The Attentional Suppressive Surround: Eccentricity, Location-Based and Feature-Based Effects and Interactions

Yoo, Sang-Ah; Tsotsos, John K.; Fallah, Mazyar

doi:10.3389/fnins.2018.00710

Cited by 10 publications

(14 citation statements)

References 70 publications

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“…For short SRTs, there were no statistically significant differences between similarity levels when examining the angle and curvature metrics over increasing OS (as the objects became less similar). However, there was a significant DoG-shaped pattern for endpoint deviation, consistent with a non-spatial suppressive surround, found for simple features such as color, orientation, and direction of motion (Tsotsos et al, 2005;Tombu and Tsotsos, 2008;Störmer and Alvarez, 2014;Yoo et al 2018;Kehoe et al, 2018b). Our results show, for the first time, an object-based suppressive surround active during the discrimination phase of target-distractor competition.…”

Section: Effects Of Similarity On Saccade Trajectoriessupporting

confidence: 86%

Target-distractor competition modulates saccade trajectories in space and object-space

Giuricich

Green

Jordan

et al. 2022

Preprint

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Saccade planning and execution can be affected by a multitude of factors present in a target selection task. Recent studies have shown that the similarity between a target and nearby distractors affects the curvature of saccade trajectories, due to target-distractor competition. To further understand the nature of this competition, we varied the distance between and the similarity of complex target and distractor objects in a delayed match-to-sample task to examine their effects on saccade trajectories and better understand the underlying neural circuitry. For trials with short saccadic reaction times (SRTs) when target-distractor competition is still active, we found a robust effect of distance consistent with saccade vector averaging, whereas the effect of similarity suggested the existence of an object-based suppressive surround. At longer SRTs there was sufficient time for competition between the objects to complete and the distractor to be inhibited, which resulted in saccade trajectory deviations exhibiting the effects of a spatial suppressive surround. In terms of similarity, as the target-distractor similarity decreased, the initial saccade angle shifted towards the target, reflecting stronger distractor inhibition. There were no interactions between distance and similarity at any point in the time-course of target-distractor competition. Taken together, saccade trajectories reflect target-distractor competition that is affected independently by both spatial and object-space suppressive surrounds. The differences in saccade trajectories at short and long SRTs distinguish between active and completed decision-making processes. Thus, saccade responses are more beneficial than manual responses in studies of decision-making models.

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Section: Effects Of Similarity On Saccade Trajectoriessupporting

confidence: 86%

Target-distractor competition modulates saccade trajectories in space and object-space

Giuricich

Green

Jordan

et al. 2022

Preprint

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“…This interpretation suggests that feature-based attention might interact with spatial attention (spatial attention refers to the allocation of attention to spatial locations, while feature-based attention refers to the allocation of attention to different features independently of their spatial locations). Our previous study provided supporting evidence for the interaction between spatial and feature-based surround suppression [35]. We showed that the suppressive effect is greatest when the two (spatially) adjacent targets are also close in feature space.…”

Section: Effects Of Feature-based Attention On Motion Direction Tunin...supporting

confidence: 67%

“…p = 0.017, BH critical value = 0.013). To provide converging evidence, we fit a quadratic model to the data as an approximation of the surround suppression [ 35 ] and compared its goodness-of-fit with that of a linear model. Whereas the linear model did not properly fit the data (adjusted R 2 = − 0.305, AIC = 11.344), the quadratic model, an approximation of the surround suppression [ 35 ], showed a better fit (adjusted R 2 = 0.734, AIC = 6.826).…”

Section: Resultsmentioning

confidence: 99%

“…To provide converging evidence, we fit a quadratic model to the data as an approximation of the surround suppression [ 35 ] and compared its goodness-of-fit with that of a linear model. Whereas the linear model did not properly fit the data (adjusted R 2 = − 0.305, AIC = 11.344), the quadratic model, an approximation of the surround suppression [ 35 ], showed a better fit (adjusted R 2 = 0.734, AIC = 6.826). Reduction of motion repulsion at around 30 ~ 40° difference suggests that feature-based surround suppression played a role by inhibiting an unattended motion direction that was near the attended motion direction.…”

Section: Resultsmentioning

confidence: 99%

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Attention to visual motion suppresses neuronal and behavioral sensitivity in nearby feature space

et al. 2022

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Background Feature-based attention prioritizes the processing of the attended feature while strongly suppressing the processing of nearby ones. This creates a non-linearity or “attentional suppressive surround” predicted by the Selective Tuning model of visual attention. However, previously reported effects of feature-based attention on neuronal responses are linear, e.g., feature-similarity gain. Here, we investigated this apparent contradiction by neurophysiological and psychophysical approaches. Results Responses of motion direction-selective neurons in area MT/MST of monkeys were recorded during a motion task. When attention was allocated to a stimulus moving in the neurons’ preferred direction, response tuning curves showed its minimum for directions 60–90° away from the preferred direction, an attentional suppressive surround. This effect was modeled via the interaction of two Gaussian fields representing excitatory narrowly tuned and inhibitory widely tuned inputs into a neuron, with feature-based attention predominantly increasing the gain of inhibitory inputs. We further showed using a motion repulsion paradigm in humans that feature-based attention produces a similar non-linearity on motion discrimination performance. Conclusions Our results link the gain modulation of neuronal inputs and tuning curves examined through the feature-similarity gain lens to the attentional impact on neural population responses predicted by the Selective Tuning model, providing a unified framework for the documented effects of feature-based attention on neuronal responses and behavior.

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“…They reported that changes in spatial attention, rather than memory load accounted better for memory performance. Attended regions can have inhibitory surrounds [ 58 , 59 ], and crowding is also known to occur when distractor items approach attended items [ 60 , 61 ]. In spite of these characteristics of attention, we modeled the effects of attention as a single Gaussian distribution because we modeled behavioral data that were averaged across many trials.…”

Section: Materials and Methodsmentioning

confidence: 99%

Capacity and Allocation across Sensory and Short-Term Memories

Wang

Tripathy

Öğmen

2022

Vision

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Human memory consists of sensory memory (SM), short-term memory (STM), and long-term memory (LTM). SM enables a large capacity, but decays rapidly. STM has limited capacity, but lasts longer. The traditional view of these memory systems resembles a leaky hourglass, the large top and bottom portions representing the large capacities of SM and LTM, whereas the narrow portion in the middle represents the limited capacity of STM. The “leak” in the top part of the hourglass depicts the rapid decay of the contents of SM. However, recently, it was shown that major bottlenecks for motion processing exist prior to STM, and the “leaky hourglass” model was replaced by a “leaky flask” model with a narrower top part to capture bottlenecks prior to STM. The leaky flask model was based on data from one study, and the first goal of the current paper was to test if the leaky flask model would generalize by using a different set of data. The second goal of the paper was to explore various block diagram models for memory systems and determine the one best supported by the data. We expressed these block diagram models in terms of statistical mixture models and, by using the Bayesian information criterion (BIC), found that a model with four components, viz., SM, attention, STM, and guessing, provided the best fit to our data. In summary, we generalized previous findings about early qualitative and quantitative bottlenecks, as expressed in the leaky flask model and showed that a four-process model can provide a good explanation for how visual information is processed and stored in memory.

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The Attentional Suppressive Surround: Eccentricity, Location-Based and Feature-Based Effects and Interactions

Cited by 10 publications

References 70 publications

Target-distractor competition modulates saccade trajectories in space and object-space

Target-distractor competition modulates saccade trajectories in space and object-space

Attention to visual motion suppresses neuronal and behavioral sensitivity in nearby feature space

Capacity and Allocation across Sensory and Short-Term Memories

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