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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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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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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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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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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