# Research Help! Why do we overestimate variability when objects are more similar to each other?

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Hello! I am a first year phd student in a Cog Psyc program. My research focus is in the area of ensemble perception -- which is our visual systems amazing ability to accurately and efficiently compute the mean of a set of similar objects. For example, if you are presented with a display of circles varying in size and asked what the average size of the circles are, you will estimate the mean very accurately! This is also referred to as summary statistics.

My research focuses on the perception of variability. Through a series of experiments where I present a sequential set of 9 objects (different line orientations for one experiment), I have found a consistent bias to overestimate objects when they are more similar to each other (they vary by 1 degree) compared to when they are different from each other (they vary by 5 degrees). For example, if participants see a set of 9 lines that all vary within a range of 1 degree (low variability), they overestimate the variability in those lines by 55% where as they are pretty good at estimating the variability when the lines vary by 6 degrees (high variability). I have thought that maybe people are relying on the first few items or the last few items in a set to make their estimates, but I don't find this when I run the analysis. I also don't really find that they are using a specific group of lines to make their  estimates. It also doesn't seem like they are using a range heuristic to make their estimates. That is, taking the largest and smallest of the set to estimate the variability.

I haven't found any clear literature that hones in on why people may be biased to overestimate a sequentially presented group of objects (that is, objects that are presented one at a time) that are more similar to each other. Are there any fellow cog psyc (maybe even psychophysicists) out there who may have a plausible mechanisms that may explain this? Any help brainstorming would be amazing!

Edited by MiaCorinne
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Just my 2 cents, We are likely to see to notice the differences rather than the similarities. Therefore, it is easier to overestimate th differences rather than the similarities. I dont know if there is a exact name for this. But here is my thought.

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Do you mean that it is easier to overestimate similarities because we notice differences more than we do when things are similar? I think that could be possible. One thing that I have been thinking about is that we overestimate things that are more similar so that we are able to notices those subtle differences. For example, imagine that you are in the rainforest where there are various shades of greens and browns, meaning that there would be a lot of variability in the colors of greens and browns. It would be more advantageous if you were able to overestimate the variability in greens when they are mostly similar because this would allow you to detect some prey or a predator. My issue with this proposal is that I don't know how to make this into a testable mechanism or theory to explain why we overestimate the variability in objects that are similar. Mostly because I'm not in evolutionary psychology. I am in a vision lab, where we use psychophysics methodologies.

Edited by MiaCorinne
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This is above my paygrade haha. But yes, figuring out how to test hypothesis is the point of grad school. You are not alone, talk to your peers,PI about it, start with what you already know how to do and build from there. If you have this question, someone else probably has it too, you might be the first one to crack it.

** you also need to becareful with overloading the brain. Look into something called chunking, this overestimation might be due to the chunks' already created by that person. So similar objects are chunked together, but this chucking is different from person to person.

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Thanks NK! Chunking/clustering is an interesting point that I am aware of but hadn't thought about in relation to my work! Hopefully I"ll get to talk more with my peers in the fall when things try to go back to normal. Unfortunately, my PI switched to this area of research recently, so they are very new to the area which makes it hard to discuss in depth.

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