An item/order tradeoff explanation of word length and generation effects

[Abstract]: The item-order hypothesis suggests that under certain conditions increased item processing can lead to deficits in order processing, and that this produces a dissociation in performance between item and order tasks. The generation effect is one such example. The word length effect is seen as another instance where this tradeoff might be observed. The following experiments compare word length and generation effects under serial recall and single item recognition conditions. Short words are better recalled than long words on the serial recall task but long words were better recognised than short words. The results are consistent with the item-order approach and support a novel explanation for the word length effect

Item Order Effects on Attitude Measures

The purpose of this dissertation was to examine the effects of altered item order on attitude measures for both computerized adaptive and conventional survey formats. Based on items modified from a dissertation/thesis completion survey (Green & Kluever, 1997) with three scales, three survey versions were generated with items ordered by difficulty as hard-to-easy (H-E), easy-to-hard (E-H), and five medium trait level items presented first followed by randomly ordered items (M-R) for conventional survey format. Significant differences in item difficulty and item discrimination were found for two of the three scales. Differences in scale reliability were detected for the procrastination and responsibility scales. Also, significant correlations between scale total score and scale attitude strength were discovered with each survey version. Further, two computerized adaptive survey version were generated. One began with items at medium and the other at extremely high trait levels. Results showed significant differences in number of items administered to achieve a set level of precision for two scales and significant differences in reaction time were found for one scale between the two versions. The version of item starting at the extreme trait level required more items, and took longer to respond to. Further, significant differences in the estimated person parameter were found for one scale between the two survey versions. Based on the results of both survey formats indicating item order effects pose a problem for assessing attitude

Serial position functions for recognition of olfactory stimuli

Two experiments examined item recognition memory for sequentially presented odours. Following a sequence of six odours participants were immediately presented with a series of 2-alternative forced choice (2AFC) test odours. The test pairs were presented in either the same order as learning or the reverse order of learning. Method of testing was either blocked (Experiment 1) or mixed (Experiment 2). Both experiments demonstrated extended recency, with an absence of primacy, for the reverse testing procedure. In contrast, the forward testing procedure revealed a null effect of serial position. The finding of extended recency is inconsistent with the single-item recency predicted by the two-component duplex theory (Phillips and Christie, 1977). We offer an alternative account of the data in which recognition accuracy is better accommodated by the cumulative number of items presented between item learning and item test

Deep Item-based Collaborative Filtering for Top-N Recommendation

Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users. In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g. similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI

Investigating invariant item ordering in the Mental Health Inventory : an illustration of the use of different methods

Invariant item ordering is a property of scales whereby the items are scored in the same order across a wide range of the latent trait and across a wide range of respondents. In the package ‘mokken’ in the statistical software R, the ability to analyse Mokken scales for invariant item ordering has recently been available and techniques for inspecting visually the item response curves of item pairs, have also been included. While methods to assess invariant item ordering are available, there have been indications that items representing extremes of distress in mental well-being scales, such as suicidal ideation, may lead to claiming invariant item ordering where it does not exist. We used the Mental Health Inventory to see if invariant item ordering was indicated in any Mokken scales derived and to see if this was being influenced by extreme items. A Mokken scale was derived indicating invariant item ordering. Visual inspection of the item pairs indicated that the most difficult item (suicidal ideation) was located far from the remaining cluster of items. Removing this item lowered invariant item ordering to an unacceptable level

The Production Effect and Item-Order Encoding

When reading a mixed list of words, participants show better memory for uncommon words compared to common words (McDaniel & Bugg, 2008). The research suggests differential memory effects in item-order encoding between mixed and pure lists. Uncommon words lead to item-specific encoding whereas common words lead to order encoding. Similarly, the production effect shows that, when reading a mixed list (some words aloud, others silently), participants show better memory for the words read aloud, but the effect does not obtain for pure lists. The purpose of this study is to examine if the production effect is due to differences in item-order encoding. Sixty-five John Carroll University undergraduates read six lists of sixteen words one at a time. Some participants read all words aloud (pure aloud), some read all words silently (pure silent); some read half of the words aloud and half silently depending on font color (mixed). At the end of each list, all participants completed a one-minute free recall task. After the final free recall task for the last list, all participants completed an order reconstruction task. Recall accuracy, input-output correspondence, and order reconstruction were examined using ANOVAs and t-tests. Basic production effect findings were replicated; aloud words were better remembered than silent words only for the mixed list group (Jones & Pyc, 2013). Further, because aloud words can be considered “uncommon”, we saw a decrease in order measures for the aloud items on a mixed list compared to the pure list. Similarly, order measures increased for silent words, which can be considered “common”. Thus, the production effect can be considered another example of how item-order encoding varies in mixed/pure list learning

The hippocampus, prefrontal cortex, and perirhinal cortex are critical to incidental order memory.

Considerable research in rodents and humans indicates the hippocampus and prefrontal cortex are essential for remembering temporal relationships among stimuli, and accumulating evidence suggests the perirhinal cortex may also be involved. However, experimental parameters differ substantially across studies, which limits our ability to fully understand the fundamental contributions of these structures. In fact, previous studies vary in the type of temporal memory they emphasize (e.g., order, sequence, or separation in time), the stimuli and responses they use (e.g., trial-unique or repeated sequences, and incidental or rewarded behavior), and the degree to which they control for potential confounding factors (e.g., primary and recency effects, or order memory deficits secondary to item memory impairments). To help integrate these findings, we developed a new paradigm testing incidental memory for trial-unique series of events, and concurrently assessed order and item memory in animals with damage to the hippocampus, prefrontal cortex, or perirhinal cortex. We found that this new approach led to robust order and item memory, and that hippocampal, prefrontal and perirhinal damage selectively impaired order memory. These findings suggest the hippocampus, prefrontal cortex and perirhinal cortex are part of a broad network of structures essential for incidentally learning the order of events in episodic memory

The "Thirty-seven Percent Rule" and the Secretary Problem with Relative Ranks

We revisit the problem of selecting an item from $n$ choices that appear before us in random sequential order so as to minimize the expected rank of the item selected. In particular, we examine the stopping rule where we reject the first $k$ items and then select the first subsequent item that ranks lower than the $l$-th lowest-ranked item among the first $k$. We prove that the optimal rule has $k \sim n/{\mathrm e}$, as in the classical secretary problem where our sole objective is to select the item of lowest rank; however, with the optimally chosen $l$, here we can get the expected rank of the item selected to be less than any positive power of $n$ (as $n$ approaches infinity). We also introduce a common generalization where our goal is to minimize the expected rank of the item selected, but this rank must be within the lowest $d$