Causal learning: how different is it from normal learning?

I was browsing a write-up on Causal reasoning by Mixing Memory, and came across this article by Lagnado et al, regarding the Causal Structure underlying causal reasoning.

In brief , Causal reasoning refers to that ability of the humans by which they can classify some events as causes and some events as effects and also determine either deterministically or probabilistically as to which effects are caused by which causes. In simple words, the ability to assign causes to effects.

Historically, Causal reasoning has focused on the statistical methods of covariance or correlation between two events and used the strength of the correlation to calculate and predict the causal relation between the two events. This suffers from several drawbacks like inability to determine the direction of causation or the inability to rule out a third common cause of which the two observed events are the effects.

Langrado et al, in their paper, present a refreshing new perspective on causal reasoning by differentiating between the qualitative Causal Structure between two or more events and the quantitative Causal Strength of that relationship. For example, a causal structure may causally relate the presence of fever with bacterial infection thus identifying bacterial infection as a cause of fever; but the causal strength between bacterial infection and fever would determine what probability we assign to a particular case of fever to have been caused due to bacterial infection (diagnostic learning) or the probability that given bacterial infection a person would develop fever (predictive learning).

The authors contend that the issues involved in causal strength learning and causal structure learning are different and should be addressed differently. Further, they contend that most of the historical research has been limited to causal strength learning, ignoring the prior and more fundamental stage of causal structure learning; as in their theory, the causal strength of any relation can only be learned once one has some a priori qualitative assumptions about the underlying causal relationships. Their paper thus focuses what cues/mechanisms are involved in the formation of the causal structure.

Causal-model theory was a relatively early, qualitative attempt to capture the distinction between structure and strength. According to this proposal causal induction is guided by top-down assumptions about the structure of causal models. These hypothetical causal models guide the processing of the learning input. The basic idea behind this approach is that we rarely encounter a causal learning situation in which we do not have some intuitions about basic causal features, such as whether an event is a potential cause or effect. If, for example, the task is to press a button and observe a light, we may not know whether these events are causally related or not, but we assume that the button is a potential cause and the light is a potential effect. Once a hypothetical causal model is in place, we can start estimating causal strength by observing covariation information. The way covariation estimates are computed and interpreted is dependent on the assumed causal model.

They list the cues that humans use to form their Causal structures as

• Statistical relations
• Temporal order
• Intervention
• Prior knowledge

Before discussing, in depth, each of these cues and how they may affect causal reasoning, it is instructive to note that the concept of a Causal Structure underlying a given set of phenomena is quite close to the idea of a Cognitive Map underlying a given environment (say the maze or the mouse trap). While the latter is a spatial mental map of the objects in the surrounding 3-D space, the former may be conceived as a causal mental map of events in the temporal dimension. The reason I am using this analogy is to contrast the cues used in formulating a Causal structure with the different learning mechanisms used by mice to form a cognitive map of the mouse trap. The contention is that the same cognitive mechanisms are involved and also that these mechanisms are structured and unfold in a developmentally guided and staged manner.

The first cue to form a Causal structure or link two or more events is that of statistical relations. Here, correlation information between the events, as well as their conditional independences are used to arrive at a set of Markov equivalent causal models. Much of the learning is associative, probabilistic and maybe latent. It may not be accessible to consciousness and the learning of causal structure is more implicit, than explicit. For example, the regularities in the data may give rise to a fuzzy causal structure, where tentative causal relations are posited. Suppose from the data, it is determined that A and B are perfectly correlated. The person will have a strong sense of causation between A and B, but would be unable to determine the direction of causation. similarly if 3 events A,B and C are correlated, we would not be able to determine the directions of causation. This mechanism is very much similar to the latent learning mechanism exhibited by the mice in the mouse trap.

The second cue to form a causal structure that we consider here is that of Intervention. Here, human intervention takes place by affecting one of the events (potential cause) and by basis of that intervention or exercised choice, experiment to find out what effect that variable has on the outcome (effect). To more rigorously define Interventions, let me quote from the paper.

Informally, an intervention involves imposing a change on a variable in a causal system from outside the system. A strong intervention is one that sets the variable in question to a particular value, and thus overrides the effects of any other causes of that variable. It does this without directly changing anything else in the system, although of course other variables in the system can change indirectly as a result of changes to the intervened-on variable. What is important for the purposes of causal learning is that an intervention can act as a quasi-experiment, one that eliminates (or reduces) confounds and helps establish the existence of a causal relation between the intervened-on variable and its effects.

Suppose A and B have been found to be correlated. Further suppose that the happening of event A and B is under the control of the human subject. Then one can intervene to cause A and observe whether B occurred. If so the direction of causation is from A -> B. On the other hand if by intervening the human subject caused B to happen and did not observe A, then one could conclude that B does not cause A. To make the example concrete, consider event A as 'Fire' and event B as 'Smoke'. We find that Fire and Smoke are correlated. By intervening and conducting experiments whereby we can control the occurrence of 'fire' or 'smoke' we can come up with correct causal relation that 'fire' -> 'smoke'

Consider again, a 3 event situation whereby the relation between two causal events (A and B) and an outcome (C) has to be ascertained. Specifically, by intervening and causing A sometimes and B other times, and observing the happening of C we could ascertain the causal structure as to whether A->c or B-> C. The situation is not too different than the vicarious trail and error learning exhibited by a mouse when at a choice point. There, the mice has to, by trail-and error choosing of either right/left black /white turnings, learn which stimulus is associated with food (outcome). Thus, intervention mechanism is nothing but the refined vicarious trial-and-error learning.

The third, and perhaps the most important, mechanism that is used to form the Causal structure is Temporal ordering. This is a very simple mechanism whereby events that are occurring prior to some other event can be the cause of that event, but not vice versa.

The temporal order in which events occur provides a fundamental cue to causal structure. Causes occur before (or possibly simultaneously with) their effects, so if one knows that event A occurs after event B, one can be sure that A is not a cause of B. However, while the temporal order of events can be used to rule out potential causes, it does not provide a sufficient cue to rule them in. Just because events of type B reliably follow events of type A, it does not follow that A causes B. Their regular succession may be explained by a common cause C (e.g., heavy drinking first causes euphoria and only later causes sickness). Thus the temporal order of events is an imperfect cue to causal structure.

This mechanism is the same as the one used by mice in searching for stimulus. When two events follow each other than an active search mechanism is used to identify the salient stimulus which may have been the cause of the event. The concept of temporal ordering implying causation is inherent in this learning mechanism as are concepts of spatial and temporal contiguity and proximity. This is the normal avoidance learning mechanism in mice and in human causal structure learning may be more engaged in and relevant to identifying the causes of events that are undesirable.

The fourth cue used for identifying causal structure, that the authors do not touch on, but do hint in terms of highlighting the importance of causal mechanisms; but that I propose nonetheless, is that of causal chains construction and elaboration. This basically involves breaking the simple A-> B with intermediate and competing C, D, E etc and intervening and conducting experiments to come up with the correct causal chain. Thus, A->B may be refined as A->C->D->B or A-> E->B and experimentation done to narrow down on a particular causal chain.

This is similar to the hypothesis learning involved in mice and depends on a cognitive capacity to sequence events . Also this is normally exhibited in approach behavior and this elaboration of causal chain may be more relevant to the desirable outcomes that human subjects want to happen and all the small intermediate steps of they need to cause to make the final outcome happen.

The fifth, and for now final, cue that is used in the formation of causal structure is prior knowledge. The authors define it as follows:

Regardless of when we observe fever in a patient, our world knowledge tells us that fever is not a cause but rather an effect of an underlying disease. Prior knowledge may be very specific when we have already learned about a causal relation, but prior knowledge can also be abstract and hypothetical. We know that switches can turn on devices even when we do not know about the specific function of a switch in a novel device. Similarly we know that diseases can cause a wide range of symptoms prior to finding out which symptom is caused by which disease. In contrast, rarely do we consider symptoms as possible causes of a disease.

My take on prior knowledge is something close to that, but slightly different. The subject forms a general idea of which events are causes and which effects and also the general relationship between a primary cause and a desired/undesired later final outcome. Though, the intervening small steps of the causal chain may not be present, and thus no formal corroborating data based proof may be there, yet one can deduce the causal relationship between the primal cause and the later final outcome, ignoring the intermediate minor events down the line. A case in point would be food aversion learning, whereby one single vomit following consumption of say a spoiled food that was taken hours ago, may result in a strong automatic association and learning of that food as the cause of vomit and lead to avoidance of (or escape from) that food.

To me this mechanism is the same as that exhibited by the mice when they learn the spatial orientation in the mouse trap and are able to exhibit novel escape learning.

This summarizes the analogy between the causal learning and normal learning as of now. Will touch on the qualitatively different next 3 (causal) learning mechanisms later.

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Framing Effects in the Universal Moral Language

I have posted earlier about the similarities between the Universal Grammar concept associated with languages and the Universal Moral Grammar that Hauser has proposed. To take the analogy further, just as linguistic framing of a issue leads to different interpretations and effects in the person exposed to a sentence or a phrase or a discourse, so too it is apparent that when a moral dilemma is posed under different contextual situations or framed differently then they led to different appraisals by the same subject.

To begin with, one may note that Mixing Memory writes about the concept of personal and impersonal violations, as outlined by Greene et al, and associates them with the famous Moral Dilemma of one versus five lives on a railway track in two different conditions - the Footbridge and the Trolley. To explain the differences in responses of the people to the differing moral dilemma in the two conditions, the concept of impersonal and personal violations is introduced and it is posited that these involve different reactions in the brain - one utilizing the emotional brain, and the other a rational brain. However, I have elsewhere provided a more parsimonious explanation utilizing the stages of moral development that people are on and how that may lead to different outcomes for the same moral problem in the two conditions. Specifically those at stage 3 of good interpersonal relationship would differ in how they respond to the two dilemmas.

More relevant to our discussion here, is that the same effects could be explained by the differing framing of the Moral dilemma. In effect, the footbridge dilemma is framed in such a way as to activate the action predicate processing in a different way from the impersonal trolley condition. In the footbridge case, the action predicate is of an action involving two human beings- the action is deliberate pushing of another person- and hence of more negative connotation- than the corresponding impersonal action involving acting on an inanimate object- viz. pushing the trolley. Thus, when Action Predicate also becomes a significant player in the Moral Dilemma, then though the intention and consequence predicate may remain the same, it may lead to different evaluations of the Moral Sentence.

Second, one needs to pay attention to the effect of emotions that has been observed in the footbridge dilemma, as observed by Piercarlo Valdesolo and David DeSterno. They report that under positive affect, people are more apt to choose the more 'rational' utilitarian alternative of pushing the person down the footbridge. This clearly is due to different framing conditions. A big part of the Moral Language is definitely made up of affects as they often provide a reliable guide to instinctive moral behavior. Thus, b putting the subjects under positive affect may be tantamount to the same framing effects that are observed when concepts like Tax opposition are associated with happy sounding words like relief and thus frame the issue of taxation differently. By a similar sleight of hand, as humans do tend to associate happiness and 'happiness for largest number of people' in their mind, so the utilitarian ethic may dominate when the context surrounding the moral dilemma is of 'happiness' or positive affect. It remains to be seen, if arousing negative or other affects in the subjects lead to a decline in the utilitarian response.
Anyway, the results as they stand today, do not corroborate the Impersonal/Personal violation theory and the corresponding rational/emotional brain theory, because the results clearly show that the differential response when in positive affect footbridge condition is due to the different emotional significance attached to the dilemma in happy affect vs. neutral affect situations. If anything, in the happy affect situation, the affective influence in decision making was greater (as baseline emotional activity was greater), than in the control condition. To prove that rational decision making was strengthened in presence of positive affect, one would need to show a general increase in rational decision making when under positive affect, or if not, at least by taking MRI scan of these happy affect decision makers, show that rational brain centers were more engaged than emotional centers while making the happy-affect-utilitarian decision..

Till then, we have plenty of evidence supporting the other hypothesis that positive affect improves moral reasoning as positive affect may be an internal guide used for guiding moral action - if something feels good, then it perhaps is good. Case in point is studies that have earlier demonstrated that if a graduate student is in positive affect, then he/she is more likely to help strangers-- for example by picking up dropped books. Thus, positive affect may be intrinsically linked to more altruistic/ moralistic actions and framing a dilemma, such that it arouses positive or negative affect in the subject, may alter the way the dilemma is perceived and resolved by the subject.

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Synapse vol 1, issue 7 now online!

The ever-thoughtful GNIF Brain Blogger has just published a brand new 7th edition of the Synapse. The articles range form thoughtful arguments for de-stigmatization of mental health issues to careful analysis of the recent vegetative-state-showing-consciousness studies. Sprinkled along the way are articles elaborating the trade off between proliferation and tumor suppression in human brain.

Have a happy reading!

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Follow Fifi and others as they roam around the Gombe Chimpanzee Park

As per this new Google initiative, one can now follow Fifi and other Chimpanzees in real-time as they roam around the Gombe Chimpanzee park using the Google Earth Featured Content. All you have to do is download Google Earth , choose the Jane Goodall's Gombe Chimpanzee Blog in the Featured Content section visible in the left sidebar and enjoy!

Although, I was not able to zoom in a live image of a chimpanzee ( as all of them were foraging in the dense forest and thus not visible), but with perseverance one may catch a live video of a chimp playing in an open area. Also, this would be of help to the primatologists amongst us, who could track the movements of these chimpanzees.

More power to Google!

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How to maximise your bets : become a schizophrenic or damage your amygdala, the orbitofrontal cortex, or the right insular cortex!

A couple of recent news articles on neuroeconomics, lead to some surprising insights regarding how addictions like Gambling could be self-addictive and how some specific neurological malfunctioning may lead to people fairing better in games of chances and making more 'rational' gambles.

The first article in the New Scientist refers to a recent research by Chris Frith et al at University College London, UK in which the authors found that people who had been given dopamine agonists (like L-DOPA) were able to determine the winning strategy involved in a gambling game early then those who were given placebo. The study contained choosing symbols - some of whom were associated with large chances of winning, while others were associated with average chances and still others were associated with financial penalties and should ideally be learned as avoidable symbols.

What they found was that dopamine facilitated the early learning of the symbols that were associated with (monetary) winning outcomes or rewards as compared to controls, but had no effect on the learning of the avoiding or punishment symbols. This, they hypothesize is due to the fact that people get a Dopamine surge whenever 'rewarded' and when base dopamine levels are high (it has already been administered prior to the betting game) this leads to greater strength of dopamine reward signal , thus leading to faster learning of the winning strategy. The fact that dopamine does not affect the learning of negative outcomes, confirms that the effect selective and due to the 'rewarding' nature of dopamine as opposed to a general improvement in learning due to dopamine administration.

The participants played a computer game in which they were repeatedly shown pairs of unmatched symbols, and had to choose one or the other without being told anything about them beforehand.

Unknown to the participants, one symbol gave them an 80% higher chance of winning £1, whereas another symbol gave them only a 20% higher chance of winning. Other symbols incurred financial penalties.

The volunteers on dopamine prospered because they identified the winning symbols faster than the haloperidol treated patients. And the winning effect was more pronounced if they actually received money in the study.

The dopamine recipients only noticed winning symbols, however. The chemical did not appear to alert recipients to “losing” symbols.

Learning from losing is controlled by other chemicals in the brain, the most dominant probably being serotonin, a chemical linked with depression, Frith concludes.

This brings up some interesting scenarios. If one has started gambling somehow, then as one keeps gambling further, the successive wins would generate more and more dopamine surges (as baseline dopamine increases after a few wins), the gambler would start identifying the winning patterns, and the strength of winning patterns and rewards associated with them would continue to get stronger in the gambler's mind; there would be no corresponding effect on the learning of negative or losing strategies by him and consequently his learning would be skewed in such a way that winning outcomes would be disproportionately perceived as being rewarding as compared to the losing outcomes - thus in the gamblers mind loses are processed in a 'normal' way ; but wins or winning strategies are perceived differently in the sense that they would be learned more strongly, earlier and more persistently - as each win would result in more and more dopamine surge and thus skew the learning in favor of the winning strategy more and more. this is a vicious circle- the gambler is getting more and more dopamine surge and is also becoming better and better at identifying the winning strategies- thus its difficult to convince him otherwise that he is gambling in vain- what he doesn’t realize that he is not attaching a corresponding increased negative outcome to losses or is learning the losing strategies also at the same rate.


The other article is a good review of the field of neuroeconomics in the New Yorker. It touches on many current issues in neuroeconomics, but what is most relevant to us here is the concept of loss aversion, whereby people perceive losses of what they already have as more aversive than a wasted chance of making an equivalent or more gain. To paraphrase from the article:

If you present people with an even chance of winning a hundred and fifty dollars or losing a hundred dollars, most refuse the gamble, even though it is to their advantage to accept it: if you multiply the odds of winning—fifty per cent—times a hundred and fifty dollars, minus the odds of losing—also fifty per cent—times a hundred dollars, you end up with a gain of twenty-five dollars. If you accepted this bet ten times in a row, you could expect to gain two hundred and fifty dollars. But, when people are presented with it once, a prospective return of a hundred and fifty dollars isn’t enough to compensate them for a possible loss of a hundred dollars. In fact, most people won’t accept the gamble unless the winning stake is raised to two hundred dollars.

Further, the article notes that this loss aversion is due to the fact that under ambiguous situations (or situations that involve probabilistic estimates in face of incomplete information to make the probabilistic judgments), our 'emotional' brain takes precedence over the 'rational' brain and prevents us from making 'rational' decisions.

In one study, Camerer and several colleagues performed brain scans on a group of volunteers while they placed bets on whether the next card drawn from a deck would be red or black. In an initial set of trials, the players were told how many red cards and black cards were in the deck, so that they could calculate the probability of the next card’s being a certain color. Then a second set of trials was held, in which the participants were told only the total number of cards in the deck.

The first scenario corresponds to the theoretical ideal: investors facing a set of known risks. The second setup was more like the real world: the players knew something about what might happen, but not very much. As the researchers expected, the players’ brains reacted to the two scenarios differently. With less information to go on, the players exhibited substantially more activity in the amygdala and in the orbitofrontal cortex, which is believed to modulate activity in the amygdala. “The brain doesn’t like ambiguous situations,” Camerer said to me. “When it can’t figure out what is happening, the amygdala transmits fear to the orbitofrontal cortex.”

The results of the experiment suggested that when people are confronted with ambiguity their emotions can overpower their reasoning, leading them to reject risky propositions. This raises the intriguing possibility that people who are less fearful than others might make better investors, which is precisely what George Loewenstein and four other researchers found when they carried out a series of experiments with a group of patients who had suffered brain damage.

Further, the article notes that people with orbitofrontal, right insular or amygdala damage, are less fearful or are less able to integrate the fearful or 'emotional' response of the brain and are thus able to make decisions that are more risky then their normal counterparts. Thus, the counterintuitive conclusion that damages to these areas may make one a better investor/ gambler etc.

Each of the patients had a lesion in one of three regions of the brain that are central to the processing of emotions: the amygdala, the orbitofrontal cortex, or the right insular cortex. The researchers presented the patients with a series of fifty-fifty gambles, in which they stood to win a dollar-fifty or lose a dollar. This is the type of gamble that people often reject, owing to loss aversion, but the patients with lesions accepted the bets more than eighty per cent of the time, and they ended up making significantly more money than a control group made up of people who had no brain damage. “Clearly, having frontal damage undermines the over-all quality of decision-making,” Loewenstein, Camerer, and Drazen Prelec, a psychologist at M.I.T.’s Sloan School of Management, wrote in the March, 2005, issue of the Journal of Economic Literature. “But there are situations in which frontal damage can result in superior decisions.”

If we club the two studies together, one may come to a surprising conclusion that to become a good speculative investor or gambler you may need to temporarily knock out your parts of the brain involved in emotional decision making (one may use TMS here) and also additionally take a dopamine does to learn the rewarding strategies and actions early on. This may be the only way for us to counter the tyranny of loss aversion that nature has imposed on us and move towards that ideal of Homo Economicus.

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Effect of enriched environments on the brain

Nature Reviews Neuroscience has an interesting article that summarizes the latest findings about neurogenesis and synaptic plasticity in adult mice and how exposure to enriched environments and experience leads to later onset of diseases in transgenic mice models of human diseases like Huntington's disease, Alzheimer's disease and Parkinson's disease, fragile X and Down syndrome, as well as various forms of brain injury.

This is exciting news and lends credence to the fact that for full flowering and upkeep of your mental faculties, mental exercises and stimulating mental environment is a must.

Hat Tip : The Frontal Cortex

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Book proposals sought in Cognitive Neuroscience

If you have always been interested in writing a book concerned with Cognitive Neuroscience, Psychology press is currently soliciting proposals for the same. More information at Cognitive Neuroscience Arena.
Please find below the detailed requirements
>>
Contemporary Approaches in Cognitive Neuroscience

Psychology Press are launching a new series called "Contemporary Approaches in Cognitive Neuroscience".

Series Editors:

* Stanislas Dehaene, Collège de France, Paris
* Alvaro Pascual-Leone, Harvard Medical School
* Jamie Ward, University College London

Invitation to Authors:

Reflecting contemporary and controversial issues in the study of cognitive neuroscience, the series aims to present a multi-disciplinary forum for cutting edge debate that will help shape this burgeoning discipline.

It offers leading figures in the field and the best new researchers an opportunity to showcase their own work, expand on their own theories and place these in the wider context of the field.

If you would like to submit a proposal to be included in this series we would like to hear from you! Titles in the series may be authored or edited; the only requirement is that each book must aim to make a contribution to a specific topic by reviewing and synthesising the existing research literature, by advancing theory in the area, or by some combination of these missions.

Please send your proposals to: book.proposals@psypress.co.uk
>>

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The Synapse, Vol. 1, issue 6

Welcome to the sixth edition of Synapse, a biweekly Carnival, devoted to aggregating the best neuroscience postings and research on the web.

History of Psychology:
Let's learn our lessons from the history of Psychology. Here, we have a very informative posting from the Neurophilosopher delineating the gradual historical process through which the concept of a Neuron got established. I am tempted to post a snippet from the post, which describes the etymology of this Carnival's name.

Also during this decade Sir Charles Sherrington described the junction between nerve and muscle, and named it the ’synapse’ (from the Greek roots syn, meaning ‘together,’ and haptein, meaning ‘to clasp’) in 1897.

Interesting New Findings:
Dave from Cognitive Daily presents an interesting research that shows that adults and children have different abilities to detect Musical Phrases and that some of the musical abilities, like language, may involve a critical period of acquisition. A lively discussion ensues on the blog!

Linking It UP:
Chris from Developing Intelligence summarizes the latest findings on Memory consolidation and how this new protein kinease M-Zeta pathway and the earlier Armitage-destruction-in-synapse pathways may lead to a futuristic scenario wherein you may be able to selectively forget the memory of one day earlier. This is the psychological equivalent of the morning-after pill!!

News and views:
PsychNotes posts on the same study regarding Kinease M-Zeta and links it to memory maintainence and LTP.

Informed Criticisms:
The Neurocritic takes issue with the popular press coverage of a study published in Nature which purportedly links Parietal lobe with categorization , and gently points that as per the original; article only LIP is involved and the categorization was limited to direction of motion and thus does not take away all that earlier glory associated with the Ventral stream!

In Depth:
If you want to learn more about attentional blink and whether the data can be explained by distracter-interference vs. two-stage bandwidth limited models, then join Chris from Developing Intelligence as he explores the phenomenon in depth.

Theoretical Developments:
In this section, yours truly, extends the observations made by Marc Hauser for an innate Universal Moral Grammar and adds to it concepts like Intention and Consequence Predicates.
Yours truly, also tries to integrate different factors and stages involved in Pretend play and how that may relate to Language acquisition.

Methodological Advances:
Jake from Pure Pedantry highlights the new sophisticated methodology of using c14 isotope levels and the fact that c14 levels in atmosphere changed drastically before and after the Nuclear Test Ban Treaty of 1963 to prove that no new neurons are formed in the adult human cortex.

Future Trends:
The Neurophiliosopher takes us on a voyage of a hybrid nanowire - rat neurons device in which artificial synapses are created between the Neurons and the silicon nanowires.

(Don’t)Try this at Home (take consent of your physician first!):
Village Smitty, from the Hippocampy, lists a simple exercise for balance, posture and spatial awareness that was found useful for a person suffering from cerebellar meningioma.

In Focus:
Last, but not the least, the In Focus cover article for this special Mouse Trap issue of Synapse, that has the theme of Mouse embedded in it- an article by Jake from Pure Pedantry about various methods used for measuring 'depression' in mice and how knocking the TREK-1 gene bestows the same effects on mice as if they had been treated by anti-depressants and also works by the same pathway. Long live the mice!

The next edition of Synapse would be hosted at GNIF Brain Blogger on Sept.17^th. Do submit your articles before the September 16^th deadline. Submission guidelines here.

Happy blogging till then!

Technorati Tags: carnival , cognition

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