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. In Experiment 1, human classifiers were shown one of three sets of eye movements: Fixations, which were displayed as blue circles, with larger circles meaning longer fixation durations; Scanpaths, which were displayed as yellow arrows; and Videos, in which a neon green dot moved around the screen. There was an additional Scene manipulation in which eye movement properties were displayed either on the original scene where the task (Search, Memory, or Rating) was performed or on a black background in which no scene information was available. Experiment 2 used similar methods but only displayed Fixations and Videos with the same Scene manipulation. The results of both experiments showed successful classification of Search. Interestingly, Search was best classified in the absence of the original scene, particularly in the Fixation condition. Memory also was classified above chance with the strongest classification occurring with Videos in the presence of the scene. Additional analyses on the pattern of correct responses in these two conditions demonstrated which eye movement properties successful classifiers were using. These findings demonstrate conditions under which humans can extract information from eye movement characteristics in addition to providing insight into the relative success/failure of previous computer classifiers

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. In Experiment 1, human classifiers were shown one of three sets of eye movements: Fixations, which were displayed as blue circles, with larger circles meaning longer fixation durations; Scanpaths, which were displayed as yellow arrows; and Videos, in which a neon green dot moved around the screen. There was an additional Scene manipulation in which eye movement properties were displayed either on the original scene where the task (Search, Memory, or Rating) was performed or on a black background in which no scene information was available. Experiment 2 used similar methods but only displayed Fixations and Videos with the same Scene manipulation. The results of both experiments showed successful classification of Search. Interestingly, Search was best classified in the absence of the original scene, particularly in the Fixation condition. Memory also was classified above chance with the strongest classification occurring with Videos in the presence of the scene. Additional analyses on the pattern of correct responses in these two conditions demonstrated which eye movement properties successful classifiers were using. These findings demonstrate conditions under which humans can extract information from eye movement characteristics in addition to providing insight into the relative success/failure of previous computer classifiers

Guidance of Attention from Visual Working Memory is Feature-based, not Object-based: Implications for Models of Feature Binding

All data from our study investigating whether the representational unit of working memory used to guide attention are directly bound objects or individual features. Across our three main experiments, participants remembered the color and shape of two presented items. We evaluated the effects of incidental and strategic guidance of remembered information during a visual search task. Control experiments show that both the color and shape of remembered items influenced behavioral performance. Our main experiments provide evidence against a direct-integration model, suggesting that items in working memory are represented as individual features. Data have been uploaded as .txt files

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. We also varied the numbers of PCs that were retained for the decoding analysis. We evaluated the resulting decoding accuracy for seven common event-related potential components (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity). We also examined more challenging decoding tasks, including decoding of face identity, facial expression, stimulus location, and stimulus orientation. The datasets also varied in the number and density of electrode sites. Our findings indicated that none of the PCA approaches consistently improved decoding performance related to no PCA, and the application of PCA frequently reduced decoding performance. Researchers should therefore be cautious about using PCA prior to decoding EEG data from similar experimental paradigms, populations, and recording setups