Major conscious and unconcoscious processes in the brain: part 3: Robot minds

This article continues my series on major conscious and unconscious processes in the brain. In my last two posts I have talked about 8 major unconscious processes in the brain viz sensory, motor, learning , affective, cognitive (deliberative), modelling, communications and attentive systems. Today, I will not talk about brain in particular, but will approach the problem from a slightly different problem domain- that of modelling/implementing an artificial brain/ mind.

I am a computer scientist, so am vaguely aware of the varied approaches used to model/implement the brain. Many of these use computers , though not every approach assumes that the brain is a computer.

Before continuing I would briefly like to digress and link to one of my earlier posts regarding the different  traditions of psychological research in personality and how I think they fit an evolutionary stage model . That may serve as a background to the type of sweeping analysis and genralisation that I am going to do. To be fair it is also important to recall an Indian parable of how when asked to describe an elephant by a few blind man each described what he could lay his hands on and thus provided a partial and incorrect picture of the elephant. Some one who grabbed the tail, described it as snake-like and so forth.

With that in mind let us look at the major approaches to modelling/mplementing the brain/intelligence/mind. Also remember that I am most interested in unconscious brain processes till now and sincerely believe that all the unconscious processes can, and will be successfully implemented in machines.   I do not believe machines will become sentient (at least any time soon), but that question is for another day.

So, with due thanks to @wildcat2030, I came across this book today and could immediately see how the different major approaches to artificial robot brains are heavily influenced (and follow) the evolutionary first five stages and the first five unconscious processes in the brain.
The book in question is 'Robot Brains: Circuits and Systems for Conscious Machines' by Pentti O. Haikonen and although he is most interested in conscious machines I will restrict myself to intelligent but unconscious machines/robots.

The first chapter of the book (which has made to my reading list) is available at Wiley site in its entirety and I quote extensively from there:

Presently there are five main approaches to the modelling of cognition that could be used for the development of cognitive machines: the computational approach (artificial intelligence, AI), the artificial neural networks approach, the dynamical systems approach, the quantum approach and the cognitive approach. Neurobiological approaches exist, but these may be better suited for the eventual explanation of the workings of the biological brain.

The computational approach (also known as artificial intelligence, AI) towards thinking machines was initially worded by Turing (1950). A machine would be thinking if the results of the computation were indistinguishable from the results of human thinking. Later on Newell and Simon (1976) presented their Physical Symbol System Hypothesis, which maintained that general intelligent action can be achieved by a physical symbol system and that this system has all the necessary and sufficient means for this purpose. A physical symbol system was here the computer that operates with symbols (binary words) and attached rules that stipulate which symbols are to follow others. Newell and Simon believed that the computer would be able to reproduce human-like general intelligence, a feat that still remains to be seen. However, they realized that this hypothesis was only an empirical generalization and not a theorem that could be formally proven. Very little in the way of empirical proof for this hypothesis exists even today and in the 1970s the situation was not better. Therefore Newell and Simon pretended to see other kinds of proof that were in those days readily available. They proposed that the principal body of evidence for the symbol system hypothesis was negative evidence, namely the absence of specific competing hypotheses; how else could intelligent activity be accomplished by man or machine? However, the absence of evidence is by no means any evidence of absence. This kind of ‘proof by ignorance’ is too often available in large quantities, yet it is not a logically valid argument. Nevertheless, this issue has not yet been formally settled in one way or another. Today’s positive evidence is that it is possible to create world-class chess-playing programs and these can be called ‘artificial intelligence’. The negative evidence is that it appears to be next to impossible to create real general intelligence via preprogrammed commands and computations.

The original computational approach can be criticized for the lack of a cognitive foundation. Some recent approaches have tried to remedy this and consider systems that integrate the processes of perception, reaction, deliberation and reasoning (Franklin, 1995, 2003; Sloman, 2000). There is another argument against the computational view of the brain. It is known that the human brain is slow, yet it is possible to learn to play tennis and other activities that require instant responses. Computations take time. Tennis playing and the like would call for the fastest computers in existence. How could the slow brain manage this if it were to execute computations?

The artificial neural networks approach, also known as connectionism, had its beginnings in the early 1940s when McCulloch and Pitts (1943) proposed that the brain cells, neurons, could be modelled by a simple electronic circuit. This circuit would receive a number of signals, multiply their intensities by the so-called synaptic weight values and sum these modified values together. The circuit would give an output signal if the sum value exceeded a given threshold. It was realized that these artificial neurons could learn and execute basic logic operations if their synaptic weight values were adjusted properly. If these artificial neurons were realized as hardware circuits then no programs would be necessary and biologically plausible artificial replicas of the brain might be possible. Also, neural networks operate in parallel, doing many things simultaneously. Thus the overall operational speed could be fast even if the individual neurons were slow. However, problems with artificial neural learning led to complicated statistical learning algorithms, ones that could best be implemented as computer programs. Many of today’s artificial neural networks are statistical pattern recognition and classification circuits. Therefore they are rather removed from their original biologically inspired idea. Cognition is not mere classification and the human brain is hardly a computer that executes complicated synaptic weight-adjusting algorithms.

The human brain has some 10 to the power of 11 neurons and each neuron may have tens of thousands of synaptic inputs and input weights. Many artificial neural networks learn by tweaking the synaptic weight values against each other when thousands of training examples are presented. Where in the brain would reside the computing process that would execute synaptic weight adjusting algorithms? Where would these algorithms have come from? The evolutionary feasibility of these kinds of algorithms can be seriously doubted. Complicated algorithms do not evolve via trial and error either. Moreover, humans are able to learn with a few examples only, instead of having training sessions with thousands or hundreds of thousands of examples. It is obvious that the mainstream neural networks approach is not a very plausible candidate for machine cognition although the human brain is a neural network.

Dynamical systems were proposed as a model for cognition by Ashby (1952) already in the 1950s and have been developed further by contemporary researchers (for example Thelen and Smith, 1994; Gelder, 1998, 1999; Port, 2000; Wallace, 2005). According to this approach the brain is considered as a complex system with dynamical interactions with its environment. Gelder and Port (1995) define a dynamical system as a set of quantitative variables, which change simultaneously and interdependently over quantitative time in accordance with some set of equations. Obviously the brain is indeed a large system of neuron activity variables that change over time. Accordingly the brain can be modelled as a dynamical system if the neuron activity can be quantified and if a suitable set of, say, differential equations can be formulated. The dynamical hypothesis sees the brain as comparable to analog feedback control systems with continuous parameter values. No inner representations are assumed or even accepted. However, the dynamical systems approach seems to have problems in explaining phenomena like ‘inner speech’. A would-be designer of an artificial brain would find it difficult to see what kind of system dynamics would be necessary for a specific linguistically expressed thought. The dynamical systems approach has been criticized, for instance by Eliasmith (1996, 1997), who argues that the low dimensional systems of differential equations, which must rely on collective parameters, do not model cognition easily and the dynamicists have a difficult time keeping arbitrariness from permeating their models. Eliasmith laments that there seems to be no clear ways of justifying parameter settings, choosing equations, interpreting data or creating system boundaries. Furthermore, the collective parameter models make the interpretation of the dynamic system’s behaviour difficult, as it is not easy to see or determine the meaning of any particular parameter in the model. Obviously these issues would translate into engineering problems for a designer of dynamical systems.

The quantum approach maintains that the brain is ultimately governed by quantum processes, which execute nonalgorithmic computations or act as a mediator between the brain and an assumed more-or-less immaterial ‘self’ or even ‘conscious energy field’ (for example Herbert, 1993; Hameroff, 1994; Penrose, 1989; Eccles, 1994). The quantum approach is supposed to solve problems like the apparently nonalgorithmic nature of thought, free will, the coherence of conscious experience, telepathy, telekinesis, the immortality of the soul and others. From an engineering point of view even the most practical propositions of the quantum approach are presently highly impractical in terms of actual implementation. Then there are some proposals that are hardly distinguishable from wishful fabrications of fairy tales. Here the quantum approach is not pursued.

The cognitive approach maintains that conscious machines can be built because one example already exists, namely the human brain. Therefore a cognitive machine should emulate the cognitive processes of the brain and mind, instead of merely trying to reproduce the results of the thinking processes. Accordingly the results of neurosciences and cognitive psychology should be evaluated and implemented in the design if deemed essential. However, this approach does not necessarily involve the simulation or emulation of the biological neuron as such, instead, what is to be produced is the abstracted information processing function of the neuron.

A cognitive machine would be an embodied physical entity that would interact with the environment. Cognitive robots would be obvious applications of machine cognition and there have been some early attempts towards that direction. Holland seeks to provide robots with some kind of consciousness via internal models (Holland and Goodman, 2003; Holland, 2004). Kawamura has been developing a cognitive robot with a sense of self (Kawamura, 2005; Kawamura et al., 2005). There are also others. Grand presents an experimentalist’s approach towards cognitive robots in his book (Grand, 2003).

A cognitive machine would be a complete system with processes like perception, attention, inner speech, imagination, emotions as well as pain and pleasure. Various technical approaches can be envisioned, namely indirect ones with programs, hybrid systems that combine programs and neural networks, and direct ones that are based on dedicated neural cognitive architectures. The operation of these dedicated neural cognitive architectures would combine neural, symbolic and dynamic elements.

However, the neural elements here would not be those of the traditional neural networks; no statistical learning with thousands of examples would be implied, no backpropagation or other weight-adjusting algorithms are used. Instead the networks would be associative in a way that allows the symbolic use of the neural signal arrays (vectors). The ‘symbolic’ here does not refer to the meaning-free symbol manipulation system of AI; instead it refers to the human way of using symbols with meanings. It is assumed that these cognitive machines would eventually be conscious, or at least they would reproduce most of the folk psychology hallmarks of consciousness (Haikonen, 2003a, 2005a). The engineering aspects of the direct cognitive approach are pursued in this book.

Now to me these computational approaches are all unidimensional-

1. The computational approach is suited for symbol-manipulation and information-represntation and might give good results when used in systems that have mostly 'sensory' features like forming a mental represntation of external world, a chess game etc. Here something (stimuli from world) is represented as something else (an internal symbolic represntation).
2. The Dynamical Systems approach is guided by interactions with the environment and the principles of feedback control systems and also is prone to 'arbitrariness' or 'randomness'. It is perfectly suited to implement the 'motor system' of brain as one of the common features is apparent unpredictability (volition) despite being deterministic (chaos theory) .
3. The Neural networks or connectionsim is well suited for implementing the 'learning system' of the brain and we can very well see that the best neural network based systems are those that can categorize and classify things just like 'the learning system' of the brain does.
4. The quantum approach to brain, I haven't studied enough to comment on, but the action-tendencies of 'affective system' seem all too similar to the superimposed,simultaneous states that exits in a wave function before it is collapsed. Being in an affective state just means having a set of many possible related and relevant actions simultaneously activated and then perhaps one of that decided upon somehow and actualized. I'm sure that if we could ever model emotion in machine sit would have to use quantum principles of wave functions, entanglemnets etc.
5. The cognitive approach, again I haven't go a hang of yet, but it seems that the proposal is to build some design into the machine that is based on actual brain and mind implemntations. Embodiment seems important and so does emulating the information processing functions of neurons. I would stick my neck out and predict that whatever this cognitive approach is it should be best able to model the reasoning and evaluative and decision-making functions of the brain. I am reminded of the computational modelling methods, used to functionally decompose a cognitive process, and are used in cognitive science (whether symbolic or subsymbolic modelling) which again aid in decision making / reasoning (see wikipedia entry)

Overall, I would say there is room for further improvement in the way we build more intelligent machines. They could be made such that they have two models of world - one deterministic , another chaotic and use the two models simulatenously (sixth stage of modelling); then they could communicate with other machines and thus learn language (some simulation methods for language abilities do involve agents communicating with each other using arbitrary tokens and later a language developing) (seventh stage) and then they could be implemented such that they have a spotlight of attention (eighth stage) whereby some coherent systems are amplified and others suppressed. Of course all this is easier said than done, we will need at least three more major approaches to modelling and implementing brain/intelligence before we can model every major unconscious process in the brain. To model consciousness and program sentience is an uphill task from there and would definitely require a leap in our understandings/ capabilities.

Do tell me if you find the above reasonable and do believe that these major approaches to artificial brain implementation are guided and constrained by the major unconscious processes in the brain and that we can learn much about brain from the study of these artificial approaches and vice versa.

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Major conscious and unconcoscious processes in the brain: part 2

This is the second in the series about major conscious and unconscious processes in the brain.  The first part can be found here. This post  tries to document a few more processes / functions in the brain and their neural substrates.
To recap, the major processes  in brain (along with sample broad brain regions (grossly simplified) associated) are :

1. Sensory (occipital)
2. Motor (parietal)
3. Learning (hippocampal formation in medial temporal)
4. Affective (amygdalar and limbic system)
5. Evaluative/decisional (frontal)

These are supplanted by the following processes and mechanisms.

6. Modeling system/ Hemispheric laterlaization: Another system/ mechanism that the brain may find useful and develop is the ability to model the world and model the self and others . This presents the following problem. The world consist of objects that follow deterministic casual laws thus lending order to it as well as seeming agents that act by their own volition and thus leading to chaos. The modeling required to model causal, deterministic world may suffer from different design constraints than that required to model a chaotic, agentic world.  The brain, I propose, solves this, by having two hemispheres, one specialized for interacting with the world based on the model of the world as orderly, deterministic , statistically regular world; while the other hemisphere specialized for interacting with the world assuming it as a chaotic , agentic, probabilistically undetermined world. The two hemispheres co-operate with each other and respond using the advantages offered by the different strategies of both hemispheres. To recap, left hemisphere is specialized for causal patterns, sequences, analysis and interpretation, classifying objects (categorical spatial represnetation) , verbal abilities depending on analysis of sequences, uses prototypes (statistical mean) and uses Match strategy of responding in a statistical pattern, Music lyrics, and works on local stimuli (components) and parses high spatial frequency and values relativity. The right brain on the other hand is specialized for random/unperdicatble associations, scenes, synthesis and documentation, acting on objects (co-ordinate spatial representation), Spatial abilities depending on synthesis of objects making the scene, uses exemplars (actual events) and uses Maximizing strategy of responding as per probability at the moment, Music melody, and works on  global stimuli (wholes) and parses low spatial frequency and values absoluteness. To summarize, left hemisphere is best suited to model temporal dimensions in an analytical and causal manner, while right hemisphere is best suited to model the spatial dimensions in an holistic and agentic manner. This modeling, it needs to be emphasized, need not be  conscious, but could be entirely unconscious and have unconscious effects. 

7. Communciation system/ perisylvian area/ mirror neurons?: Once an organism has discovered/ realized unconsciously that there are other agents/ con specifics in the world , a brain system that communicates (on an unconscious level) with the others can evolve. A system can evolve that signals the emotional/internal state to others and can also sense the emotional/ internal state of others. This can be used as an aid to predict how the agent will act - as the agent is similar to oneself, one can predict how the other will act based on its internal state by simulating how one would act himself , given the same internal state. Sensing the internal state of others is one side of the coin, the other part is signalling your own internal state honestly to others to aid communication and enhance fitness by group selection. Remember that none of these consdireations need to be conscious. Even unicellular bacteria that live in colonies/ cultures evolve communication systems based on sensing and emitting chemicals etc.  In humans the mirror neuron system activated by others actions, the emotional expression and contagious unconscious empathy may all be the unconscious communciation system driven by non-verbal communication based on mirroring and mirror neurons.

8. Attention system : The last (for now!) system to evolve might be related to directing attention or selectivity processing relevant inputs, actions, affects, evaluations, associations, models and communciations while suppressing irrelevant ones. At any time , one is bombarded by many (all unconscious ) different stimuli, urges, activated associations, body states, values, models and communications from con specifics- these may or may not be relevant to current situation/ goal.  Not everything can be processed equally as the brain has limited computational resources. This leads to a mechanism/system to gauze relevance and thus bias the other systems by selectively processing some aspects in detail while ignoring others. This attentional/orientational mechanism may be covert, may be unconscious and might be triggered by external events/ voluntarily directed; important thing to realize is that  attention seems to integrate the output and inputs of other brain systems/ mechanisms  by selectivity highlighting a few features that are relevant and coherent. This also ultimately leads to  opening the doors to the next higher level of processing by brain - the conscious processing, which is computationally more demanding and thus requires attention to restrict the inputs that it can process. The attentional system opens the floodgates of heaven (consciousness) for the humans/ animals that are able to use it appropriately.

The spotlight of attention once created leads to conscious experiences of perception, agency, memory, feelings, thoughts, self-awareness, inner speech and identity. That of course is material for another post!

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Major conscious and unconcoscious processes in the brain

Today I plan to touch upon the topic of consciousness (from which many bloggers shy) and more broadly try to delineate what I believe are the important different conscious and unconscious processes in the brain. I will be heavily using my evolutionary stages model for this.

To clarify myself at the very start , I do not believe in a purely reactive nature of organisms; I believe that apart from reacting to stimuli/world; they also act , on their own, and are thus agents. To elaborate, I believe that neuronal groups and circuits may fire on their own and thus lead to behavior/ action. I do not claim that this firing is under voluntary/ volitional control- it may be random- the important point to note is that there is spontaneous motion.

1. Sensory system: So to start with I propose that the first function/process the brain needs to develop is to sense its surroundings. This is to avoid predators/ harm in general. this sensory function of brain/sense organs may be unconscious and need not become conscious- as long as an animal can sense danger, even though it may not be aware of the danger, it can take appropriate action - a simple 'action' being changing its color to merge with background. 
   
2. Motor system:The second function/ process that the brain needs to develop is to have a system that enables motion/movement. This is primarily to explore its environment for food /nutrients. Preys are not going to walk in to your mouth; you have to move around and locate them. Again , this movement need not be volitional/conscious - as long as the animal moves randomly and sporadically to explore new environments, it can 'see' new things and eat a few. Again this 'seeing' may be as simple as sensing the chemical gradient in a new environmental.
3. Learning system: The third function/process that the brain needs to develop is to have a system that enables learning. It is not enough to sense the environmental here-and-now. One needs to learn the contingencies in the world and remember that both in space and time. I am inclined to believe that this is primarily pavlovaion conditioning and associative learning, though I don't rule out operant learning. Again this learning need not be conscious- one need not explicitly refer to a memory to utilize it- unconscious learning and memory of events can suffice and can drive interactions. I also believe that need for this function is primarily driven by the fact that one interacts with similar environments/con specifics/ predators/ preys and it helps to remember which environmental conditions/operant actions lead to what outcomes. This learning could be as simple as stimuli A predict stimuli B and/or that action C predicts reward D .
4. Affective/ Action tendencies system .The fourth function I propose that the brain needs to develop is a system to control its motor system/ behavior by making it more in sync with its internal state. This I propose is done by a group of neurons monitoring the activity of other neurons/visceral organs and thus becoming aware (in a non-conscious sense)of the global state of the organism and of the probability that a particular neuronal group will fire in future and by thus becoming aware of the global state of the organism , by their outputs they may be able to enable one group to fire while inhibiting other groups from firing. To clarify by way of example, some neuronal groups may be responsible for movement. Another neuronal group may be receiving inputs from these as well as say input from gut that says that no movement has happened for a time and that the organism has also not eaten for a time and thus is in a 'hungry' state. This may prompt these neurons to fire in such a way that they send excitatory outputs to the movement related neurons and thus biasing them towards firing and thus increasing the probability that a motion will take place and perhaps the organism by indulging in exploratory behavior may be able to satisfy hunger. Of course they will inhibit other neuronal groups from firing and will themselves stop firing when appropriate motion takes place/ a prey is eaten. Again nothing of this has to be conscious- the state of the organism (like hunger) can be discerned unconsciously and the action-tendencies biasing foraging behavior also activated unconsciously- as long as the organism prefers certain behaviors over others depending on its internal state , everything works perfectly. I propose that (unconscious) affective (emotional) state and systems have emerged to fulfill exactly this need of being able to differentially activate different action-tendencies suited to the needs of the organism. I also stick my neck out and claim that the activation of a particular emotion/affective system biases our sensing also. If the organism is hungry, the food tastes (is unconsciously more vivid) better and vice versa. thus affects not only are action-tendencies , but are also, to an extent, sensing-tendencies.
5. Decisional/evaluative system: the last function (for now- remember I adhere to eight stage theories- and we have just seen five brain processes in increasing hierarchy) that the brain needs to have is a system to decide / evaluate. Learning lets us predict our world as well as the consequences of our actions. Affective systems provide us some control over our behavior and over our environment- but are automatically activated by the state we are in. Something needs to make these come together such that the competition between actions triggered due to the state we are in (affective action-tendencies) and the actions that may be beneficial given the learning associated with the current stimuli/ state of the world are resolved satisfactorily. One has to balance the action and reaction ratio and the subjective versus objective interpretation/ sensation of environment. The decisional/evaluative system , I propose, does this by associating values with different external event outcomes and different internal state outcomes and by resolving the trade off between the two. This again need not be conscious- given a stimuli predicting a predator in vicinity, and the internal state of the organism as hungry, the organism may have attached more value to 'avoid being eaten' than to 'finding prey' and thus may not move, but camouflage. On the other hand , if the organisms value system is such that it prefers a hero's death on battlefield , rather than starvation, it may move (in search of food) - again this could exist in the simplest of unicellular organisms.

Of course all of these brain processes could (and in humans indeed do) have their conscious counterparts like Perception, Volition,episodic Memory, Feelings and Deliberation/thought. That is a different story for a new blog post!

And of course one can also conceive the above in pure reductionist form as a chain below:

sense-->recognize & learn-->evaluate options and decide-->emote and activate action tendencies->execute and move.

and then one can also say that movement leads to new sensation and the above is not a chain , but a part of cycle; all that is valid, but I would sincerely request my readers to consider the possibility of spontaneous and self-driven behavior as separate from reactive motor behavior. 

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Best of Tweets: 20-05-09

I am starting an experimental new feature today called Best of Tweets. Many other bloggers do weekly link fests and I had somehow refrained form doing one myself. Using twitter, I am able to share many more links that I find interesting instantly , but I know that many of you are not on twitter; so perhaps a weekly best of tweets post that aggregates the best of my tweets for the past week may be useful to the mouse trap blog readers. Do tell me via comments whether you find this useful.Remember that this is a manually compiled by me list of best of tweets and is not auto generated, so I am putting some additional efforts here.

Without further ado, here is the best of tweets for week ending 20-05-09:

1. RT @Wildcat2030: In defense of distraction--it's not a bug, it's a feature of a new techno-nomadic culture. (via @LynJ) http://bit.ly/sYwcl
2. Debates on free will / perchance or predetermined / now silence reigns, courtesy free won't #haiku #scaiku (ver 2) #philosophy (for the background of this tweet go read the 4 way convesration I had on twitter on free-will yesterday)
   
3. I believe in a libertarian free will concept and thus found the recent Nature article based on randomness in... re: http://ff.im/1Zo2A
4. a 5 part npr series on brain/spirituality RT @kdwashburn: Great interactive graphic on the brain and spirituality: http://tr.im/lI7F
5. RT @kdwashburn: "reading someone else's attention involves the same brain circuits that control one's own attention" http://tr.im/lIoZ 
6. Yes! 50 Scientifically Proven Ways to Be Persuasive http://ff.im/-30dO4
7. Narrative gravity /go spin a yarn/define yourself .http://bit.ly/14h8sX via @LeadonYoung: http://bit.ly/aTo0e & http://bit.ly/13Qqto #scaiku 
8. Creativity ,esp. musical / 'a seething cauldron of ideas'/ Jonah peeps in your brain : http://bit.ly/leSwf : #scaiku #haiku #science
9.  Triune Ethics: On Neurobiology and Multiple Moralities « Neuroanthropology http://ff.im/-2Vui8
10. Your will is free / not everything is a reaction/ behold the fly acting random! http://bit.ly/hzBGh #scaiku #science #haiku #cognitive 
11. Depression and Mania / which one comes first / a serpent eating its tail ? : http://bit.ly/18ycU0 #scaiku #science #haiku
12. psychosis or a dream/ hard to tell/an overactive default network : http://ff.im/-2QNny #scaiku #haiku #science
13. "We can’t control the world, but we can control how we think about it": Mischel . Sounds a lot like Viktor Frankl. http://bit.ly/118zfl
14. via @anibalmastobiza : cool genomic imprinting paper that predates Badcock/Crespi's work on Autism/Psychosis http://bit.ly/o8ppS
    

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Attention allocation / Same as action selection/ New insight on ADHD #haiku #scaiku

The title of my above post is a scaiku (scientific haiku in 140 chars on twitter) that I posted last night on twitter.I am using this title as the inspiration for this post is twitter itself.

Last night, after a hard day full of tweeting (yes tweeting and keeping up with all the friends' tweets is a lot of hard work- go check the 4-way conversation I had on cosnsciousness and free will), I was not able to relax myself, but found myself in a constant state of distraction and restlessness, and getting up in middle of night to update my status.  Of course I have heard of twitter addiction and would rubbish that off; but I could not rubbish off the unique demands on attention and juggling that twittering makes on you. First off, you need to read a lot of tweets and find the needle in the haystack- the tweets that need to be retweeted/replied to and ignore/forget the rest of them as soon as possible. Secondly, I at least, juggle constantly between windows and tabs of tweetdeck and other application trying to do optimal scavenging (feeding off good content tweeted by others) and foraging (finding a good tweetable link myself).

So to sum up, I found that twitter had taxed, at least yesterday, my attentional system- leading to a habitual distractibility and also my motor system hat had constantly flitted between open windows and tabs leading to a habitual distractibility. I am sure that was a very short term and temporary phenomenon, but that set me thinking  I have already devoted an entire post to how attention allocation and action selection may be similar and have drawn many parallels. The fundamental problem in  both the cases is to choose an action/ stimuli to attend to, that can maximize the rewards from the world/ predictability of the world.  At any given time, there are many more stimuli to attend to and acts to indulge in than are the attentional/intentional resources required for the same and thus one has to choose between alternatives. Mathematicaly, different acts have different probabilities associated with them that they would lead to a rewarding state- this wave function needs to be collapsed such that only one act is actually intended. One way to do is my maximizing Utility (ExV) associated with different acts and choosing the maximal one always; another solution is to randomly choose an act from the given set  in accordance with  the probability distribution  that is a function of their utilities.I believe that instead of maximizers most of us are staisficers and especially in time-sensitive decisions go for an undeliberate choice that does'nt actually maximize the utility over all possible behavioral acts, but choses one of them randomly/probabilistically as per their prior known probabilities of rewards. Thus, we can be both maximizers as well as satisficers and which system we engage depends both on situational factors as well as our personality tendencies/ habits.

Anyway that was a lot of digression from the main line of argument. To continue with the digression for some more time, if one extends the analogy to attending to stimuli, on can either attend to stimuli that leads to greatest predictability (P= ExR) ;  or one can attend to a stimuli from a given set in accordance with a probability distribution that is a function of their prior predictabilities. again I haven't even got into Bayesian models where thing should get more complicated; suffice it to note for now that both attention-allocation and action-selection involve choosing an act / stimuli from a set.

A look at the Utility function of acts (U=ExV) and  Predictability function of stimuli (P = ExR) , immediately outlines the importance of dopamine in the above choosing mechanism as it encodes both (reward) expectancy as well as incentive salience/Value for acts;  on the attentional side of things, it should encode  both the strength of conditioned association (E) as well as (stimuli) Relevance for minimizing surprise. As such it should detect novelty in stimuli that can indicate that things have changed and the internal model needs updating. 

I also talked in my last post about a general energy level that leads to more propensity to indulge in operant acts and a general arousal level that leads to more propensity to attend to external stimuli. Today I want to elaborate on that concept using ADHD as a guide - ADHD has primarily two varieties (and in most general case both co-exist) - the inattentive type and the hyperactive-impulsive type. In the inattentive type, one is easily distracted or to put in my conceptualization - has a high baseline arousal leading to more frequent monitoring to the world/ external stimuli . The attention-reallocation happens faster than controls and may be triggered by irrelevant stimuli too. In the hyperactive-impulsive type,  one is overly active and impulsive or to put in mu conceptualization- has a high baseline energy level leading to more frequent shifts in activities and possibly triggering unvalued acts (impulses that are not really rewarding) .

It is important to note that dopamine and dopamine mediated regions like smaller PFC, cerebellum and basal ganglia, dopamine related genes like DAT1 and DRD4  and Ritalin that works primarily on dopamine have been implicated in ADHD.  All the above points to a dopamine signalling aberration in ADHD. Once one embraces the overarching framework of action-allocation and action-selection as similar in nature and possibly involving dopamine neurons, it is easy to see why ADHD children should have both hyperactive-impulsive and inattentive syndromes and subgroups.

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Child Psychology: The Mouse Trap turns 3

The Mouse Trap turns 3 today. It was exactly three years and 334 posts earlier that the Mouse Trap was born. The Mouse Trap has indeed learnt to walk on its own and has also developed adequate linguistic skills in the meantime. The toddler years are all but over, as it now becomes more playful and enters play age of early childhood. Already people are demanding that it not be developmentally delayed, but start indulging in rich imaginative pretend play with topics being requested like symbolic interactionsim and social epistemology.

Some stock taking and reality check is in store. The wiki page on toddler lists the following last milestones for 25-36 months and I hope the Mouse trap is doing fine. To recap:

1. Speaking in sentences: Hopefully the strands of mouse trap blog posts now form more cohesive sentences (like the theme of autism-psychosis, stage theories etc) and are not disjointed phrases and one-off utterances.
2. Ability to be independent to primary care giver: I hope that the reader partcipation has increased and with more reader participatory initiatives like Skribit suggestions, Google FriendConnect etc., the Mouse Trap is able to become more and more independent of its primary caregiver, that is me, and instead make deep attachments with other secondary caregivers like its prized readers and subscriber base.
3. Easily learns new words, places and people's names: Hopefully as the Mouse trap matures, it is learning to expand its horizons and foraying into topics left hitherto untouched; with better reader connect features , like twitter/Frinedfeed etc it is surely remembering peoples names and where they come form!
4. Anticipates routines: The mouse trap hopefully has learnt to anticipate the routine articles and topics that its readership likes to read and is doing a decent job on that score. do suggest your topics if the mouse trap doesn't anticipate them!
5. Toilet learning continues : Once th emouse trap might have been suffering from blogorrehea, but now it knows that passing motion (posting articles) once a week is adequate enough an dthat one should write a article only when one is full of it! There does exist scope for more routinized daily motion passing though!!
6. Plays with toys in imaginative ways: I am experimenting a lot with social media (my favorite web 2.0 toy) so as to engage more readers in a conversation. If you have any imaginative ideas of how to play with this toy, do let me know!!
7. Attempts to sing in-time with songs: Hopefully, the mouse trap has learnt to sing in tune with the zeitgeist of the day; though here I believe Mouse trap more has an original, unsynchronised with others voice and singing profile. Hope to change that and be more in sync with what others in the science blogosphere are singing (but definitely not the atheism/evolution debate which just bores me)

So, the Mouse trap is just about doing fine. It has been consistently featured in wikio top 100 science blogs, is amongst the top 5 blogs in India as ranked by Indiblogger.in, has a google page rank of 6 and has a subscriber base of close to 450 dedicated RSS feed subscribers, besides those that visit it daily on web via search. Also , the twitter followers of @sandygautam are increasing steadily and have reached 450 and the rate at which they are growing it seems they'll grow way beyond the Mouse trap feed subscribers. With micro-blogging and twitter/ FriendFeed, I have found a new way to share links and ideas and deepen conversations and connect with my readers, that was not possible with just the Mouse Trap.



I would also like to take this opportunity to encourage all feed subscribers to join me at twitter (@sandygautam) to keep up to date on links that I don't find exciting enough to write a blog post about or do not have much to add to, but which still are related to theme of what I write about and would make for a good read and need to be shared. I would also encourage new as well as veteran readers and subscribers, just for today,  to visit the mouse trap blog on the web and not in their feed readers (to celebrate its B'day, you are invited to the party at the web) so that they can become familiar with new social media tools I have put together on the Mouse Trap blog, like the 'recommended by readers' widget, the 'top posts by PostRank' widget or the 'suggest topics to write' widget.

Lastly as a primary caregiver, though my investment in the mouse trap has been more and my pride consequently in its progress has been immense; I must also thank all the other caregivers like you , the reader, or the peers like the other science blogs that have provided a safe and playful environment in which the Mouse Trap could flower or learn by peer play/ imitation learning. You all are a part and parcel of the Mouse Trap blog, so thanks everyone and take pride in your child's development and maturation and now that it becomes more independent come forward and supplant the primary caregiver and let it achieve its full potential! Amen!

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Read the Mouse Trap on your Kindle

Amazon today broadened their kindle blog offerings and I have used that facility to make the Mouse Trap blog feed available on the Kindle. I am not sure how many of the Mouse trap blog readers do indeed possess a kindle and whether they would find it useful to read the blog with a monthly subscription fee of 1.99 dollars; but there was no option to have a say and make the Mouse Trap blog feed available for free, so that's what we have ended up with. You can always continue to read the blog freely using other means, but experimenting with a 14 day free kindle trial may be a good idea! More details here.

If you do subscribe using kindle , do let me know your experience via the comments.

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synaptic plasticity: angelman's/autism and psychosis

There is a recent article in Nature Neuroscience by Philpot et al regarding how experience-dependent synaptic plasticity is downregulated in Angelmans' syndrome and perhaps in Autism too, as the Ube3a gene involved is implicated in both disorders.

First a little history about Angelman- it is a disorder caused by deletion/lack of a maternally imprinted UBE3a gene in chromosomal region 15q11-q13 . It is typically contrasted with Prader-Willi syndrome which is caused by a paternally imprinted gene malfunction in the same chromosomal region. Christopher Badcock has used this to contrast Autism (related to Angelman) and Psychosis (more common in PWS) to argue that Autism and Psychosis are due to a genomic imprinting tug of war between fathers and mothers genes.

I have written about Badcock's and Crespi's thesis before and how it fits in with my views on Autism and Psychosis; suffice it to say that I am seeing the new study primarily from this prism of Autism and Psychosis dichotomy.

First , let us see what the study tells us:

It uses mouse model that contains silenced maternal Ube3a genes (Ube3a m-p+ mouse), thus trying to make a mouse model of Angelman.

What it found was:

1)    Ube3a expression was markedly reduced in Ube3am-/p+ mice compared with wild-type mice in all three brain regions (visual neocortex, hippocampus,cerebellam). Consistent with previous observations, this attenuation was brain specific, as Ube3a was highly expressed in the liver of both Ube3am+/p- and Ube3am-/p+ mice.

2) To determine the physiological consequences of Ube3a loss on neocortical development, we examined the developmental acquisition of spontaneous excitatory synaptic transmission by recording miniature excitatory postsynaptic currents (mEPSCs) in layer 2/3 pyramidal neurons of visual cortex (see Supplementary Table 1 online for intrinsic membrane properties of recorded neurons). Consistent with previous findings24, 25, mEPSC amplitudes decreased and frequency increased during development in wild-type mice . Just before eye opening (postnatal day 10, P10), mEPSC frequency and amplitude were indistinguishable between wild-type and Ube3am-/p+ mice . Thereafter, mEPSC frequency failed to develop normally in Ube3am-/p+ mice

3)Although dark rearing had no measurable effect on mEPSC amplitude in wild-type mice at P25 , sensory deprivation strongly attenuated the normal developmental increase in mEPSC frequency in wild-type mice . In contrast, dark rearing did not affect mEPSC amplitude or frequency in Ube3am-/p+ mice. Consequently, mEPSC frequency in normally reared Ube3am-/p+ mice was not significantly different from that of dark-reared wild-type mice . These findings demonstrate that, although Ube3a is not necessary for the initial sensory-independent establishment of synaptic connectivity, it is selectively required for experience-dependent maturation of excitatory circuits.

4)We therefore compared the properties of neocortical long-term depression (LTD) and LTP at layer 2/3 synapses in visual cortex of wild-type and Ube3am-/p+ mice at both young (P25) and adult (P100) ages. Because layer 2/3 pyramidal neurons receive major inputs from layer 4 pyramidal neurons, layer 2/3 field potentials were evoked by layer 4 stimulation . We began by measuring LTD in young mice using a standard stimulation protocol (1 Hz for 15 min). Although LTD was reliably induced in young wild-type mice, it was absent in young Ube3am-/p+ mice . We also observed deficits in LTP induction. A relatively weak induction protocol (three 1-s trains of 40-Hz stimulation) elicited LTP in young wild-type mice, but failed to reliably induce LTP in young Ube3am-/p+ mice . To test whether the neocortex of Ube3am-/p+ mice was capable of expressing LTP, we also applied a strong LTP stimulation protocol (two 1-s trains of 100-Hz stimulation). This protocol consistently induced LTP in both Ube3am-/p+ and wild-type mice. Thus, as with LTP deficits in hippocampus8, 9, the LTP induction machinery is impaired in the visual cortex of Ube3am-/p+ mice and this deficit in LTP can be overcome with strong stimulation.

5)To determine whether the plasticity deficits in Angelman syndrome mice persisted into adulthood, we tested LTD and LTP in adults (P100). In adult wild-type mice, LTD induced by 1-Hz stimulation was absent, as expected27, whereas LTP could be induced with strong stimulation. In adult Ube3am-/p+ mice, however, neither of these protocols were effective at modifying synaptic strength. These results indicate that wild-type mice show attenuated neocortical plasticity as they mature and that this attenuation of plasticity is more severe in the absence of Ube3a . Furthermore, these data indicate that plasticity defects in Angelman syndrome mice persist into adulthood.

..and so on (go read the full paper)

In a nutshell, what they found was that in presence of visual stimuli, the plasticity (measured by LTP/LTD ) of visual cortex was adversely affected. As sensory stimulus would normally be available while developing, this would adversely affect the plasticity in adolescence/ critical periods and also continue into adulthood.

Thus, Autism/ Angelman are charechterised by less synaptic plasticity in adulthood and during critical development periods. Paradoxically, this loss of synaptic plasticity is concomitant on their it being experience-dependent or having sensory stimuli. If the organism is sensory deprived, it may still retain the normal synaptic plasticity exhibited by similar sensory deprived normal people.

How does this relate to Psychosis? If my thesis is correct that autism and Psychosis are opposites, then I would predict that in either prader-willi or in Psychosis (scheziphrenia etc) there should be excessive experience-dependent plasticity. I was glad to learn that I am not the first one to make that proposition, but someone back in 1995 has argued for Hippocampal synaptic plasticity as an endophenotyoe for Episodic Psychosis. I now quote heavily form that article.

Here is the abstract:

Structural change in the hippocampal formation has become popular as a proposed neurobiological substrate for schizophrenic disorders. It is postulated that behavioral plasticity in the form of long-term potentiation of hippocampal synaptic transmission is an attractive putative mechanism for the mediation of transient psychosis. Moreover, the disturbed hippocampal neuroarchitecture found in schizophrenic brain may be susceptible to potentiation and dysfunctional to the degree that delusions and hallucinations develop. Partial and selective blockade of the receptors mediating potentiation may prove to be an efficient means of preventing psychotic episodes and avoiding further damage to the involved network. Basic research, utilizing experimental models such as intraventricular kainic acid injection, may help to clarify the anatomical and physiological substrate of psychosis.

The Main thesis of the paper is:

1. Anatomical, physiological, pharmacological, and behavioral findings are most consistent with the view that neuropathological changes within the limbic system, specifically within the hippocampal formation, may represent a biological substrate of schizophrenia.

2. The biological mechanism underlying transient psychosis may be long-term potentiation (LTP) of synaptic transmission within the hippocampal formation.

3. The effects of dopamine manipulation on these behaviors may be mediated by direct actions on the compromised limbic system of the psychotic patient.

Further:

Associative plasticity within hippocampus occurs in the form of long-term potentiation (LTP), an experience-dependent increase in synaptic efficacy. Experimentally, LTP is produced by tetanic stimulation of afferent systems (Bliss and Lomo 1973) and has been shown to facilitate simple associative learning (Berger 1984) but disrupt more complex forms of associative plasticity (Robinson et al 1989). Hippocampal LTP has been observed to occur as a consequence of stimulus pairings in classical conditioning (Weisz et al 1984) and appears to be mediated by N-methyl-Daspartate (NMDA) receptors (Harris et al 1984). Pharmacological blockade of NMDA receptors has been shown to disrupt learning and memory in a variety of forms, including simple associations (Stillwell and Robinson 1990), spatial learning (Morris et al 1986; Heale and Harley 1990; Shapiro and Caramanos 1990), conditioned fear (Miserendino et al 1990; Kim et al 1991), olfactory memory (Staubli et al 1989) and gustatory memory (Welzl et al 1990). Some evidence, however, suggests that deficits involve motor impairment as well as disrupted learning (Keith and Rudy 1990)

Hippocampal function is particularly sensitive to neurochemical modulation, and the expression of monoamine receptors in the temporal lobe is altered in schizophrenics (Joyce 1993). Antipsychotics that reduce endogenous dopamine levels (Losonczy et al 1987) exert significant effects on the hippocampus and LTP. Trifluoperazine inhibits induction of LTP in hippocampus (Finn et al 1980), whereas the dopamine antagonist domperidone has been shown to prevent the maintenance of LTP (Frey et al 1990). Long-term effects of antipsychotic drugs include functional supersensitivity of hippocampal pyramidal neurons (Bijak and Smialowski 1989). Thus, individuals with deranged hippocampal neuroarchitecture would be prone to cognitive dysfunction (including, perhaps, perceptual distortion and other schizophrenic symptoms), differentially susceptible to stress, and responsive to amelioration of symptoms via dopamine antagonism. It may be more than coincidence that the time lag between administration of antipsychotic medication (which results in near immediate decrement in dopamine levels) and the attenuation of psychotic symptoms weeks later (Kane 1987) is remarkably consistent with the time parameters of LTP decay (Douglas and Goddard 1975). Also, the selective disruption of "weak" associative responses by antipsychotic drugs (van der Heyden and Bradford 1988) is consistent with interactions between NMDA-receptor blockade and stimulation intensity on induction of LTP (Reed and Robinson 1991).

From the above, at least to me, it is clear that anti-psychotics may work by decreasing LTP/LTD that is enhanced in episodic psychosis. A propensity towards increased experience-dependent enhancement of synaptic palsticty may be at work here and paradoxically the same approach of sensory deprivation, as in Angelman/ Autism may work here too.

Here is the summary:

In summary, potentiation of hippocampal synaptic transmission may be the neurophysiological basis of episodic psychosis. (Post [1993] has proposed a similar process in the amygdala as a useful model in understanding the progression of recurrent affective disorders.) More selective blockade of the NMDA receptor, which mediates LTP, may prove an effective means of attenuating positive symptoms and preventing further accrual of cellular damage in hippocampus.

In my own summation, I am convinced that we would find more synaptic plasticity in Psychotic people and that hyper-plasticity to hypo-plasticity is another dimension on which the autistics and psychotics differ and this again is a result of the genomic imprinting mediated tug-pf-war between the maternal and paternal genomes.


PORT, R., & SEYBOLD, K. (1995). Hippocampal synaptic plasticity as a biological substrate underlying episodic psychosis Biological Psychiatry, 37 (5), 318-324 DOI: 10.1016/0006-3223(94)00128-P
Koji Yashiro, Thorfinn T Riday, Kathryn H Condon, Adam C Roberts, Danilo R Bernardo, Rohit Prakash, Richard J Weinberg, Michael D Ehlers & Benjamin D Philpot (2009). Ube3a is required for experience-dependent maturation of the neocortex Nature Neuroscience

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