Ahmad, Sheeraz ; Huang, He ; Yu, Angela J (2013)
Context-sensitive active sensing in humans.
Twenty-seventh Conference on Neural Information Processing Systems (NIPS 2013). Lake Tahoe, Nevada (05.12.2013-10.12.2013)
Conference or Workshop Item, Bibliographie
Abstract
Humans and animals readily utilize active sensing, or the use of self-motion, to focus sensory and cognitive resources on the behaviorally most relevant stimuli and events in the environment. Understanding the computational basis of natural active sensing is important both for advancing brain sciences and for developing more powerful artificial systems. Recently, a goal-directed, context-sensitive, Bayesian control strategy for active sensing, termed C-DAC (Context-Dependent Active Controller), was proposed (Ahmad & Yu, 2013). In contrast to previously proposed algorithms for human active vision, which tend to optimize abstract statistical objectives and therefore cannot adapt to changing behavioral context or task goals, C-DAC directly minimizes behavioral costs and thus, automatically adapts itself to different task conditions. However, C-DAC is limited as a model of human active sensing, given its computational/representational requirements, especially for more complex, real-world situations. Here, we propose a myopic approximation to C-DAC, which also takes behavioral costs into account, but achieves a significant reduction in complexity by looking only one step ahead. We also present data from a human active visual search experiment, and compare the performance of the various models against human behavior. We find that C-DAC and its myopic variant both achieve better fit to human data than Infomax (Butko & Movellan, 2010), which maximizes expected cumulative future information gain. In summary, this work provides novel experimental results that differentiate theoretical models for human active sensing, as well as a novel active sensing algorithm that retains the context-sensitivity of the optimal controller while achieving significant computational savings.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2013 |
| Creators: | Ahmad, Sheeraz ; Huang, He ; Yu, Angela J |
| Type of entry: | Bibliographie |
| Title: | Context-sensitive active sensing in humans |
| Language: | English |
| Date: | 2013 |
| Place of Publication: | Red Hook, New York |
| Publisher: | Curran Associates, Inc. |
| Journal or Publication Title: | Advances in Neural Information Processing Systems |
| Book Title: | Advances in Neural Information Processing Systems 26 (NIPS 2013) |
| Series Volume: | 26 |
| Event Title: | Twenty-seventh Conference on Neural Information Processing Systems (NIPS 2013) |
| Event Location: | Lake Tahoe, Nevada |
| Event Dates: | 05.12.2013-10.12.2013 |
| URL / URN: | https://papers.nips.cc/paper_files/paper/2013/hash/bcc0d4002... |
| Abstract: | Humans and animals readily utilize active sensing, or the use of self-motion, to focus sensory and cognitive resources on the behaviorally most relevant stimuli and events in the environment. Understanding the computational basis of natural active sensing is important both for advancing brain sciences and for developing more powerful artificial systems. Recently, a goal-directed, context-sensitive, Bayesian control strategy for active sensing, termed C-DAC (Context-Dependent Active Controller), was proposed (Ahmad & Yu, 2013). In contrast to previously proposed algorithms for human active vision, which tend to optimize abstract statistical objectives and therefore cannot adapt to changing behavioral context or task goals, C-DAC directly minimizes behavioral costs and thus, automatically adapts itself to different task conditions. However, C-DAC is limited as a model of human active sensing, given its computational/representational requirements, especially for more complex, real-world situations. Here, we propose a myopic approximation to C-DAC, which also takes behavioral costs into account, but achieves a significant reduction in complexity by looking only one step ahead. We also present data from a human active visual search experiment, and compare the performance of the various models against human behavior. We find that C-DAC and its myopic variant both achieve better fit to human data than Infomax (Butko & Movellan, 2010), which maximizes expected cumulative future information gain. In summary, this work provides novel experimental results that differentiate theoretical models for human active sensing, as well as a novel active sensing algorithm that retains the context-sensitivity of the optimal controller while achieving significant computational savings. |
| Divisions: | 03 Department of Human Sciences 03 Department of Human Sciences > Institute for Psychology |
| Date Deposited: | 30 Oct 2023 08:40 |
| Last Modified: | 31 Oct 2023 06:51 |
| PPN: | 512762120 |
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