The two basic phenomena that define the problem of visual attention can be illustrated in a simple example. Consider the arrays shown in each panel of Figure 1. In a typical experiment, before the arrays were presented, subjects would be asked to report letters appearing in one color (targets, here black letters), and to disregard letters in the other color (nontargets, here white letters). The array would then be briefly flashed, and the subjects, without any opportunity for eye movements, would give their report. The display mimics our. usual cluttered visual environment: It contains one or more objects that are relevant to current behavior, along with others that are irrelevant. The first basic phenomenon is limited capacity for processing information. At any given time, only a small amount of the information available on the retina can be processed and used in the control of behavior. Subjectively, giving attention to any one target leaves less available for others. In Figure 1, the probability of reporting the target letter N is much lower with two accompa­ nying targets (Figure la) than with none (Figure Ib). The second basic phenomenon is selectivity-the ability to filter out un­ wanted information. Subjectively, one is aware of attended stimuli and largely unaware of unattended ones. Correspondingly, accuracy in identifying an attended stimulus may be independent of the number of nontargets in a display (Figure la vs Ie) (see Bundesen 1990, Duncan 1980).

Neural Mechanisms of Selective Visual Attention

Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

Five important trends have emerged from recent work on computational models of focal visual attention that emphasize the bottom-up, image-based control of attentional deployment. First, the perceptual saliency of stimuli critically depends on the surrounding context. Second, a unique 'saliency map' that topographically encodes for stimulus conspicuity over the visual scene has proved to be an efficient and plausible bottom-up control strategy. Third, inhibition of return, the process by which the currently attended location is prevented from being attended again, is a crucial element of attentional deployment. Fourth, attention and eye movements tightly interplay, posing computational challenges with respect to the coordinate system used to control attention. And last, scene understanding and object recognition strongly constrain the selection of attended locations. Insights from these five key areas provide a framework for a computational and neurobiological understanding of visual attention.

/pdf/computational-modelling-of-visual-attention-3wtbok1ou7.pdf

Computational modelling of visual attention.

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Psychophysical and physiological evidence indicates that the visual system of primates and humans has evolved a specialized processing focus moving across the visual scene. This study addresses the question of how simple networks of neuron-like elements can account for a variety of phenomena associated with this shift of selective visual attention. Specifically, we propose the following: (1) A number of elementary features, such as color, orientation, direction of movement, disparity etc. are represented in parallel in different topographical maps, called the early representation. (2) There exists a selective mapping from the early topographic representation into a more central non-topographic representation, such that at any instant the central representation contains the properties of only a single location in the visual scene, the selected location. We suggest that this mapping is the principal expression of early selective visual attention. One function of selective attention is to fuse information from different maps into one coherent whole. (3) Certain selection rules determine which locations will be mapped into the central representation. The major rule, using the conspicuity of locations in the early representation, is implemented using a so-called Winner-Take-All network. Inhibiting the selected location in this network causes an automatic shift towards the next most conspicious location. Additional rules are proximity and similarity preferences. We discuss how these rules can be implemented in neuron-like networks and suggest a possible role for the extensive back-projection from the visual cortex to the LGN.

Shifts in selective visual attention: towards the underlying neural circuitry.

In terms of functional anatomy, where does learning occur when, for a basic visual discrimination task, performance improves with practice (perceptual learning)? We report remarkable long-term learning in a simple texture discrimination task where learning is specific for retinal input. This learning is (i) local (in a retinotopic sense), (ii) orientation specific but asymmetric (it is specific for background but not for target-element orientation), and (iii) strongly monocular (there is little interocular transfer of learning). Our results suggest that learning involves experience-dependent changes at a level of the visual system where monocularity and the retinotopic organization of the visual input are still retained and where different orientations are processed separately. These results can be interpreted in terms of local plasticity induced by retinal input in early visual processing in human adults, presumably at the level of orientation-gradient sensitive cells in primary visual cortex.

https://www.pnas.org/content/pnas/88/11/4966.full.pdf

Where practice makes perfect in texture discrimination: evidence for primary visual cortex plasticity

Several paradigms of perceptual learning suggest that practice can trigger long-term, experience-dependent changes in the adult visual system of humans. As shown here, performance of a basic visual discrimination task improved after a normal night's sleep. Selective disruption of rapid eye movement (REM) sleep resulted in no performance gain during a comparable sleep interval, although non-REM slow-wave sleep disruption did not affect improvement. On the other hand, deprivation of REM sleep had no detrimental effects on the performance of a similar, but previously learned, task. These results indicate that a process of human memory consolidation, active during sleep, is strongly dependent on REM sleep.

https://science.sciencemag.org/content/sci/265/5172/679.full.pdf

Dependence on REM sleep of overnight improvement of a perceptual skill

Lateral interactions between spatial channels: suppression and facilitation revealed by lateral masking experiments.

SEVERAL examples of experience-dependent perceptual improve-ment (perceptual learning) suggest that plasticity in specific neu-ronal loci could underlie the learning process1–6. For a basic visual discrimination task (using an optimal stimulus for ‘automatic’ pre-attentive texture segregation7–10), discrete retinal input-dependent changes within a very early stage in the stream of visual processing were indicated as the locus of a large and consistent learning effect5. When do these changes occur? Here we report that except for a fast, rapidly saturating improvement early in the first practice session, performance was very stable within sessions. Indeed, observers showed little or no improvement until up to 8 hours after their last training session (latent phase). But large improvements occurred thereafter. Finally, there was almost no forgetting; what was gained was retained for at least 2–3 years. We conjecture that some types of perceptual experience trigger permanent neural changes in early processing stages of the adult visual system. These may take many hours to become functional.

The time course of learning a visual skill

The present paper presents a model for texture discrimination based on Gabor functions. In this model the Gabor power spectrum of the micropatterns corresponding to different textures is calculated. A function that measures the difference between the spectrum of two micropatterns is introduced and its values are correlated with human performance in preattentive detection tasks. In addition, a two stage algorithm for texture segregation is presented. In the first stage the input image is transformed via Gabor filters into a representation image that allows discrimination between features by means of intensity differences. In the second stage the borders between areas of different textures are found using a Laplacian of Gaussian operator. This algorithm is sensitive to energy differences, rotation and spatial frequency and is insensitive to local translation. The model was tested by means of several simulations and was found to be in good correlation with known psychophysical characteristics as texton based texture segregation and micropattern density sensitivity. However, this simple model fails to predict human performance in discrimination tasks based on differences in the density of "terminators". In this case human performance is better than expected.

Dov Sagi

Papers

Where practice makes perfect in texture discrimination: evidence for primary visual cortex plasticity

Dependence on REM sleep of overnight improvement of a perceptual skill

Lateral interactions between spatial channels: suppression and facilitation revealed by lateral masking experiments.

The time course of learning a visual skill

Gabor filters as texture discriminator