Recent studies showed that real-world items are better remembered in visual working memory (VWM) than visually similar stimuli that are stripped of their semantic meaning. However, the exact nature of this advantage remains unclear. We used meaningful and meaningless stimuli in a location-reproduction VWM task. Employing a mixture-modeling analysis, we examined whether semantic meaning enables more item locations to be remembered, whether it improves the precision of the locations stored in memory, or whether it improves binding between the specific items and their locations. Participants were presented with streams of four (Experiments 1 & 2) or six (Experiment 3) real-world items, or their scrambled, meaningless counterparts. Each item was presented at a unique location, and the task was to reproduce one item's location. Overall, location memory was consistently better for real-world items compared with their scrambled counterparts. Furthermore, the results revealed that participants were less likely to make swap errors for the meaningful items, but there was no effect of conceptual meaning on the guess rate or the precision of the report. In line with previous findings, these results indicate that conceptual meaning enhances VWM for arbitrary stimulus properties such as item location, and this improvement is primarily due to a more efficient identity-location binding rather than an increase in the quantity or quality (precision) of the locations held in memory.
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http://dx.doi.org/10.3758/s13421-024-01611-x | DOI Listing |
J Imaging
January 2025
Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia.
Crop field monitoring using unmanned aerial vehicles (UAVs) is one of the most important technologies for plant growth control in modern precision agriculture. One of the important and widely used tasks in field monitoring is plant stand counting. The accurate identification of plants in field images provides estimates of plant number per unit area, detects missing seedlings, and predicts crop yield.
View Article and Find Full Text PDFBehav Sci (Basel)
January 2025
School of Psychology, Central China Normal University, Wuhan 430079, China.
Words are the basic units of language and vital for comprehending the language system. Lexical processing research has always focused on either conceptual or affective word meaning. Previous studies have indirectly compared the conceptual and affective meanings of words.
View Article and Find Full Text PDFNeuropsychologia
January 2025
Neuroscience Area, SISSA, Trieste, Italy; Dipartimento di Medicina dei Sistemi, Università di Roma-Tor Vergata, Roma, Italy.
Although gesture observation tasks are believed to invariably activate the action-observation network (AON), we investigated whether the activation of different cognitive mechanisms when processing identical stimuli with different explicit instructions modulates AON activations. Accordingly, 24 healthy right-handed individuals observed gestures and they processed both the actor's moved hand (hand laterality judgment task, HT) and the meaning of the actor's gesture (meaning task, MT). The main brain-level result was that the HT (vs MT) differentially activated the left and right precuneus, the left inferior parietal lobe, the left and right superior parietal lobe, the middle frontal gyri bilaterally and the left precentral gyrus.
View Article and Find Full Text PDFIntroduction: Broca's Aphasia (BA) is a language disorder that causes grammatical errors in the language production skills of patients. Contemporary studies revealed the fact that BA patients also have difficulty in analyzing the meaning of phrases and sentences and comprehending the real meaning of the discourse produced by the speaker. The purpose of this study is to investigate possible effect of syntactic movement by changing the word positions in the sentence with morphological markers in order to produce clauses without changing the meaning on the phrasal comprehension skills of Turkish speaking patients with BA.
View Article and Find Full Text PDFR Soc Open Sci
January 2025
School of Physics, The University of Sydney, Sydney, Australia.
Clustering short text is a difficult problem, owing to the low word co-occurrence between short text documents. This work shows that large language models (LLMs) can overcome the limitations of traditional clustering approaches by generating embeddings that capture the semantic nuances of short text. In this study, clusters are found in the embedding space using Gaussian mixture modelling.
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