Subjectively deterministic-looking features of scatter plots

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In summary: If a person has a neural network that has learned to recognize a particular feature, that person will see that feature in the pattern. But in reality, the pattern is just random. In summary, the conversation discusses a scatter plot showing the points where ##|\zeta(1/2 + it)|## has local maxima. There is a debate about whether there is a deterministic quality or texture in the scatter, with some seeing groups of points that seem to form curves and white spaces. However, others argue that this is just a subjective perception and that truly random distributions will have clumps and voids. The conversation also mentions the possibility of studies on the psychology of pattern discernment in random distributions and how individual neural networks can affect perception.
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Swamp Thing
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This is a scatter plot of the points where ##|\zeta(1/2 + it)|## has local maxima.

On seeing it, my first thought was that there seems to be a certain amount of deterministic quality, a sort of "texture", in the scatter. There seem to be groups of points that look like skeins, closed curves etc. There are white spaces roughly bounded by closed curves, and "no-go" areas surrounded by dense populations. It is as if some kind of "attractor" phenomenon is at work underlying the randomness.

So as an example of an "attractor" modulating a chaotic scatter, here are two plots of the so-called Hopalong Attractor discovered by Barry Martin and popularized in Scientific American a few decades ago.

The first one is a zoomed-in version of the second one.
1661824888744.png
1661824910497.png


But there was always the possibility that the features I perceived in the first plot were all in my head, so I plotted a Gaussian distribution and zoomed in on the middle, just to see if the same apparent groupings appeared:

1661824993186.png

To my eye, this seems to have a surprising amount of "texture", with groups of points seeming to form somewhat non-random curves, skeins, and even bounded white spaces here and there. This suggests that any deterministic appearance I see in the first plot is entirely subjective, i.e. "all in my head".

But I can't quite make up my mind. After all, it is not unreasonable that the peaks of a quasi-periodic function could show some pattern underneath the randomness. And the first plot (peaks of zeta) has a qualitatively different and more distinct texture.

So... any thoughts?

Also wondering if there have been studies of the psychology of pattern discernment in random or nearly random distributions.
 
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Swamp Thing said:
On seeing it, my first thought was that there seems to be a certain amount of deterministic quality, a sort of "texture", in the scatter. There seem to be groups of points that look like skeins, closed curves etc. There are white spaces roughly bounded by closed curves, and "no-go" areas surrounded by dense populations. It is as if some kind of "attractor" phenomenon is at work underlying the randomness.
I don't see any of that.

Swamp Thing said:
To my eye, this seems to have a surprising amount of "texture", with groups of points seeming to form somewhat non-random curves, skeins, and even bounded white spaces here and there.
I also don't see any of that.

Swamp Thing said:
Also wondering if there have been studies of the psychology of pattern discernment in random or nearly random distributions.
I am not sure that this is related, but there are studies that show that it is very difficult for humans to actually produce a random distribution. We tend to produce distributions of points that are much more uniform than is correct for a truly random distribution. If it is random then there will randomly be clumps and voids. A distribution that avoids any clumps and voids would be uniform, not random.
 
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Swamp Thing said:
Also wondering if there have been studies of the psychology of pattern discernment in random or nearly random distributions.
Different people will believe they see different things in a random distribution. That is because the brain tries to extract features to make sense of the pattern. Different people develop different neural networks because they have been subjected to different learning experiences.
 
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FAQ: Subjectively deterministic-looking features of scatter plots

What are subjectively deterministic-looking features of scatter plots?

Subjectively deterministic-looking features of scatter plots refer to patterns or trends that appear to be non-random and can be interpreted as having a cause-and-effect relationship. These features may include a linear or curved relationship between two variables, clusters or groupings of data points, or outliers that deviate significantly from the overall trend.

How can we determine if a scatter plot has subjectively deterministic-looking features?

One way to determine if a scatter plot has subjectively deterministic-looking features is to visually inspect the plot and look for patterns or trends that appear to be non-random. Another approach is to use statistical methods, such as correlation coefficients or regression analysis, to quantify the strength and direction of the relationship between the variables.

What is the significance of subjectively deterministic-looking features in scatter plots?

Subjectively deterministic-looking features in scatter plots can provide valuable insights into the relationship between two variables and can help us make predictions or identify potential causal factors. They can also guide further investigation and analysis to better understand the underlying mechanisms driving the observed patterns.

Can subjectively deterministic-looking features in scatter plots change over time?

Yes, subjectively deterministic-looking features in scatter plots can change over time as the underlying relationship between variables may also change. For example, a linear relationship between two variables may become stronger or weaker over time, or a previously observed trend may disappear due to external factors or changes in the data.

How can we account for subjectively deterministic-looking features in scatter plots in our analysis?

When analyzing data with subjectively deterministic-looking features in scatter plots, it is important to consider the potential impact of these features on our results. This may involve controlling for these features in statistical models or conducting sensitivity analyses to assess the robustness of our findings. It is also important to acknowledge any limitations or uncertainties associated with these features in our interpretations and conclusions.

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