Salience (neuroscience)

This article is about salience in cognitive neuroscience. For other uses, see Salience.

The salience (also called saliency) of an item – be it an object, a person, a pixel, etc. – is the state or quality by which it stands out relative to its neighbors. Saliency detection is considered to be a key attentional mechanism that facilitates learning and survival by enabling organisms to focus their limited perceptual and cognitive resources on the most pertinent subset of the available sensory data.

Saliency typically arises from contrasts between items and their neighborhood, such as a red dot surrounded by white dots, a flickering message indicator of an answering machine, or a loud noise in an otherwise quiet environment. Saliency detection is often studied in the context of the visual system, but similar mechanisms operate in other sensory systems. What is salient can be influenced by training: for example, for human subjects particular letters can become salient by training.[1][2]

When attention deployment is driven by salient stimuli, it is considered to be bottom-up, memory-free, and reactive. Attention can also be guided by top-down, memory-dependent, or anticipatory mechanisms, such as when looking ahead of moving objects or sideways before crossing streets. Humans and other animals have difficulty paying attention to more than one item simultaneously, so they are faced with the challenge of continuously integrating and prioritizing different bottom-up and top-down influences.

Neuroanatomy of salience

The hippocampus participates in the assessment of salience and context using past memories to filter new incoming stimuli; placing those that are most important into long term memory. The entorhinal cortex is the pathway into and out of the hippocampus and is damaged early on in Alzheimer's disease.
The pulvinar nuclei (in the thalamus) modulate physical saliency in attentional selection.[3]

Salience in psychology

The term is widely used in the study of perception and cognition to refer to any aspect of a stimulus that, for any of many reasons, stands out from the rest. Salience may be the result of emotional, motivational or cognitive factors and is not necessarily associated with physical factors such as intensity, clarity or size. Although salience is thought to determine attentional selection, salience associated with physical factors does not necessarily influence selection of a stimulus.[4]

Aberrant salience hypothesis of schizophrenia

Kapur (2003) proposed that a hyperdopaminergic state, at a "brain" level of description, leads to an aberrant assignment of salience to the elements of one's experience, at a "mind" level.[5] Dopamine mediates the conversion of the neural representation of an external stimulus from a neutral bit of information into an attractive or aversive entity, i.e. a salient event.[6] Symptoms of schizophrenia may arise out of 'the aberrant assignment of salience to external objects and internal representations', and antipsychotic medications reduce positive symptoms, by attenuating aberrant motivational salience, via blockade of the dopamine D2 receptors (Kapur, 2003).

Visual saliency modeling

In the domain of psychology, efforts have been made in modeling the mechanism of human attention, including the learning of prioritizing the different bottom-up and top-down influences.[7]

In the domain of computer vision, efforts have been made in modeling the mechanism of human attention, especially the bottom-up attentional mechanism.[8] Such a process is also called visual saliency detection.

Generally speaking, there are two kinds of models to mimic the bottom-up saliency mechanism. One way is based on the spatial contrast analysis. For example, in "A Model of Saliency-Based Visual Attention for Rapid Scene Analysis",[9] a center-surround mechanism is used to define saliency across scales, which is inspired by the putative neural mechanism. The other way is based on the frequency domain analysis. This method was first proposed by Hou et al.[10] While they used the amplitude spectrum to assign saliency to rarely occurring magnitudes, Guo et al. use the phase spectrum instead.[11] Recently, Li et al. introduced a system that uses both the amplitude and the phase information. [12]

A key limitation in many such approaches is their computational complexity which produces less than real-time performance, even on modern computer hardware.[9][11] Some recent work attempts to overcome these issues but at the expense of saliency detection quality under some conditions.[13] Other work suggests that saliency and associated speed-accuracy phenomena may be a fundamental mechanisms of recognition determined during recognition through gradient descent and does not have to be spatial in nature.[14]

See also

References

  1. ↑ Schneider, W. and Shiffrin, R. M. (1977). "Controlled and automatic human information processing: I. detection, search, and attention". Psychological Review 84 (1): 1–66. doi:10.1037/0033-295x.84.1.1.
  2. ↑ Shiffrin, R. M. and Schneider, W. (1977). "Controlled and automatic human information processing: II perceptual learning, automatic attending and a general theory". Psychological Review 84 (2): 127–190. doi:10.1037/0033-295x.84.2.127.
  3. ↑ Snow JC, Allen HA, Rafal RD, Humphreys GW (March 2009). "Impaired attentional selection following lesions to human pulvinar: evidence for homology between human and monkey". Proc. Natl. Acad. Sci. U.S.A. 106 (10): 4054–9. doi:10.1073/pnas.0810086106. PMC 2656203. PMID 19237580.
  4. ↑ Tsakanikos, E. (2004). Latent inhibition, visual pop-out and schizotypy: is disruption of latent inhibition due to enhanced stimulus salience? Personality and Individual Differences, 37, 1347-1358.
  5. ↑ Kapur, S. (2003). Psychosis as a state of aberrant salience: a framework linking biology, phenomenology, and pharmacology in schizophrenia. American Journal of Psychiatry,160, 13–23.
  6. ↑ Berridge, K.C. & Robinson, T.E. (1998). What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience? Brain Research Review, 28, 309-369.
  7. ↑ van de Laar, P., Heskes, T. and Gielen S. (August 1997). "Task-Dependent Learning of Attention". Neural Networks 10 (6): 981–992. doi:10.1016/S0893-6080(97)00031-2.
  8. ↑ Frintrop S., Rome E. and Christensen H.I. (2010). "Computational Visual Attention Systems and their Cognitive Foundation: A Survey". ACM Transactions on Applied Perception 7 (1).
  9. 1 2 Itti L., Koch C., Niebur E. (1998). "A Model of Saliency-Based Visual Attention for Rapid Scene Analysis". IEEE Trans Pattern Anal Mach Intell. 20 (11): 1254–1259. doi:10.1109/34.730558.
  10. ↑ Hou X, Zhang L (2007). "Saliency Detection: A Spectral Residual Approach". IEEE CVPR.
  11. 1 2 Guo C., Ma Q. and Zhang L. (2008). "Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform". IEEE Conference on Computer Vision and Pattern Recognition.
  12. ↑ Li J, Levine MD, An X, Xu X, He H (2012). "Visual Saliency Based on Scale-Space Analysis in the Frequency Domain" (PDF). IEEE Trans Pattern Anal Mach Intell. 35 (4): 996–1010. doi:10.1109/TPAMI.2012.147. PMID 22802112.
  13. ↑ Katramados, I., Breckon, T.P. (September 2011). "Real-time Visual Saliency by Division of Gaussians". Proc. International Conference on Image Processing (PDF). IEEE. pp. 1741–1744. doi:10.1109/ICIP.2011.6115785. Retrieved 8 April 2013.
  14. ↑ Achler T. (2013). "Supervised Generative Reconstruction: An Efficient Way To Flexibly Store and Recognize Patterns". arXiv:1112.2988.

External links

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