I recently used an automatic tracker to learn how I was spending my time online. I learned that my perceptions were systemically biased: I spend less time than I thought on purely non-productive sites, and far more time on sites that are quasi-productive.

For example, I felt that I was spending too much time reading the news, but I learned that I spend hardly time doing so. I didn't feel that I was spending much time reading Hacker News, but I was spending a huge amount of time there!

Is this a specific case of a more general error?

A general framing: "Paying too much attention to the grouping whose items have the most extreme quality, when the value of focusing on this grouping is eclipsed by the value of focusing on a larger grouping of less extreme items".

So in this case, once I had formed the desire to be more productive, I overestimated how much potential productive time I could gain by focusing on those sites that I felt were maximally non-productive, and underestimated the potential of focusing on marginally more productive sites.

In pseudo-technical terms: We think about items in groups. But then we think of the total value of a group as being closer to average_value than to average_value * size_of_group.

This falls under the category of Extension Neglect, which includes errors caused by ignoring the size of a set. Other patterns in this category are:

• Base rate neglect: Inferring the category of an item as if all categories were the same size.
• The peak-end rule: Giving the value of the ordered group as a function of max_value and end_value.
• Not knowing how set size interacts with randomness.

For the error given above, some specific examples might be:

• Health: Focusing too much on eating desert at your favorite restaurant; and not enough on eating pizza three times a week.
• Love: Fights and romantic moments; daily interaction.
• Stress: Public speaking; commuting
• Ethics: Improbable dilemmas; reducing suffering (or doing anything externally visible)
• Crime: Serial killers; domestic violence