Connectionists: Weird beliefs about consciousness

[Brad Wyble]

>
> Hi Balazs,
>

You wrote:

> That is a very interesting question and I would love to know more about
> the reconciliation of the two views. From what I understand, saliency in
> cognitive science is dependent on both 1) the scene represented by pixels
> (or other sensors) and 2) the state of mind of the perceiver (focus, goal,
> memory, etc.). Whereas the current paradigm in computer vision seems to me
> that perception is bottom up, the "true" salience of various image parts
> are a function of the image, and the goal is to learn it from examples.
> Furthermore, it seems to me that there is a consensus that salience
> detection is pre-inferential, so it cannot be learned in the classical
> supervised way: to select and label the data to learn salience, one would
> need to have the very faculty that determines salience, leading to a loop.
>
> I'm very cautious on all this since it's far from my main expertise, so my
> aim is to ask for information rather than to state anything with certainty.
> I'm reading all these discussions with a lot of interest, I find that this
> channel has a space between twitter and formal scientific papers.
>
>
Very good point and it's absolutely true that computational approaches to
salience are a shallow version of how humans compute salience.  A great
example I like to use is that if you show someone a picture with a Sun in
it, noone looks at the sun, regardless of how salient it is according
Itti-et al. 1998.  We incorporate meaning into our assessment of what is
important, and this controls even the very first eye movements in response
to viewing a new visual scene.

However, my point was that using NN's to compute salience is a very active
area of research with a wide variety of approaches being used, including
more recently the involvement of meaning.  Recent work is starting to tease
apart what recent approaches to salience are missing, e.g.

https://www.nature.com/articles/s41598-021-97879-z#:~:text=Deep%20saliency%20models%20represent%20the,look%20in%20real%2Dworld%20scenes.&text=We%20found%20that%20all%20three,feature%20weightings%20and%20interaction%20patterns
.

So while these approaches are still far from getting it right (just like
the rest of AI), I just wanted to highlight that there is a lot of work in
active progress.

Thanks!
-Brad








-- 
Brad Wyble
Associate Professor
Psychology Department
Penn State University

http://wyblelab.com
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