How Mood and felt Energy are related to thought variability and speed

There is a recent article by Pronin and Jacobs, on the relationship between mood, thought speed and experience of 'mental motion' that builds up on their previous work.

Let us see how they describe thought speed and variability and what their hypothesis is:

1. The principle of thought speed. Fast thinking, which involves many thoughts per unit time, generally produces positive affect. Slow thinking, which involves few thoughts per unit time, generally produces less positive affect. At the extremes of thought speed, racing thoughts can elicit feelings of mania, and sluggish thoughts can elicit feelings of depression.

2. The principle of thought variability. Varied thinking generally produces positive affect, whereas repetitive thinking generally produces negative affect. This principle is derived in part from the speed principle: when thoughts are repetitive, thought speed (thoughts per unit time) diminishes. At its extremes, repetitive thinking can elicit feelings of depression (or anxiety), and varied thinking can elicit feelings of mania (or reverie).

Let me clarify at the outset that they are aware of the effects of though speed on variability and vice versa; as well as the effects of mood on felt energy and vice versa; thus they know that one can confound the other. Another angle they consider is the relationship between thought speed/variability i.e the form of thought and the contents of thought (whether having emotional salience or neutral) and investigated whether the effects of speed and variability were confounded with though content; they found negative evidence for this inetrcationist view.

Let me also clarify that I differ slightly (based on my interpreation of their data) from their original hypothesis, in the sense that I believe that their data shows that speed affects felt energy and variability affects affect and that the effects of speed on mood may be mediated by the effect of speed on felt energy and similarly the effect of variability on felt energy may be mediated by its effects on mood.

Thus my claim is that:

1. Thought speed leads to more felt energy. Extremes of 'racing thoughts' leads to the manic feeling of being very energetic (when accompanied with positive mood, this may give rise to feelings of grandiosity- I have the energy to achieve anything), while also may lead to anxiety states (when accompanied with negative affect) in which one cannot really suppress a negative chain of thoughts - one following the other in fast succession, regarding the object of ones anxiety. The counterpart to this the state where thoughts come slowly (writer's block etc) and when accompanied with negative affect, this can easily be viewed as depression.
2. Thought variability leads to more positive affect: Extremes of 'tangential thoughts' leads to the manic feeling of being in a good mood (when accompanied with high energy , this manifest as feelings of euphoria); while the same tangential thoughts when accompanied by low felt energy may actually be felt as serenity/ calmness/ reverie. The counterpart to this is the state of thoughts that are stuck in a rut - when accompanied with low energy this leads to feelings of depression and sadness.

Thus, to put simply : there are two dimensions one needs to take care of - mood (thought variability) x energy (thought speed) and high and low extremes on these dimensions are all opposites of their counterpart.

Before we move on, I'll let the authors present their other two claims too:

3. The combination principle. Fast, varied thinking prompts elation; slow, repetitive thinking prompts dejection. When speed and variability oppose each other, such that one is low and the other high, individuals’ affective experience will depend on factors including which one of the two factors is more extreme. The psychological state elicited by such combinations can vary apart from its valence, as shown in Figure 1. For example, repetitive thinking can elicit feelings of anxiety rather than depression if that repetitive thinking is rapid. Notably, anxious states generally are more energetic than depressive states. Moreover, just as fast-moving physical objects possess more energy than do identical slower objects, fast thinking involves more energy (e.g., greater wakefulness, arousal, and feelings of energy) than does slow thinking.

4. The content independence principle. Effects of thought speed and variability are independent of the specific nature of thought content. Powerful affective states such as depression and anxiety have been traced to irrational and dysfunctional cognitions (e.g., Beck, 1976). According to the independence principle, effects of mental motion on mood do not require any particular type of thought content.

They review a number of factors and studies that all point to a causal link between thought speed and energy and between thought variability and mood. More importantly they show the independent effects of though speed and variability from the effects of thought content on mood. I'll not go into the details of the studies and experiments they performed, as their article is available freely online and one can read for oneself (it makes for excellent reading); suffice it to say that I believe they are on the right track and have evidence to back their claims.

What are the implications of this:

The speed and repetition of thoughts, we suggest, could be manipulated in order to alter and alleviate some of the mood and energy symptoms of mental disorders. The slow and repetitive aspects of depressive thinking, for example, seem to contribute to the disorder’s affective symptoms (e.g., Ianzito et al., 1974; Judd et al., 1994; Nolen-Hoeksema, 1991; Philipp et al., 1991; Segerstrom et al., 2000). Thus, techniques that are effective in speeding cognition and in breaking the cycle of repetitive thought may be useful in improving the mood and energy levels of depressed patients. The potential of this sort of treatment is suggested by Pronin and Wegner’s (2006) study, in which speeding participants’ cognitions led to improved mood and energy, even when those cognitions were negative, self-referential, and decidedly depressing. It also is suggested by Gortner et al.’s (2006) finding that an expressive writing manipulation that decreased rumination (even while inducing thoughts about an upsetting experience) rendered recurrent depression less likely.

There also is some evidence suggesting that speeding up even low-level cognition may improve mood in clinically depressed patients. In one experiment, Teasdale and Rezin (1978) instructed depressed participants to repeat aloud one of four letters of the alphabet (A, B, C, or D) presented in random order every 1, 2, or 4 s. They found that those participants required to repeat the letters at the fastest rate experienced the most reduction in depressed mood. Similar techniques could be tested for the treatment of other mental illnesses. For example, manipulations might be designed to decrease the mental motion of manic patients, perhaps by introducing repetitive and slow cognitive stimuli. Or, in the case of anxiety disorders, it would be worthwhile to test interventions aimed at inducing slow and varied thought (as opposed to the fast and repetitive thought characteristic of anxiety). The potential effectiveness of such interventions is supported by the fact that mindfulness meditation, which involves slow but varied thinking, can lessen anxiety, stress, and arousal.

 hat tip: Ulterior Motives


Pronin, E., & Jacobs, E. (2008). Thought Speed, Mood, and the Experience of Mental Motion Perspectives on Psychological Science, 3 (6), 461-485 DOI: 10.1111/j.1745-6924.2008.00091.x
Pronin, E., & Wegner, D. (2006). Manic Thinking: Independent Effects of Thought Speed and Thought Content on Mood Psychological Science, 17 (9), 807-813 DOI: 10.1111/j.1467-9280.2006.01786.x

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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

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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Low Latent Inhibition, high faith in intuition and psychosis/creativity

Well, the cluster goes together. Previous research has found that Low LI and psychosis (schizophrenia) and creativity are related; previous research has also found that psychotic /some types of creative people have more faith in intuition; and this research ties things by showing that Low LI and high faith in intuition are correlated.

The research under question is by Kaufman and in it he explores the dual-process theories of cognition- the popular slow high road of deliberate conscious reasoning and the fast low road of unconscious processing. I would rather have the high road consist of both cognitive and affective factors and similarly the unconscious low road consist of both cognitive and affective factors. Kaufman focuses on the unconscious low road and his factor analysis reveal three factors: Faith in intuition: a meta cognition about ones tendency to use intuition; Holistic intuition: the cognitive factor; and affective intuition: the affective factor. with this in mind let us see what Kaufman's thesis is:

He first introduces the low road and the high road:

In recent years, dual-process theories of cognition have become increasingly popular in explaining cognitive, personality, and social processes (Evans & Frankish, 2009). Although individual differences in the controlled, deliberate, reflective processes that underlay System 2 are strongly related to psychometric intelligence (Spearman, 1904) and working memory (Conway, Jarrold, Kane, Miyake, & Towse, 2007), few research studies have investigated individual differences in the automatic, associative, nonconscious processes that underlay System 1. Creativity and intelligence researchers might benefit from taking into account dual-process theories of cognition in their models and research, especially when exploring individual differences in nonconscious cognitive processes.

Then he explain LI:

Here I present new data, using a measure of implicit processing called latent inhibition (LI; Lubow, Ingberg-Sachs, Zalstein-Orda, & Gewirtz, 1992). LI reflects the brain’s capacity to screen from current attentional focus stimuli previously tagged as irrelevant (Lubow, 1989). LI is often characterized as a preconscious gating mechanism that automatically inhibits stimuli that have been previously experienced as irrelevant from entering awareness, and those with increased LI show higher levels of this form of inhibition (Peterson, Smith, & Carson, 2002). Variation in LI has been documented across a variety of mammalian species and, at least in other animals, has a known biological basis (Lubow & Gerwirtz, 1995). LI is surely important in people’s everyday lives—if people had to consciously decide at all times what stimuli to ignore, they would quickly become overstimulated.
Indeed, prior research has documented an association between decreased LI and acute-phase schizophrenia (Baruch, Hemsley, & Gray, 1988a, 1988b; Lubow et al., 1992). It is known, however, that schizophrenia is also associated with low executive functioning (Barch, 2005). Recent research has suggested that in highfunctioning individuals (in this case, Harvard students) with high IQs, decreased LI is associated with increased creative achievement (Carson et al., 2003). Therefore, decreased LI may make an individual more likely to perceive and make connections that others do not see and, in combination with high executive functioning, may lead to the highest levels of creative achievement. Indeed, the link between low LI and creativity is part of Eysenck’s (1995) model of creative potential, and Martindale (1999) has argued that a major contributor to creative thought is cognitive disinhibition.

He then relates this to intuition and presents his thesis:

A concept related to LI is intuition. Jung’s (1923/1971, p. 538) original conception of intuition is “perception via the unconscious.” Two of the most widely used measures of individual differences in the tendency to rely on an intuitive information-processing style are Epstein’s Rational- Experiential Inventory (REI; Pacini & Epstein, 1999) and the Myers-Briggs Type Indicator (MBTI) Intuition/Sensation subscale (Myers, McCaulley, Quenk, & Hammer, 1998). Both of these measures have demonstrated correlations with openness to experience (Keller, Bohner, & Erb, 2000; McCrae, 1994; Pacini & Epstein, 1999), a construct that has in turn shown associations with a reduced LI (Peterson & Carson, 2000; Peterson et al., 2002), as well as with divergent thinking (McCrae, 1987) and creative achievement.
...
The main hypothesis was that intuitive cognitive style is associated with decreased latent inhibition.

He found support for the hypothesis from his data. It seemed people with low LI were high in faith in intuition factor. Here is what he discusses:

The results of the current study suggest that faith in intuition, as assessed by the REI and the MBTI Thinking/Feeling subscale, is associated with decreased LI. Furthermore, a factor consisting of abstract, conceptual, holistic thought is not related to LI. Consistent with Pretz and Totz (2007), exploratory factor analysis revealed a distinction between a factor consisting of REI Experiential and MBTI Thinking/Feeling and a factor consisting of MBTI Intuition/Sensation and REI Rational Favorability. This further supports Epstein’s (1994) theory that the experiential system is directly tied to affect. The finding that MBTI Intuition/Sensation and REI Rational Favorability loaded on the same factor supports the idea that the type of intuition that is being measured by these tasks is affect neutral and more related to abstract, conceptual, holistic thought than to the gut feelings that are part of the Faith in Intuition factor.

Here are the broader implications:

The current study adds to a growing literature on the potential benefits of a decreased LI for creative cognition. Hopefully, with further research on the biological basis of LI, as well as its associated behaviors, including interactions with IQ and working memory, we can develop a more nuanced understanding of creative cognition. There is already promising theoretical progress in this direction.

Peterson et al. (2002) and Peterson and Carson (2000) found a significant relationship between low LI and three personality measures relating to an approach-oriented response and sensation-seeking behavior: openness to experience, psychoticism, and extraversion. Peterson et al. found that a combined measure of openness and extraversion (which was referred to as plasticity) provided a more differentiated prediction of decreased LI.

Peterson et al. (2002) argued that individual differences in a tendency toward exploratory behavior and cognition may be related to the activity of the mesolimbic dopamine system and predispose an individual to perceive even preexposed stimuli as interesting and novel, resulting in low LI. Moreover, under stressful or novel conditions, the dopamine system in these individuals will become more activated and the individual will instigate exploratory behavior. Under such conditions, decreased LI could help the individual by allowing him or her more options for reconsideration and thereby more ways to resolve the incongruity. It could also be disadvantageous in that the stressed individual risks becoming overwhelmed with possibilities. Research has shown that the combination of high IQ and reduced LI predicts creative achievement (Carson et al., 2003). Therefore, the individual predisposed to schizophrenia may suffer from an influx of experiential sensations and possess insufficient executive functioning to cope with the influx, whereas the healthy individual low in LI and open to experience (particularly an openness and faith in his or her gut feelings) may be better able to use the information effectively while not becoming overwhelmed or stressed out by the incongruity of the situation. Clearly, further research will need to investigate these ideas, but an understanding of the biological basis of individual differences in different forms of implicit processing and their relationship to openness to experience and intuition will surely increase our understanding of how certain individuals attain the highest levels of creative accomplishment.

To me this is exciting, the triad of creative/psychotic cognitive style, intuition and Latent Inhibition seem to gel together. the only grip eI have is that the author could also have measured intuition directly by using some insight problems requiring 'aha' solutions; maybe that is a project for future!

Kaufman, S. (2009). Faith in intuition is associated with decreased latent inhibition in a sample of high-achieving adolescents. Psychology of Aesthetics, Creativity, and the Arts, 3 (1), 28-34 DOI: 10.1037/a0014822

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Exploration/ Exploitation == Maximisers/ Satisficers?

There is an interesting research coverage at We are Only Human blog regarding whether people may have two different cognitive styles- one based on exploration of novel ideas and the other based on exploitation or focus on a particular familiar idea. The study employs evolutionary concepts and theorizes that these different cognitive styles may be a reflection of the different foraging styles that might have been selected for and relevant in EEA.

Specifically, while foraging for food in a habitat where the food supply and resources are unpredictable , one is faced with a choice when one has discovered a food source: whether to exploit this food source (a jungle area having sparse edible leaves) or to move ahead in search of a potentially better food source (a jungle area having abundant edible and nutritious fruits) . Both strategies , that of exploring or exploiting can be advantageous and may have been selected for. It is also possible that humans can use either of the strategies based on the environment- (food source distribution) , but may be inclined towards one strategy or the other. The authors of the study surmised that both the strategies have been selected for and we have the potential to use either of the strategy. Moreover, the same foraging strategy we use or are primed of, would also be visible in the cognitive strategy we use.

They used an ingenious technique to prime the subjects with either of the foraging strategies (go read the excellent We are only human blog post) and found that humans were flexible in the use of the appropriate strategy, given the appropriate context, and that the foraging strategy primed the corresponding cognitive strategy. To boot, those primed with an exploratory foraging strategy would be more prone to using exploratory cognitive strategies when confronted with a cognitive task and vice versa. They also found systematic differences between individuals cognitive and foraging styles- some were more exploratory than the others.

This reminds me of the Maximizers/ Satisficers distinction in decision-making style that Barry Scwatrz has introduced and brought to public attention. Basically a Maximizer , when faced with a decision and choice, would go on computing the utility of different choices and try to choose the option that maximizes his utility and is the 'best'. A Satisficer, on the other hand would also explore options, but stop his exploration, when he finds an option that is 'good enough'. I wonder, if just like the exploratory/ Exploitative cognitive and foraging styles, this is just another dimension of the same underlying phenomenon- whether to explore more - or to exploit what is available. To take an example, for marriage, a satisficing strategy may work best - as told in "The Little Prince" one should stop searching for more flowers if one has already had the fortune of possessing a flower.

"People where you live," the little prince said, "grow five thousand roses in one garden... yet they don't find what they're looking for..."

"They don't find it," I answered.

"And yet what they're looking for could be found in a single rose, or a little water..."

An interesting experiment would be to see, if the foraging style, the cognitive style, and the decisions style are all correlated within individuals and if priming one can influence the outcome of the other style.

If so, could there be an underlying neural phenomenon , common to all?

Wray, the author of We are only human blog makes a bold conjecture and relates this to the finding that dopamine levels.

Exploratory and inattentive foraging—actual or abstract—appears linked to decreases in the brain chemical dopamine.

He even relates this to cognitive disorders like Autism and ADHD.

By analogy, in conditions where baseline dopamine is more, like in bipolar and psychosis, one may be more inclined to a more staisficing/ 'I'm feeling Lucky' strategy in which the very first option is acceptable. This may explain the 'jumping-to-conclusions' bias in schizophrenia/ psychosis.

To make things more explicit, though the leading dopamine theory in vogue now is of 'error-prediction' , a competing, and to me more reasonable, view of dopamine function is incentive salience i.e. what 'value'/ importance does the stimuli have for the person in question. The importance can be both positive and negative and thus we have found that dopamine is involved in both dread and desire. The dominant reward prediction theory faces many challenges, the least of which is response of dopamine neurons to novel events. A dopamine burst is also associated with 'novel' events and thus dopamine is somehow involved in/ triggered by Novelty. Baseline dopamine may constrain the dopamine surge felt on a novel event. Thus, in schizophrenia/ psychosis , with baseline dopamine high, a dopamine burst on novelty detection may be high enough so that it is meaningful and may not lead to more exploratory behavior. While in the disorders where baseline dopamine is low, one may require a more profound dopamine burst before the stimuli becoming meaningful and thus may go on seeking novel stimulus till one finds one 'big enough to trigger salience'.

We may extend the salience argument to other domains than incentive. If the chief function of dopamine is to mark salience, then it may also be instrumental in memory and attention. Only what is Salient gets attention, and only what is salient gets into Working Memory. Thus,a high dopamine level may predispose to treating almost everything as salient, leading to delusions of reference (everything is meaningfully related to self etc) etc. Working Memory may be taxed due to everything trying to get in- and thus poor WM in people with schizophrenia. Also, every trivial thing may grab attention- leading to poor sensory gating and conditions like lack of pre-pulse inhibition. On the flip side, while making sense of ones experience, one may accept the first possible explanation and do not search further - thus leading to persistence of delusions.

An opposite scenario would be when one keeps exploring the environment and nothing seems novel due to low dopamine levels. This would be the classical Autistic repetitive and stereotype behaviors. There would be sensory over stimulation, as nothing is salient and one needs to explore more and more. On the other hand, WM capabilities may be good/ savant like, as not every piece of information grabs attention. Everything should seem insignificant and the only way to arrive at decision / choose action would be via exhaustive enumeration and logical evaluations of all options. even after obvious explanations for phenomenon, one may keep looking for a better explanation. No wonder , as per my theory, more scientists would be autistic.

Perhaps, I am stretching things too far, but to me the dopamine connection to Salience/ Meaning/ Importance is sort of worth exploring and I will write more about that in future. For now, let us be willing to associate Salience not just with stimuli related to motivation, but also with stimuli relevant in sensation, perception,learning and memory. If so the common underlying mechanism responsible for differentiating us as a exploratory and expolitatory forager (food) may also be related to our different cognitive styles, our different decision-making styles and our different baseline dopamine levels.

Dopamine though is most strongly related to food and sex. I could even stretch this argument and say this may be related to r and K reproductive styles (note these styles are species specific, but I believe individuals in a specie may also vary on the reproductive strategy along this dimension). Thus, while explorers may have r type of reproductive style, the exploiters may have a K reproductive style.

At one extreme are r-strategies, emphasizing gamete production, mating behavior, and high reproductive rates, and at the other extreme are K-strategies, emphasizing high levels of parental care, resource acquisition, kin provisioning, and social complexity.

If K-strategy is what humans have chosen, maybe exploitation in all areas (cognitive, decision-making, foraging) is more relevant and in tune with our nature. Maybe that's why I'll always be on the side of Psychosis than Autism!! Though, to put things in perspective, maybe humans have evolved to use both strategies as the situations demands , and the best thing would be to use the strategy situation-specific and not lean towards either extremes.

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Developmental Stages: New Age concurs

I recently came across a series of article by Bill Harris, director of Centerpointe institute, regarding cognitive development and I found them relatively well-informed. Bill is a new Age Guru, but his articles were relatively well -informed regarding Piaget's developmental stages; moreover he shares my enthusiasm for developmental stages and believes in extending these stages beyond Piaget's four. The series is still incomplete and I link to the first two posts in the series.

I liked his linking these stages with the Jean Gebser's structure of consciousness and the consequent archaic, magical, mythical, mental and integral stages. I also liked his emphasis on perspective taking as an integral part of developmental process and I have covered that in detail here. However, he doesn't differentiate between the stages whereby one starts understanding that others have a different viewpoint/ perspective ( social-informational perspective) vis-a-vis when one starts adopting the viewpoint of another (self-reflective perspective). See my earlier post for more on these perspective stages as outlined by Robert Selman.

What I didn't like though, and found many issues with , was the various pathologies he associated with failures of developmental tasks at each stage. These he seemed to just pull out of his hat , with neither empirical support or strong theoretical foundations. Nevertheless, the series of articles may serve as a good refreshed for Piaget's theories of cognitive development for readers of this blog.

Some excerpts:

Cognitive development refers to our ability to perform various types of operations on what we encounter in the world and in our awareness. To live in the world, accomplish various things, and deal with the challenge of being human, we first learn to ”work with” (deal with, manage, get things done with) our body, then with objects, then with symbols, concepts, and ideas, and–if development continues to the highest transpersonal or transrational levels of development–we eventually add ways of dealing with life that are beyond the realm of ideas.

Sensorimotor, Piaget’s first stage (the stage before preoperational), is sometimes referred to as archaic in other naming conventions (in this case, in that of Jean Gebser).

Piaget divided cognitive development into four broad stages: 1) sensorimotor (0-2 years), 2) preoperational, or “preop” (2-7 years), concrete operational, or “conop” (7-11 years), and formal operational, or “formop” (11 years onward). Each of these can be divided into several substages. The ages are averages, and since a person could stop and remain at any level, you can find many adults at each level (though not many are found at the sensorimotor stage).

In this discussion I’ll also use some of the stage names used by Jean Gebser and Ken Wilber: archaic (similar to sensorimotor), magic (similar to early preoperational), magic-mythic (late preoperational), mythic (early concrete operational), mythic-rational (late concrete operational), and rational (formal operational). This is just to confuse you, of course.

In the sensorimotor stage, the infant uses senses and motor abilities to understand the world, beginning at first with reflexes and eventually using complex combinations of sensorimotor skills. At the beginning of this stage, the infant cannot yet distinguish itself from its environment (what some have called an experience of oceanic oneness). This has also been called a state of “primary narcissism,” because the infant is embedded in or undifferentiated from the environment.

I suggest, this should be enough to whet your appetite and that you go to the original source to get additional servings.

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Simulating the future and remebering the past: Are we prediction machines?

This post is about an article by Schacter et al (pdf) regarding how the constructiveness of memories may crucially be due to the need to simulate future scenarios. But before I go to the main course, I would like to touch upon a starter: Jeff Hawkins Heirarchical Temporla Memory (HTM) hypothesis. I would recommend that you watch this excellent video.

As per Jeff Hawkins, we humans are basically prediction machines, constantly predicting the external causes and our responses to them. Traditionally, the behaviorist account has been that we are nothing but a bundle of associations- either conditioned pavlovian associations between stimuli and stimulus-response or a skinerrian association between our operant actions and environmental rewards. Thus every behavior we indulge in is guided by our memory of past associations and the impending stimulus. Jeff Hawkins refines this by postulating that we are not passive responders to environmental stimuli, but actively predict what future causes (stimuli) are expected and what our response to those stimuli may be. Thus in his HTM model, the memory of past events not only exerts influence via a bottom up process of responding to impending stimulus; but it is also used for a top-down expectation or prediction of incoming stimulus and our responses to it. Thus, we are also prediction machines constantly using our memory to predict future outcomes and our possible responses.

Now lets get back to the original Schacter article. Here is the abstract:

Episodic memory is widely conceived as a fundamentally constructive, rather than reproductive, process that is prone to various kinds of errors and illusions. With a view toward examining the functions served by a constructive episodic memory system, we consider recent neuropsychological and neuroimaging studies indicating that some types of memory distortions reflect the operation of adaptive processes. An important function of a constructive episodic memory is to allow individuals to simulate or imagine future episodes, happenings, and scenarios. Because the future is not an exact repetition of the past, simulation of future episodes requires a system that can draw on the past in a manner that flexibly extracts and re-combines elements of previous experiences. Consistent with this constructive episodic simulation hypothesis, we consider cognitive, neuropsychological, and neuroimaging evidence showing that there is considerable overlap in the psychological and neural processes involved in remembering the past and imagining the future.

As per the paper the same brain areas and mechanisms are involved in both remembering a past event and imagining a future one - and the regions involved include the hippocampus. These findings in itself are not so fascinating, but the argument that Schacter et al give for , as to why, the same regions are involved in both memory retrieval and future imaginings, and how this leads to confabulations and false recognitions is very fascinating. As per them , because we need to simulate the future events, and as the future events are never an exact replica of past events, hence we do not store the past events verbatim, but store a gist of the event, so that we can recombine the nebulous gist to create different possible future scenarios. Due to this fact (the need for simulation of future events), the memory is not perfect, and in normal individuals it is possible that they confabulate (attribute the source of their memory erroneously) or make false recognitions on memory tests like the DRM.

Fisrt a bit of background on DRM paradigm. In this test, a list of related words are presented to a subject: eg yawn, bed, night, pillow, dream, rest etc. All of these relate to the theme of sleep. Later in a recall test, when this thematically related word is presented to normal subjects, they most often say that they had encountered the word sleep earlier. However given an unrelated word like hunger, most are liable to recognize that the word was not encountered previously. What Schachter et al found was , that in those subjects that had damage to hippocampus/ other memory areas and were amnesics, this effect of confabulating the gist word was reduced. In other words, those with brain damage to memory areas were less likely to say that they had encountered the related word sleep during the original trial. this, despite their poor performance in overall remembering of old list items as compared to controls. This clearly indicates that remembering the gist vis-a-vis details is very important memory mechanism.

I believe that we should also take into account the prototype versus exemplar differences in categorization between the males and females into account here. I would be very interested to know whether the data collected showed the expected differences between males and females and hopefully the results are not confounded due to not taking this gender difference into account.

Anyway , returning to the experimental methodology, another sticking point seems to be the extending of results obtained with semantic memory (like that for word lists) to episodic memory.

Keeping that aside, the gist and false recognition data results clearly indicate that the constructive nature of memory is an adaptation (it is present in normal subjects) and is disrupted in amnesics/ people with dementia.

Thus, now that it is established that memory is reconstructive and that this reconstruction is adaptive, the question arises why it is reconstructive and not reproductive. To this Schacter answers that it is because the same brain mechanism used for reconstructing memory from gist are also used for imagining or simulating future scenario. They present ample neuropsychological, neuroimaging and cognitive evidence on this and I find that totally convincing.

The foregoing research not only provides insights into the constructive nature of episodic memory, but also provides some clues regarding the functional basis of constructive memory processes. Although memory errors such as false recognition may at first seem highly dysfunctional, especially given the havoc that memory distortions can wreak in real-world contexts (Loftus 1993; Schacter 2001), we have seen that they sometimes reflect the ability of a normally functioning memory system to store and retrieve general similarity or gist information, and that false recognition errors often recruit some of the same processes that support accurate memory decisions. Indeed, several researchers have argued that the memory errors involving forgetting or distortion serve an adaptive role.

However, future events are rarely, if ever, exact replicas of past events. Thus, a memory system that simply stored rote records of what happened in the past would not be well-suited to simulating future events, which will likely share some similarities with past events while differing in other respects. We think that a system built along the lines of the constructive principles that we and other have attributed to episodic memory is better suited to the job of simulating future happenings. Such a system can draw on elements of the past and retain the general sense or gist of what has happened. Critically, it can flexibly extract, recombine, and reassemble these elements in a way that allows us to simulate, imagine, or ‘pre-experience’ (Atance & O’Neill 2001) events that have never occurred previously in the exact form in which we imagine them. We will refer to this idea as the constructive episodic simulation hypothesis: the constructive nature of episodic memory is attributable, at least in part, to the role of the episodic system in allowing us to mentally simulate our personal futures.

I'll finally like to end with the conclusions the author drew:

In a thoughtful review that elucidates the relation between, and neural basis of, remembering the past and thinking about the future, Buckner and Carroll (2007) point out that neural regions that show common activation for past and future tasks closely resemble those that are activated during “theory of mind” tasks, where individuals simulate the mental states of other people (e.g., Saxe & Kanwisher 2003). Buckner and Carroll note that such findings suggest that the commonly activated regions may be specialized for, and engaged by, mental acts that require the projection of oneself in another time, place, or perspective”, resembling what Tulving (1985) referred to as autonoetic consciousness.

This Seems to be a very promising direction. The 'another time and place' can normally be simulated withing hippocampus that also specializes in cognitive maps. We may use the cognitive maps to not only remember past events, but also simulate new events. In this respect the importance of dreams may be paramount. Dreams (and asleep) may be the mechanism whose primary purpose is not memory consolidation; rather I suspect that the primary function of dreams is to work on the gist of the memory from the previous day, simulate possible future scenarios, and then keep in store those memories that would help and are likely to be encountered in future. Thus, while dreaming we are basically predicting future scenarios and sorting information as per their future relevance. Not a particularly path-breaking hypothesis, but I'm not aware of any thinking is this direction. Do let me know of any other similar hypothesis regarding the function of dream as predictors and not merely as consolidators.

Tags: memory, sleep, cognition

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Brave Heart: does will power reside in heart?

I have written earlier regarding the Heart Rate Variability, that is primarily caused by the Autonomous Nervous System (the opposite effects of PNS and SNS), and how a flexible HRV is related to better response to stress and reduced anxiety in face of external stressors. While looking at the evidence and linkages between HRV and emotional regulation, I had also speculated in it that a lower baseline or resting HRV may be reflective of depression and low regulation/motivation; while a high resting or baseline HRV reflective of Mania and high regulation/ motivation.

A recent study has looked into the issue of whether cognitive self -regulation (will power / motivation) is also associated with HRV. The study reported that higher baseline HRV was associated with more will-power and ability to resist temptation. Also, as they had surmised that will-power is a limited resource and hence the ability to resist temptation must exhaust the will- power ability, hence if the subjects showed higher HRV during the resisting temptation phase, then they would have exhausted their will-power reserves and would not persist in subsequent demanding tasks and this is exactly what they found.

The study consisted of measuring HRV, while the subjects were given a choice of eating cookies/candies or carrots. those who chose carrots over candies (thus exhibiting more will-power to resist the temptation of candies) also showed higher HRV.

In the second experiment, after the subjects chose candy or carrot , and hence supposedly exhausted their limited will-power cognitive reserves, they were asked to do a tough anagram exercise. Those who had chosen carrots were more likely to give up the task earlier. Yet those with higher baseline HRV showed high motivation and will -power regardless of whether they chose candies or not.

This I believe is a good corroborator of Higher resting HRV to be related to better self-regulation and mania , while lower baseline HRV to be related with depression and poor self-regulation. So maybe our hearts do tell us a lot about ourselves, our abilities to resist temptations and our will -powers.

Tags: cognition, emotion

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The courage of a mouse to say 'No': A case of metacognition or risk-aversion?

A recent article in Current Biology by Foote et al (courtsey Ars Technica) posits that rats have metacognition abilities. till now only Humans and primates were assumed to have metacognitive abilities. One feature or defining characteristic of metacognition is knowing what you know and also knowing what you don't know. It means one can think about one's own mental states and determine what knowledge one already has and what knowledge one has not yet learned. So a related ability would be the ability to decline a test of knowledge if one thinks that one has not learned enough to ace the test. For those who gave GRE/ any other exam recently and maybe postponed that exam, they would have no difficulty appreciating this that postponing/declining a test involves metacognition.

Taking this line of reasoning further, Foote et al surmise that if a rat could decline a test, under conditions when the rat was not sure of its learned knowledge regarding the test and doubted its ability to successfully complete the test, then such a declining behavior would indicate that the rat has metacognitive abilities. I find no flaws in this reasoning, but have a few quips about their particular experimental setup, which may have confounded the results by not factoring in the risk aversion.

First regarding their hypothesis of the experiment:

Here, we demonstrate for the first time that rats are capable of metacognition—i.e., they know when they do not know the answer in a duration-discrimination test. Before taking the duration test, rats were given the opportunity to decline the test. On other trials, they were not given the option to decline the test. Accurate performance on the duration test yielded a large reward, whereas inaccurate performance resulted in no reward. Declining a test yielded a small but guaranteed reward. If rats possess knowledge regarding whether they know the answer to the test, they would be expected to decline most frequently on difficult tests and show lowest accuracy on difficult tests that cannot be declined [4]. Our data provide evidence for both predictions and suggest that a nonprimate has knowledge of its own cognitive state.

Now on to the actual experimental setup:

Each trial consisted of three phases: study, choice, and test phases (Figure 1). In the study phase, a brief noise was presented for the subject to classify as short (2–3.62 s) or long (4.42–8 s). Stimuli with intermediate durations (e.g., 3.62 and 4.42 s) are most difficult to classify as short or long [11, 12]. By contrast, more widely spaced intervals (e.g., 2 and 8 s) are easiest to classify. In the choice phase, the rat was sometimes presented with two response options, signaled by the illumination of two nose-poke apertures. On these choice-test trials, a response in one of these apertures (referred to as a take-the-test response) led to the insertion of two response levers in the subsequent test phase; one lever was designated as the correct response after a short noise, and the other lever was designated as the correct response after a long noise. The other aperture (referred to as the decline-the-test response) led to the omission of the duration test. On other trials in the choice phase, the rat was presented with only one response option; on these forced-test trials, the rat was required to select the aperture that led to the duration test (i.e., the option to decline the test was not available), and this was followed by the duration test. In the test phase, a correct lever press with respect to the duration discrimination produced a large reward of six pellets; an incorrect lever press produced no reward. A decline response (provided that this option was, indeed, available) led to a guaranteed but smaller reward of three pellets.

The test they have used is a stimulus discrimination test. Their results indicated that indeed the rats declined more often on difficult trials (trials in which the stimulus were closely spaced around the men of 4s) as compared to easy trial (in which they had to discriminate widely spaced stimulus (say 2s and 8s). This neatly demonstrates that the rats were internally calculating what their odds of passing the test were, and in case of the difficult test they took the better option of choosing the decline-the-test condition. However I would like to see more of their data and factor out the effcets of risk aversion.

We all know that humans are prone to risk aversion. That is if I present to you an option of choosing a sure amount of 100 rs or a 50% chance of winning 200rs , you would normally choose the fist option, though if one compares the utility function it is the same. In first case you have and expected value of 100 and in the second case too you have an expected value of 100 (0.5*0 +0.5*200). Thus it doesnt make much sense why one would use one over the other. This becomes more interseting when we increase the amount of the risky option. suppose we now have 100 rs assured vis-avis a 50 % chance of 300 rs still , most of us end up choosing the assured sum.

In this setup the utility of declining the test is 3 pellets; while if we assume that the rats have not learned how to discriminate the stimuli; then assuming that they press the levers at random and thus each option of the test condition is equally probable we have the utility as 0.5 *0 +0.5 *6 = 3 pellets. so we have the same situations as with humans. Now taking risk aversion into account, one would find that the rats would decline the test more often in the difficult stimulus conditions as that is a safe and assured option as compared to the take-the-test condition. As a matter of fact I am surprised that there were some rats who did choose the take-the-test condition. I guess men are more meek than mice!!

So the best thing to do would be to take risk-aversion into account and then after factoring it out decide on whether the rats knew (in a conscious sense) that the test is difficult. Risk aversion is mostly sub-conscious and would not involve metacognition. However, the trend of rising declining behaviors with test difficulty does point to the fact that the rats did have some metacognition.

I would love to have this study replicated using a maze (mouse trap sort of) task. In a amze the cognitive map of the maze provides a good indicataor of how much the mice know about the test/ test difficulty and measuring the declining in this case may be directly related to their meta-cognitive abilities.

Tags: cognition, cognitive map

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Religion continued: Throwing the baby with the bathwater?

I recently came across the work of Norenzayen et al regarding the linkage between religion and tolerance (courtesy Mixing Memory) and found some surprising commonalities with the views I have espoused earlier.

For one they talk about the need for religion and accept it as a human universal. They also note some aspects of the religious belief that are universal.

Anthropologically-speaking, there is a near universality of 1) belief in supernatural agents who 2) relieve existential anxieties such as death and deception, but 3) demand passionate and self-sacrificing social commitments, which are 4) validated through emotional ritual (Atran & Norenzayan, 2004). There are salient similarities to be found between even the most radically divergent cultures and religions (Norenzayan & Heine, 2005).

One can see that these have clear parallels with the autism/ schizophrenic differences on four dimensions I highlighted yesterday. Specifically:

1. Agency: belief in supernatural agency in religious people
   
2. Meaning: religious beliefs give meaning and relive existential anxieties like those of death (Terror Management theory)
3. Causal and Magical thinking: leading to rituals, and pro-social behaviors in the religious people
   
4. Experience: An emotional and ecstatic experience of oneness with others in the devotees and mediators.

The book chapter goes on to describe the two aspects of religiosity: a subjective/natural one and a objective-coalitional one. To put in simple words, one is belief in a personal , felt and experienced god (combining 1 and 4 above) and the other is the traditional scripture and culture driven coalitional religion that binds people together and provides them wioth a sense of meaning and purpose (combining 2 and 3 above).

For centuries, those who have attempted to explain religion (and even those who have propagated certain religions) have often distinguished two aspects of religion, treating them not only as distinct but also as opposites.

Dual understandings of religion generally consider a sense of the omnipresence of the divine (whether sensed directly and spontaneously or with the aid of prayer, meditation or drug-ingestion) more subjective/natural than it is socially transmitted/cultural.

Some illustrative examples are: James’ (1982/1902) distinction between the “babbling brook” from which all religions originate (p. 337) and the “dull habit” of “second hand” religion “communicated … by tradition” (p. 6) as well as that between “religion proper” and corporate and dogmatic dominion (p. 337); Freud’s (1930/1961) distinction between the “oceanic feeling” as an unconscious memory of the mother’s womb and “religion” as acceptance of religious authority and morality as a projection of the father; Weber’s (1947, 1978) distinction between religious charisma in its basic and “routinized” forms; Adorno’s distinction between “personally experienced belief” and “neutralized religion” (Adorno, Frenkel-Brunswick, Levinson, & Sanford, 1950); Rappaport’s (1979) distinction between the “numinous”—the experience of pure being--and the “sacred” or doctrinal; and, more recently, Sperber’s (1996) cognitive distinction between “intuitive” beliefs—“the product of spontaneous and unconscious perceptual and inferential process” (89), and “reflective” beliefs “believed in virtue of other second-order beliefs about them.”

The authors then go on to synthesize material on tolerance- religiosity linkages and explain how the subjective-natural religiosity is inversely related to intolerance while the coalitional- objective religiosity is directly related to intolerance and co-occurs with intolerance and prejudice. A note of caution though, the authors do not consider the two dimensions of religion independent, but find a positive correlation between the two.

The measures we are most concerned with are those tapping religious devotion, rooted in supernatural belief, and coalitional religiosity, rooted in the costly commitment to a community of believers—a community that is morally and epistemically elevated above other communities. Religious devotion centers on the awareness of and attention to God or the “divine” broadly conceived.

Coalitional religiosity, on the other hand, should be approximated by validated scales measuring what social psychologists consider coalitional boundary-setting social tendencies, such as authoritarianism, fundamentalism, dogmatism and related constructs (e.g., Kirkpatrick 1999).

The authors then go on to explain coalitional religiosity in terms of sexual selection and costly signalling, instead of group selection as we had discussed yesterday.

Coaltional religiosity is likely rooted in the costly sacrifice to the community of believers that is the hallmark of religion. As evolutionary theorists have noted, sacrificial displays can be selected for if carriers of honest signals of group membership are more likely to be reciprocated by a community of cooperators. Even in rights-oriented “individualist” cultures, one is expected to sacrifice all selfish gains that might accrue from being on the benefiting end of injustice towards others. Atran (2002) and others (Atran & Norenzayan, 2004; Sosis & Alcorta, 2003) note that sincere expressions of willingness to make any kind of sacrifice (including the potential ultimate sacrifice of one’s own life) only occasionally necessitate actually following through on that sacrifice in a way that has long term costs to the potential for survival and reproduction of the genes carried by that individual. However, the material and social support benefits that can accrue to those who sincerely express or demonstrate such willingness are both more likely to occur and are of more obvious value to the long term survival of one’s genes—unless one is among the unlucky individuals whose sincere demonstration involves actually dying before reproductive potential is maximized (and even then, socially-given benefits to close kin may offset the genetic loss of one individual). This “adaptive sacrifice display” explanation for religious devotion is related to the evolutionary concept of “costly signaling”, a process that explains many forms of sacrificial displays in the animal kingdom, for example, why male peacocks who burden themselves with more costly plumage may nevertheless be more likely to pass on their genes, by increasing their chances of mating with a receptive female. Costly signaling theory offers an explanation of why humans engage in altruistic displays such as sacrifice and ritual without treating the group as a unit of selection (Sosis & Alcorta, 2003).

While I disagree with the above explanations for coalitional religiosity, I still believe that it works primarily to ensure altruism/ pro-social behavior and to manage existential anxieties. the evolutionary rationale for subjective or intrinsic religiosity (spirituality) is much more problematic. The authors believe it is selected as it enables us to empathize and to become transcendent to group boundaries.

That coalitional religiosity encourages intolerance towards outgroups seems obvious. But it is less clear why devotional religiosity can, under some conditions, foster tolerance. Some evidence from neuroscience may help us with a novel speculation as to the process by which devotional experience may lead to transcendence of group boundaries.Some investigations (e.g. Holmes, 2001; d’Aquili & Newberg, 1998, 1999; Newberg, d’Aquili & Rause, 2001) have found that when people are subjectively experiencing a transcendent or supernatural-oriented state, there is often decreased activity in the parietal lobe or other object association areas, where perceptions that distinguish self from non-self are processed.....These areas may play a role in any relationship prayer might have to greater tolerance, empathy or other-concern, since they all seem potentially relevant to whether sense of self is experienced in a more limited or more expansive way. Perhaps commonplace empathic experiences of seeing oneself in another or caring for another as one would care for oneself have some family relationship to rarer mystical experiences of ”oneness” and even to more extreme cases where the self-other boundary melts down completely.

They finally get to why transcendence is needed or what function it serves.

Coalitional religiosity arguably reflects a limited kind of self-transcendence that simply upgrades individual selfishness to group selfishness, sometimes with dramatically violent consequences. Yet religious devotion’s independent relationship to tolerance suggests that religion has the potential to transcend group selfishness as well. It is almost as if a more limited religious transcendence is in tension with a more thoroughgoing transcendence. What lies beyond group selfishness we may dub “God-selfishness,” a focus of oneself on a God or divine being or principle that is transcendent of all individuals and groups, including oneself and one’s own groups. God-selfishness would appear to be what religious devotion measures tap into when the variance of coalitional religiosity is controlled for. To the extent that this broader transcendence of self often manifests itself as a tolerant sense of kinship with all, then it would appear to render Dawkins’ pessimism about religion unwarranted.

With that note I'll end the post and explore the readers not to throw the baby with the bath water, when it comes to religion/ spirituality.

Tags: religion, cognition

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The Mind - Brain dichotomy: What it means to have a mind

Researchers at Harvard, Gray et al, are conducting an ongoing mind survey, and have also reported some findings from that online survey, based ona asmaple of more than 2,000 people.

The survey attempts to make one think about different forms of entities that may have a mind and to assign different degrees of consciousness/ mind on them.

Gray worked alongside fellow psychologists Heather Gray and Daniel Wegner on the study, which presented respondents with 13 characters: 7 living human forms (7-week-old fetus, 5-month-old infant, 5-year-old girl, adult woman, adult man, man in a persistent vegetative state, and the respondent himself or herself), 3 non-human animals (frog, family dog, and wild chimpanzee), a dead woman, God, and a sociable robot.

Participants were asked to rate the characters on the extent to which each possessed a number of capacities, ranging from hunger, fear, embarrassment, and pleasure to self-control, morality, memory and thought. Their analyses yielded two distinct dimensions by which people perceive the minds of others, agency and experience.

The participants attribute different degrees of these factors to the characters based on a forced choice between a pair of characters on a particular ability related to a mind capacity like feeling fear or making moral decisions. I believe they than id factor analysis or some such statistical method to come up with two independent dimensions or factor underlying the concept of mind: Agency or Experience.

Agency seems to be related to the fact that people (entities with mind) can take volitional actions and are thereby responsible for their actions. They can thus also be judged morally based on their actions and the choices they make.

Experience seems related to the fact that people (entities with mind) have an ability to feel and are emotional entities that have subjective experience of emotions like pain, fear and hunger and also have desires, longings and feelings etc.

The ability to perceive qualia surprisingly didn't come out as a separate entity and consciousness or ability to perceive qualia is supposedly covered under the Experience factor.

These dimensions are independent: An entity can be viewed to have experience without having any agency, and vice versa. For instance, respondents viewed the infant as high in experience but low in agency -- having feelings, but unaccountable for its actions -- while God was viewed as having agency but not experience.

"Respondents, the majority of whom were at least moderately religious, viewed God as an agent capable of moral action, but without much capacity for experience," Gray says. "We find it hard to envision God sharing any of our feelings or desires."

The regular readers of this blog will remember that one of the important distinction that I hypothesized between Schizophrenia and Autism was that due to agency: with schizophrenics attributing too much Agency; and Autistic attributing too less Agency to others (other people or other entities that may have mind). Also as God is perceived as having too much Agency, but not much Experience, thus when the Schizophrenia end of spectrum kicks in, they may also attribute too much agency to themselves and feel God-like or Divine. The negative symptoms related to less of experience would also fit the fact of being God-like or being an angel/ special person and thus not having too much emotions. The Autistic end of the spectrum however would be guided by too-less-mind sort of attributions and thinking; and thus they may view themselves and others as brains and not minds. They might thus be more capable with inanimate objects and rules of nature (thus making them good scientists/ engineers/ systemizers) ; but poor at social/ ethical aspects that require attributing minds to animals for example.

One should also distinguish between the two dimensions of Agency and Experience. Thus Autistic may have a defect due to Agency, but may have mirror neurons or other systems that confer on them the ability to feel , not only subjective feelings of self - but empathetic feelings of others too.

Also, it has been this blogs contention that the Dimension of experience is best seen as a dimension on one end of which is the Bipolar patients and on the other end of which is the Deprosanalisation/ apathetic / derealization spectrum. while the Bipolar feels too much emotions and motivations; the depersonalised/ derealized person may show too less emotion/ motivation.

Thus in mind at one end we have people having too much mind/ believing in too much mind (and exemplified by Schizophrenic and Bipolar ) and at the other end we have too people having too much brain/ believing in too much brain (exemplified by Autistic/ depersonalised people). One gives great Art, the other great Science.

Returning to the current study:

"The perception of experience to these characters is important, because along with experience comes a suite of inalienable rights, the most important of which is the right to life," Gray says. "If you see a man in a persistent vegetative state as having feelings, it feels wrong to pull the plug on him, whereas if he is just a lump of firing neurons, we have less compunction at freeing up his hospital bed."

This is exactly one of the pertinent point made by the film Munnabhai MBBS- that coma patients have feelings and have a right of life. While I have featured the effects of Lage Raho Munnabhai earlier; I would also like to pay tribute to its prequel/ precursor.

On that note, let us keep our antennas up for how thinking about us as entities with Agency and Experince can lead to Art; while thinking of us as brains can lead to good scince. I'm sure you'll agree that we need both of these concepts about us humans.

Tags: schizophrenia, mania, depression, cognition, consciousness, moral sense

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