Chapter 1 Top-down facilitation of visual object recognition: object-based and context-based contributions

doi:10.1016/S0079-6123(06)55001-0

Progress in Brain Research

Volume 155, Part B, 2006, Pages 3-21

https://doi.org/10.1016/S0079-6123(06)55001-0 Get rights and content

Abstract

The neural mechanisms subserving visual recognition are traditionally described in terms of bottom-up analysis, whereby increasingly complex aspects of the visual input are processed along a hierarchical progression of cortical regions. However, the importance of top-down facilitation in successful recognition has been emphasized in recent models and research findings. Here we consider evidence for top-down facilitation of recognition that is triggered by early information about an object, as well as by contextual associations between an object and other objects with which it typically appears. The object-based mechanism is proposed to trigger top-down facilitation of visual recognition rapidly, using a partially analyzed version of the input image (i.e., a blurred image) that is projected from early visual areas directly to the prefrontal cortex (PFC). This coarse representation activates in the PFC information that is back-projected as “initial guesses” to the temporal cortex where it presensitizes the most likely interpretations of the input object. In addition to this object-based facilitation, a context-based mechanism is proposed to trigger top-down facilitation through contextual associations between objects in scenes. These contextual associations activate predictive information about which objects are likely to appear together, and can influence the “initial guesses” about an object's identity. We have shown that contextual associations are analyzed by a network that includes the parahippocampal cortex and the retrosplenial complex. The integrated proposal described here is that object- and context-based top-down influences operate together, promoting efficient recognition by framing early information about an object within the constraints provided by a lifetime of experience with contextual associations.

Section snippets

An object-based cortical mechanism for triggering top-down facilitation

The traditional view regarding visual processing is that an input image is processed in a bottom-up cascade of cortical regions that analyze increasingly complex information. This view stems from the well-defined functional architecture of the visual cortex, which has a clear hierarchical structure. However, several models propose that both bottom-up and top-down analyses can occur in the cortex simultaneously (Grossberg, 1980; Kosslyn, 1994; Ullman, 1995; Desimone, 1998; Engel et al., 2001;

A context-based cortical mechanism for triggering top-down facilitation

In addition to the top-down benefit provided by prefrontal analysis of cursory object information, recognition efficiency can be increased through processes that take advantage of naturally occurring regularities in the environment (Gibson, 1969). A lifetime of visual experience can thereby guide expectations about which objects are likely to appear in a given setting to aid subsequent recognition of those objects through their contextual associations (Biederman, 1972, Biederman, 1981; Palmer,

Integrated object- and context-based top-down facilitation of recognition

In this overview, we have described how top-down facilitation of recognition can be achieved either (1) through an object-based mechanism that generates “initial guesses” about an object's identity using rapidly analyzed coarse information about the input image or (2) through the predictive information provided by contextual associations between an object or a scene and the other objects that are likely to appear together in a particular setting. However, it is clear that objects do not appear

Abbreviations

fMRI:

functional magnetic resonance imaging

MEG:

magnetoencephalography

OFC:

orbitofrontal cortex

PFC:

prefrontal cortex

PHC:

parahippocampal cortex

PPA:

parahippocampal place area

RSC:

retrosplenial complex

RT:

response time

Acknowledgments

This work was supported by NINDS R01-NS44319 and RO1-NS050615, NCRR P41-RR14075, and the MIND Institute.

References (89)

G.K. Aguirre et al.
An area within human ventral cortex sensitive to “building” stimuli: evidence and implications
Neuron
(1998)
J. Bachevalier et al.
Visual recognition impairment follows ventromedial but not dorsolateral prefrontal lesions in monkeys
Behav. Brain Res.
(1986)
M. Bar et al.
Cortical analysis of visual context
Neuron
(2003)
M. Bar et al.
Cortical mechanisms of explicit visual object recognition
Neuron
(2001)
I. Biederman et al.
Scene perception: detecting and judging objects undergoing relational violations
Cogn. Psychol.
(1982)
J. Bullier et al.
Parallel versus serial processing: new vistas on the distributed organization of the visual system
Curr. Opin. Neurobiol.
(1995)
R. Epstein et al.
Viewpoint-specific scene representations in human parahippocampal cortex
Neuron
(2003)
K. Friston
Learning and inference in the brain
Neural Networks
(2003)
C.D. Gilbert et al.
The neural basis of perceptual learning
Neuron
(2001)
M.A. Goodale et al.
Separate visual pathways for perception and action
Trends Neurosci
(1992)

K. Grill-Spector et al.

The lateral occipital complex and its role in object recognition

Vision Res.

(2001)

R.N. Henson

Neuroimaging studies of priming

Prog. Neurobiol.

(2003)

O. Jackson et al.

Encoding activity in anterior medial temporal lobe supports subsequent associative recognition

Neuroimage

(2004)

W. Koutstaal et al.

Perceptual specificity in visual object priming: functional magnetic resonance imaging evidence for a laterality difference in fusiform cortex

Neuropsychologia

(2001)

A. Leonova et al.

Spatial frequency processing in inferred PC- and MC-pathways

Vision Res.

(2003)

C. Lustig et al.

Preserved neural correlates of priming in old age and dementia

Neuron

(2004)

R. Malach et al.

The topography of high-order human object areas

Trends Cogn. Sci.

(2002)

M.B. Miller et al.

Creating false memories for visual scenes

Neuropsychologia

(1998)

Y. Miyashita et al.

Neural representation of visual objects: encoding and top-down activation

Curr. Opin. Neurobiol.

(2000)

C. Ranganath et al.

Category-specific modulation of inferior temporal activity during working memory encoding and maintenance

Brain Res. Cogn. Brain Res.

(2004)

C. Ranganath et al.

Dissociable correlates of recollection and familiarity within the medial temporal lobes

Neuropsychologia

(2004)

D.L. Schacter et al.

Priming and the brain

Neuron

(1998)

J.S. Simons et al.

Neural mechanisms of visual object priming: evidence for perceptual and semantic distinctions in fusiform cortex

Neuroimage

(2003)

S. Sternberg

Separate modifiability, mental modules, and the use of pure and composite measures to reveal them

Acta Psychol. (Amst.)

(2001)

G.K. Aguirre et al.

The parahippocampus subserves topographical learning in man

Cereb. Cortex

(1996)

Aminoff, E. Gronau, N. and Bar, M. (submitted for publication). The parahippocampal cortex mediates spatial and...

M. Bar

A cortical mechanism for triggering top-down facilitation in visual object recognition

J. Cogn. Neurosci.

(2003)

M. Bar

Visual objects in context

Nat. Rev. Neurosci.

(2004)

M. Bar et al.

Top-down facilitation of visual recognition

Proceedings of the National Academy of Science

(2006)

M. Bar et al.

Spatial context in recognition

Perception

(1996)

F. Barceló et al.

Prefrontal modulation of visual processing in humans

Nat. Neurosci.

(2000)

I. Biederman

Perceiving real-world scenes

Science

(1972)

I. Biederman

On the semantic of a glance at a scene

I. Biederman et al.

On the information extracted from a glance at a scene

J. Exp. Psychol.

(1974)

J.B. Brewer et al.

Making memories: brain activity that predicts how well visual experience will be remembered

Science

(1998)

C.B. Cave et al.

The role of parts and spatial relations in object identification

Perception

(1993)

M.M. Chun et al.

Implicit, long-term spatial contextual memory

J. Exp. Psychol. Learn. Mem. Cogn.

(2003)

L. Davachi et al.

Multiple routes to memory: distinct medial temporal lobe processes build item and source memories

Proc. Natl. Acad. Sci. USA.

(2003)

J.L. Davenport et al.

Scene consistency in object and background perception

Psychol. Sci.

(2004)

R. Desimone

Visual attention mediated by biased competition in extrastriate visual cortex

Philos. Trans. R. Soc. Lond. B Biol. Sci.

(1998)

E. Düzel et al.

Human hippocampal and parahippocampal activity during visual associative recognition memory for spatial and nonspatial stimulus configurations

J. Neurosci.

(2003)

A.K. Engel et al.

Dynamic predictions: oscillations and synchrony in top-down processing

Nat. Rev. Neurosci.

(2001)

R. Epstein et al.

A cortical representation of the local visual environment

Nature

(1998)

Fenske, M.J., Boshyan, J. and Bar, M. (submitted) Can a gun prime a hairbrush? The “initial guesses” that drive...

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Chapter 1 Top-down facilitation of visual object recognition: object-based and context-based contributions

Abstract

Section snippets

An object-based cortical mechanism for triggering top-down facilitation

A context-based cortical mechanism for triggering top-down facilitation

Integrated object- and context-based top-down facilitation of recognition

Abbreviations

Acknowledgments

Neuron

Behav. Brain Res.

Neuron

Neuron

Cogn. Psychol.

Curr. Opin. Neurobiol.

Neuron

Neural Networks

Neuron

Trends Neurosci

Vision Res.

Prog. Neurobiol.

Neuroimage

Neuropsychologia

Vision Res.

Neuron

Trends Cogn. Sci.

Neuropsychologia

Curr. Opin. Neurobiol.

Brain Res. Cogn. Brain Res.

Neuropsychologia

Neuron

Neuroimage

Acta Psychol. (Amst.)

The parahippocampus subserves topographical learning in man

Cereb. Cortex

A cortical mechanism for triggering top-down facilitation in visual object recognition

J. Cogn. Neurosci.

Visual objects in context

Nat. Rev. Neurosci.

Top-down facilitation of visual recognition

Proceedings of the National Academy of Science

Spatial context in recognition

Perception

Prefrontal modulation of visual processing in humans

Nat. Neurosci.

Perceiving real-world scenes

Science

On the semantic of a glance at a scene

On the information extracted from a glance at a scene

J. Exp. Psychol.

Making memories: brain activity that predicts how well visual experience will be remembered

Science

The role of parts and spatial relations in object identification

Perception

Implicit, long-term spatial contextual memory

J. Exp. Psychol. Learn. Mem. Cogn.

Multiple routes to memory: distinct medial temporal lobe processes build item and source memories

Proc. Natl. Acad. Sci. USA.

Scene consistency in object and background perception

Psychol. Sci.

Visual attention mediated by biased competition in extrastriate visual cortex

Philos. Trans. R. Soc. Lond. B Biol. Sci.

Human hippocampal and parahippocampal activity during visual associative recognition memory for spatial and nonspatial stimulus configurations

J. Neurosci.

Dynamic predictions: oscillations and synchrony in top-down processing

Nat. Rev. Neurosci.

A cortical representation of the local visual environment

Nature