The Psychological & Neuroscientific musings of sandygautam
autism
Autism and ADHD as opposites based on fly models?
Feb 23rd
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Regular readers of this blog will know my fascination with autism and psychosis as opponents on a continuum theory . I have already been privately speculating that ADHD in childhood may be a risk factor for Psychosis in later adolescence, especially as both are supposed to have underling dopamine abnormalities, so this new study by Brembs et al caught my attention.
Recently I came cross this paper by Bjorn Brembs et al that investigated attention-like processes in mutant fly models that showed memory deficits. They weer aqble to show that despite overt similar olfactory memory deficits, the attention-like processes worked in opposite ways in the two mutant models. In the rutabaga/dunce model, the atention ws hyperfocussed , resembling human autism; while in radish models , attention was flexible and distract-able resembling human ADHD.
It is increasingly apparent that many classical Drosophila learning and memory mutants are also defective in short-term processes relevant to selective attention. Previous studies have shown that short-term memory as well as long-term memory mutants display attention-like defects (van Swinderen, 2007; van Swinderen et al., 2009), and the current study reveals radish mutants to be defective as well, albeit with distinctly different symptoms. The Drosophila mutants dunce1, rutabaga2080, and radish1 share olfactory memory defects but differ conspicuously for short-term processes relevant to visual attention. Whereas the more persistent optomotor behavior of dunce1 and rutabaga2080, both affecting the cAMP-associated pathways (Davis et al., 1995), are reminiscent of the persistent preoccupation of some patients afflicted with autism, the phenotype of radish mutant flies described here is similar to some of the symptoms of patients with ADHD.
They further speculate as o why these two phenotypes may be present and relate it to exploit/explore conundrum.
Attaining the right balance between persistence and flexibility is a crucial feature of adaptive behavior, because it reflects the balance between exploration and exploitation of natural resources. It is tempting to speculate that radish and dunce/rutabaga may constitute the two respective extremes of this balance. Recent work investigating torque behavior of wild-type flies (similar to our shorter experiments here) has shown that, during extended flights, the occurrence of turning maneuvers can be described by a Le´vi distribution (Maye et al., 2007). Such distributions of behavioral output, seen in foraging behavior in many animals, are characteristically long-tailed. This means that animals may occasionally persist with one behavioral choice for unusually long, but most often choices alternate at a more regular, normally distributed rate. The advantage of allowing for occasional long forays into one direction is presumably to chance on a new resource away from the proximal search space. Such behavior has been found to be ecologically advantageous, but mechanisms driving such alternation tendencies have not been documented in the Drosophila brain. One interpretation of our results is that the mushroom body circuits defined by dunce/ rutabaga/radish expression are involved in establishing the balance between persistence and flexibility [i.e., the explore/exploit dilemma (Daw et al., 2006)]. A separate set of results has independently also arrived at a similar conclusion, suggesting that the mushroom bodies could be involved in maintaining a period of behavioral flexibility (i.e., attention-like processes) before a longer-term transition to habit formation or motor learning (Brembs, 2009).
To me, this research adds another intriguing possibility to the autism-psychosis dimension, that of ADHD as a childhood phenotype/risk factor for later psychosis.
van Swinderen, B., & Brembs, B. (2010). Attention-Like Deficit and Hyperactivity in a Drosophila Memory Mutant Journal of Neuroscience, 30 (3), 1003-1014 DOI: 10.1523/JNEUROSCI.4516-09.2010
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Dec 2nd
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I have blogged extensively about the Autism and Schizophrenia as opposites on a continuum theory. I remember first putting this theory in words in an article 3 yrs back on the mouse trap titled Autism and Schizophrenia: the two cultures. That 2006 article, in turn, was inspired by Daniel Nettle’s 2005 article in Journal of Research in Personality where Nettle had also proposed the dichotomy and that paper helped crystallize my thoughts on the subject, a theory which I had been building on my own and now supported by someone like Nettle who I respect a lot. Important to note that at that time I was blissfully unaware of Badcock or Crespi and their work. It is to the credit of Badcock that he had published in 2006 his own theory of Autism and Schizophrenia as opposites on a continuum based on parental imprinting of genes and proposed a mechanism. Crespi I guess got involved in Badcocks’s efforts later on and gave it more experimental and theoretic grounding. I firts became aware of Badcock and Crespi’s work in early 2008.
The wider world became aware of the Autism/Schizophrenia dichotomy sometime in late 2008 (November 2008) . at that time too, I was a little disappointed because most of the coverage did not mention Daniel Nettle, who I think should be credited for this work and line of reasoning too. As a consolation, some reports did mention Chris Frith who has also been partly supporting the thesis.
I wanted to give a historical perspective, because I am sure the recent Crespi article would be grabbed on by mainstream media and the pioneers Chris Frith/Nettle perhaps overlooked- but to me they too are heroes for having come up with such profound early insights. this is not to discredit teh work of Badcock and Crespi- they are doing a thorough job of convincing the skeptics and delineating the exact mechanism and genetics involved.
While we are on the topic of historical perspective , let me also pat myself on the back. In May 2008, a study came out that de novo Copy Number Variations’s (CNVs) were quite high in schizophrenics and they are in the same region as that for autistics who also have high CNVs in the same region. While some took that result to imply that Schizophrenia and Autism are same and are not different, I persisted and proposed a mechanism, whereby they could still be opposites : To quote:
Now as it happens previous research has also found that CNVs are also found to a higher extent in autistics. Moreover, research has indicated that the same chromosomal regions have CNVs in both Autism and Schizophrenia. To me this is exciting news. Probably the chromosomal region (neurexin related is one such region) commonly involved in both schizophrenia and autism is related to cognitive style, creativity and social thinking. Qualitatively (deletions as opposed to duplications) and quantitatively (more duplications) different type of CNVs may lead to differential eruption of either Schizophrenia or Autism as the same underlying neural circuit gets affected due to CNVs, though in a different qualitative and quantitative way.
Now one and half year later Crespi et al report the results of their study which has found exactly the same- that is, if deletions in some locus lead to autism, duplications lead to schizophrenia and vice versa. That to me is clinching evidence of my thesis. Who says Science does not happen on blogs- I proposed something to flow as a consequence of theory and exactly the same thing is found as per the hypothesis. I feel vindicated and emotional to some extent. Loves labor has not been lost to deaf ears.
Let us then return to the new and latest study that has sort of proven that Autism and Schizophrenia are opposites, genetically. Crespi et al, report in the latest PNAS edition that comparative genomics leads to that conclusion. What Crespi et al did was look at theCNV s and the locus whee CNV in both Autism and Schizophrenia are involved and sure enough they found the pattern I had proposed. I’ll now quote from the abstract and the article extensively:
We used data from studies of copy-number variants (CNVs), singlegene associations, growth-signaling pathways, and intermediate phenotypes associated with brain growth to evaluate four alternative hypotheses for the genomic and developmental relationships between autism and schizophrenia: (i) autism subsumed in schizophrenia, (ii) independence, (iii) diametric, and (iv) partialoverlap. Data from CNVs provides statistical support for the hypothesis that autism and schizophrenia are associated with reciprocal variants, such that at four loci, deletions predispose to one disorder, whereas duplications predispose to the other. Data from single-gene studies are inconsistent with a hypothesis based on independence, in that autism and schizophrenia share associated genes more often than expected by chance. However, differentiation between the partial overlap and diametric hypotheses using these data is precluded by limited overlap in the specific genetic markers analyzed in both autism and schizophrenia. Evidence from the effects of risk variants on growth-signaling pathways shows that autism-spectrum conditions tend to be associated with upregulation of pathways due to loss of function mutations in negative regulators, whereas schizophrenia is associated with reduced pathway activation. Finally, data from studies of head and brain size phenotypes indicate that autism is commonly associated with developmentally-enhanced brain growth, whereas schizophrenia is characterized, on average, by reduced brain growth.These convergent lines of evidence appear most compatible with the hypothesis that autism and schizophrenia represent diametric conditions with regard to their genomic underpinnings, neurodevelopmental bases, and phenotypic manifestations as reflecting under-development versus dysregulated over-development of the human social brain.
Copy Number Data. Rare copy-number variants (CNVs) at seven loci, 1q21.1, 15q13.3, 16p11.2, 16p13.1, 17p12, 22q11.21, and 22q13.3 (Tables S1 and S2), have been independently ascertained and associated with autism and schizophrenia in a sufficient number of microarray-based comparative genomic hybridization (aCGH) and SNP-based studies to allow statistical analysis of the frequencies of deletions versus duplications in these two conditions (Table 1, Tables S3–S9). For five of the loci (1q21.1, 16p11.2, 16p13.1, 22q11.21, and 22q13.3), specific risk variants have been statistically supported for both autism and schizophrenia using case-control comparisons, which allows direct evaluation of the alternative hypotheses in Fig. 1. One locus (16p13.1) supports a model of overlap, and four loci support the reciprocal model, such that deletions are associated with increased risk of autism and duplications with increased risk of schizophrenia (16p11.2, 22q13.3), or deletions are associated with increased risk of schizophrenia and duplications with increased risk of autism (1q21.1, 22q11.21). For 1q21.1 and 22q11.21, contingency table analyses also indicate highly significant differences in the frequencies of deletions compared with duplications for the two disorders, such that schizophrenia is differentially associated with deletions and autism with duplications. By contrast, for 16p11.2 and 22q13.3 such analyses show that autism is differentially associated with deletions and schizophrenia with duplications.
I cannot cut n paste the table, but a look at the table clears all doubts. They also look at gene association data and come to a similar conclusion ruling out model A (autism, subsumed in schizophrenia) or model B (autism and schizophrenia are independent of each other).
Models 1C (diametric) and 1D (overlapping) both predict broad overlap in risk genes between autism and schizophrenia, and do not necessarily predict an absence or paucity of genes affecting one condition but not the other. In theory, these models can be differentiated by using data on specific risk alleles for specific loci (such as single-nucleotide polymorphisms, haplotypes, or genotypes), which should be partially shared under the overlapping model but different under the diametric model. For the genes DAO, DISC1, GRIK2, GSTM1, and MTHFR, the same allele, genotype, or haplotype was associated with both autism and schizophrenia, and for the genes AHI1, APOE, DRD1, FOXP2, HLA-DRB1, and SHANK3, alternative alleles, genotypes, or haplotypes at the same loci appear to mediate risk of these two conditions (SI Text). For the other genes that have been associated with both conditions, heterogeneity in the genetic markers used, heterogeneity among results from multiple studies of the same genes, and the general lack of functional information preclude interpretation in terms of shared or alternative risk factors.
Models of autism as a subset of schizophrenia (Fig. 1A), and autism and schizophrenia as independent or separate (model 1B), can be rejected with some degree of confidence, but models involving diametric etiology (model 1C) or partial overlap (model 1D) cannot be clearly rejected. Taken together, most of the data and analyses described here appear to support the hypothesis of autism and schizophrenia as diametric conditions, based primarily on the findings that reciprocal variants at 1q21.1, 16p11.2, 22q11.21, and 22q13.3 represent statistically-supported, highly-penetrant risk factors for the two conditions (Table 1), and that for a number of genes, alternative alleles or haplotypes appear to mediate risk of autism versus schizophrenia. Additional lines of evidence supporting the diametric hypothesis, from previous studies of autism and schizophrenia, include:
- 1. Data showing notable rarity of familial coaggregation of autism with schizophrenia (38), in contrast, for example, to strong patterns of co-occurance within pedigrees of schizophrenia, schizoaffective disorder, and bipolar disorder (39).
- 2. Psychiatric contrasts of Smith-Magenis syndrome with Potocki-Lupski syndrome (due to the reciprocal duplication at the Smith-Magenis locus), Williams syndrome with cases of Williams-syndrome region duplication, and Klinefelter syndrome with Turner syndrome, each of which tends to involve psychotic-affective spectrum phenotypes in the former syndrome, and autistic spectrum conditions in the latter (5, 40).
- 3. Effects of autism and schizophrenia risk alleles on common growth-signaling pathways, such that autism has been associated with loss of function in genes, such as FMR1, NF1, PTEN, TSC1, and TSC2 that act as negative regulators of the PI3K, Akt, mTOR, or other growth-signaling pathways (41–45), whereas schizophrenia tends to be associated with reduced function or activity of genes that up-regulate the PI3K, Akt, and other growth-related pathways (46–49).
- 4. Increased average head size, childhood brain volume, or cortical thickness in individuals with: (i) idiopathic autism (50–53), (ii) the autism-associated duplications at 1q21.1 (17) and 16p13.1 (32) and the autism-associated deletions at 6p11.2 (31), and (iii) autism due to loss of function (or haploinsufficiency) of FMR1 (54), NF1 (55), PTEN (56) and RNF135 (57). By contrast, reduced average values for brain size and cortical thickness, due to some combination of reduced growth and accelerated gray matter loss, have been demonstrated with notable consistency across studies of schizophrenia (58–62), and such reduced head or brain size has also been associated with the schizophrenia-linked CNVs at 1q21.1 and 22q11.21 (17, 63, 64), and with deletions of 16p13.1 (65).
I am more than pleased with these results. Badcock too is. You can read his comments here. What about you? What would it take to convince you?
Crespi, B., Stead, P., & Elliot, M. (2009). Evolution in Health and Medicine Sackler Colloquium: Comparative genomics of autism and schizophrenia Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.0906080106
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Nov 9th
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Two main underlying deficits have been proposed in autism- one concerning an inactive or non-existent Theory of Mind module and another a tendency towards Weak Central Coherence. ToM defects reflect in the communicative, social and imaginative deficits seen in autistics; while the savant skills as well as restrictive and repetitive behavior (restricted repertoire of interests ;obsessive desire for sameness – islets of ability – idiot savant abilities – excellent rote memory – preoccupation with parts of objects ) are best explained by taking recourse to the Autism-as-a cognitive-style having weak Central Coherence argument. I’ve discussed the crucial aspects of both of these two dimensions in y series of posts on autism and psychosis and shown how they have to be seen on a continuum and more as deviation from the normal range with one end as autism and the other as psychosis. We also know that psychosis itself is two dimensional with one dimension being that of schizophrenic spectrum and the other the bipolar spectrum. Thus what I propose is that we start seeing Autism also as a two dimensional disorder with TOM defect subtype a mirror image of schizophrenia; while the Weak CC subtype a mirror image of bipolar or manic depressive phenotype. Here are autistic and psychotic features on these dimensions (from Autism, Happe, the autistic deficits and assets table):
- ordering behavioural pictures (Baron-Cohen et al. 1986) vs ordering mentalistic pictures understanding “see” (Perner et al. 1989)
- understanding “know” protoimperative pointing (Baron-Cohen 1989c) vs protodeclarative pointing sabotage (Sodian & Frith 1992)
- deception false photographs (Leekam & Perner 1991, Leslie & Thaiss 1992) vs. false beliefs recognising happiness and sadness (Baron-Cohen et al. 1993a)
- recognizing surprise object occlusion (Baron-Cohen 1992) vs. information occlusion
- literal expression (Happé 1993) vs. metaphorical expression
- elicited structured play (Wetherby & Prutting 1984)vs. spontaneous pretend play
- instrumental gestures (Attwood et al. 1988) vs. expressive gestures
- talking about desires and emotions (Tager-Flusberg 1993) vs. talking about beliefs and ideas
- using person as tool (Phillips 1993) vs. using person as receiver of information
- showing “active” sociability (Frith et al. 1994) vs. showing “interactive” sociability
It is also pertinent in this regard to revisit the question of co-occurrence of autism and schizophrenia. Happe maintains that psychois can only be relaibly seen in Asperge’s group who might have a late developing ToMm ability. To quote:
The higher incidence of psychiatric disorders in this group (asperger’s group) (Tantam 1991, Szatmari et al. 1989b) is well explained by this hypothesis. Depression will be more common since these people have greater insight into their own difficulties and their own feelings and thoughts. Positive symptoms of psychosis, such as hallucinations and delusions would be found only in Asperger’s syndrome cases by this account, if one takes Frith & Frith’s (1991) view of these symptoms as resulting from an “over-active” theory of mind. Asperger’s syndrome people, who gain theory of mind late and therefore abnormally, may be at high risk for having their theory of mind “go wrong”. On this hypothesis it would be impossible for a Kanner-type autistic person (who has no theory of mind) to show these psychotic or positive symptoms. In this sense (according to Frith & Frith’s theory) Asperger’s syndrome would be something of a midpoint between autism and (positive or florid) schizophrenia; while the former is due to a lack of theory of mind, and the latter due to over-active theory of mind, some people with Asperger’s syndrome may show both the scars of early lack and the florid symptoms of late acquired theory of mind working abnormally hard.
There is some preliminary evidence to support the suggestion that the term “Asperger’s syndrome” could meaningfully be restricted to those subjects with autism who have achieved some ability to think about thoughts. Ozonoff et al. (1991) found that their group labelled (perhaps arguably) as having Asperger’s syndrome did not show impairments relative to controls.
It is interesting to note the ‘over-active’ theory of mind reference to Frith and Frith. I could not locate that paper but came across another paper by Abu-akkel that propose over-active ToM as a mechanism of psychosis. There are also some full text related articles available online that may be of interest to the serious reader. As for me, it is heartening to note that others concur with the theory of autism and psychosis as opposites on a continuum.
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Sep 15th
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I serendipitously came cross this article today about how our brains are self-organized criticality or systems living on the edge of chaos. There are many interesting ideas and gold nuggets in that article, and I’ll briefly quote from it.
In reality, your brain operates on the edge of chaos. Though much of the time it runs in an orderly and stable way, every now and again it suddenly and unpredictably lurches into a blizzard of noise.
Neuroscientists have long suspected as much. Only recently, however, have they come up with proof that brains work this way. Now they are trying to work out why. Some believe that near-chaotic states may be crucial to memory, and could explain why some people are smarter than others.
In technical terms, systems on the edge of chaos are said to be in a state of “self-organised criticality”. These systems are right on the boundary between stable, orderly behaviour – such as a swinging pendulum – and the unpredictable world of chaos, as exemplified by turbulence.
The quintessential example of self-organised criticality is a growing sand pile. As grains build up, the pile grows in a predictable way until, suddenly and without warning, it hits a critical point and collapses. These “sand avalanches” occur spontaneously and are almost impossible to predict, so the system is said to be both critical and self-organising. Earthquakes, avalanches and wildfires are also thought to behave like this, with periods of stability followed by catastrophic periods of instability that rearrange the system into a new, temporarily stable state.
Self-organised criticality has another defining feature: even though individual sand avalanches are impossible to predict, their overall distribution is regular. The avalanches are “scale invariant”, which means that avalanches of all possible sizes occur. They also follow a “power law” distribution, which means bigger avalanches happen less often than smaller avalanches, according to a strict mathematical ratio. Earthquakes offer the best real-world example. Quakes of magnitude 5.0 on the Richter scale happen 10 times as often as quakes of magnitude 6.0, and 100 times as often as quakes of magnitude 7.0.
These are purely physical systems, but the brain has much in common with them. Networks of brain cells alternate between periods of calm and periods of instability – “avalanches” of electrical activity that cascade through the neurons. Like real avalanches, exactly how these cascades occur and the resulting state of the brain are unpredictable.
Two of the power laws that are found in human brains relate to the phase shift and phase lock periods of EEG/fMRI or human brain systems etc. As per this PLOS comp biology paper:
Self-organized criticality is an attractive model for human brain dynamics, but there has been little direct evidence for its existence in large-scale systems measured by neuroimaging. In general, critical systems are associated with fractal or power law scaling, long-range correlations in space and time, and rapid reconfiguration in response to external inputs. Here, we consider two measures of phase synchronization: the phase-lock interval, or duration of coupling between a pair of (neurophysiological) processes, and the lability of global synchronization of a (brain functional) network. Using computational simulations of two mechanistically distinct systems displaying complex dynamics, the Ising model and the Kuramoto model, we show that both synchronization metrics have power law probability distributions specifically when these systems are in a critical state. We then demonstrate power law scaling of both pairwise and global synchronization metrics in functional MRI and magnetoencephalographic data recorded from normal volunteers under resting conditions. These results strongly suggest that human brain functional systems exist in an endogenous state of dynamical criticality, characterized by a greater than random probability of both prolonged periods of phase-locking and occurrence of large rapid changes in the state of global synchronization, analogous to the neuronal “avalanches” previously described in cellular systems. Moreover, evidence for critical dynamics was identified consistently in neurophysiological systems operating at frequency intervals ranging from 0.05–0.11 to 62.5–125 Hz, confirming that criticality is a property of human brain functional network organization at all frequency intervals in the brain’s physiological bandwidth.
Further, as per research by Thatcher et al, the EEG phase shift is larger in people with high IQ, while phase lock is smaller in the people with high IQ.
Phase shift duration (40–90 ms) was positively related to intelligence (P < .00001) and the phase lock duration (100–800 ms) was negatively related to intelligence (P < .00001). Phase reset in short interelectrode distances (6 cm) was more highly correlated to I.Q. (P < .0001) than in long distances (> 12 cm).
Further, in this paper , thatcher eta look at autistics and conclude that the people with autism show some deficits in phase shift and phase lock.
Results: In both short (6 cm) and long (21 – 24 cm) inter-electrode distances phase shift duration in ASD subjects was significantly shorter in all frequency bands but especially in the alpha-1 frequency band (8 – 10 Hz) (P < .0001). Phase lock duration was significantly longer in the alpha-2 frequencyband (10 – 12 Hz) in ASD subjects (P < .0001). An anatomical gradient was present with the occipitalparietal regions the most significant.Conclusions: The findings in this study support the hypothesis that neural resource recruitment occurs in the lower frequency bands and especially the alpha-1 frequency band while neural resource allocation occurs in the alpha-2 frequency band. The results are consistent with a general GABA inhibitory neurotransmitter deficiency resulting in reduced number and/or strength of thalamo-cortical connections in autistic subjects
It is interesting that in the original new scientist article , thatcher speculates that the pattern in schizophrenia may be reverse of what is seen in autism (exactly my thoughts, though the confounding of low IQ with autism may explain his autism results to an extent):
He found that the length of time the children’s brains spent in both the stable phase-locked states and the unstable phase-shifting states correlated with their IQ scores. For example, phase shifts typically last 55 milliseconds, but an additional 1 millisecond seemed to add as many as 20 points to the child’s IQ. A shorter time in the stable phase-locked state also corresponded with greater intelligence – with a difference of 1 millisecond adding 4.6 IQ points to a child’s score (NeuroImage, vol 42, p 1639). Thatcher says this is because a longer phase shift allows the brain to recruit many more neurons for the problem at hand. “It’s like casting a net and capturing as many neurons as possible at any one time,” he says. The result is a greater overall processing power that contributes to higher intelligence. Hovering on the edge of chaos provides brains with their amazing capacity to process information and rapidly adapt to our ever-changing environment, but what happens if we stray either side of the boundary? The most obvious assumption would be that all of us are a short step away from mental illness. Meyer-Lindenberg suggests that schizophrenia may be caused by parts of the brain straying away from the critical point. However, for now that is purely speculative. Thatcher, meanwhile, has found that certain regions in the brains of people with autism spend less time than average in the unstable, phase-shifting states. These abnormalities reduce the capacity to process information and, suggestively, are found only in the regions associated with social behaviour. “These regions have shifted from chaos to more stable activity,” he says. The work might also help us understand epilepsy better: in an epileptic fit, the brain has a tendency to suddenly fire synchronously, and deviation from the critical point could explain this. “They say it’s a fine line between genius and madness,” says Liley. “Maybe we’re finally beginning to understand the wisdom of this statement.”
Thus, it seems Autism and Psychosis are just two ways in which self-organized criticality can cease to do what it was designed to do- live on the edge , without falling on either side of order or chaos. GD Star Ratingloading...GD Star Ratingloading... Sphere: Related Content Wikio Wikio Effecient Related Posts:
What it is like to be a zombie?
May 30th
I am sure many of you are already familiar with Nagel’s perennial question ‘what it is like to be a bat?‘ (see this one with some added commentary too). Today I propose to ask a slightly different question ‘what it is like to be a zombie‘? That may seem absurd at the outset, as in many people’s mind Zombies are synonymous with no consciousness. I beg to differ. As I have already indicated in my last post on major conscious and unconscious processes in the brain, there is an easy problem of A-consciousness and there is a hard problem of P-consciousness. I have already tried to breakup A-consciousness in its parts and I similarly think that P-consciousness is much more that qualia (qualia I envisage as more grounded in sensory or perceptive systems). So given the fact that most zombies are behaviorurally indistinguishable from normal humans, and given the fact that most people who argue for zombie models of humans (that ‘there is no one home to watch/direct the picture’) do still endow the zombie and themselves with the A-consciousness aspects - they do not deny that a representation is made and is consciously available for processing (the theater of consciousness) , it is reasonable to speculate that although lacking full P-consciousness, it would still be something like to feel like a zombie. Let me draw an analogy, in some dissociative disorders, one starts seeing the world as unreal (derealization) and the self as unreal (depersonalization) ; yet even though one believes oneself to be unreal there is still something it is like to exist in that ‘unreal’ state.
Similarly, though one may model oneself and others as zombies, still it would be something like it is to be in a state that thinks and believes that one is a zombie and also acts accordingly. I am making a leap here. I am assuming that awareness or modeling of ones A-conscious experiences leads to or affects one’s phenomenal consciousness. Thus, in my view , someone who models oneself and others as a zombie, would have a different sort of P-consciousness or what it feels-to-be-like, than a person who models oneself and others as sentient agents and his P-cosnsciousness would be of a different nature.
Now consider the problem we face when confronted with a world which is deterministic and chaotic at the same time, and which is inhabited by agents which seem to be unpredicatable and constrained at the same time. I have already indicated elsewhere, that people may form tow types of model- one is a statistical/ deterministic model that they may apply to the world; another is a probabilisitic/agentic model that they may apply to the self (as well as other sentient beings). If one keeps these domains of folk-physics and folk-psychology separate, all is hunky dory; all hell breaks lose (pun intended; zombies are correlated with dead apocalypse scenarios in popular culture) when one applies a deterministic model (that fits the world) to the self/others. Similarly all hell breaks loose, when one applies an agentic/indetrminsitc model (that fits the sefl/others) to the world.
For today, we will focus on the problem of modeling self, and leave the problem of modeling world for a later day. A self may act differently in many similar/same situations. If it acts the same on each occasion, given the same situation; we can easily say that the situation causes the action. This poses no problem for the zombie (I will refer to a zombie as a person whose self/other conceptualization is as that of oneself/others as machines), as one has a deterministic rule that defines the self- (given situation A-> action B), and thus one can keep one’s model of self as-a-deterministic-being consistent. On the other hand, if the situation A sometimes leads to action B, but at other times to action C, then one has to explain the variance in the behavioral output. Consider first the problem of explaining the variance between-subjects. Given the same situation A, subject Z acts in way B while the subject Y acts in way C. There is considerable variance. If one assumes all selves as created equal, then all should have behaved similarly. Either one has to grant an extraordinariness and uniqueness to all selves, or if one has a statistical and ordinary nature of human beings, one has to grant that the subject given the same situation, should have behaved identically. But we all see that there is considerable variance. This variance is individualistc and one may try to explain this between-subjects variance using subject’s personal history (prior conditioning: a behavioristic model; or repressed emotional experiences/memories: psychoanalytical theory), one may also look at subject’s common ancestral history and use that to explain behavior (genetic differences: evolutionary biology; cultural differences : anthropology ) or one may even look at his holistic experiences and use that individualistic experiential history as a basis for explaining behavior ( consider two identical twins that because of their different sampling of environment may end up as differently conditioned etc). Phew that covers all the major psychological theories that I could remember.
Now lets focus on the problem of explaining within-subjects variance ; given the fact that the Situation is the same (situation A) and the subject is the same (subject Z), why does the same subject react differently to the same situation (acts in ways B and C). This is a relatively hard problem. One could deny the problem itself and claim that no situation is identical, but hey we are doing armchair philosophy right now, and we have already agreed to the premise of existence of a same situation A when we discussed between-subjects variance above, so it doesn’t hurt to concede that the situation A can be same for subject Z, but he may still react differently in ways B and C. None of the above psychological approaches, if applied in a strict, causal deterministic sense can explain the same subject Z reacting differently to situation A , as the subject Z’s personal history (conditioning, repressed memories) or ancestral history (genes, cultural influences) or even previous experiences and choices remain the same and thus should ideally have led to the same behavior. I am making an assumption here that situation A is repeated twice or more in succession (closely in time) so that one cannot counter and say that conditioning (to take an example) has changed in meanwhile due to situation A itself and thus, as the subject Z (at time t=1) has changed to an extent (by delta effect of situation A on the ‘earlier’ subject Z at time t=0) , so he may react differently at tome t=1 from how he reacted at time t=0. What we are really doing is doing away with a term of the equation; we are saying subject Z is not constant (it keep changing- self as constantly changing- a Buddhist philosophical premise and also favored by many in psychology) , but in the spirit of Camus’s Absurdity argument in Myth of Sisyphus, I am not satisfied with doing away one of the variables of the equation itself, so let us see, where this model of self-as-a-deterministic-being leads us to. Now that subject Z remains the same for two iterations of situation A, how can one explain the variance that results in action B at one time and action C at the other. One can again try to dissolve the equation by claiming that there is no unified self in space (earlier argument was that there is no unified self in time- it is a constantly changing in time self) – that is we are not a single self , but made up of many different selves- some conscious, some unconscious etc. Different selves may compete with each other and whoever wins at the moment, directs the show. Again assuming different selves cohabiting the same person doesn’t really feel what-it-is like to-be-oneself , and apart from some multiple-personality disorder (DID) this has not been frequently reported; but more importantly . Granting multiple selves to subject Z again vanishes one of the terms of the equation, and I am not interested, I want to stay and see where my inquiry takes me to.
If the situation is same, the subject is same and a single one, than what explains the within-subject variance? One has to grant unpredictability to a self that was assumed to be deterministic to begin with. One can now take two routes, either resort to the magical mumbo-jumbo of quantum world and indeterminacy and uncertainty; or stay in the deterministic world but look at complex systems/ chaos theory etc to explain the apparent indeterminacy. I believe a zombie will prefer the second route and model the self as a complex-system/chaotic self. One could say that the self/ others are still completely determined, but due to an initial ‘butterfly flapping wings effect‘ the self seems or appears to be unpredictable and will continue to remain unpredicatble because of that ‘original sin’. The original sin may be how the infant took the first breath, whether he cried or laughed when born; what the time of conception was etc etc. Whatever may be the initial condition that escaped measuring, it leads to an unperdicatble self, a chaotic self that one cannot measure in the present and thus cannot predict in the long term- a self that is as fickle and as perdicatble as the waether.
There are important implications to seeing / modeling the self as a chaotic system. That leads to a diminished sense of agency / responsibility as perhaps there is not much one can do to correct the original sin and thus modify/ change ones long term behavior. This diminished p-consciousness of agency and the consequent differential experiences of sensations/ perceptions should also lead to diminished qualia or what-it-feels-to be-like feeling. Maybe the zombies do feel really like zombies- mechanical and chaotic- going along the life stream in a mechanical , predetermined manner- seeing all and understanding all, even acting and reacting, but feeling impotent and lifeless, perhaps just fulfilling a role which has been scripted by someone else (the initial butterfly flapping its wings or the original sin).
This is a good point to stop, but I would like to thank Melbren, a reader of this blog, who commented on my last post and asked me if I would re-define , give a new name to Autism spectrum disorders. Thta made me think and somehow led to this post. But first his comment:
Very cool post. And I love your blog. I am trying to think about this particular post in terms of your psychotic spectrum–most specifically as it relates to autism. But I am impeded by an overwhelming feeling that if we have a new spectrum–we’ll need new terms. The term “autism” has outgrown its usefulness, don’t you think?
For one thing–if we are to use the framework of a psychosis spectrum–I think there will be a lot of people currently diagnosed with autism who are, in fact, organically more biased toward the opposite end of the spectrum. However, such individuals may still have “stereotypies” that we have come to associate with the term “autism.”
That being said–if you were appointed “word czar of the day,” and, as such, had the authority to scrap all of our conventional terminology and come up with “new and improved” terms that are more in alignment with a psychosis spectrum–what new terms would you choose?
I conceptualize autism as defect whereby people falsely apply a deterministic model (relevant for the world/ non-living things) to the self/others (living things) ; I consider of psychosis as the reverse, whereby one applies an agentic model to the world, thus exhibiting magical thinking etc. Because psychotic spectrum is consptualised in terms of a disability (loss of contact with reality), I would rechristen autism spectrum as the zombie spectrum (loss of contact with agency); of course, If I indeed am the ‘word czar of the day’ I’ll probably rename both as consciousness-orientation (psychotic spectrum) and reality-orientation (autistic spectrum) and highlight the good aspects of both- shaministic Altered states of consciousness and creativity of schizotypals and the scientific and savantic abilities of the Aspergers. Of course, in a lighter vein, perhaps the autistic spectrum people are ‘muggles’ (believers in ordinariness ) who still have to come to terms with the ‘magic’ (believers in extraordinariness) of consciousness. GD Star Ratingloading...GD Star Ratingloading... Sphere: Related Content Wikio Wikio Effecient Related Posts:
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