How Does Our Brain Go About Seeing Data?

7, 4, 2, 6, 8, 2, 5, 8, 9, 1, 3, 5 

The average human attention span is 1,000 milliseconds shorter than that of a goldfish.  Also, our attention span is getting shorter and shorter due to the constant bombardment of information.  In this six post blog series we explore why our brain is not well equipped to handle large data volumes quickly.  But we can overcome with knowledge and analytics. 

The Science Behind It

The human mind is a true wonder.  Many neuroscientists and psychologists have tried to determine the true capacity of the brain.  Some, including the theories founders Richard Atkinson and Richard Shiffrin, believe the mind works on 3 states of memory: sensory register, short-term store, and long-term store.  The sensory register is the input of information into the brain via the 5 senses.  Then short-term memory holds small bits of information for a short period of time for instant recall.  Finally, long-term memory is information that has been repeated (or rehearsed) in short-term memory to cause semi-permanent retention of the information.

We want to make the most of our short-term memory when it comes to data.  Unfortunately, the fundamental problem is that short-term memory lacks capacity and duration.  George A Miller published an article defining what is known as Miller’s Law, or the Magic number 7 Plus or Minus 2.  The human memory diminishes at the 7th task that needs recalling (plus or minus 2 depending on the subject).  Atkinson and Shiffrin theorize that short-term memory is only available for 15 to 30 seconds before being lost.  Assuming the subject does not rehearse the memory (i.e. repeating the task out loud or in some other physical form). 

7 Data Points

Our minds can only consume 7 data points and keep them for 30 seconds or less.  As a result, our bias influences our perception of data.  Take the classic data visualization problem Anscombe’s Quartet. 

A Picture is Worth a Thousand WordsLooking at this table, it is likely hard to make sense of this data.  There are 4 data sets: 1, 2, 3, and 4.  Each data set has an X and Y coordinate.  There are 11 rows per data set.  In total, there are 88 data 
points, not including the labels.  Interestingly, all four data sets have a mean X value of 9 and y value of about 7.50.  Also, they share a lot of other statistically relevant values.  But do you see the direction of the data in set 1?  How about the arch in set 2?  Or the outliers in sets 3 and 4? 

Or in our case, a chart is worth 22 data points each.  So with the Anscombe’s Quartet data, we experience our lack of attention.  Don’t believe me?  What was the value in the 1st column, 3rd row?  How many individual data points can you recall?  With the visualized data, we observe that set 1 is slightly random, set 2 is an arch, set 3 is diagonally linear with an outlier, and set 4 is vertically linear with an outlier.  All data consumption is done within our limited attention span and fits into our limited capacity and duration of short-term memory. 


Our memory and fleeting attention span create boundaries.  Remember the random sequence of numbers at the beginning of this post…can you repeat them?  How many do you remember?  How many numbers were there?

The next post in this series identifies how our brain can make snap judgments about presented information and how to best visualize information for these quick decisions.   


Cited Sources

McLeod, S.  (2009).  Short Term Memory. Retrieved November 7, 2018 from

McLeod, S.  (2007).  Multi Store Model of Memory.  Retrieved November 7, 2018 from

Atkinson-Shiffrin memory model.  In Wikipedia.  Retrieved November 7, 2018 from

Anscombe’s quartet.  In Wikipedia.  Retrieved November 7, 2018 from

The Magical Number Seven, Plus or Minus Two.  In Wikipedia.  Retrieved November 7, 2018 from,_Plus_or_Minus_Two

Trevor Schulte

Trevor has 10+ years in Reporting and Analytics across a plethora of industries, such as investments, retail, finance, and manufacturing. He is a member of the eCapital Analytics Team where he specializes in data visualization and dashboard analytics however has deep experiences in environment management, managed reporting and distribution, and ETL.

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