An Electroencephalographic Analysis of Strategy Usage During Social Dilemmas

The neuroscience of strategy usage during social dilemmas is said to require Theory of Mind (ToM). The process of ToM takes into consideration the actions of another person, so implications with strategies like tit-for-tat (TFT) or win-stay-lose-switch (WSLS) are logical. However, using either TFT or WSLS does not require ToM since these strategies are based on the previous opponent choice. Thus using a quantifiable strategy like TFT or WSLS during a social dilemma game may not necessarily incur activation of ToM. To investigate whether this is possible, 31 participants participated in the Prisoner’s Dilemma while being recorded via electroencephalography (EEG). 16 participants were trained and performed either TFT or WSLS (strategy group) while 15 participants did not train or perform either TFT or WSLS (control group). The strategy group differed from the control group by showing significantly less alpha desynchrony than the control group in ToM areas (channels AF3 and P8) when faced with an opponent whose cooperation rate varied from one half of the experiment to the other half. The differences suggest choice processing differs when selecting by TFT or WSLS versus selecting by ToM. Furthermore, greater ToM activity for the control group may indicate strategy downregulates ToM during social dilemmas

Avian surface reconstruction in free-flight with application to flight stability analysis of a barn owl and peregrine falcon

Birds primarily create and control the forces necessary for flight through changing the shape and orientation of their wings and tail. Their wing geometry is characterised by complex variation in parameters such as camber, twist, sweep and dihedral. To characterise this complexity, a multi-stereo photogrammetry setup was developed for accurately measuring surface geometry in high-resolution during free-flight. The natural patterning of the birds was used as the basis for phase correlation-based image matching, allowing indoor or outdoor use while being non-intrusive for the birds. The accuracy of the method was quantified and shown to be sufficient for characterising the geometric parameters of interest, but with a reduction in accuracy close to the wing edge and in some localized regions. To demonstrate the method's utility, surface reconstructions are presented for a barn owl (Tyto alba) and peregrine falcon (Falco peregrinus) during three instants of gliding flight per bird. The barn owl flew with a consistent geometry, with positive wing camber and longitudinal anhedral. Based on flight dynamics theory this suggests it was longitudinally statically unstable during these flights. The peregrine flew with a consistent glide angle, but at a range of airspeeds with varying geometry. Unlike the barn owl, its glide configuration did not provide a clear indication of longitudinal static stability/instability. Aspects of the geometries adopted by both birds appeared to be related to control corrections and this method would be well suited for future investigations in this area, as well as for other quantitative studies into avian flight dynamics.Flight O1 - original uncompressed tif images for flight O1 of the barn owlO1_images.zipFlight O2 - original uncompressed tif images for flight O2 of the barn owlO2_images.zipFlight O3 - original uncompressed tif images for flight O3 of the barn owlO3_images.zipFlight P1 - original uncompressed tif images for flight P1 of the peregrineP1_images.zipFlight P2 - original uncompressed tif images for flight P2 of the peregrineP2_images.zipFlight P3 - original uncompressed tif images for flight P3 of the peregrineP3_images.zipREADM

Iterative Averaging in the Quest for Best Test Error

We analyse and explain the increased generalisation performance of iterate averaging using a Gaussian process perturbation model between the true and batch risk surface on the high dimensional quadratic. We derive three phenomena \latestEdits{from our theoretical results:} (1) The importance of combining iterate averaging (IA) with large learning rates and regularisation for improved regularisation. (2) Justification for less frequent averaging. (3) That we expect adaptive gradient methods to work equally well, or better, with iterate averaging than their non-adaptive counterparts. Inspired by these results\latestEdits{, together with} empirical investigations of the importance of appropriate regularisation for the solution diversity of the iterates, we propose two adaptive algorithms with iterate averaging. These give significantly better results compared to stochastic gradient descent (SGD), require less tuning and do not require early stopping or validation set monitoring. We showcase the efficacy of our approach on the CIFAR-10/100, ImageNet and Penn Treebank datasets on a variety of modern and classical network architectures

Natural Categorization: Electrophysiological Responses to Viewing Natural Versus Built Environments

Environments are unique in terms of structural composition and evoked human experience. Previous studies suggest that natural compared to built environments may increase positive emotions. Humans in natural environments also demonstrate greater performance on attention-based tasks. Few studies have investigated cortical mechanisms underlying these phenomena or probed these differences from a neural perspective. Using a temporally sensitive electrophysiological approach, we employ an event-related, implicit passive viewing task to demonstrate that in humans, a greater late positive potential (LPP) occurs with exposure to built than natural environments, resulting in a faster return of activation to pre-stimulus baseline levels when viewing natural environments. Our research thus provides new evidence suggesting natural environments are perceived differently from built environments, converging with previous behavioral findings and theoretical assumptions from environmental psychology

The convection connection: how ocean feedbacks affect tropical mean moisture and MJO propagation

The response of the Madden-Julian oscillation (MJO) to ocean feedbacks is studied with coupled and uncoupled simulations of four general circulation models (GCMs). Monthly mean SST from each coupled model is prescribed to its respective uncoupled simulation, to ensure identical SST mean state and low-frequency variability between simulation pairs. Consistent with previous studies, coupling improves each model's ability to propagation MJO convection beyond the Maritime Continent. Analysis of the MJO moist static energy budget reveals that improved MJO eastward propagation in all four coupled models arises from enhanced meridional advection of column water vapor (CWV). Despite the identical mean state SST in each coupled and uncoupled simulation pair, coupling increases mean-state CWV near the Equator, sharpening equatorial moisture gradients and enhancing meridional moisture advection and MJO propagation. CWV composites during MJO and non-MJO periods demonstrate that the MJO itself does not cause enhanced moisture gradients. Instead, analysis of low-level subgrid-scale moistening conditioned by rainfall rate (R) and SST anomaly reveals that coupling enhances low-level convective moistening for R > 5 mm/day; this enhancement is most prominent near the Equator. The low-level moistening process varies among the four models, which we interpret in terms of their ocean model configurations, cumulus parameterizations, and the sensitivity of convection to column relative humidity

Development and early experience from an intervention to facilitate teamwork between general practices and allied health providers: the Team-link study

Abstract. Background. This paper describes the development and implementation of an intervention to facilitate teamwork between general practice and outside allied and community health services and providers. Methods. A review of organizational theory and a qualitative study of 9 practices was used to design an intervention which was applied in four Divisions of General Practice and 26 urban practices. Clinical record review and qualitative interviews with participants were used to determine the key lessons from its implementation. Results. Facilitating teamwork across organizational boundaries was very challenging. The quality of the relationship between professionals was of key importance. This was enabled by joint education and direct communication between providers. Practice nurses were key links between general practices and allied and community health services. Conclusions. Current arrangements for Team Care planning provide increased opportunities for access to allied health. However the current paper based system is insufficient to build relationships or effectively share roles as part of a patient care team. Facilitation is feasible but constrained by barriers to communication and trust. 2010 Harris et al; licensee BioMed Central Ltd

Avian surface reconstruction in free-flight with application to flight stability analysis of a barn owl and peregrine falcon

Birds primarily create and control the forces necessary for flight through changing the shape and orientation of their wings and tail. Their wing geometry is characterised by complex variation in parameters such as camber, twist, sweep and dihedral. To characterise this complexity, a multi-stereo photogrammetry setup was developed for accurately measuring surface geometry in high-resolution during free-flight. The natural patterning of the birds was used as the basis for phase correlation-based image matching, allowing indoor or outdoor use while being non-intrusive for the birds. The accuracy of the method was quantified and shown to be sufficient for characterising the geometric parameters of interest, but with a reduction in accuracy close to the wing edge and in some localized regions. To demonstrate the method's utility, surface reconstructions are presented for a barn owl (Tyto alba) and peregrine falcon (Falco peregrinus) during three instants of gliding flight per bird. The barn owl flew with a consistent geometry, with positive wing camber and longitudinal anhedral. Based on flight dynamics theory this suggests it was longitudinally statically unstable during these flights. The peregrine flew with a consistent glide angle, but at a range of airspeeds with varying geometry. Unlike the barn owl, its glide configuration did not provide a clear indication of longitudinal static stability/instability. Aspects of the geometries adopted by both birds appeared to be related to control corrections and this method would be well suited for future investigations in this area, as well as for other quantitative studies into avian flight dynamics.Flight O1 - original uncompressed tif images for flight O1 of the barn owlO1_images.zipFlight O2 - original uncompressed tif images for flight O2 of the barn owlO2_images.zipFlight O3 - original uncompressed tif images for flight O3 of the barn owlO3_images.zipFlight P1 - original uncompressed tif images for flight P1 of the peregrineP1_images.zipFlight P2 - original uncompressed tif images for flight P2 of the peregrineP2_images.zipFlight P3 - original uncompressed tif images for flight P3 of the peregrineP3_images.zipREADM

Transformer Transforms Salient Object Detection and Camouflaged Object Detection

The transformer networks are particularly good at modeling long-range dependencies within a long sequence. In this paper, we conduct research on applying the transformer networks for salient object detection (SOD). We adopt the dense transformer backbone for fully supervised RGB image based SOD, RGB-D image pair based SOD, and weakly supervised SOD within a unified framework based on the observation that the transformer backbone can provide accurate structure modeling, which makes it powerful in learning from weak labels with less structure information. Further, we find that the vision transformer architectures do not offer direct spatial supervision, instead encoding position as a feature. Therefore, we investigate the contributions of two strategies to provide stronger spatial supervision through the transformer layers within our unified framework, namely deep supervision and difficulty-aware learning. We find that deep supervision can get gradients back into the higher level features, thus leads to uniform activation within the same semantic object. Difficulty-aware learning on the other hand is capable of identifying the hard pixels for effective hard negative mining. We also visualize features of conventional backbone and transformer backbone before and after fine-tuning them for SOD, and find that transformer backbone encodes more accurate object structure information and more distinct semantic information within the lower and higher level features respectively. We also apply our model to camouflaged object detection (COD) and achieve similar observations as the above three SOD tasks. Extensive experimental results on various SOD and COD tasks illustrate that transformer networks can transform SOD and COD, leading to new benchmarks for each related task. The source code and experimental results are available via our project page: https://github.com/fupiao1998/TrasformerSOD.Comment: Technical report, 18 pages, 22 figure