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

The Architecture of Object-Based Attention

The allocation of attention to objects raises several intriguing questions: What are objects, how does attention access them, what anatomical regions are involved? Here, we review recent progress in the field to determine the mechanisms underlying object-based attention. First, findings from unconscious priming and cueing suggest that the preattentive targets of object-based attention can be fully developed object representations that have reached the level of identity. Next, the control of object-based attention appears to come from ventral visual areas specialized in object analysis that project downward to early visual areas. How feedback from object areas can accurately target the object's specific locations and features is unknown but recent work in autoencoding has made this plausible. Finally, we suggest that the three classic modes of attention may not be as independent as is commonly considered, and instead could all rely on object-based attention. Specifically, studies show that attention can be allocated to the separated members of a group-without affecting the space between them-matching the defining property of feature-based attention. At the same time, object-based attention directed to a single small item has the properties of space-based attention. We outline the architecture of object-based attention, the novel predictions it brings, and discuss how it works in parallel with other attention pathways.

Different attentional focus sizes modulate the size-eccentricity effect

The size-eccentricity effect is a perceptual distortion phenomenon in which a peripherally located object is perceived to be smaller than a centrally located object. Although the increase in apparent object size caused by attention has been documented, little is known about the effect of different sizes of attentional focus on object appearance. The present study investigated how different sizes of attentional focus affect the size-eccentricity effect using a spatial pre-cueing paradigm. Additionally, we examined the influence of different task types on size perception. A peripheral object following a small attentional focus appeared larger, without observation of the size-eccentricity effect. In contrast, a peripheral object appeared smaller following a large attentional focus in both larger and smaller judgement tasks. These results suggest that the relative size of the attentional focus has opposite effects on the perception of object size, independent of task type. Furthermore, in addition to the structural properties of the retina and the locus of attention, the size of attentional focus determines the extent to which an object appears smaller in the periphery. The present study complements the attentional attraction field model of the size and density of population receptive fields in V1 and further explains how the effect of attention is restricted by retinal structure.

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

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01428-7.

The role of temporal cortex in the control of attention

Attention is an indispensable component of active vision. Contrary to the widely accepted notion that temporal cortex processing primarily focusses on passive object recognition, a series of very recent studies emphasize the role of temporal cortex structures, specifically the superior temporal sulcus (STS) and inferotemporal (IT) cortex, in guiding attention and implementing cognitive programs relevant for behavioral tasks. The goal of this theoretical paper is to advance the hypothesis that the temporal cortex attention network (TAN) entails necessary components to actively participate in attentional control in a flexible task-dependent manner. First, we will briefly discuss the general architecture of the temporal cortex with a focus on the STS and IT cortex of monkeys and their modulation with attention. Then we will review evidence from behavioral and neurophysiological studies that support their guidance of attention in the presence of cognitive control signals. Next, we propose a mechanistic framework for executive control of attention in the temporal cortex. Finally, we summarize the role of temporal cortex in implementing cognitive programs and discuss how they contribute to the dynamic nature of visual attention to ensure flexible behavior.

Capacity and Allocation across Sensory and Short-Term Memories

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.