Feature-based attention causes a ring-like modulation of motion direction tuning curves in areas MT and MST of macaques

AbstractAttention to a stimulus feature prioritizes its processing while strongly suppressing the processing of similar features, a non-linear phenomenon called surround suppression. Here we investigated this phenomenon using neurophysiology and psychophysics. We recorded responses of motion direction-selective neurons in area MT/MST of monkeys in different conditions. When attention was allocated to a stimulus moving in the neurons’ preferred direction responses to a distractor were strongly suppressed for directions nearby the preferred direction. These effects were modeled as the interaction between two Gaussian fields representing narrowly-tuned excitatory and widely-tuned inhibitory inputs into a neuron, with attention more strongly modulating the gain of the inhibitory inputs. We additionally demonstrated a corresponding behavioral effect in humans: Feature-based attention strongly reduced motion repulsion in the vicinity of the attended motion direction. Our results demonstrate that feature-based attention can induce non-linear changes in neuronal tuning curves via unbalanced gain changes to excitatory and inhibitory inputs into neurons ultimately translating into similar effects during behavior.

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Motion direction representation in multivariate electroencephalography activity for smooth pursuit eye movements

10.1101/519074 ◽

2019 ◽

Author(s):

Joonyeol Lee ◽

Woojae Jeong ◽

Seolmin Kim ◽

Yee-Joon Kim

Keyword(s):

Steady State ◽

Smooth Pursuit ◽

Open Loop ◽

Smooth Pursuit Eye Movements ◽

Motion Direction ◽

Eeg Activity ◽

Tuning Curves ◽

Pursuit Eye Movements ◽

Retinal Motion ◽

Direction Tuning

AbstractVisually-guided smooth pursuit eye movements are composed of initial open-loop and later steady-state periods. Feedforward sensory information dominates the motor behavior during the open-loop pursuit, and a more complex feedback loop regulates the steady-state pursuit. To understand the neural representations of motion direction during open-loop and steady-state smooth pursuits, we recorded electroencephalography (EEG) responses from human observers while they tracked random dot kinematograms as pursuit targets. We estimated population direction tuning curves from multivariate EEG activity using an inverted encoding model. We found significant direction tuning curves as early as 20 ms from motion onset. Direction tuning responses were generalized to later times during the open-loop smooth pursuit, but they became more dynamic during the later steady-state pursuit. The encoding quality of retinal motion direction information estimated from the early direction tuning curves was predictive of trial-by-trial variation in initial pursuit directions. These results suggest that the movement directions of open-loop smooth pursuit are guided by the representation of the retinal motion present in the multivariate EEG activity.

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Visual coding with a population of direction-selective neurons

Journal of Neurophysiology ◽

10.1152/jn.00919.2014 ◽

2015 ◽

Vol 114 (4) ◽

pp. 2485-2499 ◽

Cited By ~ 23

Author(s):

Michele Fiscella ◽

Felix Franke ◽

Karl Farrow ◽

Jan Müller ◽

Botond Roska ◽

...

Keyword(s):

Microelectrode Array ◽

Ganglion Cells ◽

Tuning Curve ◽

Real Data ◽

Cell Types ◽

Average Error ◽

Retinal Ganglion ◽

Motion Direction ◽

Tuning Curves ◽

Stimulus Parameters

The brain decodes the visual scene from the action potentials of ∼20 retinal ganglion cell types. Among the retinal ganglion cells, direction-selective ganglion cells (DSGCs) encode motion direction. Several studies have focused on the encoding or decoding of motion direction by recording multiunit activity, mainly in the visual cortex. In this study, we simultaneously recorded from all four types of ON-OFF DSGCs of the rabbit retina using a microelectronics-based high-density microelectrode array (HDMEA) and decoded their concerted activity using probabilistic and linear decoders. Furthermore, we investigated how the modification of stimulus parameters (velocity, size, angle of moving object) and the use of different tuning curve fits influenced decoding precision. Finally, we simulated ON-OFF DSGC activity, based on real data, in order to understand how tuning curve widths and the angular distribution of the cells' preferred directions influence decoding performance. We found that probabilistic decoding strategies outperformed, on average, linear methods and that decoding precision was robust to changes in stimulus parameters such as velocity. The removal of noise correlations among cells, by random shuffling trials, caused a drop in decoding precision. Moreover, we found that tuning curves are broad in order to minimize large errors at the expense of a higher average error, and that the retinal direction-selective system would not substantially benefit, on average, from having more than four types of ON-OFF DSGCs or from a perfect alignment of the cells' preferred directions.

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A Bayesian approach for characterizing direction tuning curves in the supplementary motor area of behaving monkeys

Journal of Neurophysiology ◽

10.1152/jn.00449.2012 ◽

2013 ◽

Vol 109 (11) ◽

pp. 2842-2851 ◽

Cited By ~ 1

Author(s):

Hadas Taubman ◽

Eilon Vaadia ◽

Rony Paz ◽

Gal Chechik

Keyword(s):

Supplementary Motor Area ◽

Tuning Curve ◽

Motor Area ◽

Movement Direction ◽

Neural Responses ◽

Shape Estimation ◽

Tuning Curves ◽

Coarse Resolution ◽

Behaving Monkeys ◽

Direction Tuning

Neural responses are commonly studied in terms of “tuning curves,” characterizing changes in neuronal response as a function of a continuous stimulus parameter. In the motor system, neural responses to movement direction often follow a bell-shaped tuning curve for which the exact shape determines the properties of neuronal movement coding. Estimating the shape of that tuning curve robustly is hard, especially when directions are sampled unevenly and at a coarse resolution. Here, we describe a Bayesian estimation procedure that improves the accuracy of curve-shape estimation even when the curve is sampled unevenly and at a very coarse resolution. Using this approach, we characterize the movement direction tuning curves in the supplementary motor area (SMA) of behaving monkeys. We compare the SMA tuning curves to tuning curves of neurons from the primary motor cortex (M1) of the same monkeys, showing that the tuning curves of the SMA neurons tend to be narrower and shallower. We also show that these characteristics do not depend on the specific location in each region.

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Feature-based attention biases perception of motion direction

Frontiers in Computational Neuroscience ◽

10.3389/conf.neuro.10.2009.14.095 ◽

2009 ◽

Vol 3 ◽

Author(s):

Series Peggy

Keyword(s):

Motion Direction ◽

Feature Based

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Feature-based attention is not confined by object boundaries: spatially global enhancement of irrelevant features

10.31234/osf.io/356vk ◽

2018 ◽

Cited By ~ 2

Author(s):

Angus F. Chapman ◽

Viola S. Störmer

Keyword(s):

Visual Field ◽

Visual Features ◽

Motion Direction ◽

Detection Rates ◽

The Core ◽

Unit Of Selection ◽

Object Based ◽

Feature Based ◽

Task Irrelevant ◽

Object Features

Theories of visual attention differ in what they define as the core unit of selection. Feature-based theories emphasize the importance of visual features (e.g., color, size, motion), demonstrated through enhancement of attended features across the visual field, while object-based theories propose that attention enhances all features belonging to the same object. Here we test how within-object enhancement of features interacts with spatially global effects of feature-based attention. Participants attended a set of colored dots (moving coherently upwards or downwards) to detect brief luminance decreases, while simultaneously detecting speed changes in another set of dots in the opposite visual field. Participants had higher speed detection rates for the dot array that matched the motion direction of the attended color array, although motion direction was entirely task-irrelevant. This effect persisted even when it was detrimental for task performance. Overall, these results indicate that task-irrelevant object features are enhanced globally, surpassing object boundaries.

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Ocular tracking responses to background motion gated by feature-based attention

Journal of Neurophysiology ◽

10.1152/jn.00810.2013 ◽

2014 ◽

Vol 112 (5) ◽

pp. 1074-1081 ◽

Cited By ~ 3

Author(s):

David Souto ◽

Dirk Kerzel

Keyword(s):

Peak Velocity ◽

Horizontal Direction ◽

Motion Onset ◽

Motion Direction ◽

Ocular Tracking ◽

Feature Based ◽

Background Motion ◽

Ocular Following Response ◽

Tracking Response

Involuntary ocular tracking responses to background motion offer a window on the dynamics of motion computations. In contrast to spatial attention, we know little about the role of feature-based attention in determining this ocular response. To probe feature-based effects of background motion on involuntary eye movements, we presented human observers with a balanced background perturbation. Two clouds of dots moved in opposite vertical directions while observers tracked a target moving in horizontal direction. Additionally, they had to discriminate a change in the direction of motion (±10° from vertical) of one of the clouds. A vertical ocular following response occurred in response to the motion of the attended cloud. When motion selection was based on motion direction and color of the dots, the peak velocity of the tracking response was 30% of the tracking response elicited in a single task with only one direction of background motion. In two other experiments, we tested the effect of the perturbation when motion selection was based on color, by having motion direction vary unpredictably, or on motion direction alone. Although the gain of pursuit in the horizontal direction was significantly reduced in all experiments, indicating a trade-off between perceptual and oculomotor tasks, ocular responses to perturbations were only observed when selection was based on both motion direction and color. It appears that selection by motion direction can only be effective for driving ocular tracking when the relevant elements can be segregated before motion onset.

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Emergence of Position-Independent Detectors of Sense of Rotation and Dilation with Hebbian Learning: An Analysis

Neural Computation ◽

10.1162/neco.1993.5.4.597 ◽

1993 ◽

Vol 5 (4) ◽

pp. 597-612 ◽

Cited By ~ 38

Author(s):

Kechen Zhang ◽

Martin I. Sereno ◽

Margaret E. Sereno

Keyword(s):

Hebbian Learning ◽

Velocity Fields ◽

Original Model ◽

Linear Speed ◽

Tuning Curves ◽

Translation Velocity ◽

Hebb Rule ◽

Input Layer ◽

Mst Neurons ◽

Direction Tuning

We previously demonstrated that it is possible to learn position-independent responses to rotation and dilation by filtering rotations and dilations with different centers through an input layer with MT-like speed and direction tuning curves and connecting them to an MST-like layer with simple Hebbian synapses (Sereno and Sereno 1991). By analyzing an idealized version of the network with broader, sinusoidal direction-tuning and linear speed-tuning, we show analytically that a Hebb rule trained with arbitrary rotation, dilation/contraction, and translation velocity fields yields units with weight fields that are a rotation plus a dilation or contraction field, and whose responses to a rotating or dilating/contracting disk are exactly position independent. Differences between the performance of this idealized model and our original model (and real MST neurons) are discussed.

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