How Animacy and Verbal Information Influence V2 Sentence Processing: Evidence from Eye Movements

Abstract

There exists a clear association between animacy and the grammatical function of transitive subject. The grammar of some languages require the transitive subject to be high in animacy, or at least higher than the object. A similar animacy preference has been observed in processing studies in languages without such a categorical animacy effect. This animacy preference has been mainly established in structures in which either one or both arguments are provided before the verb. Our goal was to establish (i) whether this preference can already be observed before any argument is provided, and (ii) whether this preference is mediated by verbal information. To this end we exploited the V2 property of Dutch which allows the verb to precede its arguments. Using a visual-world eye-tracking paradigm we presented participants with V2 structures with either an auxiliary (e.g. Gisteren heeft X … ‘Yesterday, X has …’) or a lexical main verb (e.g. Gisteren motiveerde X … ‘Yesterday, X motivated …’) and we measured looks to the animate referent. The results indicate that the animacy preference can already be observed before arguments are presented and that the selectional restrictions of the verb mediate this bias, but do not override it completely.

Full text

Open Linguistics 2019; 5: 630–649
Peter de Swart*, Geertje van Bergen
How Animacy and Verbal Information
Influence V2 Sentence Processing: Evidence
from Eye Movements
https://doi.org/10.1515/opli-2019-0035
Received October 30, 2017; accepted November 26, 2019
Abstract: There exists a clear association between animacy and the grammatical function of transitive
subject. The grammar of some languages require the transitive subject to be high in animacy, or at least
higher than the object. A similar animacy preference has been observed in processing studies in languages
without such a categorical animacy effect. This animacy preference has been mainly established in structures
in which either one or both arguments are provided before the verb. Our goal was to establish (i) whether
this preference can already be observed before any argument is provided, and (ii) whether this preference
is mediated by verbal information. To this end we exploited the V2 property of Dutch which allows the verb
to precede its arguments. Using a visual-world eye-tracking paradigm we presented participants with V2
structures with either an auxiliary (e.g. Gisteren heeft X … ‘Yesterday, X has …’) or a lexical main verb (e.g.
Gisteren motiveerde X … ‘Yesterday, X motivated …’) and we measured looks to the animate referent. The
results indicate that the animacy preference can already be observed before arguments are presented and
that the selectional restrictions of the verb mediate this bias, but do not override it completely.
Keywords: Verb-initial sentences; predictive processing; visual world; Dutch; lexical information;
selectional restrictions; argument structure; grammar-performance connection
1 Introduction
What we talk about influences how we talk about it. This especially is true of the properties of the
referents we talk about. Animacy is one recurrent referential feature that influences the form of linguistic
expressions. Animacy concerns the property of being alive or sentient and is known to affect a wide variety
of linguistic domains, including grammatical gender (Dahl 2000, Siemund 2008), agreement (Ortmann
1998), case (de Swart 2007, Malchukov 2008, Fauconnier 2011) and word order (Tomlin 1986, Siewierska
1988, Branigan et al. 2008, van Bergen 2011). Thus, we find that many languages of different genealogical
and geographic background only overtly mark direct objects that refer to animate entities, but not those
that refer to inanimate entities (differential object marking, Aissen 2003, de Swart 2007, Malchukov 2008).
This is just one example of many. In fact, the effect of animacy seems “so pervasive in the grammars of
human languages that it tends to be taken for granted and become invisible” (Dahl & Fraurud 1996: 47).
The concept of animacy and hence the distinction between animate and inanimate entities is often hard
to define and classification may change from one language or even situation to the next (e.g. Yamamoto
1999). Nevertheless, linguists quite consistently speak about animacy proper in terms of a three-way
Research Article
*Corresponding author: Peter de Swart, Centre for Language Studies, Radboud University Nijmegen, Nijmegen, 6500 HD, The
Netherlands, E-mail: p.deswart@let.ru.nl
Geertje van Bergen, Max Planck Institute for Psycholinguistics, Nijmegen, 6525 XD, The Netherlands
Open Access. ©  Peter de Swart, Geertje van Bergen published by De Gruyter. This work is licensed under the Creative
Commons Attribution . Public License.

Animacy and verbal information in V2 sentence processing  631
hierarchy ranking humans above animals (non-human animates), which in turn are ranked above inanimate
entities: human > animate > inanimate (cf. Comrie 1989). Discussion exists about the grammatical nature
and universality of such prominence hierarchies (see Lockwood & Macaulay 2012, and the contributions to
Bornkessel-Schlesewsky et al. 2015).
We will focus in this paper on what is known as the relational use of the animacy hierarchy: in many
languages two arguments have to be compared with respect to the hierarchy in order to determine the
grammatically licensed form or interpretation of an utterance. A classic example that falls into this category
is word order in Navajo (e.g. Young & Morgan 1987). In this language the element that ranks highest on the
animacy hierarchy has to occur first in the sentence. A related restriction is reported for Mam-Maya (Minkoff
2000). In this language the subject of an active transitive sentence has to rank highest on the hierarchy. This
means that if the agent ranks lower on the hierarchy than the patient, a speaker has to revert to a passive
construction (e.g. the man was seen by the dog). These two examples seem to fit into a wider cross-linguistic
pattern in which the assignment of Agent (subject) and Patient (object) is determined by the generalization
in (1), which we will refer to as the animacy preference:
(1) Agent should outrank Patient on the animacy hierarchy H
l
(where Hl is a language-specific ranking)
The association of entities higher in animacy with the Agent role is not surprising, given that several
agentive properties, such as volitionality and sentience, entail animacy (see Primus 2012 for discussion).
We will be concerned with the reflection of the generalization in (1) in languages in which the animacy
hierarchy has not grammaticalized, or more precisely, does not have a categorical effect, in contrast to
Navajo and Mam-Maya and many other languages. It has long been observed that animacy induces statistical
tendencies in language use that are very similar to the categorical effects discussed for the languages
above. Bresnan et al. (2001) dubbed this pattern ‘hard constraints mirror soft constraints’ and took it as
a motivation for a grammatical model in which constraints can be ranked lower, resulting in statistical
patterns, or high, resulting in categorical patterns. This approach is reminiscent of the notion of cue validity
in the Competition model (MacWhinney & Bates 1989). The idea that there exists a connection between
cross-linguistic grammatical patterns and patterns in language use is, for instance, also incorporated in
the Performance-Grammar Correspondence Hypothesis of Hawkins (2004) and the Interface Hypothesis of
Incremental Argument Interpretation of Bornkessel-Schlesewsky & Schlesewsky (2009).
The grammatical patterns in Mam-Maya and Navajo were, for instance, found to be mirrored in
sentence production in Germanic languages and beyond, as witnessed by corpus frequencies and controlled
experiments (e.g., Øvrelid 2004, Kempen & Harbusch 2004, Snider & Zaenen 2006, see also Branigan
et al. 2008 for discussion). The effect of animacy on language comprehension is also well-documented.
Animacy is, for instance, known to influence the processing difficulty associated with object-relative
clauses in comparison to subject-relative clauses (Trueswell et al 1994, Mak et al. 2002, Mak et al. 2006).
Both Mak et al. (2006) for Dutch and Wu et al. (2012) for Mandarin Chinese propose an animacy preference
constraint that prefers animate subjects over inanimate ones. Similar effects have been observed for simple
transitive sentences. Bornkessel-Schlesewsky & Schlesewsky (2009) provide an overview of cross-linguistic
investigations showing the relational effect of animacy on sentence processing. In particular, when an
animate P-argument has been unambiguously identified and is being followed by an inanimate A-argument
this results in additional processing costs. They incorporated a constraint as in (1) above into their model to
guide role assignment during on-line language processing. Finally, Paczynski & Kuperberg (2011) propose
that a direct mapping from the animacy hierarchy to linear order affects processing, such that less animate
arguments should appear later (the animate-first effect). They observed increased processing costs for
English sentences with an animate direct object in comparison to those with an inanimate one. The effect
was independent of semantic role assignment.
The influence of animacy on language comprehension in languages without categorical animacy effects
is thus firmly established. Most evidence for the reliance on the animacy preference in (1) in language
use comes from studies based on verb-final or verb-medial structures. This means that the observed effect

632  Peter de Swart, Geertje van Bergen
of animacy is completely or partially independent from verbal information. Bornkessel-Schlesewsky &
Schlesewsky (2009) argue that this verb-independent nature is a prerequisite for efficient communication.
Given that many languages have a verb-final word order, the use of an independent heuristic such as that
in (1) above, allows language users to establish an interpretation without delay and before the verb has
been encountered. Nevertheless, the verb may have a profound effect on the assignment of semantic roles
through its selectional restrictions. Due to close connections between animacy and certain role types,
such as agent and experiencer (e.g. Van Valin & LaPolla 1997, Primus 2012), animacy characteristics in
combination with the argument structure of the verb may guide the interpretation of sentences. Thus, when
an animate and inanimate argument are combined with an agentive verb like break the former will be
associated with the Agent slot and hence the subject function in an active sentence, but when combined
with an object-experiencer verb like please it will be associated with the object function. This suggests
that the preference in (1) can be counteracted by the verbal argument structure (e.g. Lamers 2012, Lamers
& de Hoop 2014, Czypionka 2014). The main goal of the current study is to investigate this interaction
between verbal information and the animacy preference in (1). In addition, we want to establish whether
the animacy preference in (1) is already observed before any of the arguments have been encountered. The
studies discussed above inferred the significance of the animacy preference for language comprehension
from the effects that emerge when one or both of the arguments have been processed. The significance of
the animacy preference will be strengthened when similar effects can be observed before any arguments
have been encountered. If language users indeed rely on animacy information to establish an (expectation
of an) interpretation, we would expect to find this reflected in measures of predictive processing. This would
provide complementary evidence that the animacy preference in (1) is used as a heuristic during sentence
comprehension.
It is well known by now that online language comprehension is not a passive integration process:
readers and listeners use various types of (non)linguistic cues to predict upcoming input (for reviews, see
e.g., Federmeier 2007, Kamide 2008, van Petten & Luka 2012, Pickering & Garrod 2013, Kuperberg & Jaeger
2016, among many others). Ways of measuring predictive processing include anticipatory eye movements to
depicted referents while listening to speech (Visual World eye-tracking, e.g., Tanenhaus et al. 1995, Altmann
& Kamide 1999, see also Salverda & Tanenhaus 2017), and modulations of event-related potentials (ERPs)
on linguistic elements preceding the critical word during reading/listening (e.g., DeLong et al. 2005, van
Berkum et al. 2005). For instance, in a seminal study using Visual World eye-tracking, Altmann & Kamide
(1999) showed that language comprehenders use the selectional restrictions of the verb as a cue to predict
which direct object will follow. While listening to sentences like The boy will eat ..., participants fixated the
only edible object (cake) in the visual display more often than non-edible referents, well before they heard
the target word. These semantic restrictions of the verb can also be animacy-based, e.g., The policeman
will arrest … can only be followed by an animate argument. Other evidence for animacy-based predictions
during incremental language comprehension comes from Szewczyk & Schriefers (2013). Using an ERP
reading study, they showed that native Polish listeners use animacy information to predict upcoming direct
objects following a subject and a transitive verb. In their experiment, participants read contexts that were
biasing towards either an animate or an inanimate direct object. If the animacy of the direct object did
not match the expected animacy value, they found enhanced neural activity (N400) on the (differentially
inflected) adjective preceding the critical noun, that is, before the direct object had been fully encountered.
Whereas the above studies demonstrate that animacy information is indeed used to anticipate upcoming
arguments, note that the prediction effects were established after the subject and the verb had already been
processed. As a result, the animacy information was no longer needed for argument disambiguation. Sauppe
(2016) correctly points out that subject-initial (i.e., verb-medial or verb-final) languages conflate different
information types (i.e., grammatical function, semantic role, word order) on a single argument, i.e., the
direct object, hence concealing the precise nature of prediction processes. In order to disentangle effects of
syntactic function, word order and thematic role, he focuses on the verb-initial language Tagalog. Using a
Visual World eye-tracking paradigm similar to Altmann & Kamide (1999), he found that anticipatory looks
were influenced by the semantic rather than the syntactic information from the verb: after having heard
the verb, listeners fixated the agent irrespective of its syntactic function or linear position in the sentence.

Animacy and verbal information in V2 sentence processing  633
Based on his findings, he stresses the importance of the prominence of the agent role in processing and
predicting event structures (e.g., Kemmerer 2012, Cohn & Paczynski 2013, Bornkessel-Schlesewsky &
Schlesewsky 2013, cf. also Primus 2012), and argues that an investigation of more diverse languages in the
world will help to refine psycholinguistic theories.
We underline Sauppe’s (2016) plea to make use of typological diversity in language research in general,
but in this case very similar and even more detailed insights can be deduced from ‘usual suspect’ languages
(Levinson 2012, Norcliffe et al. 2015), i.e., verb-final languages like Dutch and German, by exploiting their
verb second (V2) property. The V2 property dictates that the finite verb takes up the second position in main
clauses. The finite verb can either be an auxiliary (as in (2a)) or a lexical finite verb (2b,c) (the finite verb is
underlined):
(2) a. Zoals verwacht heeft de medaille de zwemmer gemotiveerd.
as expected has the medal the swimmer motivate.PTCP
‘As expected, the medal motivated the swimmer.’
b. Zoals verwacht motiveerde de medaille de zwemmer.
as expected motivate.PST the medal the swimmer
‘As expected, the medal motivated the swimmer.’
c. Zoals verwacht ontving de zwemmer de medaille.
as expected receive.PST the swimmer the medal
‘As expected, the swimmer received the medal.’
Dutch grammar dictates that the first NP following the finite verb has to be interpreted as the subject (unlike
German which allows subject-object reversal, cf. Lenerz 1977). This means that upon encountering the finite
verb, Dutch listeners will anticipate a subject. Crucially, however, the difference in lexical information
between the two configurations in (2) will affect predictions about which argument will be the subject. The
auxiliary in (2a) will be neutral, or at most elicit a preference for an agentive or (through the connection
between agentivity and animacy) animate subject, but the lexical finite verb in (2b) allows for more fine-
grained preferences. Given the animate and inanimate arguments in (2b), the only possible configuration
upon hearing the verb is one where the swimmer takes up the direct object position and hence will be named
second. Changing the verb to ontvangen (‘to receive), as in (2c), will anticipate a reverse interpretation,
assigning the subject function to the swimmer. The V2 property thus allows us to compare the effect of verbal
information in verb-initial contexts with that in verb-final contexts within one language. This provides a
clean view on the precise role of verbal information and its interaction with the animacy preference.
Surprisingly little work has been reported on the effect of verbal information in the (Germanic) V2
position. Scheepers et al. (2000) investigated the effect of verb position on the comprehension of German
SO and OS sentences with different types of verbs. The position of the verb did not have an effect on the
acceptability of the sentences (Experiment 1): in both cases, SO sentences were rated higher than OS
sentences. Moreover, it did not affect the reading times in an eye-tracking-during-reading experiment
(Experiment 3). Bader & Bayer (2006, Chapter 6, Experiment 2), by contrast, report an effect of verb position
across experiments. They found that globally ambiguous sentences with the lexical verb in second position
elicited a garden-path effect when disambiguated towards both an SO reading and an OS reading. However,
when the second position was taken up by an auxiliary (Chapter 6, Experiment 1) this only elicited a garden-
path effect for OS sentences. Moreover, the garden-path effect was weaker for sentences with the lexical
verb in second position. This suggests that processing is affected by verbal information preceding two
arguments, attenuating the generally strong SO preference found in German.
Dahan & Tanenhaus (2004) also provide evidence for the use of verbal information in the V2 position,
even though this was not their main objective. Using a Visual World experiment, they targeted effects of
the selectional restrictions of verbs on word recognition in Dutch. They used intransitive sentences with
either the neutral auxiliary is ‘is’ (the non-constraining context) or a lexical verb (e.g. klom ‘climbed’, the
constraining context) in second position (V2), and compared fixations to a target referent (e.g. bok ‘male
goat’) and a phonological competitor (bot ‘bone’). Fixations to the target and the phonological competitor

634  Peter de Swart, Geertje van Bergen
were found to diverge much earlier in the constraining context compared to the non-constraining context,
which strongly suggests that listeners readily use thematic constraints derived from verbal information
(‘only a goat can climb’).
Both the findings of Bader & Bayer (2006) and those of Dahan & Tanenhaus (2004) suggest that verbal
information in the second sentence position is used by the language comprehension system. Since Dahan
and Tanenhaus focused on intransitive sentences, and Bader and Bayer considered only sentences with
two animate arguments, it remains as of yet unclear how the animacy preference in (1) interacts with verbal
information in the incremental understanding of transitive sentences. In the current study, we address
this question by presenting Dutch listeners with transitive sentences as the ones in (2) above, using a
methodology similar to Dahan & Tanenhaus (2004). We will use sentences containing an animate and an
inanimate argument that are either the subject or object, depending on the verb (e.g. ontvangen ‘receive’ vs.
motiveren ‘motivate’); the lexical verb will take up either the V2 position or the verb final position. These
manipulations result in two predictions. First, in the absence of lexical verbal information we expect more
fixations to the animate referent than to the inanimate referent. This would reflect the animacy preference,
and the preference for an agentive reading associated with the auxiliary heeft (as explained above).
Second, if listeners immediately use the verbal information in the verb to predict upcoming arguments, we
hypothesize that the animacy preference is less strong in the case of verbs that select an inanimate subject
compared to verbs that select an animate subject, which would pattern similar to the auxiliary verb.
2 Method
2.1 Participants
Eighty-nine students of the University of Groningen participated in the experiment (73 females, 16 males,
mean age = 21.8 years); two participants had to be excluded because of technical error. All were native
speakers of Dutch. They were paid 5 EUR for their participation.
2.2 Materials and design
2.2.1 Experimental stimuli
We constructed 16 sets of experimental items, consisting of a combination of an animate noun - inanimate
noun pair with two verbs, one taking an animate subject and an inanimate object (animate verb), the other
one taking the opposite pattern (inanimate verb). Although many verbs could in principle take both animate
and inanimate nouns as either their subject or object, the specific combination with the selected noun pairs
left only one plausible configuration per verb (see Appendix A for a list of all noun-verb combinations
used). For each item set we constructed four sentences, manipulated for A C (animate
subject – inanimate object vs. inanimate subject – animate object) and V T (auxiliary vs. lexical),
referring to the finite verb in V2 position. All sentences followed the template in (3):
(3) XP - Vfin - NP1[=subject] - NP2[=object] - (Past Participle) - PP
In sentences containing an auxiliary, the lexical verb occurred as a past participle directly following
NP2; in sentences with the lexical verb in V2 position, the past participle was omitted. An example of an
experimental item set in each condition is given in (4):
(4) a.    *   –  
Zoals verwacht heeft de zwemmer de medaille ontvangen na het toernooi.
as expected has the swimmer the medal receive.PTCP after the tournament

Animacy and verbal information in V2 sentence processing  635
‘As expected, the swimmer received the medal after the tournament.’
b.    *   –  
Zoals verwacht heeft de medaille de zwemmer gemotiveerd na het toernooi.
as expected has the medal the swimmer motivate.PTCP after the tournament
‘As expected, the medal motivated the swimmer after the tournament.’
c.    *   –  
Zoals verwacht ontving de zwemmer de medaille na het toernooi.
as expected receive.PST the swimmer the medal after the tournament
‘As expected, the swimmer received the medal after the tournament.’
d.    *   –  
Zoals verwacht motiveerde de medaille de zwemmer na het toernooi.
as expected motivate.PST the medal the swimmer after the tournament
‘As expected, the medal motivated the swimmer after the tournament.’
The plausibility of verb-noun pair combinations was assessed in a pretest. Thirty participants who did not
participate in the actual experiment rated the plausibility of the sentences in the present perfect form (cf. 4a
and 4b) on a 7-point scale (7=highly plausible). A linear mixed-effect analysis with maximal random effect
structure revealed no difference (p = 0.15) between the two types of A C (animate
subject-inanimate object M = 5.64, SD = 1.44; inanimate subject-animate object M = 5.41, SD = 1.78).
Experimental items were counterbalanced across four lists following a Latin square design, such that
each participant heard each verb-noun pair combination in only one condition. V T was manipulated
between subjects, i.e., half of the participants heard only sentences with the auxiliary in V2 position ((4a)
and (4b)) and the other half was exposed only to sentences with the lexical verb in V2 position ((4c) and
(4d)). A C was varied within subjects, such that each participant encountered each
animacy configuration eight times. Each list occurred in two pseudo-randomized orders.
2.2.2 Filler items
In addition to the experimental items we constructed 40 filler sentences, which were constructed using the
general format in (3) above. Eight items included an animate referent acting on another animate referent
(e.g. The mailman greeted the butcher); eight items presented two inanimate referents (e.g. the towel covered
the bucket). Twenty-four items involved animals and/or vehicles acting on one another, all of which were
part of another experiment. Twelve of these items consisted of animals and vehicles pulling or pushing one
another (e.g. the cow pushed the bus, the motorboat pulled the seal), six items involved two animals acting
on another (e.g. the cat grabbed the pigeon), and six items figured two vehicles (e.g. the police car overtook
the truck). Six practice trials were constructed representing a combination of experimental and filler items.
2.2.3 Visual stimuli
Visual displays contained two images depicting the referents of the nouns (see Bosker et al. 2014 for a
similar set-up). We opted for two-picture displays without any competitors, as competitors would also have
an animacy value and hence would introduce an effect of plausibility. Photographs were acquired through
Google Image searches and from the photo-stock website Dreamstime.com, and edited (e.g., background
removed where possible) to reduce visual complexity; all were converted to grey-scale and resized to a
square format (675x675 pixels) to minimize effects of visual salience. To control for looking order, the
position of the pictures on the screen (animate left vs. animate right) was counterbalanced.

636  Peter de Swart, Geertje van Bergen
2.2.4 Auditory stimuli
Sentences were recorded by a male native speaker of Dutch (age = 29 years), speaking at normal speed. To
reduce the dynamic range of the audio recordings the audio files were leveled using the Audacity software.
To increase the time between the offset of the verb and the onset of the following NP, a silent pause (duration
130 ms) was manually added to the sound file. For the auxiliary conditions, the same instance of the verb
(heeft) including the 130 ms pause was spliced into all audio recordings.
2.3 Procedure
The experiment was conducted in the EyeLab at the University of Groningen. Eye-movements of both
eyes were recorded with a Tobii T120 eye tracker sampling at 120Hz. The experiment was programmed
in E-prime2 with the extensions for Tobii (Psychology Software Tools 2009). Participants were seated in a
semi-dark room at approximately 65cm from the screen, wearing headphones for the audio stimuli.
After a short calibration phase, the participants were first familiarized with the pictures and the names
of their referents. They saw each picture and heard the corresponding name spoken by the same male
native speaker of Dutch who recorded the sentence stimuli. The familiarization task was self-paced, as
participants could proceed by hitting the space bar. Success of familiarization was not recorded.
Experimental trials (Figure 1) started with a gaze-contingent fixation cross presented at the center of the
screen. When participants fixated the cross, it disappeared and the two pictures of the referents appeared
on the screen at equal horizontal distance from the center. After a preview window of 300ms, the sound file
with the corresponding stimulus sentence started to play. Participants were instructed to look at the screen
and listen carefully to the sentences. In order to assure that participants were paying attention to the audio
stream, we included questions on 37 of the 61 trials. These questions were statements about the sentences,
which could be either true or false. Participants had to respond by pressing marked keys on the keyboard
(‘c’ for correct and ‘m’ for false). Incorrect statements were formed by replacing either one of the nouns, the
verb, the adverbial expression or the prepositional phrase, or by reversing the direction of the action. The
experiment lasted approximately 30 minutes.
Figure 1: The structure of a trial.

Animacy and verbal information in V2 sentence processing  637
2.4 Analysis
We manually determined the onsets and offsets of the finite verb, as well as the onsets of NP1 and NP2,
using the Audacity and Praat software (Broersma & Weenink 2016). This information was used to time-lock
fixation proportions to different time points in the sentence. Fixations of less than 50ms were excluded,
as were fixations outside the areas of interest. We analyzed fixations in four time windows (see Figure 2):
(i) the pre-verbal window, (ii) the pre-argument window, (iii) the subject integration window, and (iv) the
object integration window.
Fixations in the pre-verbal window were analyzed to establish initial looking preferences, without any
influence of the following verb or arguments (before the verb, sentences are identical in all experimental
conditions). This window runs from sentence onset until 200ms after the onset of the verb (mean duration
1125ms). As it takes minimally 200ms to plan and launch an eye movement (Salverda et al. 2014), we take
200ms after verb onset to be the earliest point in time at which fixations could in principle be affected by
verbal information.
We analyzed gaze behavior in the pre-argument window to assess anticipatory eye movements
towards upcoming subject arguments. This window runs from verb offset until 200ms after subject onset
(duration 330ms). This window is most crucial to our research question as it will speak directly to the use
of verbal information and its interaction with the animacy preference. We did not choose verb onset +
200ms as the starting point of this time window because of the differences in length between the auxiliary
(duration 310ms) and the lexical verbs (mean duration 704ms): after 310ms, the verb would have been fully
encountered in the auxiliary condition, but not in the lexical verb condition. Verb offset is the moment
at which we can be sure that the verb has been completely encountered in both conditions. Although the
earliest changes in fixations can only be detected after 200ms, the influence of verbal information on gaze
behavior probably starts before the verb has been completely perceived, given that many, and especially
multisyllabic words (which 27 out of 32 lexical verbs were) are recognized well before word offset (Marslen-
Wilson, 1987). Moreover, the verbs occurred in the past tense, which for regular verbs (75% of the lexical
Figure 2: Schematic representation of the windows of analysis with an example sentence per condition: (i) the pre-verbal
window (sentence onset – verb onset + 200ms), (ii) the pre-argument window (verb offset – NP1 onset + 200ms), (iii) the
subject integration window (NP1 onset + 200ms – NP2 onset + 200ms), and (iv) the object integration window (NP2 onset –
duration of NP2).

638  Peter de Swart, Geertje van Bergen
verbs) means that they end in an inflectional suffix (-de/-te), which does not contribute to the lexical
content of the verb. The verb in the auxiliary condition (heeft) is relatively short, but the same verb occurred
in all sentences; as a result of this repetition, heeft was probably also recognized well before its offset. Taken
together, this time window provides the least biased window across conditions to detect changes in gaze
patterns related to verbal information.
Additionally, we analyzed fixations in the two argument windows, to explore whether and when
differences between conditions in the pre-argument window affected the integration of subsequent
arguments. As our study was not designed to test the effects on integration directly, the analyses of these
windows should be considered exploratory. Nevertheless, this exploration may provide initial insight on
how the availability of verbal information affects subsequent processing. Clearly, future studies have to be
designed that target this issue directly.
The subject integration window was divided into two equal parts, i.e., an early integration window
and a late integration window. These windows were determined by calculating for each trial the duration
from NP1 onset + 200ms until NP2 onset + 200ms, and dividing it into two equal halves. An early and a
late object integration window were created with a similar logic, starting at NP2 onset + 200ms. Since the
noun pairs we used were identical across conditions, we obtained NP2 durations by taking the duration of
the corresponding NP1. Hence, both argument integration windows used the same duration per item (M =
640ms, SD = 90.8).
For each time window, we calculated the total duration of AOI fixations per trial and per participant, and
computed the logit-transformed odds ratio of fixations to the animate referent over those to the inanimate
referent. We used a linear mixed effects regression model to predict the probability of fixations to the
animate referent over those to the inanimate referent based on the fixed factors A C
and V T and their interaction. Picture order (animate left, animate right) and trial order were
included as control variables. Factors were centered or sum coded. Our final models included the maximal
random effects structure justified by the data (Barr et al. 2013).
3 Results
One participant was discarded from the analysis because they did not reach the threshold of 80% correct
on target item questions. For the remainder of the participants, mean accuracy on target items was very
high (M = 95.1%, SD = 4.9). No further trials were excluded based on correctness of answering the question.
Forty-three trials (3%) were excluded due to extensive track loss, defined as track loss for both eyes on more
than one third of the time during the trial.
Figure 3 shows the fixation proportions to the animate over the inanimate referent in the four conditions,
time-locked to subject onset.
3.1 Pre-verbal window
To assess whether there was an inherent animacy preference, we first analyzed the probability of fixating
animate over inanimate referents. We did so by inspecting the intercept of the statistical model. As the
intercept presents the grand mean, an intercept that equals zero would indicate that there is no inherent
looking preference. The intercept did, however, deviate significantly from zero (β = 1.74, SE = 0.23, p < .001),
indicating a preference to look at the animate referent. As expected, there was no significant effect of
A C (p = .61) or V T (p = .17); nor their interaction (p = .36).

Animacy and verbal information in V2 sentence processing  639
3.2 Pre-argument window
As Figure 3 shows, the animacy preference observed in the pre-verbal window seems to extend into this later
window. Indeed, also in this case the intercept deviated significantly from zero (β = 1.75, SE = .45, p = < .001).
In addition, the proportion of animate fixations seems to be higher for sentences containing an auxiliary
in comparison to those with a lexical verb; this is also reflected in overall gaze durations (Figure 4). The
statistical analysis confirmed that the main effect of V T was significant (β = 0.99, SE = 0.34, p = .0053);
we found no significant main effect of A C (p = .62), nor an interaction (p = .61).
Figure 3: Gaze plot of animate fixation proportions in each condition time-locked to subject onset. The lines indicate mean
fixation proportions and the transparent bands indicate 1 SE above/below the means. Blue lines indicate conditions with
an auxiliary finite verb, orange lines indicate conditions with a lexical finite verb, dashed lines indicate conditions with an
animate subject, and straight lines indicate conditions with an inanimate subject.
Figure 4: Violin/boxplot of fixation duration to the animate referent per condition in the pre-argument window. The red dot
encodes the mean, the black vertical line encodes the median, the top and bottom edges of the box encode the interquartile
range and the transparent beans provide a density plot. Note that the statistical analysis was performed on the logit-transfor-
med odds ratio of fixations to the animate referent over those to the inanimate referent.

640  Peter de Swart, Geertje van Bergen
3.3 Subject integration window
Figure 3 suggests that the animacy preference perseveres into the subject integration window, and that it
is particularly strong. Even when participants heard an inanimate subject mentioned (the straight lines)
the fixation proportion did not drop below 0.5. The preference is most clearly visible for sentences with
an auxiliary and an inanimate subject (straight blue line): the probability of fixating the animate starts
decreasing only around 400ms after subject onset. The effect of verb type also seems to extend to the subject
integration window: there seems to be a lower probability of fixating animate referents after semantically
rich verbal information has become available. However, after having encountered an animate noun, looks
to the animate referent seem to increase quickly in the lexical verb condition.
3.3.1 Early subject integration
Figure 5 shows the average fixation durations across conditions in the early subject integration window.
Statistical analysis revealed a main effect of V T trending towards significance (β = 0.36, SE = 0.19,
p = .060), with less looks to the animate referent in sentences with a lexical verb. The main effect of
A C, pointing in the direction of more looks to the animate referent in sentences with
an animate subject, missed significance (β = 0.34, SE = 0.16, p = .051), The interaction was not significant (p
= .32). Again, the model’s intercept deviated significantly from zero (β = 1.71, SE = 0.25, p < .001), confirming
the persistence of the animacy preference.
3.3.2 Late subject integration
Figure 6 shows the duration of animate fixations in the late subject integration window. Statistical analysis
revealed a significant main effect of A C (β = -1.38, SE = 0.35, p = .0003), indicating
there were fewer looks to the animate referent when the subject was inanimate. There was no main effect of
V T (p = .20), nor a significant interaction (p = .67). The model’s intercept deviated significantly from
zero (β = 1.13, SE = 0.19, p < .001), again confirming the persistence of the animacy preference.
Figure 5: Violin/boxplot of fixation duration to the animate referent per condition in the early subject integration window.
The red dot encodes the mean, the black vertical line encodes the median, the top and bottom edges of the box encode the
interquartile range and the transparent beans provide a density plot. Note that the statistical analysis was performed on the
logit-transformed odds ratio of fixations to the animate referent over those to the inanimate referent.

Animacy and verbal information in V2 sentence processing  641
3.4 Object integration window
Figure 7 shows the fixation proportions to the animate over the inanimate referent in the four conditions,
time-locked to object onset. The gazes during the object window contrast sharply with the patterns observed
during processing of the subject NP. Figure 7 suggests that the gaze patterns closely mirror the audio input:
participants focus on the animate referent in the conditions with an animate object (straight lines) and on
the inanimate referent in conditions with an inanimate object (dotted lines). Verbal information seems to
have an effect in the early window: for animate objects, fixations to the animate referent seem lower if the
sentence contains an auxiliary, but higher when the object is inanimate.
3.4.1 Early object integration
Figure 8 shows the fixation durations in the early object integration window. Statistical analysis revealed
no significant main effects of V T (p = .65) or A C (p = .41). The interaction,
reflecting that the probability of fixating the inanimate referent is higher after hearing an inanimate object
compared to an animate object, but only in the lexical verb condition, was not significant (p = .070). Also,
the model’s intercept did not significantly deviate from zero (β = 0.14, SE = 0.21, p = .52), indicating there
was no longer a bias towards the animate referent.
3.4.2 Late object integration
Figure 9 shows the fixation durations in the late object integration window. Statistical analysis revealed a
significant main effect of A C (β = 3.06, SE = 0.36, p < .001), indicating there were more
looks to the animate referent when the object was animate. There was no main effect of V T (p = .21)
nor a significant interaction between the two factors (p = .21). The model’s intercept deviated significantly
from zero (β = 0.40, SE = 0.18, p = .038), indicating more looks to the animate referent.
Figure 6: Violin/boxplot of fixation duration to the animate referent per condition in the late subject integration window.
The red dot encodes the mean, the black vertical line encodes the median, the top and bottom edges of the box encode the
interquartile range and the transparent beans provide a density plot. Note that the statistical analysis was performed on the
logit-transformed odds ratio of fixations to the animate referent over those to the inanimate referent.

642  Peter de Swart, Geertje van Bergen
4 Discussion
The animacy hierarchy dictates the grammaticality and interpretation of transitive sentences in many
languages in the world. This hierarchy has also been argued to influence language production and
comprehension in languages where it has no grammatical status. The present study set out to determine
whether the animacy preference is visible in the latter type of languages during incremental sentence
comprehension; moreover, it investigated whether such a preference would be affected by the lexical
semantic information provided by a verb preceding the arguments in the sentence. Our results indicate that
both questions can be answered affirmatively.
Figure 7: Gaze plot of animate fixation proportions in each condition time-locked to object onset. The lines indicate mean
fixation proportions and the transparent bands indicate 1 SE above/below the means. Blue lines indicate conditions with an
auxiliary finite verb, orange lines indicate conditions with a lexical finite verb, dashed lines indicate conditions with an inani-
mate object, and straight lines indicate conditions with an animate object.
Figure 8: Violin/boxplot of fixation duration to the animate referent per condition in the early object integration window.
The red dot encodes the mean, the black vertical line encodes the median, the top and bottom edges of the box encode the
interquartile range and the transparent beans provide a density plot. Note that the statistical analysis was performed on the
logit-transformed odds ratio of fixations to the animate referent over those to the inanimate referent.

Animacy and verbal information in V2 sentence processing  643
The visual world paradigm we used rests upon a linking hypothesis, which assumes that as a
consequence of utterance planning or comprehension, visual attention moves to a particular object in the
visual display, resulting in a rapidly following saccadic eye movement to bring that attended area into
foveal vision (Salverda & Tanenhaus 2017). This kind of looking behavior was clearly observed during
object integration: people fixated object referents quickly after encountering the direct object in the speech
stream. During subject integration, however, we observed a persistent preference to attend to animate over
inanimate referents, even when the audio signal mentioned an inanimate argument. In fact, the preference
to attend to animate over inanimate entities was already present in the pre-verbal time-window, i.e.,
before any relevant linguistic information became available. We would not want to argue that this early
and persistent animacy bias is necessarily related to the incoming linguistic signal, i.e., that listeners were
anticipating an animate subject (although with the current results we cannot exclude this possibility).
The increased attention to animate over inanimate referents more likely reflects an inherent animacy bias,
i.e., animate entities are more prominent, or salient, than inanimate entities (e.g. Bock & Warren 1985),
regardless of their linguistic status or frequency considerations. But whether or not the animacy bias is
related to predictive sentence processing, we can conclude that it is a very strong processing cue. Our results
show that it was strong enough to outrank the influence of verbal information during subject integration:
even when the verbal information pushed the interpretation of the subject towards the inanimate referent,
the proportion of looks to the inanimate referent did not increase to more than 0.5.
We did find that verbal semantics modulated the animacy preference: we observed that in the lexical verb
conditions, the proportion of animate fixations decreased quickly after the lexical information of the verb
became available (in the pre-argument window). We thus corroborate earlier findings showing that lexical
information in V2 position influences incremental argument interpretation (Dahan & Tanenhaus 2004,
Bader & Bayer 2006). The exploratory analysis of the object integration window may provide a preliminary
additional pointer to the effect of verbal information: in sentences with an animate object, the proportion
of animate fixations in the early object window seemed lower when the sentence had an auxiliary verb
than when it had a lexical verb. This effect was in this direction, although not significant. Speculatively,
it may suggest that it is more difficult to integrate an animate object in the absence of supporting verbal
information, which is a corollary of the animacy preference (dictating to assign the animate argument the
subject function). This could extend the findings of Paczynski & Kuperberg (2011), who observed an increase
in processing cost (i.e., more enhanced N400 amplitude) for animate vs. inanimate objects in sentences
Figure 9: Violin/boxplot of fixation duration to the animate referent per condition in the late object integration window.
The red dot encodes the mean, the black vertical line encodes the median, the top and bottom edges of the box encode the
interquartile range and the transparent beans provide a density plot. Note that the statistical analysis was performed on the
logit-transformed odds ratio of fixations to the animate referent over those to the inanimate referent.

644  Peter de Swart, Geertje van Bergen
where the lexical verb preceded the object: our findings suggest that the preceding verbal information
could reduce these costs. Clearly, more research is needed to confirm this.
Assuming that listeners immediately use the thematic restrictions of the verb to anticipate an upcoming
subject (cf. Dahan & Tanenhaus 2004), we hypothesized a difference between verb types within the lexical
verb condition: we expected inanimate verbs to elicit more predictive looks to the inanimate referent than
animate verbs. This prediction was not borne out: animate and inanimate verbs, although requiring their
subjects to have opposite animacy values, elicited similar looking behavior. This suggests that listeners did
not make full use of the lexical information provided by the verb to predict upcoming arguments.
A possible explanation for the absence of this interaction effect might be a more shallow parsing
mechanism underlying the observed fixation patterns. It may, for instance, involve mere semantic
association between the verbs and the two referents. It could well be that hearing a verb like extinguish will
elicit more looks to a fire than to a baker, the former being more strongly associated with the meaning of
the verb. Indeed, Kukona et al. (2011), also using a Visual World paradigm, found evidence for this kind of
thematic priming, in addition to prediction effects based on structural characteristics. Participants were
found to fixate an associated agent like policeman when hearing the verb arrest, even though a different
agent had already been mentioned (Toby arrested the crook). If thematic priming also played a role in our
experiment, e.g., if animate verbs happen to be associated more strongly with their inanimate noun, this
could overshadow potential differences between verb types on predictive argument processing.
To investigate possible effects of such thematic priming, we administered two post-hoc tests to determine
the association strength between the verbs and the corresponding nouns for each experimental item.
Twenty-three participants (18 female, mean age 22.5 years), who did not take part in the main experiment,
filled out an on-line forced choice task. They were presented with a verb and a corresponding noun pair,
and had to indicate which of the nouns was most strongly associated with the verb. Each participant saw
all noun pairs; verbs were counterbalanced across two lists, such that each participant saw each noun pair
in combination with either the animate or the inanimate verb. Twenty-five other participants (18 female,
mean age 21.8 years) filled out a rating task in which they were presented with a verb and a single noun, and
had to indicate their association on a five-point Likert scale. Each participant was presented with all verbs;
nouns were counterbalanced across two lists, such that each participant saw each verb in combination with
either the animate or the inanimate noun. Statistical analyses of the results revealed that verbs were indeed
more strongly associated with inanimate nouns than with animate nouns in both tasks. A logistic mixed-
effects analysis of the results of the forced choice task showed that on average there was a significantly
higher probability of associating the verb with the inanimate noun than with the animate noun (β = 1.74,
SE = 0.42, p < .001), although 7 of the 32 verbs showed the opposite pattern; the odds did not significantly
differ between verb types (p = 0.97). For the rating task, results from a linear mixed-effects analysis showed
that on average the mean association rating between verbs and inanimate nouns was significantly higher
than between verbs and animate nouns (β = 0.42, SE = 0.13, p < .001), although 8 of the 32 verbs showed the
opposite pattern; again, we found no significant difference between verb types (p = 0.54), nor an interaction
effect (p = 0.90).
To assess whether thematic priming was a predictor for predictive argument fixations, we reclassified
the verbs into two association categories (animate vs. inanimate association) using the results of the rating
task. Eight verbs were classified as showing animate association and the other 24 as inanimate association.
We re-analyzed the fixations in the pre-argument window with a mixed-effects logistic regression model
substituting this new factor for A C as a fixed effect. Given that thematic priming can
only occur when the lexical verb precedes the arguments, we only analyzed trials with the lexical verb in
second position. Results from this model still showed an overall preference to look at the animate referents;
we found no significant differences in predictive looks between verbs that are more strongly associated with
animate nouns than with inanimate nouns (p = 0.52). It should of course be kept in mind that our study
was not designed to address this question, and the verb association categories are not equally divided
(there were more inanimate-associated than animate-associated verbs), but the results seem to suggest that
thematic priming is not the driving force behind the pre-argument fixation patterns.

Animacy and verbal information in V2 sentence processing  645
An alternative explanation of the decrease in looks to the animate referent after encountering the verb
could be that verb-based predictions do not concern the first upcoming argument, but both upcoming
arguments. Upon hearing the lexical verb, the listener can start building up a representation of the
corresponding transitive event (see also Sauppe 2016). As both the animate and the inanimate referent
will figure in the event (be it as the actor or the undergoer), this will elicit an increase in visual attention
to both referents, i.e., the proportion of animate and inanimate fixations will be more equally divided.
The auxiliary heeft then may be semantically too empty for building up any specific event representation.
Hence, a listener's visual attention will likely be drawn to the inherently most salient referent in the display.
We have shown that specific linguistic information can facilitate the integration of a globally
unexpected animate object, but the lexical information from the verb alone was not sufficient to counter the
inherent animacy bias. The question is whether the animacy preference can be fully countered on the basis
of sufficient linguistic cues. Can we build up a linguistic context that is so strong that its effects outrank the
animacy preference? Results from Szewczyk & Schriefers (2011, 2013) suggest that this is indeed possible:
they show that if the predictability of an animate object on the basis of the preceding linguistic context
is very high (cloze probability 99%), an inanimate object elicits enhanced processing difficulty (and the
other way around). In their experiment, expectations about upcoming objects were based on much more
linguistic input than just a lexical verb: participants read short stories, in which the direct object occurred
as the final word of the story-final sentence. We thus seem to need ample linguistic evidence going against
the global animacy bias to change our predictions about upcoming arguments (see also Muralikrishnan et
al. 2015).
5 Conclusions
Our results add to the existing body of research showing the role of animacy during language
comprehension. It suggests that the animacy preference in (1) is active also in languages in which it has not
fully grammaticalized. At the same time, our results add to the (as of yet) limited knowledge of the effect on
language processing of lexical verbal information preceding arguments.
It is well established by now that the animacy preference plays a role in language processing and in
the grammar of languages around the world. It is still an open question how to model this relation between
grammar and processing. The ‘soft constraints mirror hard constraints’ approach of Bresnan et al. (2001)
provides a principled account of this relation. The constraint-based nature of this grammatical model can
also easily deal with the fact that the animacy preference interacts with other principles, such as information
from the verb. This model is, however, by no means the only possible approach. Alternatively, one could
maintain, as envisaged by Minkoff (2000), that the animacy preference is only part of the human parser
and that grammatical reflexes of this preference, as for instance found in Mam-Maya, are the result of the
interaction of this parsing preference with grammatical properties of the language; in this case the fact that
the language has pro-drop and is verb-initial. Which approach is more parsimonious remains to be seen.
Distinguishing between these two alternative approaches is not trivial and requires a great deal of work on
languages with diverse linguistic characteristics.
Acknowledgements: The authors gratefully acknowledge financial support of the Netherlands
Organisation for Scientific Research (NWO Veni grants 275-89-003 (de Swart) and 275-89-022 (van Bergen)).
Gisi Cannizzaro has been extremely important in setting up the experiment and in the initial stage of data
processing. We would like to thank Laura Hemstra for running the eye-tracking experiment. The comments
of two anonymous reviewers have helped us to improve the paper. Finally, we would like to thank Virve
Vihman and Diane Nelson, the editors of this issue, for their patience and support.
Abbreviations: PST – past tense; PTCP – participle; V2 – verb second

646  Peter de Swart, Geertje van Bergen
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Animacy and verbal information in V2 sentence processing  649
APPENDIX A
List of noun-verb combinations used in the stimuli with their English translations. The label ‘(in)animate
verb’ indicates that given the combination with the two nouns, the (in)animate noun is the one taking
up subject position. Although many verbs could in principle take both animate and inanimate nouns as
either their subject or object, the specific combination with the selected noun pairs left only one plausible
configuration per verb. The label should thus not be taken to imply that the given verb cannot occur with
an (in)animate subject at all.
Inanimate noun Animate noun Inanimate verb Animate verb
camera dief registreren stelen
‘camera’ ‘thief’ ‘to record’ ‘to steal’
roman koning ontroeren lezen
‘novel’ ‘king’ ‘to move, touch’ ‘to read’
tekening bisschop grieven verscheuren
‘drawing’ ‘bishop’ ‘to hurt, offend’ ‘to tear (up)’
medaille zwemmer motiveren ontvangen
‘medal’ ‘swimmer’ ‘to motivate’ ‘to receive’
waterval fotograaf inspireren vereeuwigen
‘waterfall’ ‘photographer’ ‘to inspire’ ‘to immortalize (e.g. on film)’
sneeuw piloot hinderen trotseren
‘snow’ ‘pilot’ ‘to impede’ ‘to defy’
explosie politieman doden voorkomen
‘explosion’ ‘policeman’ ‘to kill’ ‘to prevent’
brand bakker duperen blussen
‘fire’ ‘baker’ ‘to dupe/victimize’ ‘to extinguish (a fire)’
sluier tandarts benadelen weigeren
‘veil’ ‘dentist’ ‘to put at a disadvantage’ ‘to refuse’
 piano verhuizer verpletteren verplaatsen
‘piano’ ‘mover’ ‘to crush’ ‘to move’
 golf surfer overspoelen gebruiken
‘wave’ ‘surfer’ ‘to wash over’ ‘to use’
 bliksem visser treffen zien
‘lightning’ ‘fisherman’ ‘to hit’ ‘to see’
 kerkklok zwerver wekken horen
‘church bell’ ‘tramp’ ‘to wake’ ‘to hear’
 kogel militair verwonden ontwijken
‘bullet’ ‘soldier’ ‘to wound’ ‘to dodge’
 ketting bruid verrassen bewonderen
‘necklace’ ‘bride’ ‘to surprise’ ‘to admire’
 sjaal kleuter beschermen dragen
‘scarf’ ‘pre-schooler’ ‘to protect’ ‘to wear’