January 30, 2014

Fitting the Science to the Hype

This is a response to a recent article in Forbes by Steve Kotler [1], co-author of the book "Abundance" and techno-optimist. Even though it is intended as a blue-sky vision of an emerging technology, it also suffers from something I will call "fitting science to hype" [2], which happens a lot in media coverage of emerging technologies. That being said, I had a number of problems with this article, from its provocative title to its conclusions.


Point 1: It is actually inconclusive as to whether or not video games are addictive, at least in the same way as heroin. They may be addictive in the sense that a person prone to addictions will use video games as an addiction, but that is putting the cart before the horse (in a causal sense). To start off with this premise, and then build an argument off of this "self-evident truth" using an array of neurochemical facts (but not directly relevant evidence) is a bit misleading.

Point 2: The idea that "video games causes dopamine stimulation, therefore they are addictive" is likewise misleading. Aside from being a basis of addictive experiences, dopamine is also a very general signaling molecule for behavioral motivation. For example, dopaminergic signaling serves as the basis for economic transactions (e.g. rewards), so generalizing this to addiction is a large step indeed. Can we say then that because they are tightly associated with increases in dopamine, virtual environments will necessarily become essential economic tools? Probably not.

Dopamine machines, or dopaminergic-friendly activity (so to speak)? For more on the potential addictive aspects of financial trading and risk-taking, see [3].

Point 3: Video games are indeed dopamine-production machines. So are sports cars, jet skis, motorcycles, and guns. I'm not quite sure that one could make the argument that sports cars and jet skis cause the dopaminergic excesses associated with addiction. Aside from the dodgy reasoning of "machine-therefore-dopamine-therefore-addiction", human variation is an important determinant of addiction [4].

But the real problem here is that there is no null hypothesis to compare against. For example, the logical extreme of this argument would be that video games are always addictive. This, of course, would sound foolish. A proper way to disentangle this would be to look at dopamine production across the process of addiction, and then compare this with dopamine production in people who play video games but do not become addicted to them.

Point 4: Why will the our current lack of knowledge regarding how to control neurochemistry change? And why would we want to create virtual world addicts? This is how the sixth paragraph reads to me. There are areas of research such as Augmented Cognition [5] and allostatic regulation [6] and that would be relevant here, but alas nothing was discussed.

Addiction in the context of allostasis and allostatic load. COURTESY: Figure 5 in [6].

Point 5: As a source of explaining addiction as a result of over-allotted human cognition, Kotler's use of flow theory is oddly seductive. It allows for a higher state of awareness during focused activities (e.g. a magical mechanism) to be substituted for what is not well-known about cognitive processes like multisensory integration, attention, and memory. Or perhaps there is a selective interface between, say, flow and attention which allows for some "super" response. In any case, this is a unique form of inductive brain science, the ultimate result of which will be more questions than answers.

While this might be a bit of positivist bias on my part, it is worth keeping in mind that flow always needs to be rigorously defined in its application. Paragraph eight provides an example: the claim is made that flow results in (or from, which is not clear) an extremely potent neurochemical cocktail. This mimics a rapid hit of illicit drugs, and flow itself is the source of intrinsic motivation. A claim like this makes the direction of causality (e.g. enhanced neurochemistry result in flow, or flow results in enhanced neurochemistry) quite important.

Point 6: When I see the words "Moore's Law", I reach for my gun. Or my keyboard to protest. This is not an issue of too much dopamine production, but an issue of gross overuse. In any event, it's almost certain that Moore's Law does not apply here, as what is needed to understand the effects of video games on brains are better models to interpret the neurological data, not increasingly greater amounts of data on its own.

I think the idea of deep embodiment, while well-placed in this article, also masks the magnitude of challenges inherent in this type of work. From my own work, I would predict that deep embodiment might only be achieved through the use of a multivariate, closed-loop interface. Closed-loop control allows for entrainment between the environment and physiological dynamics.

The substrate involved with the setting of physiological state includes many moving parts, including nonlinear control components such as delays and superadditive responses. So take the latest hyperrealistic video game title. A human might experience deep embodiment for awhile as their physiology learns the intensity of the experience. This is a proto-learning that is similar to a more generalized plastic response. Yet, as with all forms of learning, there will be diminishing returns and acquisition will saturate. Thus, the person will adapt over time.

While one could say the person has been "sensitized" to the virtual environment, in reality we are witnessing a form of accomodation (complex as it is) mimicking the effects of addiction. Unless the person is explicitly trained, there will be a large effect between sensory performance in the real versus virtual world.

A model of brain-VR interactions without the pretense of parsimony.

UPDATE (3/1/2014): Here is a new paper [7] on the neural correlates of internet addiction. Given individuals who are identified as addicted to the internet, neuroimaging reveals the brain structures associated with addictive stimuli.

Neural Correlates of Internet Addiction. COURTESY: Figure 1 in [7].

[1] Kotler, S. and Edwards, L.A.   Legal Heroin: is virtual reality our next hard drug? Forbes, January 15 (2014).

[2] Fitting the science to the hype (or putting hype before the data) is much like putting models before the data, except that hype resembles an intentionally distorted model. On RationalWiki, this type of hype before the data is classified as "Science Woo". While I would not go that far, blue-sky hype is always something to be skeptical about.

[3] Coates, J.   The Hour Between Dog and Wolf. Penguin Books. YouTube video (2012).

[4] Volkow, N.D., Wang, G.J., Fowler, J.S., and Tomasi, D.   Addiction circuitry in the human brain. Annual Reviews in Pharmacology and Toxicology, 52, 321-336 (2012) AND Kuss, D.J.   Internet gaming addiction: current perspectives. Psychological Research and Behavioral Management, 6, 125-137 (2013).

[5] Schmorrow, D. and Fidopiastis, C.M.   Foundations of Augmented Cognition. Lecture Notes in Computer Science, Volume 8027 (2013).

[6] Koob, G.F. and Le Moal, M.   Drug Addiction, Dysregulation of Reward, and Allostasis. Neuropsychopharmacology, 24, 97-129 (2001).

[7] Gallinat, J. and Kuhn, S.   Brains online: structural and functional correlates of habitual Internet use. Addiction Biology, doi:10.1111/adb.12128 (2014).

January 24, 2014

On Bet-hedging and Evolutionary Futures

When someone mentions "bet-hedging", the first thing that comes to mind is an economic investment or gambling strategy, one that maximizes a human's return on their investment. This necessitates cognitive mechanisms for decision-making and valuation. For example, a bet-hedger might keep their investments in two places (e.g a rowboat and a hang-glider). When the return on one potential source of income is exhausted (e.g. rowboat goes over the falls), the other investment can be drawn upon to pick up the slack. Overall, total losses are minimized and potential gains are maximized.

Bet-hedging as a human investment strategy. Hence the rowboat and hang-glider analogy.

But bet-hedging has also been used as a means to explain biological "decision-making" with respect to adaptive changes in genotype and/or phenotype. In this case, decision-making refers to directed changes in a lineage that emerge from stochastic mechanisms. There is no need for a set of formal cognitive mechanisms (or a designer), because in this case natural selection plays the role of a brain (e.g. information processing). In the case of biological bet-hedging, an organism hedges between two or more phenotypic/genotypic states (or behavioral strategies), one becoming predominant only when encouraged by environmental conditions.

Figure 1. A statistical view of biological bet-hedging. COURTESY: Discussion of bet-hedging and adaptation to environmental stresses in [1].

To understand how biological bet-hedging might work, we can use a Venn diagram to take a statistical view of the process (Figure 1). Some form of switching (phenotypic, genotypic) is induced by a subset of environmental stimuli. A smaller subset of positive responses are triggered by environmental stress. This relatively small resulting set of adaptive responses (switching due to an environmental stress signal) should (in theory) be the outcomes of highest fitness value. 

There are many ways this bet-hedging can be accomplished, as the existing literature is quite diverse. I will focus on two candidate mechanisms from the literature: selection on random noise and selection on standing variation.

Selection on random noise is driven by inherent oscillations in gene expression. Gene expression differences (e.g. differential gene expression) are usually thought to define distinct biological processes and cell types. However, gene expression also exhibits wide-band fluctuations [2] and "bursty" changes over time [3], even within the same process or cell type. A recent review in Science on oscillating gene expression over time (e.g. gene expression noise) and biological bet-hedging [4] discusses these mechanisms in more detail.

Figure 2. Type A transcriptional oscillations, sensu [4]. One example of genotypic bet-hedging.

The first type of bet-hedging (type A) is indirect, and involves retaining two or more context-dependent mechanisms for the expression of a single gene. In Figure 2, the successful transcription of a downstream target gene depends on the synchronized activity of two upstream transcription factors. In this case, each upstream gene fluctuates with respect to time. Only when their fluctuations become coordinated in-phaseare they capable of activating a downstream target. In fact, in order to exhibit this selective synchronization, the upstream gene expression must be oscillatory. Otherwise, the downstream gene would not be sensitive to environmental context.

Figure 3. Type B transcriptional oscillations, sensu [4]. Another example of genotypic bet-hedging.

By contrast, type B bet-hedging (direct) is a matter of selecting between alternating genotypic or phenotypic states. In this case, upstream genetic mechanisms create a bistable switch which allows the organism to switch between states given an environmental signal. In the case of stochastic bet-hedging, switching can be like a roulette wheel, in which all possible states are generated spontaneously. The most (as opposed to transient) stable states are those which are most strongly supported by the environment. This is a candidate model for how cells acquire and maintain their identity in microenvironmental niches. Yet it is also relevant to evolutionary change.

Instead of individual phenotypes being the focus of selection, perhaps the ability to switch between phenotypes itself is selected as a trait. For example, stochastic phenotype switching in bacteria results in persistence in the face of rapid environmental variation [5]. Using Pseudomonas fluorescens lineages, phenotypic switching can be experimentally evolved. In fact, the capacity for switching can be isolated to a single gene mutation (CarB), which is both sufficient and necessary for the colony switching trait.

Although stochastic switching is a single trait, it is by no means a simple one. Not only does CarB enable colony switching, but lineages that carry this mutation have a higher fitness compared to those that do not among mixed cultures in a static environment. In the experiments of [5], it took multiple rounds of selection to evolve the CarB mutant. This may also be due to enabling mutations and lineage-specific dependencies which set up the transition to a CarB mutant phenotype.

CarB mutation, examined within a single bet-hedging lineage. TOP: fitness, MIDDLE: genotypes, BOTTOM: phenotypes.

Now we will jump to a highly-speculative infographic [6] on future events in Earth's history. "Timeline of the Far Future" proposes events from the present to Earth's best-case scenario astrological death. In 100 quadrillion (1020) years, the Earth's orbit is predicted to fall into the Sun, if the red giant Sun does not engulf the Earth before then.

In any case, five events on this timeline are relevant to the future of life and worthy of discussion. Keep in mind that this infographic is not sequentially consistent, as it is based on multiple sources of data and overlapping potential scenarios. These events include:

* end of Eukaryotic life, in 1.3 billion years.

* end of Prokaryotic and Archean life, in 2.8 billion years.

* end of C3 photosynthesis, in 600 million years.

* end of C4 photosynthesis, in 800 million years.

* Earth's temperature rises to 47C due to an increase in solar luminosity, in 1 billion years.

Some of the predictions are provocative, such as the extinction of the Y chromosome [7]. However, one set of predictions intersect with the literature on bet-hedging: the end of photosynthesis. What we know about the possible end of photosynthesis comes from studies [8, 9] that examine the instability and breakdown of photosynthetic reactions at high temperatures. A primary producer's photosynthesis rate becomes unstable above a threshold temperature [8]. When temperature is lowered, this rate returns to normal.

When applied in a laboratory or experienced during heat waves, severe heat stress can cause cellular damage. In fact, large-scale process-based models of photosynthesis assumes that the rate returns to normal after environmental shocks [9]. However, what happens in cases where the average temperature reaches or exceeds this threshold? In such cases, extreme temperatures are not experienced as shocks, but as the physiological set-point.

View of a "red giant" Sun from a now-barren (far future) Earth. Humans, the oceans, and perhaps all extremophiles are all gone at this point.

According to the infographic's predictions, in 0.6 to 2.8 billion that will be that (although I'm not sure why primary producers would be the first type of life to die out). Yet there might be three fundamental changes to life's complexity as we reach a red giant Sun that enable it to survive until perhaps the physical end of the Earth (and/or Sun): 

* the radical restructuring of organismal physiology to suit temperatures that will approach the boiling point of water. This might include hard insulating shells, smaller areas of exposed surface, and "interesting" changes to metabolism. The hedging concept could come into play here, as the expression of genes and phenotypes associated with metabolic function in all organisms (not just single-celled ones) could fluctuate significantly across life-history.

* the radical restructuring of food webs so as to reduce any one source of primary or secondary production. Bets would be hedged in order to survive ever increasing extreme conditions. The increase of energy in the biosphere could lead to more energy being available in general. But of course there could be biospheric tradeoffs (atmospheric composition) which could limit ecological complexity. The sources of primary production would of course need to adapt to take advantage of this situation.

* the evolution of primary production itself. Like complex organisms, we should not expect photosynthesis to simply disappear. Recall that C4 photosynthesis was dominant in the Paleozoic era, only to be eclipsed by the C3 variety during the Mesozoic era. And this transition occurred despite C4 photosynthesis being more efficient than C3 in times of heat stress and draught [10]. It might turn out that a new type of hyper-efficient and multiphasic photosynthesis could evolve that takes advantage of late solar system conditions.

These points are speculative as well, but remember -- fully-functioning ecosystems that are gradually exposed to extreme conditions will likely adapt instead of coming to an abrupt end. It would be hard to transplant existing organisms (even extremophiles) into these conditions, but it might not be as hard to evolve responses to the extremities of late Earth. 

[1] Mostowy, R.   Evolution of Stress Response in the Face of Unreliable Environmental Signals. Rafal Mostowy blog, August 20 (2012).

[2] Eldar, A. and Elowitz, M.B.   Functional roles for noise in genetic circuits. Nature, 467, 167-173 (2010).

[3] Goh, K-I. and Barabasi, A-L.   Burstiness and Memory in Complex Systems. arXiv:0610233.

[4] Levine, J.H., Lin, Y., and Elowitz, M.B.   Functional Roles of Pulsing in Genetic Circuits. Science, 342, 1193 (2013).

[5] Beaumont, H.J.E., Gallie, J., Kost, C., Ferguson, G.C., and Rainey, P.B.   Experimental evolution of bet hedging. Nature, 462, 90-93 (2009).

[6] Timeline of the Far Future. BBC Future, January 6 (2014).

[7] This is an example of a scientific debate disguised as persistent myth in the popular press. For two perspectives (the former Y-optimist, the latter Y-pessimist), please see:

a) Hughes, J.F. et.al   Strict evolutionary conservation followed rapid gene loss on human and rhesus Y chromosomes. Nature, 483, 82-86 (2012).

b) Aitken, R.J. and Marshall-Graves, J.A.   Human Spermatozoa: the future of sex. Nature, 415, 963 (2002).

[8] Sage, R.F. and Kubien, D.S.   The temperature response of C3 and C4 photosynthesis. Plant, Cell, and Environment, 30, 1086-1106 (2007).

[9] Huve, K., Bichele, I., Rasulov, B., and Niinemets, U.   When it is too hot for Photosynthesis: heat-induced instability of photosynthesis in relation to respiratory burst, cell permeability changes and H2O2 formation. Plant, Cell, and Environment, 34, 113-126 (2011).

[10] Liu, Z., Sun, N., Yang, S., Zhao, Y., Wang, X., Hao, X., and Qiao, Z.   Evolutionary transition from C3 to C4 photosynthesis and the route to C4 rice. Biologia, 68(4), 577-586 (2013).

January 17, 2014

Fireside Science: Bitcoin Angst with an Annotated Blogroll

This content is being cross-posted to Fireside Science.

This post is about the crypto-currency Bitcoin. If you are interested in the technical aspects of Bitcoin (WARNING: highly technical Computer Science and Mathematics content), read the following reference paper or check out the Bitcoin category on Self-evident blog . Otherwise, please read on. Citations:

Nakamoto, Satoshi   Bitcoin: A Peer-to-Peer Electronic Cash System. Internet Archive, ark:/13960/t71v6vc06.  (2009).

Friedkl, S.   An Illustrated Guide to Cryptographic Hashes. Steve Friedl's Unixwiz.net Tech Tips. (2005).

Khan Academy: Bitcoin tutorial videos.

Being a techno-optimist (or realist, depending on which metric you use), I can't help but be fascinated by the Bitcoin phenomena. I have an interest in Economics and alternative social systems, so the promise of Bitcoin is all the more attractive. Orthogonal Research is looking into the utility of the Bitcoin model (particularly the cryptographic hash function and wagering capabilities) for understanding the evolution and emergence of economic value. 

I am generally skeptical of trends and propaganda. Therefore, once I learned that there are a finite number of Bitcoins in the world, I became unconvinced that Bitcoin could ever replace governmental currencies in the long-term. This inflexibility (which may have its roots in representative money and goldbug psychology) is one potential cause for periods of Bitcoin deflation (e.g. the value has gone up relative to real-world goods and services). This deflation has increased the hype of mining opportunities, as mining activity for high-valued Bitcoins resembles a gold rush. Conversely, Bitcoin is also vulnerable to bouts of severe inflation, which has occurred quite recently due to its use in major criminal rings and the downside of the Gartner hype cycle.

Trouble brewing on Mt. Gox! This is only temporary, though.

A lot of the Bitcoin hype is confusing to say the least. And it is not clear to me if Bitcoin mining is a totally above-board activity (this will be addressed in the articles at the end of this post). Nevertheless, Bitcoin is a significant step beyond virtual currencies such as Linden Dollars. This has been demonstrated by its interchange with conventional money and the trust a critical mass of people have placed in the currency. In addition, its cryptographic features may make Bitcoin (or something similar) a prime candidate as the currency of choice for secure internet transactions.

Below is an annotated bibilography of articles and blog posts on the phenomenon known as Bitcoin mining/trading and its libertarian underpinnings. In this discussion, I have noticed a pattern similar to the public discussion surrounding MOOCs. Much like MOOCs, technology people dominated the first few years of development, and the discussion was almost universally positive. After the initial hype, more critical voices emerged, usually from more traditional fields related to the technology. With MOOCs, these are University professors and instructors, but with Bitcoin the criticism is from financial and economics types.

COURTESY: Hardy, Q.   Bitcoin and the Fictions of Money. NY Times, January 27 (2014).

Annotated Bibliography of Bitcoin: A diversity of viewpoints from academics and journalists, mostly critical. If you want a more blue sky view of Bitcoin, there are plenty of those on the web as well. Hope you find this educational.

1) Kaminska, I.   Wikicoin. Dizzynomics, December 7 (2013).
Proposes a Bitcoin-like system for adding value to Wikipedia, without relying on the rules of Wikipedia. No competition for CPUs, reward people for valuable contributions (rather than content by the word), and new coins create new resources.

2) Stross, C.   Why I want Bitcoin to die in a fire. Charlie's Diary, December 18 (2013).
Bitcoin economy has a number of major flaws, including: high Gini coefficient (measure of economic inequality), prevalence of fraudulent behavior due to scarcity, use as a proxy for black market exchanges, mining is computationally expensive and encourages spyware and theft schemes.

3) 13) McMillan, R.   Bitcoin stares down impending apocalypse (again). January 10 (2014).
An article that discusses the distribution of Bitcoins (and hence inequality) among candidate miners. Read as a counterpoint to article (2).

4) Mihm, S.   Bitcoin Is a High-Tech Dinosaur Soon to Be Extinct. Bloomberg News, December 31 (2013).
A historical survey of private and fiat currencies, and how they work against central currencies. According to this view, Bitcoin represents the dustbin of history rather than the future of currency.

5) Krugman, P.   Bitcoin is evil. The Conscience of a Liberal blog, December 28 (2013).
A skeptical take on the viability of Bitcoin, and a primer on how Bitcoin is similar to a faux gold standard. Is Bitcoin a reliable store of value? Unlikely, given its recent performance and reputation.

6) Roche, C.   The Biggest Myths in Economics. Pragmatic Capitalism, January 8 (2014).
A refresher/primer on the theories (and mythical ideas) behind monetary policy and currency circulation. No explicit mention of Bitcoin but still relevant. Read along with article (5).

7) McMillan, R. and Metz, C.   Bitcoin Survival Guide: Everything You Need to Know About the Future of Money. Wired Enterprise, November 25 (2013).
Comprehensive overview of the Bitcoin enterprise, but nary a skeptical word. Describes the intentionally-designed upper limit on the number of Bitcoin that can circulate, as well as the cryptographic hash which enables transactions and discourages counterfeiting.

8) Yglesias, M.   Why I Haven't Changed My Mind About Bitcoin. Moneybox, December 2 (2013).
Begins with an exchange of tweets regarding the counterfeiting protections afforded by Bitcoin. Additional discussion about how the currency can be used to evade national currency regulations.

9) Coppola, F.   Bubbles, Banks, and Bitcoin. Forbes, December 30 (2013).
Explores the notion of the "entanglement" of crypto- (e.g. Bitcoin) and state (e.g. Dollars, Euros, Yuan) currencies. If a private currency system is bailed out by public ones, we will end up with a situation like the Lehman Brothers bailout. Furthermore, the uncertainty of Bitcoin as a store of value will undermine the trustworthiness of the currency, which leads to other troubles.

10) Kaminska, I.   The economic book of life. Decmeber 31 (2013).
A blog post which follows up on the Forbes article by Coppola. Is Bitcoin a harbinger of the eventual "definancialization" of money? In the digital world, thousands of digital currencies might exist side-by-side. The connections between the futurist/extropian notion of "Abundance" and crypto-currencies are also explored.

11) Salmon, F.   The Bitcoin Bubble and the Future of Currency. Medium, November 27 (2013).
A historical and speculative take on the current Bitcoin bubble and the future of money. Is Bitcoin the future? Probably not, but may very well point the way ahead.

Hype vs. valuation: a Month-long comparison.

12) Authers, J.   Time to take the Bitcoin bubble seriously. FT.com, December 11 (2013).
Argues that Bitcoin is now a serious contender as a crypto-currency due to attention paid by Wall Street and major investment firms.

13) Liu, A.   Is it time to take Bitcoin Seriously? Vice Motherboard (2013).
A review of Bitcoin's place in the contemporary social and financial landscape. Is it time to take Bitcoin seriously? Many people already are. Make points that are complementary to the discussion in (12).

14) Gans, J.   Time for a Little Bitcoin Discussion. Economist's View, December 25 (2013).
A re-evaluation of one Economist's view of Bitcoin. Very thoughtful and informative.

January 10, 2014

Thought (Memetic) Soup, it's All in the Timing Edition

This content has been cross-posted to Tumbld Thoughts.

It may be question of time, but at what scale?

Here are a few readings on the passage of time in perspective and future prediction. The first is an infographic from Visual.ly [1], which plots out the passage of time on scales from a single day to the lifetime of the universe. This is similar to the classic "Powers of 10" demonstration that shows how different spatial scales relate to one another. Only in this case, we are dealing with time.

The second reading is on Isaac Asimov's predictions (made in 1964) on life in 2014 [2]. While some of them were fairly accurate, others fell short (as we might expect). Asimov uses both an "averaging" strategy (e.g. robots prediction) coupled with an informed consensus (e.g. population numbers prediction).

Time percpetion + funhouse mirror = ???

Here are a few new readings on the manipulability of time perception. You know, as advertised in "Inception" [3]. The first paper [4] studies the relationship between continuous time and discrete events. A combinatorial experimental design is used to examine which types of manipulations result in intervals that influence judgement in a way that is independent of psychophysics. These findings provide a basis for understanding the underlying "current" of time in our daily mental lives.

COURTESY: Figure 1 in [5].

The second paper (a review) [5] examines the vulnerability of time perception to measurable distortions and illusions. In this study, perceived durations of time are distorted by saccades, oddball stimuli, and stimulus complexity/magnitude. Time perception is found to be particularly susceptible to such interference. 

Perhaps we have a partial answer....

Here is the latest preprint from Orthogonal Research, called "Animal-oriented Virtual Environments: illusion, dilation, and discovery". Now available on PeerJ Preprints

This paper is a semi-review/theoretical paper on the utilization and promise of virtual environments for animal behavioral and neurobiological research. Also demonstrates how the temporal dilation of perception can be achieved in animals for experimental investigations of genomic diversity and brain function.

COURTESY: Figure 3 in [6].

[1] A perspective on time. Visual.ly, October 17 (2013).

[3] Spiers, H. and Bendor, D.   Enhance, Delete, Incept: manipulating hippocampus-dependent memories. Brain Research Bulletin, doi: 10. 1016/j.brainresbull.2013.12.011 (2014).

[4] Liverence, B.M.   Discrete events as units of perceived time. Journal of Experimental Psychology, 38(3), 549-554 (2012).

[5] Eagleman, D.M.   Human time perception and its illusions. Current Opinion in Neurobiology, 18, 131-136 (2008).

[6] Alicea,  B.   Animal-oriented  Virtual  Environments: illusion, dilation, and discovery. PeerJ Preprints, doi:10.7287/peerj.preprints.193v1 (2014).

January 4, 2014

Informed Intuition > Pure Logic, Reason + No Information = Fallacy?

This content has been cross-posted to Tumbld Thoughts.

The peer-review committee for pure rationality. COURTESY: [1]

My notes on logical fallacies: perhaps they are not as bad as you think. People can make what are clearly errors in logic, and sometimes such fallacies are used as decision-making heuristics or cultural blends. This helps us make difficult decisions in the absence of information, or make sense of situations with little precedent.

This is like the 12-step program for skeptics and humanists (or those who aspire to these values). Much like 12-step programs, they leave a lot to be desired. These rules are largely naive of propagandist techniques, and the innate cognitive and cultural biases of their readers. The rhetoric argument does not fare with respect to this list. Fallacies on this list that I have an issue with:

1) Special pleading (and appeal to emotion): in cases where people fail to understand the context of a decision, special pleading might help to offset the damage done by a purely logical decision. Legal decisions that do not take in special cases (e.g. Grandfather clauses) are particularly of note.

2) Black-or-white: if decision-making were entirely deliberative (e.g. purely logical), we would never arrive at a decision. In this sense, decision-making must include a impulsive (or emotional) component. 

3) Ad hominem: while attacking the person rather than the argument is a convenient way to win an argument, this idea also assumes that people always argue in good faith and from a position of pure objectivity. This leaves no room for a theory of motivation, particularly when an argument has a thinly-veiled ulterior motive.

4) Slippery Slope: in cases of ambiguous moral or logical clarity, the slippery slope might actually help us clarify boundaries between one state and another. Without this boundary, human cognition is left without a reference point, which does not allow for clear (and culturally-relevant) decisions to be made.

5) Ambiguity: ambiguity is a necessary condition of a living argument. In cases where ambiguity is resolved, argument or belief/rule system becomes constricted. Allegorical arguments depend on ambiguity to remain relevant -- perhaps this is simply support for the ambiguity fallacy, but allegories are important devices in abducing (e.g. logical abduction) new logical relationships.

6) Strawman: "misrepresentation" of an argument is often in the eye of the beholder. People tend to extract heuristics in dealing with complex arguments, so it is hard to not construct a strawman (unless it is exceedingly flimsy, as with most intelligent design endeavors). 

Unless an argument is painstakingly recapitulated, any "elevator talk" length summary is bound to fail. And sometimes arguments are inherent to one's belief system -- in fact any criticism in this case could be viewed as a misrepresentation. In any case, strawman-type approaches can be used to set up improvements to an argument.

Not so fast, deduction fans......

Now here are some new fallacies that I have come up with. These are based on personal experience, both in human interactions and artificial intelligence. They are a bit more nuanced and specific than the fallacies presented in the "12-step program" model, but then again it speaks to some of my critiques above.

1) The "economic argument"/argument from efficiency: resource allocations that benefit me or my social group are superior, and can be extended to efficiency criteria. 

2) The correlative argument: things that co-occur are always significant. The problem is not discussed in the framework of complex, multivariate causality.

3) Argument from exemplar, normalization fallacy: similar to the correlative argument, but runs in the other direction. In this case, the argument is made from a single example.

In some cases, while the argument is made from extended observation, those observations do not map to the natural phenomenon well. Alternately, comparing phenomena that do not have the same underlying statistical distribution is an example of the normalization fallacy.

4) False consensus: consensus (meeting of the minds, political coalitions, peer-review) always puts you in a better place than where you started. A variant of the normalization fallacy, but involves the assumption that intellectual triangulation will solve any problem.

5) Argument from extreme relativism: when every culture is correct, no matter how morally repulsive the practice. This comes from a misunderstanding of cultural relativism: relativism is not about values, but about the intersubjectivity of cultural variants. In other words, these variants cannot be understood in isolation, only in the context of other practices.

6) Argument from moral superiority: an argument that is rooted in moral superiority (using partially or questionably factual information to intimidate). The goal of such an argument is to reform, prosletyze, or otherwise morally manipulate the intended target.

7) Highly-contingent statistic fallacy: statistics that are the most extreme in recorded history, or the first time a double play was turned in the 5th inning by a left-handed second baseman at night. The superlative is misleading, because the situation is either highly-artificial or not conducive to replication.

Different Ways of Explaining

To conclude, I will demonstrate how there are different ways of explaining. Is one specific type always superior, or is it context-dependent? Or do the abductive and deductive approaches have their own unique advantages?

Ghosts in the machine....

Who's better at explaining things, a novice with an interest and a creative mind, or an expert at really complex concepts [2]? Here, we have an example of the former (Bjork explaining how a TV works in three minutes) and the latter (Hiroshi Ishiguro and other robotics experts explaining the uncanny valley in one minute). And, of course, Spock can address both sides of the equation in one quick caption.

Robots and Humans. Theory of Mind but no context-dependence. Weird and Unnerving.

[1] This is a list of 24 common logical fallacies, courtesy of The Skeptics Guide to the Universe and Yourlogicalfallacyis.com (Jesse Richardson, Andy Smith, and Som Meadon). Also, most of these are individually found on Wikipedia with a more detailed explanation.

[2] For an interesting example of how readers of a magazine for statistics professionals explained the Monty Hall problem to a general audience: Reader's Challenge: the Monty Hall problem. Significance magazine, October, 32-33 (2013).

January 1, 2014

A Resolute and Historical CoE #67

As announced on Tumbld Thoughts. Here's one resolution we can keep.....a new Carnival of Evolution for a new year! David Morrison at the Genealogical World of Phylogenetic Networks presents "Carnival of Evolution #67: the Alfred Russell Wallace centenary edition".

This edition features a short historical tribute to Alfred Russell Wallace, a contemporary and colleague of Darwin. Wallace is best known for his work in the area of biogeography, but has also contributed to our current understanding of phylogeny and natural selection. 

Also featured is a recent Synthetic Daisies post called "Dragons, Sandpiles, and Cavefish: an evolutionary inquiry". This post relates nonlinear dynamical statistical models to cavefish evolutionary developmental biology (evo-devo).