Good advice on giving presentations from Matt Abrahams→

Lots of gems here.

Paraphrase Your Previous Content Pausing to say, “So just to step back for a moment, I’ve already covered how X and Y are relevant … ” gives you a moment to remember point Z, and even frame it as a point you’ve been building toward.

Ask Your Audience a Question — Maybe Even a Rhetorical One “What seems to be the most important point so far?” Asking a rhetorical question not only provides you with a chance to collect your thoughts, but it also boosts your confidence because you know the answer, and launching into that answer will likely get you back in the flow.

The rprime package helps you analyze E-Prime data in R→

Haven't used this yet but it seems like a great idea.

Review: Neural reorganization and compensation in aging→

Associations between additional recruitment and better performance in older adults have led to the suggestion that the additional recruitment may contribute to preserved cognitive function in old age and may explain some of the variation among individuals in preservation of function. However, many alternative explanations are possible, and recent findings and methodological developments have highlighted the need for more systematic approaches to determine whether reorganization occurs with age and whether it benefits performance. We reevaluate current evidence for compensatory functional reorganization in the light of recent moves to address these challenges.

Neuroscience lab manager talks about the Human Brain Project→

Awesome.

Flexible information coding in auditory cortex during perception, imagery, and memory for sounds→

Auditory imagery of the same sounds evokes similar overall activity in auditory cortex as perception. However, during imagery abstracted, categorical information is encoded in the neural patterns, particularly when individuals are experiencing more vivid imagery. This highlights the necessity to move beyond traditional “brain mapping” inference in human neuroimaging, which assumes common regional activation implies similar mental representations.

Intuition and big data in neuroscience→

Thoughtful essay from Eve Marder on how science progresses.

A deeper cause for my trepidation comes from the existence of parallel pathways and multiple solutions in large brains. Detailed connectomes will assuredly show the extent to which neurons and brain regions are connected by multiple parallel pathways. While we can speculate that parallel pathways can provide additional robustness to circuits, or increase their dynamic range and flexibility, they make it far more difficult to predict the outcome of a specific pattern of activity or understand the results of a perturbation. As the number of parallel pathways and brain circuit loops increase, deducing how information flows through large and multiply interconnected circuits and why and under which contexts will be a challenge for many, many years to come.

Challenges of being a whistleblower, especially at early career stage→

Many hurdles and risks, little (obvious) reward.

“As someone in your early career stage, you don’t want to do this,” he told Broockman. “You don’t want to go public with this. Even if you have uncontroversial proof, you still shouldn’t do it. Because there’s just not much reward to someone doing this.”

Not a great incentive structure for self-correcting science.

Great academic career advice from Robert Sternberg→

Take care of your health and relationships in addition to keeping perspective on science.

Take some risks. In your 60s and 70s, your biggest regrets are likely to be not about something you did, but about all the things you didn’t do, the opportunities you passed up. Faced with a "sensible" career risk, go for it. Grow from it. Some risks will fail. Some of mine certainly have. But you’ll be a wiser and better person for those failures, rather than someone who got stuck in a small world and was afraid to leave it.

Review of deep learning ($)→

All the rage these days; looks like a good overview. From the abstract:

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Essentials of making graphs in R→

Basic introduction to plotting in R (without using other packages, like ggplot2).

NIH Program Officers do not (fully) understand what happens during review→

Good advice from drugmonkey.

The takeaway message for less NIH-experienced applicants is that the PO doesn't know everything. I'm not saying they are never helpful....they are. Occasionally very helpful. Difference between funded and not-funded helpful. So I fully endorse the usual advice to talk to your POs early and often.

Do not take the PO word for gospel, however. Take it under advisement and integrate it with all of your other sources of information to try to decide how to advance your funding strategy.

Package fslr provides wrapper functions for FSL in R→

Now on CRAN.

The cortical analysis of speech-specific temporal structure revealed by responses to sound quilts→

Superior temporal sulcus responds parametrically to increased chunks of information up to ~500 ms. Nice work from Tobias Overath and colleagues.

Rhythmic auditory cortex activity shapes stimulus–response gain and background firing→

We found that phase-dependent models better reproduced the observed responses than static models, during both stimulation with a series of natural sounds and epochs of silence. This was attributable to two factors: (1) phase-dependent variations in background firing (most prominent for delta; 1–4 Hz); and (2) modulations of response gain that rhythmically amplify and attenuate the responses at specific phases of the rhythm (prominent for frequencies between 2 and 12 Hz).

Research topic: Working memory and executive function in communication under adverse conditions→

Lots of relevant papers in this research topic.

Daniel Lakens: The perfect t-test→

Great idea from Daniel Lakens—an R script that helps you properly compare two groups.

The goal of this script is to examine whether more researcher-centered statistical tools (i.e., a one-click analysis script that checks normality assumptions, calculates effect sizes and their confidence intervals, creates good figures, calculates Bayesian and robust statistics, and writes the results section) increases the use of novel statistical procedures. Download the script here: https://github.com/Lakens/Perfect-t-test.

Cognitive aging and hearing acuity: Modeling spoken language comprehension→

Art Wingfield and colleages weigh in on the Ease of Language Understanding (ELU) model. Must-read for anyone interested in listening effort.

Encoding speech sequence probability in human temporal cortex→

In my quick first pass, this seems like a nice demonstration of phonotactic probability (likelihood of auditory transitions) being reflected in superior temporal gyrus. Though, the effects of lexicality suggests something more than pure transition probability is going on here.

Transition probability first negatively modulated neural responses, followed by positive modulation of neural responses, consistent with coordinated predictive and retrospective recognition processes, respectively. Furthermore, transition probability encoding was different for real English words compared with nonwords, providing evidence for online interactions with high-order linguistic knowledge.

Aging after noise exposure: Acceleration of cochlear synaptopathy in “recovered” ears→

Noise exposure = bad.

The synaptopathic noise (100 dB) caused 35–50 dB threshold shifts at 24 h. By 2 weeks, thresholds had recovered, but synaptic counts and ABR amplitudes at high frequencies were reduced by up to ∼45%. As exposed animals aged, synaptopathy was exacerbated compared with controls and spread to lower frequencies. Proportional ganglion cell losses followed. Threshold shifts first appeared >1 year after exposure and, by ∼20 months, were up to 18 dB greater in the synaptopathic noise group. Outer hair cell losses were exacerbated in the same time frame (∼10% at 32 kHz).

Prospective motion correction of fMRI using optical tracking→

The PMC system did not introduce extraneous artifacts for the no motion conditions and improved the time series temporal signal-to-noise by 30% to 40% for all combinations of low/high resolution and slow/fast head movement relative to the standard acquisition with no prospective correction. The numbers of activated voxels (p < 0.001, uncorrected) in both task-based experiments were comparable for the no motion cases and increased by 78% and 330%, respectively, for PMC on versus PMC off in the slow motion cases.