Tag Archives: metaphor

Doing science backwards

A recent article, (Trettenbrein, P. (2016); The Demise of the Synapse As the Locus of Memory: A Looming Paradigm Shift?; Frontiers in Systems Neuroscience, 10), questions what many consider settled science – plastic changes to synapses are the basis of learning and memory – may not be correct. Thanks to Neurosceptic for noting this paper (here).

Actually, as of today, large parts of the field have concluded, primarily drawing on work in neuroscience, that neither symbolism nor computationalism are tenable and, as a consequence, have turned elsewhere. In contrast, classical cognitive scientists have always been critical of connectionist or network approaches to cognitive architecture.”Trettenbrein is in the classical cognitive scientist camp.

First Trettenbrein assumes that the brain is a Turing machine. In other words that the coinage of thought is symbols and that they are manipulated by algorithms (programs) that write to a stable memory and read from it. The brain is assumed to deal in representation/symbols as variables, stepwise procedures as programs and random access memory, giving together a Turing machine. “The crucial feature of a Turing machine is its memory component: the (hypothetical) machine must possess a read/write memory in order to be vastly more capable than a machine that remembers the past only by changing the state of the processor, as does, for example, a finite-state machine without read/write memory. Thus, there must be an efficient way of storing symbols in memory (i.e., writing), locating symbols in memory (i.e., addressing), and transporting symbols to the computational machinery (i.e., reading). It is exactly this problem, argue Gallistel and King (2009), that has by and large been overlooked or ignored by neuroscientists. …

Synaptic plasticity is widely considered to be the neurobiological basis of learning and memory by neuroscientists and researchers in adjacent fields, though diverging opinions are increasingly being recognized. From the perspective of what we might call “classical cognitive science” it has always been understood that the mind/brain is to be considered a computational-representational system. Proponents of the information-processing approach to cognitive science have long been critical of connectionist or network approaches to (neuro-)cognitive architecture, pointing to the shortcomings of the associative psychology that underlies Hebbian learning as well as to the fact that synapses are practically unfit to implement symbols.” So an assumption that we have a Turing machine dictates that it needs a particular type of memory which is difficult to envisage with plastic synapses.

I like many others believe, science starts with observations and moves on to explanations of those observations, or to state it differently, the theories of science are based on physical evidence. It is not science to start with a theoretical assumption and argue from that assumption what has to be. Science starts with ‘what is’ not ‘what has to be’.

Trettenbrein is not thinking that the brain resembles a computer in many ways (computer metaphor), he is thinking that it IS a computer (actual Turing machine). If the brain is an actual computer than it is a Turing machine, working in a stepwise fashion controlled by an algorithmic program. Then he reasons that the memory must be individual neurons that are – what? Perhaps they are addressable items in the random access memory. Well, it seems that he does not know. “To sum up, it can be said that when it comes to answering the question of how information is carried forward in time in the brain we remain largely clueless… the case against synaptic plasticity is convincing, but it should be emphasized that we are currently also still lacking a coherent alternative.” We are not clueless (although there are lots of unknowns) and the case for synaptic plasticity is convincing (as it has convinced many/most scientists) because there is quite a bit of evidence for it. But if someone starts with an assumption, then looks for evidence and finds it hard to produce – they are doing their science backwards.

Trettenbrein is not doing neuroscience, not even biology, in fact not even science. There are a lot of useful metaphors that we use to help understand the brain but we should never get so attached to them that we believe they can take the place of physical evidence from actual brains.

Just because we use the same words does not mean that they describe the same thing. A neurological memory is not the same as a computer memory. Information in the neurological sense is not the same as the defined information of information theory. Brain simulations are not real brains. Metaphors give resemblances not definitions.

Metaphors and shapes

Judith Copithorne image

Judith Copithorne image

Metaphors (including analogs and similitudes) appear to be very basic to thought. These are very important to language and communication. A large bulk of dictionary meanings of words are actually old metaphors, that have been used so much and for so long that the words has lost its figurative root and become literal in their meaning. We simply do not recognize that it was once a metaphor. Much of our learning is metaphorical. We understand one complex idea by noticing its similarity to another complex idea that we already understand. For example, electricity is not easy to understand at first but we have learned to understand a great deal about how water flows as we have grown up by watching it. Basic electrical theory is often taught by comparing it to water. By and large, when we examine our knowledge of the world, we find it is rife with metaphors. We can trace many ways we think about things and events to ‘grounding’ in experiences of infants. The way babies establish movement and sensory information is the foundation of enormous trees and pyramids of metaphorical understanding.

But what is a metaphor? We can think of it as a number of entities that are related in some way (in space, in time, in cause-effect, or in logic etc.) to form a structure that we can understand and think of/ remember/ name/ use as a predictive model and treat as a single thing. This structure can be reused without being reinvented. The entities can be re-labeled and so can the relations between them. So if we know water flowing through a pipe will be limited by a narrower length of pipe we can envisage an electrical current in a wire being limited by a resistor. Nothing needs to be retained in a metaphor but the abstract structure. This facility of being able to manipulate metaphors is important to thinking, learning, communicating. Is there more? Perhaps.

A recent paper (Rolf Inge Godøy, Minho Song, Kristian Nymoen, Mari Romarheim Haugen, Alexander Refsum Jensenius; Exploring Sound-Motion Similarity in Musical Experience; Journal of New Music Research, 2016; 1) talks about the use of a type of metaphor across the senses and movement. Here is the abstract:

People tend to perceive many and also salient similarities between musical sound and body motion in musical experience, as can be seen in countless situations of music performance or listening to music, and as has been documented by a number of studies in the past couple of decades. The so-called motor theory of perception has claimed that these similarity relationships are deeply rooted in human cognitive faculties, and that people perceive and make sense of what they hear by mentally simulating the body motion thought to be involved in the making of sound. In this paper, we survey some basic theories of sound-motion similarity in music, and in particular the motor theory perspective. We also present findings regarding sound-motion similarity in musical performance, in dance, in so-called sound-tracing (the spontaneous body motions people produce in tandem with musical sound), and in sonification, all in view of providing a broad basis for understanding sound-motion similarity in music.”

The part of this paper that I found most interesting was a discussion of abstract ‘shapes’ being shared by various senses and motor actions.

A focus on shapes or objects or gestalts in perception and cognition has particularly concerned so-called morphodynamical theory … morphodynamical theory claims that human perception is a matter of consolidating ephemeral sensory streams (of sound, vision, touch, and so on) into somehow more solid entities in the mind, so that one may recall and virtually re-enact such ephemeral sensations as various kinds of shape images. A focus on shape also facilitates motion similarity judgments and typically encompasses, first of all, motion trajectories (as so-called motion capture data) at various timescales (fast to slow, including quasi-stationary postures) and amplitudes (from large to small, including relative stillness). But shapes can also capture perceptually and affectively highly significant derivatives, such as acceleration and jerk of body motion, in addition.

The authors think of sound objects as occurring in the time range of half a second to five seconds. Sonic objects have pitch and timbre envelopes, rhythmic, melodic and harmonic patterns. In terms of dynamics, sonic objects can: be impulsive with an envelop showing an abrupt onset and then decay, or be sustained with a gradual onset and longer duration, or be iterative with rapidly repeated sound, tremolo, or drum roll. Sonic objects could have pitch that is stable, variable or just noise. These sonic objects are related to similar motion objects – objects in the same time range that produce music or react to it. For example the sonic objects in playing a piano piece or in dancing. They also have envelopes of velocity and so on. This reminds me of the similar emotions that are triggered by similar envelopes of musical sound and speech. Or, the objects that fit with the nonsense words ‘bouba’ and ‘kiki’ being smooth or sharp. ‘Shape’ is a very good description of the vague but strong and real correspondences between objects from different domains. It is probably the root of being able to use adjectives across domains. For example, we can have soft light, soft velvet, soft rustle, soft steps, soft job, and more or less soft anything. Soft describes different things in different domains but, despite the differences, it is a metaphoric connection between domains so that concrete objects can be made by combining a number of individual sensory/motor objects which share abstract characteristics like soft.

In several studies of cross-modal features in music, a common element seems to be the association of shape similarity with sound and motion, and we believe shape cognition can be considered a basic amodal element of human cognition, as has been suggested by the aforementioned morphodynamical theory …. But for the implementation of shape cognition, we believe that body motion is necessary, and hence we locate the basis for amodal shape cognition in so-called motor theory. Motor theory is that which can encompass most (or most relevant) modalities by rendering whatever is perceived (features of sound, textures, motion, postures, scenes and so on) as actively traced shape images.

The word ‘shape’, used to describe corresponding characteristics from different domains, is very like the word ‘structure’ in metaphors and may point to the foundation of our cognition mechanisms, including much more than just the commonplace metaphor.


Can we upload our brains to computers?


Some year’s ago Chris Chatham posted a look at the differences between a brain and a computer (Chatham post) and recently Steven Donne re-visited the idea in a post (Donne post) These are both interesting reading.

I part company with Donne on several points. The first has to due with the definition of computer. Some people define ‘computer’ so widely that it includes anything that computes anything. In that case the brain is a computer and there is no metaphor to examine. On the other hand it is reasonable to include more than the stock home or business computer. Super-computers, robotic computers and those that are just around the corner are metaphor material. Donne brings up computers that are built precisely to mimic and explore the brain – simulations of the brain. As a metaphor this is lame. If I build a replica of something, there is nothing to be gained in understanding by a metaphor between the original and the replica. So we are left with brain simulations in fairly conventional but advanced computers or some more faithful replica of the brain.

Second, Donne feels that there will not be a problem with size and appeals to the idea that computing power increases exponentially so it cannot be all that long before a computer could be built that would handle a brain simulation in real time. He points to a 1 second of brain activity having been simulated. Well, that should be ‘sort-of-simulated’. The 1 second took 40 minutes to compute. (factor of 2400) Then the brain activity for the simulation was a simple network exercise – not really brain activity, missing the complications of real brain physiology. (factor of ?) The amount of brain simulated was small – 1.73 billion neurons simulated with about 83000 processors. (factor of 50) 10.4 trillion synapses were modeled. (factor 100+). I assume that the glia calcium ion communication, magnetic and chemical fields and so on were not part of the simulation. (factor ?) So I am assuming that something like 5 million times the size of this simulation would be needed for a realistic one and that would be 40-50 years of Moore’s Law type exponential growth at a bare minimum. But this would not give a brain-receiving computer that could accept the upload of a real human brain. That is a much bigger problem than a standard simulation. There would have to be an understanding of how and where all information was held in that human brain, a way to ‘read it out’ and place it in the simulation so that it has the same usefulness. Are we going to understand the brain at that level within 50 years – maybe but I doubt it.

Thirdly, Donne says that if it is possible, it will happen. I think that is possible – once. But the idea that anyone who wants to be immortal could just have their brain up-loaded on death is plainly silly. It would be too expensive to do more than a few times even if it were possible. I can imagine what would happen the first time there was not enough ‘power’ for both the living people and the simulated brains. The power would be switched off of some simulations. It seems the height of arrogance for someone to assume that they have the right to be immortal and to have future generations honour that right. The people at a time more than 50 years into the future will have more pressing problems, given current predictions of climate change, population growth, resource depletion, pollution, more destructive wars and whatever else is in store. Immortal brains in simulations seem to me part of the optimistic myopic vision of the science fiction lovers – futures of space travel, infinite resources, even time travel. Humans will be lucky to live through the century without being reduced to a rough and hard dark age.


Metaphor, Exaptation and Harnessing

We are used to the metaphor of time being related to distance, as in “back in the 1930s” or “it was a long day”. And there is a noticeable metaphor relating social relationships to distance, as in “a close friend” or “distant relatives”. But these are probably not just verbal metaphors, figures of speech, but much deeper connections. Parkinson (see citations below) has studied the neurobiology of this relationship and shows it is likely to be an exaptation, a shift in function of an existing evolutionary adaptation to a new or enlarged function. We have an old and well established brain system for dealing with space. This system has been used to also deal with time (rather than a new system being evolved), and later further co-opted to also deal with social relationships.



What spatial, temporal and social perception have in common in this system is that they are egocentric. Space is perceived as distances in every direction from here, with ourselves in the ‘here’ center. In the same way we are the center of the present ‘now’. We are also at the center of a social web with various people at a relative distance out from our center. Objects are placed in the perceptual space at various directions and distances from us. Events are placed various distances into the future or past. People are placed in the social web depending on the strength of our connection with them. It appear that with a small amount of adaptation (or learning) almost any egocentric system could be handled by the basically spatial system of the brain.



Parkinson has looked at the regions of the brain that process spatial information to see if and how they process temporal and social information. The paper has details but essentually, “relative egocentric distance could be decoded across all distance domains (spatial, temporal, social) … in voxels in a large cluster in the right inferior parietal lobule (IPL) extending into the posterior superior temporal gyrus (STG). Cross-domain distance decoding was also possible in smaller clusters throughout the right IPL, spanning both the supramarginal (SMG) and angular (AG) gyri, as well as in one cluster in medial occipital cortex”.



These findings provide preliminary support for speculation that IPL circuitry originally devoted to sensorimotor transformations and representing one’s body in space was “recycled” to operate analogously on increasingly abstract contents as this region expanded during evolution. Such speculations are analogous to cognitive linguists’ suggestions that we may speak about abstract relationships in physical terms (e.g., “inner circle”) because we think of them in those terms. Consistent with representations of spatial distance scaffolding those of more abstract distances, compelling behavioral evidence demonstrates that task-irrelevant spatial information has an asymmetrically large impact on temporal processing .” As well as the similarity to the linguistic theories of Lakoff and Johnson, this is also similar to Changizi’s ideas of cultural evolution harnessing the existing functionality of the brain for new uses such as writing.



Here is the abstract of the Parkinson 2014 paper:


Distance describes more than physical space: we speak of close friends and distant relatives, and of the near future and distant past. Did these ubiquitous spatial metaphors arise in language coincidentally or did they arise because they are rooted in a common neural computation? To address this question, we used statistical pattern recognition techniques to analyze human fMRI data. First, a machine learning algorithm was trained to discriminate patterns of fMRI responses based on relative egocentric distance within trials from one distance domain (e.g., photographs of objects relatively close to or far away from the viewer in spatial distance trials). Next, we tested whether the decision boundary generated from this training could distinguish brain responses according to relative egocentric distance within each of two separate distance domains (e.g., phrases referring to the immediate or more remote future within temporal distance trials; photographs of participants’ friends or acquaintances within social distance trials). This procedure was repeated using all possible combinations of distance domains for training and testing the classifier. In all cases, above-chance decoding across distance domains was possible in the right inferior parietal lobule (IPL). Furthermore, the representational similarity structure within this brain area reflected participants’ own judgments of spatial distance, temporal soon-ness, and social familiarity. Thus, the right IPL may contain a parsimonious encoding of proximity to self in spatial, temporal, and social frames of reference.


Parkinson C, Liu S, & Wheatley T (2014). A common cortical metric for spatial, temporal, and social distance. The Journal of neuroscience : the official journal of the Society for Neuroscience, 34 (5), 1979-87 PMID: 24478377

Parkinson C, & Wheatley T (2013). Old cortex, new contexts: re-purposing spatial perception for social cognition. Frontiers in human neuroscience, 7 PMID: 24115928

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Metaphors are basic

Metaphors are basic

A few weeks ago, a friend asked what I thought about metaphors. Actually I think they are extremely important to cognition. Many years ago I was looking at a list of rhetorical devices/figures of speech. Each had its Latin name under which it was taught as part of rhetoric in ancient and medieval times. What stood out was how different metaphor, simile, allegory, analogue (and the figurative by any other name) were from the other devices and how similar they were to each other. It was as if these were ways of thinking as well as forms of speaking.

This prompted me to look at investigators such as Lakeoff and Johnson. Many of the ideas and theories about metaphor are very well known and I do not want to repeat them here. I want to deal with some less well known ideas.

Embodied cognition bridges the gap between babies being born with an empty mind, a ‘blank slate’, and having to figure everything out for themselves; and the other extreme in which babies are born with all the cognitive concepts they need to understand the world. Neither of these extremes are credible. But being born with some very useful starting points and tools, but quite a small group of them, can allow the child to get to a general understanding relatively quickly. We can think of metaphor in this sense. The child has embodied cognition that uses metaphor to get from a physical grounding point to complex and abstract notions.

Take the structure that can be built from the child’s idea of motion that is grounded in the child’s own ability to engage in intentional movement. We could draw a little map of this: there is ‘here’ where I am now, there is ‘start’ where I was when this movement started, ‘target’ where I want to get to, ‘path’, ‘goal’, ‘obstacle’, ‘finish’ and so on. As the child matures other grounded concepts get added. Eventually the child has the concept of a journey which is more complex but still heavily grounded in the child’s physical experience. But journey can become another map including many more ingredients in its structure. Lakeoff did a lot of work on this particular metaphoric structure and I will not repeat those structures (like career, life, transport, exploration) here. As adults we end up (metaphorically) with nested piles of maps, each giving a structure: concepts and relationship between the concepts of a group things that can be related by metaphor.

If I want to explain a computer memory, I say that each bit of data is stored in memory in a particular address. What does this do? The word address brings up a map set, let’s call it the postal system map set. Here everything has an address and there is a standard way to identify an address. Things (letters) can be delivered to an address by a system (postal system) using various forms of transport etc. Once we understand the postal system, we can understand many other systems with similar structures by relabeling the concepts and making small modifications to the relationships, a little tweaking and a new map goes on the pile. In a sense what the words in a language do is to point out to the listener appropriate metaphorical maps to aid in understanding what is being said. It is not just language, we can get these prods and nudges from many things in our environment and from our own thoughts. There are visual, auditory, kinesthetic metaphorical ‘maps’ too. One of the problems with experiments in this area is that very small unnoticed clues can affect the results – a sort of human ‘clever Hans’ effect.

There is a sense in which language is just one huge metaphoric machine. There are dead metaphors. If you take a page of a dictionary and examine a word’s different meanings and etymology you can see how many words are obviously derived from metaphors that have lost their figurativeness through long use and become literal. Look at the word ‘go’ as a good example. What does it mean to die as a metaphor and become literal? One, it is processed in a different part of the brain. Two, it has lost some of its poetic and emotional power. But more importantly, its metaphoric base has changed type; it no longer seems to cause recall its metaphorical roots.

It is a very important question for neuroscience and linguistics to answer: how is what I have (metaphorically) described as grounding – mapping – dieing – pointing-to etc. actually happen in the brain. In terms of autism, it is also a medical question. How is this powerful tool of learning, thinking and communicating realized in the flesh?

Questioning the brain-computer metaphor

I have noted before that the brain does not do algorithms in the sense that computers do. We do not compute in a step-wise fashion, unless we are doing something consciously in a step-wise fashion. Take an example: to find if a number is even, first find the last digit, find if that digit is zero or is divisible by 2 without remainder, if so it is even and if not it is odd. If we consciously do this task stepwise, then those are the steps we will note consciously. 798 has 8 as its last digit and 8 is divisible by 2 therefore 798 is even. But of course we rarely go though the steps consciously, we look at the number and say whether it is odd or even. And we assume that we have unconsciously followed the appropriate steps. We have no proof that we have followed the steps – in fact we have no idea how we got the answer. There is no reason to assume that unconsciously we are following an algorithm.



Here is the abstract from a paper: Gary Lupyan; The difficulties of executing simple algorithms: Why brains make mistakes computers don’t; Cognition, 2013; 129 (3): 615.


It is shown that educated adults routinely make errors in placing stimuli into familiar, well-defined categories such as triangle and odd number. Scalene triangles are often rejected as instances of triangles and 798 is categorized by some as an odd number. These patterns are observed both in timed and untimed tasks, hold for people who can fully express the necessary and sufficient conditions for category membership, and for individuals with varying levels of education. A sizeable minority of people believe that 400 is more even than 798 and that an equilateral triangle is the most “trianglest” of triangles. Such beliefs predict how people instantiate other categories with necessary and sufficient conditions, e.g., grandmother. I argue that the distributed and graded nature of mental representations means that human algorithms, unlike conventional computer algorithms, only approximate rule-based classification and never fully abstract from the specifics of the input. This input-sensitivity is critical to obtaining the kind of cognitive flexibility at which humans excel, but comes at the cost of generally poor abilities to perform context-free computations. If human algorithms cannot be trusted to produce unfuzzy representations of odd numbers, triangles, and grandmothers, the idea that they can be trusted to do the heavy lifting of moment-to-moment cognition that is inherent in the metaphor of mind as digital computer still common in cognitive science, needs to be seriously reconsidered.”



The last sentence is particularly important. “… the metaphor of mind as digital computer still common in cognitive science, needs to be seriously reconsidered.”