The Role of Semantic Features in Verb Processing | SpringerLink

J Psycholinguist Res (2008) 37:199–217
DOI 10.1007/s10936-007-9066-7
ORIGINAL ARTICLE
The Role of Semantic Features in Verb Processing
Isabelle Bonnotte
Published online: 6 November 2007
© Springer Science+Business Media, LLC 2007
Abstract The present study examined the general hypothesis that, as for nouns, stable
representations of semantic knowledge relative to situations expressed by verbs are available
and accessible in long term memory in normal people. Regular associations between verbs
and past tenses in French adults allowed to abstract two superordinate semantic features in
the representation of verb meaning: durativity and resultativity. A pilot study was designed
to select appropriate items according to these features: durative, non-resultative verbs and
non-durative, resultative verbs. An experimental study was then conducted to assess semantic priming in French adults with two visual semantic-decision tasks at a 200- and 100-ms
SOA. In the durativity decision task, participants had to decide if the target referred to a
durable or non-durable situation. In the resultativity decision task, they had to decide if
it referred to a situation with a directly observable outcome or without any clear external
outcome. Targets were preceded by similar, opposite, and neutral primes. Results showed
that semantic priming can tap verb meaning at a 200- and 100-ms SOA, with the restriction
that only the positive value of each feature benefited from priming, that is the durative and
resultative values. Moreover, processing of durativity and resultativity is far from comparable since facilitation was shown on the former with similar and opposite priming, whereas it
was shown on the latter only with similar priming. Overall, these findings support Le Ny’s
(in: Saint-Dizier, Viegas (eds) Computational lexical semantics, 1995; Cahier de Recherche
Linguistique LanDisCo 12:85–100, 1998; Comment l’esprit produit du sens, 2005) general
hypothesis that classificatory properties of verbs could be interpreted as semantic features
and the view that semantic priming can tap verb meaning, as noun meaning.
Keywords Verb processing · Semantic features · Visual semantic-decision tasks · Semantic
priming paradigm · Similar and opposite priming context
I. Bonnotte (B)
Unité de Recherches sur l’Evolution du Comportement et l’Apprentissage (URECA EA 1059),
UFR de Psychologie, Université Charles de Gaulle Lille 3, Pont de Bois, B.P. 60149,
59653 Villeneuve d’Ascq cedex, France
e-mail: [email protected]
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Introduction
How does conceptual knowledge affect verb processing? Is it similar to noun processing?
One of the common thread of theories of semantic memory is that word meaning is viewed
as represented, at least in part, in terms of semantic features (spreading activation theories:
e.g., Collins and Loftus 1975; compound-cue theories: e.g., Ratcliff and McKoon 1988; distributed network models: e.g., Cree et al. 1999; McRae and Boisvert 1998; McRae et al.
1997a, 1999; Masson 1995; Plaut 1995; Plaut and Booth 2000). The main goal of the present study of verbal semantics was to investigate the dynamics of computing verb meaning.
Namely, the current project aimed at determining how people understand verbs and whether
verb meaning is computed in the same way as noun meaning. If semantic features underlie
people’s understanding of verb meaning, how do people represent and retrieve them in long
term memory when they process verbs?
Brief Review of Empirical Works on Semantic Memory
Research on semantic processing has concentrated mainly on noun concepts. Since the seminal research of semantic priming by Meyer and Schvaneveldt (1971), the semantic memory
literature abounds with studies devoted to the impact of the semantic relation between two
concepts on semantic processing. Semantic priming occurs for word pairs that are either
purely semantically related (e.g., categorically related) or associatively related in a variety
of tasks (Becker 1980; Meyer et al. 1975).
Association strength is classically conceived as resulting from temporal contiguity in
speech or text (McKoon and Ratcliff 1992), or word co-occurrence within proposition
(McNamara 1992). By contrast, pure semantic relatedness is commonly defined and measured as feature overlap, and gave rise, in the last years, to an increasing amount of research.
Most of these research concerned adults (Cree et al. 1999; de Mornay Davies and Funnell
2000; Lund et al. 1995, 1996; McRae and Boisvert 1998; McRae et al. 1997a, 1999; Moss
et al. 1995; Perea and Rosa 2002; Plaut 1995; Plaut and Booth 2000; Rosch and Mervis
1975; Rosch et al. 1976; Shelton and Martin 1992; Williams 1996). These online studies
used the semantic priming paradigm, in which a trial consists of two events presented in the
center of a screen computer. A semantic context is provided by the presentation of a first
series of letters (the prime), which generally requires no overt response. It is then replaced
by a second series of letters (the target). The interval between the prime and the onset of the
target is called the SOA (Stimulus Onset Asynchrony). In the literature, two types of SOA
are contrasted: short and long SOAs. Very briefly, at short SOAs (less than 250 ms), semantic
priming could result from automatic semantic processing, whereas at long SOAs, it is essentially due to generating expectancies (e.g., de Groot 1984; den Heyer et al. 1983; Neely 1977,
1991; Posner and Snyder 1975). The semantic priming paradigm can be associated with the
lexical decision task (participants have to decide if the target is a word or not), the naming
task (participants have to say the target aloud), or a semantic decision task (participants have
to decide if the target is characterized by a particular semantic feature; e.g., ‘is it animate?’,
‘is it an object?’).
In the semantic literature, an important question is relative to how to account for semantic priming effects. Plaut and Booth (2000) proposed a distributed connectionist network in
which each concept is represented by a particular pattern of activity over a large number of
processing units. In processing a word, units cooperate and compete across weighted connections until the network as a whole settles into a stable pattern of activity that represents
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the meaning of the word. The authors hypothesized that at short SOAs, there is facilitation dominance for both categorical and associative priming, whereas at long SOAs, there
is facilitation dominance for associative priming, but inhibition dominance for categorical
priming (see also Plaut 1995). Categorical facilitation tends to be weak because only some
features overlap between the prime and target, whereas categorical inhibition tends to be
strong because many features do not overlap. In their empirical studies, the prime-target
pairs were chosen from free-association norms (Nelson et al. Unpublished manuscript), but
were not controlled on categorical relation. Participants (adults and children) performed a
visual lexical-decision priming task. Priming effects were evaluated by contrasting related,
unrelated, and neutral (nonword) priming. Neutral priming was added insofar as it allows to
establish explicitly the magnitude of facilitation and inhibition by comparing decision latencies (DLs) to target words following neutral primes to those following related and unrelated
primes. Results showed that priming effects in adults, at a long SOA (800 ms), were due
to a combination of facilitation from related primes and inhibition from unrelated primes,
whereas at a brief SOA (200 ms), they reflected facilitation only.
It is worth noting that Plaut and Booth’s empirical contribution did not permit to determine if semantic priming is due to association strength or feature overlap since only strongly
associated items were tested, which were not controlled on categorical relatedness. This
issue has been examined in many semantic priming studies (see McNamara 2005; Neely
1991, for reviews; Hutchison 2003; Lucas 2000, for meta-analytic reviews). Among other
problems, researchers have to deal with the difficulty in separating these two semantic relations. Although it has been hypothesized that association norms reflect primarily the phrasal
contiguity between items, they also contain other types of semantic relations (e.g., Moss
et al. 1995). As a consequence, two words can be associated in many ways. They can be
synonyms, antonyms, members of the same natural or artificial category; a perceptual or
functional property can connect them, and so on. Nevertheless, the meta-analysis conducted
by Lucas (2000) indicated that, in adults, there is strong evidence of an overall pure semantic priming effect in automatic semantic priming, which can then occur without association.
Hutchison (2003) proposed very different conclusions and underlined that automatic priming
is due to both associative relatedness and feature overlap. Although the semantic memory
literature, essentially devoted to noun processing, is still searching a consensus about this
issue, many research are carrying on in this domain, and unfortunately, there are no similar
data on verb processing.
Semantics Relative to Verb Categories
Studies that addressed the issue of what kinds of factors contribute to verb processing in
semantic tasks explored semantic argument structure (i.e., hierarchical configurations of
syntactically relevant semantic information such as thematic roles; e.g., Ferretti et al. 2001;
McRae et al. 1997b, 1998), or the associations between verb concepts and noun concepts
(e.g., the degree of semantic congruence between verbs and nouns as possible patients of the
former: Franquart-Declercq et al. 2004; see also the hypothesis that metaphors might depend
on semantic congruence fixed in memory: Le Ny and Franquart-Declercq 2001, 2002).
Other research relative to brain-damaged people explored specifically verb concepts (e.g.,
Kemmerer and Tranel 2000a,b), or compared verb concepts to noun concepts (e.g., Bird et al.
2000a,b; Caramazza and Hillis 1991; Kim and Thompson 2000).
To summarize, research on word processing is mainly devoted to noun concepts, whereas
research on verbal semantics implies essentially relations between verbs and nouns or
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brain-damaged patients. Therefore, verb processing in normal people has not yet being studied, as noun processing has been done.
The current work explores the general hypothesis that, as for nouns, stable representations
of semantic knowledge relative to situations expressed by verbs are available and accessible
in long term memory in normal people. If so, it would be possible to interpret classificatory
properties of verb categories as semantic features (Le Ny 1995, 1998, 2005).
But how choosing concept features of verbs, which play a crucial role in their semantic representation? The study of verbal semantics is complex not only because it is far less
familiar than the study of nouns, but also because the semantic composition of situational
representations is of far greater complexity than that of static entities, precisely because of
their dynamic and transitory nature. It is therefore challenging to specify their representation.
Indeed, in comparison with concrete noun categories, verbs express situations (events and
states), which can be directly observed, but without stable referents in our world. So, they
seem more transient, and are described essentially by resorting to introspective processes.
Two lines of works, one in linguistics, the other in psycholinguistics, provide initial guidelines for selecting concept features of verbs.
In linguistics, many verb classifications have been proposed (e.g., François 2003). Among
others, references to Vendler’s classification (1967) were frequent in the verbal literature.
On the basis of linguistic criteria, Vendler proposed three classificatory properties, each
associated with two values: dynamicity (static versus dynamic), durativity (durative versus
non-durative), and resultativity (resultative versus non-resultative). From this property list,
four main verb categories are distinguished: (1) states defined as static (homogeneous),
durative, and non-resultative situations (e.g., to exist); (2) activities differ from states on dynamicity; they are dynamic (heterogeneous), durative, and non-resultative situations (e.g., to
chat); (3) accomplishments differ from activities on resultativity; they are dynamic,
durative, and resultative situations (e.g., to remove); (4) achievements differ from accomplishments on durativity; they are dynamic, non-durative, and resultative situations (e.g., to
blow up). Some authors considered iterativity as an additional classificatory property and
opposed continuous actions to discontinuous actions. In all, some verbal classifications are
developed on the basis of four classificatory properties: dynamicity, durativity, resultativity,
and iterativity.
The second source of available information for selecting concept features of verbs comes
from empirical research in psycholinguistics. Both verbal production and verbal comprehension activities allow to construct regular associations over time between verb categories
and tenses. Tense selection for reporting situations is a purposive behavior, depending on the
speaker’s intentions. And it is worth noting that tenses play at least a twofold role (Comrie
1976, 1985; Fuchs and Leonard 1979; Reichenbach 1947; Vendler 1967; Vet 1980). First,
they indicate that the reported situations are past, present, or future, relative to the speech
moment, or to another moment of reference situated in the discourse (temporal function).
Second, they can highlight or minor characteristics of the reported situations (aspectual function). The aspectual function refers to the viewpoint or perspective taken by the speaker with
respect to the temporal structure of the reported situations. Speaker perspective essentially
corresponds to the ‘imperfective/perfective’ opposition. Imperfective tenses present situations in progress and/or from within their temporal structure (their beginning and their end
are not considered). By contrast, perfective tenses present situations from an external perspective and underline the reaching of a terminal point independently of their internal structure.
In some languages, like English, this ‘imperfective/perfective’ opposition is available in all
tenses. However, in other languages, like French, this opposition is only available in the past
and essentially corresponds to the distinction between the imperfect (imparfait), on the one
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hand, and the perfect (passé composé), past historic (passé simple), and pluperfect (plusque-parfait), for the most frequent, on the other hand. For example, the durative aspect of a
durative, non-resultative verb inflected with an imperfective tense (e.g., il gouvernait, he was
governing) is strengthened. By contrast, it is weakened when the same verb is inflected with
a perfective tense (e.g., il a gouverné or il gouverna, he governed; il avait gouverné, he had
governed), whereas the accomplishment of the situation in relation to the speech moment
seems more important.
The generation of a tense associated with a verb is normally an effortless and fast process.
Yet, when considered in detail, this process is a very complex one, because the aspectual
function of tenses is mastered late during development (Bonnotte and Fayol 1997; Fayol
et al. 1988, 1989, 1993). Specifically, the ability to associate past tenses in French and verb
categories is a very long process since semantic features of verb categories exert a weaker
influence in children in primary school, including 5th graders, than in adults. Off-line data
relative to the use of past tenses by French-speaking adults showed that the probability of
a situation expressed by a verb to be reported with an imperfective tense versus a perfective tense depends on its characteristics. Thus, the probability of a durative, non-resultative
situation (e.g., jouer, to play) to be reported with an imperfective tense is higher than its
probability to be reported with a perfective tense, whereas the reverse is generally observed
for a non-durative, resultative situation (e.g., casser, to break). By contrast, with durative,
resultative verbs (e.g., repasser, to iron), imperfective and perfective tenses are equally used.
Then, the use of tenses appears more as a probabilistic system than as a regular system. To
explain these privileged associations, it is worth noting that verb categories are considered as
semantic and grammatical categories. Furthermore, tenses as grammatical markers are also
considered as carrying semantic information. As previously underlined, imperfective tenses
present situations in progress and/or from within their temporal structure (their beginning
and their end are not considered) and, therefore, highlight the durative value of durative,
non-resultative situations. By contrast, perfective tenses focus on situation accomplishment,
and therefore highlight the resultative value of non-durative, resultative situations. Finally,
imperfective and perfective tenses are equally used with durative, resultative verbs since their
durative value favors the use of an imperfective tense, whereas their resultative value favors
the use of a perfective tense. To sum up, in adults, two verb categories are clearly linked
to two tense types: durative, non-resultative verbs to imperfective tenses, and non-durative, resultative verbs to perfective tenses. Privileged associations between both categories
and past tenses in French might explain the relative weightings of two important
superordinate features within semantic representations of verb concepts, which are durativity and resultativity. Thus, pure semantic relatedness was analyzed by considering these two
superordinate features, but not the number of features shared by verb concepts (i.e., featural
overlap).
The Present Study
Verb concept representation, like noun concept representation, is viewed as being distributed
across features. As emphasized by Barsalou (1999), the total number of features available in
the semantic representation of a concept could be potentially very high. Hence, the abstraction of crucial features in semantic representation of word meaning is a major challenge
for researchers. Like research relative to noun categories (e.g., McRae et al. 1997a), this
study examined the role of superordinate features of verb categories. It was hypothesized
that verb concepts could be decomposed with a short list of superordinate features. Regular
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associations over time between tenses and verb categories might lead to elaborate privileged
connections and to weight features, and consequently exert an impact on their greater or
lesser probability being taken into account during cognitive processing.
However, it might be difficult to identify which superordinate featural values are
critically related to the meaning of a verb, because of its variation according to linguistic
contexts. Indeed, verbs are highly polysemous, and the situation expressed by a verb could
vary considerably from one linguistic context to another (Langacker 1999). For example, in
French, the same verb casser (to break) can be used to report different situations. Phrases
such as casser une noix (to crack a walnut) and casser un carreau (to break a pane) evoke
non-durative, resultative situations. The phrase casser des noix (to crack walnuts) evokes a
durative, discontinuous, resultative situation. And the phrase casser les pieds à quelqu’un
(to bore somebody stiff) evokes a durative, non-resultative situation. Thus, the meaning of a
verb is undoubtedly contextually dependent. In other words, a verb concept can differ widely
from one sentential context to another. Despite these contextual differences, it seems however possible to extract a primary sense (the core meaning; Barsalou 1993, 1999), and to
distinguish it from a secondary sense (the peripheral meaning activated in specific contexts).
Then, while considering that the meaning of a verb concept depends on context, it might be
possible to consider different meaning levels for this verb concept.
Insofar as the current work investigated verb core meaning, a pilot study (an untimed
graphic coding task of individual verbs presented in the infinitive form) was conducted to
select typical items (i.e., items on which a high agreement across participants was obtained)
for two verb categories: the durative, non-resultative verb category (e.g., défiler, to march
past), and the non-durative, resultative verb category (e.g., accrocher, to hang). So, there
was no overlap in featural values between both categories.
In the experimental study, two semantic decision-priming tasks were used at a 200- and
100-ms SOA: a durativity and a resultativity decision task. They were chosen to be at the
same level as those proposed by McRae et al. (1997a) in their Experiment 2A (Is it animate?
Is it an object? Is it made by humans? Does it grow?). In the present study, participants had to
decide if the target refers to a durable versus non-durable situation in the durativity decision
task, and to a situation with a directly observable outcome versus without any clear, external
outcome in the resultativity decision task. Targets were preceded by similar, opposite, and
neutral primes. This study aimed at examining if semantic information retrieval might depend
on the characteristics of the prime preceding the target.
The Pilot Study
The purpose of the pilot study was to collect data concerning adults’ semantic knowledge of
situations entailed by individual verbs (i.e., verbs presented in the infinitive form, without
sentential context). In order to select typical items for both verb categories analyzed in this
study, a graphic coding task was used (see Bonnotte and Fayol 1997, for an extended presentation). The graphic coding system was initially devised to assess four semantic features,
each associated with two values: dynamicity (static versus dynamic), durativity (durative
versus non-durative), resultativity (resultative versus non-resultative), and iterativity (continuous versus discontinuous). In this study, only two superordinate features were of interest:
durativity and resultativity.
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Method
Participants
Thirteen graduate native French-speaking students at the University Charles de Gaulle Lille
3 (France) volunteered to participate in the pilot study.
Materials
Three hundred and fifty-three verbs were selected in the Brulex Database (Content et al.
1990). All verbs were frequent, trisyllabic, 6-, 7-, 8-, or 9-letter words, and regular (i.e., in
the infinitive form, ending with -er; e.g., admirer, to admire).
Procedure
The graphic coding task was conducted in a paper-and-pencil format. Each participant performed the task for all verbs at home, with the instruction to accomplish the graphic coding
in several short sessions. The whole set of verbs was presented in the infinitive form, in a
random order in a booklet of stapled A4 sheets of paper. Each participant received the following instructions: “Verbs refer to states or actions. When verbs refer to states (i.e., stable
and durable situations without any clear, external outcome), you will trace a horizontal line,
always starting from the left end of the page and stopping at its right end. When verbs refer to
actions, you will have to graphically respond to three questions. The first question is relative
to the action duration: Is the action durable or non-durable? For durable actions, you will
trace a long horizontal line, always starting from the left end of the page, but stopping before
reaching its right end. For non-durable actions (i.e., with a very short duration), you will trace
a very short horizontal line, always starting from the left end of the page. The second question
concerns the continuous or discontinuous aspect of a durable action: Is the durable action
continuous or discontinuous? For continuous actions, you will trace a continuous horizontal
line. For discontinuous actions (i.e., composed of several small identical actions), you will
trace a discontinuous horizontal line (i.e., composed of dashes). The third question concerns
the outcome of an action: Is the action giving rise to a directly observable outcome or not?
For actions giving rise to a directly observable outcome, you will add a vertical bar at the end
of the horizontal line. For actions without any clear, external outcome, you will add nothing”.
The instructions were illustrated by all graphic coding possibilities and examples.
Verb selection
Data concerning the four semantic features were binary coded. For dynamicity, situations
were categorized as dynamic when the horizontal line stopped before reaching the right end
of the page, and static when it stopped at its right end. Durativity (symbolized by the length
of the horizontal line) was assessed by classifying situations as durative when the horizontal
line was long, and non-durative when it was very short. For iterativity, situations were classified as continuous when the horizontal line was continuous, and discontinuous when the
horizontal line was composed of dashes. Finally, for resultativity, actions were considered as
resultative when a vertical bar (symbolizing the result) occurred at the end of the line, and
non-resultative otherwise. Since only durative, non-resultative and non-durative, resultative
verbs were studied in the experimental study, those for which these dominant graphic codings
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were characterized by high interindividual agreement rates (mean rate: .826; range: .62–1.00)
were selected as stimuli (Appendix A). All these verbs were 7-, 8-, or 9-letter words.
The Experimental Study: Semantic Decision-priming Tasks with a 200- and 100-ms
SOA
Because word meaning is computed in 100–300 ms (Gough and Cosky 1977; Rayner 1978), a
short SOA (less than 250 ms) was assumed to be sufficient to allow a stable semantic pattern to
be processed for a prime. Previous research have shown that automatic semantic priming taps
noun meaning (e.g., McRae and Boisvert 1998; McRae et al. 1997a; Perea and Rosa 2002;
Plaut and Booth 2000; Williams 1996). In addition, Plaut and Booth have hypothesized that
at short SOAs, there is facilitation dominance for both categorical and associative priming,
whereas at long SOAs, there is facilitation dominance for associative priming, but inhibition
dominance for categorical priming (see also Plaut 1995). Categorical facilitation tends to be
weak because only some features overlap between the prime and target, whereas categorical
inhibition tends to be strong because many features do not overlap. Insofar as the present study
concerned superordinate categorical priming, without featural overlap, except for two superordinate features, it could be hypothesized that if semantic priming taps verb meaning, then it
would be evidenced at short SOAs, but not at long SOAs. Thus, this first contribution to semantic priming in verb processing was devoted to the analysis of automatic semantic priming,
with two short SOAs: 200- and 100-ms. Moreover, it examined the question of links in semantic memory between similar and opposite featural values, and aimed at determining whether
similar and opposite featural values activate one another in semantic decision-priming tasks.
In both semantic decision-priming tasks (durativity and resultativity decision tasks), priming effects were evaluated, at a 200- and 100-ms SOA, by contrasting similar related, opposite
related, and neutral (Xs string) priming. As in Plaut and Booth’s research (2000), neutral priming was added insofar as it allows to establish explicitly the magnitude of facilitation and
inhibition by comparing decision latencies (DLs) to targets following neutral primes to those
following similar and opposite related primes. Targets and related primes were durative, nonresultative verbs and non-durative, resultative verbs, always presented in the infinitive form
(e.g., for similar related prime-target pairs: respirer caresser, to breathe to stroke, écraser
retirer, to crush to remove; for opposite related prime-target pairs: bavarder détacher, to
chat to untie, dérober visiter, to steal to visit). Participants had to decide as quickly and accurately as possible if the target expressed a durative or non-durative situation in the durativity
decision task, and a resultative or non-resultative situation in the resultativity decision task.
Method
Participants
Eighty native undergraduate French-speaking students at the University Charles de Gaulle
Lille 3 (France) volunteered to participate in the experimental study. All participants had
either normal or corrected-to-normal vision. None had participated in the pilot study.
Materials
The stimuli for both priming tasks were 54 prime-target pairs in three priming
contexts: similar, opposite, and neutral priming contexts. Two lists were constructed
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(see Appendix A) insofar as verbs in prime-target pairs with similar or opposite featural
values reversed: a first list was constructed with one verb as prime and another verb as target
(e.g., admirer défiler, to admire to march past), whereas a second list reversed these assignments (e.g., défiler admirer, to march past to admire). Neutral prime-verb target pairs were
identical in both lists. Within each list, there were six pair types, each with nine pairs: two
similar prime-target pair types (i.e., two durative, non-resultative verbs, and two non-durative, resultative verbs), two opposite prime-target pair types (i.e., a durative, non-resultative
verb preceding a non-durative, resultative verb; and a non-durative, resultative verb preceding a durative, non-resultative verb), and two neutral pair types (i.e., a Xs string preceding a
durative, non-resultative verb; and a Xs string preceding a non-durative, resultative verb). In
addition, in both similar and opposite priming contexts, verbs shared no meaning. Finally, in
all conditions, the prime and target in each pair were identical in length.
It is worth noting that, in the durativity decision task, durative, non-resultative targets possessed the positive featural value, whereas non-durative, resultative targets had the negative
featural value. By contrast, in the resultativity decision task, non-durative, resultative targets
were characterized by the positive featural value, whereas durative, non-resultative targets
had the negative featural value.
To summarize, prime-target pairs were constructed in order to propose six combinations
in both semantic decision tasks. In the durativity decision task, the six combinations were:
(1) for similar priming, durative prime—durative target, and non-durative prime—non-durative target, (2) for opposite priming, durative prime—non-durative target, and non-durative
prime—durative target, and (3) for neutral priming, Xs string prime—durative target, and
Xs string prime—non-durative target. In the resultativity decision task, the six combinations were: (1) for similar priming, resultative prime—resultative target, and non-resultative
prime—non-resultative target, (2) for opposite priming, resultative prime—non-resultative
target, and non-resultative prime—resultative target, and (3) for neutral priming, Xs string
prime—resultative target, and Xs string prime—non-resultative target.
Procedure
Participants were tested individually using PsyScope experimental software (Cohen et al.
1993) on a Macintosh Powerbook 1400cs. They were randomly assigned to one decision
task and to one list, and only tested under one SOA. A priming trial consisted of a fixation
point “+” displayed in the center of the screen for 750 ms, followed by the prime for 200 or
100 ms, then the target which remained on the screen until the participant responded. The
inter-trial interval (ITI) was 750 ms. All stimuli were presented in black upper-case letters on
a gray background. Order of trials was randomized for each participant. The task began with
the experimenter reading instructions, which were also presented on the computer monitor
placed about 50 cm in front of the participant. Participants were told that all words were meant
to be verbs, and that two verb categories could be proposed. In the durativity decision task,
some verbs referred to durable situations, whereas others referred to non-durable situations
(i.e., with a very short duration). In the resultativity decision task, some verbs referred to
situations with a directly observable outcome, whereas others referred to situations without
any clear, external outcome. Illustrative examples were provided. Finally, participants were
instructed to read or identify the “first letter string” (the prime) silently and to respond as
quickly and accurately as possible “only to the second letter string” (the target), that is, to
decide if the verb refers to a durable versus non-durable situation, or a situation with a directly
observable outcome versus without any clear, external outcome. Participants responded by
pressing one of two buttons on a CMU button box that provided ms accuracy. Insofar as
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positive and negative featural values were relevant for each semantic decision task, half of
the participants responded ‘durable situation’ or ‘situation with a directly observable outcome’ with the right hand and ‘non-durable situation’ or ‘situation without any clear, external
outcome’ with the left hand, and the other half did the reverse. In the durativity decision task,
there were 50% “durable situation” and 50% “non-durable situation” trials. In the resultativity decision task, there were 50% “situation with a clear outcome” and 50% “situation with
no outcome” trials. Participants completed 12 practice trials, then the 54 experimental trials.
Semantic decision latency was recorded as the time between the onset of the target and the
participant’s response. The experiment took approximately ten minutes.
Design
The dependent measures were decision latency and accuracy. In all subsequent analyses of
variance, decision (durativity versus resultativity decision) and SOA (200- versus 100-ms
SOA) were between-subjects factors, whereas featural value (positive versus negative) and
priming context (neutral, similar, opposite) were within-subjects factors.
Results
In all decision latency analyses reported in this article, trials on which an error occurred
were excluded. Latencies greater than two standard deviations above the mean by Decision
Task × SOA × Featural Value condition were replaced by the cutoff value. This procedure
affected 4.8% of the scores.
The global analysis of decision latencies (DLs) yielded a main effect of SOA, F (1, 76) =
5.091, MSE = 684344.514, p < .05, featural value, F (1, 76) = 17.91, MSE = 53746.959,
p < .001, and priming context, F (2, 152) = 18.959, MSE = 20838.595, p < .001, a
two-way Decision × Priming Context interaction, F (2, 152) = 5.914, MSE = 20838.595,
p < .01, a two-way Featural Value × Priming Context interaction, F (2, 152) = 4.805,
MSE = 27068.381, p < .01, a three-way Decision × Featural Value × SOA interaction,
F (1, 76) = 4.311, MSE = 53746.959, p < .05, and a three-way Decision × Featural
Value × Priming Context interaction, F (2, 152) = 3.158, MSE = 27068.381, p < .05.
The global analysis of errors yielded a two-way Decision × Priming Context interaction, F (2, 152) = 9.724, MSE = .01, p < .001, a two-way Featural Value × Priming Context
interaction, F (2, 152) = 3.301, MSE = .012, p < .05, and a three-way Decision × Featural
Value × Priming Context interaction, F (2, 152) = 3.826, MSE = .012, p < .05.
Insofar as different sources of variation in the DLs or errors analyses implied decisions
and SOAs, separate analyses of variance were then conducted by decision and SOA, then by
featural value. These latter analyses were necessary for comparisons. Priming effects were
tested (Sidàk’s tests) by contrasting neutral priming with respectively similar priming and
opposite priming in order to determine whether they resulted from facilitation or inhibition.
Results are presented in Table 1.
Durativity Decision at the 200-ms SOA
The DLs analysis yielded a main effect of featural value, F (1, 19) = 9.878, MSE =
47933.663, p < .01, and priming context, F (2, 38) = 12.638, MSE = 22247.948,
p < .001. However, separate analyses of variance conducted by featural value revealed a
priming context effect on the positive value (i.e., a durative, non-resultative target), F (2, 38) =
11.585, MSE = 22303.237, p < .001, but not on the negative value (i.e., a non-durative,
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209
Table 1 Mean semantic decision latencies in ms (percent errors) for each decision, according to SOAs,
featural values, and priming context; and priming effects
SOA × featural value
Priming context
Priming effects
Neutral
Similar
Opposite
Similar
Opposite
Durativity decision
200 ms—Positive
200 ms—Negative
100 ms—Positive
100 ms—Negative
1466 (13.8)
1514 (13.7)
1717 (19.8)
1646 (13.2)
1258 (10.5)
1394 (13.2)
1529 (15.9)
1586 (12.7)
1283 (6.6)
1476 (13.2)
1518 (7.2)
1630 (8.8)
208 (3.3)
120 (0.5)
188 (3.9)
60 (0.5)
183 (7.2)
38 (0.5)
199 (12.6)
16 (4.4)
Resultativity decision
200 ms—Positive
200 ms—Negative
100 ms—Positive
100 ms—Negative
1300 (12.6)
1315 (9.4)
1384 (12.1)
1492 (11)
1191 (10.4)
1315 (14.9)
1276 (9.9)
1496 (19.8)
1276 (17.6)
1312 (10.5)
1386 (14.3)
1483 (17.6)
109 (2.2)
0 (−5.5)
108 (2.2)
−4 (−8.8)
24 (−5)
3 (−1.1)
−2 (−2.2)
9 (−6.6)
Note: SOA, stimulus onset asynchrony
For the durativity decision, the positive value corresponded to a durative target, and the negative value to a
non-durative target
For the resultativity decision, the positive value corresponded to a resultative target, and the negative value to
a non-resultative target
resultative target), F (2, 38) = 2.319, MSE = 32669.581, n.s. In addition, on the positive
value, facilitation was displayed for similar priming (208 ms), F (1, 38) = 19.475, p < .01, and
opposite priming (183 ms), F (1, 38) = 14.988, p < .01. By contrast, on the negative value,
no differences reached the significance level: for similar priming (120 ms), F (1, 38) = 4.443,
n.s.; for opposite priming (38 ms), F < 1.
The analysis of errors did not reveal any source of variation. As well, separate analyses of
variance conducted by featural value did not show any priming context effect on the positive
value, F (2, 38) = 2.829, MSE = .009, n.s., or on the negative value, F < 1. However, on
the positive value, facilitation was displayed for opposite priming (7.2%), F (1, 38) = 5.647,
p < .05, but not for similar priming (3.3%), F (1, 38) = 1.203, n.s. By contrast, on the negative value, no differences attained the significance level: for similar and opposite priming
(0.5%), Fs < 1.
Durativity Decision at the 100-ms SOA
The DLs analysis yielded a main effect of priming context, F (2, 38) = 6.014, MSE =
30241.821, p < .01, and a two-way Featural Value × Priming Context interaction,
F (2, 38) = 3.389, MSE = 25905.544, p < .05. Separate analyses of variance conducted
by featural value revealed a priming context effect on the positive value, F (2, 38) = 6.689,
MSE = 37413.062, p < .01, but not on the negative value, F (2, 38) = 1.038, MSE =
18734.302, n.s. In addition, on the positive value, facilitation was displayed for similar priming (188 ms), F (1, 38) = 9.472, p < .05, and opposite priming (199 ms), F (1, 38) = 10.564,
p < .01. By contrast, on the negative value, no differences got to the significance level: for
similar priming (60 ms), F (1, 38) = 1.939, n.s.; for opposite priming (16 ms), F < 1.
The analysis of errors only displayed a main effect of priming context, F (2, 38) = 6.701,
MSE = .012, p < .01. However, separate analyses of variance conducted by featural value
showed a priming context effect on the positive value, F (2, 38) = 6.175, MSE = .014,
p < .01, but not on the negative value, F (2, 38) = 1.019, MSE = .011, n.s. In addition, on
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the positive value, facilitation was displayed for opposite priming (12.6%), F (1, 38) = 11.751,
p < .01, but not for similar priming (3.9%), F (1, 38) = 1.088, n.s. By contrast, on the negative value, no differences reached the significance level: for similar priming (0.5%), F < 1,
and opposite priming (4.4%), F (1, 38) = 1.716, n.s.
Resultativity Decision at the 200-ms SOA
The DLs analysis only yielded a main effect of priming context, F (2, 38) = 3.536, MSE =
9156.348, p < .05. However, separate analyses of variance conducted by featural value revealed a marginally priming context effect on the positive value (i.e., a non-durative, resultative target), F (2, 38) = 3.135, MSE = 20852.427, p = .055, but not on the negative value
(i.e., a durative, non-resultative target), F < 1. In addition, on the positive value, facilitation
was displayed for similar priming (109 ms), F (1, 38) = 5.683, p < .05, but not for opposite
priming (24 ms), F < 1. By contrast, on the negative value, no differences were apparent: for
similar priming (0 ms) and opposite priming (3 ms), Fs < 1.
The analysis of errors revealed a two-way Featural Value × Priming Context interaction, F (2, 38) = 3.569, MSE = .01, p < .05. Indeed, separate analyses of variance conducted
by featural value showed a priming context effect on the positive value, F (2, 38) = 3.789,
MSE = .007, p < .05, but not on the negative value, F (2, 38) = 1.402, MSE = .012, n.s. However, no differences were apparent on the positive value (for similar priming: 2.2%, F < 1;
for opposite priming: −5%, F (1, 38) = 3.461, n.s.), or on the negative value (for similar
priming: −5.5%, F (1, 38) = 2.519, n.s.; for opposite priming: −1.1%, F < 1).
Resultativity Decision at the 100-ms SOA
The DLs analysis only yielded a main effect of featural value, F (1, 19) = 14.359, MSE =
41757.95, p < .01. However, separate analyses of variance conducted by featural value revealed a priming context effect on the positive value, F (2, 38) = 4.363, MSE = 18046.625,
p < .05, but not on the negative value, F < 1. In addition, on the positive value, facilitation
was displayed for similar priming (108 ms), F (1, 38) = 6.384, p < .05, but not for opposite
priming (−2 ms), F < 1. By contrast, on the negative value, no differences were apparent:
for similar priming (−4 ms) and opposite priming (9 ms), Fs < 1.
The analysis of errors did not reveal any source of variation. However, separate analyses of
variance conducted by featural value showed a priming context effect on the negative value,
F (2, 38) = 3.309, MSE = .013, p < .05, but not on the positive value, F < 1. On the negative
value, inhibition was displayed for similar priming (−8.8%), F (1, 38) = 6.124, p < .05, but
not for opposite priming (−6.6%), F (1, 38) = 3.406, n.s. By contrast, on the positive value,
no differences were apparent: for similar priming (2.2%) and opposite priming (−2.2%),
Fs < 1.
General Discussion
The purpose of the research presented in this article was to gain further insight into the nature
of word processing by investigating verb concepts and the influence of their featural representations in semantic processing. Indeed, in psycholinguistics, off-line data relative to the
use of past tenses according to linguistic properties of verbs are relatively numerous, whereas
processing implied in the representation of these linguistic properties are not studied.
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Durativity and resultativity have been thus consistently shown to affect the selection of past
tenses among French adults in sentences (Bonnotte and Fayol 1997; Fayol et al. 1988, 1989)
and narrative texts (Fayol et al. 1993). Durative, non-resultative verbs attract more frequently
imperfective tenses and less frequently perfective tenses than non-durative, resultative verbs.
This empirical study aimed then at determining whether durativity and resultativity, as
superordinate features of verbs, could be processed in semantic-decision priming tasks at
short SOAs (200- and 100-ms). To sum up, the current work aimed at testing, in French
adults, the semantic priming hypothesis. Providing support to this hypothesis would confirm
Le Ny’s (1995, 1998, 2005) general hypothesis that classificatory properties of verbs could
be interpreted as semantic features. This work was also designed to investigate the role of
similar versus opposite priming context in semantic priming.
The pilot study provided a set of feature norms that was used to construct semantic representations in terms of individual semantic features. These representations were used to
create stimuli and to predict performance in the experimental study. Two verb categories
were analyzed: the durative, non-resultative verb category and the non-durative, resultative
verb category.
The experimental study used a 200- and 100-ms SOA with two semantic decision-priming
tasks: a durativity decision task (to decide if the target expressed a durative versus non-durative situation) and a resultativity decision task (to decide if the target expressed a resultative
versus non-resultative situation). The target was preceded by a similar prime, an opposite
prime, or a neutral prime (Xs string).
Results showed that semantic processing differed according to superordinate features and
featural values (i.e., verb categories).
On durativity, the DLs analyses showed, at both SOAs, a priming context effect on durative, non-resultative verbs, whereas no priming context effect was apparent on non-durative,
resultative verbs. Additionally, this effect, which was facilitation, was displayed with both
similar and opposite priming. In the same time, the errors analyses showed, at both SOAs, a
facilitation on durative, non-resultative verbs when preceded by an opposite prime.
On resultativity, the DLs analyses showed, at both SOAs, a priming context effect on
non-durative, resultative verbs, whereas no priming context effect was apparent on durative,
non-resultative verbs. Furthermore, this effect, which was facilitation, was only displayed
with similar priming. The errors analyses only showed, at the 100-ms SOA, a priming context effect on durative, non-resultative verbs. This effect, which was inhibition, was only
exhibited with similar priming.
Overall, results confirmed the semantic priming hypothesis by showing that semantic
priming can tap verb meaning. As a first result, it is worth noting that on the speed processing, only the positive value of each feature benefited from priming, that is the durative and
resultative values. However, processing of durativity and resultativity is far from comparable
since facilitation was shown on the former with similar and opposite priming, whereas it was
shown on the latter with similar priming only. From this perspective, it is worth recalling that
the graphic coding task, which was used to select typical items for both verb categories, was
conceived as a binary task. Resultativity could be undoubtedly considered as a binary feature.
By contrast, durativity could also be considered as a continuous feature. And the variation
concerning their semantic processing could be partly explained by differences relative to
their representation: binary versus continuous. Indeed, our results suggested that the activation spread should be faster on a continuous feature than on a binary feature. Specifically,
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durativity should display a swiftness in semantic activation spread. This should explain why
durativity and resultativity differ quantitatively in semantic priming.
Semantic-decision priming tasks generally gave rise to severe criticisms since there is
considerable doubt as to whether the results from such tasks can reflect anything about the
underlying representations of the words in semantic memory (de Groot 1990; Hutchison
2003; Lucas 2000). The detractors highlighted that these tasks confound response congruency with semantic relatedness since related pairs (e.g., dog-cat in an “animate/inanimate”
decision) are associated with the same response, whereas unrelated pairs (e.g., table-cat)
are associated with different responses. The response congruency could thus give briefer
decision latencies on related pairs than on unrelated pairs even if the prime exerts no effect
on lexical access to the target. This argument seems weakened by the results exhibited on
durativity since facilitation was registered on durative, non-resultative verbs with similar and
opposite priming. Indeed, in this last case, there was incongruency of the responses on the
prime and the target (e.g., the prime redresser, to straighten, was labeled as ‘non-durable’,
whereas the target sangloter, to sob, was categorized as ‘durable’).
To conclude, this research relative to verb processing emphasized two findings. First,
durativity and resultativity, as classificatory properties, can be considered as superordinate
semantic features (Le Ny 1995, 1998, 2005) since semantic priming was exhibited in semantic decision tasks. Second, semantic priming can tap verb meaning (with the restriction that
durativity and resultativity processing are far from equivalent), and agree with results relative
to noun meaning (e.g., McRae et al. 1997a; McRae and Boisvert 1998). Though, results of
this first empirical contribution have to be confirmed in other research. Indeed, at present, it
was not possible to conclude for an automatic semantic priming effect in our two semanticdecision tasks since average decision latencies were very long (about 1,500 ms for durativity
and 1,350 ms for resultativity, i.e., virtually twice as long as those recorded for lexical decisions), and errors were relatively frequent (nearing 13%). In other words, these two measures
tended to indicate that participants found both semantic-decision tasks difficult. Finally, both
superordinate features examined in this study are highly abstract and general. Their content
might be partly implicit in semantic memory. This implicit characteristic doesn’t prevent
them from exerting an important role in cognition (Le Ny 2005). Now, this first empirical
contribution of their role in semantic processing must be followed by studies that test the
automatic semantic priming hypothesis, in particular with the lexical-decision priming task.
It would be also better to construct prime-target pairs so that each target appears in all priming
contexts in order to reduce the variability in responses and provide more consistent priming
effects.
Acknowledgements I would like to thank Patrick Lemaire for his useful comments on the preparation of
the experimental study, and Mireille Lecacheur for her help in using PsyScope experimental Software.
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Appendix A
Prime-target pairs used in the experimental study according to priming context (they are
grouped together on a line, e.g., in list 1, figurer espérer, to represent to hope; in list 2,
espérer figurer, to hope to represent)
For similar priming
In lists 1 and 2, durative, non-resultative prime-target pairs
Primes in list 1
Targets in list 2
LW
LF
AR
Targets in list 1
Primes in list 2
LW
LF
AR
figurer, to represent
admirer, to admire
exister, to exist
respirer, to breathe
incarner, to incarnate
célébrer, to celebrate
concerner, to concern
pratiquer, to practice
balbutier, to stammer
Mean
7
7
7
8
8
8
9
9
9
5224
8512
20391
7683
1101
1910
6577
1914
1548
6096
.92
1.00
.92
.85
.85
.92
.92
.77
.62
.86
espérer, to hope
défiler, to march past
résider, to reside
caresser, to stroke
préférer, to prefer
raconter, to relate
sapprécier, to appreciate
chuchoter, to whisper
gouverner, to govern
Mean
7
7
7
8
8
8
9
9
9
12877
1518
1816
3369
9023
13962
2373
1259
2424
5402
.92
.92
1.00
.77
.92
.85
1.00
.69
.77
.87
In lists 1 and 2, non-durative, resultative prime-target pairs
Primes in list 1
Targets in list 2
LW
LF
AR
Targets in list 1
Primes in list 2
LW
LF
AR
écraser, to crush
déposer, to lay down
replier, to fold back up
entourer, to surround
enfoncer, to drive in
déplacer, to shift
accrocher, to hang
renverser, to knock over
retourner, to turn
Mean
7
7
7
8
8
8
9
9
9
3943
2493
1348
4883
5228
2386
2969
2522
15447
4580
.85
.85
.69
.77
.77
.69
.92
.92
.69
.79
retirer, to remove
libérer, to free
arrêter, to stop
déchirer, to tear
arracher, to pull out
abaisser, to pull down
repousser, to push back
restituer, to return
bousculer, to bump into
Mean
7
7
7
8
8
8
9
9
9
7806
2420
26912
3322
6700
1671
3709
1038
1391
6108
1.00
.69
.85
.77
1.00
.69
.69
.77
.69
.79
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For opposite priming
In list 1, durative, non-resultative prime—non-durative, resultative target pairs; in list 2,
non-durative, resultative prime—durative, non-resultative target pairs
Primes in list 1
Targets in list 2
LW
LF
tolérer, to tolerate
méditer, to meditate
ignorer, to be unaware
bavarder, to chat
demeurer, to live
rayonner, to shine forth
respecter, to respect
prolonger, to prolong
reprocher, to reproach
Mean
7
7
7
8
8
8
9
9
9
1233
2088
11473
1459
15047
1420
3552
4296
5466
5115
AR
1.00
.92
.92
.92
.92
.92
1.00
.62
.69
.88
Targets in list 1
Primes in list 2
LW
LF
AR
allumer, to light
écarter, to move away
émerger, to emerge
détacher, to untie
refermer, to close again
renvoyer, to send again
rattraper, to recapture
débarquer, to land
souligner, to underline
Mean
7
7
7
8
8
8
9
9
9
5254
7040
1425
4803
3097
2880
2280
1595
1595
3330
.85
.85
.69
.92
.85
.69
.69
.85
.62
.78
In list 1, non-durative, resultative prime—durative, non-resultative target pairs; in list 2,
durative, non resultative prime—non-durative, resultative target pairs
Primes in list 1
Targets in list 2
LW
LF
dérober, to steal
éclater, to burst
séparer, to separate
déborder, to overflow
emporter, to take away
délivrer, to set free
redresser, to straighten
supprimer, to suppress
provoquer, to provoke
Mean
7
7
7
8
8
8
9
9
9
2909
8134
8759
2352
8955
3739
3616
3199
4518
5131
AR
.85
.92
.62
.77
.77
.69
1.00
.77
.69
.79
Targets in list 1
Primes in list 2
LW
LF
visiter, to visit
habiter, to inhabit
rigoler, to laugh
posséder, to possess
circuler, to move
détester, to detest
sangloter, to sob
comporter, to comprise
conserver, to keep
Mean
7
7
7
8
8
8
9
9
9
2905
8329
1046
11273
2531
4254
1127
4837
5722
4669
AR
.92
.92
.69
.92
.77
.92
.92
1.00
.92
.89
For neutral priming: in lists 1 and 2, Xs string primes preceded durative, non-resultative
targets and non-durative, resultative target pairs
Target in lists 1 and 2
LW
LF
ménager, to spare
mériter, to merit
ricaner, to giggle
éprouver, to feel
discuter, to discuss
refléter, to reflect
continuer, to continue
regretter, to regret
signifier, to mean
Mean
7
7
7
8
8
8
9
9
9
1829
4573
1242
15507
4356
1939
22752
6572
7521
7366
AR
.92
.85
.85
1.00
.77
.85
.85
.92
.85
.87
Target in lists 1 and 2
LW
LF
effacer, to erase
envoyer, to send
ajouter, to add
attraper, to catch
retomber, to fall again
incliner, to tilt
rattacher, to tie up again
détourner, to divert
déboucher, to uncork
Mean
7
7
7
8
8
8
9
9
9
4900
11848
23369
1471
5122
4560
2280
6589
1803
6882
Note: LW, letter word; LF, lexical frequency; AR, agreement rate
123
AR
.77
.69
.77
1.00
.77
.69
.69
.69
.62
.74
J Psycholinguist Res (2008) 37:199–217
215
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