Special issue: ReviewRepresentation of shape, space, and attention in monkey cortex
Section snippets
Attention: single-cell versus population representations
Attention is a critical component of executive functions (Lyon and Krasnegor, 1996, Sereno and Bolding, 2017). Executive functions are a set of cognitive functions that are essential for goal-oriented behavior and drive our ability to adapt to a constantly changing environment. Executive function deficits lead to disproportionate impairment in function and activities of daily living (Cahn-Weiner et al., 2002, Rabinovici et al., 2015). Attention is essential in order for individuals to generate
Representations: extrinsic versus intrinsic approaches
Perceptual and cognitive representations are encoded in a collective fashion by populations of neurons and not individual neurons in isolation. Dealing with the collective behavior of neural populations leads to complex and sometimes non-intuitive properties that often require extensive quantitative methods to characterize. This has led to a widespread theoretical literature dealing with population codes, (e.g., Averbeck et al., 2006, Deneve et al., 1999, Földiák, 1993, Jazayeri and Movshon,
Cortical streams: object recognition versus spatial processing
Much early research (e.g., Brown and Schafer, 1888, Ferrier and Yeo, 1884) supports the idea of a division in visual processing between a ventral and a dorsal cortical region, which subserve object (Fig. 1) and spatial (Fig. 2) vision, respectively. More recent work also demonstrates evidence for functionally and anatomically distinct multisynaptic streams or projections from striate cortex (Ungerleider & Mishkin, 1982). These distinct visual cortical streams were first characterized as a
Cortical streams: segregation of properties or segregation of function?
Notwithstanding proliferation and identification of sub-streams, the idea remains that cortical processing of visual information from the retina is anatomically segregated into two fundamental streams, ventral versus dorsal, important for object and spatial vision, respectively. Despite this consensus, evidence has accumulated from our own and others’ work demonstrating object representations in the dorsal stream (Durand et al., 2007, Konen and Kastner, 2008, Lehky and Sereno, 2007, Murata
Intrinsic advantages: data-driven categorization and discrimination
Intrinsic methods, using data-driven (agnostic) techniques such as MDS, are able to reveal relationships among individual neurons, such as category membership, without any a priori knowledge or assumptions about the properties of individual neurons beyond their firings (for example, without time-consuming measurements of neural tuning curves across a population) (Lehky et al., 2013). For fMRI data, the same data analysis techniques can be used, substituting voxels for neurons. Intrinsic
Intrinsic advantages: data-driven cross-stream comparison of shape and space encoding
In our own work in monkeys at a cellular or fMRI level, we report many mixed findings with shape selectivity in dorsal stream neurons (Fig. 4, Fig. 5) (Peng et al., 2008, Sereno and Maunsell, 1998, Sereno et al., 2002) and retinal and eye position spatial selectivities in ventral stream neurons (Fig. 6) (Lehky et al., 2008, Sereno and Lehky, 2011a, Sereno et al., 2014). By using an intrinsic approach on the population data collected under identical conditions, we directly compared cortical
Intrinsic modeling: elucidation of key physiological characteristics
By using an identical intrinsic approach with model neurons, we can explore and elucidate how single cell properties influence these population representations. Specifically, we can search for those key physiological characteristics that can explain the differences in encoding we see across cortical regions or instead identify invariances in these population representations (those changes in physiological properties that do not affect the encoding representation). We highlight here one example
Effects of attention on population representations across cortical streams
Neurophysiological effects of attention appear to be ubiquitous in both the ventral and dorsal visual streams (Chelazzi et al., 2011, Colby and Goldberg, 1999, Desimone and Duncan, 1995, Maunsell, 2015, Reynolds and Chelazzi, 2004). In the ventral stream, this includes attentional effects in V4 (McAdams and Maunsell, 1999, Moran and Desimone, 1985, Nandy et al., 2017) as well as inferotemporal cortex (e.g., Chelazzi et al., 1998, Zhang et al., 2011). In the dorsal stream, attentional effects
Future directions: intrinsic modeling of attention
In order to better understand these attentional differences (i.e., population coding differences at a representational level due to an attention manipulation), and more specifically, the effects of various cellular attentional modulations on the response distance or discriminability of stimulus items in a high-dimensional neural representation, a model with synthetic responses based on prior physiological data would be helpful. In our own work, we have previously had much success applying MDS
Summary and implications
Despite a wealth of knowledge about single-cell response for shape, space, and attention across many brain regions, little is understood about how these properties relate to differences in population representations across cortical regions, important for behavior and intervention in human disease. Recent work has blurred distinctions between properties in the ‘what’ (ventral) and ‘where’ (dorsal) cortical visual streams, with findings demonstrating single-cell shape, space, and attention
Conclusion
Although much has been learned about the detailed response properties at the single-cell level of many regions of the brain, a deeper understanding of how these properties collectively lead to different functions at the population level in various cortical regions and how they relate to behaviors such as attention remains elusive. Without a clearer understanding of behaviors such as attention, current interventions remain rather haphazard, and optimal interventions in human disease stay a
Acknowledgments
This research was supported in part by Purdue University start-up funds (ABS) and NIH R01 MH63340.
References (162)
- et al.
Effects of haloperidol on cognition in schizophrenia patients depend on baseline performance: A saccadic eye movement study
Progress in Neuropsychopharmacology and Biological Psychiatry
(2011) - et al.
A source for feature-based attention in the prefrontal cortex
Neuron
(2015) - et al.
Cross-talk connections underlying dorsal and ventral stream integration during hand actions
Cortex
(2018) - et al.
Using neuronal populations to study the mechanisms underlying spatial and feature attention
Neuron
(2011) - et al.
Visual areas in the temporal cortex of the macaque
Brain Research
(1979) - et al.
Anterior regions of monkey parietal cortex process visual 3D shape
Neuron
(2007) - et al.
The dorsal "action" pathway
Handbook of Clinical Neurology
(2018) - et al.
Separate neural pathways for the visual analysis of object shape in perception and prehension
Current Biology
(1994) - et al.
Separate visual pathways for perception and action
Trends in Neurosciences
(1992) - et al.
Visual cognition: A new look at the two-visual systems model
Neuropsychologia
(2005)
A brief comparative review of primate posterior parietal cortex: A novel hypothesis on the human toolmaker
Neuropsychologia
The ventral visual pathway: An expanded neural framework for the processing of object quality
Trends in Cognitive Sciences
Matching categorical object representations in inferior temporal cortex of man and monkey
Neuron
Frontal lobe dysfunctions in schizophrenia--II. Impairments of psychological and brain functions
Journal of Psychiatric Research
Attentional modulation strength in cortical area MT depends on stimulus contrast
Neuron
Feature-based attention increases the selectivity of population responses in primate visual cortex
Current Biology
Feature-based attention in visual cortex
Trends in Neurosciences
Object vision and spatial vision: Two cortical pathways
Trends in Neurosciences
Differential attention-dependent response modulation across cell classes in macaque visual area V4
Neuron
Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4
Neuron
Organization of the macaque extrastriate visual cortex re-examined using the principle of spatial continuity of function
Journal of Neurophysiology
Number of neurons in a voxel Retrieved Dec. 1, 2018, from
Selection for action: Some behavioral and neurophysiological considerations of attention and action
Activity in LIP, but not V4, matches performance when attention is spread
Cerebral Cortex
Neural correlations, population coding and computation
Nature Reviews Neuroscience
Shape similarity, better than semantic membership, accounts for the structure of visual object representations in a population of monkey inferotemporal neurons
PLoS Computational Biology
Parallel and serial neural mechanisms for visual search in macaque area V4
Science
Neuronal activity in the lateral intraparietal area and spatial attention
Science
View-invariant representations of familiar objects by neurons in the inferior temporal visual cortex
Cerebral Cortex
Cortical connections of the macaque anterior intraparietal (AIP) area
Cerebral Cortex
Dissociations and associations between shape and category representations in the two visual pathways
Journal of Neuroscience
Eye position effects in macaque area V4
Neuroreport
Is coding a relevant metaphor for the brain?
Behavioral and Brain Sciences
An investigation into the functions of the occipital and temporal lobes of the monkey's brain
Philosophical Transactions of the Royal Society of London B
Tests of executive function predict instrumental activities of daily living in community-dwelling older individuals
Applied Neuropsychology
The code for facial identity in the primate brain
Cell
Neural basis of visual selective attention
Wiley Interdisciplinary Reviews Cognitive Science
Responses of neurons in inferior temporal cortex during memory-guided visual search
Journal of Neurophysiology
Attention improves performance primarily by reducing interneuronal correlations
Nature Neuroscience
Visual, presaccadic, and cognitive activation of single neurons in monkey lateral intraparietal area
Journal of Neurophysiology
Space and attention in parietal cortex
Annual Review of Neuroscience
The representation of biological classes in the human brain
Journal of Neuroscience
Support-vector networks
Machine Learning
Attention during natural vision warps semantic representation across the human brain
Nature Neuroscience
The role of early visual cortex in visual integration: A neural model of recurrent interaction
European Journal of Neuroscience
Reading population codes: A neural implementation of ideal observers
Nature Neuroscience
Visual attention mediated by biased competition in extrastriate visual cortex
Philosophical Transactions of the Royal Society of London Series B Biological Sciences
Stimulus-selective properties of inferior temporal neurons in the macaque
Journal of Neuroscience
Neural mechanisms of selective visual attention
Annual Review of Neuroscience
Cortical connections of inferior temporal area TEO in macaque monkeys
Journal of Comparative Neurology
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2022, NeuropsychologiaCitation Excerpt :In general, however, considering shape processing to be an exclusive property of the ventral visual pathway appears to be an oversimplification. Neurons in the lateral parietal area (LIP) for example, show selectivity for simple shapes of different forms (e.g. Sereno and Maunsell, 1998; Sereno et al., 2020), and one fMRI study found preferential activation to line segments forming coherent shapes compared to non-coherent ones in several areas along the ventral and the dorsal stream, including the intraparietal sulcus (Braddick et al., 2000). Other imaging and neuropsychological studies pointed at the involvement of the parietal cortex in perception of more complex objects, Gestalt perception, or in binding shape and surface details together (Humphreys, 2003; Eger et al., 2007; Himmelbach et al., 2009; Huberle and Karnath, 2012; Zaretskaya et al., 2013; Rennig et al., 2015).
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