THE USE OF L1 IN TEACHING ENGLISH

This research aims at finding the functions of the use of L1 in teaching English. It is descriptive research. Purposive sampling technique was used to choose the sample. There were two samples in this research. The researcher used tape recorder to collect the data. It is found that the core functions of the use L1 is higher than the social function of the use L1. The core functions are record keeping; language analysis; presenting the rules of grammar, phonology, morphology, and grammar; checking comprehension; translating L1; explaining new vocabulary or material; brain storming; eliciting; testing; highlight the recent item of material and checking for sense of the students’ language and social functions are classroom management; instruction or prompts; giving advice, asking the students’ conditions; checking attendance; joking; and threaten the students to maintain discipline. As long as the lecturers or teacher use L1 appropriately it can support the teaching process.

Session Recommendation via Recurrent Neural Networks over Fisher Embedding Vectors

Recommendation services bear great importance in e-commerce, shopping, tourism, and social media, as they aid the user in navigating through the items that are most relevant to their needs. In order to build recommender systems, organizations log the item consumption in their user sessions by using different sensors. For instance, Web sites use Web data loggers, museums and shopping centers rely on user in-door positioning systems to register user movement, and Location-Based Social Networks use Global Positioning System for out-door user tracking. Most organizations do not have a detailed history of previous activities or purchases by the user. Hence, in most cases recommenders propose items that are similar to the most recent ones viewed in the current user session. The corresponding task is called session based, and when only the last item is considered, it is referred to as item-to-item recommendation. A natural way of building next-item recommendations relies on item-to-item similarities and item-to-item transitions in the form of “people who viewed this, also viewed” lists. Such methods, however, depend on local information for the given item pairs, which can result in unstable results for items with short transaction history, especially in connection with the cold-start items that recently appeared and had no time yet to accumulate a sufficient number of transactions. In this paper, we give new algorithms by defining a global probabilistic similarity model of all the items based on Random Fields. We give a generative model for the item interactions based on arbitrary distance measures over the items, including explicit, implicit ratings and external metadata to estimate and predict item-to-item transition probabilities. We exploit our new model in two different item similarity algorithms, as well as a feature representation in a recurrent neural network based recommender. Our experiments on various publicly available data sets show that our new model outperforms simple similarity baseline methods and combines well with recent item-to-item and deep learning recommenders under several different performance metrics.

Temporal-Order-Based Attentional Priority Modulates Mnemonic Representations in Parietal and Frontal Cortices

The respective roles of occipital, parietal, and frontal cortices in visual working memory maintenance have long been under debate. Previous work on whether parietal and frontal regions convey mnemonic information has yielded mixed findings. One possibility for this variability is that the mnemonic representations in high-level frontoparietal regions are modulated by attentional priority, such as temporal order. To test this hypothesis, we examined whether the most recent item, which has a higher attentional priority in terms of temporal order, is preferentially encoded in frontoparietal regions. On each trial, participants viewed 2 gratings with different orientations in succession, and were cued to remember one of them. Using fMRI and an inverted encoding model, we reconstructed population-level, orientation representations in occipital (V1–V3), parietal (IPS), and frontal (FEF) areas during memory maintenance. Unlike early visual cortex where robust orientation representations were observed regardless of serial order, parietal, and frontal cortices showed stronger representations when participants remembered the second grating. A subsequent experiment using a change detection task on color rings excluded the possibilities of residual stimulus-driven signals or motor preparative signals for responses. These results suggest that mnemonic representations in parietal and frontal cortices are modulated by temporal-order-based attentional priority signals.

Recency order judgments in short term memory: Replication and extension of Hacker (1980)

The classic finding from short-term relative JOR tasks is that correct response time (RT) depends on the lag to the more recent item but not to the less recent item (Hacker, 1980). For decades, researchers have argued that this finding is consistent with a self-terminating backward scanning model (Muter, 1979; Hacker, 1980; Hockley, 1984; McElree & Dosher, 1993). This finding has taken on new importance in light of recent proposal that many forms of memory depend on a compressed representation of the past (Howard, Shankar, Aue, & Criss, 2015). This paper replicates and extends the results of the classic papers. A Bayesian t-test showed substantial evidence for the null effect of lag to the less recent item on correct RT. In addition, this paper reports that correct RT is a sub-linear function of lag to the more recent probe and replicates the classic finding that error RT depends on lag to the less recent probe. These findings place new constraints on models of short-term memory scanning.

Monkey prefrontal neurons during Sternberg task performance: full contents of working memory or most recent item?

To explore the brain mechanisms underlying multi-item working memory, we monitored the activity of neurons in the dorsolateral prefrontal cortex while macaque monkeys performed spatial and chromatic versions of a Sternberg working-memory task. Each trial required holding three sequentially presented samples in working memory so as to identify a subsequent probe matching one of them. The monkeys were able to recall all three samples at levels well above chance, exhibiting modest load and recency effects. Prefrontal neurons signaled the identity of each sample during the delay period immediately following its presentation. However, as each new sample was presented, the representation of antecedent samples became weak and shifted to an anomalous code. A linear classifier operating on the basis of population activity during the final delay period was able to perform at approximately the level of the monkeys on trials requiring recall of the third sample but showed a falloff in performance on trials requiring recall of the first or second sample much steeper than observed in the monkeys. We conclude that delay-period activity in the prefrontal cortex robustly represented only the most recent item. The monkeys apparently based performance of this classic working-memory task on some storage mechanism in addition to the prefrontal delay-period firing rate. Possibilities include delay-period activity in areas outside the prefrontal cortex and changes within the prefrontal cortex not manifest at the level of the firing rate. NEW & NOTEWORTHY It has long been thought that items held in working memory are encoded by delay-period activity in the dorsolateral prefrontal cortex. Here we describe evidence contrary to that view. In monkeys performing a serial multi-item working memory task, dorsolateral prefrontal neurons encode almost exclusively the identity of the sample presented most recently. Information about earlier samples must be encoded outside the prefrontal cortex or represented within the prefrontal cortex in a cryptic code.