When I first heard of the book title ” Why Quitters Win: Decide to be excellent“, to say the least, I was very much intrigued. Was Nick trying to say something like stop doing something mid-way if you know that it is going to fail- and ignore the sunk costs…or was it about quitting when faced with unreasonable odds- rather than doubling your efforts and commitment. I believe in sticking with the choices you make, till you have given it your last shot, and so was slightly apprehensive.
However, what Nick Tasler means, is not about starting many things sequentially, and then quitting them early, if they are likely to fail; but what he means, instead, in a broad sense, is not starting off and getting absorbed in too many parallel threads, in the first place- but defining a theme or decision pulse and sticking with it and let it guide your day-to-day decisions; and also actively quitting doing the million other things that are not inline with that main theme / decision pulse.
To elaborate, the book is about advice in a business/ organizational scenario, where an organization, should spend time to spell out its one-time decision pulse- a guiding value that enables managers at all levels to determine for themselves as to whether the decision they will take will be for the good of the organization or not (is in harmony with the decision pulse or not). Seems like a reasonable and obvious advice , but only in hindsight. Practically, it’s very difficult to determine what exactly is/ should be one’s guiding value. And then what is even more difficult is to focus on that one value/ principle and *stop* doing/ being driven by other values/ value propositions.
Easier said then done. Nick proposes a three-step guiding cheat sheet: Know:( find out/ define your decision pulse); Think ( appraise action-plan in light of decision pulse and also taking alternative scenarios and contrary views into account.) and Do (execute by getting everyone aligned with single focus and take action rather than falling into the trap of making a decision either way by stalling or not acting/ deciding). And quitting other options/ burning bridges behind is important at each step. For e.g. your vision/ decision pulse cannot be vague or over-inclusive- it has to be sharp and concrete enough and focus on one thing and consciously exclude other options- so that it is useful when decisions involve tradeoffs between competing values- as they always do in real world scenarios. .Also, while its important to have action plans, its more important to have a non-action plan: given your new priorities and direction, what are the things you need to stop doing- given that taking up something new and fitting in your day-to-day schedule would force time away from some other activities. Lastly, when executing its best to leave plan B’s foreclosed- for success of plan A, Plan B and Plan C must be sacrificed.
Nick has enough evidence based studies to back his proposition, but the way he goes around elaborating these themes is by taking use of anecdotes and business case studies, which make for engaging reading. Illustrating for e.g. , how Starbucks , whose primary value proposition was being a coffee place, was sort of getting waylaid by having cheese sandwiches as breakfast, and whose cheesy aroma spoiled the coffee aroma, and how the Starbucks founder used the guiding value to put an end to the lucrative breakfast/ sandwich business to realign the Starbucks with its roots; is illuminating and makes the principles involved clear.
The book is full of such illuminating examples, which makes one see the power of these ‘quitting’ actions, in action and make one appreciate the theory and ideas in light of real world historical examples.
The book is an absorbing and light read, and is sure to grip you till the end. In the last chapter, Nick also elaborates how the same strategic framework can be applied to personal planning and self-development. He list support for some eight universal personal values and how one should ideally choose one of those values and let all one’s personal decision be guided by that value. I could fit those eight values in my ABCD and fundamental four frameworks and would like to spell them out here for the benefit of the readers:
they are sort of eight values, a pair slightly opposed to each other:
1. Security- Freedom (pain-pleasure Affect based polarity)
2. Stimulation- Authority (active – passive Behavior based polarity)
3. Achievement- Relationships (self-other Drive/ motivation based polarity)
4. Power – Humanity (broad- narrow Cognition based polarity)
Of course, this is just a peripheral part of what Nick’s book is about, but it resonated with me most.
Lastly, I am at a stage in my life, where , although I do have a guiding decision pulse i.e. ” anythings and everything that helps me achieve and leverage positive psychology based knowledge and interventions in workplace and school settings” I am still too broadly spread: for e.g I am doing a plethora of MOOCs ranging from topics related to management and leadership , to evolution and genetics, and to psychology and neuroscience. Also, I simultaneously manage a full-time job, read a lot of psychology books , do book reviews, am writing a psychology book of my own and have 3-4 active blogs, to which I should contribute on regular basis. I am planning on attending a 15-day cognition workshop in near future. On top of this I pride myself as curator and share stuff on scoop.it, twitter, Facebook etc. I definitely needed the advice Nick has so timely provided- to make a non-action plan and quit doing somethings.
It’s rare for me to proclaim books as life changing- but this book does seem to be right up the alley- I can’t vouch for you, but at least I am planning to apply its principles to my life in earnest- and am sure that it will be a life changing experience. Thanks Nick for writing this book and sharing it so graciously with me for review. Hope many more people get to be aware of your ideas and are able to apply them to their lives.
Bozo Sapiens :why to err is human, is a book that tries to document the frailties of our decision making process and the underlying psychological mechanisms behind them.
Written with a lay audience in mind,it is written in an easy to read manner and is fun to read. As per the site, it is in the tradition of books like Blink and Stumbling on happiness and plans to cater to the same market segment of people who are interested in psychology and how it affects day-to-day lives. while most of the psychology studies were already familiar to me, they would be novel for a lay audience and would definitely interest and entertain and also inform and guide. I,myself, cam across a few new and worthwhile studies and feel enriched having been made aware of them. As is prone to writing for a popular audience, the Kaplans often gloss over or do not highlight all the subtleties involved, but it must go to their credit that they are able to explain the studies lucidly and clearly,without significantly diluting on the scientese involved. the only peeve I have is that the sections and studies covered in them somehow felt unconnected and not flowing in a smooth manner from one to the other.
The organization of the chapters is decent- one chapter focusing on perceptual errors, another on action-based errors while yet another on errors based on group mentality. The section on perception seemed to me better and the section on groups perhaps the weakest. Despite its title it is not a bleak view of humanity and knowing our heuristics, biases and design features/bugs will only help us act better. It is an easy read and perhaps would be savored by those who do have a general interest in psychology; for the experts there are some nuggets spread here-and-there and that may make it worthwhile skimming through the book.
Disclaimer: I received a free e-copy of the book for review.
PS: would my readers like to see more book reviews featured on the mouse trap ? some books that I would love to review and highlight include books by Nettle : happiness, personality; gazzaniga: mind’s past riddley: genome, nature via nurture etc etc. Do let me kno wvia commnets/ skribit suggestions using left sidebar.
This is the fifth post in my ongoing series on major conscious and unconscious processes in the brain. For earlierparts, clickhere.
Today , I would like to point to a few physical models and theories of consciousness that have been proposed that show that consciousness still resides in the brain, although the neural/ supportive processes may be more esoteric.
I should forewarn before hand that all the theories involve advanced understanding of brains/ physics/ biochemistry etc and that I do not feel qualified enough to understand/ explain all the different theories in their entirety (or even have a surface understanding of them) ; yet , I believe that there are important underlying patterns and that applying the eight stage model to these approaches will only help us further understand and predict and search in the right directions. The style of this post is similar to the part 3 post on robot minds that delineated the different physical approaches that are used to implement intelligence/ brains in machines.
With that as a background, let us look at the major theoretical approaches to locate consciousness and define its underlying substrates. I could find six different physical hypothesis about consciousness on the Wikipedia page:
* Orch-OR theory
* Electromagnetic theories of consciousness
* Holonomic brain theory
* Quantum mind
* Space-time theories of consciousness
* Simulated Reality
Now let me briefly introduce each of the theories and where they seem to have been most successful; again I believe that though this time visually-normal people are perceiving the elephant, yet they are hooked on to its different aspects and need to bind their perspectives together to arrive at the real nature of the elephant.
The Orch OR theory combines Penrose’s hypothesis with respect to the Gödel theorem with Hameroff’s hypothesis with respect to microtubules. Together, Penrose and Hameroff have proposed that when condensates in the brain undergo an objective reduction of their wave function, that collapse connects to non-computational decision taking/experience embedded in the geometry of fundamental spacetime. The theory further proposes that the microtubules both influence and are influenced by the conventional activity at the synapses between neurons. The Orch in Orch OR stands for orchestrated to give the full name of the theory Orchestrated Objective Reduction. Orchestration refers to the hypothetical process by which connective proteins, known as microtubule associated proteins (MAPs) influence or orchestrate the quantum processing of the microtubules. Hameroff has proposed that condensates in microtubules in one neuron can link with other neurons via gap junctions. In addition to the synaptic connections between brain cells, gap junctions are a different category of connections, where the gap between the cells is sufficiently small for quantum objects to cross it by means of a process known as quantum tunnelling. Hameroff proposes that this tunnelling allows a quantum object, such as the Bose-Einstein condensates mentioned above, to cross into other neurons, and thus extend across a large area of the brain as a single quantum object. He further postulates that the action of this large-scale quantum feature is the source of the gamma (40 Hz) synchronisation observed in the brain, and sometimes viewed as a correlate of consciousness . In support of the much more limited theory that gap junctions are related to the gamma oscillation, Hameroff quotes a number of studies from recent year. From the point of view of consciousness theory, an essential feature of Penrose’s objective reduction is that the choice of states when objective reduction occurs is selected neither randomly, as are choices following measurement or decoherence, nor completely algorithmically. Rather, states are proposed to be selected by a ‘non-computable’ influence embedded in the fundamental level of spacetime geometry at the Planck scale. Penrose claimed that such information is Platonic, representing pure mathematical truth, aesthetic and ethical values. More than two thousand years ago, the Greek philosopher Plato had proposed such pure values and forms, but in an abstract realm. Penrose placed the Platonic realm at the Planck scale. This relates to Penrose’s ideas concerning the three worlds: physical, mental, and the Platonic mathematical world. In his theory, the physical world can be seen as the external reality, the mental world as information processing in the brain and the Platonic world as the encryption, measurement, or geometry of fundamental spacetime that is claimed to support non-computational understanding.
To me it seems that Orch OR theory is more suitable for forming platonic representations of objects – that is invariant/ideal perception of an object. This I would relate to the Perceptual aspect of A-consciousness.
The electromagnetic field theory of consciousness is a theory that says the electromagnetic field generated by the brain (measurable by ECoG) is the actual carrier of conscious experience. The starting point for these theories is the fact that every time a neuron fires to generate an action potential and a postsynaptic potential in the next neuron down the line, it also generates a disturbance to the surrounding electromagnetic (EM) field. Information coded in neuron firing patterns is therefore reflected into the brain’s EM field. Locating consciousness in the brain’s EM field, rather than the neurons, has the advantage of neatly accounting for how information located in millions of neurons scattered throughout the brain can be unified into a single conscious experience (sometimes called the binding problem): the information is unified in the EM field. In this way EM field consciousness can be considered to be ‘joined-up information’. However their generation by synchronous firing is not the only important characteristic of conscious electromagnetic fields — in Pockett’s original theory, spatial pattern is the defining feature of a conscious (as opposed to a non-conscious) field. In McFadden’s cemi field theory, the brain’s global EM field modifies the electric charges across neural membranes and thereby influences the probability that particular neurons will fire, providing a feed-back loop that drives free will.
To me, the EM filed theories seem to be right on track regarding the fact that the EM filed itself may modify / affect the probabilities of firing of individual neurons and thus lead to free will or sense of agency by in some sense causing some neurons to fire over others. I believe we can model the agency aspect of A-consciousness and find neural substrates of the same in brain, using this approach.
The holonomic brain theory, originated by psychologist Karl Pribram and initially developed in collaboration with physicist David Bohm, is a model for human cognition that is drastically different from conventionally accepted ideas: Pribram and Bohm posit a model of cognitive function as being guided by a matrix of neurological wave interference patterns situated temporally between holographic Gestalt perception and discrete, affective, quantum vectors derived from reward anticipation potentials. Pribram was originally struck by the similarity of the hologram idea and Bohm’s idea of the implicate order in physics, and contacted him for collaboration. In particular, the fact that information about an image point is distributed throughout the hologram, such that each piece of the hologram contains some information about the entire image, seemed suggestive to Pribram about how the brain could encode memories. According to Pribram, the tuning of wave frequency in cells of the primary visual cortex plays a role in visual imaging, while such tuning in the auditory system has been well established for decades. Pribram and colleagues also assert that similar tuning occurs in the somatosensory cortex. Pribram distinguishes between propagative nerve impulses on the one hand, and slow potentials (hyperpolarizations, steep polarizations) that are essentially static. At this temporal interface, he indicates, the wave interferences form holographic patterns.
To me, the holnomic approach seems to be the phenomenon lying between gestalt perception and quantum vectors derived from reward-anticipation potentials or in simple English between the perception and agency components of A-consciousness. this is the Memory aspect of A-consciousness. The use of hologram used to store information as a model, the use of slow waves that are tuned to carry information, the use of this model to explain memory formation (including hyperpolarization etc) all point to the fact that this approach will be most successful in explaining the autobiographical memory that is assited wuith A-cosnciousness.
The quantum mind hypothesis proposes that classical mechanics cannot fully explain consciousness and suggests that quantum mechanical phenomena such as quantum entanglement and superposition may play an important part in the brain’s function and could form the basis of an explanation of consciousness. Recent papers by physicist, Gustav Bernroider, have indicated that he thinks that Bohm’s implicate-explicate structure can account for the relationship between neural processes and consciousness. In a paper published in 2005 Bernroider elaborated his proposals for the physical basis of this process. The main thrust of his paper was the argument that quantum coherence may be sustained in ion channels for long enough to be relevant for neural processes and the channels could be entangled with surrounding lipids and proteins and with other channels in the same membrane. Ion channels regulate the electrical potential across the axon membrane and thus play a central role in the brain’s information processing. Bernroider uses this recently revealed structure to speculate about the possibility of quantum coherence in the ion channels. Bernroider and co-author Sisir Roy’s calculations suggested to them that the behaviour of the ions in the K channel could only be understood at the quantum level. Taking this as their starting point, they then ask whether the structure of the ion channel can be related to logic states. Further calculations lead them to suggest that the K+ ions and the oxygen atoms of the binding pockets are two quantum-entangled sub-systems, which they then equate to a quantum computational mapping. The ions that are destined to be expelled from the channel are proposed to encode information about the state of the oxygen atoms. It is further proposed the separate ion channels could be quantum entangled with one another.
To me, the quantum entanglement (or bond between different phenomenons)and the encoding of information about the state of the system in that entanglement seems all too similar to feelings as information about the emotional/bodily state. Thus, I propose that these quantum entanglements in these ion-channels may be the substrate that give rise to access to the state of the system, thus giving rise to feelings that is the feeling component of A-consciousness i.e access to one’s own emotional states.
Space-time theories of consciousness have been advanced by Arthur Eddington, John Smythies and other scientists. The concept was also mentioned by Hermann Weyl who wrote that reality is a “…four-dimensional continuum which is neither ‘time’ nor ‘space’. Only the consciousness that passes on in one portion of this world experiences the detached piece which comes to meet it and passes behind it, as history, that is, as a process that is going forward in time and takes place in space”. In 1953, CD Broad, in common with most authors in this field, proposed that there are two types of time, imaginary time measured in imaginary units (i) and real time measured on the real plane. It can be seen that for any separation in 3D space there is a time at which the separation in 4D spacetime is zero. Similarly, if another coordinate axis is introduced called ‘real time’ that changes with imaginary time then historical events can also be no distance from a point. The combination of these result in the possibility of brain activity being at a point as well as being distributed in 3D space and time. This might allow the conscious individual to observe things, including whole movements, as if viewing them from a point. Alex Green has developed an empirical theory of phenomenal consciousness that proposes that conscious experience can be described as a five-dimensional manifold. As in Broad’s hypothesis, space-time can contain vectors of zero length between two points in space and time because of an imaginary time coordinate. A 3D volume of brain activity over a short period of time would have the time extended geometric form of a conscious observation in 5D. Green considers imaginary time to be incompatible with the modern physical description of the world, and proposes that the imaginary time coordinate is a property of the observer and unobserved things (things governed by quantum mechanics), whereas the real time of general relativity is a property of observed things. These space-time theories of consciousness are highly speculative but have features that their proponents consider attractive: every individual would be unique because they are a space-time path rather than an instantaneous object (i.e., the theories are non-fungible), and also because consciousness is a material thing so direct supervenience would apply. The possibility that conscious experience occupies a short period of time (the specious present) would mean that it can include movements and short words; these would not seem to be possible in a presentist interpretation of experience. Theories of this type are also suggested by cosmology. The Wheeler-De Witt equation describes the quantum wave function of the universe (or more correctly, the multiverse).
To me, the space-time theories of consciousness that lead to observation/consciousness from a point in the 4d/5d space-time continuum seem to mirror the identity formation function of stage 5.This I relate to evaluation /deliberation aspect of A-consciousness.
In theoretical physics, digital physics holds the basic premise that the entire history of our universe is computable in some sense. The hypothesis was pioneered in Konrad Zuse’s book Rechnender Raum (translated by MIT into English as Calculating Space, 1970), which focuses on cellular automata. Juergen Schmidhuber suggested that the universe could be a Turing machine, because there is a very short program that outputs all possible programmes in an asymptotically optimal way. Other proponents include Edward Fredkin, Stephen Wolfram, and Nobel laureate Gerard ‘t Hooft. They hold that the apparently probabilistic nature of quantum physics is not incompatible with the notion of computability. A quantum version of digital physics has recently been proposed by Seth Lloyd. None of these suggestions has been developed into a workable physical theory. It can be argued that the use of continua in physics constitutes a possible argument against the simulation of a physical universe. Removing the real numbers and uncountable infinities from physics would counter some of the objections noted above, and at least make computer simulation a possibility. However, digital physics must overcome these objections. For instance, cellular automata would appear to be a poor model for the non-locality of quantum mechanics.
To me the simulation argument is one model of us and the world- i.e we are living in a dream state/ simulation/ digital world where everything is synthetic/ predictable and computable. The alternative view of world as real, analog, continuous world where everything is creative / unpredictable and non-computable. One can , and should have both the models in mind – a simulated reality that is the world and a simulator that is oneself. Jagat mithya, brahma sach. World (simulation) is false, Brahma (creation) is true . Ability to see the world as both a fiction and a reality at the same time, as a fore laid stage and as a creative jazz at the same time leads to this sixth stage of consciousness the A-consciousness of an emergent conscious self that is distinct from mere body/brain. One can see oneself and others as actors acting as per their roles on the world’s stage; or as agents co-creating the reality.
That should be enough for today, but I am sure my astute readers will take this a notch further and propose two more theoretical approaches to consciousness and perhaps look for their neural substrates basde on teh remianing tow stages and componenets of A-consciousness..
This is the part 4 of the multi–partseries on conscious and unconscious processes in the brain.
I’ll like to start with a quote from the Mundaka Upanishads:
Two birds, inseparable friends, cling to the same tree. One of them eats the sweet fruit, the other looks on without eating.
On the same tree man sits grieving, immersed, bewildered, by his own impotence. But when he sees the other lord contented and knows his glory, then his grief passes away.
Today I plan to delineate the major conscious processes in the brain, without bothering with their neural correlates or how they are related to unconscious processes that I have delineated earlier. Also I’ll be restricting the discussion mostly to the easy problem of Access or A- consciousness. leaving the hard problem of phenomenal or P-consciousness for later.
The contents of consciousness include the immediate perceptual world; inner speech and visual imagery; the fleeting present and its fading traces in immediate memory; bodily feelings like pleasure, pain, and excitement; surges of feeling; autobiographical events when they are remembered; clear and immediate intentions, expectations and actions; explicit beliefs about oneself and the world; and concepts that are abstract but focal. In spite of decades of behaviouristic avoidance, few would quarrel with this list today.
With this background, let me delineate the major conscious processes/ systems that make up the A-consciousness as per me:-
Perceptual system: Once the spotlight of attention is available, it can be used to bring into focus the unconscious input representations that the brain is creating. Thus a system may evolve that has access to information regarding the sensations that are being processed or in other words that perceives and is conscious of what is being sensed. To perceive is to have access to ones sensations. In Tarts model , it is the input-processing module that ‘sees’ meaningful stimuli and ignores the rest / hides them from second-order representation. This is Baars immediate perceptual world.
Agency system: The spotlight of attention can also bring into foreground the unconscious urges that propel movement. This access to information regarding how and why we move gives rise to the emergence of A-consciousness of will/ volition/agency. To will is to have access to ones action-causes. In tarts model , it is the motor output module that enables sense of voluntary movement. In Baars definition it is clear and immediate intentions, expectations and actions.
Memory system: The spotlight of attention may also bring into focus past learning. This access to information regarding past unconscious learning gives rise to A-consciousness of remembering/ recognizing. To remember is to have access to past learning. The Tart subsystem for the same is Memory and Baars definition is autobiographical events when they are remembered.
Feeling (emotional/ mood) system: The spotlight of attention may also highlight the emotional state of the organism. An information about one’s own emotional state gives rise to the A-consciousness of feelings that have an emotional tone/ mood associated. To feel is to have access to ones emotional state. The emotions system of Tart and Baars bodily feelings like pleasure, pain, and excitement; surges of feeling relate to this.
Deliberation/ reasoning/thought system: The spotlight of attention may also highlight the decisional and evaluative unconscious processes that the organism indulges in. An information about which values guided decision can lead to a reasoning module that justifies the decisions and an A-consciousness of introspection. To think is to have access to ones own deliberation and evaluative process. Tarts evaluative and decision making module is for the same. Baars definition may be enhanced to include intorspection i.e access to thoughts and thinking (remember Descartes dictum of I think therefore I am. ) as part of consciousness.
Modeling system that can differentiate and perceive dualism: The spotlight of attention may highlight the dual properties of the world (deterministic and chaotic ). An information regarding the fact that two contradictory models of the world can both be true at the same time, leads to modeling of oneslf that is different from the world giving rise to the difference between ‘this’ and ‘that’ and giving rise to the sense of self. One models both the self and the world based on principles/ subsystems of extereocpetion and interoception and this give rise to A-consciousness of beliefs about the self and the world. To believe is to have access to one’s model of something. One has access to a self/ subjectivity different from world and defined by interoceptive senses ; and a world/ reality different from self defined by exterioceptive senses. The interocpetive and exteroceptive subsystems of Tart and Baars explicit beliefs about oneself and the world are relevant here. This system give rise to the concept of a subjective person or self.
Language system that can report on subjective contents and propositions. The spotlight of awareness may verbalize the unconscious communicative intents and propositions giving rise to access to inner speech and enabling overt language and reporting capabilities. To verbally report is to have access to the underlying narrative that one wants to communicate and that one is creating/confabulating. This narrative and story-telling capability should also in my view lead to the A-consciousness of the stream of consciousness. This would be implemented most probably by Tart’s unconscious and space/time sense modules and relates to Baars the fleeting present and its fading traces in immediate memory- a sense of an ongoing stream of consciousness. To have a stream of consciousness is to have access to one’s inner narrative.
Awareness system that can bring into focal awareness the different conscious process that are seen as coherent. : the spotlight of attention can also be turned upon itself- an information about what all processes make a coherent whole and are thus being attended and amplified gives rise to a sense of self-identity that is stable across time and unified in space. To be aware is to have access to what one is attending or focusing on or is ‘conscious’ of. Tarts Sense of identity subsystem and Baars concepts that are abstract but focal relate to this. Once available the spotlight of awareness opens the floodgates of phenomenal or P-consciousness or experience in the here-and-now of qualia that are invariant and experiential in nature. That ‘feeling of what it means to be’ of course is the subject matter for another day and another post!
This article continues my series on major conscious and unconscious processes in the brain. In my lasttwo 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.
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-
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).
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) .
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.
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.
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.
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.
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.
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.
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 .
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.
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.
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.
I have recently blogged a bit about action-selection and operant learning, emphasizing that the action one chooses, out of many possible, is driven by maximizing the utility function associated with the set of possible actions, so perhaps a quick read of last few posts would help appreciate where I come from .
To recap, whenever an organism makes a decision to indulge in an act (an operant behavior), there are many possible actions from which it has to choose the most appropriate one. Each action leads to a possibly different Outcome and the organism may value the outcomes differentially. this valuation may be both objective (how the organism actually ‘likes’ the outcome once it happens, or it may be subjective and based on how keenly the organism ‘wants’ the outcome to happen independent on whether the outcome is pleasurable or not. Also, it is never guaranteed that the action would produce the desired/expected outcome. There is always some probability associated that the act may or may not result in the expected outcome. Also, on a macro level the organism may lack sufficient energy required to indulge in the act or to carry it out successfully to completion. Mathematically, with each action one can associate a utility U= E x V (where U is utility of act; E is expectancy as to whether one would be able to carry the act and if so whether the act would result in desired outcome; and V is the Value (both subjective and objective0 that one has assigned to the outcome. The problem of action-selection then is simply to maximize the utility given different acts n and to choose the action with maximum utility.
Today I had an epiphany; doesn’t the same logic apply to allocating attention to the various stimuli that bombard us. Assuming a spotlight view of attention, and assuming that there are limited attentional resources, one is constantly faced with the problem of finding which stimuli in the world are salient and need to be attended to. Now, the leap I am making is that attention-allocation just like choosing to act volitionally is an operant and not a reactive, but pro-active process. It may be unconscious, but still it involves volition and ‘choosing’. Remember, that even acts can be reactive and thus there is room for reactive attention; but what I am proposing is that the majority of attention is pro-active- actively choosing between stimuli and focusing on one to try and better predict the world. We are basically prediction machines that want to predict beforehand the state of the world that is most relevant to us and this we do by classical or pavlovian conditioning. We try to associate stimuli (CS) with stimuli(UCS) or response (UCR) and thus try to ascertain what state of world at time T would be given that stimulus (CS) has happened. Apart from prediction machines we are also Agents that try to maximize rewards and minimize punishments by acting on this knowledge and acting and interacting with the world. There are thousands of actions we can indulge in- but we choose wisely; there are thousands of stimuli in the external world, but we attend to salient features wisely.
Let me elaborate on the analogy. While selecting an action we maximize reward and minimize punishment, basically we choose the maximal utility function; while choosing which stimuli to attend to we maximize our foreknowledge of the world and minimize surprises, basically we choose the maximal predictability function; we can even write an equivalent mathematical formula: Predictability P = E x R where P is the increase in predictability due to attending to stimulus 1 ; E is probability that stimulus 1 correctly leads to prediction of stimulus 2; and R is the Relevance of stimulus 2(information) to us. Thus the stimulus one would attend, is the one that leads to maximum gain in predictability. Also, similar to the general energy level of organism that would bias as to whether, and how much, the organism acts or not; there is a general arousal level of the organism that biases whether and how much it would attend to stimuli.
So, what new insights do we gain from this formulation? First insight we may gain is by elaborating the analogy further. We know that basal ganglia in particular and dopamine in general is involved in action-selection. Dopamine is also heavily involved in operant learning. We can predict that dopamine systems , and the same underlying mechanisms, may also be used for attention-allocation. Dopamine may also be heavily involved in classical learning as well. Moreover, the basic computations and circuitry involved in allocating attention should be similar to the one involved in action-selection. Both disciplines can learn from each other and utilize methods developed in one field for understanding and elaborating phenomenon in the other filed. For eg; we know that dopamine while coding for reward-error/ incentive salience also codes for novelty and is heavily involved in novelty detection. Is the novelty detection driven by the need to avoid surprises, especially while allocating attention to a novel stimulus.
What are some of the prediction we can make form this model: just like the abundant literature on U= E x V in decision making and action selection literature, we should be able to show the independent and interacting effects of Expectancy and Relevance on attention-grabbing properties of stimulus. The relevance of different stimuli can be manipulated by pairing them with UCR/UCS that has different degrees of relevance. The expectancy can be differentially manipulated by the strength of conditioning; more trials would mean that the association between the CS and UCS is strong; also the level of arousal may bias the ability to attend to stimuli. I am sure that there is much to learn in attention research from the research on decision-making and action-selection and the reverse would also be true. It may even be that attention-allocation is actually conceptualized in the above terms; if so I plead ignorance of knowledge of this sub-field and would love to get a few pointers so that I can refine my thinking and framework.
Also consider the fact that there is already some literature implicating dopamine in attention and the fact that dopamine dysfunction in schizophrenia, ADHD etc has cognitive and attentional implications is an indication in itself. Also, the contextual salience of drug-related cues may be a powerful effect of dapomine based classical conditioning and attention allocation hijacking the normal dopamine pathways in addicted individuals.
Lastly, I got set on this direction while reading an article on chaining of actions to get desired outcomes and how two different brain systems ( a cognitive (Prefrontal) high road one based on model-based reinforcement learning and a unconscious low road one (dorsolateral striatal) based on model-free reinforcement learning)may be involved in deciding which action to choose and select. I believe that the same conundrum would present itself when one turns attention to the attention allocation problem, where stimuli are chained together and predict each other in succession); I would predict that there would be two roads involved here too! but that is matter for a future post. for now, would love some honest feedback on what value, if any, this new conceptualization adds to what we already know about attention allocation.
Daniel Nettle, writes an article in Journal Of Theoretical Biology about the evolution of low mood states. Before I get to his central thesis, let us review what he reviews:
Low mood describes a temporary emotional and physiological state in humans, typically characterised by fatigue, loss of motivation and interest, anhedonia (loss of pleasure in previously pleasurable activities), pessimism about future actions, locomotor retardation, and other symptoms such as crying. … This paper focuses on a central triad of symptoms which are common across many types of low mood, namely anhedonia, fatigue and pessimism. Theorists have argued that, whereas their opposites facilitate novel and risky behavioural projects. These symptoms function to reduce risk-taking. They do this, proximately, by making the potential payoffs seem insufficiently rewarding (anhedonia), the energy required seem too great (fatigue), or the probability of success seem insufficiently high (pessimism). An evolutionary hypothesis for why low mood has these features, then, is that is adaptive to avoid risky behaviours when one is in a relatively poor current state, since one would not be able to bear the costs of unsuccessful risky endeavors at such times .
I would like to pause here and note how he has beautifully summed up the low mood symptoms and key features; taking liberty to define using my own framework of Value X Expectancy and distinction between cognitive(‘wanting’) and behavioral (‘liking’) side of things :
Anhedonia: behavioral inability to feel rewarded by previously pleasurable activities. Loss of ‘liking’ following the act. Less behavioral Value assigned.
Loss of motivation and interest: cognitive inability to look forward to or value previously desired activities. Loss of ‘wanting’ prior to the act. Less cognitive Value assigned.
Fatigue: behavioral inability to feel that one can achieve the desired outcome due to feelings that one does not have sufficient energy to carry the act to success. Less behavioral Expectancy assigned.
Pessimism: cognitive inability to look forward to or expect good things about the future or that good outcomes are possible. Less cognitive Expectancy assigned.
The reverse conglomeration is found in high mood- High wanting and liking, high energy and outlook. Thus, I agree with Nettle fully that low mood and high mood are defined by these opposed features and also that these features of low and high mood are powerful proximate mechanisms that determine the risk proneness of the individual: by subjectively manipulating the Value and Expectancy associated with an outcome, the high and low mood mediate the risk proneness that an organism would display while assigning a utility to the action. Thus, it is fairly settled: if ultimate goal is to increase risk-prone behavior than the organism should use the proximate mechanism of high mood; if the ultimate goal is to avoid risky behavior, then the organism should display low mood which would proximately help it avoid risky behavior.
Now let me talk about Nettle’s central thesis. It has been previously proposed in literature that low mood (and thus risk-aversion) is due to being in a poor state wherein one can avoid energy expenditure (and thus worsening of situation) by assuming a low profile. Nettle plays the devil’s advocate and argues that an exactly opposite argument can be made that the organism in a poor state needs to indulge in high risk (and high energy) activities to get out of the poor state. Thus, there is no a prior reason as to why one explanation may be more sound than the other. To find out when exactly high risk behavior pay off and when exactly low risk behaviors are more optimal, he develops a model and uses some elementary mathematics to derive some conclusions. He, of course , bases his model on a Preventive focus, whereby the organism tries to minimize getting in a state R , which is sub-threshold. He allows the S(t) to be maximized under the constraint that one does not lose sight of R. I’ll not go into the mathematics, but the results are simple. When there is a lot of difference between R (dreaded state) and S (current state), then the organism adopts a risky behavioral profile. when the R and S are close, he maintains low risk behavior, however when he is in dire circumstances (R and S are very close) then risk proneness again rises to dramatic levels. To quote:
The model predicts that individuals in a good state will be prepared to take relatively large risks, but as their state deteriorates, the maximum riskiness of behaviour that they will choose declines until they become highly risk-averse. However, when their state becomes dire, there is a predicted abrupt shift towards being totally risk-prone. The switch to risk-proneness at the dire end of the state continuum is akin to that found near the point of starvation in the original optimal foraging model from which the current one is derived (Stephens, 1981). The graded shift towards greater preferred risk with improving state is novel to this model, and stems from the stipulation that if the probability of falling into the danger zone in the next time step is minimal, then the potential gain in S at the next time step should be maximised. However, a somewhat similar pattern of risk proneness in a very poor state, risk aversion in an intermediate state, and some risk proneness in a better state, is seen in an optimal-foraging model where the organism has not just to avoid the threshold of starvation, but also to try to attain the threshold of reproduction (McNamara et al., 1991). Thus, the qualitative pattern of results may emerge quite generally from models using different assumptions.
Nettle, then extrapolates the clinical significance from this by proposing that ‘agitated’ / ‘excited’ depression can be explained as when the organism is in dire straits and has thus become risk-prone. He also uses a similar logic for dysphoric mania although I don’t buy that. However, I agree that euphoric mania may just be the other extreme of high mood and more risk proneness and goal achievements; while depression the normal extreme of low mood and adverse circumstances and risk aversion. To me this model ties up certain things we know about life circumstances and the risk profile and mood tone of people and contributes to deepening our understanding. Nettle, D. (2009). An evolutionary model of low mood states Journal of Theoretical Biology, 257 (1), 100-103 DOI: 10.1016/j.jtbi.2008.10.033
The hedonic principle says that we are motivated to approach pleasure and avoid pain. This, as per Higgins is too simplistic a formulation. He supplants this with his concepts of regulatory focus, regulatory anticipation and regulatory reference. That is too much of jargon for a single post, but let us see if we can make sense.
First, let us conceptualize a desired end-state that an organism wants to be in- say eating food and satisfying hunger. This desired end-state becomes the current goal of the organism and leads to gold-directed behavior. Now, it is proposed that given this desired end-state, the organism has two ways to go about achieving or moving towards the end-state. If the organism has promotion or achievement self-regulation focus, then it will be more sensitive to whether the positive outcome is achieved or not and will thus have an approach orientation whereby it would try to match his next state to the desired state or try approaching the desired end-sate as close as possible. On the other hand, if the organism has a prevention or safety self-regulation focus, then it will be more sensitive to the negative outcome as to whether it becomes worse off after the behavior and will have an avoidance orientation whereby it would try to minimize the mismatch between his next state and the desired state. Thus given n next states with different food availability , the person with promotion focus will choose a next state that is as close, say within a particular threshold, to the desired state of satiety ; while the person with the prevention focus will be driven by avoiding all the sates that have a sub-threshold food availability and are thus mis-matched with the end-goal of satiety. thus, the number and actual states which are available for choosing form are different for the two groups: the first set is derived from whether the states are within a particular range of the end-state; the second set is derived from excluding all the states that are not within a particular range of the end-state. Put this way it is easy to see, that these strategies of promotion or prevention focus, place different cognitive and computational demands: the former requires explortation/ maximizing, the other may be satisfied by satisficing. (see my earlier post on exploration/ exploitation and satisficers / maximisers where I believe I was slightly mistaken).
Now, that I have explained in simple terms (hopefully) the concepts of self-regulatory focus, let me quote from the article and show how Higgins arrives at the same.
The theory of self-regulatory focus begins by assuming that the hedonic principle should operate differently when serving fundamentally different needs, such as the distinct survival needs of nurturance (e.g., nourishment) and security (e.g., protection). Human survival requires adaptation to the surrounding environment, especially the social environment (see Buss, 1996). To obtain the nurturance and security that children need to survive, children must establish and maintain relationships with caretakers who provide them with nurturance and security by supporting, encouraging, protecting, and defending them (see Bowlby, 1969, 1973). To make these relationships work, children must learn how their appearance and behaviors influence caretakers’ responses to them (see Bowlby, 1969; Cooley, 1902/1964; Mead, 1934; Sullivan, 1953). As the hedonic principle suggests,children must learn how to behave in order to approach pleasure and avoid pain. But what is learned about regulating pleasure and pain can be different for nurturance and security needs. Regulatory-focus theory proposes that nurturance-related regulation and security-related regulation differ in regulatory focus. Nurturance-related regulation involves a promotion focus, whereas security related regulation involves a prevention focus. ….. People are motivated to approach desired end-states, which could be either promotion-focus aspirations and accomplishments or prevention-focus responsibilities and safety. But within this general approach toward desired end-states, regulatory focus can induce either approach or avoidance strategic inclinations. Because a promotion focus involves a sensitivity to positive outcomes (their presence and absence), an inclination to approach matches to desired end-states is the natural strategy for promotion self-regulation. In contrast, because a prevention focus involves a sensitivity to negative outcomes (their absence and presence), an inclination to avoid mismatches to desired end-states is the natural strategy for prevention self-regulation (see Higgins, Roney, Crowe, & Hymes, 1994).
Figure 1 (not shown here, go read the article for the figure) summarizes the different sets of psychological variables discussed thus far that have distinct relations to promotion focus and prevention focus (as well as some variables to be discussed later). On the input side (the left side of Figure 1), nurturance needs, strong ideals, and situations involving gain-nongain induce a promotion focus, whereas security needs, strong oughts, and situations involving nonloss-loss induce a prevention focus. On the output side (the right side of Figure 1), a promotion focus yields sensitivity to the presence or absence of positive outcomes and approach as strategic means, whereas a prevention focus yields sensitivity to the absence or presence of negative outcomes and avoidance as strategic means.
Higgins then goes on describing many experiments that support this differential regulations focus and how that is different from pleasure-pain valence based approaches. He also discusses the regulatory focus in terms of signal detection theory and here it is important to note that promotion focus leads to leaning towards (being biased towards) increasing Hits and reducing Misses ; while prevention focus means leaning more towards increasing correct rejections and reducing or minimizing false alarms. Thus,a promotion focus individual is driven by finding correct answers and minimizing errors of omission; while a preventive focused person is driven by avoiding incorrect answers and minimizing errors of commission. In Higgin’s words:
Individuals in a promotion focus, who are strategically inclined to approach matches to desired end-states, should be eager to attain advancement and gains. In contrast, individuals in a prevention focus, who are strategically inclined to avoid mismatches to desired end-states, should be vigilant to insure safety and nonlosses. One would expect this difference in self-regulatory state to be related to differences in strategic tendencies. In signal detection terms (e.g., Tanner & Swets, 1954; see also Trope & Liberman, 1996), individuals in a state of eagerness from a promotion focus should want, especially, to accomplish hits and to avoid errors of omission or misses (i.e., a loss of accomplishment). In contrast, individuals in a state of vigilance from a prevention focus should want, especially, to attain correct rejections and to avoid errors of commission or false alarms (i.e., making a mistake). Therefore, the strategic tendencies in a promotion focus should be to insure hits and insure against errors of omission, whereas in a prevention focus, they should be to insure correct rejections and insure against errors of commission .
He next discusses Expectancy x Value effects in utility research. Basically , whenever one tries to decide between two or more alternative actions/ outcomes, one tries to find the utility of a particular decision/ behavioral act based on both the value and expectance of the outcome. Value means how desirable or undesirable (i.e what value is attached) that outcome is to that person. Expectancy means how probable it is that the contemplated action (that one is deciding to do) would lead to the outcome. By way of an example: If I am hungry, I want to eat food. Lets say there are two actions or decisions that have different utility that can lead to my hunger reduction. The first involves begging for food from the shopkeeper; the second involves stealing the food from the shopkeeper. The first may be having positive value (begging might not be that embarrassing) , but low expectancy (the shopkeeper is miserly and unsympathetic) ; while the second act may have negative value (I believe that stealing is wrong and would like to avoid that act) but high expectancy (I am sure I’ll be able to steal the food and fulfill my hunger). the utility I impart to the two acts may determine what act I eventually decide to indulge in.
Higgins touches on research that showed that Expectancy X value have a multiplicative effect i.e as expectancy increases, and value increases the motivation to take that decision/ course of action increases non-linearly. He clarifies that this interaction effect is seen in promotion focus , but not in preventive focus:
Expectancy-value models of motivation assume not only that expectancy and value have an impact on goal commitment as independent variables but also that they combine multiplicatively (Lewin, Dembo, Festinger, & Sears, 1944; Tolman, 1955; Vroom, 1964; for a review, see Feather, 1982). The multiplicative assumption is that as either expectancy or value increases, the impact of the other variable on commitment increases. For example, it is assumed that the effect on goal commitment of higher likelihood of goal attainment is greater for goals of higher value. This assumption reflects the notion that the goal commitment involves a motivation to maximize the product of value and expectancy, as is evident in a positive interactive effect of value and expectancy. This maximization prediction is compatible with the hedonic or pleasure principle because it suggests that people are motivated to attain as much pleasure as possible. Despite the almost universal belief in the positive interactive effect of value and expectancy, not all studies have found this effect empirically (see Shah & Higgins, 1997b). Shah and Higgins proposed that differences in the regulatory focus of decision makers might underlie the inconsistent findings in the literature. They suggested that making a decision with a promotion focus is more likely to involve the motivation to maximize the product of value and expectancy. A promotion focus on goals as accomplishments should induce an approach-matches strategic inclination to pursue highly valued goals with the highest expected utility, which maximizes Value × Expectancy. Thus, the positive interactive effect of value and expectancy assumed by classic expectancy-value models should increase as promotion focus increases. But what about a prevention focus? A prevention focus on goals as security or safety should induce an avoid-mismatches strategic inclination to avoid all unnecessary risks by striving to meet only responsibilities that are clearly necessary. This strategic inclination creates a different interactive relation between value and expectancy. As the value of a prevention goal increases, the goal becomes a necessity, like the moral duties of the Ten Commandments or the safety of one’s child. When a goal becomes a necessity, one must do whatever one can to attain it, regardless of the ease or likelihood of goal attainment. That is, expectancy information becomes less relevant as a prevention goal becomes more like a necessity. With prevention goals, motivation would still generally increase when the likelihood of goal attainment is higher, but this increase would be smaller for high-value goals (i.e., necessities) than low-value goals. Thus, the second prediction was that the positive interactive effect of value and expectancy assumed by classic expectancy value models would not be found as prevention focus increased. Specifically, as prevention focus increases, the interactive effect of value and expectancy should be negative.
And that is exactly what they found! the paper touches on many other corroborating readers and the interested reader can go to the source for more. Here I will now focus on his concepts of regulatory expectancy and regulatory reference.
Regulatory Reference is the tendency to be either driven by positive and desired end-states as a reference end-point and a goal; or to be driven by negative and undesired end-states as goals that are most prominent. For example, eating food is a desirable end-state; while being eaten by others is a undesired end-sate. now an organism may be driven by the end-sate of ‘getting food’ and thus would be regulating approach behavior of how to go about getting food. It is important to contrast this with regulatory focus; while searching for food, it may have promotion orientation focusing on matching the end state; or may have prevention focus i.e avoiding states that don’t contain food; but it is still driven by a ‘positive’ or desired end-state. On the other hand, when the regulatory reference is a negative or undesirable end-state like ‘becoming food’, then avoidance behavior is regulated i.e. behavior is driven by avoiding the end-state. Thus, any state that keeps one away from ‘being eaten’ is the one that is desired; this may involve promotion focus as in approaching states that are opposite of the undesired state and provide safety from predator; or it may have a prevention focus as in avoiding states that can lead one closer to the undesired end-state. In words of Higgins:
Inspired by these latter models in particular, Carver and Scheier (1981, 1990) drew an especially clear distinction between self-regulatory systems that have positive versus negative reference values. A self-regulatory system with a positive reference value has a desired end state as the reference point. The system is discrepancy reducing and involves attempts to move one’s (represented) current self-state as close as possible to the desired end-state. In contrast, a self-regulatory system with a negative reference value has an undesired end-state as the reference point. This system is discrepancy-amplifying and involves attempts to move the current self-state as far away as possible from the undesired end-state.
To me Regulatory Reference is similar to Value associated with a utility decision and determines whether when we are choosing between different actions/ goals , the end-states or goals have a positive connotation or a negative connotation.
That brings us to Regulatory anticipation: that is the now well-known Desire/ dread functionality of dopamine mediated brain regions that are involved in anticipation of pleasure and pain and drive behavior. This anticipation of pleasure or pain is driven by our Expectancies of how our actions will yield the desired/undesired outcomes and can be treated as the equivalent to Expectancy in the Utility decisions. The combination of independent factors of regulatory reference and regulatory anticipation will drive what end-state or goal is activated to be the next target for the organism. Once activated, its tendencies towards promotion focus or prevention focus would determine how it strategically uses approach/ avoidance mechanisms to archive that goal or move towards the end-state. Let us also look at regulatory anticipation as described by higgins:
Freud (1920/1950) described motivation as a “hedonism of the future.” In Beyond the Pleasure Principle (Freud, 1920/1950), he postulated that people go beyond total control of the “id” that wants to maximize pleasure with immediate gratification to regulating as well in terms of the “ego” or reality principle that avoids punishments from norm violations. For Freud, then, behavior and other psychical activities were driven by anticipations of pleasure to be approached (wishes) and anticipations of pain to be avoided (fears). Lewin (1935) described how the “prospect” of reward or punishment is involved in children learning to produce or suppress, respectively, certain specific behaviors (see also Rotter, 1954). In the area of animal learning, Mowrer (1960) proposed that the fundamental principle underlying motivated learning was regulatory anticipation, specifically, approaching hoped-for desired end-states and avoiding feared undesired endstates. Atkinson’s (1964) personality model of achievement motivation also proposed a basic distinction between self-regulation in relation to “hope of success” versus “fear of failure.” Wicker, Wiehe, Hagen, and Brown (1994) extended this notion by suggesting that approaching a goal because one anticipates positive affect from attaining it should be distinguished from approaching a goal because one anticipates negative affect from not attaining it. In cognitive psychology, Kahneman and Tversky’s (1979) “prospect theory” distinguishes between mentally considering the possibility of experiencing pleasure (gains) versus the possibility of experiencing pain (losses).
Why I have been dwelling on this and how this fits into the larger framework: Wait for the next post, but the hint is that I believe that bipolar mania as well as depression is driven by too much goal-oriented activity- in mania the focus being promotion; while in depression the focus being preventive; Higgins does discuss mania and depression in his article, but my views differ and would require a new and separate blog post. Stay tuned!
Higgins, E. T. (1997). Beyond pleasure and pain American Psychologist (52), 1280-1300