The Impact of Pressure to Communicate on
Abstract and Compositional Thinking
Arvind Neelakantan
[email protected]
Abstract
Humans display phenomenal abstract and compositional thinking. We
can conceptually place the same object at multiple levels of abstraction
ignoring different irrelevant information based on the context. We can also
compose different concepts to produce novel concepts and experiences that
can sometimes be only imagined and are not even part of the real world.
In this work, we study the impact of pressure to communicate on abstract
and compositional thinking. By instructing one group to write down their
strategy while asking the other group to just focus on an abstract goal, we
aim to show that the group that was pressured to communicate is better
at forming abstract sub-goals, and composing these sub-goals to solve a
set of computer games.
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Introduction and Related Work
Humans display phenomenal abstract and compositional thinking. By abstraction, we mean the ability to remove irrelevant noise in a particular context.
For example, when there is a single window in an environment, we refer to it
just as a window while when there are multiple windows with multiple colors,
we discriminate between them using their color. Next, humans can compose
concepts to produce novel concepts and experiences. For example, consider the
following sentence “The pink elephant was dancing with a violet dog and many
green cats”.1 Anyone reasonably good in English would have no trouble in
understanding and imagining this scenario even though it has possibly never
happened in reality. While there could be many factors contributing to the
phenomenon of abstract and compositional thinking, in this work, we study the
impact of pressure to communicate on abstract and compositional thinking.
Throughout this paper, we refer to language as a framework that provides
abstract symbols with specific meaning, and rules to compose the symbols to
form other meaningful units. Natural languages and mathematics used to communicate to other humans who know the rules of that language and computer
programming languages that are used by humans to communicate with computers are some examples that fit this definition. The effect of language on
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first heard something similar from Noam Chomsky.
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thought has been studied extensively. Gordon [5] and Pica et al. [7] study the
effect of language to perform “unnatural” mathematical computations involving
large numbers, Waxman & Markow [11] show that naming distinct objects with
the same symbol makes children think about the commonalities between these
objects and Pyers et al. [8] show that language helps in navigation involving
landmarks.
The idea that interaction serves as a regularizer removing unnecessary noise
has been studied extensively [9, 3, 4]. At a high-level, in these studies an
artificial language with unnecessary noise (for example, noisy determiners) is
introduced to two or more people and they are instructed to interact. After
some amount of interaction, it is observed that the unpredictable variation or
noise is removed and the group abstracts to a predictable language. The process
of abstracting by removing noise to reduce entropy could be due to several
reasons including reducing the cognitive load and linguistic stimuli. It is easy to
see that storing a model with lesser entropy requires fewer bits of information
and thus enabling parsimonious storage. [4] argue and verify empirically that
the abstraction phenomenon possibly occurs due to both cognitive load and
linguistic stimuli. They present two interesting findings: 1) participants exhibit
more regularization as the cognitive load is increased, 2) linguistic stimuli results
in more regularization than many kinds of visual stimuli. Moreover, they observe
that as the participants interact more and more, the amount of regularization
keeps increasing.
Spelke [10] discuss the idea of core systems that are mostly innate and are
a product of evolution while the process of composing two or more systems
together is learned. They argue that while the process of modeling concepts
such as “left of wall” and “blue wall” are part of the core systems, the process of
composing these two concepts to form the concept “left of blue wall” is learned.
Moreover, they hypothesize that the ability of composition is influenced by
language. As evidence they discuss two studies. First, they discuss a study
conducted by Cheng [1] on rats. Here, a hungry rat can correctly re-orient
and go to the left or right of an object to get food but cannot additionally
combine information that uses the brightness of the wall behind the object.
Similarly, experiments in Hermer et al. [6] and other studies discussed in Spelke
[10] show that children ranging from eighteen months to four years perform like
rats when searching for toys. Children gain mastery on these composition tasks
simultaneously as they develop language.
In our study, we aim to study the impact of communication on the high-level
thought process. More specifically, we plan to have two groups of adults who
both know language and are required to solve a set of computer games. One
of them is instructed that they would have to write down their strategy while
the other group is asked to just focus on the goal. We expect more people in
the group that was pressured to communicate to display better abstract and
compositional thinking. As a result, after some amount of training, the group
that was pressured to communicate will solve games faster than the other group.
Our work differs from previous work in the following ways:
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• Previous work that studies the effect of language on compositional thinking is performed on subjects that know language versus those who do
not. Our study is an attempt to uncover a more subtle phenomenon of
the impact of pressure to communicate where both the groups consists of
subjects that know language.
• The studies on abstraction measure its impact directly on language and
not on the high-level thought process. Our study aims to measure the
impact on general abstract thinking.
• The influence of language on abstraction and compositional thinking have
been studied independently. We make an attempt towards developing an
experiment where both of these characteristics can be measured simultaneously.
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High-Level Experimental Setup
We plan to have two groups and use computer games as the testing environment.
The main resources we would need our adults who are willing to participate in
these experiments. I do not play computer games often and hence do not know
whether there are existing computer games that already have the properties
discussed later in the section. If they do not exist, I will be open to the idea of
designing the games which could be time-consuming and expensive or consider
a different testing environment.
2.1
Groups
We will have two groups of people, Group A and Group B. Both the groups will
have adults who know language well and do not have communication problems.
Group A is instructed to write down the strategy after they solve every game.
This communication pressure will hopefully make them form abstract sub-goals,
and re-use and compose the abstract sub-goals in creative ways. Group B is
instructed to just solve the computer games. We expect them to adopt a more
trial-and-error based strategy without uncovering the high-level pattern in the
set of games.
2.2
Environment
We need an environment where there is a high-level pattern that is hard to
unravel unless the subjects perform abstract and compositional thinking. We
plan to have a goal-oriented environment that consists of accomplishing some
number of sub-goals. The sub-goals have to be composed in a certain way to
reach the final goal. We plan to have many distractions in the environment
which will increase the cognitive load and might make it difficult to discover the
high-level pattern.
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3
Computer Games
In this section, we describe the set of computer games we plan to employ in
more detail. At a high-level, each game has an end goal of defeating an evil bot.
To accomplish this goal, the participants need to collect a set of resources/subgoals. The meta-pattern in the game is to have roughly equal amounts
of each of the resource to win the evil bot. We discuss the different set
of resources, the difficulty in identifying and acquiring them below.
3.1
Sub-Goals
The motivation for having sub-goals is that the participants have to abstract and
infer these sub-goals while playing them. We hope that participants with the
pressure to communicate would perform better at identifying these sub-goals.
We plan to have the following sub-goals:
• People: To win the battle, game participants have to collect people.
Within a game, we would have different kinds of people that the participant could acquire. The people would differ in their costume, sounds
they make, color of the skin and so on. While on the surface they are different, for the purposes of the war, they all carry the same value. All the
differences could potentially make it slightly hard for game participants
to abstract and consider them as the same resource. The people in the
game would be controlled by the participant through computer control.
The people in the game use the weapons described below. The decision
of when to use the weapons are again controlled by the participant using
computer control.
• Guns
• Tankers
• Bombs
• Truck: The purpose of the trucks is to transport the guns, bombs and
people to the battleground. Like previous sub-goals, the trucks would
also come in different designs and colors. The truck will be driven by the
participant using computer controls.
All the weapons would also come in different shapes, color and design. Due
to the different kinds of cognitive loads we plan to employ, we hope that without
the pressure to communicate it would be hard for participants to figure out the
abstract sub-goals.
3.2
Collecting Sub-Goals
The participant in each game is roaming around a landscape with many possible
doors at different instances. Opening the door will give some number of each
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resource described previously. For example, opening a red door with small circles
as design in the door in a green soil will give more guns than other resources.
We plan to have soil in two different colors, door in five different colors and two
different kinds of shapes as design in the door. Overall there are 20 different
combinations, with each combination giving one resource more than the other.
The participants have to plan such that at the end of the game they have opened
the correct sequence of doors so that they have roughly equal number of each
resource.
3.3
Distractions
We also plan to have many high-level distractions throughout the game that
adds to the cognitive load of the participant and makes it harder to perform
abstract and compositional thinking. The distractions we plan to employ are:
• Music that keeps changing randomly with different moods from different
genres.
• Airplanes and Helicopters flying in the air with flashy colors.
• Fire suddenly appearing in the middle of the game.
• Buildings and castles in crazy designs and shapes.
• Weird characters walking around in different costumes, and sometimes
dancing and making noise.
3.4
High-Level Strategy
The high-level strategy to solve each set of game is to collect roughly equal number of each resource. By having these combination of resources, the participant
would be able to defeat the evil bot using a simple deterministic process.
3.5
Train Time
Participants in both the groups are instructed to solve a set of games. Group A
is instructed to write down the strategy after each game. We plan to have some
number of games, say 5 during the training phase. We expect the strategy of
participants in Group A to move closer and closer towards the optimal strategy
as the training phase progresses.
3.6
Test Time
At test time, we plan to have an additional game that has the same high-level
strategy. We expect participants in Group A to solve the game much faster
than Group B because of their abstract and compositional thinking process.
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Expected Outcome
We expect Group A to solve the games at test time faster than Group B because
Group A would uncover the high-level pattern in the game. More specifically,
we expect more people in Group A to be better at identifying abstract sub-goals
and the composition pattern required to solve the game. We can inspect the
strategy of Group A by reading their strategy and check how close it is to the
optimal strategy required to solve the game.
After the test phase, we will ask the Group B participants also to write down
their strategy. Our expectation is that their strategy would be more trial-anderror based without sophisticated high-level reasoning. But, if Group B solves
the games as fast as (or faster than) Group A, then it would be interesting to
know whether their strategy is similar to Group A. If the experimental setup is
well designed, this result would mean that the pressure to communicate has no
significant impact on abstract and compositional thinking.
4.1
Time Pressure
To disentangle different factors of variations, we could consider having a third
group, Group C. The third group is instructed to solve the game as soon as possible because of which they are primed towards trial-and-error based thinking.
The rationale here is that abstract and compositional thinking usually requires
more patience without getting immediate perceptible rewards. By introducing
time pressure, we discourage the participants from performing deep thinking.
The expected outcome here is that Group C takes more time than Group B.
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Discussion
The central claim in this work is that the pressure to communicate has an impact
on abstract and compositional thinking. This essentially comes from the idea
that possibly the default mode of thinking in humans is approximate, fuzzy and
trial-and-error based. In the book“Thinking Fast and Slow”, the author makes
the argument that humans have two modes of thinking: 1) fast thinking whose
characteristics include approximate and emotional, and 2) slow thinking which
is rational, procedural and more accurate.
If we consider our history, fast thinking process is largely sufficient for survival for both animals and humans. If an organism learns quick reflexes for a
small range of tasks, there is largely no additional pressure for slow, rational
and procedural thinking. This is reflected in the experiments described in Dehaene [2] where animals are tested on the task of counting. As the number to
be counted gets larger, the animal gets to an approximate solution using fast
thinking and then proceeds by trial-and-error to the required number. Perhaps,
an optimal strategy here would be to “take a step back”, and model the highlevel process better. Somehow, animals and even humans to a large extent are
mostly restless and resort to trial-and-error based strategy too quickly.
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The slow thinking process has contributed to remarkable progress in science,
art, music and many other domains. This success is a clear indication that
humans are indeed capable of slow thinking. The slow thinking process often
requires abstract and compositional thinking. A scientist thinks in terms of the
symbols in their field of study and a musician through the symbolic notations
of music, and composing the symbols in novel and creative ways. Scientists
and musicians invent linguistic symbols and syntax not just for the process of
communication but also to create a framework that eases their thought process.
While scientists and musicians have multiple different motivations for abstract and compositional thinking such as experiencing the joy of a breakthrough, fame and legacy, there is little pressure in our daily lives to perform
abstract and compositional thinking. Most of the human workforce is involved
in jobs that does not require too much abstract and compositional thinking.
We have built a reasonably safe world where fast and approximate thinking is
usually sufficient to live comfortably. As a result, to a very large extent there
are no big pressures for abstract and compositional thinking in general.
Leaving aside the benefits of communication, language provides us an excellent framework to perform abstract and compositional thinking. The pressure
to communicate to others makes us think through language which biases us towards abstract and compositional thinking. This is possibly because language is
inherently abstract and compositional. While it is still true, that approximate,
non-linguistic thinking is sufficient for our daily survival, for certain tasks it is
far from the optimal strategy. For example, the project got clearer in my head
after writing this document! :)
Acknowledgments
I would like to thank Erik Cheries for numerous discussions, and guidance on
this topic and many other general topics in psychology. I discussed this project
with Craig Greenberg, Nicholas Monath, Aanand Srinivas, and Pat Verga.
References
[1] Cheng, K. A purely geometric module in the rat’s spatial representation.
Cognition, 1986.
[2] Dehaene, Stanislas. Taleneted and gifted animals. Oxford Univeristy Press,
1997.
[3] Feher, Olga, Wonnacott, Elizabeth, and Smith, Kenny. Structural priming
in artificial languages and the regularisation of unpredictable variation.
Journal of Memory and Language, 2016.
[4] Ferdinand, Vanessa, Kirby, Simon, and Smith, Kenny. The cognitive roots
of regularization in language. ArXiv, 2017.
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[5] Gordon, Peter. Numerical cognition without words: Evidence from amazonia. Report, 2004.
[6] Hermer, L., Spelke, E., and Katsnelson, A.S. Sources of flexibility in human
cognition: Dual-task studies of space and language. Cognitive Psychology,
1999.
[7] Pica, Pierre, Lemer, Cathy, Izard, Veronique, and Dehaene, Stanislas. Exact and approximate arithmetic in an amazonian indigene group. Science
Reports, 2004.
[8] Pyers, Jennie, Shusterman, Anna, Senghas, Ann, Spelke, Elizabeth, and
Emmorey, Karen. Evidence from an emerging sign language reveals that
language supports spatial cognition. PNAS, 2009.
[9] Smith, Kenny, Feher, Olga, and Ritt, Nikolaus. Eliminating unpredictable
linguistic variation through interaction.
[10] Spelke, Elizabeth. Innateness, choice, and language. Cognitive Science and
The Philosophy of Mind, 2010.
[11] Waxman, S.R. and Markow, D.B. Words as invitations to form categories:
Evidence from 12- to 13-month-old infants. Cognitive Psychology, 1995.
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