Neuroimaging evidence for object model verification theory: Role of prefrontal control in visual object categorization

Although the visual system rapidly categorizes objects seen under optimal viewing conditions, the categorization of objects seen under impoverished viewing conditions not only requires more time but may also depend more on top-down processing, as hypothesized by object model verification theory. Two studies, one with functional magnetic resonance imaging (fMRI) and one behavioral with the same stimuli, tested this hypothesis. FMRI data were acquired while people categorized more impoverished (MI) and less impoverished (LI) line drawings of objects. FMRI results revealed stronger activation during the MI than LI condition in brain regions involved in top-down control (inferior and medial prefrontal cortex, intraparietal sulcus), and in posterior, object-sensitive brain regions (ventral and dorsal occipitotemporal, and occipitoparietal cortex). The behavioral study indicated that taxing visuospatial working memory, a key component of top-down control processes during visual tasks, interferes more with the categorization of MI stimuli (but not LI stimuli) than does taxing verbal working memory. Together, these findings provide evidence for object model verification theory and implicate greater prefrontal cortex involvement in top-down control of posterior visual processes during the categorization of more impoverished images of objects.

[1]  J. Desmond,et al.  Making memories: brain activity that predicts how well visual experience will be remembered. , 1998, Science.

[2]  B Macwhinney,et al.  The PsyScope experiment-building system. , 1997, Spatial vision.

[3]  Lizabeth M Romanski,et al.  Domain specificity in the primate prefrontal cortex , 2004, Cognitive, affective & behavioral neuroscience.

[4]  Jack L. Lancaster,et al.  Clustered pixels analysis for functional MRI activation studies of the human brain , 1995 .

[5]  S. Kosslyn,et al.  Neural Systems Shared by Visual Imagery and Visual Perception: A Positron Emission Tomography Study , 1997, NeuroImage.

[6]  M. Kutas,et al.  Neurophysiological evidence for visual perceptual categorization of words and faces within 150 ms. , 1998, Psychophysiology.

[7]  M. Kutas,et al.  Neurophysiological evidence for two processing times for visual object identification , 2002, Neuropsychologia.

[8]  David J. Freedman,et al.  A Comparison of Primate Prefrontal and Inferior Temporal Cortices during Visual Categorization , 2003, The Journal of Neuroscience.

[9]  D. Collins,et al.  Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space , 1994, Journal of computer assisted tomography.

[10]  K. Grill-Spector,et al.  The human visual cortex. , 2004, Annual review of neuroscience.

[11]  S. Kosslyn,et al.  Mental rotation of objects versus hands: neural mechanisms revealed by positron emission tomography. , 1998, Psychophysiology.

[12]  C. Frith,et al.  The Role of Working Memory in Visual Selective Attention , 2001, Science.

[13]  Joan Gay Snodgrass,et al.  Fragmenting pictures on the apple macintosh computer for experimental and clinical applications , 1987 .

[14]  J. Fuster,et al.  Functional interactions between inferotemporal and prefrontal cortex in a cognitive task , 1985, Brain Research.

[15]  J. G. Snodgrass,et al.  A standardized set of 260 pictures: norms for name agreement, image agreement, familiarity, and visual complexity. , 1980, Journal of experimental psychology. Human learning and memory.

[16]  I. Biederman Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[17]  N. Kanwisher,et al.  Testing cognitive models of visual attention with fMRI and MEG , 2001, Neuropsychologia.

[18]  M. Petrides,et al.  Wisconsin Card Sorting Revisited: Distinct Neural Circuits Participating in Different Stages of the Task Identified by Event-Related Functional Magnetic Resonance Imaging , 2001, The Journal of Neuroscience.

[19]  R. Passingham,et al.  Specialisation within the prefrontal cortex: the ventral prefrontal cortex and associative learning , 2000, Experimental Brain Research.

[20]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[21]  Keiji Tanaka,et al.  Inferotemporal cortex and object vision. , 1996, Annual review of neuroscience.

[22]  David G. Lowe,et al.  Towards a Computational Model for Object Recognition in IT Cortex , 2000, Biologically Motivated Computer Vision.

[23]  Karl J. Friston,et al.  Where bottom-up meets top-down: neuronal interactions during perception and imagery. , 2004, Cerebral cortex.

[24]  R. C. Oldfield The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[25]  Junying Yuan,et al.  Selective gating of visual signals by microstimulation of frontal cortex , 2022 .

[26]  N. Kanwisher,et al.  The Generality of Parietal Involvement in Visual Attention , 1999, Neuron.

[27]  Edward E. Smith,et al.  Categories and concepts , 1984 .

[28]  Alex Martin,et al.  Representation of Manipulable Man-Made Objects in the Dorsal Stream , 2000, NeuroImage.

[29]  A. Dale,et al.  Building memories: remembering and forgetting of verbal experiences as predicted by brain activity. , 1998, Science.

[30]  R. E Passingham,et al.  Activations related to “mirror” and “canonical” neurones in the human brain: an fMRI study , 2003, NeuroImage.

[31]  David G. Lowe,et al.  Perceptual Organization and Visual Recognition , 2012 .

[32]  J. Jonides,et al.  Storage and executive processes in the frontal lobes. , 1999, Science.

[33]  M. D’Esposito,et al.  Directing the mind's eye: prefrontal, inferior and medial temporal mechanisms for visual working memory , 2005, Current Opinion in Neurobiology.

[34]  M. Petrides Lateral prefrontal cortex: architectonic and functional organization , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[35]  R. Malach,et al.  Object-related activity revealed by functional magnetic resonance imaging in human occipital cortex. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[36]  J. Duncan,et al.  Encoding Strategies Dissociate Prefrontal Activity from Working Memory Demand , 2003, Neuron.

[37]  E. Miller,et al.  Effects of Visual Experience on the Representation of Objects in the Prefrontal Cortex , 2000, Neuron.

[38]  Edward E. Smith,et al.  Neuroimaging studies of working memory: , 2003, Cognitive, affective & behavioral neuroscience.

[39]  Leslie G. Ungerleider,et al.  Mechanisms of visual attention in the human cortex. , 2000, Annual review of neuroscience.

[40]  Sergio Della Sala,et al.  Agnosia for object orientation: Implications for theories of object recognition , 1997, Neuropsychologia.

[41]  Tor D Wager,et al.  Neuroimaging studies of shifting attention: a meta-analysis , 2004, NeuroImage.

[42]  Shintaro Funahashi,et al.  Representation and brain , 2007 .

[43]  Robyn T. Oliver,et al.  Dorsal stream activation during retrieval of object size and shape , 2003, Cognitive, affective & behavioral neuroscience.

[44]  Mark D'Esposito,et al.  Searching for “the Top” in Top-Down Control , 2005, Neuron.

[45]  M. Corbetta,et al.  Control of goal-directed and stimulus-driven attention in the brain , 2002, Nature Reviews Neuroscience.

[46]  E. Viding,et al.  Load theory of selective attention and cognitive control. , 2004, Journal of experimental psychology. General.

[47]  J. Jonides,et al.  Overlapping mechanisms of attention and spatial working memory , 2001, Trends in Cognitive Sciences.

[48]  Toshio Inui,et al.  The role of the posterior parietal cortex in human object recognition: a functional magnetic resonance imaging study , 1999, Neuroscience Letters.

[49]  Leslie G. Ungerleider,et al.  A general mechanism for perceptual decision-making in the human brain , 2004, Nature.

[50]  J. G. Snodgrass,et al.  Priming effects in picture fragment completion: support for the perceptual closure hypothesis. , 1990, Journal of experimental psychology. General.

[51]  A M Dale,et al.  Optimal experimental design for event‐related fMRI , 1999, Human brain mapping.

[52]  Stephen M. Kosslyn,et al.  Multiple Mechanisms of Top-Down Processing in Vision , 2007 .

[53]  Alan C. Evans,et al.  Enhancement of MR Images Using Registration for Signal Averaging , 1998, Journal of Computer Assisted Tomography.

[54]  Y. Miyashita,et al.  The Wisconsin Card Sorting Test , 2022 .

[55]  A. Treisman,et al.  Parietal contributions to visual feature binding: evidence from a patient with bilateral lesions , 1995, Science.

[56]  T. Poggio,et al.  A network that learns to recognize three-dimensional objects , 1990, Nature.

[57]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[58]  K. Grill-Spector,et al.  The dynamics of object-selective activation correlate with recognition performance in humans , 2000, Nature Neuroscience.

[59]  E. Rolls,et al.  The Neurophysiology of Backward Visual Masking: Information Analysis , 1999, Journal of Cognitive Neuroscience.

[60]  M. Bar,et al.  Cortical Mechanisms Specific to Explicit Visual Object Recognition , 2001, Neuron.

[61]  S M Kosslyn,et al.  Identifying objects seen from different viewpoints. A PET investigation. , 1994, Brain : a journal of neurology.

[62]  S. Kosslyn Image and Brain , 1994 .

[63]  C. Stern,et al.  Prefrontal–Temporal Circuitry for Episodic Encoding and Subsequent Memory , 2000, The Journal of Neuroscience.

[64]  G. Mangun,et al.  The neural mechanisms of top-down attentional control , 2000, Nature Neuroscience.

[65]  T. Poggio,et al.  Neural mechanisms of object recognition , 2002, Current Opinion in Neurobiology.

[66]  T Vilis,et al.  “Active” and “passive” learning of three-dimensional object structure within an immersive virtual reality environment , 2002, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[67]  Y. Miyashita,et al.  Top-down signal from prefrontal cortex in executive control of memory retrieval , 1999, Nature.

[68]  S. Kosslyn,et al.  Identifying objects at different levels of hierarchy: A positron emission tomography study , 1995 .

[69]  Michael S. Beauchamp,et al.  A Parametric fMRI Study of Overt and Covert Shifts of Visuospatial Attention , 2001, NeuroImage.

[70]  Rafael Malach,et al.  Rapid completion effects in human high-order visual areas , 2004, NeuroImage.

[71]  M. Kilwein,et al.  Basic objects in natural categories revisited : a replication with sighted and blind college students / , 1993 .

[72]  E. Miller,et al.  An integrative theory of prefrontal cortex function. , 2001, Annual review of neuroscience.

[73]  S P Wise,et al.  The role of ventral and orbital prefrontal cortex in conditional visuomotor learning and strategy use in rhesus monkeys (Macaca mulatta). , 2001, Behavioral neuroscience.

[74]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[75]  S. Thorpe,et al.  Taking the MAX from neuronal responses , 2003, Trends in Cognitive Sciences.

[76]  E. Rolls,et al.  INVARIANT FACE AND OBJECT RECOGNITION IN THE VISUAL SYSTEM , 1997, Progress in Neurobiology.

[77]  M. Corbetta,et al.  A PET study of visuospatial attention , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[78]  David I. Perrett,et al.  Neurophysiology of shape processing , 1993, Image Vis. Comput..

[79]  S. Bunge How we use rules to select actions: A review of evidence from cognitive neuroscience , 2004, Cognitive, affective & behavioral neuroscience.

[80]  Rafael Malach,et al.  Large-Scale Mirror-Symmetry Organization of Human Occipito-Temporal Object Areas , 2003, Neuron.

[81]  J G Snodgrass,et al.  Perceptual Identification Thresholds for 150 Fragmented Pictures from the Snodgrass and Vanderwart Picture Set , 1988, Perceptual and motor skills.