When studying how people search for objects in scenes, the inhomogeneity of the visual field is often ignored. Due to physiological limitations, peripheral vision is blurred and mainly uses coarse-grained information (i.e., low spatial frequencies) for selecting saccade targets, whereas high-acuity central vision uses fine-grained information (i.e., high spatial frequencies) for analysis of details. Here we investigated how spatial frequencies and color affect object search in real-world scenes. Using gaze-contingent filters, we attenuated high or low frequencies in central or peripheral vision while viewers searched color or grayscale scenes. Results showed that peripheral filters and central high-pass filters hardly affected search accuracy, whereas accuracy dropped drastically with central low-pass filters. Peripheral filtering increased the time to localize the target by decreasing saccade amplitudes and increasing number and duration of fixations. The use of coarse-grained information in the periphery was limited to color scenes. Central filtering increased the time to verify target identity instead, especially with low-pass filters. We conclude that peripheral vision is critical for object localization and central vision is critical for object identification. Visual guidance during peripheral object localization is dominated by low-frequency color information, whereas high-frequency information, relatively independent of color, is most important for object identification in central vision.
Process-oriented theories of cognition must be evaluated against time-ordered observations. Here we present a representative example for data assimilation of the SWIFT model, a dynamical model of the control of fixation positions and fixation durations during natural reading of single sentences. First, we develop and test an approximate likelihood function of the model, which is a combination of a spatial, pseudo-marginal likelihood and a temporal likelihood obtained by probability density approximation Second, we implement a Bayesian approach to parameter inference using an adaptive Markov chain Monte Carlo procedure. Our results indicate that model parameters can be estimated reliably for individual subjects. We conclude that approximative Bayesian inference represents a considerable step forward for computational models of eye-movement control, where modeling of individual data on the basis of process-based dynamic models has not been possible so far.
Dynamical models of cognition play an increasingly important role in driving theoretical and experimental research in psychology. Therefore, parameter estimation, model analysis and comparison of dynamical models are of essential importance. In this article, we propose a maximum likelihood approach for model analysis in a fully dynamical framework that includes time-ordered experimental data. Our methods can be applied to dynamical models for the prediction of discrete behavior (e.g., movement onsets); in particular, we use a dynamical model of saccade generation in scene viewing as a case study for our approach. For this model, the likelihood function can be computed directly by numerical simulation, which enables more efficient parameter estimation including Bayesian inference to obtain reliable estimates and corresponding credible intervals. Using hierarchical models inference is even possible for individual observers. Furthermore, our likelihood approach can be used to compare different models. In our example, the dynamical framework is shown to outperform nondynamical statistical models. Additionally, the likelihood based evaluation differentiates model variants, which produced indistinguishable predictions on hitherto used statistics. Our results indicate that the likelihood approach is a promising framework for dynamical cognitive models.
Bottom-up and top-down as well as low-level and high-level factors influence where we fixate when viewing natural scenes. However, the importance of each of these factors and how they interact remains a matter of debate. Here, we disentangle these factors by analyzing their influence over time. For this purpose, we develop a saliency model that is based on the internal representation of a recent early spatial vision model to measure the low-level, bottom-up factor. To measure the influence of high-level, bottom-up features, we use a recent deep neural network-based saliency model. To account for top-down influences, we evaluate the models on two large data sets with different tasks: first, a memorization task and, second, a search task. Our results lend support to a separation of visual scene exploration into three phases: the first saccade, an initial guided exploration characterized by a gradual broadening of the fixation density, and a steady state that is reached after roughly 10 fixations. Saccade-target selection during the initial exploration and in the steady state is related to similar areas of interest, which are better predicted when including high-level features. In the search data set, fixation locations are determined predominantly by top-down processes. In contrast, the first fixation follows a different fixation density and contains a strong central fixation bias. Nonetheless, first fixations are guided strongly by image properties, and as early as 200 ms after image onset, fixations are better predicted by high-level information. We conclude that any low-level, bottom-up factors are mainly limited to the generation of the first saccade. All saccades are better explained when high-level features are considered, and later, this high-level, bottom-up control can be overruled by top-down influences.
When watching the image of a natural scene on a computer screen, observers initially move their eyes toward the center of the image—a reliable experimental finding termed central fixation bias. This systematic tendency in eye guidance likely masks attentional selection driven by image properties and top-down cognitive processes. Here, we show that the central fixation bias can be reduced by delaying the initial saccade relative to image onset. In four scene-viewing experiments we manipulated observers' initial gaze position and delayed their first saccade by a specific time interval relative to the onset of an image. We analyzed the distance to image center over time and show that the central fixation bias of initial fixations was significantly reduced after delayed saccade onsets. We additionally show that selection of the initial saccade target strongly depended on the first saccade latency. A previously published model of saccade generation was extended with a central activation map on the initial fixation whose influence declined with increasing saccade latency. This extension was sufficient to replicate the central fixation bias from our experiments. Our results suggest that the central fixation bias is generated by default activation as a response to the sudden image onset and that this default activation pattern decreases over time. Thus, it may often be preferable to use a modified version of the scene viewing paradigm that decouples image onset from the start signal for scene exploration to explicitly reduce the central fixation bias.
During visual fixation, the eye generates microsaccades and slower components of fixational eye movements that are part of the visual processing strategy in humans. Here, we show that ongoing heartbeat is coupled to temporal rate variations in the generation of microsaccades. Using coregistration of eye recording and ECG in humans, we tested the hypothesis that microsaccade onsets are coupled to the relative phase of the R-R intervals in heartbeats. We observed significantly more microsaccades during the early phase after the R peak in the ECG. This form of coupling between heartbeat and eye movements was substantiated by the additional finding of a coupling between heart phase and motion activity in slow fixational eye movements; i.e., retinal image slip caused by physiological drift. Our findings therefore demonstrate a coupling of the oculomotor system and ongoing heartbeat, which provides further evidence for bodily influences on visuomotor functioning.
During reading, saccadic eye movements are generated to shift words into the center of the visual field for lexical processing. Recently, Krugel and Engbert (Vision Research 50:1532-1539, 2010) demonstrated that within-word fixation positions are largely shifted to the left after skipped words. However, explanations of the origin of this effect cannot be drawn from normal reading data alone. Here we show that the large effect of skipped words on the distribution of within-word fixation positions is primarily based on rather subtle differences in the low-level visual information acquired before saccades. Using arrangements of "x" letter strings, we reproduced the effect of skipped character strings in a highly controlled single-saccade task. Our results demonstrate that the effect of skipped words in reading is the signature of a general visuomotor phenomenon. Moreover, our findings extend beyond the scope of the widely accepted range-error model, which posits that within-word fixation positions in reading depend solely on the distances of target words. We expect that our results will provide critical boundary conditions for the development of visuomotor models of saccade planning during reading.
Saccades move objects of interest into the center of the visual field for high-acuity visual analysis. White, Stritzke, and Gegenfurtner (Current Biology, 18, 124–128, 2008) have shown that saccadic latencies in the context of a structured background are much shorter than those with an unstructured background at equal levels of visibility. This effect has been explained by possible preactivation of the saccadic circuitry whenever a structured background acts as a mask for potential saccade targets. Here, we show that background textures modulate rates of microsaccades during visual fixation. First, after a display change, structured backgrounds induce a stronger decrease of microsaccade rates than do uniform backgrounds. Second, we demonstrate that the occurrence of a microsaccade in a critical time window can delay a subsequent saccadic response. Taken together, our findings suggest that microsaccades contribute to the saccadic facilitation effect, due to a modulation of microsaccade rates by properties of the background.