Simultaneous brain/cervical cord fMRI reveals spinal cord involvement in motor learning→

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Impressive technical achievement.

Here, for the first time, we provide evidence for local learning-induced plasticity in intact human spinal cord through simultaneous functional magnetic resonance imaging of the brain and spinal cord during motor sequence learning. Specifically, we show learning-related modulation of activity in the C6–C8 spinal region, which is independent from that of related supraspinal sensorimotor structures. Moreover, a brain–spinal cord functional connectivity analysis demonstrates that the initial linear relationship between the spinal cord and sensorimotor cortex gradually fades away over the course of motor sequence learning, while the connectivity between spinal activity and cerebellum gains strength. T

Prediction across sensory modalities: A neurocomputational model of the McGurk effect ($)→

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Here we assessed the role of dynamic cross-modal predictions in the outcome of AV speech integration using a computational model that processes continuous audiovisual speech sensory inputs in a predictive coding framework. The model involves three processing levels: sensory units, units that encode the dynamics of stimuli, and multimodal recognition/identity units. The model exhibits a dynamic prediction behavior because evidence about speech tokens can be asynchronous across sensory modality, allowing for updating the activity of the recognition units from one modality while sending top–down predictions to the other modality. We explored the model's response to congruent and incongruent AV stimuli and found that, in the two-dimensional feature space spanned by the speech second formant and lip aperture, fusion stimuli are located in the neighborhood of congruent /ada/, which therefore provides a valid match. Conversely, stimuli that lead to combination percepts do not have a unique valid neighbor. In that case, acoustic and visual cues are both highly salient and generate conflicting predictions in the other modality that cannot be fused, forcing the elaboration of a combinatorial solution.

Steps to support women in science: #just1action4WIS→

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I'm not sure about the opening defense of Tim Hunt, but Athene Donald has a fantastic list of concrete steps we can take to support women in science:

  • Call out bad behaviour whenever and wherever you see it – in committees or in the street. Don’t leave women to be victimised;
  • Encourage women to dare, to take risks;
  • Act as a sponsor or mentor (if you are just setting out there will still always be people younger than you, including school children, for whom you can act);
  • Don’t let team members get away with demeaning behaviour, objectifying women or acting to exclude anyone;
  • Seek out and remove microinequities wherever you spot them;
  • Refuse to serve on single sex panels or at conferences without an appropriate level of female invited speakers;
  • Consider the imagery in your department and ensure it represents a diverse group of individuals;
  • Consider the daily working environment to see if anything inappropriate is lurking. If so, do something about it.
  • Demand/require mandatory unconscious bias training, in particular for appointment and promotion panels;
  • Call out teachers who tell girls they can’t/shouldn’t do maths, physics etc;
  • Don’t let the bold (male or female) monopolise the conversation in the classroom or the apparatus in the laboratory, at the expense of the timid (female or male);
  • Ask schools about their progression rates for girls into the traditionally male subjects at A level (or indeed, the traditionally female subjects for boys);
  • Nominate women for prizes, fellowships etc;
  • Tap women on the shoulder to encourage them to apply for opportunities they otherwise would be unaware of or feel they were not qualified for;
  • Move the dialogue on from part-time working equates to ‘isn’t serious’ to part-time working means balancing different demands;
  • Recognize the importance of family (and even love) for men and women;
  • Be prepared to be a visible role model;
  • Gather evidence, data and anecdote, to provide ammunition for management to change;
  • Listen and act if a woman starts hinting there are problems, don’t be dismissive because it makes you uncomfortable;
  • Think broadly when asked to make suggestions of names for any position or role.

Chris Chambers weighs in on Tim Hunt→

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Right on the mark from what I can tell. A great learning experience for how even the (apparently) nicest people can also be sexist, and how we all need to work on examining our unconscious biases (and words).

Hunt’s comments were unacceptable and stupid. He has yet to offer a full apology, which just shows how little recognition he has of sexism in science. Oh but he's old, right, so that's ok? Fuck that. My dad is the same age as Hunt, has one less Nobel prize, grew up in 1950s Australia (AKA Betty Crocker Central) and could teach him a thing or two about equality.

Marcus Munafò on his lab's experience with open science→

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We’ve certainly found it a healthy and worthwhile thing to do. It shines a light on our work, makes us more accountable, and encourages us to slow down slightly and check everything one extra time. There’s been a positive reaction to it within my group. I think once you achieve a cultural change like this then it becomes self-sustaining.

Why you should use omega-squared instead of eta-squared→

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Nice post from Daniel Lakens.

The table shows the bias. With four groups of n = 20, a One-Way ANOVA with a medium effect (true η² = 0.0588) will overestimate the true effect size on average by 0.03512, for an average observed η² of = 0.0588 + 0.0347 = 0.0935. We can see that for small effects (η² = 0.0099) the bias is actually larger than the true effect size (up to ANOVA’s with 70 participants in each condition).

When there is no true effect, η² from small studies can easily give the wrong impression that there is a real small to medium effect, just due to the bias. Your p-value would not be statistically significant, but this overestimation could be problematic if you ignore the p-value and just focus on estimation.

"The median Turker...had completed 300 total academic studies"→

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Workers on Mechanical Turk are experienced participants, which may be a problem for some studies.

“If you’re running social psychology studies on Turk, watch out, because [the subjects] have gotten experienced, and that can change effects,” Rand said. “So if you run my experiment on Turk right now, you won’t get any effect. Which sucks for me.”

To be clear, extreme experience isn’t always a problem, Rand said. There are some psychological tests that are so robust that no amount of experience will override the effect, said Jesse Chandler, a researcher at the University of Michigan’s Institute for Social Research. The Stroop effect, for example, which involves identifying colors when the color of a word doesn’t match the color spelled out by the text. When the word “red,” for example, is colored green, it takes longer to override the automatic reading of the text and choose green.

“Help! My adviser won’t stop looking down my shirt!”→

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Sad story, good take from Dr. Isis.

The last part is most important. Alice’s advice is that she needs this person for his professional guidance and future recommendations. Start working to make that less true. Find other mentors at your university who you can establish a track record so that if one relationship goes to shit, you have a history with other people who might advocate for you. Your future should never be in one person’s hands.

Inside higher ed has a summary of the whole debacle.