Abstract
Visual adaptation leads to certain changes in the perceived identity of a face depending on the previous faces an observer has been exposed to. The explicit effect of adaptation on face perception is not well understood. This work presents, for what is believed to be the first time, a mathematical model to capture the effect of adaptation on face identity perception. Our model is grounded in three assumptions that are consistent with recent neurobiological findings: (1) adaptation has the highest effect on the neural activities generated at the lowest levels of visual cortex; (2) neural activities in low-level visual areas are representatives of extracted features from a stimulus face, and as a result, encoding the stimulus face in the face space is affected by the adaptation process; and (3) a stimulus face is represented by a small number of simultaneously active neurons out of a large population (sparse coding). The rate of target identification with or without adaptation obtained by the proposed computational model resembles psyhometric functions obtained in prior studies using real subjects.
| Original language | English |
|---|---|
| Title of host publication | ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 389-392 |
| Number of pages | 4 |
| Volume | 2018-January |
| ISBN (Electronic) | 9781538619117 |
| DOIs | |
| Publication status | Published - Feb 14 2018 |
| Externally published | Yes |
| Event | 24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017 - Batumi, Georgia Duration: Dec 5 2017 → Dec 8 2017 |
Conference
| Conference | 24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017 |
|---|---|
| Country | Georgia |
| City | Batumi |
| Period | 12/5/17 → 12/8/17 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Energy Engineering and Power Technology
Cite this
Adaptive face space models with dynamic neural priors and sparse coding. / Reihanian, Samira; Zollanvari, Amin; James, Alex Pappachen.
ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 389-392.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Adaptive face space models with dynamic neural priors and sparse coding
AU - Reihanian, Samira
AU - Zollanvari, Amin
AU - James, Alex Pappachen
PY - 2018/2/14
Y1 - 2018/2/14
N2 - Visual adaptation leads to certain changes in the perceived identity of a face depending on the previous faces an observer has been exposed to. The explicit effect of adaptation on face perception is not well understood. This work presents, for what is believed to be the first time, a mathematical model to capture the effect of adaptation on face identity perception. Our model is grounded in three assumptions that are consistent with recent neurobiological findings: (1) adaptation has the highest effect on the neural activities generated at the lowest levels of visual cortex; (2) neural activities in low-level visual areas are representatives of extracted features from a stimulus face, and as a result, encoding the stimulus face in the face space is affected by the adaptation process; and (3) a stimulus face is represented by a small number of simultaneously active neurons out of a large population (sparse coding). The rate of target identification with or without adaptation obtained by the proposed computational model resembles psyhometric functions obtained in prior studies using real subjects.
AB - Visual adaptation leads to certain changes in the perceived identity of a face depending on the previous faces an observer has been exposed to. The explicit effect of adaptation on face perception is not well understood. This work presents, for what is believed to be the first time, a mathematical model to capture the effect of adaptation on face identity perception. Our model is grounded in three assumptions that are consistent with recent neurobiological findings: (1) adaptation has the highest effect on the neural activities generated at the lowest levels of visual cortex; (2) neural activities in low-level visual areas are representatives of extracted features from a stimulus face, and as a result, encoding the stimulus face in the face space is affected by the adaptation process; and (3) a stimulus face is represented by a small number of simultaneously active neurons out of a large population (sparse coding). The rate of target identification with or without adaptation obtained by the proposed computational model resembles psyhometric functions obtained in prior studies using real subjects.
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UR - http://www.scopus.com/inward/citedby.url?scp=85047304269&partnerID=8YFLogxK
U2 - 10.1109/ICECS.2017.8292008
DO - 10.1109/ICECS.2017.8292008
M3 - Conference contribution
VL - 2018-January
SP - 389
EP - 392
BT - ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
ER -