Neuronal encoding of the distance traversed by covert shifts of spatial attention

A Bayesian inference model can predict the effects of attention on the serial dependence in heading estimation from optic flow

It has been demonstrated that observers can accurately estimate their self-motion direction (i.e., heading) from optic flow, which can be affected by attention. However, it remains unclear how attention affects the serial dependence in the estimation. In the current study, participants conducted two experiments. The results showed that the estimation accuracy decreased when attentional resources allocated to the heading estimation task were reduced. Additionally, the estimates of currently presented headings were biased toward the headings of previously seen headings, showing serial dependence. Especially, this effect decreased (increased) when the attentional resources allocated to the previously (currently) seen headings were reduced. Furthermore, importantly, we developed a Bayesian inference model, which incorporated attention-modulated likelihoods and qualitatively predicted changes in the estimation accuracy and serial dependence. In summary, the current study shows that attention affects the serial dependence in heading estimation from optic flow and reveals the Bayesian computational mechanism behind the heading estimation.

Attention affects the perception of self-motion direction from optic flow

Many studies have demonstrated that attention affects the perception of many visual features. However, previous studies show conflicting results regarding the effect of attention on the perception of self-motion direction (i.e., heading) from optic flow. To address this question, we conducted three behavioral experiments and found that estimation accuracies of large headings (>14°) decreased with attention load, discrimination thresholds of these headings increased with attention load, and heading estimates were systematically compressed toward the focus of attention. Therefore, the current study demonstrated that attention affected heading perception from optic flow, showing that the perception is both information-driven and cognitive.

Temporal stability of human heading perception

Humans are capable of accurately judging their heading from optic flow during straight forward self-motion. Despite the global coherence in the optic flow field, however, visual clutter and other naturalistic conditions create constant flux on the eye. This presents a problem that must be overcome to accurately perceive heading from optic flow-the visual system must maintain sensitivity to optic flow variations that correspond with actual changes in self-motion and disregard those that do not. One solution could involve integrating optic flow over time to stabilize heading signals while suppressing transient fluctuations. Stability, however, may come at the cost of sluggishness. Here, we investigate the stability of human heading perception when subjects judge their heading after the simulated direction of self-motion changes. We found that the initial heading exerted an attractive influence on judgments of the final heading. Consistent with an evolving heading representation, bias toward the initial heading increased with the size of the heading change and as the viewing duration of the optic flow consistent with the final heading decreased. Introducing periods of sensory dropout (blackouts) later in the trial increased bias whereas an earlier one did not. Simulations of a neural model, the Competitive Dynamics Model, demonstrates that a mechanism that produces an evolving heading signal through recurrent competitive interactions largely captures the human data. Our findings characterize how the visual system balances stability in heading perception with sensitivity to change and support the hypothesis that heading perception evolves over time.

Hyperexcitability in Aging Is Lost in Alzheimer's: What Is All the Excitement About?

Neuronal hyperexcitability has emerged as a potential biomarker of late-onset early-stage Alzheimer's disease (LEAD). We hypothesize that the aging-related posterior cortical hyperexcitability anticipates the loss of excitability with the emergence of impairment in LEAD. To test this hypothesis, we compared the behavioral and neurophysiological responses of young and older (ON) normal adults, and LEAD patients during a visuospatial attentional control task. ONs show frontal cortical signal incoherence and posterior cortical hyper-responsiveness with preserved attentional control. LEADs lose the posterior hyper-responsiveness and fail in the attentional task. Our findings suggest that signal incoherence and cortical hyper-responsiveness in aging may contribute to the development of functional impairment in LEAD.

Attentional ERPs distinguish aging and early Alzheimer's dementia

The early detection of Alzheimer's disease requires our distinguishing it from cognitive aging. Here, we test whether spatial attentional changes might support that distinction. We engaged young normal (YN), older normal (ON), and patients with early Alzheimer's dementia (EAD) in an attentionally cued, self-movement heading discrimination task while we recorded push-button response times and event related potentials. YNs and ONs show the behavioral effects of attentional shifts from the cue to the target, whereas EAD patients did not (p < 0.001). YNs and ONs also show the shifting lateralization of a newly described attentional event related potentials component, whereas EAD patients did not (p < 0.001). Our findings suggest that spatial inattention in EAD patients may contribute to heading direction processing impairments that distinguish them from ONs and undermine their navigational capacity and driving safety.

Recurrent competition explains temporal effects of attention in MSTd

Navigation in a static environment along straight paths without eye movements produces radial optic flow fields. A singularity called the focus of expansion (FoE) specifies the direction of travel (heading) of the observer. Cells in primate dorsal medial superior temporal area (MSTd) respond to radial fields and are therefore thought to be heading-sensitive. Humans frequently shift their focus of attention while navigating, for example, depending on the favorable or threatening context of approaching independently moving objects. Recent neurophysiological studies show that the spatial tuning curves of primate MSTd neurons change based on the difference in visual angle between an attentional prime and the FoE. Moreover, the peak mean population activity in MSTd retreats linearly in time as the distance between the attentional prime and FoE increases. We present a dynamical neural circuit model that demonstrates the same linear temporal peak shift observed electrophysiologically. The model qualitatively matches the neuron tuning curves and population activation profiles. After model MT dynamically pools short-range motion, model MSTd incorporates recurrent competition between units tuned to different radial optic flow templates, and integrates attentional signals from model area frontal eye fields (FEF). In the model, population activity peaks occur when the recurrent competition is most active and uncertainty is greatest about the relative position of the FoE. The nature of attention, multiplicative or non-multiplicative, is largely irrelevant, so long as attention has a Gaussian-like profile. Using an appropriately tuned sigmoidal signal function to modulate recurrent feedback affords qualitative fits of deflections in the population activity that otherwise appear to be low-frequency noise. We predict that these deflections mark changes in the balance of attention between the priming and FoE locations.

Driving strategy alters neuronal responses to self-movement: cortical mechanisms of distracted driving

We presented naturalistic combinations of virtual self-movement stimuli while recording neuronal activity in monkey cerebral cortex. Monkeys used a joystick to drive to a straight ahead heading direction guided by either object motion or optic flow. The selected cue dominates neuronal responses, often mimicking responses evoked when that stimulus is presented alone. In some neurons, driving strategy creates selective response additivities. In others, it creates vulnerabilities to the disruptive effects of independently moving objects. Such cue interactions may be related to the disruptive effects of independently moving objects in Alzheimer's disease patients with navigational deficits.