Connectionists: Weird beliefs about consciousness

[Danko Nikolic]

Hi all,

There are even more cool and relevant facts about attention than what Tsvi
listed. The capability to effectively apply attention is tightly correlated
to our knowledge about what we are attending to and to the capacity of
working memory; there is a linear relationship between the capacity of
working memory for a certain type of inputs and the ability to effectively
attend to those inputs. For example, non Chinese readers have poor
attention capacity and poor working memory for Chinese characters. Chinese
readers, in contrast, have great capacities for both. The part of the brain
that drives attention to X is also the part of the brain that stores X into
working memory. A tight relationship indeed.

The capability to attend also determines the time it takes to learn a
visual pattern -- which also seems to be a linear relationship.

Attention seems to require concepts. If you have a concept of A, you can
effectively attend to A.

Attention may not be a separate mechanism but a shadow of something else in
the brain that also does working memory and other stuff like decision
making and intelligence in general. We don't know what that is. (I have
given my two cents on the properties of this mechanism behind, which I
named anapoiesis.)

Danko


Dr. Danko Nikolić
www.danko-nikolic.com
https://www.linkedin.com/in/danko-nikolic/
--- A progress usually starts with an insight ---


On Thu, Feb 17, 2022 at 8:24 AM Tsvi Achler <achler at gmail.com> wrote:

>
> 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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