To Dash or to Dawdle: Verb-Associated Speed of Motion Influences Eye Movements during Spoken Sentence Comprehension

Abstract

In describing motion events verbs of manner provide information about the speed of agents or objects in those events. We used eye tracking to investigate how inferences about this verb-associated speed of motion would influence the time course of attention to a visual scene that matched an event described in language. Eye movements were recorded as participants heard spoken sentences with verbs that implied a fast ("dash") or slow ("dawdle") movement of an agent towards a goal. These sentences were heard whilst participants concurrently looked at scenes depicting the agent and a path which led to the goal object. Our results indicate a mapping of events onto the visual scene consistent with participants mentally simulating the movement of the agent along the path towards the goal: when the verb implies a slow manner of motion, participants look more often and longer along the path to the goal; when the verb implies a fast manner of motion, participants tend to look earlier at the goal and less on the path. These results reveal that event comprehension in the presence of a visual world involves establishing and dynamically updating the locations of entities in response to linguistic descriptions of events.

Full text

To Dash or to Dawdle: Verb-Associated Speed of Motion
Influences Eye Movements during Spoken Sentence
Comprehension
Shane Lindsay
1
*, Christoph Scheepers
2
, Yuki Kamide
1
1School of Psychology, University of Dundee, Dundee, United Kingdom, 2School of Psychology, University of Glasgow, Glasgow, United Kingdom
Abstract
In describing motion events verbs of manner provide information about the speed of agents or objects in those events. We
used eye tracking to investigate how inferences about this verb-associated speed of motion would influence the time
course of attention to a visual scene that matched an event described in language. Eye movements were recorded as
participants heard spoken sentences with verbs that implied a fast (‘‘dash’’) or slow (‘‘dawdle’’) movement of an agent
towards a goal. These sentences were heard whilst participants concurrently looked at scenes depicting the agent and a
path which led to the goal object. Our results indicate a mapping of events onto the visual scene consistent with
participants mentally simulating the movement of the agent along the path towards the goal: when the verb implies a slow
manner of motion, participants look more often and longer along the path to the goal; when the verb implies a fast manner
of motion, participants tend to look earlier at the goal and less on the path. These results reveal that event comprehension
in the presence of a visual world involves establishing and dynamically updating the locations of entities in response to
linguistic descriptions of events.
Citation: Lindsay S, Scheepers C, Kamide Y (2013) To Dash or to Dawdle: Verb-Associated Speed of Motion Influences Eye Movements during Spoken Sentence
Comprehension. PLoS ONE 8(6): e67187. doi:10.1371/journal.pone.0067187
Editor: Ayse Pinar Saygin, University of California San Diego, United States of America
Received January 16, 2013; Accepted May 15, 2013; Published June 21, 2013
Copyright: ß2013 Lindsay et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The present research was supported by ESRC (RES-062-23-2842 awarded to YK and CS; RES-062-23-2749 awarded to YK). (http://www.esrc.ac.uk/) The
funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: shane.lindsay@gmail.com
Introduction
We use language to refer to events in the world. Studies of
language comprehension using the situation or mental model
frameworks (e.g., [1]) have helped demonstrate that language users
keep track of a wide variety of information about those events,
such as their spatial and temporal properties [2,3]. In recent years,
the theory of mental simulation [4] has been used to provide a
representational substrate for mental models [5]. The idea is that
understanding an event described in language invokes a dynamic
multi-modal recreation of the experience of that event, using the
same sensorimotor systems that would be involved with the
sensorimotor interaction with a real event. Evidence in support of
the perceptual simulation theory has come from findings that
language comprehension leads to the activation of visual features
such as the shape [6] and orientation of objects [7] implied by
situations described in discourse. As well as static properties, there
is evidence for the activation of more dynamic visual information
such as direction of movement [8] and motion of moving objects
[9]. For example, when hearing sentences describing the throwing
of a baseball, participants were faster to verify whether two
pictures matched when the relationship between the pictures was
analogous to the motion implied by the sentence [9].
An important aspect of an event is the amount of time it takes to
occur. The duration of an event may differ for various reasons,
such as the fact that a movement from one location to another can
take a long time because the distance is large or the movement is
slow. In classic work, Kosslyn, Ball and Reiser [10] showed that
scanning time for mental images took longer when there was a
longer distance to scan, in the same way that the time it would take
to look across a real map would be related to the distance between
points on that map. These results are compatible with the theory
of mental simulation which posits that simulations are analogues of
perception of events in the world. For language processing, this
means that comprehension of sentences referring to more ‘time-
consuming’ events should lead to more time-consuming simula-
tions.
This idea has been studied in several studies of text compre-
hension. In children’s narrative comprehension [11], processing
times for sentences were longer when agents in a story were
described as travelling slowly (walking), compared with travelling
quickly (being driven in a car), consistent with the idea that their
comprehension of the narrative involved a simulation of the
events. Relatedly, Matlock [12] reported a reading study in which
processing times for implied speeds of journeys were examined.
She used grammatical constructions that Talmy [13] described as
co-extension path fictive motion. This kind of fictive motion in
English involves the use of a motion verb construction (typically
manner neutral) to describe the spatial properties of a path (e.g.,
‘‘the road runs through the valley’’) or a linearly extended entity (e.g.,
‘‘the cord runs along the wall’’). Matlock found that participants
took longer to process sentences where the implied journey was
long or slow, and only when fictive motion was described. The
speed of the journey was manipulated by different descriptions of
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the distance of the journey, the difficulty of traversing terrain on
that journey, or more explicit descriptions of the speed of the
journey (e.g., driving at 100 mph in a Ferrari vs. 40 mph in a VW
bus). Further support for the idea that simulations reflect speed of
inferred events comes from Yao and Scheepers [14], who found
that it took longer to read direct speech quotations silently when
the quotations were contextually implied to be uttered by a slow-
speaking than by a fast-speaking quoted protagonist, suggesting
that participants invoked an analogue mental simulation of the
described speech.
Since one of the claims of the simulation approach is that visuo-
spatial representations are activated by language comprehension, a
natural method to study those representations is through the use of
eye tracking. One such study was done by Spivey, Tyler,
Richardson, and Young [15], who found that when hearing
stories involving events which occur in a particular direction (e.g.,
on increasingly higher floors of an apartment building) partici-
pants’ eye movements mimicked the directionality of the event in
the story, suggesting a scanning process akin to constructing a
mental model of the described situation. Another eye tracking
method that can be used to study simulation in language is the
visual world paradigm [16], where spoken language is presented
with accompanying scenes. Matlock and Richardson [17]
employed this technique in an eye tracking study of sentences
with a fictive motion construction. They measured eye movements
while participants viewed simple scenes featuring horizontal or
vertical paths or linearly extended entities, without any agents or
goals. The scenes were accompanied by auditory presentation of
sentences with fictive motion constructions describing the spatial
properties of those paths and linearly extended entities. These
sentences were contrasted with sentences without a motion verb
construction, such as ‘‘the road is in the valley’’. They found
longer looking times along the path for the fictive motion
sentences, which they argued stemmed from fictive motion
constructions invoking a mental simulation of motion which
encouraged visual scanning along the paths or linearly extended
entities. A follow up study [18] found that time spent looking at
paths was influenced by the complexity and difficulty of traversal
of that path – additional information that was provided in context
sentences heard before scene viewing (e.g., ‘‘the valley was flat and
smooth’’ vs. ‘‘the valley was full of potholes’’). Here participants
spent more time inspecting paths in the difficult terrain condition,
but only for those sentences with the fictive motion construction.
In the above studies on fictive motion no literal motion of agents
was described; while a road may provide a means to support
motion events, a cord cannot literally run along a wall. This makes
it unclear what they reveal about comprehension of events with
moveable agents undergoing actual motion. Instead, rather than
revealing the nature of event processing or of figurative language
understanding, fictive motion constructions may be seen as a
conventionalised means of expressing a spatial property, where
verbs are paired with a prepositional phrase which emphasises the
linear-path quality of a spatial arrangement (e.g., fictive: ‘‘runs
along’’ vs. the non-fictive: ‘‘is next to’’). Taken together, research
on fictive motion supports the idea suggested by Talmy [13] that
such constructions invoke a ‘‘survey’’ type (i.e., a bird’s eye view,
[19]) emphasizing the spatial property of extension or elongation
(‘‘the path goes from the town to the beach’’). The results by
Matlock and Richardson suggest that these types of spatial
representations invoke a dynamic mental simulation which
involves scanning along the extent of the featured entity.
In the current work, we also focus on paths, but use an eye
tracking visual world paradigm to index the online allocation of
visual attention when understanding sentences involving an agent
undergoing an actual motion event. One of the advantages of this
paradigm is that it allows investigation of the coupling of the visual
world and language over time, as language users incrementally
build models of events described in language. Earlier work has
shown that language-mediated eye movements reflect the mapping
of inferred locations of objects featured in a discourse when
presenting with static visual scenes [20], with locations being
updated online in accordance with the event described by the
language. In our experiment below, we presented participants with
static scenes depicting an agent, a path and a goal. As they viewed
these scenes, they heard a sentence which described an agent
undergoing a location change signalled by a verb phrase with a
motion verb and path-denoting satellite or prepositional phrase,
explicitly signalling the path to a goal. An example scene with
accompanying sentences is shown in Figure 1. Crucially, we varied
the manner of motion described by the verb. An important aspect
denoted by manner of motion is speed, with some manners
suggesting fast motion (e.g., ‘‘sprint’’) and some slow (‘‘crawl’’).
According to the simulation theory of language comprehension,
participants should process linguistically provided information
about the speed of a journey in a similar way to how we might
perceive that journey in the world. Based on evidence that
language users dynamically map and update locations of entities in
the visual world in response to language, we expected that when
the verb in a sentence suggests a slow movement towards a goal
then that journey should be inferred to take longer than if it was a
fast movement. As a consequence, eye movements would reveal
more looking along the path on which the agent will travel. In
contrast, when the verb implies a quick journey, participants
should spend less time looking along the path and instead look
more and earlier to the goal, as the agents would be projected to
arrive at the goal earlier. In addition to eye tracking, we also
collected computer mouse movement tracking data [21] for the
same stimuli in a separate task, to give us a more direct measure of
how participants interpreted the speed at which the agents moved.
Using a mouse, participants moved the agent within the scene to
the end location described in the sentence. For slow verb sentences
we expected it would take longer to move the agent to the goal
compared with the fast verbs.
Methods
Participants
Forty-three native English speakers without visual or auditory
impairments were recruited from the University of Dundee, and
received course credits or £4 payment.
Ethics Statement
The present research was approved by the University of
Dundee Research Ethics Committee (Ref: SREC 1103). All
participants provided their written informed consent prior to
participation.
Materials and Stimulus Construction
Initially 89 candidate verbs of movement were selected, and we
conducted a two-part norming questionnaire with 14 participants.
In the first part, each verb was inserted into a sentence in which
the agent and goal were replaced with the letters X and Y, and
only the verb differed across items. For example: ‘‘The X will run
along the path to the Y’’. This was done to ensure that participants
focused on the meaning of the verb and its implied speed of
agentive travel, rather than the semantics of the agents and goals.
Participants had to rate how quickly the verb implied movement to
the goal, with a rating of 1 for ‘‘very slowly’’ and 7 for ‘‘very
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quickly’’. Participants then completed a movement suitability
questionnaire with the same items, but now had to judge how
suitable the verb was for describing a movement of X to Y along a
path, where a rating of 1 meant the verb was ‘‘very unsuitable’’ (‘‘a
strange way to describe movement from one location to another’’)
and 7 was ‘‘very suitable (‘‘would often use the verb in such a
way’’). These ratings led to the selection of the 16 fast and 16 slow
verbs with the highest or lowest ratings of speed, and suitability
ratings above 3.75 (fast: mean speed rating = 6.06, SD = 0.49,
mean suitability rating = 5.46, SD = 0.84; slow: mean speed
rating = 2.05, SD = 0.40, mean suitability rating = 5.13, SD = .65).
There was no significant difference in the spoken length of the
verbs (p..10), but fast verbs had significantly higher frequency
using log10 CELEX lemma frequencies [22] (fast: M= 1.28,
SD = 0.57; slow: M=.56, SD = .42; t(32) = 3.6, p,.001).
Thirty-six 10246768 pixel colour scenes were constructed
containing an agent and goal which were selected from a
commercially available clip-art database. The background and
path were manually created using graphics software and chosen to
thematically match the agents and goals (e.g., a canyon landscape
for a cowboy and cactus). The paths directly connected the agent
and goal with an equal division of horizontal or vertical paths, with
the agent appearing close to the middle of one side of the screen,
and the goal appearing on the opposite side (see Figure 1 for an
example scene and sentence).
Thirty-six sentences (see Table S1 for the full set) were
constructed to match the scenes, with each scene having a
matching fast and slow verb speed version. Sentences used the
same syntax with a future tense marker ‘‘will’’, the preposition
‘‘along’’, one of seven possible path nouns (e.g., ‘‘track’’, ‘‘path’’,
‘‘road’’), and a unique agent and goal combination. Each of the 18
fast and 18 slow verbs was paired with two scenes, with sentences
counterbalanced across two lists so that a participant saw all scenes
but heard each verb only once. Stimuli were recorded by a native
speaker of British English at 44,100 Hz. To create each pair of
sentences, the verb and the preposition (e.g., ‘‘run along’’) were
cross-spliced using sound editing software from a recording of the
fast version of the sentence into a recording of the slow version, to
ensure that the sentences were otherwise acoustically identical.
We also included 18 filler scenes with different verbs to the
experimental items, which involved an equal split of either
omission of the path from the scene and accompanying sentence
(e.g., ‘‘The sheep will travel to the tractor’’), removal of the goal
from the scene and sentence (e.g., ‘‘The waiter will walk along the
path’’), or had a non-movement verb without a path in the scene
or sentence (e.g., ‘‘The sailor will dream about the windmill’’).
Finally, we conducted an additional norming study on Amazon
Mechanical Turk in which 24 participants took part. Experimental
and filler sentence-picture combinations appeared in a random
order and participants had to rate the suitability of each agent-
verb combination (‘‘how suitable do you think the verb is for
combining with the agent in describing movement of that agent
along a path to another location’’) on a 7-point scale (1 for very
unsuitable, 7 for very suitable). In the instructions, it was stressed
that ratings should not be based on how suitable the path or goal
was. Along with the experimental items, we included the 18 fillers,
Figure 1. Example scene for the fast/slow sentences: ‘‘The student will run/stagger along the trail to the picnic basket’’.
doi:10.1371/journal.pone.0067187.g001
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and adapted their verbs to make them less suitable (e.g., ‘‘The tiger
will puncture to the house’’), ensuring a wide range of responses.
Ratings for suitability were slightly higher for the fast verb
sentences (fast: M=4.7, SD = 0.9; slow: M= 4.2, SD = 1.2;
t1(22) = 3.6, p,.001; t2(71) = 2.01, p= .04).
Procedure
Participants’ eye movements were recorded using a head
mounted SR Research Eyelink II tracker sampling at 500 Hz.
Viewing was binocular but eye movements were recorded from
one eye. Scenes were viewed at a distance of approximately 60 cm
on a 120 Hz 210CRT monitor and sentences were played at a
conversational level in mono across a pair of speakers positioned
on either side of the computer monitor. Participants were
instructed that they were going to view some scenes with
accompanying sentences describing an event, and their task was
to ‘‘listen carefully to each sentence while looking at the screen,
and try to understand what will happen in each scene’’. Scenes
were presented for 1000 ms before onset of the spoken sentences
to allow examination of the display before hearing the sentences.
The trial finished 8000 ms after sentence onset. Mean sentence
length was 4188 ms (SD = 354 ms). Trial order was randomised
per participant. In between each trial a centrally located dot was
presented which participants had to fixate upon before the trial
started, to allow for an automatic drift correction. After every 8
th
trial the eye tracker was recalibrated using a 9-point fixation
stimulus, which took approximately 20 s. Four practice trials were
presented to allow participants to become familiar with the
procedure. The eye tracking portion of the experiment took
approximately 25 minutes.
After a short break, the same participants performed a mouse
tracking task with the same stimuli, with a different randomised
order of trials per participant. Participants were given the
following instructions: ‘‘Your task is to listen carefully and
understand the event being described in the scene, and then after
you hear the tone you should use the mouse to drag the agent to
where the sentence describes it will move to’’. The tone appeared
1000 ms after the sentence ended, which co-occurred with the
appearance of a cursor in the middle of the screen. Participants
used their self-reported dominant hand to ‘‘drag and drop’’ the
agent using the left mouse button. There was no time limit for
responding. Trials ended after the release of the mouse button,
giving participants only one opportunity to move the agent. Only
the agent could be moved. For those sentences in which they did
not think the agent moved at all (which was implied in some of the
filler items) they were instructed to press the space bar to move on
to the next trial. Four practice trials were given. This part of the
experiment took approximately 15 minutes.
Results
Contrary to the chronological order, we report the mouse
tracking data first, as we expected that without an effect in the
mouse tracking task, differences in eye movements would be
minimal.
Mouse Movement Tracking Analysis
We used the recording of the time between the first click on the
mouse button (‘‘picking up’’ the agent) and its release (‘‘dropping’’
the agent at the end destination) as a measure of how quickly
participants interpreted the speed of movement in the scenes.
Results were analysed using Generalized Estimating Equations
(GEE) [23,24] using a Gaussian distribution and identity link
function, with Verb Speed (fast vs, slow) as a categorical predictor
and both lexical frequency of the verb (CELEX log10 frequencies)
and agent-verb suitability (Mechanical Turk ratings per item) as
continuous predictors. The frequency and suitability covariates
were included to examine whether effects of Verb Speed occur
above and beyond potential influences of these confound variables
on processing time; we will not report any significant effects of
these covariates as they are not of theoretical interest. Two types of
analyses were performed: one treating the three predictors within-
participants variables (reported as x2
p) and one treating the
predictors as within-items variables (x2
i). In each case, an
exchangeable covariance structure was assumed for repeated
measurements. Participants took significantly longer to move the
agent to the end location for the slow Verb Speed condition
(M= 1422 ms, SD = 1041 ms) compared with the fast Verb Speed
condition (M= 852 ms, SD = 384 ms) [x2
p(1) = 96.01, p,.001;
x2
i(1) = 120.93, p,.001]. These results are shown in Figure 2.
The effects were not due to the amount of distance travelled, as
there was no significant difference between conditions in terms of
number of pixels moved from start to end location (ps..05). The
mouse tracking data corroborate the results from the movement-
speed ratings by showing that, when embedded in meaningful
sentences with accompanying visual scenes, speed-associated verb
information influences the interpretation of how long it takes an
agent to reach the goal (note that speed was actually not explicitly
mentioned in the mouse tracking instructions). Indeed, there was a
very strong correlation between the speed ratings in the norming
study and the mouse tracking data (r(70) = .87, p,.001). In the
following eye tracking analysis we focused on online incremental
interpretation as the sentence was unfolding.
Eye tracking: Looking Time Analysis
To examine eye movements, three spatial Regions of Interest
(ROI) per scene were assigned manually using the SR Research
Data Viewer software. This was done by constructing rectangles
which contained boundaries of the Agent and Goal, and a four-
sided polygon which enclosed the Path. In cases where the Path
ROI overlapped with either the Agent or the Goal ROI, fixations
on those ‘ambiguous’ areas were counted as being on the Agent or
Goal, respectively.
We first analysed total dwell time, which was the summed total
duration of fixations within a ROI across a time window. As we
expected shifts in visual attention to be modulated by verb
semantics we examined the time window from verb onset to the
end of the sentence. The mean duration of the time windows were
3150 ms for the fast verb condition and 3177 ms for the slow verb
condition. Dwell time results are shown in Figure 3. Analyses
employed the same GEE modelling approach as described in the
mouse tracking section, but with separate models for looks to the
Agent, Path and Goal ROIs. Also, we now used a gamma
distribution and log link function to better account for the fact that
the dwelling time distributions were positively skewed. As
predicted, participants spent significantly longer looking at the
Path for slow verb sentences compared with fast verb sentences [x2
p
(1) = 9.79, p,.001; x2
i(1) = 4.64, p=.031]. In contrast, there was
longer dwelling on the Goal ROI for fast verb sentences,
significant by-participants and marginal by-items [x2
p(1) = 5.51,
p=.019; x2
i(1) = 3.53, p=.06]. There were no significant differ-
ences in dwell times for the Agent ROI (ps..4).
Eye tracking: Proportions of Fixations over Time Analysis
Complementing our analysis of dwell time, we analysed
proportions of fixations over time. While this measure is typically
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correlated with dwell time (more fixations should lead to longer
overall looking times), the present analysis has the advantage of
revealing fine-grained changes in attention to different ROIs over
time.
Mean proportions of fixations over time (separately for each
ROI) are shown in Figure 4, with aggregation of data by 50 ms
time bins. In line with the dwell-time results, Figure 4 shows that
looks to the Agent ROI were not affected much by experimental
condition, therefore further analyses focused on the theoretically
most relevant Goal and Path ROIs only. Descriptively, Figure 4
suggests that the cross-condition difference in dwell time for the
Path ROI is globally attributable to a difference in overall
likelihood of looks, with a higher ‘peak’ of the curve in the slow
than in the fast Verb Speed condition. Particularly noticeable for
the Goal ROI is an early ’anticipatory’ bias towards this region,
shown by curves rising well in advance of the goal noun being
mentioned at the end of the sentence. This was not unexpected, as
the nature of the scenes and sentences used made the Goal easy to
predict, and these anticipatory effects have been previously
documented in visual world experiments [25]. What was also
apparent for the Goal ROI was a cross-condition difference in
these anticipatory looks. The bottom panel of Figure 4 suggests
that this cross-condition difference can be characterised as a
difference in timing of visual attention: relative to the slow Verb
Speed condition, the upwards curve for the fast Verb Speed
condition appears to be shifted more to the left on the time
dimension.
To qualify these observations more formally we divided the
considered time period into four smaller time windows of interest
(on a by-trial basis) based on critical linguistic information
available. These four windows are shown in Figure 4: the Verb
window (mean length = 1159 ms; from onset of the verb to onset
of the path noun; e.g., ‘‘run_along_the_’’; ‘‘_’’ indicates a pause in
the speech streams); the Path window (mean length = 1340 ms;
from onset of the prepositional noun to the onset of the goal noun;
e.g., ‘‘trail_to_the_’’); the Goal window (mean length = 647 ms;
from goal noun onset to the end of sentence, e.g., ‘‘picnic basket.’’);
and the Post-sentence window (from end of sentence until 1000 ms
later, to establish the persistence of any verb-associated differences
in the absence of linguistic inputs).
The trial-level data per ROI (Path,Goal) and time window (Verb,
Path,Goal, and Post-sentence) were statistically modelled using GEE.
Given that responses at trial by time-bin level were dichotomous (a
fixation on a given ROI either occurs or not), we specified a
binomial distribution and logit link function in these analyses. Two
types of analyses were performed per ROI and time window of
interest: the first treated Verb Speed (fast vs. slow) and Time
(continuous predictor referring to the 50 ms time bins per window)
as within-participants predictors (x2
p) and the second treated the
two variables as within-items predictors (x2
i). Effects of Verb Speed
refer to condition-dependent differences in the overall likelihood of
looking within a time window; the slope of the continuous Time
variable indicates changes in proportions of looks over time, with
the coefficient indicating an increase (positive slope), decrease
(negative slope) or no change at all (slope around zero). To account
for the fact that time series data tend to be more correlated for
adjacent time bins than for time bins that are further apart, a first
order auto-regressive (AR1) covariance structure was assumed. The
results from these analyses are shown in Table 1 (looks to the Path
ROI) and Table 2 (looks to the Goal ROI) and will be summarised
in the following sections.
Figure 2. Mean movements times for the movement tracking task. Error bars indicate within-participant 95% confidence intervals [35].
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Although it would have been desirable to also include the verb
frequency and agent-verb suitability covariates in these models
(analogous to the previous mouse tracking and dwell time
analyses), this turned out to be computationally less feasible with
the present data. Test runs using a full four-factorial design (Verb
Speed6Agent-Verb Suitability6Lexical Frequency6Time) yield-
ed an estimated processing time requirement of about 190 hours
across all ROIs and time windows of interest. Moreover, there was
a risk that the four-factorial design would overfit the data or even
fail to converge in some instances (note that parameters are harder
to estimate with binary data).
Instead, we ran additional binary logistic GEE analyses per
ROI and time window of interest (using the same distribution, link
function, and covariance settings as before), but replacing the Verb
Speed6Time design with an Agent-Verb Suitability6Lexical
Frequency6Time design. Hence, the new ‘‘competitor models’’
comprised Time, Lexical Frequency, and Agent-Verb Suitability
as continuous predictors in a full-factorial model. We then
compared the goodness of fit of the competitor models (Agent-
Verb Suitability6Lexical Frequency6Time) with the goodness of
fit of the original models (Verb Speed6Time) for each ROI and
time window of interest. The measure used for this purpose was
the Quasi-likelihood Information Criterion (QIC) metric specifi-
cally developed to quantify goodness of fit in GEE [26]. The
results of these model comparisons are shown in the DQIC
columns in Tables 1 and 2, where DQIC refers to the QIC-
difference between the original model and the competitor model.
Whenever DQIC is negative, the original Verb Speed6Time
model obtained a better fit of the data than the alternative Agent-
Verb Suitability6Lexical Frequency6Time model, indicating that
potential Verb Speed effects were not fully attributable to the
covariates.
Proportions of looks to the Path ROI. Within the Verb
window, proportions of looks to the Path ROI grew about equally
positively in each Verb Speed condition, as reflected in a
significant main effect of Time (associated with a positive slope
coefficient), and no main effect of, or interaction with, Verb Speed.
Within the Path window no reliable effect or interaction with Time
was detected; instead, there was a significant main effect of Verb
Speed such that looks to the Path ROI were generally more likely
in the slow than in the fast condition) [x2
p(1) = 10.33, p=.001;
x2
i(1) = 12.48, p,.001]. For the Goal window there was a main
effect of Verb Speed (more looks to the Path ROI in the slow than
in the fast verb condition, as before) [x2
p(1) = 5.92, p=.015;
x2
i(1) = 8.87, p=.003], and a main effect of Time which was
associated with a negative slope coefficient (meaning decreasing
probabilities of looks to the Path ROI over subsequent time-bins)
Figure 3. Mean total dwell time (total looking time per trail from verb onset to sentence offset) for the Agent, Path and Goal ROI.
Error bars indicate 95% within-participants confidence intervals.
doi:10.1371/journal.pone.0067187.g003
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[x2
p(1) = 4.61, p=.032; x2
i(1) = 5.15, p=.023]. The same pattern of
results also emerged in the Post-sentence window, but the
corresponding effects were reliable by items only [Speed:
x2
p(1) = 3.35, p=0.067; x2
i(1) = 5.67, p=.017; Time:x2
i(1) = 1.56,
p=.211; x2
i(1) = 4.23, p=.040].
The DQIC values in Table 1 reveal generally superior fits of the
Verb Speed6Time model in comparison to the alternative Agent-
Verb Suitability6Lexical Frequency6Time model, especially in
time windows with significant Verb Speed effects.
Proportions of looks to the Goal ROI. For the Verb
window, a reliable main effect of Time emerged [x2
p(1) = 5.50,
p=.019; x2
i(1) = 6.78, p=.009] which was further modulated by a
significant Verb Speed6Time interaction [x2
p(1) = 3.99, p=.046;
x2
i(1) = 5.64, p=.018]; the main effect was due to a reliably positive
slope coefficient for the Time variable (meaning increasing
probabilities of looks to the Goal over time) and the interaction
was due to the fact that this slope coefficient was significantly more
positive (steeper slope) in the fast than in the slow Verb Speed
condition. This finding corroborates the pattern in Figure 4 which
indicates a tendency to look earlier at the Goal ROI in the fast
Verb Speed condition, and helps provide a more nuanced account
for the longer dwell times on the Goal ROI: participants started to
look earlier at the Goal for the fast verb sentences, and this led to
longer dwell times overall.
Results for the two time windows that follow (Path and Goal)
were qualitatively the same, showing a main effect of Time (due to
reliably positive slope coefficients overall) and a main effect of
Verb Speed (due to proportions of looks to the Goal ROI being
higher in the fast than in the slow Verb Speed condition), but no
interaction [Path Time Window -Speed:x2
p(1) = 10.37, p=.001;
x2
i(1) = 10.48, p=.01; Path Time Window - Time:x2
p(1) = 23.90,
p,.001; x2
i(1) = 25.87, p,.001; Goal Time Window - Speed:
x2
p(1) = 6.97, p=.008; x2
i(1) = 9.32, p=.002; Goal Time Window -
Time:x2
p(1) = 7.91, p=.005; x2
i(1) = 7.24, p=.007]. In the final time
window of interest (Post-sentence; from end of sentence until
1000 ms later) the main effect of Verb Speed disappeared,
Figure 4. Mean proportions of fixations for Agent, Path and Goal regions, where 0 ms = verb onset. Shaded area around mean line
indicates 16std. error from the mean.
doi:10.1371/journal.pone.0067187.g004
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implying that the two curves rose to roughly the same peak
proportion of looks to the Goal ROI at maximum time. The only
close to reliable effect in this time window was a main effect of
Time with negative coefficient [x2
p(1) = 3.38, p=.066; x2
i(1) = 3.98,
p=.046].
Again, the DQIC values in Table 2 were consistently negative,
indicating that Verb Speed6Time yielded a better account of the
data than Agent-Verb Suitability6Lexical Frequency6Time.
Item Correlation Analysis
To further demonstrate that our effects were driven by the
speed of journey implied by the verb (rather than some other
factor), we correlated the movement times (in ms) from the mouse
tracking task with the dwell time data from the eye tracking task on
a by-item basis. There was highly reliable positive correlation
between the mean amount of time dwelling on the Path (eye
tracking) and how long it took participants to move the agent to
Table 1. Binary logistic GEE results for the Path ROI (per time window of interest).
By-participants (N = 43) By-items (N = 36)
Window Effect Est.
SE
x2
p
p
D
QIC
Est.
SE
x2
i
p
D
QIC
Verb Speed +.306 .178 2.67 .10 +.307 .187 2.64 .11
Time +.058 .008 15.18 .001 +37.5 +.062 .010 24.11 .001 248.3
S6T2.017 .010 2.60 .11 2.016 .011 2.41 .12
Path Speed 2.500 .132 10.33 .001 2.501 .116 12.48 .001
Time 2.002 .007 0.04 .85 2108.7 2.002 .005 0.09 .76 2200.0
S6T+.005 .009 0.36 .55 +.006 .006 1.01 .31
Goal Speed 2.498 .166 5.92 .02 2.502 .146 8.87 .003
Time 2.029 .014 4.61 .04 2113.2 2.030 .012 5.15 .03 2193.4
S6T+.004 .019 0.05 .83 +.005 .012 0.15 .70
Post-sent. Speed 2.364 .189 3.35 .07 2.362 .142 5.67 .02
Time +.004 .009 1.56 .21 258.6 +.004 .002 4.23 .04 2183.5
S6T+.013 .012 1.28 .26 +.013 .008 2.26 .13
Table shows GEE modelling results for occurrences of looks to the Path ROI as a function of Verb Speed (fast vs. slow) and Time (continuous predictor referring to the
50 ms time-bins per window). Shown are the by-participant/by-item GEE parameter estimates and corresponding SEs (both in logit units) along with generalised score
chi square statistics and related p-values (at df = 1). As for interactions (S6T), a positive parameter estimate indicates a more positive slope for the Time predictor in the
fast than in the slow Verb Speed condition. DQIC refers to the goodness of fit of the Verb Speed6Time model in relation to a competitor Agent-Verb Suitability6Lexical
Frequency6Time model (negative DQICs indicate superior fit of the Verb Speed6Time model).
doi:10.1371/journal.pone.0067187.t001
Table 2. Binary logistic GEE results for the Goal ROI (per time window of interest).
By-participants (N = 43) By-items (N = 36)
Window Effect Est.
SE
x2
p
p
D
QIC
Est.
SE
x2
i
p
D
QIC
Verb Speed 2.017 .138 0.02 .90 2.021 .132 0.03 .87
Time +.009 .004 5.50 .02 234.9 +.010 .004 6.78 .01 2176.7
S6T+.017 .008 3.99 .05 +.018 .006 5.64 .02
Path Speed +.424 .118 10.37 .001 +.422 .110 10.48 .001
Time +.033 .005 23.90 .001 2192.3 +.036 .005 25.87 .001 2325.1
S6T2.004 .006 0.35 .55 2.004 .006 0.44 .52
Goal Speed +.380 .133 6.97 .01 +.377 .104 9.32 .002
Time +.029 .010 7.91 .005 266.5 +.030 .012 7.24 .01 291.1
S6T2.018 .012 2.09 .15 2.017 .011 1.92 .17
Post-sent. Speed +.130 .092 1.91 .17 +.128 .098 1.63 .20
Time 2.006 .004 3.38 .07 22.9 2.007 .003 3.98 .05 293.8
S6T2.001 .007 0.03 .88 2.001 .006 0.03 .87
Table shows GEE modelling results for occurrences of looks to the Goal ROI as a function of Verb Speed (fast vs. slow) and Time (continuous predictor referring to the
50 ms time-bins per window). Shown are the by-participant/by-item GEE parameter estimates and corresponding SEs (both in logit units) along with generalised score
chi square statistics and related p-values (at df = 1). As for interactions (S6T), a positive parameter estimate indicates a more positive slope for the Time predictor in the
fast than in the slow Verb Speed condition. DQIC refers to the goodness of fit of the Verb Speed6Time model in relation to a competitor Agent-Verb Suitability6Lexical
Frequency6Time model (negative DQICs indicate superior fit of the Verb Speed6Time model).
doi:10.1371/journal.pone.0067187.t002
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the goal location (mouse tracking): r
(70)
= .54, p,.001). In contrast,
there was a non-significant negative correlation between dwelling
times on the Goal and the mouse-movement times ( =2.16,
p= .19). We also correlated mean gaze proportions (from verb
onset to sentence end) with the time it took participants to move
the agent to the goal in the mouse-tracking task. Again, we found a
significant positive correlation between the mouse-tracking data
and gaze proportions on the Path (r
(70)
= .43, p,.001), and a
significant negative correlation between the mouse-tracking data
and gaze proportions on the Goal (r
(70)
=2.36, p,.01). No such
correlations were obtained between eye-tracking data on the agent
ROI and the mouse-tracking data (all ps..1). Unsurprisingly,
given that the offline speed ratings were highly correlated with the
mouse-tracking data, correlations between speed ratings and eye
tracking measures gave a similar pattern of results. In summary,
verb-related speed of motion influenced participants’ interpreta-
tion of how fast the agent would move to the goal, as reflected in
the corresponding mouse-tracking data and in participants’ eye
movement behaviour.
Discussion
Taken together, the present results show that dynamic
information associated with the inferred speed of a linguistically
described motion event influences visual attention and motor
execution in a way consistent with simulation-based accounts of
language comprehension [5,27]. In other words, our results
support the idea that participants simulate the movement of the
agent along the path to the goal. For sentences with verbs that
imply a slow motion towards a goal, we expected that participants
would allocate greater visual attention along the path of travel to
that goal. This was confirmed by our eye tracking results which
showed that slow verb sentences led to more time looking along
the path and more looks to the path. For sentences with verbs
implying faster movement towards the goal, we found that
participants were actually faster to look at the goal than for
sentences with verbs implying slow movement towards the goal.
Because of this, they also spent more time in total on the goal
region in the fast verb condition. Thus, if the verb implies fast
movement towards the goal, listeners spend less time looking at the
path region and more quickly shift their visual attention towards
the goal region (in line with a simulated fast movement);
conversely, if the verb implies a slow movement towards the goal,
listeners are less quick to attend to the goal and spend more time
looking at the path region instead (in line with a simulated slow
movement). These speed-associated differences in interpretation
were confirmed in our mouse tracking task which showed that fast
verbs led to faster movements of the agent to the goal than the
slow verbs.
In interpreting these findings it is important to clarify how
simulation contributes to language comprehension in the visual
world paradigm. One perspective on simulation in language
comprehension is that it provides a tool for enriching inferences
about the visual-spatial properties of situations, the kinematics of
motor actions, and emotional and introspective states. This is
beneficial as the semantic specification provided via language is
schematic [28]. The scenes viewed in the visual world paradigm
provide additional information to help flesh out skeletal language
semantics, allowing for a richer specification of the visual-spatial
properties of the described event than provided by language alone.
Our assumption is that attention in the visual world paradigm can
index how participants track the locations and changes in location
of objects as indicated by unfolding linguistic information. Our use
of the term ‘‘simulation’’ is a description of the process of mapping
out these dynamic visuo-spatial inferences onto a static visual scene.
In keeping with the theory of simulation, this may involve some of
the same sensorimotor systems used in apprehending such events
in the real world. Furthermore, the visual world context may
provide a way of cognitive out-sourcing, where an internal mental
model which combines linguistic and visual world information is
dynamically mapped out onto the visual world, using the latter as
an external representational system [29].
In this study we manipulated the speed of the journey implied in
an event by use of different manner of motion verbs. Potentially
there are other ways to manipulate speed of journey, such as
changing the difficulty of the path [18], the intentions of the agent
[11], the mode of transport (e.g., by car vs. on foot, [11]), or the
implied speed of the agent (a racing boat vs. a tug boat). In
principle, if the construction of a mental model for a movement
event involves a multimodal simulation, then we should find that
visual attention to a depiction of that event should be modulated
by the mental model consistent with an ‘‘acting out’’ of that event,
regardless of whether crucial information for that simulation
process stems from the verb, (visible) situational affordances, or
linguistic context.
While our results depend on the use of a visuo-spatial context
that promotes activation of perceptual representations, other
studies have shown evidence for simulation of event speed in the
absence of a visual world, such as in the rate of talking in
quotations of direct speech [14,30]. However, there may be
limitations in how simulation based accounts can straightforwardly
explain comprehension of temporality in language. Based on the
findings that sentences describing longer events can take longer to
process, Coll-Florit and Gennari [31] argued that processing time
differences may occur because longer events are associated with
greater complexity and therefore lead to greater number of
inferences. While these results are not entirely incompatible with
simulation approaches, for long and complex events a simple
analogue relationship between the event described and a
simulation of that event is not tenable. For example, it is hard to
conceive how one could simulate the running of a whole
marathon. Instead, simulation would appear to need scaling and
temporal compression, where the time involved in a simulation of
an event should have a proportional relationship to the length of
an event, but simulations are compressed to allow the generation
of inferences appropriate to the discourse context in the time
available for discourse comprehension. This compression may
involve particular focus on start states, intermediate states, end
states and the transitions between them.
Along with manner of motion, verbs can provide information
about paths. Our results indicate that language semantics
modulates attention to depicted paths when the language explicitly
describes agents travelling on them. In future work we are
exploring how path information that is not visually depicted but
implied by the verb (e.g., ‘‘jump’’ being associated with an
upwards path) influences visual attention. It remains to be seen
whether similar results could be found without explicit naming of
the path and its depiction in the visual world. Since fixations in the
visual world are primarily drawn to denoted objects rather than
blank areas of the screen, the present naming and depiction of the
path could have promoted looks to the region in between the agent
and the goal which otherwise might not have occurred. Along with
focusing attention on the path and the journey along it,
mentioning the path in the sentence may also provide sufficient
time for participants to incrementally and rapidly update the
position of the agent as they hear the sentence. We also suspect
that the results may have been influenced by the richness of other
types of manner information associated with the verbs. Informal
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examination of the verbs suggests that the slow verbs (such as
‘‘stagger’’) were generally more manner rich than the fast verbs,
which included verbs like ‘‘hurry’’ and ‘‘rush’’. One prediction
from the application of the simulation theory to visual world
language comprehension is that participants map visual inferences
about characteristic manners of motion onto the visual world. If
so, then manner rich verbs such as ‘‘stagger’’ could enhance
attention to the agent and/or the path along which the agent
moves, compared with verbs like ‘‘go’’ which do not focus on the
manner of movement. Note that we found no differences in visual
attention to the agent as a function of verb-related speed of
motion. However, if participants dynamically update the inferred
location of the agent as they hear the sentence, then inferences
about manner would be projected onto the updated location of the
entity, such as its position on the path or in reaching the goal,
rather than its starting position visible on screen. As speed of
motion is at least partly determined by manner of motion (i.e.,
‘‘staggering’’ is slow because it is a cumbersome and stilted
movement) it is difficult to disentangle their individual contribu-
tions on the basis of the present data. This question awaits future
clarification.
We have recently come across a similar study to ours which also
manipulated the implied speed of verbs using a visual word world
paradigm [32]. They used sentences where no path was
mentioned (e.g. ‘‘the lion ambled/dashed to the balloon’’), but
with scenes that included a path. Across two experiments, they
found that dwelling times on the goal were longer for slow verb
sentences than for fast verb sentences, a result opposite to our
findings (however, note that they did not report any data for the
path region). Dwell time was calculated from the start of the
sentence to approximately 1500 ms after the end of the sentence
(compared with verb onset to sentence end in our study). One
explanation for the discrepancy in results is found in the
descriptive data they present for proportions of looks over time,
which should be highly correlated with dwell time. Like in our
results, there appeared to be a tendency to look at the goal earlier in
the fast verb condition, but in contrast to our study, there was a
much steeper decline in goal-directed looks following the end of
fast-verb sentences, which suggests that their dwell time results
could have been more strongly influenced by processes that were
initiated after hearing the sentence. Note that our sentences were
actually much longer (due to an additional prepositional phrase
denoting the path), which is likely to allow more time to construct
an online spatial representation of the event. In contrast, the
shorter sentences in [32] may have caused participants to delay
this spatial simulation process until after the end of the sentence.
From the mouse tracking results we found that participants
moved the agent consistent with the speed implied by the verb.
This manipulation differs from experiments involving sentence
comprehension where participants respond to sentences using a
motor movement which is indirectly related to the event described
in the language [33,34]. The authors of these studies have argued
that activation of motor information is an automatic by-product of
language comprehension through a simulation process. While the
mouse-tracking data may have reflected such perceptual simula-
tion processes as well, we are more cautious about making such a
strong claim here. Instead, the mouse tracking data largely
confirm the results of the speed-rating norming study. In the
mouse-tracking instructions, we only asked participants to move
the described agent to the described goal, but participants may
have interpreted this as being about verb-related manner of
motion as well. Individual mouse-movement patterns suggest that
this might be the case for at least some participants. For example,
given the verb ‘‘limp’’ several participants moved the agent up and
down consistent with a limping movement. In the eye tracking
study (which always preceded the mouse-tracking task) such
demand characteristics are less likely to explain the results because
the fixation data were collected online during sentence processing,
rather than offline as in the mouse-tracking task. Eye movements
are rapid, ballistic motor responses and it appears implausible that
participants ‘acted out’ the manner of motion implied by the verb
using their eyes.
To conclude, our results indicate that the verb-related speed at
which an event would take place influences the way in which
listeners shift their visual attention online as language unfolds.
These results support the idea that language comprehension
involves dynamic mental simulation of linguistically described
situations. Challenges remain in clarifying how online methods
such as visual world eye tracking reveal the integration of
simulation-driven mental representations with their referents in
the visual world, and how these processes operate in conjunction
with other non-simulation based language comprehension mech-
anisms. In the meanwhile, our results contribute to a better
understanding of the links between the processing of motion events
in the language domain and consequential attention shifts in the
visual domain.
Supporting Information
Table S1 Experimental sentences used in experiment.
(DOCX)
Acknowledgments
We thank Karl Smith Byrne, Aneta Lisowska, and Robert Petrie for their
help with data collection, and Anuenue Kukona, Gabriella Vigliocco and
an anonymous reviewer for their comments.
Author Contributions
Conceived and designed the experiments: SL CS YK. Performed the
experiments: SL YK. Analyzed the data: SL CS YK. Contributed
reagents/materials/analysis tools: SL CS YK. Wrote the paper: SL CS
YK.
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