Author’s Accepted Manuscript698673/UQ698673_OA.pdf · Vigliocco et al., 2004). It is worth...

Author’s Accepted Manuscript

Interference from related actions in spoken wordproduction: Behavioural and fMRI evidence

Greig de Zubicaray, Douglas Fraser, KoriRamajoo, Katie McMahon

PII: S0028-3932(17)30010-6DOI: http://dx.doi.org/10.1016/j.neuropsychologia.2017.01.010Reference: NSY6231

To appear in: Neuropsychologia

Received date: 4 May 2016Revised date: 9 January 2017Accepted date: 9 January 2017

Cite this article as: Greig de Zubicaray, Douglas Fraser, Kori Ramajoo and KatieMcMahon, Interference from related actions in spoken word production:Behavioural and fMRI evidence, Neuropsychologia,http://dx.doi.org/10.1016/j.neuropsychologia.2017.01.010

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RUNNING HEAD: Interference from related actions 1

Interference from related actions in spoken word production: Behavioural and

fMRI evidence

Greig de Zubicaray1,*

, Douglas Fraser2, Kori Ramajoo

1, Katie McMahon

3.

1 Faculty of Health and Institute of Health and Biomedical Innovation, Queensland

University of Technology, Brisbane, Australia 2 School of Public Health, University of Queensland, Brisbane, Australia

3 Centre for Advanced Imaging, University of Queensland, Brisbane, Australia

*Corresponding author: Greig de Zubicaray Faculty of Health and Institute of Health

and Biomedical Innovation, Queensland University of Technology, Kelvin Grove,

Brisbane, QLD 4059. Australia. [email protected]

Abstract

Few investigations of lexical access in spoken word production have

investigated the cognitive and neural mechanisms involved in action naming. These

are likely to be more complex than the mechanisms involved in object naming, due to

the ways in which conceptual features of action words are represented. The present

study employed a blocked cyclic naming paradigm to examine whether related action

contexts elicit a semantic interference effect akin to that observed with categorically

related objects. Participants named pictures of intransitive actions to avoid a confound

with object processing. In Experiment 1, body-part related actions (e.g., running,

walking, skating, hopping) were named significantly slower compared to unrelated

actions (e.g., laughing, running, waving, hiding). Experiment 2 employed perfusion

functional Magnetic Resonance Imaging (fMRI) to investigate the neural mechanisms

involved in this semantic interference effect. Compared to unrelated actions, naming

related actions elicited significant perfusion signal increases in frontotemporal cortex,

including bilateral inferior frontal gyrus (IFG) and hippocampus, and decreases in

bilateral posterior temporal, occipital and parietal cortices, including intraparietal


sulcus (IPS). The findings demonstrate a role for temporoparietal cortex in

conceptual-lexical processing of intransitive action knowledge during spoken word

production, and support the proposed involvement of interference resolution and

incremental learning mechanisms in the blocked cyclic naming paradigm.

Keywords: semantic interference, action naming, fMRI

1. Introduction

A considerable body of psycholinguistic research has demonstrated that

lexical access – the process of retrieving words from the mental lexicon – can be

affected by production contexts that are similar in meaning. The majority of evidence

has come from object naming paradigms. Using these paradigms, categorically related

contexts have been demonstrated to reliably impede spoken word production

compared to unrelated contexts in both healthy participants and patients with brain

damage (e.g., Damian, Vigliocco, & Levelt., 2001; Schnur et al., 2006). The origin of

these semantic interference effects has been attributed to a conceptual preparation

stage of processing in which activation spreads between related object concepts and

their features, and subsequently to their linked lexical representations (see Belke,

2013, for a review).

Relatively few studies have investigated semantic context effects in bare

action naming. Of these, most have employed the picture-word interference (PWI)

paradigm in which target pictures are named in context with related versus unrelated

distractor words (e.g., Roelofs, 1993; Schnur, Costa, & Caramazza, 2002, Experiment

1; Vigliocco et al., 2004, Experiment 4; Vigliocco, Vinson, & Siri, 2005). As our


main interest here is in investigating analogous semantic interference effects in bare

action and object naming, we will not review studies that involved manipulations of

distractor and target grammatical class and/or required participants to name action

pictures using sentential constraints (e.g., Mahon et al., 2007; Schriefers, Teruel, &

Meinhausen, 1998). Several PWI studies have reported a semantic interference effect

in bare action naming (e.g., picture - SHAVE, distractor - comb), with the authors

concluding that similar conceptual and lexical mechanisms are likely to be engaged

for both actions and objects (e.g., Roelofs, 1993; Schnur et al., 2002; Vigliocco et al.,

2004, 2005).

Two factors complicating interpretations of semantic interference effects in

bare action naming and PWI paradigms in particular are transitivity and grammatical

ambiguity particularly when conducting experiments in English. Depictions of

transitive (i.e., object oriented) actions tend to be complex, requiring identification of

both actors and objects (even if they aren’t named) and their functional

interrelationships (including syntactic relations). For example, consider the PWI

target-distractor pairing SHAVE-comb: The depiction of the action SHAVE in the

International Picture Naming Project (IPNP) database involves an actor using a razor

in front of a mirror (Szekeley et al., 2004). Additionally, the grammatically

ambiguous distractor comb can denote both an action and an object noun categorically

related to razor. Thus, studies demonstrating semantic interference effects with

transitive actions in bare naming have likely confounded conceptual feature overlap

among object category-coordinates (e.g., Roelofs, 1993; Vigliocco et al., 2004). This

raises the question of whether “pure” semantic interference effects in intransitive

action naming are actually observable.


To address this question, spoken word production models need to specify how

action meanings are organized in the semantic system so that activation can spread

between related actions and their features, and then to their linked lexical

representations (e.g., Roelofs, 1993). The conceptual features of actions are proposed

to be primarily motoric and functional, whereas objects have a greater weighting of

sensory features (Bird, Howard, & Franklin, 2000). Words referring to actions are

therefore considered to be more abstract than words referring to objects (Vigliocco et

al., 2004). In Roelofs’ (1993) model, primitive features create an abstract conceptual-

lexical representation of the given action via a process known as chunking. In the

production system, words are accessed by using this abstract representation (see also

Vigliocco et al., 2004).

It is worth emphasizing that few production models explicitly mention actions,

and those that do fail to distinguish transitivity (e.g., Roelofs, 1993; Vigliocco et al.,

2004). According to these models, in order for intransitive actions to elicit semantic

interference in a manner analogous to categorically related objects, coactivation due

to conceptual feature overlap would need to spread to lexical competitors via a shared

superordinate category node, such as body-part relation (e.g., Abdel Rahman &

Melinger, 2009; Belke, 2013; Damian et al., 2001). In a normative study using a

body-part association task, Maouene, Hidaka and Smith (2008) showed many

individual action words learned in early childhood are systematically attributed to

specific body parts (e.g., leg/foot, hand/arm, face), while others are associated with

multiple body parts. Examples of the latter type of “whole body” action words include

swing, hide, rest and climb. Intransitive actions might therefore produce semantic


interference if body part representations are organized along the lines of category

coordinates in semantic memory. However, in the absence of empirical evidence, this

remains an unresolved question for production models.

Recently, Hirschfeld and Zwitserlood (2012) employed the blocked cyclic

naming paradigm to test whether actions induced a semantic interference effect. The

paradigm involves small blocks of pictures (e.g., 4–6) presented repeatedly over

several cycles (e.g., 4–6). Related/homogeneous blocks usually comprise object

category exemplars (e.g., all animals) while unrelated/heterogeneous blocks comprise

pictures from different categories (e.g., animals, vehicles, furniture, fruit). Healthy

participants are typically slower to name objects in related compared to unrelated

blocks when they are repeated from the second cycle onward – a semantic

interference effect (Damian et al., 2001; Damian & Als, 2005; see Belke & Stielow,

2013 for review). It is generally accepted that the interference effect in blocked cyclic

naming originates during conceptual processing, with categorically related contexts

priming the activation levels of lexical candidates via feature sharing (see Belke,

2013; Oppenheim et al., 2010). The relative persistence of the effect has been

attributed to an incremental learning mechanism operating in the links between

conceptual and lexical representations (e.g., Damian & Als, 2005; Oppenheim et al.,

2010).1 In Hirschfeld and Zwitserlood’s (2012) experiment, related blocks comprising

pictures of different actions performed by the same body-part (hand, face and foot)

1 There is also debate about whether lexical selection occurs via competitive or non-competitive

mechanisms. The former mechanism assumes selection of the target utterance is made more difficult in

related contexts due to the priming of conceptual representations raising the lexical activation levels of

competitors (e.g., Belke, 2013; Damian et al., 2001). The latter assumes selection is accomplished

when a predetermined activation threshold or number of time steps is reached (e.g., Oppenheim et al.,

2010). The present study is primarily concerned with the conceptual representations engaged during

semantic interference effects in action naming rather than adjudicating between different lexical

selection mechanisms.


elicited a significant semantic interference effect. However, the stimuli included

depictions of both object-oriented actions (e.g., painting) and intransitive actions

accompanied by objects (e.g., singing via a microphone).

Evidence from lesion, neuroimaging and non-invasive brain stimulation

studies of blocked cyclic object naming has been incorporated in production models,

with key roles proposed for two left-hemisphere cortical regions: posterior middle and

superior temporal gyri (pMTG/STG) and inferior frontal gyrus (IFG) (e.g., Belke &

Stielow, 2013; Oppenheim et al., 2010; Schnur et al., 2009). There is relatively

consistent evidence that the left pMTG/STG area plays a role in mediating

conceptual-lexical processing. For example, the lesion-symptom mapping (LSM) and

perfusion neuroimaging studies of Harvey and Schnur (2015) and de Zubicaray et al.

(2014) show good agreement with clusters reported with peak maxima with Montreal

Neurological Institute (MNI) atlas coordinates of -52, -40, -5 and -46, -42, 2,

respectively for semantic interference. The non-invasive brain stimulation studies of

Pisoni et al. (2012), Krieger-Redwood and Jefferies (2014) and Meinzer et al. (2016)

likewise showed significant effects targeting similar MNI coordinates (-50, -46, 1 and

-54, -49, -2).

Reports of left IFG involvement in the block cyclic naming paradigm are

somewhat less consistent, and the proposed roles vary. Some neuroimaging studies

have observed differential activity for semantic interference (e.g., Schnur et al., 2009),

while others have not (e.g., de Zubicaray et al., 2014), and studies of aphasics with

left IFG lesions have produced different results for interference reflected in naming

latencies versus error rates (e.g., Biegler et al., 2008; Harvey & Schnur, 2015; Riès et


al., 2014; Schnur et al., 2009). Two anodal transcranial direct stimulation (aTDCS)

stimulation studies have shown short-lived facilitative effects of LIFG versus sham

stimulation on semantic interference (i.e., over the first four cycles only; Pisoni et al.,

2012; Meinzer et al., 2016), and a transcranial magnetic stimulation (TMS) study

reported an effect of LIFG stimulation in the first cycle only (Krieger-Redwood &

Jefferies, 2014). According to Schnur et al. (2009), left IFG biases interactions among

incompatible, non-target representations to help resolve lexical competition during

blocked cyclic naming. Belke and Stielow (2013) proposed a similar, top-down

account of LIFG involvement. Oppenheim et al. (2010) “tentatively” linked the left

IFG to a different mechanism that boosts all (i.e., target and non-target) lexical

activity until the difference between the most highly active candidate and the next

most active exceeds a threshold for selection.

It is worth noting interpretations of the neuropsychological evidence are

complicated by the potential involvement of two separate mechanisms in blocked

cyclic naming (see Belke & Stielow, 2013; Damian & Als., 2005; Krieger-Redwood

& Jefferies, 2014). Damian and Als (2005) were the first to propose a two-factor

account, noting naming latencies are occasionally faster in related blocks in the first

cycle, perhaps indicating a semantic priming mechanism, and the longer-lasting

semantic interference effect emerges only with repetition in subsequent cycles (see

also Belke & Stielow, 2013; Navarrete et al., 2014). Belke and Stielow (2013) have

also proposed the initial presentation cycle allows participants to establish or

memorise a task set (i.e., in terms of category membership), and then use this

information in subsequent cycles to bias selection. Yet, the majority of

neuropsychological studies have analysed data collapsed over all presentation cycles


(for review, see de Zubicaray et al., 2014). Hence, reports of left IFG and pMTG/STG

involvement from these studies potentially reflect contributions from more than one

mechanism. Recently, de Zubicaray et al. (2014) examined activity from the second

cycle onward with perfusion fMRI, observing significant differential signal changes

solely in the left pMTG/STG and the hippocampus. They interpreted the involvement

of the latter structure as reflecting the engagement of an incremental learning

mechanism (e.g., Damian & Als, 2005; Oppenheim et al., 2010; see Gluck, Meeter, &

Myers, 2003).

1.1 The present study

The purpose of the present study was to investigate whether a semantic

interference effect could be observed during bare naming of intransitive actions, and

to determine the nature and extent of the cognitive and neural mechanisms involved.

Our first behavioural experiment established a “pure” semantic interference effect

could be elicited with intransitive actions using the blocked cyclic naming paradigm.

In a second experiment using perfusion functional magnetic resonance imaging

(fMRI) with the same paradigm, we aimed to identify the neural mechanisms

involved.

Measuring cerebral perfusion changes during speech production with arterial

spin labeling (ASL) fMRI has several advantages over the more conventionally used

blood oxygen level dependent (BOLD) contrast mechanism (e.g., de Zubicaray et al.,

2014). For example, compared to BOLD signal, it is relatively insensitive to speech-

related motion-by-susceptibility artifacts in perisylvian cortical regions (e.g., Kemeny


et al., 2005; Detre et al., 2012; Liu & Brown, 2007). Perfusion fMRI also provides a

quantitative estimate of signal change that is more directly related to neural activity

(see Cavusoglu et al., 2012; Huppert et al., 2006). It additionally shows increased

sensitivity to group-level effects, due to the relatively smaller inter-individual

variability in perfusion compared to BOLD signal changes (Detre et al., 2012).

Within the production domain, neuroimaging, lesion and cortical stimulation

studies have provided converging evidence for left mid-posterior temporal cortex,

IPL, IFG and premotor cortex involvement in bare action naming (e.g., Breier &

Papanicolaou, 2008; Corina et al., 2005; Kemmerer et al., 2012; Liljeström et al.,

2008; Saccuman et al., 2006). Hence, all of these regions are plausible candidates for

mediating semantic interference effects during production of words denoting

intransitive actions. Modality specific activity in motor cortical areas during action

word comprehension is typically interpreted as supporting embodied/grounded

accounts of action meaning representation (for review, see Kemmerer, 2015; but see

de Zubicaray, Arciuli & McMahon, 2013 and Watson et al., 2013). However, it is

worth emphasizing that the aim of the current study is to determine whether a “pure”

semantic interference effect can be observed when naming intransitive actions in the

absence of object processing confounds, rather than to adjudicate between models of

embodied and amodal action concept representation. Indeed, as a number of authors

have pointed out, the mere detection of activity in motor cortical areas during action

word processing is insufficient to distinguish between these accounts (see de

Zubicaray, Arciuli, et al., 2013; Mahon & Caramazza, 2008; Watson et al., 2013).


Many neuroimaging studies of action comprehension have also shown activity

in left mid-to-posterior temporal cortex and inferior parietal lobe (IPL). These latter

regions are proposed to play roles of convergence zones whose activity reflects

processing of heteromodal, abstract representations of actions. For example,

Noppeney et al. (2005) reported that semantic decisions on action words increased

activation in the left anterior intraparietal sulcus (IPS) and mid-posterior temporal

cortex, and Van Dam et al. (2010) reported increased IPL activity for action verbs

associated with specific (e.g., to wipe) versus general (e.g., to clean) movements of

body parts. Thus, if these regions are involved in processing of action knowledge

during production, semantic interference in bare naming of intransitive actions should

be reflected in differential cerebral perfusion signal responses in posterior

temporoparietal regions. Finally, other regions implicated in more domain general

mechanisms during blocked cyclic naming, such as the left IFG and hippocampus, are

also expected to show differential activity (de Zubicaray et al., 2014; Harvey &

Schnur, 2015).

2. Experiment 1

2.1 Methods

2.1.1 Participants

Twenty-one healthy, native English-speaking adults participated (15 female,

mean age 20.29 years, range 17-29 years). All were undergraduate psychology

students who received partial course credit for participating in this experiment. All

had normal or corrected-to-normal vision. None reported any history of neurological

or psychiatric disorder, substance use, or hearing deficits. All provided informed


consent in accordance with the protocol approved by the Behavioural and Social

Sciences Ethical Review Committee of the University of Queensland.

2.1.2 Materials

A set of sixteen black-and-white line drawings served as targets, selected from

a range of action picture corpora and the internet (Druks & Masterson, 2000; Miozzo,

Fischer-Baum, & Postman, 2010; Szekely et al., 2004). Pictures comprised four

exemplars from each of four intransitive action contexts (face, arm, leg or whole-body

movements; Maouene et al., 2008) and were distributed orthogonally to create four

unrelated blocks (see Appendix). Transitivity was established using the online

Wordsmyth dictionary (Parks, Ray, & Bland, 1998).2 Blocks of four related (A) and

four unrelated (B) pictures were used to create counterbalanced lists of ABBA and

BAAB blocks in which trials were pseudo-randomly ordered such that no consecutive

items were identical or phonologically related (see Figure 1). Six presentation cycles

were created for each A and B block via Mix software (van Casteren & Davis, 2006),

with the requirement that consecutive trials never comprised the same picture or

phonological onsets.

2 Note that it is not possible to include a transitivity manipulation within the blocked cyclic naming

experiment without introducing a confound as the semantic interference effect is known to generalize

to novel items that share features/relations (e.g., Belke, Meyer, & Damian, 2005; see also Riès et al.,

2014). This would result in spread of semantic activation across related objects and actions/body parts

and to lexical representations, introducing a significant confound for interpreting a selective

interference effect for intransitive actions.


Figure 1. An example sequence of trials over two consecutive cycles in the blocked

cyclic naming paradigm employed in both experiments. The homogeneous context

shows trials from the leg action category; the heterogeneous context shows trials from

all categories.

2.1.3 Apparatus

Picture presentation and response recording were accomplished on a PC with a

15” display using the Cogent 2000 toolbox extension (v1.32;

www.vislab.ucl.ac.uk/cogent_2000.php) for MATLAB Software (The MathWorks

Inc., Massachusetts, USA). A Logitech Desktop Microphone with noise cancelling

technology was used to record responses on digital audio files. Naming latencies were

determined online with a voice-key implemented in the Cogent2000 toolbox. All

responses were verified off-line using Audacity software

(http://audacity.sourceforge.net).

2.1.4 Procedure

Participants completed a familiarization phase in which they named all 16

action pictures in random order, first with the correct gerundial form (e.g., laughing)

printed below and then without. The experimenter corrected participants if a mistake

was made. Two runs of 96 experimental items followed the familiarization phase,

with participants allowed a brief rest break in between. On each trial, a fixation cross

was presented for 500 ms, followed by the picture for 1500 ms, and a blank screen for

1000 ms. Participants were instructed to name each picture as quickly and as

accurately as possible using the gerundial form that names the action.

http://audacity.sourceforge.net/


2.1.5 Analyses

We conducted repeated measures analyses of variance (ANOVAs) with

semantic context and presentation cycle as within participant variables, with

participants (F1) and items (F2) as random factors. Note that when item variability is

experimentally controlled by matching or by counterbalancing, which is the case in

the blocked cyclic naming paradigm, the traditional F1 is the most informative test

statistic (see Raaijmakers, Schrijnemakers, & Gremmen, 1999).

3. Results

Trials on which the voice key failed to detect a response or non-speech noises

triggered the voice key (N=4; 0.01%) were excluded from analyses. In addition,

correct trials with naming latencies deviating more than 2.5 standard deviations from

a participant’s mean response time (RT) within context (N=345; 8.55 %) were

considered outliers and excluded from analysis. Speech errors and dysfluencies were

rare (N=21; 0.52%). Due to the low rate, these errors were not subjected to analysis.

A total of 3662 trials were available for analysis. Figure 2 shows mean naming

latencies as a function of action meaning context (body-part related vs. unrelated) and

cycle. Mean naming latencies as a function of body-part relation are provided in

Supplementary Material Table S1.

The analysis revealed significant main effects of action meaning context [F1(1,

20) = 45.69, MSE = 1387.84, p < .001, ηρ² = .70; F2(1, 15) = 34.8, MSE = 1276.98, p

< .001, ηρ² = .70], such that response times were slower overall for the body-part


related (M = 589 ms) compared to unrelated context (M = 557 ms) and presentation

cycle [F1(5, 100) = 18.77, MSE = 884.382, p < .001, ηρ² = .48; F2(5, 75) = 17.28,

MSE = 772.45, p < .001, ηρ² = .54]. There was also a significant interaction [F1(5,

100) = 2.96, MSE = 1059.23, p = .016, ηρ² = .123; F2(5, 75) = 4.61, MSE = 562.9, p

= .001, ηρ² = .24]. As Figure 2 shows, naming latencies become slower from the

second cycle onward for the body-part related compared to unrelated sets, which is

the typical pattern seen for categorical object relations (see Belke & Stielow, 2013,

for a review). Following Belke and Stielow (2013) a second ANOVA was conducted

excluding data from the first cycle to determine if the interference effect was

cumulative over subsequent cycles (e.g., Oppenheim et al., 2010). This revealed a

significant main effect of action meaning context [F1(1, 20) = 74.50, MSE = 1020.86,

p < .001, ηρ² =.79; F2(1, 15) = 43.48, MSE = 1272.42, p < .001, ηρ² = .74] with body-

part related latencies again slower (584 ms vs. 546 ms) but not presentation cycle

[F1(4, 80) < 1, p = .50; F2(4, 60) < 1, p = .54]. The interaction was not significant

[F1(4, 80) < 1, p = .51; F2(4, 60) < 1, p = .46]. A paired t-test conducted on the means

(Mdiff = 0 ms, 95% CI = 29 ms) from the first presentation cycle was not significant [t1

< -1, p = .99; t1 < -1, p = .71].


Figure 2. Mean naming latencies as a function of context & cycle in Experiment 1.

Error bars are standard errors of the mean (SEM).

3.1 Discussion

We observed a significant interference effect in blocked cyclic action naming,

with slower naming latencies for body-part related actions. To our knowledge, this is

the first demonstration that related intransitive actions produce an interference effect

akin to categorical object relations in the blocked cyclic naming paradigm. However,

naming latencies did not differ in the first cycle or accumulate over cycles. It is worth

emphasising that these latter effects are also not consistently observed in the object

naming variant of the paradigm (e.g., Belke & Stielow, 2013).

4. Experiment 2


4.1 Methods

In Experiment 2, we sought to replicate the interference effect observed for

body-part related action contexts, and to investigate the neural mechanisms associated

with this effect. We therefore conducted a perfusion fMRI investigation. As

mentioned in the Introduction, our a priori hypotheses primarily targeted differential

perfusion responses in motor cortical and temporo-parietal regions that might reflect

grounded and/or heteromodal conceptual processing of actions, respectively.

4.1.1 Participants

Twenty-one healthy, native English-speaking adults participated (11 female,

mean age 23.33 years, range 19-30 years). All had normal or corrected-to-normal

vision. None reported any history of neurological or psychiatric disorder, substance

use, or hearing deficits. All were reimbursed AUD$30 for their participation. They

provided informed consent according to the protocol approved by the Medical

Research Ethics Committee of the University of Queensland. None participated in

Experiment 1.

4.1.2 Materials

Identical to Experiment 1.

4.1.3 Apparatus

Picture presentation and response recording were accomplished via a PC using

the Cogent 2000 toolbox extension for MATLAB as per Experiment 1. Pictures were

projected in black with a luminous white background onto a screen positioned at the


rear of the MRI system that participants viewed through a mirror mounted on the head

coil. The size of the pictures including background was approximately 10 cm wide by

10 cm high, and subtended approximately 10o of visual angle when each participant

was positioned for imaging. A 30 db attenuating headset was used to reduce gradient

noise. Naming responses were recorded on digital audio files using a custom

positioned fibre-optic dual-channel noise-cancelling microphone attached to the head

coil (FOMRI-III, Optoacoustics Ltd., Or-Yehuda, Israel;

http://www.optoacoustics.com). As per Experiment 1, naming latencies were

determined online with voice-key code implemented in the Cogent2000 toolbox, and

responses verified off-line using Audacity software (http://audacity.sourceforge.net).

4.1.4 Procedure

Participants completed a familiarization phase identical to Experiment 1. Two

runs of 96 experimental items followed the familiarization phase, with participants

allowed a brief rest break in between. On each trial, a fixation cross was presented for

500 ms, followed by the picture for 1000 ms, and a blank screen for 2000 ms. There

was a 4000 ms delay between blocks, during which a blank screen was shown. The

relatively longer inter-trial interval was employed in the fMRI experiment to assist in

resolving the perfusion response to each trial while the pause between blocks enabled

the perfusion response to return to baseline and avoided carry-over when switching

between homogeneous and heterogeneous contexts. Participants were instructed to

name each picture as quickly and as accurately as possible using the gerundial form.

4.1.5 Image Acquisition

http://www.optoacoustics.com/

http://audacity.sourceforge.net/


Images were acquired using a 3 Tesla Siemens MAGNETOM Trio TIM

System (Siemens Medical Solutions, Erlangen, Germany) equipped with a 32-channel

receive-only phased-array head coil. Perfusion data were acquired using a quantitative

imaging of perfusion with a single subtraction, thin-slice TI1 periodic saturation

(Q2TIPS) with a proximal inversion with a control for off-resonance effects

(PICORE) labeling technique (Luh et al., 1999). The saturation slab was applied

inferior to the imaging slices, and was 20 mm thicker than the imaging slab, with a 10

mm margin at each edge, to ensure optimum inversion. In each of two consecutive

sessions, an initial M0 image followed by 152 interleaved control and label images

were acquired using a gradient-echo single shot echoplanar imaging (EPI) readout

with the following parameters: TI1 = 700 ms, TI2 = 1800 ms, TR/TE = 2500/11 ms,

matrix = 64×64, voxel in-plane resolution = 3×3 mm, flip angle = 90° and parallel

imaging (PI) reduction factor of 2 for optimal image quality (Ferré et al., 2012).

Volumes comprised 16 slices, 6 mm thick with a 1.5 mm gap, and were oriented to

ensure coverage of the whole cerebrum and most of the cerebellum. Prior to these

sessions, we elected to acquire a separate M0 image with a longer TR of 10000 ms to

maximize SNR for the equilibrium brain tissue magnetization used to normalize the

difference perfusion maps. The first five volumes of each 153 volume session

(consisting of the manufacturer’s M0 and two control and label images) were

discarded. Head movement was limited by foam padding within the head coil. A T1-

weighted structural image was acquired last using a magnetisation-prepared rapid

acquisition gradient-echo (MPRAGE) sequence (512 x 512 matrix, in plane resolution

.45 x .45 mm, 192 slices, slice thickness .9 mm, flip angle 7o, TI 1100ms, TR 2530

ms, TE 2.32ms).


4.1.6 Behavioural and Imaging Analyses

For the behavioural data, we conducted repeated measures ANOVAs with

semantic context (action related, unrelated) and presentation cycle (six cycles) as

within participant variables, all with participants (F1) and items (F2) as random

factors, in an identical manner to Experiment 1.

Image data preprocessing and analysis were conducted with the ASL toolbox

(ASLtbx; Wang, Z., et al., 2008) within statistical parametric mapping software

(SPM12; Wellcome Department of Imaging Neuroscience, Queen Square, London,

UK). Motion correction for the ASL image series was carried out using INRIalign

(Freire, Roche & Mangin, 2002), realigning subsequent images to the first image of

the first series. The realigned series were smoothed with a 6 mm FWHM isotropic

Gaussian kernel to reduce signal outliers by improving the spatial signal-to-noise ratio

(SNR) of both control and label images (Wang, Z., et al., 2008). The T1-weighted

image was segmented using the ‘New Segment’ procedure, and an intracranial mask

generated to exclude extracranial voxels for CBF calculation. Perfusion imaging time

series were then constructed for each participant by implementing a pairwise simple

subtraction between temporally adjacent label (tagged) and control acquisitions,

resulting in image volumes with an effective TR of 5 s (Liu & Wong, 2004). A mean

image was created from the perfusion time-series, and coregistered to the T1-

weighted structural image. The deformation fields produced by the ‘New Segment’

procedure for spatial normalisation to MNI atlas space were then applied to the

perfusion imaging time series, and volumes resliced to 2 mm3 voxels.


We conducted two-stage, mixed-effects model statistical analyses. At the

participant/fixed effects level, event types corresponding to the items in the two

experimental blocking contexts (action related and unrelated blocks) in each of the six

cycles were modeled as effects of interest with delta functions representing each

picture onset, and convolved with a synthetic haemodynamic response function

(HRF) for each session. First order time (i.e., linear) modulations for all event types

were included to accommodate between session variability. Error trials were modeled

separately as a regressor of no interest per session. Global perfusion signal

fluctuations were included per session as nuisance regressors to reduce between

session and between subject variability and enhance SNR (Wang, Z., 2012). In

addition, the segmented grey matter image from each participant was included as an

explicit mask. Temporal filtering was not employed due to its deleterious effects on

perfusion analyses (Wang, J., et al., 2005; Wang, Z., et al., 2008).

Linear contrasts were applied to each participant’s parameter estimates at the

fixed effects level. These contrasts were then smoothed with a 5 mm FWHM isotropic

Gaussian kernel to reduce between participant variability in brain structure and error

of voxel displacement during normalization (Wang, Z., et al., 2008) and entered in a

group level random effects repeated measures analysis of variance (ANOVAs) with

condition and cycle as within participant factors. Covariance components were

estimated using a restricted maximum likelihood (REML) procedure to correct for

non-sphericity (Friston et al., 2002). Our primary analyses involved planned contrasts

performed on correctly identified items according to blocking context and cycle

following the approach with the behavioural data. Specifically, we contrasted (1)

mean perfusion signal for related vs. unrelated blocks over all cycles; (2) mean


perfusion signal for related vs. unrelated blocks from cycle two onward (semantic

interference effect) and; (3) mean perfusion signal for related vs. unrelated blocks in

the first cycle only.

As we had a priori hypotheses concerning specific cortical regions associated

with various processing stages involved in action meaning representation and speech

production, we opted to first restrict voxel-wise analyses to a set of predefined regions

of interest (ROIs) via small volume corrections (SVC) within SPM12, thereby

controlling for multiple comparisons only in those voxels. The following ROIs were

selected from the Hammers et al. (2003) probabilistic atlas: left mid-temporal cortex

(mid-MTG/STG identified by the Indefrey & Levelt, 2004 meta-analysis associated

with lexical concept selection; see also Indefrey, 2011), LIFG (top down biasing or

booster mechanism; e.g., Belke & Stielow, 2013; Oppenheim et al., 2010; see Schnur

et al., 2009), and inferior parietal cortex (action word naming; e.g., Corina et al.,

2005; Liljeström et al., 2008; Saccuman et al., 2006). The left hippocampus

(incremental learning mechanism; de Zubicaray et al., 2014; see also Gluck et al.,

2003) and motor area ROIs (embodied action representations; Gallese & Lakoff,

2005; Kemmerer, 2015; Pulvermüller, 2005) were derived from the SPM Anatomy

toolbox (Eickhoff et al., 2006) based on cytoarchitectonic maximum probability maps

of Brodmann areas 6 and 4ap (Amunts, Schleicher, & Zilles, 2007).

We employed SVC as our hypotheses typically concerned a subset of voxels

within each ROI, rather than the mean activity across all voxels. However, by

estimating SVC thresholds from all voxels within the larger ROI, this approach

produces a more conservative threshold for controlling type 1 error. A height


threshold of p < .001 was adopted in conjunction with spatial cluster extent thresholds

of p < .05 (family-wise error [FWE] corrected) established independently for the

whole brain and each ROI.

5. Results

5.1 Behavioural data

Trials on which the voice key failed to detect a response or non-speech noises

triggered the voice key (N=54; 1.4%) were excluded from analyses. In addition,

correct trials with naming latencies deviating more than 2.5 SDs from a participant’s

mean RT within context (N=45; 1.1%) were considered outliers and excluded from

analysis. Speech errors and dysfluencies were rare (N=27; 0.67%). Due to the low

rate, these errors were not subjected to analysis. A total of 3852 trials were available

for analysis. Figure 3 shows mean naming latencies as a function of action meaning

context (body-part related vs. unrelated) and cycle. Mean naming latencies as a

function of body-part relation are provided in Supplementary Material Table S2.

The analysis revealed significant main effects of action meaning context [F1(1,

20) = 73.07, MSE = 530.25, p < .001, ηρ² = .79; F2(1, 15) = 56.46, MSE = 514.04, p

< .001, ηρ² = .79], such that response times were slower overall for the body-part

related (M = 750 ms) compared to unrelated context (M = 726 ms) and presentation

cycle [F1(5, 100) = 48.08, MSE = 570.89, p < .001, ηρ² = .71; F2(5, 75) = 33.94, MSE

= 655.3, p < .001, ηρ² = .69]. There was also a significant interaction [F1(5, 100) =

2.38, MSE = 1342.48, p = .044, ηρ² = .11; F2(5, 75) = 3.22, MSE = 371.45, p = .011,

ηρ² = .18]. As Figure 3 shows, naming latencies become slower from the second


cycle onward for the body-part related compared to unrelated sets. A second ANOVA

was conducted excluding data from the first cycle to determine if the interference

effect was cumulative over subsequent cycles (e.g., Oppenheim et al., 2010). This

revealed significant main effects of action meaning context [F1(1, 20) = 72.63, MSE =

567.57, p < .001, ηρ² =.78; F2(1, 15) = 46.6, MSE = 660.04, p < .001, ηρ² = .76] and

presentation cycle [F1(4, 80) = 3.50, MSE = 461.17, p = .011, ηρ² =.15; F2(4, 60) =

2.92, MSE = 473.71, p = .028, ηρ² = .16] with body-part related latencies again

slower (742 ms vs. 714 ms) and naming latencies becoming faster over cycles.

However, the interaction was not significant by participants [F1(4, 80) = 1.5, p = .22]

but was by items [F2(4, 60) = 2.74, MSE = 329.31, p = .037, ηρ² =.15]. A paired t-test

conducted on the means (Mdiff = 8.7 ms, 95% CI = 13.3 ms) from the first presentation

cycle was not significant [t1 = -1.36, p = .19; t2 = -1.27, p = .23].

5.2 Imaging data

5.2.1 A priori defined ROI analyses. Across all six cycles, comparisons of action

related vs. unrelated contexts revealed significant perfusion signal increases (i.e.,

action related > unrelated) and reductions (i.e., action related < unrelated) in multiple

ROIs. Increases were observed in left IFG, middle temporal cortex and hippocampus.

Perfusion signal decreases were observed in anterior middle and posterior temporal

cortex (see Table 1 and Figure 4).


Figure 3. Mean naming latencies as a function of context & cycle in Experiment 2.

Error bars are standard errors of the mean (SEMs).

For the first presentation cycle data, the left IFG and hippocampus ROIs

revealed significant perfusion signal increases (Figure 4). Significant perfusion signal

decreases were observed in the left anterior middle temporal cortex for the opposite

contrast (action related < unrelated). However, it should be noted that this contrast is

relatively underpowered as it involves (maximally) only 16 trials per condition per

participant. Comparisons involving data from the second cycle onward (i.e., the

semantic interference effect) again revealed significant perfusion signal increases

(i.e., action related > unrelated) in left IFG, hippocampus, and middle temporal

cortex. The opposite contrast revealed significant perfusion decreases in anterior

middle temporal and posterior temporal cortices and in the anterior intraparietal

sulcus (aIPS) (see Table 1 and Figure 5). No other ROIs showed significant

differential responses.


Table 1. Cerebral regions showing significant activity as a function of action context

(related vs. unrelated) and cycle in the fMRI experiment

Peak MNI

(x y z)

Z

score

Cluster Size

(Voxels)

Related > Unrelated Actions: All

Cycles

Left inferior frontal gyrusa,b

-42 42 -18 >8 8836

Right inferior frontal gyrusa 40 46 -2 7.08 1926

Left hippocampusb -14 -6 -26 6.71 283

Left middle temporal gyrusb -58 -28 -6 4.53 97

Related < Unrelated Actions: All

Cycles

Bilateral occipitotemporal cortexa 26 -68 -8 >8 18378

Left anterior middle temporal gyrusb -54 -4 -18 5.78 175

Left posterior middle and superior

temporal gyria, b

-44 -46 8 4.70 17

Related > Unrelated Actions: Cycle

1


-42 42 -18 6.11 2328


Related < Unrelated Actions: Cycle

1

Bilateral Occipitotemporal cortexa 2 -98 20 7.81 9396


Related > Unrelated Actions: Cycles

2 to 6 (Semantic Interference)


-42 42 -18 >8 8437

Right inferior frontal gyrusa,b

40 46 -2 6.72 1427


Left middle temporal gyrusb -58 -28 -6 4.17 40

Related < Unrelated Actions: Cycles

2 to 6 (Semantic Interference)

Bilateral Occipitotemporoparietal

cortexa

26 -68 -8 >8 8523

Left inferior parietal sulcusa, b

-32 -60 38 4.76 94


Left posterior middle and superior

temporal gyria, b

-44 -46 10 4.51 15

Height threshold p < .001 and p < .05 cluster FWE corrected aWhole brain corrected.

bROI corrected.


Figure 4. Cortical surface renderings showing (from left to right) perfusion increases

(i.e., action related > unrelated contexts) and decreases (i.e., action related < unrelated

contexts) in (top row) left lateral hemispheres over all cycles, and (bottom row)

during the first cycle. IFG = inferior frontal gyrus; aMTG = anterior middle temporal

gyrus; pMTG = anterior middle temporal gyrus. Responses are height thresholded at p

< .001 (uncorrected) and clusters > 50 voxels for visualization purposes.

5.2.2 Exploratory whole brain analyses. Across all six cycles, significant perfusion

increases and decreases were observed for the comparison of action related vs.

unrelated contexts, in large (i.e., spatially extensive) bilateral perisylvian cortical

networks. In addition to signal changes extending throughout the left hemisphere

ROIs noted above, signal increases were observed bilaterally extending throughout

the IFG (including pars orbitalis, pars triangularis and pars opercularis), rectal and

medial frontal gyri, and anterior hippocampi. Signal reductions were also observed in

both hemispheres, extending laterally and medially throughout occipital, posterior

temporal and parietal cortices, with a peak in the right fusiform gyrus (Table 1 and

Figure 4). For the first presentation cycle data, perfusion signal increases were

observed bilaterally in the IFG, in addition to portions of the medial and rectal frontal


gyri. The reverse contrast revealed signal decreases bilaterally throughout occipital,

posterior temporal and parietal cortices. For cycles two onward, signal increases were

observed bilaterally extending throughout the IFG (including pars orbitalis, pars

triangularis and pars opercularis), rectal and medial frontal gyri, and anterior

hippocampi. Signal reductions were also observed in both hemispheres, extending

laterally and medially throughout occipital and posterior temporal cortices and IPS,

with a peak in the right fusiform gyrus (Table 1 and Figure 5).

Figure 5. Cortical surface renderings showing (from left to right) perfusion increases

(i.e., action related < unrelated contexts) on lateral and medial views of the left

hemisphere (top row) and perfusion decreases (i.e., action related < unrelated

contexts) on lateral views of left and right hemispheres over cycles 2-6 (bottom row).

IFG = inferior frontal gyrus; aMTG = anterior middle temporal gyrus; pMTG =

anterior middle temporal gyrus; IPS = intraparietal sulcus. Responses are height

thresholded at p < .001 (uncorrected) and clusters > 50 voxels for visualization

purposes.


5.3 Discussion

We replicated the significant interference effect in naming latencies observed

in Experiment 1. The perfusion fMRI data over all cycles showed significant signal

changes in an extensive fronto-temporo-parietal cortical network for the contrast of

action related vs. unrelated contexts. Perfusion signal increases were observed

anteriorly in IFG and hippocampus, while decreases tended to be observed more

posteriorly in occipitotemporal and parietal (IPS) cortices in addition to anterior

temporal lobe. However, the motor area ROI did not show a significant context effect

for any of the contrasts.

6. General Discussion

In two experiments with a blocked cyclic paradigm, naming intransitive

actions in body-part related compared to unrelated contexts resulted in a significant

slowing of responses from the second cycle onward. This interference effect was

associated with significant perfusion signal increases and decreases in bilateral

cerebral networks encompassing predominantly frontal and medial temporal vs.

occipitotemporal and parietal cortices, respectively. No significant differences in

naming latencies according to context were observed in the first cycle in either

experiment. However, significant perfusion signal changes were observed in similar,

less extensive networks during the first cycle, and in more extensive networks when

all cycle data were combined. Below we discuss the implications of these findings for

models of spoken word production.


The novel finding of a reliable context effect across both experiments

indicates a “pure” semantic interference effect can be elicited during bare naming of

intransitive actions, i.e., the effect cannot be attributed to object feature confounds

that potentially occur with the use of transitive actions (e.g., Hirschfeld &

Zwitserlood, 2014). This semantic interference effect manifested from the second

cycle of naming onward, analogous to that observed for related object contexts (e.g.,

Belke & Stielow, 2013). Leaving aside the issue of the type of lexical selection

mechanism that might operate during blocked cyclic naming, production models

typically assume that semantic interference effects in naming have their origin in

conceptual representations or in the links between conceptual and lexical

representations (see Belke, 2013; Oppenheim et al., 2010).

Significant perfusion reductions were observed in a large bilateral network

encompassing lateral temporal cortex and intraparietal sulcus (IPS). These latter

regions have been proposed to play roles as convergence zones for processing of

heteromodal action meanings (e.g., Noppeney et al., 2005). A role for the anterior

temporal lobe in amodal conceptual processing across a variety of tasks is well-

established (see Binder & Desai, 2011). The peak in posterior MTG (-44, -46, 10)

accords well with those reported for object category coordinates in blocked cyclic

naming (e.g., -52, -40, -5 and -46, -42, 2; de Zubicaray et al., 2014; Harvey & Schnur,

2015), and suggests a processing mechanism in this region that is generic to naming

of both objects and actions. Reduced left MTG activity for semantic interference in

object naming has been observed across both PWI and blocked cyclic paradigms (e.g.,

de Zubicaray, Hansen, & McMahon, 2013; de Zubicaray et al., 2014; de Zubicaray &

McMahon, 2009; Piai et al., 2013, 2014). This relative decrease (related < unrelated)


in signal has been variously interpreted in terms of semantic priming (Piai et al.,

2014) or lateral inhibition between competing representations (de Zubicaray &

McMahon, 2009). The finding of increased perfusion signal accompanying the

semantic interference effect in another, more middle portion of the left MTG is

interesting as it suggests a processing distinction within this cortical structure, perhaps

reflecting a different mechanism for operations involving intransitive actions.

The differential perfusion signal in the anterior wall of the IPS (aIPS) is a

novel finding as this region has not been implicated in studies of the semantic

interference effect in blocked cyclic object naming.3 In his updated meta-analysis of

the neuroimaging data for spoken word production, Indefrey (2011) noted a

“probable, yet to date unclear role of the inferior parietal cortex”. This is perhaps

because the original Indefrey and Levelt (2004) meta-analysis collapsed data across

both object and action picture naming and word production studies. The present

findings clarify a role for the aIPS in conceptual-lexical processing of intransitive

actions during word production.

What type of conceptual-lexical processing might the aIPS engagement

reflect? Embodied accounts of action meaning representation propose that mirror

neurons in premotor cortex and IPL contribute to action understanding (Gallese &

Lakoff, 2005; Rizzolatti & Craighero, 2004). Consequently, the perfusion signal

3 As an anonymous reviewer noted, de Zubicaray et al. (2001) reported left IPL activity for a contrast

of semantically related distractors vs. a lexical control condition (a row of Xs) during object naming in

the PWI paradigm, i.e., a Stroop-like effect. Aside from the obvious differences in paradigm and

contrast employed, the current peak is 22 mm lateral and 26 mm posterior to that of the earlier PWI

result, and so in a macroanatomically (supramarginal gyrus vs. aIPS) and cytoarchitectonically (PFt vs.

hlP3) distinct region (see Caspers et al., 2006). Contrasts of related vs. unrelated distractors (i.e.,

semantic interference) during object naming in PWI typically do not elicit significant left IPL activity

(e.g., de Zubicaray & McMahon, 2009; de Zubicaray et al., 2013; Piai et al., 2013, 2014).


changes in the aIPS might be attributable to mirror neuron activity, and so reflect

modality-specific meaning activation. Although Rizzollati and Craighero (2004)

described intransitive actions as being capable of producing mirror system activation

in humans, they emphasized this activation was restricted to premotor cortex and did

not involve IPL, unlike transitive actions that activated both regions. Yet, premotor

cortex did not show significant perfusion signal changes associated with semantic

interference in the present study, reducing the likelihood that mirror neurons or motor

simulation were engaged (e.g., Gallese & Lakoff, 2005; Kemmerer, 2015;

Pulvermüller, 2005). Consequently, it seems unlikely that the perfusion changes

occurring in the aIPS could be attributed to a mirror neuron mechanism for action

meaning representation.

Neuroimaging and cortical stimulation studies have confirmed a role for the

IPL in representing action intentions, rather than actual movements. For example,

Desmurget et al., (2009) showed that electrical stimulation of left IPL regions in

awake surgical patients led to reports of an intention to move specific body parts

without movement, whereas stimulation of premotor cortex produced actual

movements. Using voxel-based lesion symptom mapping in stroke patients, Kalénine,

Shapiro and Buxbaum (2013) reported that processing action means (i.e., intention to

perform a particular movement) rather than outcomes (i.e., object related goals) relied

on the integrity of the left IPL. Thus, one possibility is that the aIPS activity during

semantic interference with intransitive actions reflects processing of a relatively

abstract nature, consistent with the proposals of prominent production models (e.g.,

Roloefs, 1992; Vigliocco et al., 2004), and spreading-activation accounts of

conceptual processing of actions (e.g., Mahon & Caramazza, 2008).


Indirect evidence for some of these regions’ involvement in processing of

action meaning during production comes from analyses of aphasic patients’ object

naming errors. For example, Schwartz et al. (2011) found posterior MTG and IPL

lesions were selectively associated with thematic errors during object naming (e.g.,

apple-worm, dog-bone), and proposed this reflected activation of action knowledge

linking the objects in an event context (e.g., eating). Neuroimaging studies of the PWI

paradigm have likewise shown differential activity in posterior MTG and IPL for

thematically related contexts with obvious action linkages (e.g., CHEESE-mouse; de

Zubicaray et al., 2013). However, thematic relations that do not emphasise actions in

obvious event contexts do not elicit significant interference or IPL activity in the

blocked cyclic naming paradigm (e.g., cowboy, wagon, rifle, buffalo; de Zubicaray et

al., 2014). This is consistent with Abdel Rahman and Melinger’s (2011) study that

showed interference in blocked cyclic naming only occurred for apparently unrelated

objects (e.g., stool, knife, bucket, and river) when the blocks were preceded by a

verbal cue describing an event context (e.g., fishing trip) and thus could be integrated

into a common theme.

Three other findings of potential interest deserve mention here. One

interesting result is the observation of significant perfusion increases in the IFG in all

analyses. The whole brain analyses indicated this was part of a larger cluster

extending into rectal and medial frontal gyrii. As reviewed in the Introduction to this

paper, the evidence for left IFG involvement in semantic interference in blocked

cyclic object naming has been less consistent than that for the pMTG. However,

actions typically take longer to name than objects (e.g., Szekely et al., 2005), and


might thus afford greater involvement of top down regulation when retrieving target

representations from among competing candidates. The present findings are

consistent with proposals that IFG is required for resolving lexical-semantic

competition, perhaps via a domain general mechanism that top-down biases selection

processes (e.g., Belke & Stielow, 2013; Harvey & Schnur, 2015; Schnur et al., 2009).

The relatively extensive IFG activity might also reflect greater difficulty in teasing

apart lexical activations due to the more abstract conceptual representations of

intransitive actions. Further, as IFG involvement was observed across both initial and

subsequent cycles, it clearly reflects a process (or processes) operating throughout

task performance, perhaps including a representation of the task itself (e.g., Belke &

Stielow, 2013). An interpretation in terms of a semantic priming mechanism operating

solely in initial cycles is also less likely due to the absence of a significant context

difference in naming latencies in the first cycle data across both experiments.

The second interesting finding is the observation of significant perfusion

increases in the left hippocampus. Hippocampal involvement has also been reported

for object category coordinates (de Zubicaray et al., 2014; Llorens et al., 2016).

Several authors have noted the need for production models to include a mechanism to

explain the persistence of semantic interference in paradigms such as blocked cyclic

naming, where the effect has been shown to survive intervening filler trials. One

proposed mechanism is incremental learning (e.g., Damian & Als, 2005; Oppenheim

et al., 2010). Neurophysiologically informed models have attributed a key role to the

hippocampus in incremental learning (see Gluck et al., 2003; Meeter et al., 2005).

Whether an incremental learning mechanism is necessary for the semantic

interference effect to emerge (e.g., Navarrete et al., 2014; Oppenheim et al., 2010), or


is simply engaged as a consequence of the blocked cyclic naming paradigm’s

repetition of items, remains to be demonstrated.

The third and final result of interest is the reduced perfusion signal responses

in the visual extrastriate cortices, also observed in PWI studies of semantic context

effects with object naming (e.g., de Zubicaray et al., 2013). This likely reflects

meaning dependent modulation of early perceptual processing (for a review, see

Collins & Olson, 2014). For example, sequential visual matching of objects is known

to be affected by semantic context (e.g., Gauthier et al., 2003), and category learning

has been shown to enhance visual perception of objects along dimensions relevant to

the learned categories (Folstein et al., 2015). Consequently, presenting intransitive

actions in categorically related versus unrelated contexts might enhance their

perceptual processing along coordinate dimensions (e.g., body part), resulting in

reduced perfusion signal.

6.1 Conclusions

Over two experiments, we observed a reliable semantic interference effect

during bare naming of intransitive actions in related versus unrelated contexts. This

novel finding may be interpreted as indicating conceptual representations of action

words are organized according to body part coordinate relations in semantic memory

(e.g., Maouene et al., 2008), and may thus inform future production models. The

interference effect was associated with perfusion increases and decreases in bilateral

cerebral networks encompassing predominantly frontal and medial temporal vs.

occipitotemporal cortices and the IPS, respectively. As the semantic interference

effect is generally assumed to have its origins in a conceptual preparation stage of


processing or in conceptual-to-lexical connections (e.g., Belke, 2013; Oppenheim et

al., 2010), the latter findings confirm a role for middle temporal cortex and IPS in the

conceptual-lexical processing of intransitive actions.

Acknowledgements

This research was supported by an Australian Research Council (ARC) Future

Fellowship FT0991634 (GZ). We are grateful to Michele Miozzo for providing some

of the action pictures.

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Appendix

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lock

Related block

laughing yelling winking sneezing Face

pointing saluting clapping waving Arm/hand

running walking skating hopping Leg/Foot

swinging hiding resting climbing Whole body

Highlights

Naming of intransitive actions is slower in related versus unrelated contexts

Associated with perfusion fMRI signal changes in frontal and temporo-parietal regions

Frontal cortex activity may reflect domain general mechanisms for resolving interference

Temporo-parietal activity may reflect conceptual-lexical processing of actions

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