Assessing the effectiveness of spatial PCA on SVM-based decoding of EEG data.

Principal component analysis (PCA) has been widely employed for dimensionality reduction prior to multivariate pattern classification (decoding) in EEG research. The goal of the present study was to provide an evaluation of the effectiveness of PCA on decoding accuracy (using support vector machines) across a broad range of experimental paradigms. We evaluated several different PCA variations, including group-based and subject-based component decomposition and the application of Varimax rotation or no rotation.

Using multivariate pattern analysis to increase effect sizes for event-related potential analyses.

Multivariate pattern analysis (MVPA) approaches can be applied to the topographic distribution of event-related potential (ERP) signals to "decode" subtly different stimulus classes, such as different faces or different orientations. These approaches are extremely sensitive, and it seems possible that they could also be used to increase effect sizes and statistical power in traditional paradigms that ask whether an ERP component differs in amplitude across conditions. To assess this possibility, we leveraged the open-source ERP CORE data set and compared the effect sizes resulting from conventional univariate analyses of mean amplitude with two MVPA approaches (support vector machine decoding and the cross-validated Mahalanobis distance, both of which are easy to compute using open-source software).

Using Multivariate Pattern Analysis to Increase Effect Sizes for Event-Related Potential Analyses.

Multivariate pattern analysis approaches can be applied to the topographic distribution of event-related potential (ERP) signals to 'decode' subtly different stimulus classes, such as different faces or different orientations. These approaches are extremely sensitive, and it seems possible that they could also be used to increase effect sizes and statistical power in traditional paradigms that ask whether an ERP component differs in amplitude across conditions. To assess this possibility, we leveraged the open-source ERP CORE dataset and compared the effect sizes resulting from conventional univariate analyses of mean amplitude with two multivariate pattern analysis approaches (support vector machine decoding and the cross-validated Mahalanobis distance, both of which are easy to compute using open-source software).

Guidance of attention from visual working memory is feature-based, not object-based: Implications for models of feature binding.

A classic question in visual working memory (VWM) research is whether features from the same object are bound directly in an integrated representation or are maintained separately and bound only indirectly though shared location. Here, we examined this question using a novel method that probed the of VWM on the guidance of attention (rather than requiring explicit access to VWM content, as has typically been used). Participants remembered two color-shape conjunction objects.

Categorical cuing: Object categories structure the acquisition of statistical regularities to guide visual search.

Recent statistical regularities have been demonstrated to influence visual search across a wide variety of learning mechanisms and search features. To function in the guidance of real-world search, however, such learning must be contingent on the context in which the search occurs and the that is the target of search. The former has been studied extensively under the rubric of .

The architecture of working memory: Features from multiple remembered objects produce parallel, coactive guidance of attention in visual search.

Theories of working memory (WM) differ in their claims about the number of items that can be maintained in a state that directly interacts with other, ongoing cognitive operations (termed the ). A similar debate has arisen in the literature on visual working memory (VWM), focused on the number of items that can simultaneously interact with attentional priority. In 3 experiments, we used a redundancy-gain paradigm to provide a comprehensive test of the latter question.

Feature-based guidance of attention by visual working memory is applied independently of remembered object location.

Visual working memory (VWM) has been implicated both in the online representation of object tokens (in the object-file framework) and in the top-down guidance of attention during visual search, implementing a feature template. It is well established that object representations in VWM are structured by location, with access to the content of VWM modulated by position consistency. In the present study, we examined whether this property generalizes to the guidance of attention.

Contrasting episodic and template-based guidance during search through natural scenes.

Visual search through natural scenes can be guided by knowledge of where a target object has been observed previously (episodic guidance) and knowledge of that object's visual properties (template guidance). In the present experiments, we compared the relative contributions of these two sources of guidance. Episodic guidance was implemented in a contextual cuing task: participants searched multiple times through a set of scenes for a target letter that appeared in a consistent location within each scene.

Feature-based statistical regularities of distractors modulate attentional capture.

Ignoring salient distracting information is paramount to efficiently guiding attention during visual search. Learning to reject or suppress these strong sources of distraction leads to more effective visual search for targets. Participants can learn to overcome salient distractors if given reliable search regularities.

The architecture of interaction between visual working memory and visual attention.

In five experiments, we examined whether a task-irrelevant item in visual working memory (VWM) interacts with perceptual selection when VWM must also be used to maintain a template representation of a search target. This question is critical to distinguishing between competing theories specifying the architecture of interaction between VWM and attention. The single-item template hypothesis (SIT) posits that only a single item in VWM can be maintained in a state that interacts with attention.

Contrasting gist-based and template-based guidance during real-world visual search.

Visual search through real-world scenes is guided both by a representation of target features and by knowledge of the sematic properties of the scene (derived from scene gist recognition). In 3 experiments, we compared the relative roles of these 2 sources of guidance. Participants searched for a target object in the presence of a critical distractor object.

Human classifier: Observers can deduce task solely from eye movements.

Computer classifiers have been successful at classifying various tasks using eye movement statistics. However, the question of human classification of task from eye movements has rarely been studied. Across two experiments, we examined whether humans could classify task based solely on the eye movements of other individuals.