Sleep reduces the semantic coherence of memory recall: An application of latent semantic analysis to investigate memory reconstruction

The effect of mood on shaping belief and recollection following false feedback

The current study examined how mood affects the impact of false feedback on belief and recollection. In a three-session experiment, participants first watched 40 neutral mini videos, which were accompanied by music to induce either a positive or negative mood, or no music. Following a recognition test, they received false feedback to reduce belief in the occurrence of the events displayed in some of the videos (Session 2). This was followed by an immediate memory test and a delayed memory assessment one week later (Session 3). The results revealed that participants in negative mood reported higher belief scores compared to those in positive moods, despite an overall decline in belief scores for all groups following the false feedback. Notably, individuals in negative moods exhibited less reduction in their belief scores after encountering challenges, thereby maintaining a higher accuracy in their testimonies. Over time, a reduction in the clarity of participants' memory recall was observed, which correspondingly reduced their testimony accuracy. This study thus indicates that mood states play a role in shaping belief and memory recall under the influence of false feedback.

The cost of changing language context: The language-dependent recall of fictional stories

Language-dependent recall refers to the language-specific retrieval of memories in which the retrieval success depends on the match between the languages of encoding and retrieval. The present study investigated language-dependent recall in terms of memory accuracy, false memory, and episodic memory characteristics in the free recall of fictional stories. We also asked how language-dependent memories were influenced by language proficiency and visual imagery. One hundred and thirty-seven native Turkish (L1) speakers who were second-language learners of English (L2) were divided into four groups in which they read fictional stories and then recalled them: (1) Turkish reading-Turkish recall, (2) English reading-English recall, (3) English reading-Turkish recall, (4) Turkish reading-English recall. Regardless of the match between L1 or L2, accuracy was higher when participants read and recalled the stories in the same language than when they did it in different languages, showing the language-dependent recall effect. Notably, the effect of match or mismatch between encoding and retrieval languages on accuracy did not depend on L2 proficiency and visual imagery. In addition, false memories were salient, particularly for participants who read the stories in L2 but retrieved them in L1. Overall, our findings suggest that accuracy-oriented memory research provides a comprehensive investigation of language-dependent recall, addressing the links of language-dependent memories with accuracy, false memory, and episodic memory characteristics.

Sensorimotor distance: A grounded measure of semantic similarity for 800 million concept pairs

Experimental design and computational modelling across the cognitive sciences often rely on measures of semantic similarity between concepts. Traditional measures of semantic similarity are typically derived from distance in taxonomic databases (e.g. WordNet), databases of participant-produced semantic features, or corpus-derived linguistic distributional similarity (e.g. CBOW), all of which are theoretically problematic in their lack of grounding in sensorimotor experience. We present a new measure of sensorimotor distance between concepts, based on multidimensional comparisons of their experiential strength across 11 perceptual and action-effector dimensions in the Lancaster Sensorimotor Norms. We demonstrate that, in modelling human similarity judgements, sensorimotor distance has comparable explanatory power to other measures of semantic similarity, explains variance in human judgements which is missed by other measures, and does so with the advantages of remaining both grounded and computationally efficient. Moreover, sensorimotor distance is equally effective for both concrete and abstract concepts. We further introduce a web-based tool ( https://lancaster.ac.uk/psychology/smdistance ) for easily calculating and visualising sensorimotor distance between words, featuring coverage of nearly 800 million word pairs. Supplementary materials are available at https://osf.io/d42q6/ .

Organization and Priming of Long-term Memory Representations with Two-phase Plasticity

Background / Introduction

In recurrent neural networks in the brain, memories are represented by so-called Hebbian cell assemblies. Such assemblies are groups of neurons with particularly strong synaptic connections formed by synaptic plasticity and consolidated by synaptic tagging and capture (STC). To link these synaptic mechanisms to long-term memory on the level of cognition and behavior, their functional implications on the level of neural networks have to be understood.

Methods

We employ a biologically detailed recurrent network of spiking neurons featuring synaptic plasticity and STC to model the learning and consolidation of long-term memory representations. Using this, we investigate the effects of different organizational paradigms, and of priming stimulation, on the functionality of multiple memory representations. We quantify these effects by the spontaneous activation of memory representations driven by background noise.

Results

We find that the learning order of the memory representations significantly biases the likelihood of activation towards more recently learned representations, and that hub-like overlap structure counters this effect. We identify long-term depression as the mechanism underlying these findings. Finally, we demonstrate that STC has functional consequences for the interaction of long-term memory representations: 1. intermediate consolidation in between learning the individual representations strongly alters the previously described effects, and 2. STC enables the priming of a long-term memory representation on a timescale of minutes to hours.

Conclusion

Our findings show how synaptic and neuronal mechanisms can provide an explanatory basis for known cognitive effects.

Identifying Major Research Areas and Minor Research Themes of Android Malware Analysis and Detection Field Using LSA

Contemporary technologies have ensured the availability of high-quality research data shared over the Internet. This has resulted in a tremendous availability of research literature, which keeps evolving itself. Thus, identification of core research areas and trends in such ever-evolving literature is not only challenging but interesting too. An empirical overview of contemporary machine learning methods, which have the potential to expedite evidence synthesis within research literature, has been explained. This manuscript proposes Simulating Expert comprehension for Analyzing Research trends (SEAR) framework, which can perform subjective and quantitative investigation over enormous literature. TRENDMINER is the use case designed exclusively for the SEAR framework. TRENDMINER uncovered the intellectual structure of a corpus of 444 abstracts of research articles (published during 2010–2019) on Android malware analysis and detection. The study concludes with the identification of three core research areas, twenty-seven research trends. The study also suggests the potential future research directions.