What is Degree of Attention?

What happens when you pay more attention to something?

By Parag Jasani o www.YourDailyLife.blog
Posted on
Self

Despite exponential progress in mind and brain sciences, without a causal account of the human brain, knowledge about degree of attention, a phenomenon as ubiquitous as breathing, is still a mystery. Following is the first ever explanation of how the mechanism underlying degree of attention (i.e. intensity of attention) works:

Design of the human brain is not developed on a preconceived plan or a blueprint. It has evolved over thousands of generations based on the process of natural selection in a gradual manner.

Such gradual development has enabled it to transit from processing information using lower amount of resources in earlier generations to higher amount of resources in succeeding generations, an ability it has retained that helps in optimizing its operations.

In detail: According to an estimate, our brain can store 1 petabyte of information. A computer with the same amount of information would require 1 gigawatt of power to process it, which our brain does with just 25 watts of power. Such optimization is a result of the optimizing aspect of natural selection 1.

One of the many ways it optimizes its operations is by using a mechanism I call “Rescaling Mechanism”, which is designed to give more attention to significant interactions and vice-versa. It does so by controlling what we call the “degree of attention”, in which system 1 controls the resources required to process its ongoing interactions.

How Rescaling Mechanism Works: System 2, with the help of system 1, evaluates how significant each ongoing interaction is to its goals, proportional to which, it instructs system 1 to allot the amount of resources that are required to process it. Higher amount of processing resources are allotted when the significance is high, which offers more comprehensive processing, whereas lower amount of processing resources are allotted when the significance is low, which saves energy.

In other words, when you pay more attention to something, system 1 allots and utilizes higher amount of resources to process it and vice versa. It’s like having a digital camera with, e.g. a 5 megapixel sensor that is capable of steplessly scaling up to 20 megapixel sensor or scaling down to 2 megapixel sensor on demand.

An example of the same is when you hear a news item; system 2, with the help of system 1, evaluates it. If it detects it as significant for you, e.g. a road accident very close to your house (as it may involve someone close to you), it instructs system 1 to allot proportionately higher amount of resources to process it.

Along with allotting higher amount of processing resources for more comprehensive thought processing, system 1 also allows higher amount of visual data to fall on the retina for more comprehensive visual processing by dilating pupils of our eyes, i.e. making our pupils larger.

As pupil dilation does not consume much energy and as requirement of comprehensive visual processing may spontaneously arise at any time during an interaction, they are hard-wired to dilate as a reflex in proportion to the processing resources allotted for thought processing, whether vision is involved in the interaction or not.  

When you are involved in comprehensive thinking like deliberating between multiple options (e.g. negotiating a business deal), focusing on the subject of interest (e.g. attending to someone you love), etc., the reflexively dilating pupils of your eyes act as a window to your mind. 

To summarize, when your brain detects something of high significance, it allots proportionately higher amount of resources to process it, offering more comprehensive processing and thus, better capability to interact with something that is significant to you. Conversely, when your brain detects something of low significance, it allots proportionately lower amount of resources to process it, thereby saving on energy.



References

1 Optimizing Aspect of Natural Selection

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What is Degree of Attention?

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