Don’t Think Twice, It’s Alright

We arrive at most of our beliefs unreflectively. As we navigate the world, beliefs about our surroundings are, inevitably, simply produced in us. Similarly, the vast majority of our actions are unreflective. We don’t have to think about every little thing we do; we simply act. But we also, at times, stop to reflect: Is this what I should believe? Is this what I should do? What does such reflective activity achieve? Some philosophers have suggested that reflecting about what we should believe is necessary if our beliefs are to be justified. In the case of action, some philosophers have suggested that reflecting about what one should do is necessary for freedom of the will. One might think that there are more humble benefits as well. Beliefs which are the product of reflective activity are more likely to be true than beliefs unreflectively arrived at; actions reflectively produced are more likely to be successful in achieving their goals than unreflective actions. This is just, it seems, good common sense. This paper challenges both common sense views about the benefits of reflection as well as a good deal of recent philosophical thinking. It would be silly to think that reflection is never valuable, but I will argue that both common sense, and much philosophical thought about the nature and importance of reflection, have vastly overestimated its value

Trauma Early Mortality Prediction Tool (TEMPT) for assessing 28-day mortality.

Background:Prior mortality prediction models have incorporated severity of anatomic injury quantified by Abbreviated Injury Severity Score (AIS). Using a prospective cohort, a new score independent of AIS was developed using clinical and laboratory markers present on emergency department presentation to predict 28-day mortality. Methods:All patients (n=1427) enrolled in an ongoing prospective cohort study were included. Demographic, laboratory, and clinical data were recorded on admission. True random number generator technique divided the cohort into derivation (n=707) and validation groups (n=720). Using Youden indices, threshold values were selected for each potential predictor in the derivation cohort. Logistic regression was used to identify independent predictors. Significant variables were equally weighted to create a new mortality prediction score, the Trauma Early Mortality Prediction Tool (TEMPT) score. Area under the curve (AUC) was tested in the validation group. Pairwise comparison of Trauma Injury Severity Score (TRISS), Revised Trauma Score, Glasgow Coma Scale, and Injury Severity Score were tested against the TEMPT score. Results:There was no difference between baseline characteristics between derivation and validation groups. In multiple logistic regression, a model with presence of traumatic brain injury, increased age, elevated systolic blood pressure, decreased base excess, prolonged partial thromboplastin time, increased international normalized ratio (INR), and decreased temperature accurately predicted mortality at 28 days (AUC 0.93, 95% CI 0.90 to 0.96, P<0.001). In the validation cohort, this score, termed TEMPT, predicted 28-day mortality with an AUC 0.94 (95% CI 0.92 to 0.97). The TEMPT score preformed similarly to the revised TRISS score for severely injured patients and was highly predictive in those having mild to moderate injury. Discussion:TEMPT is a simple AIS-independent mortality prediction tool applicable very early following injury. TEMPT provides an AIS-independent score that could be used for early identification of those at risk of doing poorly following even minor injury. Level of evidence:Level II

Probing clustering in neural network representations

Neural network representations contain structure beyond what was present in the training labels. For instance, representations of images that are visually or semantically similar tend to lie closer to each other than to dissimilar images, regardless of their labels. Clustering these representations can thus provide insights into dataset properties as well as the network internals. In this work, we study how the many design choices involved in neural network training affect the clusters formed in the hidden representations. To do so, we establish an evaluation setup based on the BREEDS hierarchy, for the task of subclass clustering after training models with only superclass information. We isolate the training dataset and architecture as important factors affecting clusterability. Datasets with labeled classes consisting of unrelated subclasses yield much better clusterability than those following a natural hierarchy. When using pretrained models to cluster representations on downstream datasets, models pretrained on subclass labels provide better clusterability than models pretrained on superclass labels, but only when there is a high degree of domain overlap between the pretraining and downstream data. Architecturally, we find that normalization strategies affect which layers yield the best clustering performance, and, surprisingly, Vision Transformers attain lower subclass clusterability than ResNets

Latency and Selectivity of Single Neurons Indicate Hierarchical Processing in the Human Medial Temporal Lobe

Neurons in the temporal lobe of both monkeys and humans show selective responses to classes of visual stimuli and even to specific individuals. In this study, we investigate the latency and selectivity of visually responsive neurons recorded from microelectrodes in the parahippocampal cortex, entorhinal cortex, hippocampus, and amygdala of human subjects during a visual object presentation task. During 96 experimental sessions in 35 subjects, we recorded from a total of 3278 neurons. Of these units, 398 responded selectively to one or more of the presented stimuli. Mean response latencies were substantially larger than those reported in monkeys. We observed a highly significant correlation between the latency and the selectivity of these neurons: the longer the latency the greater the selectivity. Particularly, parahippocampal neurons were found to respond significantly earlier and less selectively than those in the other three regions. Regional analysis showed significant correlations between latency and selectivity within the parahippocampal cortex, entorhinal cortex, and hippocampus, but not within the amygdala. The later and more selective responses tended to be generated by cells with sparse baseline firing rates and vice versa. Our results provide direct evidence for hierarchical processing of sensory information at the interface between the visual pathway and the limbic system, by which increasingly refined and specific representations of stimulus identity are generated over time along the anatomic pathways of the medial temporal lobe

Platelet Imaging

The knowledge gained through imaging platelets has formed the backbone of our understanding of their biology in health and disease. Early investigators relied on conventional light microscopy with limited resolution and were primarily able to identify the presence and basic morphology of platelets. The advent of high resolution technologies, in particular, electron microscopy, accelerated our understanding of the dynamics of platelet ultrastructure dramatically. Further refinements and improvements in our ability to localize and reliably identify platelet structures have included the use of immune-labeling techniques, correlative-fluorescence light and electron microscopy, and super-resolution microscopies. More recently, the expanded development and application of intravital microscopy in animal models has enhanced our knowledge of platelet functions and thrombus formation in vivo, as these experimental systems most closely replicate native biological environments. Emerging improvements in our ability to characterize platelets at the ultrastructural and organelle levels include the use of platelet cryogenic electron tomography with quantitative, unbiased imaging analysis, and the ability to genetically label platelet features with electron dense markers for analysis by electron microscopy