Sensory sluggishness dissociates saccadic, manual, and perceptual responses: An S-cone study

Sensory information travels to visual and motor areas via several distinct pathways, some of them being fastVlike the achromatic magnocellular and retinotectal routesVand others slowerVthose carrying chromatic signals, in particular S-opponent signals. It is debated whether common visual processing stages are used for different types of responses, such as initiating saccadic or manual responses or making perceptual judgments. The present paper casts new light on this question by comparing the participation of fast and slow pathways across these responses. In the first experiment, we measured manual and saccadic reaction times to luminance and S-cone signals, equated in detectability for each participant and presented on either sides of fixation. Our results show that both manual and saccadic responses are slower for S-cone stimuli. Most interestingly, this reaction time difference was twice as large for saccadic responses as for manual responses, suggesting that saccades rely more on the fast signals, not supported by S-cone stimuli, than do manual responses. In a second experiment, our participants performed temporal order judgments on pairs of luminance and S-cone stimuli. Our results show no evidence of perceived time discrepancy between the two signals, which may imply that perceptual judgments utilize different signals from either manual or saccadic responses

Interaction between contours and eye movements in the perception of afterimages: A test of the signal ambiguity theory

An intriguing property of afterimages is that conscious experience can be strong, weak, or absent following identical stimulus adaptation. Previously we suggested that postadaptation retinal signals are inherently ambiguous, and therefore the perception they evoke is strongly influenced by cues that increase or decrease the likelihood that they represent real objects (the signal ambiguity theory). Here we provide a more definitive test of this theory using two cues previously found to influence afterimage perception in opposite ways and plausibly at separate loci of action. However, by manipulating both cues simultaneously, we found that their effects interacted, consistent with the idea that they affect the same process of object interpretation rather than being independent influences. These findings bring contextual influences on afterimages into more general theories of cue combination, and we suggest that afterimage perception should be considered alongside other areas of vision science where cues are found to interact in their influence on perception

The effect of eye movements and blinks on afterimage appearance and duration

The question of whether eye movements influence afterimage perception has been asked since the 18th century, and yet there is surprisingly little consensus on how robust these effects are and why they occur. The number of historical theories aiming to explain the effects are more numerous than clear experimental demonstrations of such effects. We provide a clearer characterization of when eye movements and blinks do or do not affect afterimages with the aim to distinguish between historical theories and integrate them with a modern understanding of perception. We found neither saccades nor pursuit reduced strong afterimage duration, and blinks actually increased afterimage duration when tested in the light. However, for weak afterimages, we found saccades reduced duration, and blinks and pursuit eye movements did not. One interpretation of these results is that saccades diminish afterimage perception because they cause the afterimage to move unlike a real object. Furthermore, because saccades affect weak afterimages but not strong ones, we suggest that their effect is modulated by the ambiguity of the afterimage signal

Saccadic inhibition reveals the timing of automatic and voluntary signals in the human brain

Neurophysiological and phenomenological data on sensorimotor decision making are growing so rapidly that it is now necessary and achievable to capture it in biologically inspired models, for advancing our understanding in both research and clinical settings. However, the main impediment in moving from elegant models with few free parameters to more complex biological models in humans lies in constraining the more numerous parameters with behavioral data (without human single-cell recording). Here we show that a behavioral effect called “saccadic inhibition” (1) is predicted by existing complex (neuronal field) models, (2) constrains crucial temporal parameters of the model, precisely enough to address individual differences, and (3) is not accounted for by current simple decision models, even after significant additions. Visual onsets appearing while an observer plans a saccade knock out a subpopulation of saccadic latencies that would otherwise occur, producing a clear dip in the latency distribution. This overlooked phenomenon is remarkably well time locked across conditions and observers, revealing and characterizing a fast automatic component of visual input to oculomotor competition. The neural field model not only captures this but predicts additional features that are borne out: the dips show spatial specificity, are lawfully modulated in contrast, and occur with S-cone stimuli invisible to the retinotectal route. Overall, we provide a way forward for applying precise neurophysiological models of saccade planning in humans at the individual level

Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties

Determining, understanding, and predicting the so-called structure-property relation is an important task in many scientific disciplines, such as chemistry, biology, meteorology, physics, engineering, and materials science. Structure refers to the spatial distribution of, e.g., substances, material, or matter in general, while property is a resulting characteristic that usually depends in a non-trivial way on spatial details of the structure. Traditionally, forward simulations models have been used for such tasks. Recently, several machine learning algorithms have been applied in these scientific fields to enhance and accelerate simulation models or as surrogate models. In this work, we develop and investigate the applications of six machine learning techniques based on two different datasets from the domain of materials science: data from a two-dimensional Ising model for predicting the formation of magnetic domains and data representing the evolution of dual-phase microstructures from the Cahn-Hilliard model. We analyze the accuracy and robustness of all models and elucidate the reasons for the differences in their performances. The impact of including domain knowledge through tailored features is studied, and general recommendations based on the availability and quality of training data are derived from this

The effect of eye movements and blinks on afterimage appearance and duration

The question of whether eye movements influence afterimage perception has been asked since the 18th century, and yet there is surprisingly little consensus on how robust these effects are and why they occur. The number of historical theories aiming to explain the effects are more numerous than clear experimental demonstrations of such effects. We provide a clearer characterization of when eye movements and blinks do or do not affect afterimages with the aim to distinguish between historical theories and integrate them with a modern understanding of perception. We found neither saccades nor pursuit reduced strong afterimage duration, and blinks actually increased afterimage duration when tested in the light. However, for weak afterimages, we found saccades reduced duration, and blinks and pursuit eye movements did not. One interpretation of these results is that saccades diminish afterimage perception because they cause the afterimage to move unlike a real object. Furthermore, because saccades affect weak afterimages but not strong ones, we suggest that their effect is modulated by the ambiguity of the afterimage signal

Cognitive control and automatic interference in mind and brain: A unified model of saccadic inhibition and countermanding

Countermanding behavior has long been seen as a cornerstone of executive control—the human ability to selectively inhibit undesirable responses and change plans. However, scattered evidence implies that stopping behavior is entangled with simpler automatic stimulus-response mechanisms. Here we operationalize this idea by merging the latest conceptualization of saccadic countermanding with a neural network model of visuo-oculomotor behavior that integrates bottom-up and top-down drives. This model accounts for all fundamental qualitative and quantitative features of saccadic countermanding, including neuronal activity. Importantly, it does so by using the same architecture and parameters as basic visually guided behavior and automatic stimulus-driven interference. Using simulations and new data, we compare the temporal dynamics of saccade countermanding with that of saccadic inhibition (SI), a hallmark effect thought to reflect automatic competition within saccade planning areas. We demonstrate how SI accounts for a large proportion of the saccade countermanding process when using visual signals. We conclude that top-down inhibition acts later, piggy-backing on the quicker automatic inhibition. This conceptualization fully accounts for the known effects of signal features and response modalities traditionally used across the countermanding literature. Moreover, it casts different light on the concept of top-down inhibition, its timing and neural underpinning, as well as the interpretation of stop-signal reaction time (RT), the main behavioral measure in the countermanding literature