Eye tracking. Applications within cognitive science Dr. Christa van Mierlo. Why is eye tracking used in cognitive science?.
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Applications within cognitive science
Dr. Christa van Mierlo
Scan patterns give delayed information about the mental processes that are developing in a person’s mind and reveal what visual information is (going to be) used by these processes.
low spatial frequencies attentuated
high spatial frequencies unaffected.
Even when the location of the target is known, highly salient features that are known not to be associated with the target can still capture attention (Christ & Abrams, 2006).
Searching is faster for more explicitly specified targets. Why?
Subjects had to look for a specific object within a visual scene.
Divide scanpaths in different epochs:
Picture rather than word cues resulted in:
Longer SOA’s resulted in faster search initiation, but no interaction with cue type
Knowledge of a target’s appearance prior to search benefits scanning in 2 ways:
In conscious search:
Compare fixations on detected targets with fixations on undetected targets
the detected pair
the undetected pair
So fixations are needed to identify the target; detection is not an inherent property of the stimulus!
Does the large number of fixations give rise to detection or is it a result of detection?
So it seems that the increase in fixations near the target are necessary for its detection and do not result from verification!
Why a short burst of fixations near target cards just before detection?
It may be difficult to keep many cards in working memory at the same time, so that fixations need to be close to each other to associate place with identity. Since the sequential distance decreases when approaching detection; perhaps a necessary condition for detection is that two cards be represented concurrently in working memory.
Stable individual differences in search strategy? The effects of task demands and motivational factors on scannning strategy in visual searchBoot et al., JOV 2009
This study seeks to further evaluate and understand individual differences in visual search behaviour in the context of search tasks in which poor strategies can have a major impact on performance.
A surprisingly large range in accuracy: effects of task demands and motivational factors on scannning strategy in visual search
some participants almost always detected the new dot
others missed 50% or more of the onset events
Their current study seeks to explore: in the display, the fewer targets they detected.
Study scanning strategies during:
If participants use the same scan strategy in different tasks, regardless of whether or not this strategy is adaptive, then the rates of eye movements on the different search tasks should be correlated.
Performance in the dynamic dot detection task has been shown to be almost exclusively driven by strategy (Boot et al., 2006).
If the eye movements on this task differ individually but are similar to that those seen on other visual search tasks for each subject, these differences in scan pattern are likely to be caused by differences in strategy choice, not differences in visual processing ability.
In difficult and inefficient search tasks, a covert search strategy would be highly maladaptive due to the difficulty of discriminating complex stimuli in the periphery.
In an efficient or easy search task, eye movements might hinder performance by focusing attention on individual items rather than allowing the unique target item to pop-out.
‘incorrect; you missed the target/no target present’ or
‘correct – target/no target’ present’
‘You were fast!’ or ‘A bit slow!’
Fastest participant received an additional 20 dollars in payment
Why are there differences in default strategy? average, participants tended to adopt more overt or covert strategies depending on the demands of the visual search task at hand, even without explicit feedback about performance.
Maybe to compensate for:
As a result of differences in the structure and function of various brain regions known to control endogenous eye movements
Subjects had to familiarize themselves with a novel and abstract silhouette and decide whether a second silhouette was identical to the one that they familiarized themselves with.
With each fixation, the model takes a foveated measurement of the stimulus:
Entropy = - Σp (x) log p(x).
Can this global strategy model predict subjects’ eye movements across the shapes?
Fixated locations were found to be spatially distributed in a donut shape for three of four subjects.
Human fixations are closer to the global strategy than to random fixation.
For all of our observers, the global model is significantly better than chance at predicting the next fixation than random fixations.
ROC curves shift for each observer toward the diagonal but the AUC is still significantly greater than 0.5.
To better understand this difference, imagine two nearby locations that have similar prediction values. The global strategy might be to fixate between them to maximize information about both locations, whereas the local uncertainty strategy would fixate the one with slightly higher uncertainty (more information).
Both the saliency and local uncertainty strategies produce a donut-shaped distribution, but neither strategy shows a distribution of saccade amplitudes exactly like the observers.
Fixation error the AUC is still significantly greater than 0.5.
The discrepancy between fixation error and the ROC finding could be explained if observers consistently undershoot the maximum of the local uncertainty prediction but still land within a hot spot.
The spatial distribution of predicted fixations has a more compact donut shape and looks strikingly similar to the human pattern. This improved distribution is reflected in the decrease in fixation error.
Given observed fixation locations and different values of w, the observer’s intended fixation can be calculated and superimposed on the local uncertainty strategy map. Using the prediction values from these maps, again ROC curves are computed.
For all subjects, centroids of small shapes (Melcher & Kowler, 1999).
the local uncertainty strategy with centroid weighting provides the best prediction of
human fixation locations.