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Salience (neuroscience)

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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 from 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. Conversely, 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

The brain component named the hippocampus helps with the assessment of salience and context by using past memories to filter new incoming stimuli, and placing those that are most important into long term memory. The entorhinal cortex is the pathway into and out of the hippocampus, and is an important part of the brain's memory network; research shows that it is a brain region that suffers damage early on in Alzheimer's disease,[3] one of the effects of which is altered (diminished) salience.[4]

The pulvinar nuclei (in the thalamus) modulates physical/perceptual salience in attentional selection.[5]

One group of neurons (i.e., D1-type medium spiny neurons) within the nucleus accumbens shell (NAcc shell) assigns appetitive motivational salience ("want" and "desire", which includes a motivational component), aka incentive salience, to rewarding stimuli, while another group of neurons (i.e., D2-type medium spiny neurons) within the NAcc shell assigns aversive motivational salience to aversive stimuli.[6][7]

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.[8]

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.[9] These aberrant salience attributions have been associated with altered activities in the mesolimbic system, including the striatum, the amygdala, the hippocampus, and the parahippocampal gyrus.[10] 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.[11] 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).

Alternative areas of investigation include supplementary motor areas, frontal eye fields and parietal eye fields. These areas of the brain are involved with calculating predictions and visual salience. Changing expectations on where to look restructures these areas of the brain. This cognitive repatterning can result in some of the symptoms of these symptoms found in such disorder.

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.[12]

In the domain of computer vision, efforts have been made in modeling the mechanism of human attention, especially the bottom-up attentional mechanism[13], including both spatial and temporal attention. 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, a center-surround mechanism is used to define saliency across scales, which is inspired by the putative neural mechanism. [14] The other way is based on the frequency domain analysis. This method was first proposed by Hou et al.[15] While they used the amplitude spectrum to assign saliency to rarely occurring magnitudes, Guo et al. use the phase spectrum instead.[16] Recently, Li et al. introduced a system that uses both the amplitude and the phase information.[17]

A key limitation in many such approaches is their computational complexity which produces less than real-time performance, even on modern computer hardware.[14][16] Some recent work attempts to overcome these issues but at the expense of saliency detection quality under some conditions.[18] 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.[19]

See also

References

  1. ^ Schneider, Walter; Shiffrin, Richard 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, Richard M.; Schneider, Walter (1977). "Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory". Psychological Review. 84 (2): 127–90. doi:10.1037/0033-295x.84.2.127.
  3. ^ Khan, Usman A; Liu, Li; Provenzano, Frank A; Berman, Diego E; Profaci, Caterina P; Sloan, Richard; Mayeux, Richard; Duff, Karen E; Small, Scott A (2013). "Molecular drivers and cortical spread of lateral entorhinal cortex dysfunction in preclinical Alzheimer's disease". Nature Neuroscience. 17 (2): 304–11. doi:10.1038/nn.3606. PMC 4044925. PMID 24362760.
  4. ^ Balthazar, Marcio L. F.; Pereira, Fabrício R. S.; Lopes, Tátila M.; Da Silva, Elvis L.; Coan, Ana Carolina; Campos, Brunno M.; Duncan, Niall W.; Stella, Florindo; Northoff, Georg; Damasceno, Benito P.; Cendes, Fernando (2014). "Neuropsychiatric symptoms in Alzheimer's disease are related to functional connectivity alterations in the salience network". Human Brain Mapping. 35 (4): 1237–46. doi:10.1002/hbm.22248. PMID 23418130.
  5. ^ Snow, J. C.; Allen, H. A.; Rafal, R. D.; Humphreys, G. W. (2009). "Impaired attentional selection following lesions to human pulvinar: Evidence for homology between human and monkey". Proceedings of the National Academy of Sciences. 106 (10): 4054–9. Bibcode:2009PNAS..106.4054S. doi:10.1073/pnas.0810086106. JSTOR 40428498. PMC 2656203. PMID 19237580.
  6. ^ Malenka RC, Nestler EJ, Hyman SE (2009). Sydor A, Brown RY (eds.). Molecular Neuropharmacology: A Foundation for Clinical Neuroscience (2nd ed.). New York: McGraw-Hill Medical. pp. 147–148, 367, 376. ISBN 978-0-07-148127-4. VTA DA neurons play a critical role in motivation, reward-related behavior (Chapter 15), attention, and multiple forms of memory. This organization of the DA system, wide projection from a limited number of cell bodies, permits coordinated responses to potent new rewards. Thus, acting in diverse terminal fields, dopamine confers motivational salience ("wanting") on the reward itself or associated cues (nucleus accumbens shell region), updates the value placed on different goals in light of this new experience (orbital prefrontal cortex), helps consolidate multiple forms of memory (amygdala and hippocampus), and encodes new motor programs that will facilitate obtaining this reward in the future (nucleus accumbens core region and dorsal striatum). In this example, dopamine modulates the processing of sensorimotor information in diverse neural circuits to maximize the ability of the organism to obtain future rewards. ...
    The brain reward circuitry that is targeted by addictive drugs normally mediates the pleasure and strengthening of behaviors associated with natural reinforcers, such as food, water, and sexual contact. Dopamine neurons in the VTA are activated by food and water, and dopamine release in the NAc is stimulated by the presence of natural reinforcers, such as food, water, or a sexual partner. ...
    The NAc and VTA are central components of the circuitry underlying reward and memory of reward. As previously mentioned, the activity of dopaminergic neurons in the VTA appears to be linked to reward prediction. The NAc is involved in learning associated with reinforcement and the modulation of motoric responses to stimuli that satisfy internal homeostatic needs. The shell of the NAc appears to be particularly important to initial drug actions within reward circuitry; addictive drugs appear to have a greater effect on dopamine release in the shell than in the core of the NAc.
  7. ^ Baliki, M. N.; Mansour, A.; Baria, A. T.; Huang, L.; Berger, S. E.; Fields, H. L.; Apkarian, A. V. (2013). "Parceling Human Accumbens into Putative Core and Shell Dissociates Encoding of Values for Reward and Pain". Journal of Neuroscience. 33 (41): 16383–93. doi:10.1523/JNEUROSCI.1731-13.2013. PMC 3792469. PMID 24107968.
  8. ^ Tsakanikos, Elias (2004). "Latent inhibition, visual pop-out and schizotypy: Is disruption of latent inhibition due to enhanced stimulus salience?". Personality and Individual Differences. 37 (7): 1347–58. doi:10.1016/j.paid.2004.01.005.
  9. ^ Kapur, Shitij (2003). "Psychosis as a State of Aberrant Salience: A Framework Linking Biology, Phenomenology, and Pharmacology in Schizophrenia". American Journal of Psychiatry. 160 (1): 13–23. doi:10.1176/appi.ajp.160.1.13. PMID 12505794.
  10. ^ Lee, Seon-Koo; Chun, Ji Won; Lee, Jung Suk; Park, Hae-Jeong; Jung, Young-Chul; Seok, Jeong-Ho; Kim, Jae-Jin (2014). "Abnormal Neural Processing during Emotional Salience Attribution of Affective Asymmetry in Patients with Schizophrenia". PLoS ONE. 9 (3): e90792. Bibcode:2014PLoSO...990792L. doi:10.1371/journal.pone.0090792. PMC 3949688. PMID 24619004.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  11. ^ Berridge, Kent C; Robinson, Terry E (1998). "What is the role of dopamine in reward: Hedonic impact, reward learning, or incentive salience?". Brain Research Reviews. 28 (3): 309–69. doi:10.1016/s0165-0173(98)00019-8. PMID 9858756.
  12. ^ Van De Laar, Piërre; Heskes, Tom; Gielen, Stan (1997). "Task-Dependent Learning of Attention". Neural Networks. 10 (6): 981–992. doi:10.1016/S0893-6080(97)00031-2. PMID 12662494.
  13. ^ Frintrop, Simone; Rome, Erich; Christensen, Henrik I. (2010). "Computational visual attention systems and their cognitive foundations". ACM Transactions on Applied Perception. 7 (1): 1–46. doi:10.1145/1658349.1658355.
  14. ^ a b Itti, L.; Koch, C.; Niebur, E. (1998). "A model of saliency-based visual attention for rapid scene analysis". IEEE Transactions on Pattern Analysis and Machine Intelligence. 20 (11): 1254–9. doi:10.1109/34.730558.
  15. ^ Hou, Xiaodi; Zhang, Liqing (2007). "Saliency Detection: A Spectral Residual Approach". 2007 IEEE Conference on Computer Vision and Pattern Recognition. pp. 1–8. doi:10.1109/CVPR.2007.383267. ISBN 1-4244-1179-3.
  16. ^ a b Chenlei Guo; Qi Ma; Liming Zhang (2008). "Spatio-temporal Saliency detection using phase spectrum of quaternion fourier transform". 2008 IEEE Conference on Computer Vision and Pattern Recognition. pp. 1–8. doi:10.1109/CVPR.2008.4587715. ISBN 978-1-4244-2242-5.
  17. ^ Li, Jian; Levine, Martin D.; An, Xiangjing; Xu, Xin; He, Hangen (2013). "Visual Saliency Based on Scale-Space Analysis in the Frequency Domain". IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (4): 996–1010. doi:10.1109/TPAMI.2012.147. PMID 22802112.
  18. ^ Katramados, Ioannis; Breckon, Toby P. (2011). "Real-time visual saliency by Division of Gaussians". 2011 18th IEEE International Conference on Image Processing. pp. 1701–4. doi:10.1109/ICIP.2011.6115785. ISBN 978-1-4577-1303-3.
  19. ^ Achler T. (2013). "Supervised Generative Reconstruction: An Efficient Way To Flexibly Store and Recognize Patterns". arXiv:1112.2988 [cs.CV].
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