of 10KHz (low-pass ® ltered at 4.8KHz) and
stored in a disk ® le.
ADAPTIVE VALUE OF CONNOTATION
183
Procedure. Digitised speech ® les were played for the participants over
headphones at a comfortable listening level, with a delay of 1500msec
between items. Participants were directed to make a speeded lexical
decision about each item. Each participant used his/her dominant hand
to make responses on a button board, pushing one button for words and
another for pseudowords. Participants were tested in groups of one to three
in a sound-attenuating chamber. The order of stimulus presentation was
randomised for each group of participants.
RTs were measured from the uniqueness point (UP) of each word, which
was located by both auditory and visual criteria using a commercial
waveform editor.
Results and Discussion
Participants whose responding was excessively slow (mean RT > 1000msec)
or inaccurate (error rate > .15) were excluded from the analyses. Five
participants’ data were excluded by these criteria. For the remaining 65
subjects, data were discarded for trials on which the word/pseudoword
decision was made incorrectly (6.5% of the data). The dependent variable
in all analyses was RT on correct trials.
Identi® cation of Relevant Control Variables. The relevant control variables were identi® ed for inclusion in the regression equation by the following process. First, the mean RT for each item was calculated for the 65
subjects (on correct trials only). Next, the correlations between mean RT
and each of the 18 potential control variables were calculated. Control
variables were retained for use in the main regression analysis if they were
signi® cantly correlated with mean RT, with the stipulation that multiple
measures of the same construct were excluded. For example, word length
was signi® cantly correlated with mean RT whether de® ned in terms of
milliseconds or number of phonemes. However, only the phoneme
measure was retained, because its bivariate association with mean RT
was the stronger of the two.
Five control variables emerged from this preliminary analysis. Table 1
lists these variables and shows how they correlate with each other and with
mean RT.
Main Regression Analysis. The analytic strategy used in the current
study parallels that of our earlier studies (Vakoch & Wurm, 1997; Wurm &
Vakoch, 1996). Lorch and Myers (1990) provide a discussion of repeatedmeasures regression analyses in cognitive research that is extremely
valuable in the present context (see also Cohen & Cohen, 1983).
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WURM AND VAKOCH
TABLE 1
Bivariate Intercorrelations for Control Variables and Mean ReactionTime
(RT)
Length
Familiarity
Word frequency
No. of neighbours
Stress
Famil.
Word
Freq.
No. of
Neighbours
Stress
Mean
RT
2 .40***
±
±
±
±
2 .14
.27**
±
±
±
2 .61***
.34**
.21*
±
±
2 .49***
.10
.09
.31**
±
.48***
2 .43***
2 .28**
2 .41***
2 .20*
*P < .05; **P < .01; ***P < .001.
A hierarchical multiple regression analysis was conducted, in which the
main effects of Danger and Usefulness were assessed, along with their
interaction. The results of this analysis are shown in Table 2. Variables
were entered in four steps, with simultaneous entry for all variables within
a given step. Step 1 of the analysis was to partition the between-subjects
variance. In repeated-measures regression analyses this is done by entering
a block of 64 (i.e. N2 1) dummy variables that represent the participants.
At Step 2 of the analysis, the ® ve control variables were entered: word
length (B 5 16.52, SE B 5 1.56, b 5 .17); familiarity (B 5 2 0.01, SE B 5
0.01, b 5 2 .11); log word frequency (B 5 2 24.49, SE B 5 3.55, b 5 2 .09);
number of neighbours (B 5 2 2.85, SE B 5 0.55, b 5 2 .07); and stress
(B 5 4.32, SE B 5 6.25, b 5 .01).
Step 3 of the analysis assessed the main effects of Danger and Usefulness (entering Danger and Usefulness in a separate step after the control
variables was done solely for conceptual clarityÐ entering them in the same
step as the control variables [i.e. collapsing Steps 2 and 3], does not change
their regression coef® cients). Higher ratings on both Danger and
Usefulness were associated with faster RTs [B 5 2 2.13, SE B 5 0.95,
b 5 2 .03, F(1,64) 5 6.75, P < .05; and B 5 2 10.48, SE B 5 1.22, b 5 2 .11,
F(1,64) 5 106.56, P < .001, respectively]. The fact that the regression
coef® cient ( b ) for Usefulness is 3.7 times larger than that for Danger will
guide our interpretation later (Pedhazur, 1982).
Step 4 of the analysis assessed the Danger 3 Usefulness interaction,
entered as the product of the two ratings for each word. The interaction
was signi® cant [B 5 2.84, SE B 5 0.52, b 5 .14, F(1,64) 5 34.00, P < .001].
The remaining values in Table 2 provide the appropriate error terms for the
three effects of interest (see Lorch & Myers, 1990).
The signi® cant interaction means that the slope of the relationship
between RTs and Danger varies signi® cantly, depending on the particular
value of Usefulness (or conversely, that the slope of the relationship
between RTs and Usefulness varies, depending on the value of Danger).
ADAPTIVE VALUE OF CONNOTATION
185
TABLE 2
Summary of Hierarchical Regression Analysis for Variables
Predicting Reaction Time
Variable(s)
df
Mean
Square
Between Subjects
Control Variables
Main Effects
Danger
Usefulness
Danger 3 Usefulness
Error Terms
Subjects 3 Danger
Subjects 3 Usefulness
Subjects 3 Danger 3 Usefulness
64
5
2
1
1
1
458,538
3,842,602
802,557
109,835
1,596,586
638,510
64
64
64
14,984
16,264
18,777
F
6.75*
106.56***
34.00***
*P < .05; ***P < .001.
Figure 1 shows two regression lines for our data set and illustrates this
change in slope.
The four values shown in Fig. 1 were computed using the regression
equation. In each of the four computations, the mean value for each of the
® ve control variables was multiplied by its corresponding regression coef® cient. The values that were multiplied by the Danger, Usefulness, and
Danger 3 Usefulness coef® cients changed, depending on the level of
Danger or Usefulness currently under consideration. For High we used
the mean rating plus one standard deviation and for Low we used the mean
rating minus one standard deviation, which gives the four combinations of
Danger and Usefulness shown in the ® gure (see Aiken & West, 1991;
Cohen & Cohen, 1983).
The main effect of Usefulness in Fig. 1 is striking; words with referents
that are useful have much faster RTs (an average of 34msec faster, for the
two lines plotted). The effect of Danger, although it was statistically
signi® cant, is small by comparison (3msec).
One always needs to interpret main effects in light of any signi® cant
interactions, however. Figure 1 suggests that the meaning of the Danger
dimension to the organism is different in the context of high Usefulness
than in the context of low Usefulness. For items that are of little use,
increasing Danger has the effect of speeding the organism’s response.
This ® nding has a natural explanation in terms of adaptiveness and
survival: Dangerous things call for quick decisions. On the other hand,
for words with highly useful referents, increasing Danger slows the
organism’s response. For these cases, there is a con¯ ict: The items in
question are useful and thus desirable, but they are also dangerous, and
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WURM AND VAKOCH
FIG. 1. Regression lines showing the results of the reaction time analysis. Predicted reaction
time (RT) is shown, in milliseconds (msec), as a function of dimension weight on Danger and
Usefulness. For Danger and Usefulness, High means that a value 1 standard deviation above
the mean for that dimension was used in plotting a particular point. Low means that a value 1
standard deviation below the mean was used (see Aiken & West, 1991; Cohen & Cohen, 1983).
thus to be avoided if possible. Con¯ icting responses are called into play,
which slows down processing.
The extreme values represented in Fig. 1 present interesting cases. One
would predict on the basis of a resource-gathering goal that items that are
highly useful and not dangerous would be responded to very quickly. These
words refer to objects that are unambiguously desirable and that might
speed away if action were not taken quickly (e.g. ® sh or chicken). As
inspection of the ® gure shows, these items tend to have the fastest RTs.
The theory being put forward does not make strong predictions about
words whose referents are low on both Danger and Usefulness. These refer
to objects that need not be avoided, but at the same time, there is no
particular reason to try to obtain them (e.g. balloon, lint, or dust). These
words have the longest predicted RT in Fig. 1.
What about objects that are unambiguously dangerous? One might have
predicted that items with low Usefulness and high Danger would have the
fastest predicted RTs, but as the ® gure shows, they instead have mid-range
predicted RTs. Our interpretation of this is that the classi® cation of something along the Usefulness dimension takes precedence; classi® cation along
the Danger dimension, although certainly important, is not as important.
ADAPTIVE VALUE OF CONNOTATION
187
GENERAL DISCUSSION
The results of the current study provide evidence for the hypothesised
importance of Danger and Usefulness as dimensions underlying lexical
access, at least for nouns with referents that can be classi® ed on those
dimensions. References to objects in the external world may be most salient
in terms of their Usefulness to an organism, but the relationship is modulated by Danger: Lexical decision times are fastest for words with referents
that are useful and not particularly dangerous. This pattern of results
suggests that recognition times for these words are characterised by the
resource gathering motive rather than the danger avoidance motive.
The idea that constructs such as Danger and Usefulness should influence the perception of spoken language may not be obvious. Language is,
after all, a relative newcomer in evolutionary history. However, paying
attention to the Danger and Usefulness of objects in the external world
has served organisms well for millions of years. This strategy would not
(and could not) be abandoned simply because organisms stood erect and
began to speak. Our position is that language developed on top of this
existing, long-standing framework of survival goals and strategies.
As noted in the Introduction, we might expect some overlap between
Osgood’s Evaluation dimension and the dimensions used in the current
study. After all, the impetus for the current study was our earlier ® ndings
using Evaluation, Potency, and Activity. However, the current study more
directly tests a speci® cally evolutionary model because Danger and Usefulness were rated explicitly with respect to human survival (e.g. endpoints of
the Danger rating scale were labelled ``not dangerous to human survival’’
and ``dangerous to human survival’’ ).
The results of the current study seem to agree with those of the Vakoch
and Wurm (1997) study of words from the general lexicon. In that study,
participants responded most quickly to words that were either high on both
Evaluation and Activity, or high on both Evaluation and Potency. The
interpretation of those results emphasised the approach motive. The
current study addresses this earlier study’s chief limitation, which was
the ambiguity about how to interpret the endpoints of dimensions.
Because the current study made use of the dimensions of Danger and
Usefulness, the interpretational dif® culties inherent in explaining adaptive
strategies with reference only to Osgood’s three dimensions are avoided.
The similarity between the words in the current study and those in the
study of the general lexicon (Vakoch & Wurm, 1997), may explain why the
same adaptive goal should be evident in both. All words in the current
study and 77% of the words in the general lexicon study were nouns. Most
nouns make reference to objects or concepts external to the individual, and
it is precisely these cases where one would expect to ® nd evidence of the
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WURM AND VAKOCH
resource desirability motive. In contrast, all of the target words in the
Wurm and Vakoch (1996) study of emotion words were adjectives, which
may modify either external referents (e.g. ``the sad dog’ ’ ) or subjective
emotional experiences (e.g. ``I feel sad ’ ’). In either case, the contact with
the external world is less direct than it is for nouns. It is also worth noting
that the emotion words used were particularly appropriate for identifying
the avoidance goal, because they were speci® cally selected to cover the full
range of the ® ght-¯ ight dimension of Potency.
Another possible explanation for the difference between the studies is
the method by which the stimuli were presented. A secondary purpose of
the study of emotion words was to look for an auditory Stroop effect in
cases where the semantic content of a word con¯ icts with the tone of voice
in which the word is spoken. The emotion words were presented in carrier
phrases that had intonation contours characteristic of several basic
emotions, which could be either similar to or different from the semantic
content of the word. For example, the word happy was for some participants presented in a carrier phrase designed to convey a sense of happiness,
whereas other participants heard the same word embedded in carrier
phrases that were in tones of voice conveying other emotions such as
disgust or fear. Future research should explore this possible explanation
for the differential processing that was found.
Manuscript received 23 October 1998
Revised manuscript received 28 May 1999
REFERENCES
Aiken, L.S., & West, S.G. (1991). Multiple regression: Testing and interpreting interactions.
Newbury Park, CA: Sage.
Anderson, J.R. (1991). The adaptive nature of human categorization. Psychological Review,
98, 409± 429.
Apple, W., & Hecht, K. (1982). Speaking emotionally: The relation between verbal and vocal
communication of affect. Journal of Personality and Social Psychology, 42, 864± 875.
Bargh, J.A. (1990). Auto-motives: Preconscious determinants of social interaction. In E.T.
Higgins & R.M. Sorrentino (Eds.), Handbook of motivation and cognition (Vol. 2, pp. 93±
130). New York: Guilford Press.
Bargh, J.A., Chaiken, S., Govender, R., & Pratto, F. (1992). The generality of the automatic
attitude activation effect. Journal of Personality and Social Psychology, 62, 893± 912.
Bargh, J.A., Chaiken, S., Raymond, P., & Hymes, C. (1996). The automatic evaluation effect:
Unconditional automatic attitude activation with a pronunciation task. Journal of Experimental Social Psychology, 32, 104± 128.
Bower, G.H. (1987). Commentary on mood and memory. Behaviour Research and Therapy,
25, 443± 455.
Chartrand, T.L., & Bargh, J.A. (1996). Automatic activation of impression formation and
memorization goals: Nonconscious goal priming reproduces effects of explicit task
instructions. Journal of Personality and Social Psychology, 71, 464± 478.
ADAPTIVE VALUE OF CONNOTATION
189
Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the
behavioural sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
Coltheart, M., Davelaar, E., Jonasson, J.T., & Besner, D. (1977). Access to the internal
lexicon. In S. Dornic (Ed.), Attention and performance (Vol. VI, pp. 535± 555). Hillsdale,
NJ: Erlbaum.
Cutler, A., & Norris, D. (1988). The role of strong syllables in segmentation for lexical access.
Journal of Experimental Psychology: Human Perception and Performance, 14, 113± 121.
Daly, E.M., Lancee, W.J., & Polivy, J. (1983). A conical model for the taxonomy of emotional
experience. Journal of Personality and Social Psychology, 45, 443± 457.
Darwin, C. (1968). The origin of species (J.W. Burrow, Ed.). New York: Penguin. (Original
work published 1859).
Darwin, C. (1871). The descent of man. London: John Murray.
Davidson, R. J. (1992). Prolegomenon to the structure of emotion: Gleanings from neuropsychology. Cognition and Emotion, 6, 245± 268.
Fazio, R.H. (1990). Multiple processes by which attitudes guide behaviour: The MODE
model as an integrative framework. In M.P. Zanna (Ed.), Advances in experimental
psychology (Vol. 23, pp. 75± 109). San Diego, CA: Academic Press.
Fazio, R.H., Sanbonmatsu, D.M., Powell, M.C., & Kardes, F.R. (1986). On the automatic
activation of attitudes. Journal of Personality and Social Psychology, 5, 229± 238.
Francis, W.N., & KucË era, H. (1982). Frequency analysis of English usage: Lexicon and
grammar. Boston, MA: Houghton Mif¯ in.
Goldinger, S.D., Luce, P.A., & Pisoni, D.B. (1989). Priming lexical neighbors of spoken
words: Effects of competition and inhibition. Journal of Memory and Language, 28,
501± 518.
Green, R.S., & Cliff, N. (1975). Multidimensional comparisons of structures of vocally and
facially expressed emotion. Perception and Psychophysics, 17, 429± 438.
Grosjean, F., & Gee, J. (1987). Prosodic structure and spoken word recognition. Cognition,
25, 135± 155.
Lorch, R.F., & Myers, J.L. (1990). Regression analyses of repeated measures data in cognitive
research. Journal of Experimental Psychology: Learning, Memory and Cognition, 16,
149± 157.
Luce, P.A., Pisoni, D.B., & Goldinger, S.D. (1990). Similarity neighborhoods of spoken
words. In G.T.M. Altmann (Ed.), Cognitive models of speech processing: Psycholinguistic
and computational perspectives. Cambridge, MA: MIT Press.
MacLeod, C., & Mathews, A. (1991). Biased cognitive operations in anxiety: Accessibility of
information or assignment of processing priorities? Behaviour Research and Therapy, 29,
599± 610.
Marslen-Wilson, W.D. (1984). Function and process in spoken word recognition. In
H. Bouma & D.G. Bouwhuis (Eds.), Attention and performance: Control of language
processes (Vol. X, pp. 125± 150). Hove, UK: Lawrence Erlbaum Associates Ltd.
Marslen-Wilson, W., & Welsh, A. (1978). Processing interactions and lexical access during
word recognition in continuous speech. Cognitive Psychology, 10, 29± 63.
Mayzner, M.S., & Tresselt, M.E. (1965). Tables of single-letter and digram frequency counts
for various word-length and letter-position combinations. Psychonomic Monograph
Supplements, 1, 13± 32.
Mogg, K., Mathews, A., Eysenck, M.W., & May, J. (1991). Biased cognitive operations in
anxiety: Artifact, processing priorities, or attentional search? Behaviour Research and
Therapy, 29, 459± 467.
Murphy, S.T., & Zajonc, R.B. (1993). Affect, cognition, and awareness: Affective priming
with optimal and suboptimal stimulus exposures. Journal of Personality and Social
Psychology, 64, 723± 739.
190
WURM AND VAKOCH
Osgood, C.E. (1969). On the whys and wherefores of E, P, and A. Journal of Personality and
Social Psychology, 12, 194± 199.
Osgood, C.E., May, W.H., & Miron, M.S. (1975). Cross-cultural universals of affective meaning. Urbana, IL: University of Illinois Press.
Osgood, C.E., Suci, G.J., & Tannenbaum, P.H. (1957). The measurement of meaning. Urbana,
IL: University of Illinois Press.
Pedhazur, E.J. (1982). Multiple regression in behavioural research: Explanation and prediction (2nd ed.). New York: Holt, Rinehart & Winston.
Quinlan, Philip T. (1987). Oxford Psycholinguistic Database [Computer Software]. Oxford,
UK: Oxford University Press.
Schneirla, T.C. (1965). Aspects of stimulation and organisation in approach/withdrawal
processes underlying vertebrate behavioural development. Advances in the Study of
Behavior, 1, 1± 74.
Vakoch, D.A., & Wurm, L.H. (1997). Emotional connotation in speech perception: Semantic
associations in the general lexicon. Cognition and Emotion, 11, 337± 349.
Wurm, L.H. (1997). Auditory processing of pre® xed English words is both continuous and
decompositional. Journal of Memory and Language, 37, 438± 461.
Wurm, L.H., & Vakoch, D.A. (1996). Dimensions of speech perception: Semantic associations in the affective lexicon. Cognition and Emotion, 10, 409± 423.
APPENDIX
Stimuli used in the main reaction time experiment
Word
ant
appendix
apple
arrow
badger
balloon
banana
basket
blanket
bottle
buffalo
burglar
butter¯ y
cancer
cannon
canteen
canyon
card
carrot
chicken
clothing
club
Mean
Danger
Rating
Mean
Usefulness
Rating
1.97
2.25
1.22
6.09
2.97
1.84
1.16
1.19
1.31
2.69
3.44
6.50
1.12
7.81
6.62
1.62
3.53
1.16
1.38
1.97
1.41
4.53
2.00
3.22
5.97
4.72
2.73
1.81
5.97
3.66
6.03
4.66
3.88
1.16
2.19
1.28
2.78
5.22
2.09
1.81
5.34
5.88
6.88
3.28
Word
collision
constellation
cork
corner
crime
crossbow
crow
desert
desk
dust
dynamite
eagle
echo
egg
electricity
elephant
® re
® sh
¯ ag
food
fork
hammer
Mean
Danger
Rating
Mean
Usefulness
Rating
6.34
1.07
1.22
1.72
6.81
5.61
1.97
3.69
1.56
1.88
7.28
2.20
1.25
1.69
5.00
3.50
7.03
2.50
1.91
1.94
2.56
3.97
1.62
2.69
2.69
2.34
1.81
4.81
2.47
1.91
2.97
1.41
3.16
3.47
1.44
5.50
6.03
3.19
7.16
5.47
2.25
7.97
3.91
4.56
ADAPTIVE VALUE OF CONNOTATION
Word
hook
hurricane
joke
kerosene
knife
ladder
lamp
lava
leaf
lightning
lint
lion
machete
moose
mugger
nail
needle
ointment
opera
parsley
pesticide
philosophy
pin
plague
poetry
poison
pollution
potato
Mean
Danger
Rating
Mean
Usefulness
Rating
4.12
7.06
1.50
4.94
6.38
2.44
1.59
6.06
1.56
6.06
1.34
5.75
6.12
3.75
6.75
3.94
3.41
1.66
1.25
1.12
5.69
1.62
2.72
7.81
1.12
7.59
5.94
1.31
4.34
1.56
3.97
4.78
5.75
3.25
4.41
1.50
4.75
2.72
1.22
3.03
3.91
3.16
1.09
4.78
5.00
5.25
1.81
3.25
3.34
2.62
2.66
1.09
2.00
1.41
1.38
6.03
Word
quicksand
rabbit
razor
scorpion
shoe
skunk
snake
spear
spoon
stove
strawberry
sunset
syringe
tarantula
telescope
thorn
tiger
toad
tobacco
tonsil
tornado
toxin
tree
twig
waltz
water
wood
wool
191
Mean
Danger
Rating
Mean
Usefulness
Rating
5.94
1.72
5.34
6.44
1.66
1.78
5.84
6.28
1.56
3.31
1.34
1.16
5.28
5.81
1.34
2.59
6.12
1.97
6.44
2.44
7.00
7.22
2.72
1.38
1.09
3.19
1.91
1.22
1.19
3.84
4.00
1.94
5.12
2.31
2.28
5.72
3.4
5.69
5.16
3.50
5.75
1.94
2.34
1.69
2.97
2.16
1.53
3.44
1.41
1.38
6.88
2.75
1.78
7.97
6.91
5.41
Pseudowords used: *abufe, *ain, *armo, *arthrotis, *bacperia, *bedpug, *buffeen, *bullek,
*burdel, *butchel, *buttol, *cacsus, *carpek, *cas, *cazern, *chalt, *chemital, *chenotherapy,
*cleavel, *coj, *commitrent, *corm, *cos, *crig, *cruj, *cuj, *diager, *dod, *dollal, *dunch,
*elt, *fod, *gachelle, *gase, *grach, *haim, *hawm, *horche, *hornep, *impappe, *insolnia,
*jaim, *lavelder, *laja, *locomopive, *maif, *medichine, *mirrot, *morphone, *mosquoto,
*motorkycle, *muse, *musip, *nabe, *navo, *nep, *nesk, *nuneral, *onuf, *oxypen, *painking, *papen, *pencim, *penno, *pid, *pillay, *pozerty, *prepalation, *propatanda, *restaureent, *roke, *rusp, *ruv, *salvotion, *sheeg, *slizer, *smope, *spinack, *stalepate,
*steamdoller, *stobe, *stooke, *syphirris, *thiftle, *thulder, *thun, *tightripe, *toothkrush,
*tordedo, *towen, *trag, *trassic, *turple, *uver, *vampare, *vilus, *vulpure, *wereyolf,
*wheem, *wristyatch.
Note: Danger and Usefulness were rated on scales from 1 to 8 (N 5
32 participants).