The present study employed Dynamic Causal Modeling to investigate the effective functional connectivity between regions of the neural network involved in top-down letter processing. We used an illusory letter detection paradigm in which participants detected letters while viewing pure noise images. When participants detected letters, the response of the right middle occipital gyrus (MOG) in the visual cortex was enhanced by increased feed-backward connectivity from the left inferior frontal gyrus (IFG). In addition, illusory letter detection increased feed-forward connectivity from the right MOG to the left inferior parietal lobules. Originating in the left IFG, this top-down letter processing network may facilitate the detection of letters by activating letter processing areas within the visual cortex. This activation in turns may highlight the visual features of letters and send letter information to activate the associated phonological representations in the identified parietal region.
letter processing; word processing; top-down processing; fMRI; dynamic causal modeling
We report the use of an array of electrically gated ~200 nm solid-state pores as nanofluidic transistors to manipulate the capture and passage of DNA. The devices are capable of reversibly altering the rate of DNA capture by over three orders of magnitude using sub-1V biasing of a gate electrode. This efficient gating originates from the counter-balance of electrophoresis and electroosmosis, as revealed by quantitative numerical simulations. Such a reversible electronically-tuneable biomolecular switch may be used to manipulate nucleic acid delivery in a fluidic circuit, and its development is an important first step towards active control of DNA motion through solid-state nanopores for sensing applications.
nanopore; nanofluidic transistor; DNA capture; gate manipulation; biomolecular switch
To study top-down face processing, the present study used an experimental paradigm in which participants detected non-existent faces in pure noise images. Conventional BOLD signal analysis identified three regions involved in this illusory face detection. These regions included the left orbitofrontal cortex (OFC) in addition to the right fusiform face area (FFA) and right occipital face area (OFA), both of which were previously known to be involved in both top-down and bottom-up processing of faces. We used Dynamic Causal Modeling (DCM) and Bayesian model selection to further analyze the data, revealing both intrinsic and modulatory effective connectivities among these three cortical regions. Specifically, our results support the claim that the orbitofrontal cortex plays a crucial role in the top-down processing of faces by regulating the activities of the occipital face area, and the occipital face area in turn detects the illusory face features in the visual stimuli and then provides this information to the fusiform face area for further analysis.
Face processing; Top-down processing; Bottom-up processing; Dynamic Causal Modeling (DCM); Orbitofrontal cortex (OFC)
Even without feedback, test practice enhances delayed performance compared to study practice, but the size of the effect is variable across studies. We investigated the benefit of testing, separating initially retrievable items from initially non-retrievable items. In two experiments, an initial test determined item retrievability. Retrievable or non-retrievable items were subsequently presented for repeated study or test practice. Collapsing across items, in Experiment 1, we obtained the typical crossover interaction between retention interval and practice type. For retrievable items, however, the crossover interaction was quantitatively different, with a small study benefit for an immediate test and a larger testing benefit after a delay. For non-retrievable items, there was a large study benefit for an immediate test, but one week later there was no difference between the study and test practice conditions. In Experiment 2, initially non-retrievable items were given additional study followed by either an immediate test or even more additional study, and one week later performance did not differ between the two conditions. These results indicate that the effect size of study/test practice is due to the relative contribution of retrievable and non-retrievable items.
testing effect; retrievability; forgetting
How is the meaning of a word retrieved without interference from recently viewed words? The ROUSE theory of priming assumes a discounting process to reduce source confusion between subsequently presented words. As applied to semantic satiation, this theory predicted a loss of association between the lexical item and meaning. Four experiments tested this explanation in a speeded category-matching task. All experiments used lists of 20 trials that presented a cue word for 1 second followed by a target word. Randomly mixed across the list, 10 trials used cues drawn from the same category whereas the other 10 trials used cues from 10 other categories. In Experiments 1a and 1b, the cues were repeated category labels (FRUIT-APPLE) and responses gradually slowed for the repeated category. In Experiment 2, the cues were nonrepeated exemplars (PEAR-APPLE) and responses remained faster for the repeated category. In Experiment 3, the cues were repeated exemplars in a word matching task (APPLE-APPLE) and responses again remained faster for the repeated category.
Semantic satiation; Reading; discounting; Habituation; Repetition priming; Semantic priming; Lexical representation; Semantic processing; Categories; Semantic retrieval
The open-source toolbox “TopoToolbox” is a suite of functions that use sensor topography to calculate psychologically meaningful measures (similarity, magnitude, and timing) from multisensor event-related EEG and MEG data. Using a GUI and data visualization, TopoToolbox can be used to calculate and test the topographic similarity between different conditions (Tian and Huber, 2008). This topographic similarity indicates whether different conditions involve a different distribution of underlying neural sources. Furthermore, this similarity calculation can be applied at different time points to discover when a response pattern emerges (Tian and Poeppel, 2010). Because the topographic patterns are obtained separately for each individual, these patterns are used to produce reliable measures of response magnitude that can be compared across individuals using conventional statistics (Davelaar et al. Submitted and Huber et al., 2008). TopoToolbox can be freely downloaded. It runs under MATLAB (The MathWorks, Inc.) and supports user-defined data structure as well as standard EEG/MEG data import using EEGLAB (Delorme and Makeig, 2004).
This fMRI study investigated top-down letter processing with an illusory letter detection task. Participants responded whether one of a number of different possible letters was present in a very noisy image. After initial training that became increasingly difficult, they continued to detect letters even though the images consisted of pure noise, which eliminated contamination from strong bottom-up input. For illusory letter detection, greater fMRI activation was observed in several cortical regions. These regions included the precuneus, an area generally involved in top-down processing of objects, and the left superior parietal lobule, an area previously identified with the processing of valid letter and word stimuli. In addition, top-down letter detection also activated the left inferior frontal gyrus, an area that may be involved in the integration of general top-down processing and letter-specific bottom-up processing. These findings suggest that these regions may play a significant role in top-down as well as bottom up processing of letters and words, and are likely to have reciprocal functional connections to more posterior regions in the word and letter processing network.
word processing; letter processing; top-down processing; fMRI
We report an experiment that compared two explanations for the effect of congruency between a word's on screen spatial position and its meaning. On one account, congruency is explained by the match between position and a mental simulation of meaning. Alternatively, congruency is explained by the polarity alignment principle. To distinguish between these accounts we presented the same object names (e.g., shark, helicopter) in a sky decision task or an ocean decision task, such that response polarity and typical location were disentangled. Sky decision responses were faster to words at the top of the screen compared to words at the bottom of the screen, but the reverse was found for ocean decision responses. These results are problematic for the polarity principle, and support the claim that spatial attention is directed by mental simulation of the task-relevant conceptual dimension.
spatial attention; concepts; polarity alignment; grounded cognition
Traditionally, expensive and time consuming techniques such as mass spectrometry and Western Blotting have been used for characterization of protein–protein interactions. In this paper, we describe the design, fabrication, and testing of a rapid and inexpensive sensor, involving the use of microelectrodes in a microchannel, which can be used for real-time electrical detection of specific interactions between proteins. We have successfully demonstrated detection of target glycoprotein–glycoprotein interactions, antigen-antibody interactions, and glycoprotein-antigen interactions. We have also demonstrated the ability of this technique to distinguish between strong and weak interactions. Using this approach, it may be possible to multiplex an array of these sensors onto a chip and probe a complex mixture for various types of interactions involving protein molecules.
Electrical biosensor; electrical impedance detection; microfluidics; protein–protein interactions
The current study compared three models of recognition memory in their ability to generalize across yes/no and two-alternative forced-choice (2AFC) testing. The unequal-variance signal-detection model assumes a continuous memory strength process. The dual-process signal-detection model adds a threshold-like-recollection process to a continuous familiarity process. The mixture signal-detection model assumes a continuous memory strength process, but the old item distribution consists of a mixture of two distributions with different means. Prior efforts comparing the ability of the models to characterize data from both test formats did not consider the role of parameter reliability, which can be critical when comparing models that differ in flexibility. Parametric bootstrap simulations revealed that parameter regressions based on separate fits of each test type only served to identify the least flexible model. However, simultaneous fits of ROC data from both test types with goodness-of-fit adjusted using AIC successfully recovered the true model that generated the data. With AIC and simultaneous fits to real data, the unequal-variance signal-detection model was found to provide the best account across yes/no and 2AFC testing.
signal detection theory; yes/no recognition memory; two-alternative forced-choice recognition memory; model flexibility; parameter reliability
Evidence suggests that the neural system associated with face processing is a distributed cortical network containing both bottom-up and top-down mechanisms. While bottom-up face processing has been the focus of many studies, the neural areas involved in the top-down face processing have not been extensively investigated due to difficulty in isolating top-down influences from the bottom-up response engendered by presentation of a face. In the present study, we used a novel experimental method to induce illusory face detection. This method allowed for directly examining the neural systems involved in top-down face processing while minimizing the influence of bottom-up perceptual input. A distributed cortical network of top-down face processing was identified by analyzing the functional connectivity patterns of the right fusiform face area (FFA). This distributed cortical network model for face processing includes both “core” and “extended” face processing areas. It also includes left anterior cingulate cortex (ACC), bilateral orbitofrontal cortex (OFC), left dorsolateral prefrontal cortex (DLPFC), left premotor cortex, and left inferior parietal cortex. These findings suggest that top-down face processing contains not only regions for analyzing the visual appearance of faces, but also those involved in processing low spatial frequency (LSF) information, decision making, and working memory.
top-down processing; psychophysiological interaction (PPI); distributed cortical network; fMRI; face processing
We report three behavioral experiments on the spatial characteristics evoking illusory face and letter detection. False detections made to pure noise images were analyzed using a modified reverse correlation method in which hundreds of observers rated a modest number of noise images (480) during a single session. This method was originally developed for brain imaging research, and has been used in a number of fMRI publications, but this is the first report of the behavioral classification images. In Experiment 1 illusory face detection occurred in response to scattered dark patches throughout the images, with a bias to the left visual field. This occurred despite the use of a fixation cross and expectations that faces would be centered. In contrast, illusory letter detection (Experiment 2) occurred in response to centrally positioned dark patches. Experiment 3 included an oval in all displays to spatially constrain illusory face detection. With the addition of this oval the classification image revealed an eyes/nose/mouth pattern. These results suggest that face detection is triggered by a minimal face-like pattern even when these features are not centered in visual focus.
vision; face perception; reverse correlation; letter perception; top down; false detection