Cisek theory

In the near future, Paul Cisek’s latest paper will appear and the abstract on the pre-publication is:

How does the brain decide between actions? Is it through comparisons of abstract representations of outcomes or through a competition in a sensorimotor map defining the actions themselves? Here, I review strengths and limitations of both of these proposals, and suggest that decisions emerge through a distributed consensus across many levels of representation.

This peaked my interest in Cisek’s work. Here is how he introduces his research on his website:

I am interested in how the brain controls behavior. Many scientists approach this very large question by starting with perception and asking how the brain builds an internal representation of the world, and how it then uses this representation to guide action. In contrast, I study behavior by starting with a concrete task such as a voluntary movement and asking what parameters of the task the brain must specify and control, and what information from the environment it may employ toward that specification. The goal here is an understanding of brain mechanisms for mediating interaction with the world, not necessarily of mechanisms for representing the world. A research program based on such an approach begins with questions concerning motor control and gradually works its way toward the perceptual systems which guide that control. One could say I’m going backwards through the brain…

This is not the usual approach. Although, he is not the first person to say it would be better to start with action rather than the senses, he is pursuing the idea consistently in his research.


A couple of years ago his group published a review paper (citation below). This is its abstract:

The neural bases of behavior are often discussed in terms of perceptual, cognitive, and motor stages, defined within an information processing framework that was originally inspired by models of human abstract problem solving. Here, we review a growing body of neuro-physiological data that is difficult to reconcile with this influential theoretical perspective. As an alternative foundation for interpreting neural data, we consider frameworks borrowed from ethology, which emphasize the kinds of real-time interactive behaviors that animals have engaged in for millions of years. In particular, we discuss an ethologically-inspired view of interactive behavior as simultaneous processes that specify potential motor actions and select between them. We review how recent neuro-physiological data from diverse cortical and subcortical regions appear more compatible with this parallel view than with the classical view of serial information processing stages.


He starts with animal behaviour (ethology) rather than human cognition because:

Contrary to popular belief, brain evolution has been remarkably conservative. Since the development of the telencephalon, the basic outline of the vertebrate nervous system has been strongly conserved . The conservative nature of brain evolution motivates us to think about large-scale theories of neural organization from the perspective of the kinds of behaviors that animals engaged in many millions of years ago, when that neural organization was being laid down. … their nervous systems have been preoccupied by almost constant interaction with a complex and ever changing environment, which continuously offers a potentially bewildering variety of opportunities and demands for action.


He describes his theory, Affordance Competition Hypothesis , in which ‘action selection’ and ‘action specification’ are simultaneous. He concludes:

  • Brains evolved for sensorimotor control and retained much of that architecture—even the neocortex is still part of that old circuit.

  • Natural interactive behavior requires sensorimotor control and selection systems to operate continuously and in parallel.

  • Distinctions between perceptual, cognitive, and motor processes, although descriptively useful, might not reflect the natural categories of the brain’s functional organization.

  • Decisions appear to be made through a distributed consensus that emerges in competitive populations.

  • Neurophysiological data may be more readily interpreted from the perspective of interactive behavior than from the perspective of serial information processing.


This has bearing on the interpretation of the Libet and similar experiments. If there is not a serial sensory-cogitive-motor pattern that is separated in time and location, it is very unlikely that conscious intention precedes action, possibly it is impossible.

Paul Cisek, & John Kalaska (2010). Neural Mechanisms for Interacting with a World Full of Action Choices Annu. Rev. Psychol. DOI: 10.1146/annurev.neuro.051508.135409

Innate facial expressions

ScienceDaily has an item (here) on D. Matsumoto’s comparison of blind and sighted Olympic and Paralympic judo athletes from 23 different countries.

“The statistical correlation between the facial expressions of sighted and blind individuals was almost perfect,” Matsumoto said. “This suggests something genetically resident within us is the source of facial expressions of emotion.”

Matsumoto found that sighted and blind individuals manage their expressions of emotion in the same way according to social context. For example, because of the social nature of the Olympic medal ceremonies, 85 percent of silver medalists who lost their medal matches produced “social smiles” during the ceremony. Social smiles use only the mouth muscles whereas true smiles, known as Duchenne smiles, cause the eyes to twinkle and narrow and the cheeks to rise.

“Losers pushed their lower lip up as if to control the emotion on their face and many produced social smiles,” Matsumoto said. “Individuals blind from birth could not have learned to control their emotions in this way through visual learning so there must be another mechanism. It could be that our emotions, and the systems to regulate them, are vestiges of our evolutionary ancestry.”

This does not mean that there are no cultural, learned instances of facial expressions. Obvious facial reactions are encouraged in some cultures and discouraged in others, for example, giving them a slightly different physical form and social use. I assume these are minor changes when looking at the really basic expressions.

But there is a sense in which facial expressions are more emotionally truthful than words. So when someone says that they really, really are not angry but their face, voice, posture and colour all show anger, they are probably deceiving themselves as well as you. Their introspection is less credible than your eyes.

Used up self-control

There has been some back and forth on the subject of whether self-control is a limited resource. Not too surprisingly, marketing researchers are interested in this subject as well as other psychologists and neuro-scientists. They will, after all, be working on how to overcome the self-control that limits spending. William Hedgcock has just published a paper in the Journal of Consumer Psychology on self-control. According to a University of Iowa press release:

“The reason that people have diminished self-control after first exerting self-control is not really well understood, so we just thought the MRI might be a good way to get some information about why this happens to people and it might help us try to prevent it in the future. What we’re seeing with the MRI is that a certain part of the brain has less activity. Actually, specifically what we’re studying is blood flow, but there’s a pretty good link between blood flow and neurons firing so we think that part of the brain is less active when people fail to exert self-control.” Once that well of self-control has been tapped dry, Hedgcock says there seems to be just one way to fill it up again. “For the most part, the only thing we know that can help with that is just time. If you take a break and you don’t have to exert self-control, it will replentish that resource.”

I think there are several ways to deal with depleted self-control: leave, just walk away from temptation with your last little drop of self-control; don’t tempt yourself (like the recovering addict) to prove you have self-control; use firm habits to reduce the need for decisions ‘on the spot’ that requiring self-control; discover an great interest in something else; take a nap.

Brain states

Tang, Rothbart and Posner have a new paper reviewing the neural correlates of three brain states (citation below). The states that were reviewed were rest, being alert (waiting for a target and responding to it), and meditation (Integrative Body-Mind training in three stages of training). They looked at the switching between these and the maintenance of them over time.


One very interesting result: the resting state is not ‘restful’ in the ordinary sense.

Although the resting state does not involve an external task, it shows strong activation of a number of brain areas, some of which show reduced activation during task performance, and it consumes a greater amount of energy in comparison with task-evoked states. The resting state accounts for much of the metabolic activity of the human brain, with specific activations during tasks involving only small changes (about 5%).


The active networks in rest are:

medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), and posterior cingulate cortex (PCC), often called the Default Mode Network (DMN), along with a number of other areas that are active and correlated at rest.


The alert state seems to be a suppression of activity when the target is expected, until it appears. Before the target the parasympathetic system is more active and during response, it is the sympathetic that is active. The authors assume that this is the result of ACC levels of activity.

(The suppression of activity is show in the) Contingent Negative Variation (CNV): a negative direct current shift in electrophysiological recordings that occurs when a warning signal leads one to prepare for an upcoming target. … This negative change appears to arise, at least in part, in ACC and adjacent structures. The CNV may arise within 100 ms after the warning .


They discuss the release of norepinepherine (NE) from the locus coeruleus . This results in changes in the recognition of the target.

How does the alert state influence task performance? The warning increases rapid motor responses to signals. However, it appears to accomplish this in a specific way . The alert state speeds the motor response without altering signal quality. This accords with the anatomical distribution of NE (norepinepherine), which does not appear to influence the ventral pathways involved in object recognition. … whereas the response is speeded, the quality of information to which the person responds is reduced, resulting in increased error rates. Posner argued that these effects can be understood if the warning signal allows faster input to parts of the brain that mediate conscious detection of the target. This allows not only for enhanced overt response, but also for the priority of the signal to consciousness.


The work on mediation is very specific to a particular type and its stages of learning. There may be great similarities between the neural correlates of the different methods of mediation – or there may not. The neural correlates involved in changes of state are summarized in the IAS hypothesis.

Key neural correlates of changing brain states include the insula, ACC, and striatum (IAS). The ACC is involved in maintaining a state by reducing conflict with other states; the insula serves a primary role in switching between states; the striatum is linked to the reward experience and formation of habits required to make state maintenance easier.


I hope this group and others continue this type of study. Here is the paper’s abstract:

Although the study of brain states is an old one in neuroscience, there has been growing interest in brain state specification owing to MRI studies tracing brain connectivity at rest. In this review, we summarize recent research on three relatively well-described brain states: the resting, alert, and meditation states. We explore the neural correlates of maintaining a state or switching between states, and argue that the anterior cingulate cortex and striatum play a critical role in state maintenance, whereas the insula has a major role in switching between states. Brain state may serve as a predictor of performance in a variety of perceptual, memory, and problem solving tasks. Thus, understanding brain states is critical for understanding human performance.

Yi-Yuan Tang, Mary K. Rothbart, & Michael I. Posner (2012). Neural correlates of establishing, maintaining, and switching brain states Trends in Cognitive Sciences, 16 (6) DOI: 10.1016/j.tics.2012.05.001

does dopamine equal reward?

Bradley Voytek at Oscillatory Thoughts Blog has a great posting (here). It is about Jonah Lehrer’s book on creativity (sort of). More important, it is about how misleading neuroscience can be. The great thing for me was that he very clearly discussed why it is not reasonable to assume that dopamine equals reward.

… And that’s assuming that the “dopamine = reward” hypothesis is even true. Most people–neuroscientists included–take this as gospel truth. Of course dopamine equals reward! Dopamine neurons fire in response to rewarding stimuli, and the neurons “learn” to predict the rewards! Addicts’ brains show activity in dopaminergic regions when shown images of drug paraphernalia. And on and on. … dopaminergic neurons don’t get any sensory inputs early enough to make a “decision” about the reward value of visual stimuli. In fact, they’re probably encoding salience (relevance). Which explains why drug users have increased activity when shown pictures of drug paraphernalia, and mothers pictures of their children, or even iPhone users pictures of iPhones versus Androids: because those things are more familiar and relevant to them.
To really hammer this point home, there is one disease we know of that is caused by the death of dopaminergic neurons: Parkinson’s disease. It seems to me the clearest support for the argument that “dopamine = reward” would be seen in people missing most of their dopamine. Parkinson’s patients shouldn’t be able to experience any reward/pleasure because that whole system is obliterated.
Clinically, not feeling pleasure from experiences is known as “anhedonia”, and a systematic review of the literature on Parkinson’s and anhedonia in 2011 was inconclusive. In that review the authors found that, if anything, any signs of anhedonia in Parkinson’s patients was likely caused by their associated depression.

Voytek has more to say about simplistic conclusions on the posting – link above.

Memory types needed for future imaginings

Below is the abstract from a new paper: M Irish, DR Addis, JR Hodges, O Piguet (May 2012) in Brain, Considering the role of semantic memory in episodic future thinking: evidence from semantic dementia.



The paper looks at the differences in the semantic and episodic memory in the production of images of the future and implies that imaging future events requires a semantic framework in which to organize episodic fragments.

Semantic dementia is a progressive neurodegenerative condition characterized by the profound and amodal loss of semantic memory in the context of relatively preserved episodic memory. In contrast, patients with Alzheimer’s disease typically display impairments in episodic memory, but with semantic deficits of a much lesser magnitude than in semantic dementia. Our understanding of episodic memory retrieval in these cohorts has greatly increased over the last decade, however, we know relatively little regarding the ability of these patients to imagine and describe possible future events, and whether episodic future thinking is mediated by divergent neural substrates contingent on dementia subtype. Here, we explored episodic future thinking in patients with semantic dementia (n = 11) and Alzheimer’s disease (n = 11), in comparison with healthy control participants (n = 10). Participants completed a battery of tests designed to probe episodic and semantic thinking across past and future conditions, as well as standardized tests of episodic and semantic memory. Further, all participants underwent magnetic resonance imaging. Despite their relatively intact episodic retrieval for recent past events, the semantic dementia cohort showed significant impairments for episodic future thinking. In contrast, the group with Alzheimer’s disease showed parallel deficits across past and future episodic conditions. Voxel-based morphometry analyses confirmed that atrophy in the left inferior temporal gyrus and bilateral temporal poles, regions strongly implicated in semantic memory, correlated significantly with deficits in episodic future thinking in semantic dementia. Conversely, episodic future thinking performance in Alzheimer’s disease correlated with atrophy in regions associated with episodic memory, namely the posterior cingulate, parahippocampal gyrus and frontal pole. These distinct neuroanatomical substrates contingent on dementia group were further qualified by correlational analyses that confirmed the relation between semantic memory deficits and episodic future thinking in semantic dementia, in contrast with the role of episodic memory deficits and episodic future thinking in Alzheimer’s disease. Our findings demonstrate that semantic knowledge is critical for the construction of novel future events, providing the necessary scaffolding into which episodic details can be integrated. Further research is necessary to elucidate the precise contribution of semantic memory to future thinking, and to explore how deficits in self-projection manifest on behavioural and social levels in different dementia subtypes.

Changing the way we see the world

My reason for starting this blog, four years ago, was to help people come to terms with what science was saying and going to say about consciousness. What I see is people twisting themselves in knots in order to preserve a conscious mind, thinking and in control, rather than consciousness being a simple, partial and barely accurate awareness. Now I find that it may never be possible to completely lose the naïve psychology that we may be born with.


A paper by Shtulman and Valcarcel (citation below) argues that even though we know that the earth goes around the sun, we still have hidden away the idea that the sun goes around the earth. Their experiment takes a number of statements about astronomy, evolution, fractions, genetics, germs, matter, mechanics, physiology, thermodynamics, and waves, and asks if they are true. (For example, statements could be ‘the moon goes around the earth’, ‘the sun goes around the earth’.) The speed of answering and the accuracy were compared for statements that have the same validity both scientifically and naïvely, and statements were the validity is different. In all the subject categories the answers were slower and less accurate if there was dissonance between the scientific answer and the naïve one. I assume that neuroscience statements would give the same result, based on the ideas of teleological (design and purpose is found in all things) and animistic (all events are the product of animated intention) bias.

They (the findings) are, however, consistent with previous findings regarding the re-emergence of teleological thought and animistic thought under cognitive impairment or cognitive load. And they do not merely replicate those findings; they extend them across multiple domains of knowledge – from the life sciences to the physical sciences to mathematics – and across multiple concepts within those domains. Indeed, the consistency of the effect within and across domains suggests that it is not merely the byproduct of a few particularly resilient intuitions but is rather a domain-general consequence of conceptual restructuring.


So it seems when we learn (or discover) scientific knowledge it must replace naïve concepts. How this is done is probably very important.

Science educators are thus charged with two tasks: not only must they help students learn the correct, scientific theory at hand, but they must also help students unlearn their earlier, less accurate theories. Psychologists who have studied this process – typically termed ‘‘conceptual change’’ – have characterized the transition from naïve theories to scientific theories in several ways. … Common to all characterizations is a commitment to knowledge restructuring, or the conversion of one conceptual system into another by radically altering the structure (and not just the content) of that system. Implicit in the idea of knowledge restructuring is the idea that early modes of thought, once restructured, should no longer be accessible, for the basic constituents of the earlier system are no longer represented.


But this study shows that that naïve system of understanding is still there, in the background, interfering and ready to come to the fore in old age and situations of confusion.


Given the nature of naïve psychology: present very early, very deep and associated with personal identity; it will be very difficult to shake the naïve in order to accept the scientific.

Shtulman, A., & Valcarcel, J. (2012). Scientific knowledge suppresses but does not supplant earlier intuitions Cognition DOI: 10.1016/j.cognition.2012.04.005

Damasio in music

Here is a poem written by the neuroscientist, Antonio Damasio, for the composer, Bruce Adolphe, and musician, Yo-Yo Ma. I would like to thank the blogger that brought this back to the surface from 2009, but unfortunately I have lost the link – sorry. To learn more and hear the music go to here .

I. When Mind First (in the Body Bloomed)

Mind first bloomed quietly
and no one knows when,
although we know where:
within a brain that lived within a body.

Each mind bloomed quietly,
quietly made of images
(well, brain maps actually),
images of its own body
in repose and in motion—
images of smell and taste,
of touch and sound, of sights.

But no one knew that minds existed
least of all the beings within whom
minds had now emerged.
Unannounced and undetected,
minds had entered life.

Once minds began blooming
nothing was ever the same.
But who would know
that the universe had changed?
No one. Nothing could yet be known.

II. Self Came to Mind

When knowing began
it bloomed as quietly as mind first had.
Knowing came from the very same secret—
making images—

Each mind composed a portrait of
its own organism
with images from within the body
(well, brain maps actually).
The organism moved about and sensed around
but now the mind had a protagonist —
the portrait of the organism at the center of the action —
and now the mind could tell the story
of what was happening to it.

This first story had no words, just images.
There were images of the body—they became feelings,
and there were images of things done to the body —
sound and sight and touch are things done to the body.
The images of what the body underwent
were attributed to the images of what the body was.
Mind had fashioned
a self without words,
the would be I, the would be me.

Thus self came to mind,
and when it did,
some part of the mind knew
that the rest existed.
Or so it seemed.
Ever blind, nature
had conjured up an apprentice.

The conscious mind was timid, at first,
as it noticed simple things
inside and around the body boundary.
But in humans the flourishing was
bold and intense.
What the mind did notice it could record,
and what it recorded could be recalled,
And not just the past, not just the now—
the future too, could be imagined and once imagined could be recorded.
Loss could be foreseen, but so could gain,
and so could hope.

From its simple beginnings, the conscious mind
revealed some part of existence.
But then conscious human minds banded together,
and invented languages of gesture and of music,
languages of words and of math,
and partial revelation became discovery.
Mind had stepped into the light.

Those were glory days.

III. Discovery

What conscious human minds first discovered
was that existence was drama.
Had there been no knowledge, no drama would have been.

Consciousness had revealed joy
but the price was high—
now the mind knew pain
and unattainable pleasure.

Were it not for the conscious mind,
were it not for knowing,
there would have been no bliss
and no suffering, just a mind unexamined.

But drama is not necessarily tragedy
and this is not the end of the story.

When sorrow could no longer hide
the apprentices turned against the sorcerer.
They used knowledge to transform existence
and respond defiantly to suffering.

Nature, ever blind, did not care
that a part of itself wanted to shape its future.
And still it does not.
We are allowed
to rebel against indifference.
We have a say.

Drama is not necessarily tragedy
and this is not the end of the story.

State of understanding is a long way off

There are many ways in which the brain is visualized. Each may be useful in some situations but all are misleading in others.


First example: the action of the brain is the result of the balance of neurotransmitters. Is there too much or too little dopamine? There is no doubt that the neurotransmitters and their receptors are very important but not in so simplistic a form. A while ago there was a posting by Neuroskeptic (here) where the metaphor of soup was used.

The concept of a ‘chemical imbalance’ in the human brain is one of the most fantastic oversimplifications in science, and one of the worst legacies of the modern pharmaceutical industry. … A bowl of soup could have a chemical imbalance. …Our technology for investigating the chemistry of the brain is comically crude.


Second example: the action of the brain is the result of specialized ‘areas for x’. Is there an area for love? Of course, it is true that there is a good deal of localization of various types of processing but this is a long way from ‘areas for x’. Some call it the ‘new phrenology’ and (here) is Michael Shermer’s take.

Today a popular metaphor is that the brain is like a Swiss Army knife, with specialized modules for vision, language, facial recognition, cheating detection, risk taking, spirituality and even God.

Modularity metaphors have been fueled by a new brain-scanning technology called functional magnetic resonance imaging (fMRI). We have all seen scans with highlighted (usually in red) areas where your brain “lights up” when thinking about X (money, sex, God, and so on)… There is a skeptical movement afoot to curtail abuses of the metaphor, however, and it is being driven by neuroscientists themselves.


Third example: the action of the brain is the result for a few networks, groups of local areas across the brain that act together. The favorite approach lately has been to contrast the default network and the task oriented network. This seems a very productive approach but a long way from a clear theory. The problem is that as studies look closer there are variations in the network components and overlaps depending on the type of task or rest etc. Much that is said about networks appears simplistic. G. deMarco pointed out the complications (here)

The concept supposes the existence of a dynamic interaction between interconnected, active areas and that the brain areas are expressed as networks within integrated systems. In such a system, localized areas are included in networks which become dynamic according to the cognitive task. Brain areas underlie several functions and can belong successively to several different functional networks. In other words, a given brain area does not have a single function; its resources can be exploited in several different cognitive strategies.


There are many more examples. Now the big idea is the connectome. If we knew all the connections between neurons we would know how the brain operated. The mantra is ‘you are your connectome’. To date the only connectome that has been documented is of a tiny worm with exactly 302 neurons in its nervous system. It does not seem that the connectome resulted in a complete understanding of C. elegans’ behaviour. This is because its neurons are not interchangeable widgets. A NeuroDojo post reviews the fight over the usefulness of the connectome project (here).

In one sense, the theory is trivially true. But in another sense, the theory omits so much that it doesn’t end up telling us anything we wanted to know.


So we have a lot of useful but incomplete ways to look at brain activity. Understanding is not right around the corner. But on the other hand, it is not impossible either. We should not settle for simplistic theories and we should not lose hope of a strong theory someday.





A couple of years back I looked at a paper by G. Dumas etal. (here) and was impressed. Recently he commented on a posting. This prompted me to look at the recent work of the group. They have developed a method that allows them to look at the interaction between two brains in communication and advance from individual to social theories of cognition.


Two people communicate with hand movements via a video link and at the same time EEG traces are collected from both. This really is measurement of synchrony in social interaction.

Towards a two-body neuroscience (see citation) gives a good description of the method.

Our findings result from the close collaboration between experts who study neural dynamics and developmental psychology… A new technique called hyperscanning has made it possible to study the neural activity of two individuals simultaneously. However, this advanced methodology was not sufficient in itself. What remained to be found was a way to promote real-time reciprocal social interaction between two individuals during brain recording and analyze the neural and behavioral phenomenon from an inter-individual perspective. Approaches used in infancy research to study nonverbal communication and coordination, between a mother and her child for example, have so far been poorly applied to neuroimaging experiments. We thus adapted an ecological two-body experiment inspired by the use of spontaneous imitation in preverbal infants. Numerous methodological and theoretical problems had to be overcome, ranging from the choice of a common time-unit for behavioral and brain recordings to the creation of algorithms for data processing between distant brain regions in different brains.


Currently, the integration, coordination and sharing of information by brain areas is thought to be do to neural phase synchronization. In other words, information processing relies on oscillations.

The Phase Locking Value (PLV) is a practical method for the quantification of neural synchronization between two neuroelectric signals in a specific frequency band. The fact that the perception-action loops of the two participants were intertwined in our experiment leads us to hypothesize that neural synchronizations, as measured by PLV, may exist between their two brains during periods in which the two subjects imitated one another reciprocally. Rather than using the classical PLV used to measure synchrony in the individual brain, we measured synchrony between two separated brains using a hyper-phase locking value (h-PLV). What can this h-PLV measure?


Both sensory (visual motion) and motor (hand velocity) cause oscillatory activities and this low-level sensory-motor information is being communicated.

Thus, the h-PLV could reflect information being dynamically shared through an interindividual sensory-motor loop. These loops emerge from a bi-directional coupling between the participants, with the behavior of each one influencing the other’s behavior, and inter-brain synchronizations reflecting their perception-action entanglement.


So what has this methodology produced so far. Here are the abstracts of three of the group’s papers (see citations):


Inter-Brain Synchronization during Social Interaction

During social interaction, both participants are continuously active, each modifying their own actions in response to the continuously changing actions of the partner. This continuous mutual adaptation results in interactional synchrony to which both members contribute. Freely exchanging the role of imitator and model is a well-framed example of interactional synchrony resulting from a mutual behavioral negotiation. How the participants’ brain activity underlies this process is currently a question that hyperscanning recordings allow us to explore. In particular, it remains largely unknown to what extent oscillatory synchronization could emerge between two brains during social interaction. To explore this issue, 18 participants paired as 9 dyads were recorded with dual-video and dual-EEG setups while they were engaged in spontaneous imitation of hand movements. We measured interactional synchrony and the turn-taking between model and imitator. We discovered by the use of nonlinear techniques that states of interactional synchrony correlate with the emergence of an interbrain synchronizing network in the alpha-mu band between the right centroparietal regions. These regions have been suggested to play a pivotal role in social interaction. Here, they acted symmetrically as key functional hubs in the interindividual brainweb. Additionally, neural synchronization became asymmetrical in the higher frequency bands possibly reflecting a top-down modulation of the roles of model and imitator in the ongoing interaction.


Does the brain know who is at the origin of what in an imitative interaction?

Brain correlates of the sense of agency have recently received increased attention. However, the explorations remain largely restricted to the study of brains in isolation. The prototypical paradigm used so far consists of manipulating visual perception of own action while asking the subject to draw a distinction between self- versus externally caused action. However, the recent definition of agency as a multifactorial phenomenon combining bottom-up and top-down processes suggests the exploration of more complex situations. Notably there is a need of accounting for the dynamics of agency in a two-body context where we often experience the double faceted question of who is at the origin of what in an ongoing interaction. In a dyadic context of role switching indeed, each partner can feel body ownership, share a sense of agency and altogether alternate an ascription of the primacy of action to self and to other. To explore the brain correlates of these different aspects of agency, we recorded with dual EEG and video set-ups 22 subjects interacting via spontaneous versus induced imitation (II) of hand movements. The differences between the two conditions lie in the fact that the roles are either externally attributed (induced condition) or result from a negotiation between subjects (spontaneous condition). Results demonstrate dissociations between self- and other-ascription of action primacy in delta, alpha and beta frequency bands during the condition of II. By contrast a similar increase in the low gamma frequency band (38–47 Hz) was observed over the centro-parietal regions for the two roles in spontaneous imitation (SI). Taken together, the results highlight the different brain correlates of agency at play during live interactions.


Anatomical Connectivity Influences both Intra- and Inter- Brain Synchronizations

Recent development in diffusion spectrum brain imaging combined to functional simulation has the potential to further our understanding of how structure and dynamics are intertwined in the human brain. At the intra-individual scale, neurocomputational models have already started to uncover how the human connectome constrains the coordination of brain activity across distributed brain regions. In parallel, at the inter-individual scale, nascent social neuroscience provides a new dynamical vista of the coupling between two embodied cognitive agents. Using EEG hyperscanning to record simultaneously the brain activities of subjects during their ongoing interaction, we have previously demonstrated that behavioral synchrony correlates with the emergence of inter-brain synchronization. However, the functional meaning of such synchronization remains to be specified. Here, we use a biophysical model to quantify to what extent inter-brain synchronizations are related to the anatomical and functional similarity of the two brains in interaction. Pairs of interacting brains were numerically simulated and compared to real data. Results show a potential dynamical property of the human connectome to facilitate inter-individual synchronizations and thus may partly account for our propensity to generate dynamical couplings with others.

(I asked G. Dumas to comment on this post and point out where I may have given the wrong impression of the work. He kindly sent this comment and I am adding it here in the main posting – with a thank you to him.)

1. Since there are lot of other groups using hyperscanning, I will advise you to emphasize the real-time and reciprocal dimensions here. I think notably at the title where people could believe I have developed hyperscanning although it is not the case (for fMRI it’s Montague and for EEG it’s Babiloni).

2. Instead of saying “coordination and sharing of information by brain areas is thought to be do to neural phase synchronization.” I would be less speculative and mentioned that “phase synchronization has been proved to play a role in the integration of neural information between distributed areas”. It is proposed as one of the mechanisms at play for brain coordination, but others are also potentially implicated (active de-synchornization is for instance as much important).

3. The last abstract quoted is a neurocomputational work which uses past experimental data as matter of comparison. The goal here is slightly different since it tends to supports the extension of existing computational approaches to the two-body level, and meanwhile describes how the human connectome structure constrains intra-individual and inter-individual neural dynamics. At the intra-individual level, it show for instance how the peak of the alpha rhythm could be linked to the connectome size and the conduction of neural pathways. At the inter-individual level, it seems that the structural similarity could play a role in the dynamical similarity. This open new venues for approaching social disorders such as autism and schizophrenia where the anatomical structure seems globally changed.

Dumas, G. (2011). Towards a two-body neuroscience Communicative & Integrative Biology, 4 (3), 349-352 DOI: 10.4161/cib.4.3.15110

Dumas, G., Nadel, J., Soussignan, R., Martinerie, J., & Garnero, L. (2010). Inter-Brain Synchronization during Social Interaction PLoS ONE, 5 (8) DOI: 10.1371/journal.pone.0012166

Dumas, G., Martinerie, J., Soussignan, R., & Nadel, J. (2012). Does the brain know who is at the origin of what in an imitative interaction? Frontiers in Human Neuroscience, 6 DOI: 10.3389/fnhum.2012.00128

Dumas, G., Chavez, M., Nadel, J., & Martinerie, J. (2012). Anatomical Connectivity Influences both Intra- and Inter-Brain Synchronizations PLoS ONE, 7 (5) DOI: 10.1371/journal.pone.0036414