Saccade Target Selection Research Articles

The control of stimulus-driven saccades is subject not to central, but to visual attention limitations

In three experiments, we investigated whether the control of reflexive saccades is subject to central attention limitations. In a dual-task procedure, Task 1 required either unspeeded reporting or ignoring of briefly presented masked stimuli, whereas Task 2 required a speeded saccade toward a visual target. The stimulus onset asynchrony (SOA) between the two tasks was varied. In Experiments 1 and 2, the Task 1 stimulus was one or three letters, and we asked how saccade target selection is influenced by the number of items. We found (1) longer saccade latencies at short than at long SOAs in the report condition, (2) a substantially larger latency increase for three letters than for one letter, and (3) a latency difference between SOAs in the ignore condition. Broadly, these results match the central interference theory. However, in Experiment 3, an auditory stimulus was used as the Task 1 stimulus, to test whether the interference effects in Experiments 1 and 2 were due to visual instead of central interference. Although there was a small saccade latency increase from short to long SOAs, this difference did not increase from the ignore to the report condition. To explain visual interference effects between letter encoding and stimulus-driven saccade control, we propose an extendedtheory of visual attention.

The control of stimulus-driven saccades is subject not to central, but to visual attention limitations

In three experiments, we investigated whether the control of reflexive saccades is subject to central attention limitations. In a dual-task procedure, Task 1 required either unspeeded reporting or ignoring of briefly presented masked stimuli, whereas Task 2 required a speeded saccade toward a visual target. The stimulus onset asynchrony (SOA) between the two tasks was varied. In Experiments 1 and 2, the Task 1 stimulus was one or three letters, and we asked how saccade target selection is influenced by the number of items. We found (1) longer saccade latencies at short than at long SOAs in the report condition, (2) a substantially larger latency increase for three letters than for one letter, and (3) a latency difference between SOAs in the ignore condition. Broadly, these results match the central interference theory. However, in Experiment 3, an auditory stimulus was used as the Task 1 stimulus, to test whether the interference effects in Experiments 1 and 2 were due to visual instead of central interference. Although there was a small saccade latency increase from short to long SOAs, this difference did not increase from the ignore to the report condition. To explain visual interference effects between letter encoding and stimulus-driven saccade control, we propose an extended theory of visual attention.

Saccade target selection in subjects cued to remember single or multiple visual features

Saccade target selection in subjects cued to remember single or multiple visual features

Attention, Intention, and Priority in the Parietal Lobe

For many years there has been a debate about the role of the parietal lobe in the generation of behavior. Does it generate movement plans (intention) or choose objects in the environment for further processing? To answer this, we focus on the lateral intraparietal area (LIP), an area that has been shown to play independent roles in target selection for saccades and the generation of visual attention. Based on results from a variety of tasks, we propose that LIP acts as a priority map in which objects are represented by activity proportional to their behavioral priority. We present evidence to show that the priority map combines bottom-up inputs like a rapid visual response with an array of top-down signals like a saccade plan. The spatial location representing the peak of the map is used by the oculomotor system to target saccades and by the visual system to guide visual attention.

Flexible saccade-target selection in Chinese reading

As Chinese is written without orthographical word boundaries (i.e., spaces), it is unclear whether saccade targets are selected on the basis of characters or words and whether saccades are aimed at the beginning or the centre of words. Here, we report an experiment where 30 Chinese readers read 150 sentences while their eye movements were monitored. They exhibited a strong tendency to fixate at the word centre in single-fixation cases and at the word beginning in multiple-fixation cases. Different from spaced alphabetic script, initial fixations falling at the end of words were no more likely to be followed by a refixation than initial fixations at word centre. Further, single fixations were shorter than first fixations in two-fixation cases, which is opposite to what is found in Roman script. We propose that Chinese readers dynamically select the beginning or centre of words as saccade targets depending on failure or success with segmentation of parafoveal word boundaries.

Superior colliculus inactivation biases target selection for smooth pursuit, saccades, and manual responses

Superior colliculus inactivation biases target selection for smooth pursuit, saccades, and manual responses

Saccadic target selection and crowding

Saccadic target selection and crowding

Selectivity for multiple orientations in visual search

Observer strategies in visual search for targets containing multiple orientations were considered in the study. We used our efficient search framework (Tavassoli et al., 2007), a reverse-correlation based technique distinguishing between nonfoveal and foveal processes, to examine how observers search for low-contrast targets created from sums (Experiment 1) and mosaics (Experiment 2) of Gabors. Eye movements were recorded while observers searched for one target randomly embedded in one tile of a grid of 1/f noise tiles. Fixated noise-only tiles (representing our nonfoveal false alarm category) and a subset of these noise tiles selected by the observer as target candidate (constituting our foveal false alarm category) were analyzed off-line. We present several key discoveries. First, we show a strong presence of visual guidance in saccadic targeting in search for our composite targets, demonstrated by selectivity for spatial frequencies and (in some cases) orientations close to the characteristics of each target. Second, multiple orientation attributes of the targets are shown to be represented in saccadic programming and target selection in most cases, modulated by the observer’s sensitivity / selectivity for each orientation. Third, different configurations of the Gabor mosaicing produce distinct tunings in orientation, but visibly idiosyncratic to each observer (Experiment 2). Moreover, a localized analysis has been performed for the use of phase. Fourth, a curious presence of close-to-vertical structures is observed in fixated distracters, although the search targets did not contain vertically-oriented structures (Experiment 2).

Automatic and intentional memory processes in saccade target selection

Automatic and intentional memory processes in saccade target selection

The effects of reversible inactivation of Frontal Eye Field and Superior Colliculus on saccade target selection

The effects of reversible inactivation of Frontal Eye Field and Superior Colliculus on saccade target selection

Deficits in saccade target selection after temporary inactivation of superior colliculus

Deficits in saccade target selection after temporary inactivation of superior colliculus

Facilitation of saccade target selection by object centered priming

Facilitation of saccade target selection by object centered priming

The Influence of Onsets and Offsets on Saccade Programming

When making a saccadic eye movement to a peripheral target, a simultaneous stimulus onset at central fixation generally increases saccadic latency, while offsets reduce latency (‘gap effect’). Visual onsets remote from fixation also increase latency (‘remote distractor effect’); however, the influence of remote visual offsets is less clear. Previous studies, which used a search task, found that remote offsets either facilitated, inhibited, or did nothing to saccade latencies towards a peripheral target. It cannot be excluded, however, that the target selection process in such search tasks influenced the results. We therefore simplified the task and asked participants to make eye movements to a predictable target. Simultaneously with target onset, either one or multiple remote stimulus onsets and offsets were presented. It was found that peripheral onsets increased saccade latencies, but offsets did not influence the initiation of a saccade to the target. Moreover, the number of onsets and offsets did not affect the results. These results suggest that earlier effects of remote stimulus offsets and of the number of remote distractor onsets reside in the target identification process of the visual search task rather than the competition between possible saccade goals. The results are discussed in the context of models of saccade target selection.

Predictive Activity in Macaque Frontal Eye Field Neurons During Natural Scene Searching

Generating sequences of multiple saccadic eye movements allows us to search our environment quickly and efficiently. Although the frontal eye field cortex (FEF) has been linked to target selection and making saccades, little is known about its role in the control and performance of the sequences of saccades made during self-guided visual search. We recorded from FEF cells while monkeys searched for a target embedded in natural scenes and examined the degree to which cells with visual and visuo-movement activity showed evidence of target selection for future saccades. We found that for about half of these cells, activity during the fixation period between saccades predicted the next saccade in a sequence at an early time that precluded selection based on current visual input to a cell's response field. In addition to predicting the next saccade, activity during the fixation prior to two successive saccades also predicted the direction and goal of the second saccade in the sequence. We refer to this as advanced predictive activity. Unlike activity indicating the upcoming saccade, advanced predictive activity occurred later in the fixation period, mirroring the order of the saccade sequence itself. The remaining cells without advanced predictive activity did not predict future saccades but reintroduced the signal for the upcoming saccade at an intermediate time in the fixation period. Together these findings suggest that during natural visual search the timing of FEF cell activity is consistent with a role in specifying targets for one or more future saccades in a search sequence.

The contribution of low-level features at the centre of gaze to saccade target selection

Does it matter what observers are looking at right now to determine where they will look next? We recorded eye movements and computed colour, local orientation, motion, and geometrical invariants on dynamic natural scenes. The distributions of differences between features at successive fixations were compared with those from random scanpaths of varying similarity to natural scanpaths. Although distributions show significant differences, these feature correlations are mainly due to spatio-temporal correlations in natural scenes and a target selection bias, e.g. towards moving objects. Our results indicate that low-level features at fixation contribute little to the choice of the next saccade target.

Temporal integration of sensory evidence for saccade target selection

Perceptual decision-making may be viewed as a process of integrating sensory evidence over time to a response threshold. Ludwig et al. (2005b) argued against an integration to threshold account for saccadic eye movement decisions based on data from a stochastic contrast discrimination task. They argued that evidence integration was time-limited, with the deadline being independent from the quality of the sensory evidence. In this study, the data from Ludwig et al. (2005b) were fit with a model in which sensory evidence is integrated to a time-varying threshold. The functional form of the threshold variation allowed the model to approximate a constant threshold as well as an abrupt deadline. Sensory evidence was computed on the basis of a temporally blurred representation of the stimulus sequence. The model provides an overall good fit to the latency and accuracy data. Its predictions are consistent with a short window of evidence integration, as proposed in the original study. The model produces qualitatively correct predictions for an experiment in which the availability of sensory evidence is varied systematically. Inspection of the model parameters, however, shows that integration was not terminated by an abrupt deadline, although a rather gradual deadline signal improved the fit for some observers.

Reply to Balan and Gottlieb

REPLY: Several important questions are raised in the recent comparison by Balan and Gottlieb (2009) between the description by Balan et al. (2008) of lateral intraparietal (LIP) neuron activity in monkeys performing visual search with manual responses and our recent report of frontal eye field (FEF) neuron activity in monkeys performing visual search with saccade responses (Cohen et al. 2009). Both studies manipulated the number of distractors in the search display. Balan et al. 2008 reported decreased firing rate with increasing set size, but they reported no delay in the time at which activity for the target exceeded activity for the distractors, referred to as target selection time. Cohen and colleagues (2009) also reported decreased firing rate with increasing set size, but also observed delayed target selection time with increasing set size. How should we interpret the different observations across laboratories, effectors, cortical areas, and task designs? First, we emphasize that this is not a case of one observation being correct and the other incorrect. The results of both studies are valid within their respective contexts. However, the question remains: which difference between studies explains the different outcomes? We argue that task demands are the major factors. In other words, measured under the same task demands, we predict that the same pattern of results would be obtained across effector (manual response vs. saccade) and cortical area (LIP vs. FEF). The first step of the argument is to appreciate that categorical statements about the relationship between target selection time and response time are not possible because these times vary for at least three reasons. First, neuron type matters. Multiple

Emotional scene content drives the saccade generation system reflexively.

The authors assessed whether parafoveal perception of emotional content influences saccade programming. In Experiment 1, paired emotional and neutral scenes were presented to parafoveal vision. Participants performed voluntary saccades toward either of the scenes according to an imperative signal (color cue). Saccadic reaction times were faster when the cue pointed toward the emotional picture rather than toward the neutral picture. Experiment 2 replicated these findings with a reflexive saccade task, in which abrupt luminosity changes were used as exogenous saccade cues. In Experiment 3, participants performed vertical reflexive saccades that were orthogonal to the emotional-neutral picture locations. Saccade endpoints and trajectories deviated away from the visual field in which the emotional scenes were presented. Experiment 4 showed that computationally modeled visual saliency does not vary as a function of scene content and that inversion abolishes the rapid orienting toward the emotional scenes. Visual confounds cannot thus explain the results. The authors conclude that early saccade target selection and execution processes are automatically influenced by emotional picture content. This reveals processing of meaningful scene content prior to overt attention to the stimulus.

Search time critically depends on irrelevant subset size in visual search

In order for our visual system to deal with the massive amount of sensory input, some of this input is discarded, while other parts are processed [Wolfe, J. M. (1994). Guided search 2.0: a revised model of visual search. Psychonomic Bulletin and Review, 1, 202–238]. From the visual search literature it is unclear how well one set of items can be selected that differs in only one feature from target (a 1 F set), while another set of items can be ignored that differs in two features from target (a 2 F set). We systematically varied the percentage of 2 F non-targets to determine the contribution of these non-targets to search behaviour. Increasing the percentage 2 F non-targets, that have to be ignored, was expected to result in increasingly faster search, since it decreases the size of 1 F set that has to be searched. Observers searched large displays for a target in the 1 F set with a variable percentage of 2 F non-targets. Interestingly, when the search displays contained 5% 2 F non-targets, the search time was longer compared to the search time in other conditions. This effect of 2 F non-targets on performance was independent of set size. An inspection of the saccades revealed that saccade target selection did not contribute to the longer search times in displays with 5% 2 F non-targets. Occurrence of longer search times in displays containing 5% 2 F non-targets might be attributed to covert processes related to visual analysis of the fixated part of the display. Apparently, visual search performance critically depends on the percentage of irrelevant 2 F non-targets.

Visual and motor connectivity and the distribution of calcium-binding proteins in macaque frontal eye field: implications for saccade target selection.

The frontal eye field (FEF) contributes to directing visual attention and saccadic eye movement through intrinsic processing, interactions with extrastriate visual cortical areas (e.g., V4), and projections to subcortical structures (e.g., superior colliculus, SC). Several models have been proposed to describe the relationship between the allocation of visual attention and the production of saccades. We obtained anatomical information that might provide useful constraints on these models by evaluating two characteristics of FEF. First, we investigated the laminar distribution of efferent connections from FEF to visual areas V4 + TEO and to SC. Second, we examined the laminar distribution of different populations of GABAergic neurons in FEF. We found that the neurons in FEF that project to V4 + TEO are located predominantly in the supragranular layers, colocalized with the highest density of calbindin- and calretinin-immunoreactive inhibitory interneurons. In contrast, the cell bodies of neurons that project to SC are found only in layer 5 of FEF, colocalized primarily with parvalbumin inhibitory interneurons. None of the neurons in layer 5 that project to V4 + TEO also project to SC. These results provide useful constraints for cognitive models of visual attention and saccade production by indicating that different populations of neurons project to extrastriate visual cortical areas and to SC. This finding also suggests that FEF neurons projecting to visual cortex and SC are embedded in different patterns of intracortical circuitry.