Why computers should be attentive?

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Any animal [1] from the tiniest insect [2] to humans is perfectly able to “pay attention”. Attention is the first step of perception: it analyses the outer real world and turns it into an inner conscious representation. Even during some dreaming phases known as REM (Rapid Eye Movements), the eye activity proves that the attentional mechanism is at work. But this time it analyses a virtual world coming from the inner subconscious and turns it into an inner conscious representation. Attention seems to be not only the first step of perception, but also the gate to conscious awareness.

The attentional process probably activates with the first developments of a complex sense (like auditory) which comes with the first REM dreams beginning after the sixth months of foetal development [3]. This mechanism is one of the first cognitive processes to be set up and factors like smoke, drugs, alcohol or even stress during pregnancy lead to later attention disorders and even higher chances to develop psychopathologies [4][5]. It is largely proven that for cognitive psychopathologies, the attentive process is highly affected (like in autism or schizophrenia) mainly by studying eye tracking traces which can be very different between patients and the control groups [6][7]. The attentive process is set up as early as the prenatal period when it already begins to operate during babies dreams. Until death it occurs in every single moment of the day when people are awake, but also during their dreams. This shows the importance of attention: it cannot be dissociated from perception and consciousness. Even when the person is sleeping without dreaming and the eyes are not moving, important stimuli can “wake up” a person. Attention is never turned off, it can be only lowered and in standby (excepting drug-induced states when the consciousness is altered or eliminated as in artificial coma). It is thus safe to say that if there is conscious life in a body capable to act on its environment, there is attention.

As a gate of conscious awareness at the interface between inner and outer, attention can be both conscious (attentive) and unconscious (pre-attentive) and it is the key to survival. Attention is also a sign of limited computation capabilities. Vision, audition, touch, smell or taste, they all provide the brain with a huge amount of information. Gigabits of rough sensorial data flow every second into the brain which cannot physically handle such an information rate. Attention provides the brain with the capacity of selecting the main information and building priority tasks. While there are a lot of definitions and views of attention the one core idea which justifies attention regardless the discipline, methodology or intuition is “information reduction” [8].

Attention only begun to be seriously studied from the 19th century with the arrival of modern psychology. Some thoughts about the attention concepts may be found in Descartes, but no rigorous and intensive scientific study was done until the beginning of psychology. How the philosophers missed such a key concept as attention from the antic times to almost now? Part of the answer is given by William James, the father of psychology, in his famous definition of attention: “Everybody knows what attention is”. Attention is so natural, so linked to life and partly unconscious, so obvious that … nobody really noticed it until recently.

However, little by little, a new transversal research field appeared around the concept of “attention” gathering first psychologists, than neuroscientists and even since the end of the nineties’ engineers and computer scientists. While covering the whole research on attention needs a whole series of books, the topic is here narrowed to focus on attention modelling, a crucial step towards wider artificial intelligence.

Indeed, this key process of attention is currently rarely used within computers. As with the brain, a computer is a processing unit. As with the brain it has limited computation capabilities and memory. As with the brain, computers should analyse more and more data. But unlike the brain they do not pay attention. While a classical computer will be more precise in quantifying the whole input data, an attentive computer will focus on the most “interesting” data which has several advantages:

  • It will be faster and more efficient in terms of memory storage due to its ability to process only part of the input data.
  • It will be able to find regularities and irregularities in the input signal and thus be able to detect and react to unexpected or abnormal events.
  • It will be able to optimize data prediction by describing novel patterns, and depending on the information reduction result (how efficient the information reduction was), it will be capable of being curious, bored or annoyed. This curiosity which constantly pushes to the discovery of more and more complex patterns to better reduce information is a first step towards creativity.

As in humans attention is the gate to awareness and consciousness, in computers attention can lead to novel emergent computational paradigms beyond classical pre-programmed machines. While the way towards self-modifying computers is still very long ahead, computational attention develops in an exponential way letting more and more applications benefit from it.

References

[1] Zentall, Thomas R. “Selective and divided attention in animals.” Behavioural Processes 69.1 (2005): 1-15.

[2] Hoy, Ronald R. “Startle, categorical response, and attention in acoustic behavior of insects.” Annual review of neuroscience 12.1 (1989): 355-375.

[3] Hopson, Janet L. “Fetal psychology.” Psychology Today 31.5 (1998): 44.

[4] Mick, Eric, et al. “Case-control study of attention-deficit hyperactivity disorder and maternal smoking, alcohol use, and drug use during pregnancy.” Journal of the American Academy of Child & Adolescent Psychiatry 41.4 (2002): 378-385.

[5] Linnet, Karen Markussen, et al. “Maternal lifestyle factors in pregnancy risk of attention deficit hyperactivity disorder and associated behaviors: review of the current evidence.” American Journal of Psychiatry 160.6 (2003): 1028-1040.

[6] Holzman, Philip S., et al. “Eye-tracking dysfunctions in schizophrenic patients and their relatives.” Archives of general psychiatry 31.2 (1974): 143-151.

[7] Klin, Ami, et al. “Visual fixation patterns during viewing of naturalistic social situations as predictors of social competence in individuals with autism.”Archives of general psychiatry 59.9 (2002): 809-816.

[8] Itti, Laurent, Geraint Rees, and John K. Tsotsos, eds. Neurobiology of attention. Academic Press, 2005.

Matei Mancas

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