Abstract
How do we learn what a visually seen object is? How do our brains learn without supervision to link multiple views of the same object into an invariant object category while our eyes scan a scene, even before we have a concept of the object? Indeed, why do we not link together views of different objects when there is no teacher to correct us? Why do not our eyes move around randomly? How do they explore salient features of novel objects and thereby enable us to learn view-, size-, and positionally invariant object categories? How do representations of a scene remain binocularly fused as our eyes explore it? How do we solve the Where’s Waldo problem and thereby efficiently search for desired objects in a scene? This article summarizes the ARTSCAN and ARTSCENE families of neural models, culminating in the 3D ARTSCAN Search model that clarifies how the brain solves these problems in a unified way by coordinating processes of 3D vision and figure-ground separation, spatial and object attention, object and scene category learning, predictive remapping, and eye movement search. ARTSCAN illustrates revolutionary new computational paradigms whereby the brain computes: Complementary Computing clarifies the nature of brain specialization, and Laminar Computing clarifies why all neocortical circuits exhibit a layered architecture. ARTSCAN also provides unified explanations and simulations of brain and behavioral data, and computer simulation benchmarks that support the model, which provides a blueprint for developing a new type of system for active vision and autonomous learning, recognition, search, and robotics.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Grossberg references downloadable from http://cns.bu.edu/~steve
References
Grossberg references downloadable from http://cns.bu.edu/~steve
Brown, J.M., Denney, H.I.: Shifting attention into and out of objects: evaluating the processes underlying the object advantage. Percept. Psychophys. 69, 606–618 (2007)
Cao, Y., Grossberg, S., Markowitz, J.: How does the brain rapidly learn and reorganize view- and positionally-invariant object representations in inferior temporal cortex? Neural Netw. 24, 1050–1061 (2011)
Caplovitz, G.P., Tse, P.U.: Rotating dotted ellipses: motion perception driven by grouped figural rather than local dot motion signals. Vision. Res. 47, 1979–1991 (2007)
Carpenter, G.A., Grossberg, S.: A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput Vis Graph Image Process 37, 54–115 (1987)
Carpenter, G.A., Grossberg, S.: Pattern recognition by self-organizing neural networks. MIT Press, Cambridge (1991)
Carpenter, G.A., Grossberg, S.: Normal and amnesic learning, recognition and memory by a neural model of cortico-hippocampal interactions. Trends Neurosci. 16, 131–137 (1993)
Cavanagh, P., Hunt, A.R., Alfraz, A., Rolfs, M.: Visual stability based on remapping of attention pointers. Trends Cogn. Sci. 14, 147–153 (2010)
Chang, H.-C., Grossberg, S., Cao, Y.: Where’s Waldo? How perceptual cognitive, and emotional brain processes cooperate during learning to categorize and find desired objects in a cluttered scene. Front. Integr. Neurosci. (2014). doi:10.3389/fnint.2014.0043
Chiu, Y.C., Yantis, S.: A domain-independent source of cognitive control for task sets: shifting spatial attention and switching categorization rules. J. Neurosci. 29, 3930–3938 (2009)
Chun, M.M.: Contextual cueing of visual attention. Trends Cogn. Sci. 4, 170–178 (2000)
Fazl, A., Grossberg, S., Mingolla, E.: View-invariant object category learning, recognition, and search: how spatial and object attention are coordinated using surface-based attentional shrouds. Cogn. Psychol. 58, 1–48 (2009)
Foley, N.C., Grossberg, S., Mingolla, E.: Neural dynamics of object-based multifocal visual spatial attention and priming: object cueing, useful-field-of-view, and crowding. Cogn. Psychol. 65, 77–117 (2012)
Grossberg, S.: How does a brain build a cognitive code? Psychol. Rev. 87, 1–51 (1980)
Grossberg, S.: 3-D vision and figure-ground separation by visual cortex. Percept. Psychophys. 55, 48–121 (1994)
Grossberg, S.: Cortical and subcortical predictive dynamics and learning during perception, cognition, emotion, and action. Philos. Trans. R. Soc. Lond. 364, 1223–1234 (2009)
Grossberg, S.: Adaptive resonance theory: how a brain learns to consciously attend, learn, and recognize a changing world. Neural Netw. 37, 1–47 (2013)
Grossberg, S., Huang, T.-R.: ARTSCENE: a neural system for natural scene classification. J. Vis. 9(6), 1–19 (2009)
Grossberg, S., Markowitz, J., Cao, Y.: On the road to invariant recognition: explaining tradeoff and morph properties of cells in inferotemporal cortex using multiple-scale task-sensitive attentive learning. Neural Netw. 24, 1036–1049 (2011)
Grossberg, S., Srinivasan, K., Yazdanbakhsh, A.: Binocular fusion and invariant category learning due to predictive remapping during scanning of a depth scene with eye movements. Front. Psychol: Percept. Sci. (2014). doi:10.3389/fpsyg.2014.01457
Huang, T.-R., Grossberg, S.: Cortical dynamics of contextually cued attentive visual learning and search: spatial and object evidence accumulation. Psychol. Rev. 117, 1080–1112 (2010)
Irwin, D.E.: Information integration across saccadic eye movements. Cogn. Psychol. 23, 420–456 (1991)
Li, N., DiCarlo, J.J.: Unsupervised natural experience rapidly alters invariant object representation in visual cortex. Science 321, 1502–1507 (2008)
Theeuwes, J., Mathôt, S., Kingstone, A.: Object-based eye movements: the eyes prefer to stay within the same object. Atten. Percept. Psychophys. 72, 12–21 (2010)
Tyler, C.W., Kontsevich, L.L.: Mechanisms of stereoscopic processing: stereo attention and surface perception in depth reconstruction. Perception 24, 127–153 (1995)
Zoccolan, D., Kouh, M., Poggio, T., DiCarlo, J.J.: Trade-off between object selectivity and tolerance in monkey inferotemporal cortex. J. Neurosci. 27, 12292–12307 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Grossberg, S. (2016). Toward Autonomous Intelligence: From Active 3D Vision to Invariant Object and Scene Learning, Recognition, and Search. In: Wang, R., Pan, X. (eds) Advances in Cognitive Neurodynamics (V). Advances in Cognitive Neurodynamics. Springer, Singapore. https://doi.org/10.1007/978-981-10-0207-6_4
Download citation
DOI: https://doi.org/10.1007/978-981-10-0207-6_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0205-2
Online ISBN: 978-981-10-0207-6
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)