Deeper meditation is associated with more reproducible sensory responses

Nah et al. do experiments suggesting that meditation depth enhances the functional signal-to-noise ratio of the brain. The study is attempting to turn the phenomenological notion of “clarity” into a falsifiable neural measure: how faithfully sensory activity tracks its external cause against ongoing endogenous variation. It provides a concrete extension of Laukkonen’s predictive-mind account of meditation.

Across contemplative traditions, deeper states of meditation are described as states of heightened clarity, vividness, and stillness of mind, yet what this clarity corresponds to in the brain has remained difficult to specify. The functional signal-to-noise ratio (f-SNR) framework frames mental clarity as a measurable property of neural signals: the degree to which brain activity tracks the causes of sensory signals rather than endogenous, irrelevant, fluctuations. It predicts that deepening meditation should raise f-SNR, expressing sensory events more faithfully in neural signals against ongoing background activity. We tested this prediction across different levels of meditative depth. Twenty-nine experienced Vipassana practitioners meditated while auditory tones were presented, periodically reporting their depth of meditation. f-SNR was quantified from event-related potentials (ERPs) in a fronto-central P3 window and from single-trial decodability of auditory tone-evoked activity against no-tone background EEG. High-depth states were associated with greater ERP signal-to-noise ratio, stronger single-trial signal consistency, and improved decodability of sound tones. These results suggest that meditative depth is expressed in the reproducibility and stimulus-background separability of sensory responses, consistent with deep meditation enhancing the brain’s functional signal-to-noise ratio by improving the clarity of sensory signals and reducing endogenous noise. 

The agentic self: The brain learns whether action matters before deciding what to learn

A study by Liu et al. (open access article with interesting graphics) decomposes our “agency” into at least two computations: estimating whether one is in control, and using that estimate to determine which consequences should be attributed to one's actions. This has obvious relevance to learned helplessness, depression and the construction of an agentive self. Their central claim is that Humans learn whether outcomes depend on their actions through a dorsomedial prefrontal–dorsal raphe circuit. Once estimated, controllability alters learning signals in dopamine-related midbrain structures and a separate prefrontal credit-assignment system. A few caveats are that TMS cannot selectively perturb the entire proposed dmPFC–raphe pathway, and fMRI of small brainstem nuclei is technically demanding. Laboratory controllability is simpler than the ambiguous causal structures of ordinary life. Here is their summary:

When humans and other animals assess that they have little control over outcomes, their motivation and capacity for learning change. However, how we first learn what the control level is and how it then influenceslearning remain unclear. Using functional magnetic resonance imaging (fMRI) and transcranial magneticstimulation (TMS) in human participants, we provide both recording and transient disruption evidence for an anatomically grounded model of control learning, in which dorsomedialprefrontal cortex (dmPFC) tracks confidence in each individual decision in order to then estimate controllability and changes in controllability via interactions with the dorsal raphe nucleus. Disrupting activity in this circuit, via dmPFC manipulation,impaired control learning. In addition, another mechanism was identified through which control estimates influenced learning. Participants’ estimates of controllability were associated with widespread changes in outcome signals, including in dopamine-associated midbrain nuclei and in credit-assignment-linked signals in a distinct prefrontal cortex subregion. 

 

Regulating our subjective well being - our brain's axes of arousal, valence, and agency

I've read several times through a daunting review article by Feldman et al.  in the July issue of Trends in Cognitive Science  titled "The Neurobiology of Interoception and Affect."  (motivated readers can obtain a copy of the whole article from me). The bottom line is that the subjective feelings of what is going on inside our bodies -  that taken together form our sense of well being -  rise from a an array of cortical and visceral neuroendocrine systems that are much more complex that the nerve pathways regulating our exteroception, the sensing of external signals such as sound, light, or touch.  I would recommend reading the article to get a sense of the array of players that include upper and lower cortical regions, spinal cord, the autonomic nervous system, the enteric nervous system, etc.

We can describe our feelings, our affect, along two fundamental axes, valence and arousal. Valence refers to whether something is pleasant or unpleasant,  desirable or dangerous -  do we go for it or scram?  Arousal refers to where are we on the spectrum of being calm to being excited or distressed.  A third fundamental axis is formed by our experience of agency, how powerful versus helpless we feel in a given situation.  

In this post I want to pass on a rich graphic from the article showing how we can categorize  our feelings  in language with respect to these fundamental axes.  It is based on Saif M. Mohammad's computational linguistics studies that have obtained reliable human ratings of valence, arousal, and dominance for 20,000 English words. The graphic gives us a  description of central regulators of our well being to which 2 we have subjective (interoceptive) access. (You should be able to click on this and the following images to enlarge them.)  I have found that referencing my own subjective feelings to these axes has helped me to be more aware of them and assisted in their regulation.  

 
 

If the above looks complicated, it gets even worse. There really should be a 3-dimensional rather than 2-dimensional plot (see graphic below). The further axis of regulation used by Mohammad in generating data for the above figure is our subjective experience of dominance or agency in a given situation (where are we on a gradient of helpless to powerful?)  Here is a clip from the Saif M. Mohammad reference link shown above with some definitions:

As a footnote or addendum, I will also repeat here a more simple graphic showing these axes that I have used in my lectures, taken from the work of Lisa Feldman Barrett,  (enter 'Barrett' in the search box in left column of this page to find MindBlog posts describing her studies on understanding what feelings and emotions are).

 

3

 

(this is a repost of MindBlog's  8/28/24 post)

Categorization is ‘baked’ into the brain

I've been meaning to point out this Nature Reviews Neuroscience Perspectives article by Barrett and Miller (the same Miller referenced in the previous post).  I pass on just the abstract and initial paragraphs of their model. The article has some very striking graphics, and motivated readers can request a copy of the article from me.  (It is important to point out that the choice of the limbic core as the analytic starting point is somewhat arbitrary, rather than being derived from basic data itself.)

Abstract

Categorization, the grouping of objects, living organisms, actions or events into equivalence clusters, is fundamental to adaptive behaviour. Traditionally, it is assumed that categorization begins with feature detection and ends with assigning representations stored in memory. Here we review converging evidence from neuroanatomy, electrophysiology, brain imaging and cognitive science to suggest an alternative view: categorization is not the end stage of perception but occurs throughout signal processing, from the very beginning. It is a core computational strategy of the brain, implemented through a neural context created by predictive feedback signals that organize feedforward processing. 

Introduction

A category is a group of objects, living organisms, actions or events that are similar enough to be treated as equivalent for some purpose or function. Categorization, the process of treating something as equivalent to something else, is fundamental to life. The ability to determine ‘this is like that’ allows animals to draw on past experiences to guide present actions. All animals categorize, generalizing from past experiences at temporal and spatial scales relevant to their biology and ecological niche. Categorization reduces metabolic costs by minimizing uncertainty in a constantly changing, partially predictable world.

In this Perspective, we discuss the possibility that categorization is not the pinnacle of brain processing, as is traditionally assumed. Instead, categorization is a fundamental operating principle that describes the functional consequences of signal processing throughout the brain. We integrate evidence from neuroanatomy, electrophysiology, brain imaging and cognitive science to suggest that category construction begins as patterns of predictive feedback signals for abstract motor plans. Feedback signals create a neural context that actively shapes the processing of feedforward sensory signals, organizing the reduction of their dimensionality to serve situated, functional goals. In this view, the brain categorizes sensory signals from the outset, rather than as a final-stage process following sensation, attention and perception.

We begin with a review of the main cytoarchitectural gradient that reduces the dimensionality of feedforward sensory inputs to the cerebral cortex. We then discuss the seemingly counterintuitive idea that reverse signal flow along this gradient expands dimensionality to generate prediction signals that shape the processing of and ultimately categorize those inputs to give them meaning. This universal continuous category construction starts at the limbic core of the brain, in areas that are at the core of the brain’s regulation of the body, and supports metabolic efficiency by anticipating and preparing for energy needs before they arise, a condition known as allostasis. In this way, continuous category construction and the resulting categorization satisfy a key biological constraint, energy optimization, which is an organizing principle of life and evolution

Analog cognition and consciousness

Miller et al.   suggest that brain synapses store representations while large-scale rhythmic electric fields dynamically select, route and combine them. Traveling alpha and beta waves could act as movable “stencils” controlling faster gamma activity, permitting parallel analog computation; unified consciousness would arise when such wave organization becomes sufficiently cortex-wide and integrated. They propose an "analog" computational layer on top of digital-style spikes. Waves would be organizing top-down control, flexibly route signals between neural populations, and let individual neurons take on different functional roles depending on context — the coordination problem that discrete spiking alone struggles to explain. I pass on their abstract and concluding comments (motivated readers can obtain a PDF of the article from me.)

Abstract:

Cognition and consciousness may arise from bidirectional interactions between neuronal spiking and rhythmic electric field activity (brain waves). Goal-directed behavior relies on top-down control to coordinate large neural populations into low-dimensional, task-oriented dynamics. Brain waves are well suited for this role, exerting mesoscale influence over neural excitability. They can also support analog computation, shaping activity patterns according to underlying computational principles. Brain waves can flexibly route and organize neural signals, enabling multifunctional neurons to assume context-dependent roles, a hallmark of cognition. In this view, goal-directed thought, action, and unified consciousness emerge from cortex-wide wave dynamics that both reflect and transform spiking into coherent brain states.

Concluding comments: 

Our theory shares with others the general idea that cognition and consciousness involve mesoscale coordination and thus top-down control across the brain. But instead of describing function mainly in terms of information exchange between discrete neurons, circuits, and regions, we emphasize brain-wide wave dynamics as an organizing principle.

In this view, rhythmic electric fields help unify and structure cortical activity, allowing analog computation to take place across space and time. These wave interactions not only bind distributed neural populations into coherent states but also carry out computations that support flexible thought and control. Consciousness, then, emerges when these dynamic wave patterns bring the cortex into an organized, globally integrated state, one that naturally links and influences widespread activity.

This theory is not only conceptually plausible but biologically feasible. Cortical circuits naturally generate oscillatory dynamics and propagate waves through recurrent connectivity and horizontal cortical connections across multiple scales. The dynamics we emphasize thus require no special machinery. These waves are energetically efficient, leveraging continuous field interactions rather than metabolically costly, all-or-none spiking at every step. In this sense, the brain exploits its own physics: Electric field dynamics offer a low-overhead substrate for organizing and coordinating information across cortical networks. Given strong evolutionary pressure to maximize computation per unit energy, it would be surprising if evolution did not exploit such a built-in analog computing substrate.