This is the last post on this site. I have a new blog on the same topic at http://dyslectern.info and I will be posting new articles there. Please visit it. The 600 or so postings on this site will stay with the same URLs for the foreseeable future.
It seems that many people think that the basic mechanisms of the brain are understood, at least in outline. Therefore what we don’t understand is somehow a mystery that requires a mystical explanation. One of these mystical explanations is panconsciousness.
What theory appears to do is to make consciousness a fundamental particle, like say an electron or a quark. But one that physics has not found. Why might physics have missed a fundamental particle? The obvious answer is because it has no effect on other fundamental particles due to having no mass, no charge, other electromagnetic effect, or weak force etc. There appears to be nothing it can do to conventional matter or energy and therefore cannot be ‘seen’ by physics. If it is present then it is aware of itself but nothing is aware of it. Great – this is just another dualism. It is just another mystical, magical, spiritual way to avoid the physical world.
There is no need for this. The brain is far from completely understood but there is continual progress. Don’t panic and jump into dualism.
Barbara King has some interesting things to say when reviewing Michael Corballis book, The Recursive Mind. (here)
Here is her description the situation:
“Astonishing animals show up everywhere these days. Cooperative apes, grief-stricken elephants, empathetic cats and dogs crowd our bookshop shelves. It’s all the rage to plumb the cognitive and emotional depths of the animal world, rejecting sceptics’ sneers of “anthropomorphism” to insist that we’re finally coming to see animals for who they really are: not so different from us. Pushing against this tide of animal awe is a competing cultural trope, the relentless seeking of human superiority.”
She says that Corballis believes that humans uniquely have language, thought and civilization, which is due to having mental time travel and theory of mind, which in turn is due to having recursion. Well, here we go again with Chomsky’s uniquely human key, recursion.
Corballis is also a co-author with Suddendorf of a 1997 paper on mental time travel which makes a similar argument. “This article contains the argument that the human ability to travel mentally in time constitutes a discontinuity between ourselves and other animals. Mental time travel comprises the mental reconstruction of personal events from the past (episodic memory) and the mental construction of possible events in the future. It is not an isolated module, but depends on the sophistication of other cognitive capacities, including self-awareness, meta-representation, mental attribution, understanding the perception- knowledge relationship, and dissociation of imagined mental states from one’s present mental state. These capacities are also important aspects of so-called “theory of mind”, and they appear to mature in children at around age four. Furthermore, mental time travel is generative, involving the combination and recombination of familiar elements, and in this respect may have been a precursor to language. Current evidence, although indirect or based on anecdote rather than on systematic study, suggests that nonhuman animals, including the great apes, are confined to a “present” that is limited by their current drive states. In contrast, mental time travel by humans is relatively unconstrained, and allows a more rapid and flexible adaptation to complex, changing environments than is afforded by instincts or conventional learning. Past and future events loom large in much of human thinking, giving rise to cultural, religious, and scientific concepts about origins, destiny, and time itself.” So all the animal studies between 1997 and 2013 have not had any effect on Corballis.
But Corballis does seem to be aware of many relevant animal studies, even contributing to them, and so it is surprising that he would take this outlook. But for some, and Corballis may be one, it seems there is a real need to find a single, significant difference between humans and other animals. Why? I assume it is because they cannot imagine an alternative.
As I have said in previous posts. We are unique but all species are unique. We are not uniquely unique. We are unique in the same way other animals are. We have a little more of this and a little less of that, a different pattern to the mix. But we are basically very similar to other mammals. We are very unlikely to have some characteristic with no other animal having anything similar. The mammals are different from each other but metaphorically they are painted with the same palette. A few million year’s ago there was no difference at all between us and chimps, we were one species. Our paths diverged slightly and so we became a little different and then a little more. But we are still, some millions of years on, very similar to chimps anatomically, genetically, developmentally. But definitely we are not similar culturally.
But when we look at the chimp in the jungle and the astronaut traveling to the moon there is a world of difference. It is this difference that drives people to look for some important significant ability that separates us from the chimp. They look for a different physical characteristic or ability. But the big difference between us and the chimp is cultural not biological.
Cultural evolution was very distinct from biological evolution. It is exponential. Its growth feeds more growth. Suppose we start with some stones used as tools (chimps also use stones). But after a while we start fashioning their shape a bit. Sometime later we get a bit better at fashioning and develop a skill at knapping. Slowly, but at an increasing pace, we learn to make really great stone tools. The same would be true of language. We start as chimps do with communication of a limited kind: a few calls and cries that can be sequenced to a small extent and a number of gestures. We can build on this slowly, very slowly at first but with increasing speed. We end up with a primitive language. We start taking advantage of fires when they occur naturally, then we try to keep and manage them, then make them from scratch whenever we want a fire. Every step in the accumulation of culture is faster than the one before. The culture force becomes enormous and eventually we are changing our way of living constantly. Each innovator could have said, like Newton, “If I have seen farther it is by standing on the shoulders of giants”.
How much does it take for a species that is capable of cultural transmission (as chimp and humans both are) to become two species, one with very little culture and the other with a growing and finally exploding culture. It could be as little as a slightly different niche with slightly different problems and opportunities. Some of our joint ancestors stepped onto one path and some on to another. Of course there was biological evolution in humans but it appears to be mostly driven by cultural evolution rather than the other way around.
As an example of how this works look at the effect of population. Human population has been growing since the start of agriculture in an exponential fashion. As the growers of food can produce more than their family needs, they can have more children and so can others who are fed with the surplus. As long as there is land, we have more food – more people – more farms – more food in an ever increasing spiral. The amount of innovation depends on the population density (or, more likely, the number of people that an average person has contact with in some fashion). So cities are more innovating than the agricultural countryside and cities have been growing in number and size at an ever increasing rate. Soon more than half of all humans will live in large cities. The contact between people is also increasing with travel, writing, electronic communications and so on. So one exponential growth is the basis of another exponential growth and that is the basis of yet another. As far as cultural evolution is concerned it is not just growing at an exponential rate but the rate itself is growing.
The point I am making is that a small, almost immeasurable, difference in the amount of cultural change accumulated in a generation between the group that would become humans and the group that would become chimps, can over a few million years result in the sort of differences we see now. Humans could have come from the individuals with the slightly harder environment or something like that. There need be no biological difference at first. Later there will be some genetic change that is driven by cultural change, of course. For example, once some sort of pre-language culturally acquired vocal communication starts to be an important advantage then there is going to be a selective pressure for changes to the vocal apparatus, hearing and the brain to make that communication more fixed and efficient in the population. It has become a situation where the genetic change follows the cultural one.
We do not need to look for the source of our unusual life style and achievements in some special facility that we have and other animals do not. It is more likely that our history started with some small quirk of fate like which side of the Rift Valley was our home. Our biological changes are mostly caused by our cultural changes and need not actually be very large. There is no need to look for some very significant physical difference, like a brain that can uniquely do recursion, to explain our present situation.
I feel that language is one of (probably the most) important difference between humans and other animals. This post is not an attempt to downgrade the importance of language. But if we do not notice the limits of its importance then we cannot understand all the language-less animals. Language is not the only vehicle for many aspects of thought. Many assume that without language it is impossible to think, to remember, to communicate, to have categories/plans/procedures, to have culture and to even have consciousness. Slowly it is being shown that other animals can do many of the things that used to be classed as only-with-language skills. We just do them more effectively with language.
In a recent paper (Jennifer Vonk. Matching based on biological categories in Orangutans (Pongo abelii) and a Gorilla (Gorilla gorilla gorilla). PeerJ, 2013; 1: e158 DOI: 10.7717/peerj.158) it was shown that apes could put pictures of animals into categories that relied on more than close visual resemblance. The apes used the type of categories that we use in language and in science. They did not need the use of language to construct these categories. This is just the latest in a long string of reports which show animals doing various types of cognition that were once thought only possible in humans because of human language and consciousness.
It seems obvious that musical composers think when composing and that they think in musical not semantic ideas. Sculptors think but not in words but in three dimensional shapes. Athletes, for instance soccer players, think but they think kinetically. In fact they often do not have the time to think in words and ‘choke’ when they try. In cases like these it may be possible (it also may not) to document in words the processes that ended in a tune, a statue, or a scoring goal but that is not relevant – it is not how the process actually happened but just a not very accurate description of it.
We do not need language for some thinking. What we need is a working model of the world in which what-if scenarios can be played out. It does not have to be the whole world but one in which we can hear or see or feel causal chains. There is no reason to assume that animals cannot think quite sophisticated thoughts in this sensory-motor non-symbolic way. There is also no reason why they cannot think symbolically, just in wordless concepts. They communicate with each other. They seem to have episodic memory and therefore would have conscious experiences to remember. It would be wise to assume that the highly social mammals, especially primates, have basically the same cognitive apparatus as ourselves and that all vertebrates are similar in the general way the brain works.
A recent paper by Zikepoulos and Barbas (citation below) traces the detailed anatomy of connections between the prefrontal cortex and the ventral anterior thalamus. Here is the abstract:
Pathways linking the thalamus and cortex mediate our daily shifts from states of attention to quiet rest, or sleep, yet little is known about their architecture in high-order neural systems associated with cognition, emotion and action. We provide novel evidence for neurochemical and synaptic specificity of two complementary circuits linking one such system, the prefrontal cortex with the ventral anterior thalamic nucleus in primates. One circuit originated from the neurochemical group of parvalbumin-positive thalamic neurons and projected focally through large terminals to the middle cortical layers, resembling ‘drivers’ in sensory pathways. Parvalbumin thalamic neurons, in turn, were innervated by small ‘modulatory’ type cortical terminals, forming asymmetric (presumed excitatory) synapses at thalamic sites enriched with the specialized metabotropic glutamate receptors. A second circuit had a complementary organization: it originated from the neurochemical group of calbindin-positive thalamic neurons and terminated through small ‘modulatory’ terminals over long distances in the superficial prefrontal layers. Calbindin thalamic neurons, in turn, were innervated by prefrontal axons through small and large terminals that formed asymmetric synapses preferentially at sites with ionotropic glutamate receptors, consistent with a driving pathway. The largely parallel thalamo-cortical pathways terminated among distinct and laminar-specific neurochemical classes of inhibitory neurons that differ markedly in inhibitory control. The balance of activation of these parallel circuits that link a high-order association cortex with the thalamus may allow shifts to different states of consciousness, in processes that are disrupted in psychiatric diseases.
It is interesting that these loops are similar to sensory loops, but connect to an area of the cortex that is not primarily sensory. The prefrontal cortex is active in executive functions: thought, planning, emotional control, working memory, goals, decisions, patterns of activity, levels of awareness. The thalamus is guiding/driving these cortical functions and they, in turn, are guiding/driving the ventral thalamus. Of course, both these areas are also connected to other centers – these loops are not isolated. The prefrontal cortex is strongly connected to other parts of the cortex and the ventral thalamus is strongly connected to the basal ganglia. The authors point out how important these paired loops are in understand a number of diseases including schizophrenia.
Here is the paper’s illustration of the loops and an explanation of the diagram:
Figure 11. Schematic diagram summarizing the features of reciprocal driving and modulatory pathways linking the prefrontal cortex with the ventral anterior nucleus. The thickness of the lines, connecting thalamus and cortex, indicates the strength of the projection. The size of the dot indicates the size of the terminals. Solid lines (d1 and d2) represent driving projections, and dotted lines (m1 and m2) represent modulatory projections. Green represents PV+ labeling and red CB+ labeling. There were two parallel reciprocal circuits between the prefrontal cortex and ventral anterior nucleus. One originated mostly from PV+ thalamic projection neurons (green, bottom panels), and terminated focally as large boutons in the middle-deep layers (IIIb–VI, center blue panels) of the prefrontal cortex (d1). In turn, presumed layer VI neurons projected (m1) and terminated as numerous small boutons that formed synapses mainly with PV+ thalamic projection neurons (green, bottom panels), enriched with metabotropic receptors (mGluR1a, purple). The other circuit originated from CB+ thalamic neurons and sent widespread projections (m2) mainly to the superficial layers (I–IIIa) of the prefrontal cortex (grey), and terminated mostly as small terminals traversing the borders of neighboring regions and in association with the apical dendrites of neighboring layer V neurons. In turn, a prefrontal pathway (d2) established synapses through many small and fewer large boutons mainly on CB+ thalamic projection neurons (red) with ionotropic receptors (NR1, white). The PV+ thalamo-cortical pathway (d1) terminated mostly in the middle-deep layers, which were also rich with PV+ local inhibitory neurons (green spheres). In contrast, the CB+ thalamo-cortical pathway (m2) terminated in the superficial layers, which were rich in CB+ local inhibitory neurons (red spheres).
Work like this will get us closer to understanding consciousness.
Zikipoulos B, & Barbas H (2007). Parallel Driving and Modulatory Pathways Link the Prefrontal Corex and Thalamus PLoS ONE, 2 (9) DOI: 10.1371/journal.pone.0000848
There seems to be growing displeasure with the current plans for ‘big science’ in the area of neuroscience. It is true that if enough data is collected and it is stored and manipulated well, then it can produce understanding of a system. That was the idea behind the human genome project and although it did not produce the answers expected, it did produce, and is still producing, a great deal of understanding. A ‘big science’ effort did find the higgs boson. But just data by itself does not give understanding. I have posted on this before (here).
There is a recent comment in Nature on this subject: RD Fields, Map the other brain, Nature Sept 2013 (here). Field’s message is that we are not looking at the real unknowns of the brain in these big projects – the glial cells. This reminds me of the old saying, “if all you have is a hammer, everything looks like a nail”. Tools for examining the activity of glia are far less developed than those used to examine neurons. Would we not be better off to spend money on developing tools to study glia than to collect massive information on the connectome? He makes good sense.
“I believe that exploring neural networks and developing techniques with which to do so are important goals that should be vigorously supported. But simply scaling up current efforts to chart neural connections is unlikely to deliver the promised benefits — which include understanding perception, consciousness, how the brain produces memories, and the development of treatments for diseases such as epilepsy, depression and schizophrenia.”
“In any major mapping expedition, the first priority should be to survey the uncharted regions. Our understanding of one half of the brain (the part comprised of astrocytes, oligodendrocytes and microglia) lags a century behind our knowledge of neurons. I believe that answers to questions about the brain and public support for a large-scale study are more likely to come from expanding the search into this unknown territory. As a first step, tools such as optogenetic methods and mathematical models are needed to assess the number, distribution and properties of different kinds of glial cell in different brain regions, and to identify how glia communicate with each other and with neurons, and what developmental and physiological factors affect this. This exploration into the ‘other brain’ must be done together with the proposed studies of neurons. It cannot be achieved as a by-product of them.”
There has been an example in plain view. “The 302 neurons and 7,000 connections that make up the nervous system of the roundworm Caenorhabditis elegans were mapped in the 1970s and 80s. More than two decades later, little is understood about how the worm’s nervous system produces complex behaviours.” In other words the connectome does not result in automatic understanding even in an extremely simple example.
This again seems part of a pattern. The connectome is a sort of ‘wiring diagram’ and if we think of the brain as a sort of conventional computer, the wiring diagram is very valuable to understanding the circuits. As long as we have this computer hangup, there seems not place or role for glia. So way more than half of the brain’s cells are just ignored in an expensive ‘big science’ project.
Being left-handed and dyslexic, the first area of the brain that interested me, long ago, was Broca’s area. The theory that has been around for some time in various forms – that the evolution of language is connected to the evolution of fine motor skills with the hands – has intrigued me. A recent paper gives some evidence for the co-evolution of language and complex tool knapping (see citation below).
Knapping stone is not something that can be done in a scanner. The researchers used functional transcranial Doppler ultrasonography (fTCD) to measure brain blood flow lateralization patterns. This method allow the subjects to move more or less naturally. Competent knappers made stone tools and did language exercises while the difference in blood flow between the two hemispheres was measured. The language test was cued word generation; a letter was given and the subject generated a string of words beginning with that letter. The tool making was the production of Acheulean tools, an early but sophisticated style of knapping. They found a similarity in the blood flow pattern in the two tasks during the first 10 seconds. After that the pattern diverged with the language task having more blood flow to the left hemisphere and the knapping task having more to the right. In both tasks the first few seconds are used to ‘set up’ or plan the activity.
“Our participants showed correlated LIs (lateralization indices ) during the initial 10 seconds after task onset for cued word generation and handaxe production. A number of previous studies have directly correlated LI values for different tasks and shown that tasks which draw on shared neural processing sites, such as the three language tasks described by Bishop, result in highly correlated LI measures. In contrast, cognitive tasks that draw on disparate brain areas, such as language and visuo-spatial tasks like driving, visual attention, or visual memory, lead to uncorrelated LIs. Since Acheulean stone knapping is a highly visuo-spatial task , our finding of a correlation between knapping and language requires explanation.
Making an Acheulean handaxe requires both working memory and planning memory. This careful planning is dominant in the initial phase of each experimental block in our study. This action planning draws on brain areas that are shared with language tasks, such as the left-lateralized ventral premotor areas and Broca’s area. Our subject pool shows highly correlated individual brain blood flow lateralization in the early phases of task execution for both tasks. Our findings add empirical data to the hypothesis that action planning for tool-making and language draw on shared functional brain structures. The correlated time-signatures for Acheulean knapping and language, which remain significantly correlated within subjects despite variability between subjects, indicates that the same brain networks are being activated for both tasks. They suggest that tool-making and language share a basis in more general human capacities for complex, goal-directed action. ”
If tool making and language co-evolved then this says something about the age of language.
“… the network for complex action planning might have emerged in human evolution as part of our brain size increase and reorganization, leading to both language and tool-making . We suggest the start of the Acheulean techno-complex at 1.75 million years ago as a likely candidate for this because Acheulean knapping required more complex action planning than Oldowan technologies.”
This is not a new idea. There is an interesting paper by Greenfield (see citation below) from 12 years ago. Here is the abstract:
During the first two years of human life a common neural substrate (roughly Broca’s area) underlies the hierarchical organization of elements in the development of speech as well as the capacity to combine objects manually, including tool use. Subsequent cortical differentiation, beginning at age two, creates distinct, relatively modularized capacities for linguistic grammar and more complex combination of objects. An evolutionary homologue of the neural substrate for language production and manual action is hypothesized to have provided a foundation for the evolution of language before the divergence of the hominids and the great apes. Support comes from the discovery of a Broca’s area homologue and related neural circuits in contemporary primates. In addition, chimpanzees have an identical constraint on hierarchical complexity in both tool use and symbol combination. Their performance matches that of the two-year-old child who has not yet developed the neural circuits for complex grammar and complex manual combination of objects.
This points to a very long history for the development of language. Current estimates for the start of language vary from almost 2 million years ago to 50,000. The 50,000 figure is often quoted but is probably no longer accepted by most Evolutionary Biologists but only by ‘one-mighty-leap’ theorists.
Uomini NT, & Meyer GF (2013). Shared Brain Laterialization Patterns in Language and Acheulean Stone Tool Production: A Functional Transcranial Doppler Ultrasound Study. PLoS ONE, 8 (8) : 10.1371/journal.pone.0072693
Greenfield PM (1991). Language, tools and brain: the ontogeny and phylogeny of hierachically organized sequential behavior. Behavior & Brain Sciences, 14, 531-595
I have been called a couple of times over the word ‘consciousness’. Some think it is not a useful word but a confusing one and therefore should be avoided. My answer is that it is a perfectly clear word for a very particular thing. It is the word for our being aware and experiencing ourselves in the world. We all experience things when we are awake; we don’t have to be told what it is to be conscious. It would be fairly difficult to talk about thought or feeling or behaviour without having a word for consciousness.
The problem arises when we assume that consciousness means more than simply being aware and experiencing life as it happens. As soon as we leave the phenomenal area and look at other aspects that might be grouped in with the phenomenal, we start having problems. The problems come from the different ways that people view the world, people and brains.
Max Velmans in a paper, How to Define Consciousness – and How Not to Define Consciousness, Journal of Consciousness Studies 16(5) 2009, attempts to develop a working definition of consciousness. He starts with the simple phenomenal definition. This is the definition that I think forces us to use the word – we don’t have another word for it.
This everyday understanding of consciousness based on the presence or absence of experienced phenomena provides a simple place to start. A person, or other entity, is conscious if they experience something; conversely, if a person or entity experiences nothing they are not conscious. Elaborating slightly, we can say that when consciousness is present, phenomenal content (consciousness of something) is present. Conversely, when phenomenal content is absent, consciousness is absent. This stays very close to everyday usage and it provides a simple place of departure on which widely diverging theories can agree. It also makes sense to stay as close as possible to everyday, natural language usage for related terms.
it is important to reserve the term “mind” for psychological states and processes that may or may not be “conscious”. … restricting the phenomenology of “consciousness” to the phenomenology of “thought” is too narrow. … To allow a clear distinction between consciousness of oneself and consciousness of things other than oneself, it makes more sense to reserve the term “self-consciousness” for a special form of reflexive consciousness in which the object of consciousness is the self or some aspect of the self. … it is necessary to distinguish “consciousness” in the sense of “phenomenal consciousness” from wakefulness and other states of arousal … much knowledge is unconscious, or implicit (for example, the knowledge gained over a lifetime, stored in long-term memory). So consciousness and knowledge cannot be co-extensive.
More interesting is his take on ‘conscious processes’:
I have argued that the psychological and philosophical literature confounds three distinct senses in which a process might be said to be “conscious.” It might be conscious:
in the sense that one is conscious of the process
in the sense that the operation of the process is accompanied by consciousness (of its results) and
in the sense that consciousness enters into or causally influences the process.
the content of such thoughts and the sequence in which they appear does give some insight into the way the cognitive processes (of which they are manifestations) operate over time in problem solving, thinking, planning and so on. Consequently such cognitive processes are partly conscious in sense (1), but only in so far as their detailed operation is made explicit in conscious thoughts, thereby becoming accessible to introspection. Many psychological processes are conscious in sense (2), but not in sense (1) – that is, we are not conscious of how the processes operate, but we are conscious of their results. …Crucially, having an experience that gives some introspective access to a given process, or having the results of that process manifest in an experience, says nothing about whether that experience carries out that process. That is, whether a process is “conscious” in sense (1) or (2) needs to be distinguished from whether it is conscious in sense (3). … Consciousness of a physical process does not make consciousness responsible for the operation of that process (watching a kettle does not determine when it comes to the boil).
I avoid ‘conscious process’ because I believe that my readers might take the phrase to have meaning (3) which I reject completely and would never mean. I usually specify exactly what I mean when I am talking about consciousness of the results of a process as in (2). And, I usually call (1) guessing.
Mo Costandi has an excerpt from his book, 50 Human Brain Ideas You Really Need to Know, on his Guardian blog (here). The post is about mirror cells.
It can be summed up:
Mirror neurons are cells that fire during both the execution and observation of a specific action.
They have not actually been observed in humans although they have been inferred.
What they actually do in the monkey brain is not clear.
They have been hyped out of all proportion to the knowledge that we have of them.
So we have people proposing that autism is the result of the ‘mirror system’ not functioning properly, that mirror neurons allow us to understand the actions of others, that they are needed for imitation, that they allow us to understand action ‘from the inside’, that they are the foundation of empathy, etc. etc.
I assume that someday mirror neurons will be found in humans but that they will turn out to be the action equivalent of sensory cells that recognize particular objects, or particular places. The brain can recognize particular things by activity in particular groups of neurons: a kitchen chair, a friend’s back yard, Aunt Jane, and the action of reaching for something. There does not seem to be a special explanation required. The fact that mirror neurons have taken on a sort of mystical quality implies that there is something very important missing from many people’s understanding of the brain. Some gap that has to be bridged.
In some quotes found in other articles, there is a clue to what the gap seems to be. G. Rissolatti: “We are exquisitely social creatures. Our survival depends on understanding the actions, intentions and emotions of others. Mirror neurons allow us to grasp the minds of others not through conceptual reasoning but through direct simulation. By feeling, not by thinking.” P. Greenfield: “Mirror neurons provide a powerful biological foundation for the evolution of culture. Now we see that mirror neurons absorb culture directly, with each generation teaching the next by social sharing, imitation and observation.” M Iacoboni: “Mirror neurons suggest that we pretend to be in another person’s mental shoes. In fact, with mirror neurons we do not have to pretend, we practically are in another person’s mind.” So there is the problem – how do we know what other’s intentions are without some magical mind reading?
That does not seem a problem. We guess. Simple, we guess. We are smart guessers. First, we sometimes guess wrong. Two, the guessing skill is something that can be learned. Three, we have to guess our own intentions so why not others.
Consciousness does not have a direct knowledge of intent – we guess and we are not even conscious of our guessing. We are back to the awkward knowledge that what we feel consciously (first our intention, then our decision and then our action) is not what is actually happening. Our conscious thoughts are not the cause of the action. Our conscious thought is constructed after the events have taken place. We are guessing what our intent is. Libet’s experiment is not going to fade away. We have to face up to this. We have to get used to how our motivation really works rather than put a mystical, magical power into mirror neurons.
I notice that there are still a lot of people who assume that the brain is some type of general computer that can be modeled with a Turing machine. This idea cannot be assumed – it must be indicated if not proven. Just because a problem can be solved by a Turing machine and that problem is solved by the brain does not mean that the way the brain solved the problem is by a Turing machine type method. Nor is it clear (at least to me) that problems that are not framed in symbols of some sort and manipulations of those symbols, can all be solved by a Turing machine. An identity between a Turing machine and a brain has yet to be shown.
We have no reason to believe that brains are digital, that they deal with discrete states analogous to 1s and 0s, true and false, or yes and no. Everything points to the brain dealing with continuous values rather than discrete ones. We have no reason to believe that the brain uses algorithms, pre-formulated and step-wise methods of processing. In fact the physiology of brain cells is too slow for step-wise methods for most tasks. Everything points to massive overlapping loops, covering much of the brain at any instant, which settle quickly to momentarily stable configurations. We should forget about the brain being like a Turing type computer. We can entertain analogue computers or control systems but not general digital computers.
This should not be a surprise. Take the idea of a general computer. Why would our brains have general facilities rather than be ‘purpose built’? They are the product of evolution. They evolved as part of a slowly evolving body and slowly changing niches. What we need the brain to do, it is likely to do very well. And, what we don’t need it to do, it will not be able to do. It is very good at pattern recognition and very poor at complex mental mathematics. (We use calculators but not pattern recognizers.) We can push the envelop in certain new directions but only by using facilities evolved for something else.
How did it come to be that so many people had accepted the computer metaphor in its most literal form. I recently ran across a historical perspective in a discussion of amodal symbols, symbols that are not grounded in any mode like the senses, actions, situations or emotions etc. – the sort of symbols a Turing type machine might use, ungrounded and disembodied symbols, so to speak. Symbols that are very problematic from the biological point of view. The brain is after all a biological organ and our cognition is a biological function.
From Barsalou, L. (2008). Grounded cognition. Annual Review of Psychology, 59, 617-645
“Perhaps surprisingly, grounded cognition has been the dominant view of cognition for most of recorded history. Nearly all prescientific views of the human mind going to back to ancient philosophers (e.g., Epicurus 341–270 B.C.E.) assumed that modal representations and imagery represent knowledge analogous to current simulation views. Even nativists, such as Kant (1787/1965) and Reid (1785/1969), frequently discussed modal images in knowledge (among other constructs).
In the early twentieth century, behaviorists attacked late nineteenth-century studies of introspection, banishing imagery from much of psychology for not being sufficiently scientific, along with other cognitive constructs. When cognitive constructs reemerged during the Cognitive Revolution of the mid-twentieth century, imagery was not among them, probably for two reasons. First, the new cognitivists remembered Watson’s attacks on imagery and wanted to avoid the same criticisms. Second, they were enthralled with new forms of representation inspired by major developments in logic, linguistics, statistics, and computer science. As a result, theories of knowledge adopted a wide variety of amodal representations, including feature lists, semantic networks, and frames.
When early findings for mental imagery were reported in the 1960s, the new cognitivists dismissed and discredited them. Nevertheless, the behavioral and neural evidence for imagery eventually became so overwhelming that imagery is now accepted as a basic cognitive mechanism. …amodal symbols were adopted largely because they provided elegant and powerful formalisms for representing knowledge, because they captured important intuitions about the symbolic character of cognition, and because they could be implemented in artificial intelligence.”