Narrativity and Referential Activity Predict Episodic Memory Strength in Autobiographical Memories.

Narrativity has been proposed as an indicator of episodic memory strength when people discuss their past (Nelson and Horowitz in Discourse Processes 31:307-324, 2001. https://doi.org/10.1207/S15326950dp31-3_5 ). Referential Activity, the extent to which words convey a speaker's experience of being present in the event being described, has been independently hypothesized to indicate episodic memory strength (Maskit in J Psycholinguist Res, 2021. https://doi.org/10.1007/s10936-021-09761-8 ). These hypotheses are tested using a linguistic measure of narrativity and a computerized measure of referential activity to predict previous independent ratings of episodic memory strength that used the Levine et al. (Psychol Aging 17(4):677-689, 2002. https://doi.org/10.1037//0882-7974.17.4.677 ) measure of internal details in retold personal memories provided by Schacter (Addis et al. in Psychol Sci 19(1):33-41, 2008. https://doi.org/10.1111/j.1467-9280.2008.02043.x ). Raters scored narrativity on four brief near and far past memories elicited from 32 subjects, using Nelson's narrative temporal sequence method based on Labov's (J Narrat Life Hist 7(1-4):395-415, 1997. https://doi.org/10.1075/jnlh.7.49som ) analysis of spoken narratives of personal experience; computerized weighted scores of referential activity (WRAD) were obtained on these same 128 memories. Data analysis showed that narrative temporal sequences predict internal details and WRAD predict internal details. Adding WRAD to narrative temporal sequences improved the prediction of internal details.

MCE-Net: polyp segmentation with multiple branch series-parallel attention and channel interaction via edge distribution guidance.

Objective.Accurate polyp segmentation is vital for diagnosing colorectal cancer. However, it is still challenging for accurate polyp segmentation and several bottlenecks exist, such as incomplete boundary, localization bias and lack of micro blocks along with large fragmented boundaries in uncertain regions.Approach.To address the above issues, a novel polyp segmentation network with multiple branch series-parallel attention (MBSA) and channel interaction via edge distribution guidance is proposed. Initially, the edge distribution guidance strategy is proposed to generate the edge distribution following Cauchy distribution to capture complementary edges with sufficient details. Subsequently, a MBSA module is put forward to extract features from various receptive fields to pinpoint tiny polyps by a multiple kernel dilated convolution block, while combining semantics of different dimensions to filter out noise and refining the details of micro target. Ultimately, the channel interaction model is proposed to improve the segmentation accuracy of the polyps in uncertain area by splitting channels into groups and conducts group-wise interaction to excavate subtle clues contained in different channels.Main results.Extensive experimental results demonstrate that the proposed method is superior over the state-of-the-art methods with the mean dice of 0.8972, 0.9420, 0.8312, 0.8064 and 0.9214 on five public polyp datasets.Significance.The proposed method improves the integrity of the margins and internal details for polyp segmentation, which will provide a powerful aid for doctors to achieve accurate judgments, reducing the likelihood of colorectal cancer and improving the survival chances of patients.