Behavioural study
Participants
Twenty-eight volunteers (18 females, age = 22.4±3.2 years) took part in the behavioural experiment. All participants were 18 years or older prior to participating, they gave their informed consent, accordingly with the ethical standards of the Declaration of Helsinki. Exclusion criteria were formal education in linguistics, the presence of neurological or psychiatric disorders and the use of drugs affecting the central nervous system. The study was approved by the Ethics Committee of the University “Magna Graecia” of Catanzaro and complied with the ethical standards of the Italian Psychological Society (AIP, see http://www.aipass.org/node/26) as well as the Italian Board of Psychologists (see http://www.psy.it/codice_deontologico.html). All participants were right-handed, according to the Edinburgh Handedness Inventory 107, had normal or corrected-to-normal vision and were native Italian speakers.
Apparatus, procedure and stimuli
The experiment was carried out in a sound-attenuated room, dimly illuminated by a halogen lamp directed toward the ceiling. Participants sat comfortably in front of a PC screen (LG 22′′ LCD, 1920 × 1080 pixel resolution and 60 Hz refresh rate). The eye-to-screen distance was set at 60 cm.
The experiment used a go/no-go task, in which participants were requested to respond to real nouns and images of objects and refrain from responding when presented stimuli were pseudowords and scrambled images. The experiment session consisted of 1 practice block and 1 experimental block. In the practice block, participants were presented with 16 stimuli (4 images of natural objects or tools, 4 scrambled images, 4 nouns of natural objects or tools and 4 pseudowords) which were not used in the experimental block. During the practice block, participants received feedback (“ERROR”) after giving a wrong response (i.e., responding to a meaningless or refraining from responding to a real item), as well as for responses given prior to go signal presentation (“ANTICIPATION”), or later than 1.5 s (“YOU HAVE NOT ANSWERED”). In the experimental block each stimulus was randomly presented twice, for a total of 320 trials, with the constraint that no more than three items of the same kind (verbal, visual) or referring to objects of the same category (graspable natural object, tools, meaningless) could be presented on consecutive trials. No feedback was given to participants. Thus, the experiment, which lasted about 20 min, consisted of 160 go trials (80 nouns, 50% natural graspable object nouns and 50% tools nouns, plus 80 images of objects, 50% natural graspable objects and 50% tools) and 160 no-go trials (80 pseudowords plus 80 scrambled images), and 16 practice trials, for a total of 336 trials. To sum up, the experiment used a 2 × 2 repeated measures factorial design with Category (natural graspable objects, graspable tools) and Stimulus Type (nouns, photos) as within-subjects variables.
Nouns in the 2 categories were matched for word length (mean values for nouns referring to natural objects and tools: 6.4 and 7.4; t = 0.049, p = 0.96), syllable number (mean values: 2.45 and 3.00; t = 0.018, p = 0.98) and written lexical frequency [mean values: 6.14 and 8.77 number of occurrences per million in CoLFIS (Corpus e Lessico di Frequenza dell’Italiano Scritto ∼3.798.000 words)— Laudanna et al., 1995; t = 0.52 , p = 0.60]. Pseudowords were built by substituting one consonant and one vowel in two distinct syllables of each noun (e.g., “sgalpillo” instead of “scalpello”). With this procedure, pseudowords contained orthographically and phonologically legal syllables for the Italian language. Hence, nouns and pseudowords were also matched for length.
Images depicted 20 natural graspable objects and 20 tools. They were photos of real objects and not sketches. The scrambled images were built by applying Photoshop distorting graphic filters (e.g., blur and twist) to the photos depicting both natural graspable objects and graspable tools so to make them unrecognizable and then meaningless. All photos and scrambled images were 440 × 440 pixels.
Each trial started with a black fixation cross (RGB coordinates = 0, 0, 0) displayed at the center of a grey background (RGB coordinates = 178, 178, 178). After a delay of 1000–1500 ms (in order to avoid response habituation), the fixation cross was replaced by a stimulus item, either a noun/pseudoword or an image/scrambled image. Note that the delay could be at any time between 1000 and 1500 ms. The verbal labels were written in black lowercase Courier New bold (font size = 24). Stimuli were centrally displayed and surrounded by a red (RGB coordinates = 255, 0, 0) 20 pixels-wide frame. The red frame changed to green (RGB coordinates = 0, 255, 0) 150 ms after the stimulus onset. The color change of the frame was the “go” signal for the response (Fig.3). Participants were instructed to give a motor response, as fast and accurate as possible, by pressing a key on a computer keyboard centred on participants’ body midline with their right index finger. They had to respond when the stimulus referred to a real object, and refrain from responding when it was meaningless. After the go signal, stimuli remained visible for 1350 ms or until participant’s responses. Stimulus presentation and response times (RTs) collection were controlled using the software package E-Prime 2.
Data analysis
Data analyses were performed using R 3.6.3 108. Practice trials were excluded from analysis. Participants' RTs to real stimuli were analysed. The RTs were measured from the “go” signal to the button pressing. Mean RTs of each participants were submitted to an rmANOVA, with Category (2 levels: natural graspable object and tool) and Stimulus type (2 levels: noun and image) as factors, with the Greenhouse-Geisser correction being applied in the case of a violation of sphericity assumptions.
MEG study
Participants
Fifteen volunteers (9 females, age 26.5±2.0 years) were recruited for the experiment. All participants were 18 years or older prior to participating. All participants were right-handed, according to the Edinburgh Handedness Inventory 107, had normal or corrected-to-normal vision and were native Italian speakers. Exclusion criteria were formal education in linguistics, the presence of neurological or psychiatric disorders and the use of drugs affecting the central nervous system. The experiment was carried out in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. The study was approved by the Ethics Committee of Fondazione IRCCS Istituto Neurologico Carlo Besta of Milan and the University “Magna Graecia” of Catanzaro and complied with the ethical standards of the Italian Psychological Society (AIP, see http://www.aipass.org/node/26) as well as the Italian Board of Psychologists (see http://www.psy.it/codice_deontologico.html). Participants gave their written informed consent before being included in the study.
Task
Stimuli and procedure were the same of the behavioural study, with the necessary adaptation required by the MEG setting used in the current study. Sixteen practice trials were used to train participants. To improve signal-to-noise ratio, the experiment consisted of two consecutive acquisitions in which 80 go trials (40 nouns, 50% natural object nouns and 50% tools nouns, plus 40 images of object, 50% natural objects and 50% tools) and 80 no-go trials (40 pseudowords plus 40 scrambled images) were presented, for a total of 320 experimental trials. In the two acquisitions, the presentation order of the stimuli was randomized. Hence, the MEG study used the same 2 × 2 repeated measures factorial design as the behavioural one. Stimulus presentation and RTs collection were controlled using the software package Stim2.
MEG data acquisition and pre-processing
The MEG signals were acquired using a 306-channel whole head MEG system (Triux, Elekta Oy, Helsinki, Finland). Surface EMG signals were simultaneously recorded from pairs of electrodes placed bilaterally 2–3 cm apart over the belly of the right and left flexor and extensor of wrist. Signals were sampled at 1 kHz. Also bipolar electro-oculographic and electrocardiographic signals were acquired.
The participant’s head position inside the MEG helmet was continuously monitored by five head position identification (HPI) coils located on the scalp. The locations of these coils, together with three anatomical landmarks (nasion, right and left preauriculars), and additional scalp points were digitized before the recording by means of a 3D digitizer (FASTRAK, Polhemus, Colchester, VT).
The raw MEG data were pre-processed off-line using the spatio-temporal signal-space separation method109 implemented in the Maxfilter 2.2 (Elekta Neuromag Oy, Helsinki, Finland) in order to subtract external interference and correct for head movements and then band-pass filtered at 0.1–100 Hz. Cardiac and ocular movement artifacts were removed using ICA algorithm based on EEGLAB toolbox 110 implemented in a custom-made MATLAB code (R2017b, Mathworks Inc., Natick MA, USA). MEG data were divided into epochs ranging from 2.2 s before to 2.8 s after the stimulus onset. Epochs with muscular artifacts and/or sensor jumps were excluded from further analysis. Finally, data epochs were grouped according to the four conditions (natural and tools images, natural and tools words).
Sensors analysis
Time–frequency representations (TFR) for frequencies between 15 and 30 Hz with steps of 1 Hz were computed using a Fourier transformation. Desynchronization values were obtained as percent power change in beta band (15-30 Hz) calculated with respect to mean power in the -3 to -2 s before cue onset. Finally, for each participant, the most reactive β-band frequency (individual reactive frequency, IRF) was defined as the frequency at which the maximum desynchronization was found.
Source analysis
Dynamic imaging of coherent sources (DICS) beamforming 111 was used to identify the spatial distribution in the frequency domain. The leadfield matrix was computed using realistically shaped single-shell volume conduction model based on template brain co-registered by means of digitized scalp points. Source model was obtained from a 5 mm resolution grid which covered whole brain volume. Source localizations was performed for the band IRF±1 Hz for a pre-stimulus baseline period (−1.2 to −0.5 s) and for a window of interest during stimulus presentation (0.5 to 1.2 s) using a common spatial filter based on the pooled data from both time intervals. Subject-specific relative power differences were grand-averaged and visualized on the cortical surface of the MNI brain. To obtain a time course for each trial and voxel, we used the linearly constraint minimum variance (LCMV) beamforming 112 calculating the covariance matrix of the sensor-level MEG data with 5% regularization.
Automated Anatomical Labelling atlas was used to extract the source time-series on inferior parietal lobule and precentral and postcentral areas. Subsequently, as for the sensors data, we calculated the desynchronization in IRF±1 Hz band and averaged within regions. Finally, we calculated AUC in the 0.5 to 1.5 s period.
Both sensor and source data analysis were analysed using custom Matlab (MATLAB 2017a, MathWorks, Inc., Natick, MA, USA) scripts based on SPM8 and Fieldtrip toolboxes 113,114.
Statistical analysis
The RTs and source time-series AUC were compared using rmANOVA with the factor Category (tools, natural) and Stimulus type (images, nouns) with the Greenhouse-Geisser correction being applied in the case of a violation of sphericity assumptions.
To compare TFR between different conditions in contralateral motor areas, and to identify significant beta frequencies and time points, the non-parametric permutation test in combination with cluster-level statistics and multiple comparison correction implemented in Fieldtrip toolbox was applied. Post-hoc paired two-tailed t tests were used to calculate the within-group difference between stimuli. All data are expressed as mean ± standard errors of mean. Statistical analyses were carried out using IBM SPSS, version 20 (SPSS Inc., Chicago, IL, U.S.A.).