Understanding the origins of drawing in humans requires studying the drawing behavior of our closest relatives, the great apes. This study examines the drawing behavior of chimpanzees to assess cognitive abilities in a long-term context. Our systematic analysis of previously unseen and newly documented drawings from six chimpanzees over several years revealed significant inter- and intra-individual differences, as well as seasonal variation. Chimpanzees used multiple colors, but no specific color preferences were observed. Although they use their right hand more, they can use both hands to produce a single drawing. Using Principal Component Analysis (PCA), we identified three graphically significant dimensions: filling aspect, color aspect and shape aspect, suggesting that the measures were meaningfully or intentionally regrouped. Drawing style varied between seasons, but more importantly between individuals, with each chimpanzee exhibiting a kind of unique drawing personality. In addition, changes in drawing style were observed over the years, suggesting a development in individual drawing behavior. Comparisons with previous studies on orangutans and human children suggest that chimpanzee drawing may reflect personality traits and common cognitive abilities. These findings add to our understanding of the evolutionary origins of human artistic expression.
Le texte de Miguel Espinoza Freedom in a Causally Determined World invite à réfléchir à cette caractérisation qui est faite de la cause formelle, alors qu’on y admet que sans elle le monde serait un ensemble pulvérisé d’événements aléatoires sans aucune signification.1 Le point de vue de cette réflexion est l’ontologie et non l’épistémologie, affirme l’A. dès le début. Le déterminisme causal y est vu comme l’ordre de la rationalité dans les choses. On y fait appel au problème de la non computabilité qui s’introduit dans le déterminisme au sens de la physique classique.
Immersive virtual reality (IVR) has the capacity to act as a complement to skill training. Nevertheless, discrepancies in outcomes persist, and field studies evaluating the use of IVR during multi-day training remain scarce. Furthermore, cognitive load has the potential to provide valuable insights into the instructional framework. Yet, it is often overlooked in practice. This study examines cognitive load and its interaction with other individual features (i.e., self-efficacy, presence, cybersickness and learning outcomes) when using IVR during a multiple-day training programme in molecular biology skills. The primary objective is twofold: firstly, to enhance comprehension of the role of cognitive load during multiple day training and, secondly, to refine the instructional framework for the utilisation of IVR within authentic classroom settings. A total of 54 undergraduate students were recruited for the study. Participants were split into three groups: one with only practical training, and two using IVR either before or after the hands-on training. The findings demonstrated no discrepancy in self-efficacy, cognitive load, and learning outcomes between the IVR groups after training. However, IVR groups demonstrated higher levels of cognitive load but lower learning outcomes and self-efficacy scores in comparison to the CTRL group. Correlations were also exhibited between cognitive load, self-efficacy and cybersickness. The moment of implementation of IVR did not have an effect on the assessed variables. However, in contrast to the CTRL group, the implementation of IVR increased the associated cognitive load, which may have influenced the learning process.
Critical border and migration studies have shown that border practices are often messy, ambiguous, improvised and uncertain, while datafication has amplified messiness and its effects on migrants. Describing 'mess' at borders has been a critical intervention against the myth of order, homogeneity, predictability, clarity or efficiency. Yet, 'mess' can also open spaces for agency and resisting data borders. What do these ambiguities mean for the critical potential of diagnosing mess? Drawing on research we have conducted in border zones in France, Italy, Germany and Spain, we show that the critical potential of mess cannot be gauged through the binary of mess/order. We argue that we need to introduce a third term - control - to understand the critical potential of mess. To do so, we first unpack three dimensions of 'mess' - spatial, temporal and material - to situate it in relation to diagnoses of failure, friction, improvisation and non-knowledge in bordering practices. Second, we raise questions about the limits that the co-constitution of mess/order entails for critical research on data borders. Third, we show how control transforms the binary of mess/order into a question about 'kinds of mess'.
BACKGROUND AND PURPOSE:Deep learning-based reconstruction has the potential to shorten MRI acquisition while preserving diagnostic image quality, but its reliability for MS lesion detection on 3D FLAIR requires validation. Our aim was to evaluate the diagnostic performance and image quality of a deep learning-reconstructed 3D FLAIR sequence in detecting demyelinating lesions in patients with MS, compared with the conventional reference FLAIR sequence, and to assess the impact of different head coil configurations. MATERIALS AND METHODS:In this prospective study, 76 patients with MS underwent 3T MRI using both reference and deep learning-reconstructed FLAIR sequences, with identical spatial and contrast parameters. Imaging was alternately performed using either a 20- or a 64-channel head coil. Two blinded radiologists independently assessed the image quality using a 5-point Likert scale and evaluated lesion detection. The SNR and contrast-to-noise ratios were measured, and automated lesion detection was performed using a certified artificial intelligence device. RESULTS:All clinically relevant lesions (≥3 mm) were detected on the deep learning-reconstructed FLAIR sequence with complete agreement between readers. Six subthreshold lesions (<3 mm) were missed in 3 patients (3.95%; 95% CI, 1.03%-11.88%), all scanned with the 20-channel coil. The reference FLAIR sequence had slightly higher image quality scores overall (mean, 4.86 [SD, 0.35] versus 4.72 [SD, 0.52] P = .01). However, no difference in quality or lesion detection was observed between sequences when using the 64-channel coil. The deep learning-reconstructed FLAIR sequence demonstrated significantly higher SNR and contrast-to-noise ratios across all cases (P < .001), with an additional contrast increase using the 64-channel coil (P = .007). Although subjective evaluation favored the standard reconstruction, quantitative metrics indicated improved image quality with deep learning reconstruction. Automated analysis using an artificial intelligence-based tool confirmed complete concordance in lesion detection between the two sequences. CONCLUSIONS:The deep learning-reconstructed FLAIR enables reliable detection of demyelinating lesions in MS with significantly reduced scan time without the loss of diagnostic information. Use of a 64-channel coil further enhances image quality, supporting the integration of this accelerated technique into clinical practice.
