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 (5-10 December 2013)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2013 |
| Autor(en): | Ahmad, Sheeraz ; Huang, He ; Yu, Angela J |
| Art des Eintrags: | Bibliographie |
| Titel: | Context-sensitive active sensing in humans |
| Sprache: | Englisch |
| Publikationsjahr: | 2013 |
| Ort: | Red Hook, New York |
| Verlag: | Curran Associates, Inc. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Advances in Neural Information Processing Systems |
| Buchtitel: | Advances in Neural Information Processing Systems 26 (NIPS 2013) |
| Band einer Reihe: | 26 |
| Veranstaltungstitel: | Twenty-seventh Conference on Neural Information Processing Systems (NIPS 2013) |
| Veranstaltungsort: | Lake Tahoe, Nevada |
| Veranstaltungsdatum: | 5-10 December 2013 |
| URL / URN: | https://papers.nips.cc/paper_files/paper/2013/hash/bcc0d4002... |
| Kurzbeschreibung (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. |
| Fachbereich(e)/-gebiet(e): | 03 Fachbereich Humanwissenschaften 03 Fachbereich Humanwissenschaften > Institut für Psychologie |
| Hinterlegungsdatum: | 30 Okt 2023 08:40 |
| Letzte Änderung: | 31 Okt 2023 06:51 |
| PPN: | 512762120 |
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