Showing posts with label rationalism. Show all posts
Showing posts with label rationalism. Show all posts

1.9.15

On Perception, Emotion, & Decision-Making


The following article builds upon the arguments and evidences offered in the previous post How You Know What You Know; however, the contents below stand on their own.  A further review of the History of Cognitive Science can be found at How do human minds work?: The Cognitive Revolution and Paradigm Change in Cognitive Science.

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1. Sensory Integration and Interdependence


The transition from sensations to perceptions is commonly referred to as sensory integration. The importance of this process is such that it led the Rodney A. Brooks and the robotics team at MIT to postulate it as an ‘alternative essence of intelligence’ (Brooks et al. 1998) during their first attempt at building a humanoid robot, appropriately named Cog.

Sensations are modality-specific; perceptions are not, even though we can attempt to dissociate the different sense streams and partially succeed in doing this. As evidence, consider two phenomena: sensory illusions and synesthesia.



Sensory illusions can be uni-modal (involving one sense modality like the images above and below), multi-modal (involving two or more sense modalities; see, e.g., Turatto, Mazza & Umiltà 2005), or a sense modality and some piece of standing knowledge. As remarked by Fodor (2003), early 20th century Gestalt psychologists were more than justified in offering sensory illusions against their current-day empiricist counterparts. David Hume, and the tradition that ensued, granted an individual privileged access to his sensations. But, as the Gestalt psychologists would argue, perceiving involves construction, not just passive reception. Sensations decay— what persist are perceptions flowing through ideas.

(Just in case you thought the above illusion was due to the surroundings, see the image below.)


Hume’s agglomeration of impressions and ideas into the bucket of perceptions (classifying both impressions and ideas as types of perceptions), and his implacable loathing of skeptics, led him straight to an erred view of the mind. By compromising with the skeptic and contemporary cognitive scientists, it is possible to recognize the ephemeral character of sensations and identify perception with sensory integration, which necessarily involves active construction as is the activation of learned mental representations. This move does not undermine the core tenet of empiricism (i.e., there are no innate ideas); rather, it just delineates a point where bottom-up and top-down processing converge in the constant and continuous process of real-time experience.

(For Mobile users who cannot see the video embedded above, here is the short color-creating optical illusion.)


Synesthesia is less well-known. Synesthesia is a very rare condition that has its onset in early development and for which there is no treatment. Up until recently, very little research and funding had been directed towards the study this condition, mainly because it only rarely impairs a person’s productiveness and its incidence is quite low, around 1 in every 1150 females and 1 in every 7150 males (Rich, Bradshaw, & Mattingley 2005; however, Sagiv et al. 2006 has challenged the existence of a male-female asymmetry). These numbers are still under revision as the incidence of this condition is widely debated since synesthetes rarely see their condition as a problem, rather as a gift, and hence do not seek professional counsel.

A synesthete has two or more modalities intertwined, usually uni-directionally, such that some features in one modality reliably cause some unrelated features in another modality (Cytowic 1993, Cytowic 1995, Rizzo & Eslinger 1989, but see Knoch et al. 2006, who argue that even in clear uni-directional cases there is some bidirectional activation; also Paffen et al. 2015). The patterns of association are established early during development and are stable throughout the lifespan. Moreover, no two synesthesias are alike. On the one hand, not only are many modality combinations possible, such as colored hearing, tasting tactile textures, or morphophonetic proprioception, but also, though it is extremely rare, more than two modalities can become entangled. On the other hand, even synesthetes who belong to the same class, like colored hearing, have completely different patterns of feature association. For example, colored-alphabet synesthesia involves person-specific ‘color - written letter’ mappings where each letter always appears in a specific color.

 Karen's Colored Alphabet

Carol's Colored Alphabet



But colored alphabet synesthesia is among the least invasive. In colored hearing synesthesia, certain sounds can trigger beams of colorful light situated in a personal space extending 1 meter in front of the face of the synesthete. The fact that colored hearing synesthesia typically involves a personal space is indicative of associations that were made very early on during development, as infants cannot see much past such a space. Indeed, the associations must have been made so early on as to be incorporated in the base perceptual code of the individual, a fact that illustrates not only the distinction between a sensation and a perception, but also the effect that ideas have in delimiting perception, and is firmly evidenced by the reality that, as of yet, no person with synesthesia has ever been found that remembers a time when they did not have their particular anomalous perceptions. As such, synesthesia ought to be deemed paradigmatic for any empiricist cognitive architecture because it not only shows (in an exaggerated manner) that sensory integration—perception—implies active construction, but also hints at how individual differences are the rule, rather than the exception, in the conformation of representational capacities, which would be indicative that these capacities are not innate.


In fact, synesthesia might be paradigmatic of cognition in general, so much so that it has led researchers (Baron-Cohen 1996, Maurer 1993) to seriously explore the Neonatal Synesthesia Hypothesis, which states that “early in infancy, probably up to about 4 months of age, all babies experience sensory input in an undifferentiated way. Sounds trigger both auditory and visual and tactile experiences” (Baron-Cohen 1996). Since neonatal nervous systems are in the process of approximating environmental properties and specializing in domains of processing, experience to the infant might just be one constant synesthetic flow. By adopting this view, synesthesia can be explained as a derailment of an early process of modularization that the brain undergoes as a function of neural competition in the processing of the input stream during development.

There is a second, competing explanation for synesthesia, what might be called the perceptual mapping hypothesis. According to this view, synesthesia occurs not so much as a function of modularization (although this process may still be relevant), but rather as a function of early induction of the associated pairs and subsequent entrenchment of these pairs into the base perceptual code of the individual (i.e., during some critical period; see Rich, Bradshaw, & Mattingley 2005). Since for most synesthetic associations, there is no clear source of what the target ought to be other than the input itself, the individual can go a prolonged time without knowing that their perceptions are irregular, and by then the association might be so entrenched in the representational system that it might either be too late for it to be corrected or it might be too dangerous because changing the base code would negatively affect all other cognitive capacities that are built upon it. Which account is correct is ultimately a scientific question that needs to be experimentally approached; nonetheless, either explanation affords support to present-day empiricism based on connectionism and dynamical systems theory (Beer 2014, Rumelhart 1989, van Gelder 1999).

The neuropsychological and ontological question underlying both sensory illusions and synesthesia is where to draw the line between a sensation and a perception. In the journal Current Opinion in Neurobiology, Shimojo and Shams (2001) of the California Institute of Technology go as far as to argue that there are no distinct sensory modalities, since the supposed sensory systems modulate one another continuously as a function of the transience of the stimuli. They reach this radical conclusion by considering a wealth of recent findings in neuropsychology that include the plasticity of the brain and the role that experience has on determining processing localization (i.e., emergent modularization). And they are very likely correct; sensory integration is the rule rather than the exception, even in adult ‘early’ cortical sensory processing. This claim is echoed by Ghazanfar & Schroeder (2006), who argue not only that there are no uni-modal processing regions in the neocortex at all but also that the entirety of the neocortex is composed of associative, multi-sensory processing.

So what is the difference between a sensation and a perception? Succinctly, a sensation becomes a perception when it is mediated by an idea. When a mental representation intervenes in the flow of a sensation, when it delineates its processing, the process of construction and integration begins.


2. Aspects of the Nature of Emotions


Damasio (1994) claims that what sets the stage for heuristic, full-blown human reason are limbic system structures that code for basic emotions and that help train the cortical structures on top of these, through experience, which then code for complex emotions. His somatic marker hypothesis states that emotional experiences set up markers that later guide our decision-making processes.  It is a well-known fact that when we try to solve a problem we do not consider all the alternatives, only the tiniest fraction.  These markers of past bodily state set up in our brain allow our minds to discard the vast majority of possibilities before we can even consider the vast array of options, and what is left is a small set that we may manage to ponder. Such training mechanisms are patently fruitful from an evolutionary standpoint, as illustrated by the following Artificial Life simulation.

Nolfi & Parisi (1991) simulated the evolution of agents made up of artificial neural networks whose only task was to find food in a simulated world. Two distinct types of evolution were explored. In the first, the networks that were most successful at finding food in each generation were allowed to reproduce, which meant that new neural networks would begin with similar, though not exact, connection weights. What evolves, in this scenario, is the solution to the problem of navigation and food localization. Over several generations, the resulting agents have no problems at finding food at birth, so to speak. This is the equivalent of evolution hand-coding the solution into the neural connections, that is, of evolution installing truly innate ideas. For complex organisms, however, this kind of pinpoint fixation is untenable. The second type of evolution involved agents that were made up of two distinct networks. The first network handled the navigation, as the agents in the first simulation did, and the second neural network was in charge of helping train the navigating network (that is, it did not navigate at all). In this simulation, the first network was a tabula rasa in every generation, and what was allowed to evolve were the connection weights for the training network. Upon comparison of the two end-state types of agents, Nolfi & Parisi found that the auto-teaching networks consistently performed better at the task than the agents that had the solution to the problem hard-wired at birth.

It strikes me as altogether probable, if not entirely undeniable, that tastes and emotions serve to guide the inductions of the tabula rasa toward specific ends, the same as Nolfi & Parisi’s teaching nets served the blank nets to solve the issues of their existence. Tastes and emotions are fundamental—even at birth, these instruct as to what is food and as to what can kill you. However, taking Nolfi & Parisi’s simulations at face value would mean that emotions would come preset in specific connection configurations, which are a means of mental representation. If, as has been claimed here, all mental representations are ideas, then such a solution would lead to an as-of-yet unseen kind of rationalism (an emotional rationalism - how bizarre!). But there are other ways in which nature might have implemented the mechanism. It simply might have implemented it into the brain through something other than the patterns of connections, for example, they could result from the global effects of neurotransmitters (see, e.g., Williams et al. 2006, Hariri & Holmes 2006), instead of their specific transmission, as suggests the fact that both selective serotonin reuptake inhibitors (SSRIs, like Prozac and Zoloft) and MDMA (street name: ecstasy; mechanism: makes neurons fire vast quantities of the serotonin available) affect mood significantly. Whereas with SSRIs, emotion is attenuated, with MDMA the user feels pure love, a sense of empathy that is unmatched by any drug on the market. The aforementioned hypothesis, however, is an open empirical question on which I take no stand.

For our purposes here, it might be enough to note that emotions have traditionally been included within the realm of sensations as inner sensations. As of yet, I’ve seen no evidence that even remotely challenges this ancient view. For all we know, evolution might have simply implemented a non-representational domain of sensation that serves to guide learning. Such a domain need not be innately represented in the brain because it may be induced from the body itself. This claim lies behind Schachter & Singer’s (1962) classic Attribution of Arousal Theory of Emotion, which claims that emotions are the product of the conjunction of a bodily state and an interpretation of the present environment. In fact, Antonio Damasio and his team have been hard at work attempting to figure out where basic emotions come from. In an admittedly preliminary finding (Rainville et al. 2006), they managed to reliably identify basic emotion types (e.g., fear, anger, sadness and happiness) with patterns of cardiorespiratory activity. Similarly, Moratti & Keil (2005), working independently out of the University of Konstanz in Germany, found that cortical activation patterns coding for fear depend on specific heart rate patterns (see also, e.g., Van Diest et al. 2009). Should these findings pan out, it would be indicative that emotions are a sensory modality. As a sensory modality, emotions permeate experience, leading to emotion recognition being widely-distributed (Adolphs, Tranel, & Damasio 2003) because these become intertwined in the establishment of ideas.

In the end, if emotions are sensations, they are not innate ideas. Ideas are formed from these sensations as a function of their being perceived, a process that could, in principle, account for fine-grained emotional distinctions (Damasio 1994). Be it as it may, it is clear that emotional experience lies at the base of all of cognition, even reasoning, since as a sensory modality its mode permeates directly or indirectly all other processing everywhere and always.


3. Corollaries & Implications


Contrary to what it may seem upon first inspection, there is an underlying feature that is shared by both rationalist classical cognitive architecture (Fodor & Pylyshyn 1988, Newell 1980, Chomsky1966, Chomsky 1968-2005) and traditional empiricist cognitive architectures like John Locke's and David Hume's, mainly that both suppose there is a domain of memory that constitutes a thorough and detailed model or record of states of (the body in the) world. This feature is part of a modern tendency, illustrated somewhat indirectly in the previous section, of overcrowding the mind with what it can get—and does get—for free from the body in the world. In classical architectures, this feature more prominently takes the form of sensory memory, constituting a complete and detailed imprint of the world, only part of the information of which will travel to working memory for further processing. On the empiricist side, this feature takes on a more insipid form.

Think of Hume’s use of the word impression as opposed to, for example, sensation. Whereas the term sensation emphasizes both the senses and what is sensed, the term impression mostly accentuates what is imprinted, rendering perception mainly a passive receptor (a photocopier, if you will) upon which states in the world are imprinted. Also, and more importantly, the process of imprinting in Hume’s cognitive architecture does not stop with impressions because ideas, given how he defined these, are nothing more than less lively copies of imprints of states (of the mind) in the world. Moreover, since these ideas record holistically (i.e., somewhat faded yet still complete), as opposed to Barsalou’s (1993, 1999) schematic perceptual symbols, the resulting view is a mind overcrowded with images, sounds, tastes, smells, emotions—full of all of the experiences that the body in the world ever imprints on the mind.

It is important to highlight the active character of perception by identifying perception with the real-time integration of fading sensations with lasting mental representation. Both sensory illusions and synesthesia are evidence of the active nature of perception because both phenomena illustrate the impact that ideas have upon sensations and the fact that what we perceive is not just an imprint of the world. In this respect, what must be emphasized is the character of neural networks as universal approximators of environmental properties (see How You Know What You Know for a review), allowing neural networks to get their representational constraints for free, from the information being processed. Moreover, as these approximations become entrenched in the processing mechanism, they partially delineate the processing of incoming stimuli.

The resulting view is of a mind primarily full—not of sensory impressions but—of self-organizing approximations to the patterns implicit in such sensations, approximations that serve to anchor further representations through association.  These self-organizing approximations aren't just the substrates of "higher-order" processes—higher order reasoning carry their biases, their limitations, as well as their benefits, like speed and elasticity, as ongoing research on reasoning keep finding. Human beings are not logical or rational animals.  We can become more logical by learning logic and more rational by learning argumentation and how to spot formal and informal fallacies when these are used (van Gelder 2005, 2002).

For centuries, the supposition that human thinking follows logical rules has permeated and biased explorations into our cognitive capacities. The view that we are endowed with innate ideas that underpin our thinking, that allow us to learn syntax and to think logically, has been the cornerstone of Rationalism in every epoch including our own. But this is a far-fetched fantasy. To paraphrase Bertrand Russell, logic doesn't teach you how to think, it teaches you how not to think.

Cognitive Science is gradually overcoming the rationalist bias that was set at the moment of the discipline's creation.  The more evidence mounts, the more it becomes clear that mental processing follows the associative rules of the brain.  With this realization, the computer metaphor (that mind is software to the brain's hardware) slowly but surely unravels.

Perhaps this is how dualism finally dies, not with a bang, but with a whimper.


2.7.15

How You Know What You Know



In a now classic paper, Blakemore and Cooper (1970) showed that if a newborn cat is deprived of experiences with horizontal lines (i.e., is raised in an environment that is without horizontal stripes), it will fail to develop neurons in visual areas that are sensitive to horizontal edges. If the cat is exposed to horizontal lines while the visual areas are still optimally plastic (when the effects of learning and entrenchment have yet to set in), some neurons will quickly become selective to the feature, firing reliably when horizontal lines are part of the incoming sensation. These neurons are often referred to as ‘feature detectors’ even though the actual detection of the feature is always a network effect, that is, not the result of an isolated neuron firing, leading some to use the term tuned filters instead (see, e.g., Clark 1997).




It is well-known that our ability to categorize depends on our experiences with the objects of such categorization; moreover, research keeps finding that this phenomenon has more than a mere neurological ‘substrate’, that it permeates the very fabric of the brain (Abel et al. 1995, Sitnikova et al. 2006, Doursat & Petitot 2005). The study of this fact, however, proves very difficult for ethical reasons. Ideally, neuroscientists would experiment with children in order to see how, for example, sensory distinctions or, better yet, abstract concepts are acquired and represented. But the best means of accessing such precise data are ethically inconceivable.

To name one of the best methods currently on the market, in an ongoing project at Stanford University School of Medicine that aims to study the formation and entrenchment of sensory distinctions, Niell and Smith (2005) have recently been able to study the development, in real-time, of whole populations of neurons and their connections straight from the retina to brain regions known to process visual information. The method consists of immobilizing the growing subject and effecting two-photon imaging of neurons loaded with a fluorescent calcium indicator while experimenters control for the stimulus in order to better understand the electrochemical activity. Now remember, the aim of their efforts is to study the development of the neural connectivity and sensory capacity, which means subjecting the organism to this method for extended periods of times. Obviously, you can’t do this with children, so they are doing it with zebrafish, but the procedure promises to dazzle and reveal a lot about how sensory distinctions become entrenched in neural networks as a result of experience.

For the first time, it is possible to see how populations of neurons respond selectively to certain types of features, such as movement direction or size, and see to what extent, if any, there are innate representational constraints, such as the triggering of unlearned appearance concepts (Fodor 1998). As you probably guessed, current evidence seems to back up the claim that there are no innate representations,that they are learned from experience. The following are 6 reasons to believe that there are no pieces of knowledge or ideas that are unlearned.


1. Universal Approximators



Most complex neural networks are Universal Approximators because they can approximate any continuous function in their environment given enough time (Hornik, Stinchcombe & White 1989 or see, e.g., Zhang, Stanley & Smith 2004, Elman et al. 1996).[1] The Universal Approximator description applies to 3-layer neural networks, and obviously to those networks with a higher degree of complexity.



The human cerebral cortex is composed of innumerable overlapping 6-layer networks and each neuron can have up to 10,000 connections (see Damasio 1994, for a leisurely review). Moreover, there are many 3-layer networks in subcortical structures, as well as unlayered networks which consist of nucleuses of neurons and can provide added plasticity to an already elastic arrangement.



Universal approximators make excellent blank slates. In this respect, the interesting thing to notice is that human brains approximate the functions that they do and not others because of the characteristics of their bodies and the way they afford interaction with the world. In this way, the interaction between body and world conform the environment, a set of “time-varying stochastic function[s] over a space of input units”, according to Rumelhart (1989), which the brain must approximate.

You can learn anything, relatively quickly too. But, on the flip side, you are also likely to become what you surround yourself with. If you are surrounded by a bunch of idiots, well, sooner or later...


2. Neural Representations Mirror the World



Neural representation is symbolic but not as arbitrary as linguistic symbolization. Since neural networks are sensitive to the analog aspects of environmental functions through inductive and associative means, the internal code mirrors real-world structure in many ways, linking to what is represented through learning processes that involve neural competition that lead to self-organization and self-organizing maps (Kohonen & Hari 1999, Beatty 2001).



The structure of the mental representations arises out of the structure of what is represented (Damasio et al. 2004, Dehaene et al. 2005, Elman 2004) and what is done with that therein represented (Goswami & Ziegler 2006, Churchland & Churchland 2002, P.S. Churchland 2002). Content is everything, and that information isn't linked through logic.





3. Go Ahead and Kill a Few Braincells: Neurogenesis



We all grew up being told that we ought not to drink because it kills braincells, and braincells don't come back. Well, they do... every day. You know what actually kills them? Other brain cells because you didn't learn anything today. Yes, that's right. Since new neurons consume energy and resources, other braincells will kill them if they don't have to accept them into already existing neural networks. (Corty & Freeman 2013



Contrary to the long-held scientific dogma, there is widespread neurogenesis throughout the lifespan (Taupin 2006, Zhao et al. 2003, Gould et al. 1999). That argument in favor of ingrained pieces of knowledge went bust in 1998. However, the survival of new neurons depends on their becoming integrated into existing networks (Tashiro et al. 2006, So et al. 2006), which in turn depends on some degree on the richness and variety of the perceived environment. To say it another way, the more varied your life is, the stronger your brain will be.



Nevertheless, some networks are more entrenched than others because some processing domains are very rigidly articulated (e.g., sensory modalities, like vision, where a network’s expansion could come at the unthinkable cost of losing reliability). Other processing domains admit more flexibility and open-endedness, like language processing or memorizing your new favorite songs.

So what does kill braincells? Living a monotonous life, like that of a homeowner who day by day goes through some mindless routine. In an ironic twist of fate, that person that was telling you not to kill your braincells was probably killing way more braincells than you were by refusing to live beyond his or her routine (see, e.g., "Environmental enrichment promotes neurogenesis and changes the extracellular concentrations of glutamate and GABA in the hippocampus of aged rats" by Segovia et al. 2006).





4. The Nature of Ideas



Neural representations are function (i.e., action) specific. Knowledge gained through one action that is useful for a different, supplanting action does not transfer ‘free of charge’, so to speak (see, e.g., Thelen and Smith 1994). However, neural networks bootstrap one another towards the approximation of ever more complex functions, conforming emergent properties, whereby associations between committed functional webs (called modules in scientific circles) lead to new functional webs that subsume the previous ones.[2]

As previous action representations are co-opted instead of supplanted, the new functional web inherits the representations of its onstituent functional webs insofar as these representations become associated.  But this does not occur because they transfer the information, rather because the neural networks learn to behave in concert, in tandem, for your greater good.



For 60 years, good old fashioned cognitive scientists have wanted to convince the world that we are born with some ideas (a.k.a. Classical Cognitive Architecture), based on the ideas of Kant, Descartes and Plato. Even now, the television blurs commercials about how your genetics cause this psychological disorder or that one, so take a pill for that chemical imbalance.  Their assumption is that the chemical imbalance causes the psychological issue. They are wrong, and a new generation of cognitive scientists is just waiting for them to die out so that the next paradigm, dynamical systems, can take over, this time based on evidence instead of on theoretical assumptions and wishful thinking.  Though it has been an uphill battle, the dynamical systems perspective of mind is certainly taking over.

The chemical unbalance doesn't cause the psychological disorder; it is the psychological disorder. Your mind isn't some byproduct of your nervous system. Your mind is your nervous system; hence, it processes information in the same way. Mind and body are one.




5. Artificial Neural Networks that Organize Themselves:
An example



Superimposed artificial self-organizing networks with recurrent connections (Kohonen 2006) and newly developed genetic algorithms that permit the neuron to grow, shrink, rotate, and reproduce or absorb another neuron (Ohtani et al. 2000), are bringing about an artificial medium capable of transparently exploring many computational issues that cannot be studied as precisely with biological brains.



These models do away with innate representations altogether. You can't "program" information into them; you literally have to raise them by giving them an environment fitting to what it is you actually want them to learn and do.

The following is an example developed at the Helsinki University of Technology.  An artificial neural network called a Self-Organizing Map was trained by feeding it 39 types of measurements of quality of life factors, like access and quality of education and healthcare, nutrition, among many others.  All the data used was provided by the World Bank. The following image is the map produced by the network.   



For the benefit of our understanding, this very same map was then depicted as the world map below.  My guess is that it wont take you long to figure out what colors represent a higher degree of poverty if you compare the image above to the one below.





6. How You Know What You Know



The moral of the story seems to be that neural networks have more plasticity than plastic. For example, if the visual cortex is damaged at birth, large, medium and small scale characteristics of the functional organization of normal visual cortexes appear in the auditory cortex of the damaged brain, as other functional webs specialize in the functions typically located in the visual cortex (Sharma, Angelucci & Sur 2000, Roe et al. 1990). This same effect can be replicated by surgically redirecting the optic nerves, which suggests that there is nothing special about the networks of the visual cortex or of any piece of cortex at all. So please, I beg of you, stop believing the hype that everything is genetic.

Findings like these led to the Neuronal Empiricism Hypothesis (Beatty 2001), which states that the whole of the cerebral cortex is just one large yet segmented unsupervised, knowledge-seeking, self-organizing neural network. But neural empiricism is characteristic not just of the 6-layer networks of the cerebral cortex; though these add vast computational power to the brain, neural organization as a function of experience is the rule rather than the exception even in ‘lower’ structures.

Krishnan et al. (2005), for example, show that language experience influences sensitivity to pitch in populations of neurons of the brainstem. It’s not only the 6-layer networks that just don’t need innate representations; embodied neural networks get their representational constraints for free, from the body in the world.

Early on in our development, sensations establish further yet lasting symbols in the mind, what Barsalou (1993) calls perceptual symbols. John Locke and David Hume called them simply ideas. Today, in scientific circles, these are commonly referred to as mental representations.

There is an important difference, however, between Barsalou’s account and Hume’s, mainly that in the latter ideas are construed as less lively yet still complete copies of sensory impressions and in the former the copies are only schematic. This difference merits being highlighted as it is easily overlooked, specifically because it concerns a not-oft observed difference between primarily inductive and primarily associative learning.

Given that Hume construed the emergence of ideas as he did, his tabula is a warehouse of countless sequences of images, smells, tastes, textures, emotions, in short all the objects the mind has ever had sensations of. In contrast, because schematic records are by definition abstractions, associations between properties if you will, Barsalou’s cognitive architecture may be construed as furnishing the mind firstly with analog approximations of continuous and co-occurring properties that have been experienced, approximations that can later be used through association to fill in the blanks of particular schematic representations.

This is why murder trials can no longer be had in the United States based on a single eye-witness testimony. It is also why, when a person is placed in front of a police lineup to identify the perpetrator, the police have to say, by law, something to the effect of "remember, the person that you are trying to identify may not be in the lineup". If the police do not say that, any identification becomes inadmissible in a court of law. Why? Because your memories are reconstructions through and through, and by not saying that they are inducing the person to create a false memory.

Differently stated, while it follows from Hume's theory that baby and toddler minds record the totality of experienced events — a view followed later by Sigmund Freud — only to abstract or induce (and later associate) recurrent properties from the set, contemporary findings indicate that, first and foremost, minds approximate the properties themselves, such as shapes and colors. It is only later that these approximations can be employed towards the conformation of memories of concrete sequences of images, sounds, tastes, textures, smells, and combinations thereof.

Recall is surprisingly reconstructive. The resulting view is of a mind populated - not by countless sensory impressions but - by auto-organizing approximations of sensed properties.

During the first months of life, uncommitted neural networks in the infant’s brain approximate in an associative manner the functions of color, form, movement, depth, texture, temperature, pitch, among many, many others. In so doing, the brain develops its own personal neural code, a code that is conjunctively contoured by the processing mechanism, by the individual’s experience, and by the characteristics of the input domains. As these approximations ­— these ideas — are established, they mediate the processing of incoming sensations. Perception emerges as the real-time process of mediation, as the integration of fading sensations with enduring mental representations.

You know what you know because you are one big ball of perception flowing through your own web of ideas. And you act how you act because you've been conditioned to smithereens.

Open your eyes and do things differently. Go live outside your routine. You'll be happier and healthier as a result.



[1] Sometimes it is claimed that 3-layer, feedforward neural networks are not real universal approximators, as these can only approximate problem domains that have graded structure.  While it an open question whether more complex networks (e.g., 6-layer recurrent networks) are able to reliably approximate non-graded problem domains, it should be recognized that the immense majority of problem domains have graded structure, as practically all natural variables have graded structure.  The domain of morphology is a prime example.  Generative linguistics traditionally applied a rule-governed (non-graded) approach to this domain; however, current evidence indicates that the morphological domain has gradient structure (Hay & Baayen 2005), and thus can be reliably approximated by 3-layer networks.
[2] Properties that result from a network’s functioning as a whole, i.e., that do not result from the activation of a single neuron, are known as emergent properties. Neural codes are network specific and emerge from the interaction of the implicated neurons responding to the body in the world.

Featured Original:

How You Know What You Know

In a now classic paper, Blakemore and Cooper (1970) showed that if a newborn cat is deprived of experiences with horizontal lines (i.e., ...