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Understanding actions performed by others requires us to integrate different types of information about people, scenes, objects, and their interactions. What organizing dimensions does the mind use to make sense of this complex action space? To address this question, we collected intuitive similarity judgments across two large-scale sets of naturalistic videos depicting everyday actions. We used cross-validated sparse non-negative matrix factorization to identify the structure underlying action similarity judgments. A low-dimensional representation, consisting of nine to ten dimensions, was sufficient to accurately reconstruct human similarity judgments. The dimensions were robust to stimulus set perturbations and reproducible in a separate odd-one-out experiment. Human labels mapped these dimensions onto semantic axes relating to food, work, and home life; social axes relating to people and emotions; and one visual axis related to scene setting. While highly interpretable, these dimensions did not share a clear one-to-one correspondence with prior hypotheses of action-relevant dimensions. Together, our results reveal a low-dimensional set of robust and interpretable dimensions that organize intuitive action similarity judgments and highlight the importance of data-driven investigations of behavioral representations.

Humans observe actions performed by others in many different visual and social settings. What features do we extract and attend when we view such complex scenes, and how are they processed in the brain? To answer these questions, we curated two large-scale sets of naturalistic videos of everyday actions and estimated their perceived similarity in two behavioral experiments. We normed and quantified a large range of visual, action-related, and social-affective features across the stimulus sets. Using a cross-validated variance partitioning analysis, we found that social-affective features predicted similarity judgments better than, and independently of, visual and action features in both behavioral experiments. Next, we conducted an electroencephalography experiment, which revealed a sustained correlation between neural responses to videos and their behavioral similarity. Visual, action, and social-affective features predicted neural patterns at early, intermediate, and late stages, respectively, during this behaviorally relevant time window. Together, these findings show that social-affective features are important for perceiving naturalistic actions and are extracted at the final stage of a temporal gradient in the brain.

•Does stimulus timing impact the processing of predicted visual features?.•We evaluated if expectation suppression effects are modulated by temporal predictability.•Expectation suppression was robust in both visual ERFs and feature decoding accuracy.•Visual responses to predictable stimuli are greater for predictable vs. unpredictable timing.•Sensory evidence is given less weight when timing is uncertain. Contextual information triggers predictions about the content (“what”) of environmental stimuli to update an internal generative model of the surrounding world. However, visual information dynamically changes across time, and temporal predictability (“when”) may influence the impact of internal predictions on visual processing. In this magnetoencephalography (MEG) study, we investigated how processing feature specific information (“what”) is affected by temporal predictability (“when”). Participants (N = 16) were presented with four consecutive Gabor patches (entrainers) with constant spatial frequency but with variable orientation and temporal onset. A fifth target Gabor was presented after a longer delay and with higher or lower spatial frequency that participants had to judge. We compared the neural responses to entrainers where the Gabor orientation could, or could not be temporally predicted along the entrainer sequence, and with inter-entrainer timing that was constant (predictable), or variable (unpredictable). We observed suppression of evoked neural responses in the visual cortex for predictable stimuli. Interestingly, we found that temporal uncertainty increased expectation suppression. This suggests that in temporally uncertain scenarios the neurocognitive system invests less resources in integrating bottom-up information. Multivariate pattern analysis showed that predictable visual features could be decoded from neural responses. Temporal uncertainty did not affect decoding accuracy for early visual responses, with the feature specificity of early visual neural activity preserved across conditions. However, decoding accuracy was less sustained over time for temporally jittered than for isochronous predictable visual stimuli. These findings converge to suggest that the cognitive system processes visual features of temporally predictable stimuli in higher detail, while processing temporally uncertain stimuli may rely more heavily on abstract internal expectations.

Porous alkali activated materials (AAM), can be obtained from waste glass powder and slag mixtures by alkali activation with NaOH solution. To obtain an adequate porous microstructure, the hardened AAM pastes were thermally treated at temperatures ranging between 900°C and 1000°C, for 60 or 30 minutes. Due to the intumescent behaviour specific for this type of materials, an important increase of the volume and porosity occurs during the thermal treatment. The partial substitution of waste glass powder with slag, determines the increase of compressive strength assessed before (up to 37 MPa) and after (around 10 MPa) thermal treatment; the increase of slag dosage also determines the increase of the activation temperature of the intumescent process (above 950°C). The high porosity and the specific microstructure (closed pores with various shapes and sizes) of these materials recommend them to be utilised as thermal and acoustical insulation materials.

In navigating our environment, we rapidly process and extract meaning from visual cues. However, the relationship between visual features and categorical representations in natural scene perception is still not well understood. Here, we used natural scene stimuli from different categories and filtered at different spatial frequencies to address this question in a passive viewing paradigm. Using representational similarity analysis (RSA) and cross-decoding of magnetoencephalography (MEG) data, we show that categorical representations emerge in human visual cortex at ∼180 ms and are linked to spatial frequency processing. Furthermore, dorsal and ventral stream areas reveal temporally and spatially overlapping representations of low and high-level layer activations extracted from a feedforward neural network. Our results suggest that neural patterns from extrastriate visual cortex switch from low-level to categorical representations within 200 ms, highlighting the rapid cascade of processing stages essential in human visual perception. •Low spatial frequencies support generalizable neural responses to natural scenes.•Categorical representations of scenes arise in visual cortex within 200 ms.•We find simultaneous processing of low- and high-level visual features.•Low- and high-level neural network layers have overlapping representations.

This paper presents the influence of adding black carbon nanopowder (average size 13 nm, PlasmaChem) in soybean oil in different massic concentration (0.25 %, 0.50 % and 1 %) on several tribological parameters: friction coefficient and wear scar diameter. Tests are done on a four-ball machine. The test parameters were load: 100 N, 200 N and 300 N and speed 1000 rpm, 1400 rpm, 1800 rpm. The test balls are lime polished, made of chrome alloyed steel, having 12.7±0.0005 mm in diameter, with 64-66 HRC hardness. The sample oil volume required for each test was 8±1 ml. This type of anti-wear additive, because the particle distribution is not evenly in contact during the running, could not help improving the tribological behavior. It does not reduced the friction coefficient and wear scar diameter as compared to the neat soybean oil. The authors estimate that the additive should be bonded (physically or chemically) on the triboelements for having better results.

Actions are the building blocks of our dynamic visual world, yet the neural computations supporting action perception are not well understood. How does perceptual and conceptual information unfold in the brain when we observe what others are doing? We collected EEG and fMRI data while participants viewed short videos and sentences depicting naturalistic actions. Using representational similarity analysis, we found distinct conceptual representations along the ventral, dorsal, and lateral pathways, with the target of actions specifically encoded in lateral occipitotemporal cortex (LOTC) and posterior superior temporal sulcus (pSTS). Among conceptual features, the target of actions (i.e. whether the action was directed at an object, a person, or the self) explained the most unique variance in EEG responses. Finally, EEG-fMRI fusion revealed rapid processing along the lateral and dorsal pathways. Together, our results disentangle the perceptual and conceptual components of action understanding and characterize the underlying spatiotemporal dynamics in the human brain.

The paper presents the change of functional parameters defined on the Abbott-Firestone curve in order to evaluate the surface quality of the balls from the four ball tester, after tests done with several vegetable oils. The tests were done using two grades of rapeseed oil (degummed and refined) and two grades of soybean oil (coarse and degummed) and a common transmission oil (T90). Test parameters were 200 N and 0.576 m/s (1500 rpm) for 60 minutes. For the refined rapeseed oil, the changes in shape of the Abbott-Firestone curves are more dramatic, these being characterized by high values of Spk (the average value for the wear scars on the three balls), thus being 40% of the sum Svk + Sk + Spk, percentage also obtained for the soybean oil, but the value Spk being lower. For the degummed soybean oil, the profile height of the wear scars are taller than those obtained after testing the coarse soybean oil, meaning that the degumming process has a negative influence on the worn surface quality and the lubricating capacity of this oil. Comparing the surface quality of the wear scars on fixed tested balls is a reliable method to point out the lubricant properties of the vegetable oils, especially if they are compared to a \"classical\" lubricant as a non-additivated transmission mineral oil T90. The best surface after testing was obtained for the soybean oil, followed by T90 oil and the degummed grades of the soybean oil and rapeseed oil (these three giving very close values for the functional parameters), but the refined rapeseed oil generated the poorest quality of the wear scars on the balls, under the same testing conditions.

Magnetoencephalography (MEG) is increasingly being used to study brain function because of its excellent temporal resolution and its direct association with brain activity at the neuronal level. One possible cause of error in the analysis of MEG data comes from the fact that participants, even MEG-experienced ones, move their head in the MEG system. Head movement can cause source localization errors during the analysis of MEG data, which can result in the appearance of source variability that does not reflect brain activity. The MEG community places great importance in eliminating this source of possible errors as is evident, for example, by recent efforts to develop head casts that limit head movement in the MEG system. In this work we use software tools to identify, assess and eliminate from the analysis of MEG data any possible correlations between head movement in the MEG system and widely-used measures of brain activity derived from MEG resting-state recordings. The measures of brain activity we study are a) the Hilbert-transform derived amplitude envelope of the beamformer time series and b) functional networks; both measures derived by MEG resting-state recordings. Ten-minute MEG resting-state recordings were performed on healthy participants, with head position continuously recorded. The sources of the measured magnetic signals were localized via beamformer spatial filtering. Temporal independent component analysis was subsequently used to derive resting-state networks. Significant correlations were observed between the beamformer envelope time series and head movement. The correlations were substantially reduced, and in some cases eliminated, after a participant-specific temporal high-pass filter was applied to those time series. Regressing the head movement metrics out of the beamformer envelope time series had an even stronger effect in reducing these correlations. Correlation trends were also observed between head movement and the activation time series of the default-mode and frontal networks. Regressing the head movement metrics out of the beamformer envelope time series completely eliminated these correlations. Additionally, applying the head movement correction resulted in changes in the network spatial maps for the visual and sensorimotor networks. Our results a) show that the results of MEG resting-state studies that use the above-mentioned analysis methods are confounded by head movement effects, b) suggest that regressing the head movement metrics out of the beamformer envelope time series is a necessary step to be added to these analyses, in order to eliminate the effect that head movement has on the amplitude envelope of beamformer time series and the network time series and c) highlight changes in the connectivity spatial maps when head movement correction is applied. [Display omitted] •Head movement effects on MEG resting-state measures of brain activity are evaluated.•Regressing head movement metrics out of beamformer envelope time series is essential.•Regressing head movement metrics out of network activation time series is essential.•Regressing out head movement metrics changes some spatial network connectivity maps.

Although many genetic risk factors for psychiatric and neurodevelopmental disorders have been identified, the neurobiological route from genetic risk to neuropsychiatric outcome remains unclear. 22q11.2 deletion syndrome (22q11.2DS) is a copy number variant (CNV) syndrome associated with high rates of neurodevelopmental and psychiatric disorders including autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD) and schizophrenia. Alterations in neural integration and cortical connectivity have been linked to the spectrum of neuropsychiatric disorders seen in 22q11.2DS and may be a mechanism by which the CNV acts to increase risk. In this study, magnetoencephalography (MEG) was used to investigate electrophysiological markers of local and global network function in 34 children with 22q11.2DS and 25 controls aged 10–17 years old. Resting-state oscillatory activity and functional connectivity across six frequency bands were compared between groups. Regression analyses were used to explore the relationships between these measures, neurodevelopmental symptoms and IQ. Children with 22q11.2DS had altered network activity and connectivity in high and low frequency bands, reflecting modified local and long-range cortical circuitry. Alpha and theta band connectivity were negatively associated with ASD symptoms while frontal high frequency (gamma band) activity was positively associated with ASD symptoms. Alpha band activity was positively associated with cognitive ability. These findings suggest that haploinsufficiency at the 22q11.2 locus impacts short and long-range cortical circuits, which could be a mechanism underlying neurodevelopmental and psychiatric vulnerability in this high-risk group.