Connectionists: Weird beliefs about consciousness

[Tsvi Achler]

Salience is a much more fundamental phenomena within recognition than the
spotlight attention type map suggested by Itti et al and Treisman et al
1980 (the cognitive psychology-equivalent reference).
It is also integrated into non-spatial modalities and occurs even when the
display is too fast to form an attention map in fast-masking experiments eg
(Francis & Cho 2008).
It occurs from a bottom up (through input interactions) way before there is
a chance to select a spatial region focus and is a source of "pop-out".
Salience is associated with a signal-to-noise ratio during processing which
can be measured by the speed of processing given different inputs.
These effects of salience can be measured both in spatial processing and by
reaction times and errors in humans given fast stimuli.  Salience kicks in
immediately while processing information so it is an integral part of
processing, not an attention spatial filter after-effect as hypothesized in
the old cognitive and not very much updated current neural network
literatures.

Pop-out and difficulty with similarity (Duncan & Humphreys 1989; Wolfe
2001) which are analogous signal-to-noise effects (Rosenholtz 2001) are
observed in non-visual modalities with poor spatial resolution such as
olfaction (e.g. Rinberg et al 2006).
Salience seems generated “on-the-fly” as an inseparable part of recognition
mechanisms and thus my opinion (and computational findings) are that
top-down connections back to inputs are very important for all recognition
(not an aftereffect of spatial attention).

Here are some references I have accumulated over my studies:
Francis, G. & Cho, Y. (2008). Effects of temporal integration on the shape
of visual backward masking functions. Journal of Experimental Psychology:
Human Perception & Performance, 34, 1116-1128.
Treisman, A.M. and G. Gelade, A feature-integration theory of attention.
Cogn Psychol, 1980. 12(1): p. 97-136.
Macknik S. L., Martinez-Conde S. (2007). The role of feedback in visual
masking and visual processing. Advances in Cognitive Psychology, 3, 125–152.
Enns, J.T., & Di Lollo, V. (1997). Object substitution: A new form of
visual masking in unattended visual locations. Psychological Science, 8,
135-139.
Duncan, J. and G. W. Humphreys (1989). "Visual-Search and Stimulus
Similarity." Psychological Review 96(3): 433-458.
Breitmeyer, B. G., & Öğmen, H. (2006). Visual Masking: Time Slices Through
Conscious and Unconscious Vision. Oxford: Oxford University Press.
Bichot, N. P., A. F. Rossi, et al. (2005). "Parallel and serial neural
mechanisms for visual search in macaque area V4." Science 308(5721): 529-34.
Wolfe, J.M., Asymmetries in visual search: An introduction. Perception &
Psychophysics, 2001. 63(3): p. 381-389.
Rinberg D, Koulakov A, Gelperin A (2006) Speed accuracy tradeoff in
olfaction. Neuron, 51(3), pp.351-358
Rosenholtz R (2001) Search asymmetries? What search asymmetries? Perception
& Psychophysics, 63(3), 476-489

P.S. In order not to offend as much (but dont worry I believe every field
deserves criticisms) I have put my opinion about the state of the field
here after the references.
I find the neural network community is stuck with 1950's feedforward
neurons and 1980's attention mechanisms and its associated computer science
community is stuck using data sets and paradigms that promote feedforward
methods but are unrealistic paradigms for real life environments.
The computational neuroscience community is also generally bogged down with
a large number of parameters but additionally with statistical models (not
really connectionist) with predominantly feedforward and lateral inhibition
structures.
The cognitive community sits on the most interesting data but is also stuck
with (either) overparameterized rate models or abstract non-computational
models.
The cognitive community is more open to feedback back to inputs but trying
to publish or get funds by doing something that covers all three
communities gets bogged down by sometimes conflicting requirements,
nomenclature and politics in each one.  In my opinion and experience this
is why there is little progress even if there are new ideas.
Thus brain science progress suffers a lot because of these separations.
 -Tsvi

On Wed, Feb 16, 2022 at 9:39 AM Brad Wyble <bwyble at gmail.com> wrote:

> 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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