Deserialized Cognition

Part Of: [Deserialized Cognition] sequence
Followup To: [Why Serialization?]

You’ve heard the phrase “pre-conceived notion” before. Ever wonder what it means? Let’s figure it out!

Cognitive Style: Conceptiation

Your mind is capable of generating concepts. Let us name this active process conceptiation.

How does this process work in practice? There are only two ways concepts are created: from oneself (inference conceptiation), or from others (social conceptiation):

Deserialization- Conceptiation

Inference Conceptiation attempts to get at self-motivated, non-social cognition. You process information, you form a conclusion (a result), you save this result to memory, and then you pass it along to other cognitive process. Examples:

  • A scientist trying to make progress in string theory
  • An artist teaching herself to speak Spanish

Social Conceptiation summarizes the thought process of someone immersed in a more social setting. Examples:

  • An engineer picking up an proverb (e.g., “no analogy is perfect”) from Facebook, without thinking about it much.
  • A socialite half-listening to some guy at a dinner party describing nuanced work tasks.

During both types of conceptiation, concepts are saved to your long-term memory. Call this serialization.

Cognitive Style: Deserialization

You are a lazy thinker.  Don’t take it personally, though – so is everyone else. How can we explain our inner cognitive miser? It turns out that there are at least two biological reasons for this failing:

  • Brains are slow because they rely on chemical synapses; they run at 100Hz (vs the 2 billion Hz of computers)
  • Brains are metabolically expensive, burning 800% more energy than other organs (20% of total organismic load)

Serialization techniques (discussed previously) allow our brains to be lazy. Not all concepts need to be created from scratch; if, at some point in the past, you have acquired the requisite mindware, you can always resurrect it from long-term memory, in virtue of your built-in deserialization mechanism:

Deserialization- Deserialization

Two Inputs

As mentioned, deserialization (loading) only works if the requisite concepts have been serialized (saved) at some point in the past. Since serialization comes in two flavors, we can now refer to two different kinds of deserialization:

Deserialization- Deserialization Modes (1)

Call the former inference deserialization, and the latter social deserialization.

Application: “I Love You”

Imagine you were raised to believe in the importance of regular expressions of affection to your significant other (SO). So, you say “I love you” to him/her every day. At first, you are eager to tell them the reasons behind your feelings, but after a while, novelty becomes increasingly effortful. Eventually, you settle into a simple “I love you” before falling asleep. Fast forward two years, and your SO says “I don’t feel like you are being affectionate enough”. How can we explain this?

We are now equipped to describe the “I love you” pattern as an instance of deserialized cognition, no? This form of cognition (more specifically, a behavioral pattern) was established previously, and no longer requires active conceptiation to perform. Why should your SO wish for you to employ active processing, especially if such processing yields content very similar to your habituated behavior?

Why would your SO wish you reject serialized cognition? Here’s one path an explanation may take: such an override goes against the instinct of the cognitive miser. Costly signaling is a staple concept in ethology: effort filters between those who truly hold the recipient in high regard and those who only wish to appear that way.

Speaking more generally, it seems to me that our itch for originality come from precisely this will to demonstrate rejection of deserialized cognition.


In this post, we explored how the brain uses concepts via two distinct mechanisms:

  1. In conceptiation, the brain actively constructs & uses novel concepts.
  2. In deserialization, the brain simply reuses pre-existing concepts.

The brain also employs two different ways to create concepts:

  1. Some concepts are constructed by one’s own mind.
  2. Concepts constructed in a social setting are constructed externally, but are (optionally) evaluated by the self.

Putting these together, we are now equipped to refer to four different types of cognition.

Deserialization- Cognition Taxonomy (2)

This new vocabulary opens many doors to explanation, including the question “why do people value originality?”

Credit: Some of the ideas of this post come from previous speculations about cached thoughts. However, compared to deserialization, caching has a weaker analogy strength: concept reuse has precious little to do with enforcing consistency within a memory hierarchy.

Until next time!


Policy Proposal: Metrication

Table Of Contents

  • Back To Basics
  • Meet The English System
  • A Cognition-Friendly Design
  • Global Trends
  • Policy Proposal
  • What Use Are Policy Proposals?
  • Bonus Proposal!

Hm. So, I enjoy discussing this topic. Maybe if I write about it, my Will To Rant will weaken! (Family & friends will be thanking me in no time. 😉 )

Back To Basics

Do you remember how long one meter is? Extend your arms to approximate its length. Now say “meter” about eighteen times, until you achieve semantic satiation. Okay good, I’ve confused you. Your familiarity high was stunting your ability to learn.

Why must a meter be that long? What forbids it from being defined differently?

Nothing. All measurement conventions are arbitrary. Thus, it is possible for every person to use different measurement rules.

But that isn’t how society operates. Why? How do we explain measurement convergence?

It is a cultural technology: it moves attention away from the communicative vehicle and to its content.

Does the above remind you of anything? It should. If I swap out the nouns, I’d be talking about language. The analogy strength is considerable. (Have you yet figured out the mechanism that underwrites analogy strength?)

The funny thing about language is that globalization is murdering it. Of the 6500 languages alive today, fewer than half will survive to 2100 ACE. If you combine this fact to our analogy, you are mentally equipped to forge a prediction:

  • We expect the number of measurement systems to be decreasing.

Meet The English System

In fact, only two comprehensive measurement systems remain. Here is a snapshot of one of them, the English system:



Chances are that you live in the US, and chances are you’ve wrestled with the question “how many ounces in a quart” once in your life.

Let’s be explicit about why we don’t like the above:

  • There is no discernible pattern between the equivalency values (e.g., 2, 1760, 2240, 43,560…) or words (e.g., “cup”, “pint”, “quart”, “gallon”)

Do you agree? Is this is the reason why you winced at the above table?

Even if we agree, we aren’t done. We still need to explain where our complaint comes from. And that explanation is, of course, cognitive:

  • Patterns facilitate memorization, improving performance of long-term memory.
  • Patterns allow for compression, reducing the load on working memory.

A Cognition-Friendly Design

If you were to design a solution to the above problems from scratch, how would you do it?

I doubt I would have been able to invent this technology independently: it is intimidatingly brilliant. Time to meet the quantitative prefix. The basic idea is: why don’t we link equivalency values to the grammar, and infuse mathematical meaning into our prefixes?

The metric prefix is a kind of quantitative prefix. It encodes scale, in increments of 10^3 (i.e., 1000), by the following:



You can allow your sense of familiarity back in the room. You have, of course, used quantitative prefixes all your life. Do you recognize the words “milli-meter”, “kilo-gram”, “giga-byte”? Well, now you have another tool under your belt: you can now precisely understand words you’ve become accustomed to, and rapidly absorb the meaning of new combinations. Two examples:

  1. If someone were to ask you “what does a micro-gram mean?” you could answer “a millionth of a gram!”
  2. If someone were to ask you “how many bytes in 4 gigabytes?” you could answer “4,000,000,000”! *

(* Unless the person who said gigabyte ACTUALLY meant 4 gibibytes, which is NOT the same thing, and a totally separate rant. 🙂 )


Notice that, with this technology, we have the same root word, and only need to modify the prefix to expand our vocabulary. More pleasant, no?

Global Trends

Recall our prediction, that the number of measurement systems would decrease over time. And it has. All countries marked in green use the Metric system:

Global Metrication Status

Notice any outliers? 🙂

It’s not like the United States hasn’t tried. In 1975, Congress passed the Metric Conversion Act… but its efforts were largely disbanded in 1982. You can read more here if you like.

Policy Proposal

  • Proposal: The United States should pursue metrication.

Some drawbacks: Such legislation will cost money, and be inconvenient in the short term.

Some benefits: Improved international relations, promotion of less fuzzy thinking, working memory generally freed up for other tasks.

To me, I’m more worried about the possibility of systemic failure: perhaps any political action that incur short-term-cost in exchange for long-term gain are generally considered hazardous. Perhaps, for example, we could introduce a legislation timers so that the fallout from “eat your vegetables” bills don’t fall on their signatories.

Yes, I’m aware the above example is completely broken. But it is meant to signal the kind of thinking we need: infrastructure refactoring.

What Use Are Policy Proposals?

A large amount of ink has been spilled on the metric system. Many of these contributions dive to a depth greater than mine. I do not expect my career to involve the comprehensive analysis of policy ramifications, the meticulous construction of actionable proposals. I am a voice in the wind. Why do I bother?

I will be collecting policy proposals on this blog for several reasons. Beyond my philosophy of politics, I write because it may bring value to the world, and it helps organize my mental life. I also would like to ultimately find collaborators, like-minded individuals interested in researching with me. But I also write because I hope my unconventional emphases will someday unlock relatively-novel ideas that are of good quality. Here’s an example of an idea that may come from my cognitive emphasis above (no promises on quality though :P):

The above solution of quantitative prefix was ultimately a marriage of mathematical reasoning and grammatical systems. I am unable to technically specify the full cognitive algorithm for why this combination works (yet, darn it!). But it opens the door to brainstorming: how else could we leverage language to crystallize and augment our rational capacities? And then you start casting around for ideas.

Bonus Proposal!

A stream-of-consciousness illustration of the kind of transhumanist creativity I am encouraging.

For me, I recall reading speculations that perhaps one reason Chinese kids tend to score highly in math is because the digits are easier to pronounce. I then search for “chinese digits pronunciation” and find this paper. An excerpt:

These data offer support for the hypothesis that differences in digit memory between Chinese and English speakers are derived, in part, from differences in the time required to pronounce number words in the two languages.

I then wonder if a numeric system could be engineered to supplant our “one”, “two”, “three”, etc with a system more like Chinese, to enhance students’ cognitive capacities. But not exactly Chinese numerals – that phonetic system carries other disadvantages. I envision a new numerical phonetics that, engineered with state-of-the-art computational models of working memory, brings empirically-demonstrable cognitive advantages over its “natural” competitors.

See you next time.