Some thoughts on writing ‘Bayes Glaze’ theoretical papers.

[This was a twitter navel-gazing thread someone ‘unrolled’. I was really surprised that it read basically like a blog post, so I thought why not post it here directly! I’ve made a few edits for readability. So consider this an experiment in micro-blogging ….]

In the past few years, I’ve started and stopped a paper on metacognition, self-inference, and expected precision about a dozen times. I just feel conflicted about the nature of these papers and want to make a very circumspect argument without too much hype. As many of you frequently note, we have way too many ‘Bayes glaze’ review papers in glam mags making a bunch of claims for which there is no clear relationship to data or actual computational mechanisms.

It has gotten so bad, I sometimes see papers or talks where it feels like they took totally unrelated concepts and plastered “prediction” or “prediction error” in random places. This is unfortunate, and it’s largely driven by the fact that these shallow reviews generate a bonkers amount of citations. It is a land rush to publish the same story over and over again just changing the topic labels, planting a flag in an area and then publishing some quasi-related empirical stuff. I know people are excited about predictive processing, and I totally share that. And there is really excellent theoretical work being done, and I guess flag planting in some cases is not totally indefensible for early career researchers. But there is also a lot of cynical stuff, and I worry that this speaks so much more loudly than the good, careful stuff. The danger here is that we’re going to cause a blowback and be ultimately seen as ‘cargo cult computationalists’, which will drag all of our research down both good and otherwise.

In the past my theoretical papers in this area have been super dense and frankly a bit confusing in some aspects. I just wanted to try and really, really do due-diligence and not overstate my case. But I do have some very specific theoretical proposals that I think are unique. I’m not sure why i’m sharing all this, but I think because it is always useful to remind people that we feel imposter syndrome and conflict at all career levels. And I want to try and be more transparent in my own thinking – I feel that the earlier I get feedback the better. And these papers have been living in my head like demons, simultaneously too ashamed to be written and jealous at everyone else getting on with their sexy high impact review papers.

Specifically, I have some fairly straightforward ideas about how interoception and neural gain (precision) inter-relate, and also have a model i’ve been working on for years about how metacognition relates to expected precision. If you’ve seen any of my recent talks, you get the gist of these ideas.

Now, I’m *really* going to force myself to finally write these. I don’t really care where they are published, it doesn’t need to be a glamour review journal (as many have suggested I should aim for). Although at my career stage, I guess that is the thing to do. I think I will probably preprint them on my blog, or at least muse openly about them here, although i’m not sure if this is a great idea for theoretical work.

Further, I will try and hold to three key promises:

  1. Keep it simple. One key hypothesis/proposal per paper. Nothing grandiose.
  2. Specific, falsifiable predictions about behavioral & neurophysiological phenomenon, with no (minimal?) hand-waving
  3. Consider alternative models/views – it really gets my goat when someone slaps ‘prediction error’ on their otherwise straightforward story and then acts like it’s the only game in town. ‘Predictive processing’ tells you almost *nothing* about specific computational architectures, neurobiological mechanisms, or general process theories. I’ve said this until i’m blue in the face: there can be many, many competing models of any phenomenon, all of which utilize prediction errors.

These papers *won’t* be explicitly computational – although we have that work under preparation as well – but will just try to make a single key point that I want to build on. If I achieve my other three aims, it should be reasonably straight-forward to build computational models from these papers.

That is the idea. Now I need to go lock myself in a cabin-in-the-woods for a few weeks and finally get these papers off my plate. Otherwise these Bayesian demons are just gonna keep screaming.

So, where to submit? Don’t say Frontiers…

fMRI study of Shamans tripping out to phat drumbeats

Every now and then, i’m browsing RSS on the tube commute and come across a study that makes me laugh out loud. This of course results in me receiving lots of ‘tuts’ from my co-commuters. Anyhow, the latest such entry to the world of cognitive neuroscience is a study examining brain response to drum beats in shamanic practitioners. Michael Hove and colleagues of the Max Planck Institute in Leipzig set out to study “Perceptual Decoupling During an Absorptive State of Consciousness” using functional magnetic resonance imaging (fMRI). What exactly does that mean? Apparently: looking at how brain connectivity in ‘experienced shamanic practitioners’ changes when they listen to  rhythmic drumming. Hove and colleagues explain that across a variety of cultures, ‘quasi-isochronous drumming’ is used to induce ‘trance states’. If you’ve ever been dancing around a drum circle in the full moon light, or tranced out to shpongle in your living room, I guess you get the feeling right?

Anyway, Hove et al recruited 15 participants who were trained in  “core shamanism,” described as:

“a system of techniques developed and codified by Michael Harner (1990) based on cross-cultural commonalities among shamanic traditions. Participants were recruited through the German-language newsletter of the Foundation of Shamanic Studies and by word of mouth.”

They then played these participants rhythmic isochronous drumming (trance condition) versus drumming with a more regular timing. In what might be the greatest use of a Likert scale of all time, Participants rated if [they] “would describe your experience as a deep shamanic journey?” (1 = not at all; 7 = very much so)”, and indeed described the trance condition as more well, trancey. Hove and colleagues then used a fairly standard connectivity analysis, examining eigenvector centrality differences between the two drumming conditions, as well as seed-based functional connectivity:

trance.PNG

seed.PNG

Hove et al report that compared to the non-trance conditions, the posterior/dorsal cingulate, insula, and auditory brainstem regions become more ‘hublike’, as indicated by a higher overall degree centrality of these regions. Further, they experienced stronger functionally connectivity with the posterior cingulate cortex. I’ll let Hove and colleagues explain what to make of this:

“In sum, shamanic trance involved cooperation of brain networks associated with internal thought and cognitive control, as well as a dampening of sensory processing. This network configuration could enable an extended internal train of thought wherein integration and moments of insight can occur. Previous neuroscience work on trance is scant, but these results indicate that successful induction of a shamanic trance involves a reconfiguration of connectivity between brain regions that is consistent across individuals and thus cannot be dismissed as an empty ritual.”

Ultimately the authors conclusion seems to be that these brain connectivity differences show that, if nothing else, something must be ‘really going on’ in shamanic states. To be honest, i’m not really sure anyone disagreed with that to begin with. Collectively I can’t critique this study without thinking of early (and ongoing) meditation research, where esoteric monks are placed in scanners to show that ‘something really is going on’ in meditation. This argument to me seems to rely on a folk-psychological misunderstanding of how the brain works. Even in placebo conditioning, a typical example of a ‘mental effect’, we know of course that changes in the brain are responsible. Every experience (regardless how complex) has some neural correlate. The trick is to relate these neural factors to behavioral ones in a way that actually advances our understanding of the mechanisms and experiences that generate them. The difficulty with these kinds of studies is that all we can do is perform reverse inference to try and interpret what is going on; the authors conclusion about changes in sensory processing is a clear example of this. What do changes in brain activity actually tell us about trance (and other esoteric) states ? Certainly they don’t reveal any particular mechanism or phenomenological quality, without being coupled to some meaningful understanding of the states themselves. As a clear example, we’re surely pushing reductionism to its limit by asking participants to rate a self-described transcendent state using a unidirectional likert scale? The authors do cite Francisco Varela (a pioneer of neurophenemonological methods), but don’t seem to further consider these limitations or possible future directions.

Overall, I don’t want to seem overly critical of this amusing study. Certainly shamanic traditions are a deeply important part of human cultural history, and understanding how they impact us emotionally, cognitively, and neurologically is a valuable goal. For what amounts to a small pilot study, the protocols seem fairly standard from a neuroscience standpoint. I’m less certain about who these ‘shamans’ actually are, in terms of what their practice actually constitutes, or how to think about the supposed ‘trance states’, but I suppose ‘something interesting’ was definitely going on. The trick is knowing exactly what that ‘something’ is.

Future studies might thus benefit from a better direct characterization of esoteric states and the cultural practices that generate them, perhaps through collaboration with an anthropologist and/or the application of phenemonological and psychophysical methods. For now however, i’ll just have to head to my local drum circle and vibe out the answers to these questions.

Hove MJ, Stelzer J, Nierhaus T, Thiel SD, Gundlach C, Margulies DS, Van Dijk KRA, Turner R, Keller PE, Merker B (2016) Brain Network Reconfiguration and Perceptual Decoupling During an Absorptive State of Consciousness. Cerebral Cortex 26:3116–3124.

 

Mind-wandering and metacognition: variation between internal and external thought predicts improved error awareness

Yesterday I published my first paper on mind-wandering and metacognition, with Jonny Smallwood, Antoine Lutz, and collaborators. This was a fun project for me as I spent much of my PhD exhaustively reading the literature on mind-wandering and default mode activity, resulting in a lot of intense debate a my research center. When we had Jonny over as an opponent at my PhD defense, the chance to collaborate was simply too good to pass up. Mind-wandering is super interesting precisely because we do it so often. One of my favourite anecdotes comes from around the time I was arguing heavily for the role of the default mode in spontaneous cognition to some very skeptical colleagues.  The next day while waiting to cross the street, one such colleague rode up next to me on his bicycle and joked, “are you thinking about the default mode?” And indeed I was – meta-mind-wandering!

One thing that has really bothered me about much of the mind-wandering literature is how frequently it is presented as attention = good, mind-wandering = bad. Can you imagine how unpleasant it would be if we never mind-wandered? Just picture trying to solve a difficult task while being totally 100% focused. This kind of hyper-locking attention can easily become pathological, preventing us from altering course when our behaviour goes awry or when something internal needs to be adjusted. Mind-wandering serves many positive purposes, from stimulating our imaginations, to motivating us in boring situations with internal rewards (boring task… “ahhhh remember that nice mojito you had on the beach last year?”). Yet we largely see papers exploring the costs – mood deficits, cognitive control failure, and so on. In the meditation literature this has even been taken up to form the misguided idea that meditation should reduce or eliminate mind-wandering (even though there is almost zero evidence to this effect…)

Sometimes our theories end up reflecting our methodological apparatus, to the extent that they may not fully capture reality. I think this is part of what has happened with mind-wandering, which was originally defined in relation to difficult (and boring) attention tasks. Worse, mind-wandering is usually operationalized as a dichotomous state (“offtask” vs “ontask”) when a little introspection seems to strongly suggest it is much more of a fuzzy, dynamic transition between meta-cognitive and sensory processes. By studying mind-wandering just as the ‘amount’ (or mean) number of times you were “offtask”, we’re taking the stream of consciousness and acting as if the ‘depth’ at one point in the river is the entire story – but what about flow rate, tidal patterns, fishies, and all the dynamic variability that define the river? My idea was that one simple way get at this is by looking at the within-subject variability of mind-wandering, rather than just the overall mean “rate”.  In this way we could get some idea of the extent to which a person’s mind-wandering was fluctuating over time, rather than just categorising these events dichotomously.

The EAT task used in my study, with thought probes.
The EAT task used in my study, with thought probes.

To do this, we combined a classical meta-cognitive response inhibition paradigm, the “error awareness task” (pictured above), with standard interleaved “thought-probes” asking participants to rate on a scale of 1-7 the “subjective frequency” of task-unrelated thoughts in the task interval prior to the probe.  We then examined the relationship between the ability to perform the task or “stop accuracy” and each participant’s mean task-unrelated thought (TUT). Here we expected to replicate the well-established relationship between TUTs and attention decrements (after all, it’s difficult to inhibit your behaviour if you are thinking about the hunky babe you saw at the beach last year!). We further examined if the standard deviation of TUT (TUT variability) within each participant would predict error monitoring, reflecting a relationship between metacognition and increased fluctuation between internal and external cognition (after all, isn’t that kind of the point of metacognition?). Of course for specificity and completeness, we conducted each multiple regression analysis with the contra-variable as control predictors. Here is the key finding from the paper:

Regression analysis of TUT, TUT variability, stop accuracy, and error awareness.
Regression analysis of TUT, TUT variability, stop accuracy, and error awareness.

As you can see in the bottom right, we clearly replicated the relationship of increased overall TUT predicting poorer stop performance. Individuals who report an overall high intensity/frequency of mind-wandering unsurprisingly commit more errors. What was really interesting, however, was that the more variable a participants’ mind-wandering, the greater error-monitoring capacity (top left). This suggests that individuals who show more fluctuation between internally and externally oriented attention may be able to better enjoy the benefits of mind-wandering while simultaneously limiting its costs. Of course, these are only individual differences (i.e. correlations) and should be treated as highly preliminary. It is possible for example that participants who use more of the TUT scale have higher meta-cognitive ability in general, rather than the two variables being causally linked in the way we suggest.  We are careful to raise these and other limitations in the paper, but I do think this finding is a nice first step.

To ‘probe’ a bit further we looked at the BOLD responses to correct stops, and the parametric correlation of task-related BOLD with the TUT ratings:

Activations during correct stop trials.
Activations during correct stop trials.
Deactivations to stop trials (blue) and parametric correlation with TUT reports (red)
Deactivations to stop trials (blue) and parametric correlation with TUT reports (red)

As you can see, correct stop trials elicit a rather canonical activation pattern on the motor-inhibition and salience networks, with concurrent deactivations in visual cortex and the default mode network (second figure, blue blobs). I think of this pattern a bit like when the brain receives the ‘stop signal’ it goes, (a la Picard): “FULL STOP, MAIN VIEWER OFF, FIRE THE PHOTON TORPEDOS!”, launching into full response recovery mode. Interestingly, while we replicated the finding of medial-prefrontal co-variation with TUTS (second figure, red blob), this area was substantially more rostral than the stop-related deactivations, supporting previous findings of some degree of functional segregation between the inhibitory and mind-wandering related components of the DMN.

Finally, when examining the Aware > Unaware errors contrast, we replicated the typical salience network activations (mid-cingulate and anterior insula). Interestingly we also found strong bilateral activations in an area of the inferior parietal cortex also considered to be a part of the default mode. This finding further strengthens the link between mind-wandering and metacognition, indicating that the salience and default mode network may work in concert during conscious error awareness:

Activations to Aware > Unaware errors contrast.
Activations to Aware > Unaware errors contrast.

In all, this was a very valuable and fun study for me. As a PhD student being able to replicate the function of classic “executive, salience, and default mode” ‘resting state’ networks with a basic task was a great experience, helping me place some confidence in these labels.  I was also able to combine a classical behavioral metacognition task with some introspective thought probes, and show that they do indeed contain valuable information about task performance and related brain processes. Importantly though, we showed that the ‘content’ of the mind-wandering reports doesn’t tell the whole story of spontaneous cognition. In the future I would like to explore this idea further, perhaps by taking a time series approach to probe the dynamics of mind-wandering, using a simple continuous feedback device that participants could use throughout an experiment. In the affect literature such devices have been used to probe the dynamics of valence-arousal when participants view naturalistic movies, and I believe such an approach could reveal even greater granularity in how the experience of mind-wandering (and it’s fluctuation) interacts with cognition. Our findings suggest that the relationship between mind-wandering and task performance may be more nuanced than mere antagonism, an important finding I hope to explore in future research.

Citation: Allen M, Smallwood J, Christensen J, Gramm D, Rasmussen B, Jensen CG, Roepstorff A and Lutz A (2013) The balanced mind: the variability of task-unrelated thoughts predicts error monitoringFront. Hum. Neurosci7:743. doi: 10.3389/fnhum.2013.00743

Can the reward prediction error hypothesis explain addiction and reward? Great video on incentive-salience theory.

If you are interested in predictive coding, learning, motivation, addiction, or reward, don’t miss this excellent video by Kent Berridge. The incentive salience theory has long fascinated me as it may potentially explain data not accounted for by the hedonic-aversive accounts of addiction and reward. Essentially Incentive Salience argues that rather than reward or addiction […]

Can compassion be trained like a muscle? Active-controlled fMRI of compassion meditation.

Among the cognitive training literature, meditation interventions are particularly unique in that they often emphasize emotional or affective processing at least as much as classical ‘top-down’ attentional control. From a clinical and societal perspective, the idea that we might be able to “train” our “emotion muscle” is an attractive one. Recently much has been made of the “empathy deficit” in the US, ranging from empirical studies suggesting a relationship between quality-of-care and declining caregiver empathy, to a recent push by President Obama to emphasize the deficit in numerous speeches.

While much of the training literature focuses on cognitive abilities like sustained attention and working memory, many investigating meditation training have begun to study the plasticity of affective function, myself included.  A recent study by Helen Weng and colleagues in Wisconsin investigated just this question, asking if compassion (“loving-kindness”) meditation can alter altruistic behavior and associated neural processing. Her study is one of the first of its kind, in that rather than merely comparing groups of advanced practitioners and controls, she utilized a fully-randomized active-controlled design to see if compassion responds to brief training in novices while controlling for important confounds.

As many readers should be aware, a chronic problem in training studies is a lack of properly controlled longitudinal design. At best, many rely on “passive” or “no-contact” controls who merely complete both measurements without receiving any training. Even in the best of circumstances “active” controls are often poorly matched to whatever is being emphasized and tested in the intervention of interest. While having both groups do “something” is better than a passive or no-control design, problems may still arise if the measure of interest is mismatched to the demand characteristics of the study.  Stated simply, if your condition of interest receives attention training and attention tests, and your control condition receives dieting instruction or relaxation, you can expect group differences to be confounded by an explicit “expectation to improve” in the interest group.

In this regard Weng et al present an almost perfect example of everything a training study should be. Both interventions were delivered via professionally made audio CDs (you can download them yourselves here!), with participants’ daily practice experiences being recorded online. The training materials were remarkably well matched for the tests of interest and extra care was taken to ensure that the primary measures were not presented in a biased way. The only thing they could have done further would be a single blind (making sure the experimenters didn’t know the group identity of each participant), but given the high level of difficulty in blinding these kinds of studies I don’t blame them for not undertaking such a manipulation. In all the study is extremely well-controlled for research in this area and I recommend it as a guideline for best practices in training research.

Specifically, Weng et al tested the impact of loving-kindness compassion meditation or emotion reappraisal training on an emotion regulation fMRI task and behavioral economic game measuring altruistic behavior. For the fMRI task, participants viewed emotional pictures (IAPS) depicting suffering or neutral scenarios and either practiced a compassion meditation or reappraisal strategy to regulate their emotional response, before and after training. After the follow-up scan, good-old fashion experimental deception was used to administer a dictator economics-game that was ostensibly not part of the primary study and involved real live players (both deceptions).

For those not familiar with the dictator game, the concept is essentially that a participant watches a “dictator” endowed with 100$ give “unfair” offers to a “victim” without any money. Weng et al took great care in contextualizing the test purely in economic terms, limiting demand confounds:

Participants were told that they were playing the game with live players over the Internet. Effects of demand characteristics on behavior were minimized by presenting the game as a unique study, describing it in purely economic terms, never instructing participants to use the training they received, removing the physical presence of players and experimenters during game play, and enforcing real monetary consequences for participants’ behavior.

This is particularly important, as without these simple manipulations it would be easy for stodgy reviewers like myself to worry about subtle biases influencing behavior on the task. Equally important is the content of the two training programs. If for example, Weng et al used a memory training or attention task as their active-control group, it would be difficult not to worry that behavioral differences were due to one group expecting a more emotional consequence of the study, and hence acting more altruistic. In the supplementary information, Weng et al describe the two training protocols in great detail:

Compassion

… Participants practiced compassion for targets by 1) contemplating and envisioning their suffering and then 2) wishing them freedom from that suffering. They first practiced compassion for a Loved One, such as a friend or family member. They imagined a time their loved one had suffered (e.g., illness, injury, relationship problem), and were instructed to pay attention to the emotions and sensations this evoked. They practiced wishing that the suffering were relieved and repeated the phrases, “May you be free from this suffering. May you have joy and happiness.” They also envisioned a golden light that extended from their heart to the loved one, which helped to ease his/her suffering. They were also instructed to pay attention to bodily sensations, particularly around the heart. They repeated this procedure for the Self, a Stranger, and a Difficult Person. The Stranger was someone encountered in daily life but not well known (e.g., a bus driver or someone on the street), and the Difficult Person was someone with whom there was conflict (e.g., coworker, significant other). Participants envisioned hypothetical situations of suffering for the stranger and difficult person (if needed) such as having an illness or experiencing a failure. At the end of the meditation, compassion was extended towards all beings. For each new meditation session, participants could choose to use either the same or different people for each target category (e.g., for the loved one category, use sister one day and use father the next day).

Reappraisal

… Participants were asked to recall a stressful experience from the past 2 years that remained upsetting to them, such as arguing with a significant other or receiving a lower-than- expected grade. They were instructed to vividly recall details of the experience (location, images, sounds). They wrote a brief description of the event, and chose one word to best describe the feeling experienced during the event (e.g., sad, angry, anxious). They rated the intensity of the feeling during the event, and the intensity of the current feeling on a scale (0 = No feeling at all, 100 = Most intense feeling in your life). They wrote down the thoughts they had during the event in detail. Then they were asked to reappraise the event (to think about it in a different, less upsetting way) using 3 different strategies, and to write down the new thoughts. The strategies included 1) thinking about the situation from another person’s perspective (e.g., friend, parent), 2) viewing it in a way where they would respond with very little emotion, and 3) imagining how they would view the situation if a year had passed, and they were doing very well. After practicing each strategy, they rated how reasonable each interpretation was (0 = Not at all reasonable, 100 = Completely reasonable), and how badly they felt after considering this view (0 = Not bad at all, 100 = Most intense ever). Day to day, participants were allowed to practice reappraisal with the same stressful event, or choose a different event. Participants logged the amount of minutes practiced after the session.

In my view the active control is extremely well designed for the fMRI and economic tasks, with both training methods explicitly focusing on the participant altering an emotional response to other individuals.  In tests of self-rated efficacy, both groups showed significant decreases in negative emotion, further confirming the active control. Interestingly when Weng et al compared self-ratings over time, only the compassion group showed significant reduction from the first half of training sessions to the last. I’m not sure if this constitutes a limitation, as Weng et al further report that on each individual training day the reappraisal group reported significant reductions, but that the reductions themselves did not differ significantly over time. They explain this as being likely due to the fact that the reappraisal group frequently changed emotional targets, whereas the compassion group had the same 3 targets throughout the training. Either way the important point is that both groups self-reported similar overall reductions in negative emotion during the course of the study, strongly supporting the active control.

Now what about the findings? As mentioned above, Weng et al tested participants before and after training on an fMRI emotion regulation task. After the training, all participants performed the “dictator game”, shown below. After rank-ordering the data, they found that the compassion group showed significantly greater redistribution:

The dictator task (left) and increased redistribution (right).

For the fMRI analysis, they analyzed BOLD responses to negative vs neutral images at both time points, subtracted the beta coefficients, and then entered these images into a second-level design matrix testing the group difference, with the rank-ordered redistribution scores as a covariate of interest. They then tested for areas showing group differences in the correlation of redistribution scores and changes of BOLD response to negative vs neutral images (pre vs post), across the whole brain and in several ROIs, while properly correcting for multiple comparisons. Essentially this analysis asks, where in the brain do task-related changes in BOLD correlate more or less with the redistribution score in one group or another. For the group x covariate interaction they found significant differences (increased BOLD-covariate correlation) in the right inferior parietal cortex (IPC), a region of the parietal attention network, shown on the left-hand panel:

Increased correlation between negative vs neutral imagery related BOLD and redistribution scores (left), connectivity with DLPFC (right).

They further extracted signal from the IPC cluster and entered it into a conjunction analysis, testing for areas showing significant correlation  with the IPC activity, and found a strong effect in right DLPFC (right panel). Finally they performed a psychophysiological interaction (PPI) analysis with the right DLPFC activity as the seed, to determine regions showing significant task-modulated connectivity with that DLPFC activity. The found increased emotion-modulated DLPFC connectivity to nucleus accumbens, a region involved in encoding positive rewards (below, right).

Screen shot 2013-05-23 at 3.21.15 PM
PPI shows increased emotion-modulated connectivity of nucleus accumbens and DLPFC.

Together these results implicate training-related BOLD activity increases to emotional stimuli in the parietal attention network and increased parietal connectivity with regions implicated in cognitive control and reward processing, in the observed altruistic behavior differences. The authors conclude that compassion training may alter emotional processing through a novel mechanism, where top-down central-executive circuits redirect emotional information to areas associated with positive reward, reflecting the role of compassion meditation in emphasizing increased positive emotion to the aversive states of others. A fitting and interesting conclusion, I think.

Overall, the study should receive high marks for its excellent design and appropriate statistical rigor. There is quite a bit of interesting material in the supplementary info, a strategy I dislike, but that is no fault of the authors considering the publishing journal (Psych Science). The question itself is extremely novel, in terms of previous active-controlled studies. To date only one previous active-controlled study investigated the role of compassion meditation on empathy-related neuroplasticity. However that study compared compassion meditation with a memory strategy course, which (in my opinion) exposes it to serious criticism regarding demand characteristic. The authors do reference that study, but only briefly to state that both studies support a role of compassion training in altering positive emotion- personally I would have appreciated a more thorough comparison, though I suppose I can go and to that myself if I feel so inclined :).

The study does have a few limitations worth mentioning. One thing that stood out to me was that the authors never report the results of the overall group mean contrast for negative vs neutral images. I would have liked to know if the regions showing increased correlation with redistribution actually showed higher overall mean activation increases during emotion regulation. However as the authors clearly had quite specific hypotheses, leading them to  restrict their alpha to 0.01 (due to testing 1 whole-brain contrast and 4 ROIs), I can see why they left this out. Given the strong results of the study, it would in retrospect perhaps have been more prudent to skip  the ROI analysis (which didn’t seem to find much) and instead focus on testing the whole brain results.  I can’t blame them however, as it is surprising not to see anything going on in insula or amygdala for this kind of training.  It is also a bit unclear to me why the DLPFC was used as the PPI seed as opposed to the primary IPC cluster, although I am somewhat unfamiliar with the conjunction-connectivity analysis used here. Finally, as the authors themselves point out, a major limitation of the study is that the redistribution measure was collected only at time two, preventing a comparison to baseline for this measure.

Given the methodological state of the topic (quite poor, generally speaking), I am willing to grant them these mostly minor caveats. Of course, without a baseline altruism measure it is difficult to make a strong conclusion about the causal impact of the meditation training on altruism behavior, but at least their neural data are shielded from this concern. So while we can’t exhaustively conclude that compassion can be trained, the results of this study certainly suggest it is possible and perhaps even likely, providing a great starting point for future research. One interesting thing for me was the difference in DLPFC. We also found task-related increases in dorsolateral prefrontal cortex following active-controlled meditation, although in the left hemisphere and for a very different kind of training and task. One other recent study of smoking cessation also reported alteration in DLPFC following mindfulness training, leading me to wonder if we’re seeing the emergence of empirical consensus for this region’s specific involvement in meditation training. Another interesting point for me was that affective regulation here seems to involve primarily top-down or attention related neural correlates,  suggesting that bottom-up processing (insula, amygdala) may be more resilient to brief training, something we also found in our study. I wonder if the group mean-contrasts would have been revealing here (i.e. if there were differences in bottom-up processing that don’t correlate with redistribution). All together a great study that raises the bar for training research in cognitive neuroscience!