How is the serial order of a spatial sequence represented in short-term memory (STM)? Previous research by Farrell and Lewandowsky (Farrell & Lewandowsky, 2004; Lewandowsky & Farrell, 2008) has shown that 5 alternative mechanisms for the representation of serial order can be distinguished on the basis of their predictions concerning the response times accompanying transposition errors. We report 3 experiments involving the output-timed serial recall of sequences of seen spatial locations that tested these predictions. The results of all 3 experiments revealed that transposition latencies are a negative function of transposition displacement, but with a reduction in the slope of the function for postponement, compared with anticipation errors. This empirical pattern is consistent with that observed in serial recall of verbal sequences reported by Farrell and Lewandowsky (2004), and with the predictions of a competitive queuing mechanism, within which serial order is represented via a primacy gradient of activations over items combined with associations between items and positional markers, and with suppression of items following recall. The results provide the first clear evidence that spatial and verbal STM rely on some common mechanisms and principles for the representation of serial order.
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http://dx.doi.org/10.1037/a0038223 | DOI Listing |
Cogn Neuropsychol
January 2025
Department of Psychological Sciences, Rice University, Houston, Texas, USA.
Many aspects of human performance require producing sequences of items in serial order. The current study takes a multiple-case approach to investigate whether the system responsible for serial order is shared across cognitive domains, focusing on working memory (WM) and word production. Serial order performance in three individuals with post-stroke language and verbal WM disorders (hereafter persons with aphasia, PWAs) were assessed using recognition and recall tasks for verbal and visuospatial WM, as well as error analyses in spoken and written production tasks to assess whether there was a tendency to produce the correct phonemes/letters in the wrong order.
View Article and Find Full Text PDFHum Brain Mapp
January 2025
The Mind Research Network/Lovelace Biomedical Research Institute, Albuquerque, New Mexico, USA.
Evaluation of mechanisms of action of EEG neurofeedback (EEG-nf) using simultaneous fMRI is highly desirable to ensure its effective application for clinical rehabilitation and therapy. Counterbalancing training runs with active neurofeedback and sham (neuro)feedback for each participant is a promising approach to demonstrate specificity of training effects to the active neurofeedback. We report the first study in which EEG-nf procedure is both evaluated using simultaneous fMRI and controlled via the counterbalanced active-sham study design.
View Article and Find Full Text PDFSensors (Basel)
December 2024
College of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.
In order to achieve infrared aircraft detection under interference conditions, this paper proposes an infrared aircraft detection algorithm based on high-resolution feature-enhanced semantic segmentation network. Firstly, the designed location attention mechanism is utilized to enhance the current-level feature map by obtaining correlation weights between pixels at different positions. Then, it is fused with the high-level feature map rich in semantic features to construct a location attention feature fusion network, thereby enhancing the representation capability of target features.
View Article and Find Full Text PDFWhile the genetic paradigm of cancer etiology has proven powerful, it remains incomplete as evidenced by the widening spectrum of non-cancer cell-autonomous "hallmarks" of cancer. Studies have demonstrated the commonplace presence of high oncogenic mutational burdens in homeostatically-stable epithelia. Hence, the presence of driver mutations alone does not result in cancer.
View Article and Find Full Text PDFFront Psychol
December 2024
Department of Psychology, Emory University, Atlanta, GA, United States.
Introduction: Implicit statistical learning is, by definition, learning that occurs without conscious awareness. However, measures that putatively assess implicit statistical learning often require explicit reflection, for example, deciding if a sequence is 'grammatical' or 'ungrammatical'. By contrast, 'processing-based' tasks can measure learning without requiring conscious reflection, by measuring processes that are facilitated by implicit statistical learning.
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