Pairs Of Items Research Articles

Computer mouse tracking reveals motor signatures in a cognitive task of spatial language grounding

In a novel computer mouse tracking paradigm, participants read a spatial phrase such as “The blue item to the left of the red one” and then see a scene composed of 12 visual items. The task is to move the mouse cursor to the target item (here, blue), which requires perceptually grounding the spatial phrase. This entails visually identifying the reference item (here, red) and other relevant items through attentional selection. Response trajectories are attracted toward distractors that share the target color but match the spatial relation less well. Trajectories are also attracted toward items that share the reference color. A competing pair of items that match the specified colors but are in the inverse spatial relation increases attraction over-additively compared to individual items. Trajectories are also influenced by the spatial term itself. While the distractor effect resembles deviation toward potential targets in previous studies, the reference effect suggests that the relevance of the reference item for the relational task, not its role as a potential target, was critical. This account is supported by the strengthened effect of a competing pair. We conclude, therefore, that the attraction effects in the mouse trajectories reflect the neural processes that operate on sensorimotor representations to solve the relational task. The paradigm thus provides an experimental window through motor behavior into higher cognitive function and the evolution of activation in modal substrates, a longstanding topic in the area of embodied cognition.

A Neural Circuit for Perceptual Grouping, Segmentation, and Selection

Francis et al. (2017) showed that a neural circuit for grouping and segmentation explains a variety of visual uncrowding effects. When a central target did not group with flankers, the circuit could segment out the flankers, thereby freeing the target from crowding effects. A limitation of that model is that fixed parameter values strictly determine the range of perceptual grouping. We describe a revised grouping circuit that allows for top-down control of grouping range via modification of a single timing parameter. Different values can result in different groupings of elements in a scene. Depending on how the grouping circuit connects visual elements, the segmentation process may select individual items, all items, or a subset of items. Based on the behavior of this revised model, we propose that various empirical tasks designed to measure grouping track how easily observers can segment and select relevant parts of a visual scene. Model simulations that quantify ease of grouping closely match reported subjective ratings of target distinguishability (Manassi et al., 2012). Such judgments are well explained by the segmentation process. Flankers larger than the target, for example, are not grouped with the target for a wide span of top-down range control values. In contrast, flankers equal in size to the target cannot form separate groups: either all or none of the elements group together. The same model also accounts for objective measures of grouping. Palmer and Beck (2007) varied spacing between items and had subjects locate a matching pair of adjacent items. We show that challenging conditions for this task occur when a narrow set of range control parameters are required. This set varies as a function of spacing between items. Overall, we show that the model provides a novel characterization of grouping effects that is applicable to a wide variety of situations.

Mapping visual symbols onto spoken language along the ventral visual stream

Reading involves transforming arbitrary visual symbols into sounds and meanings. This study interrogated the neural representations in ventral occipitotemporal cortex (vOT) that support this transformation process. Twenty-four adults learned to read 2 sets of 24 novel words that shared phonemes and semantic categories but were written in different artificial orthographies. Following 2 wk of training, participants read the trained words while neural activity was measured with functional MRI. Representational similarity analysis on item pairs from the same orthography revealed that right vOT and posterior regions of left vOT were sensitive to basic visual similarity. Left vOT encoded letter identity and representations became more invariant to position along a posterior-to-anterior hierarchy. Item pairs that shared sounds or meanings, but were written in different orthographies with no letters in common, evoked similar neural patterns in anterior left vOT. These results reveal a hierarchical, posterior-to-anterior gradient in vOT, in which representations of letters become increasingly invariant to position and are transformed to convey spoken language information.

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.

Superhard Choices

ABSTRACTSometimes, when comparing a pair of items, it appears that neither is better than the other, nor that they are equally good, relative to a certain value that they bear. Cases of this kind have come to be referred to as superhard comparisons. What grounds superhard comparisons? On the dominant views, held by Joseph Raz and Ruth Chang, they are grounded, at least partially, in the failure of the three classic value relations—‘better than’, ‘worse than’, and ‘equally good’. On an alternative view, which might be called the vagueness view, first developed by John Broome, they are grounded in vagueness about which of the classic value relations holds between the items. In this paper, I pay special attention to superhard comparisons in the context of choice and develop a novel argument against the dominant views on the basis of an account of decision-making under vagueness in ‘better than’. The upshot is that a new vagueness view emerges.

Measuring romantic competence in young adults: The Inventory of Romantic Relationship Competence

This article presents psychometric data for a 35-item self-report instrument measuring romantic relationship competence in two separate samples of young adult college students. In study 1 (N = 219), results from Parallel Analysis and Exploratory Factor Analysis (EFA) suggested the extraction of seven domains of romantic relationship competence: relationship locus of control, perspective taking, intimacy avoidance, emotion regulation, romantic appeal, conflict resolution skills, and temperament. In study 2 (N = 907), a Confirmatory Factor Analysis (CFA) provided support for the aforementioned factor structure albeit with minor revisions (i.e., correlated errors between three pairs of items). MIMIC modeling results provided support for partial measurement invariance across gender. A CFA-based method of estimating scale reliability demonstrated acceptable to good reliability indices. Bivariate correlations with other social competence and self-esteem measures provided support for convergent and divergent validity. An excel-based applet is available to readers who are interested in using the 35-item IRRC with individual respondents (e.g., practitioners). Researchers interested in using the measure within the context of structural equation modeling should model relevant non-invariant parameters before proceeding with the evaluation of structural parameters. This instrument demonstrates promise as an instrument for measuring domains of romantic relationship competence within the emerging adult population.

Multi-Order Attentive Ranking Model for Sequential Recommendation

In modern e-commerce, the temporal order behind users’ transactions implies the importance of exploiting the transition dependency among items for better inferring what a user prefers to interact in “near future”. The types of interaction among items are usually divided into individual-level interaction that can stand out the transition order between a pair of items, or union-level relation between a set of items and single one. However, most of existing work only captures one of them from a single view, especially on modeling the individual-level interaction. In this paper, we propose a Multi-order Attentive Ranking Model (MARank) to unify both individual- and union-level item interaction into preference inference model from multiple views. The idea is to represent user’s short-term preference by embedding user himself and a set of present items into multi-order features from intermedia hidden status of a deep neural network. With the help of attention mechanism, we can obtain a unified embedding to keep the individual-level interactions with a linear combination of mapped items’ features. Then, we feed the aggregated embedding to a designed residual neural network to capture union-level interaction. Thorough experiments are conducted to show the features of MARank under various component settings. Furthermore experimental results on several public datasets show that MARank significantly outperforms the state-of-the-art baselines on different evaluation metrics. The source code can be found at https://github.com/voladorlu/MARank.

Analysing exposure diversity in collaborative recommender systems—Entropy fusion approach

Recommender Systems are considered as essential business tools to leverage the potential growth of on-line services. Neighbourhood based collaborative filtering, a successful recommendation approach has mainly focused on improving accuracy of predictions. From user point of view, it is more valuable to obtain novel and diverse recommendations rather than monotonic preferences. Ratings given by a user for different categories of items are considered as a tool to access user exposure diversity which signifies his creative and divergent thinking. On the other hand, pair of items is concordant if highly correlated users agree in rating the items. Based on the user exposure diversity and item concordance, the neighbourhood selection process of item based collaborative recommender systems is refined. Rating predictions are made based on the newly selected neighbours. The performance of the proposed approach is investigated for accuracy and diversity of predictions on Movielens data sets. The results demonstrate that the proposed approach outperforms the state of the art recommendation approaches which address accuracy–diversity trade off. Statistical analysis is done to prove the efficiency of the proposed approach.

Learning to Match Clothing From Textual Feature-Based Compatible Relationships

This paper presents a new framework for matching clothes by considering item in-between compatibility. In contrast to the use of visual features of clothing items, we only utilized their textual descriptions, i.e., title sentences, to constitute the basic features. Specifically, a longshort-term memory (LSTM) network was used for feature embeddings of title sentences. Given item pairs of queries and candidates, their feature embeddings achieved by Siamese LSTMs were integrated into style-compatible space characterized by a compatibility matrix. Our framework is examined on three large-scaled clothing item sets collected from Amazon, Taobao, and Polyvore, respectively. Experiments confirm the efficacy of our approach compared with several baseline methods.

Local low-rank Hawkes processes for modeling temporal user–item interactions

Hawkes processes have become very popular in modeling multiple recurrent user–item interaction events that exhibit mutual-excitation properties in various domains. Generally, modeling the interaction sequence of each user–item pair as an independent Hawkes process is ineffective since the prediction accuracy of future event occurrences for users and items with few observed interactions is low. On the other hand, multivariate Hawkes processes (MHPs) can be used to handle multi-dimensional random processes where different dimensions are correlated with each other. However, an MHP either fails to describe the correct mutual influence between dimensions or become computational inhibitive in most real-world events involving a large collection of users and items. To tackle this challenge, we propose local low-rank Hawkes processes to model large-scale user–item interactions, which efficiently captures the correlations of Hawkes processes in different dimensions. In addition, we design an efficient convex optimization algorithm to estimate model parameters and present a parallel algorithm to further increase the computation efficiency. Extensive experiments on real-world datasets demonstrate the performance improvements of our model in comparison with the state of the art.

Real-world size is automatically encoded in preschoolers' object representations.

When adults see a picture of an object, they automatically process how big the object typically is in the real world (Konkle & Oliva, 2012a). How much life experience is needed for this automatic size processing to emerge? Here, we ask whether preschoolers show this same signature of automatic size processing. We showed 3- and 4-year-olds displays with two pictures of objects and asked them to touch the picture that was smaller on the screen. Critically, the relative visual sizes of the objects could be either congruent with their relative real-world sizes (e.g., a small picture of a shoe next to a big picture of a car) or incongruent with their relative real-world sizes (e.g., a big picture of a shoe next to a small picture of a car). Across two experiments, we found that preschoolers were worse at making visual size judgments on incongruent trials, suggesting that real-world size was automatically activated and interfered with their performance. In addition, we found that both 4-year-olds and adults showed similar item-pair effects (i.e., showed larger Size-Stroop effects for a given pair of items, relative to other pairs). Furthermore, the magnitude of the item-pair Stroop effects in 4-year-olds did not depend on whether they could recognize the pictured objects, suggesting that the perceptual features of these objects were sufficient to trigger the processing of real-world size information. These results indicate that, by 3-4 years of age, children automatically extract real-world size information from depicted objects. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Locally differentially private item-based collaborative filtering

Recently, item-based collaborative filtering has attracted a lot of attention. It recommends to users new items which may be of interests to them, based on their reported historical data (i.e., the items they have already been interested in). The reported historical data leads to significant privacy risks in case that the recommending service is not fully trusted. Many researches have focused on developing differential privacy mechanisms to protect personal data in various recommendations. However, most of these mechanisms can not ensure accuracy of the recommendations. The main reason for this problem is that these methods compute similarity directly from the perturbation data. The computed similarity is thus always inaccurate and this inaccurate similarity finally leads to inaccurate recommendation results. In this paper, we propose a locally differentially private item-based collaborative filtering framework, which protects users’ private historical data on the user-side, and on the server-side reconstructs the similarity to ensure recommendation accuracy. The similarities are reconstructed for every pair of items, by estimating the number of users who have rated neither, either one, or both of them. The final recommendation is generated by the reconstructed similarities. Experimental results show that our proposed method significantly outperforms the state-of-the-art methods in terms of the recommendation accuracy and the trade-off between privacy and accuracy.

Are associations formed across pairs? A test of learning by temporal contiguity in associative recognition.

In models such as the search of associative memory (SAM: Gillund & Shiffrin, Psychological Review, 91(1), 1-67 1984) model, associations in paired-associate tasks are only formed between the pair of to-be-remembered items. The temporal context model (TCM: Howard & Kahana, Journal of Mathematical Psychology, 46, 268-299 2002) deviates from SAM by positing that long-range associations are formed between the current item and all previously presented items, even in paired-associate tasks, where cross-pair associations are formed in addition to within-pair associations (Davis, Geller, Rizzuto, & Kahana, Psychonomic Bulletin & Review, 15, 64-69 2008). We tested this proposal in an associative recognition task by constructing rearranged pairs where the distance in within-list serial position between the two pair members was manipulated between one and five pairs. Models such as TCM would predict that FAR should be highest for rearranged pairs that are constructed from pair members that were adjacent to each other on the study list, whereas models such as SAM predict that FAR should be equal for rearranged pairs regardless of whether they are constructed from adjacent or remote pairs. Results from our experiment and from three archival datasets found that FAR for rearranged pairs did not depend on whether the constituent items came from nearby or remote pairs, suggesting that participants were not forming associations across pairs of items in the task.

Investigating Non-Linear Trends in Questionnaire Studies Using Contrastive Bipolar Scales

ABSTRACT The following study provides evidence that the use of a high number of test items in a subjective rating task with a bipolar scale does not affect the outcome detrimentally, a question hitherto unexplored in quantitative linguistics. By ‘contrastive bipolar scale’ we have in mind a scale with two different, explicit ‘options’ (here words), one at each end. We analyse a large-scale rating study consisting of nearly 1,000 participants and almost 700 pairs of word items per participant. A pair consisted of two synonymous words from a non-standard Silesian variety, one of German origin (a loan), one from the Polish Standard language. The judgement to be given was an estimation of the subjective frequency of the use of the German loan in comparison to its Polish equivalent in colloquial Silesian speech. We use non-linear regression analysis, GAMM, to examine if the presentation order of words affects the mean ratings. Findings show that results are not affected by either the high number of test items or the order of the presentation of word items.

Psychometric properties of the Dutch-Flemish Patient-Reported Outcomes Measurement Information System Pain Behavior item bank in patients with musculoskeletal complaints

We studied the psychometric properties of the 39-item v1.1 Dutch-Flemish Patient-Reported Outcomes Measurement Information System Pain Behavior item bank in a sample of 1,602 patients with musculoskeletal complaints. We evaluated the assumptions of the underlying item response theory (IRT) model (unidimensionality and local dependency with confirmatory factor analyses), and monotonicity with scalability coefficients). We studied the IRT model fit of all items and estimated the item parameters of the IRT model. Differential item functioning (DIF) was studied for age and gender, and DIF for language was studied as a measure of cross-cultural validity. Confirmatory factor analyses showed suboptimal fit of a unidimensional model, but a bifactor model showed low risk of bias when a unidimensional model was assumed (Omega H = .92, explained common variance of .70). Fifteen item pairs (2%) were locally dependent. Five items showed poor scalability. All items fitted the IRT model; slope parameters ranged from .60 to 2.00, and threshold parameters from –2.05 to 6.80. One item showed DIF for age, 1 item DIF for gender, and 5 items showed DIF for language, but the impact on total scores was low. Our study shows limitations of the Dutch-Flemish Patient-Reported Outcomes Measurement Information System Pain Behavior item bank when used in a primary care population with musculoskeletal complaints. PerspectiveWe studied the psychometric properties of the Dutch-Flemish Patient-Reported Outcomes Measurement Information System Pain Behavior item bank in a large primary care population of patients with musculoskeletal complaints. It showed that the Pain Behavior item bank has limitations when used in this population.

The N2cc component as an electrophysiological marker of space-based and feature-based attentional target selection processes in touch.

An electrophysiological correlate of attentional target selection processes in touch (N2cc component) has recently been discovered in lateralized tactile working memory experiments. This tactile N2cc emerges at the same time as the visual N2pc component but has a different modality-specific topography over central somatosensory areas. Here, we investigated links between N2cc components and the space-based versus feature-based attentional selection of task-relevant tactile stimuli. On each trial, a pair of tactile items was presented simultaneously to one finger on the left and right hand. Target stimuli were defined by their location (e.g., left index finger; Spatial Attention Task), by a nonspatial feature (continuous vs. pulsed; Feature-Based Attention Task), or by a combination of spatial and nonspatial features (Conjunction Task). Reliable N2cc components were observed in all three tasks. They emerged considerably earlier in the Spatial Attention Task than in the Feature-Based Attention Task, suggesting that space-based selection mechanisms in touch operate faster than feature-guided mechanisms. The temporal pattern of N2cc components observed in the Conjunction Task revealed that space-based and feature-based attention both contributed to target selection, which was initially driven primarily by spatial location. Overall, these findings establish the N2cc component as a new electrophysiological marker of the selective attentional processing of task-relevant stimuli in touch.

Metacognitive expectancy effects in source monitoring: Beliefs, in-the-moment experiences, or both?

Abstract Judgments of Learning (JOLs) may result from a priori beliefs and in-the-moment experiences (cue-utilization approach, Koriat, 1997). The authors investigated the interplay and relative importance of beliefs and experiences, and their impact on JOLs and Judgments of Source (JOS). In a source-monitoring task with expected and unexpected source–item pairs (e.g., kitchen–oven vs. bathroom–refrigerator), metamemory judgments were higher for expected than unexpected pairs (expectancy effect). In two experiments, beliefs and experiences independently contributed to the expectancy effects on JOLs and JOSs. A priori beliefs regarding item and source memory for expected and unexpected source–item pairs were heterogeneous and partially moderated the expectancy effects on JOLs and JOSs. In Experiment 2, experiences captured via study time mediated the expectancy effect on JOLs only. Beliefs and experiences were weighted differently in JOLs versus JOSs. Results highlight the importance of investigating different metamemory processes and their contributions to different judgments.

Deep Item-based Collaborative Filtering for Top-N Recommendation

Item-based Collaborative Filtering (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 relationships between items, which are insufficient to capture the complicated decision-making process of users. In this article, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationships 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. By doing this, 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.

Abstract 94: SCAR-Q: International Results and Why Individuals Report the Need for Scar Revision Surgery

PURPOSE: Millions of scars are formed annually with wide-ranging effects on the quality of life of the individual. Currently, there is no internationally validated, rigorously tested PRO measure (‘PROM’) that can be used to assess scar outcomes of all major etiologies in both children and adults. Nor, do we currently understand why patients may seek scar revision surgery. The purpose of this study is to describe the international field-test of seven clinics in four countries, the psychometric properties of the PROM, and the independent risk factors of patients requiring scar revision surgery. METHODS: Between March, 2017 and April, 2018 data were collected from 7 outpatient clinics in New Zealand, Chile, Canada, and the USA. Participants completed a questionnaire booklet with demographic and clinical questions as well as the preliminary SCAR-Q (Appearance, Symptom, and Psychosocial Impact). Rasch Measurement Theory (RMT) analysis was conducted examining item response options, item fit statistics, dependency, targeting, stability, and person separation index. In addition, Cronbach’s alpha and intraclass correlation coefficient were completed to examine reliability. In addition, a multivariable regression analysis was completed to examine independent risk factors for patients self-reporting the need for scar revision surgery. RESULTS: 773 patients were consented and 731 patients completed the survey results in full. Participants were aged 8–88 years with predominantly surgical scars (n=354, 48.4%), traumatic (n=199, 27.2%), and burn scars (n=184, 25.1%). Analysis led to the refinement of the SCAR-Q scales: Appearance, Symptoms, and Psychosocial Impact from 48 items to the final 29 items. RMT analysis showed ordered thresholds for response options, 25 items had fit residuals within the required range, and residual correlations were found only with one pair of items. Reliability was high, with Person Separation Index values of 0.90, 0.82, 0.84, Cronbach alpha values 0.96, 0.91, 0.95 and Intraclass Correlation Coefficient values of 0.92, 0.94. 0.88 respectively. Finally, having a health condition (OR 2.032; 0.944, 4.370), perceiving that one scars badly (OR 2.631, 1.195–5.792), a prior scar revision surgery (0.253; 0.127–0.672), and scoring poorly on the SCAR-Q Psychosocial Impact scale (OR 0.339; 0.171–0.672) were independent risk factors for reporting the need for future scar revision surgery. CONCLUSION: This RMT analysis showed that the 3 SCAR-Q scales performed well in this international, field-test sample, with good to excellent reliability. The SCAR-Q represents a rigorously developed PROM that can be used to evaluate scars in research, clinical care, and quality improvement initiatives. In addition, this study suggests that patients with the listed independent risk factors may require heightened attention and clinical care. These individuals may require more scar symptom management, psychosocial support in the case of social work or in severe cases a psychiatry referral, and/or scar treatment to help alleviate their appearance related concerns.

Testing the Local Independence Assumption of the Rasch Model With Q 3-Based Nonparametric Model Tests.

Local independence is a central assumption of commonly used item response theory models. Violations of this assumption are usually tested using test statistics based on item pairs. This study presents two quasi-exact tests based on the statistic for testing the hypothesis of local independence in the Rasch model. The proposed tests do not require the estimation of item parameters and can also be applied to small data sets. The authors evaluate the tests with three simulation studies. Their results indicate that the quasi-exact tests hold their alpha level under the Rasch model and have higher power against different forms of local dependence than several alternative parametric and nonparametric model tests for local independence.