scholarly journals Testing the Relationship Between Looking Time and Choice Preference in Long-tailed Macaques

2021 ◽  
Vol 8 (3) ◽  
pp. 351-375
Author(s):  
Vanessa A. D. Wilson ◽  
Carolin Kade ◽  
Julia Fischer
Keyword(s):  
Social Preference ◽  
Choice Task ◽  
Training Data ◽  
Future Research ◽  
Social Stimuli ◽  
Test Environment ◽  
Choice Preference ◽  
Social Images ◽  
Post Hoc ◽  
Visual Bias

Visual bias in social cognition studies is often interpreted to indicate preference, yet it is difficult to elucidate whether this translates to social preference. Moreover, visual bias is often framed in terms of surprise or recognition. It is thus important to examine whether an interpretation of preference is warranted in looking time studies. Here, using touchscreen training, we examined (1) looking time to non-social images in an image viewing task, and (2) preference of non-social images in a paired choice task, in captive long-tailed macaques, Macaca fascicularis. In a touchscreen test phase, we examined (3) looking time to social images in a viewing task, and (4) preference of social images in a paired choice task. Finally, we examined (5) looking time to social images in a non-test environment. For social content, the monkeys did not exhibit clear preferences for any category (conspecific/heterospecific, in-group/outgroup, kin/non-kin, young/old) in the explicit choice paradigm, nor did they differentiate between images in the viewing tasks, thus hampering our interpretation of the data. Post-hoc analysis of the training data however revealed a visual bias towards images of food and objects over landscapes in the viewing task. Similarly, across choice-task training sessions, food and object images were chosen more frequently than landscapes. This suggests that the monkeys’ gaze may indeed indicate preference, but this only became apparent for non-social stimuli. Why these monkeys had no biases in the social domain remains enigmatic. To better answer questions about attention to social stimuli, we encourage future research to examine behavioral measures alongside looking time.

scholarly journals Quantifying social preference in young children using two tasks on a mobile platform

2019 ◽  
Author(s):  
indu dubey ◽  
Simon Brett ◽  
Liliana Ruta ◽  
Rahul Bishain ◽  
Sharat Chandran ◽  
...  
Keyword(s):  
Social Preference ◽  
Choice Task ◽  
Social Reward ◽  
Typically Developing ◽  
Social Stimuli ◽  
Reward Responsiveness ◽  
Preferential Looking ◽  
Reward Responsivity ◽  
Underlying Processes ◽  
Social Rewards

Children typically prefer social stimuli (e.g. faces, smiles) over non-social stimuli (e.g. natural scene, household objects). This social preference is believed to be an essential building block for later social skills and healthy social development. Measuring social reward responsiveness poses an empirical challenge, as it encompasses multiple underlying processes. In this study, we use a preferential looking task and an instrumental choice task to capture different potential processes underlying social preference, in over 100 typically developing 3-9 year old children. Children spent longer looking at social stimuli in the preferential looking task but did not show a similar preference for social rewards on the instrumental choice task. This study highlights the importance of choice of paradigms when evaluating social preference and their potential impact on understanding social reward responsivity.

Imaging the “social brain” in schizophrenia: A systematic review of neuroimaging studies of social reward and punishment

2020 ◽  
Author(s):  
Jessica Mow ◽  
Arti Gandhi ◽  
Daniel Fulford
Keyword(s):  
Brain Activity ◽  
Interaction Network ◽  
Social Motivation ◽  
Social Impairment ◽  
Future Research ◽  
Social Reward ◽  
Schizophrenia Spectrum Disorders ◽  
Social Stimuli ◽  
Schizophrenia Spectrum ◽  
Reward And Punishment

Decreased social functioning and high levels of loneliness and social isolation are common in schizophrenia spectrum disorders (SSD), contributing to reduced quality of life. One key contributor to social impairment is low social motivation, which may stem from aberrant neural processing of socially rewarding or punishing stimuli. To summarize research on the neurobiology of social motivation in SSD, we performed a systematic literature review of neuroimaging studies involving the presentation of social stimuli intended to elicit feelings of reward and/or punishment. Across 11 studies meeting criteria, people with SSD demonstrated weaker modulation of brain activity in regions within a proposed social interaction network, including prefrontal, cingulate, and striatal regions, as well as the amygdala and insula. Firm conclusions regarding neural differences in SSD in these regions, as well as connections within networks, are limited due to conceptual and methodological inconsistencies across the available studies. We conclude by making recommendations for the study of social reward and punishment processing in SSD in future research.

scholarly journals Artificial Intelligence, 3D Documentation, and Rock Art—Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images

2021 ◽  
Author(s):  
Christian Horn ◽  
Oscar Ivarsson ◽  
Cecilia Lindhé ◽  
Rich Potter ◽  
Ashely Green ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bronze Age ◽  
Data Augmentation ◽  
Rock Art ◽  
Training Data ◽  
Future Research ◽  
Rock Surface ◽  
Southern Scandinavia ◽  
Bounding Boxes

AbstractRock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc. This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art.

Automatic information retrievement for exporting services: First project findings from the development of an AI based export decision supporting instrument

2021 ◽  
pp. 2-11
Author(s):  
David Aufreiter ◽  
Doris Ehrlinger ◽  
Christian Stadlmann ◽  
Margarethe Uberwimmer ◽  
Anna Biedersberger ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Research Process ◽  
Training Data ◽  
Future Research ◽  
Manufacturing Companies ◽  
Market Information ◽  
Annotation Scheme ◽  
Export Decisions ◽  
Decision Supporting

On the servitization journey, manufacturing companies complement their offerings with new industrial and knowledge-based services, which causes challenges of uncertainty and risk. In addition to the required adjustment of internal factors, the international selling of services is a major challenge. This paper presents the initial results of an international research project aimed at assisting advanced manufacturers in making decisions about exporting their service offerings to foreign markets. In the frame of this project, a tool is developed to support managers in their service export decisions through the automated generation of market information based on Natural Language Processing and Machine Learning. The paper presents a roadmap for progressing towards an Artificial Intelligence-based market information solution. It describes the research process steps of analyzing problem statements of relevant industry partners, selecting target countries and markets, defining parameters for the scope of the tool, classifying different service offerings and their components into categories and developing annotation scheme for generating reliable and focused training data for the Artificial Intelligence solution. This paper demonstrates good practices in essential steps and highlights common pitfalls to avoid for researcher and managers working on future research projects supported by Artificial Intelligence. In the end, the paper aims at contributing to support and motivate researcher and manager to discover AI application and research opportunities within the servitization field.

scholarly journals Biological Bases of Beauty Revisited: The Effect of Symmetry, Averageness, and Sexual Dimorphism on Female Facial Attractiveness

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 279 ◽  
Author(s):  
Alex L. Jones ◽  
Bastian Jaeger
Keyword(s):  
Sexual Dimorphism ◽  
Learning Algorithm ◽  
Facial Attractiveness ◽  
Choice Task ◽  
Future Research ◽  
Human Female ◽  
Literature Study ◽  
Skin Texture ◽  
Femininity And Masculinity ◽  
Forced Choice Task

The factors influencing human female facial attractiveness—symmetry, averageness, and sexual dimorphism—have been extensively studied. However, recent studies, using improved methodologies, have called into question their evolutionary utility and links with life history. The current studies use a range of approaches to quantify how important these factors actually are in perceiving attractiveness, through the use of novel statistical analyses and by addressing methodological weaknesses in the literature. Study One examines how manipulations of symmetry, averageness, femininity, and masculinity affect attractiveness using a two-alternative forced choice task, finding that increased masculinity and also femininity decrease attractiveness, compared to unmanipulated faces. Symmetry and averageness yielded a small and large effect, respectively. Study Two utilises a naturalistic ratings paradigm, finding similar effects of averageness and masculinity as Study One but no effects of symmetry and femininity on attractiveness. Study Three applies geometric face measurements of the factors and a random forest machine learning algorithm to predict perceived attractiveness, finding that shape averageness, dimorphism, and skin texture symmetry are useful features capable of relatively accurate predictions, while shape symmetry is uninformative. However, the factors do not explain as much variance in attractiveness as the literature suggests. The implications for future research on attractiveness are discussed.

Evaluation of the Team Workload Questionnaire (TWLQ) in a Team Choice Task

2017 ◽  
Vol 61 (1) ◽  
pp. 1317-1317
Author(s):  
Eric T. Greenlee ◽  
Gregory J. Funke ◽  
Lindsay Rice
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Factor Structure ◽  
Performance Outcomes ◽  
Task Type ◽  
Choice Task ◽  
Task Demands ◽  
Future Research ◽  
Team Dynamics ◽  
Team Task

To date, conceptual explanations of workload and development of workload measures have been focused primarily on individual workload, the workload of a single operator as they perform a task. Yet, this focus on individual workload does not consider the many situations in which operators are required to collaborate, communicate, and operate as a team to achieve successful performance outcomes. In short, conceptualization and development of team workload measures have lagged behind those of individual workload. In an effort to meet the need for a conceptually-driven team workload measure, Sellers, Helton, Näswall, Funke, and Knott (2014) recently developed the team workload questionnaire (TWLQ). In developing the measure, Sellers and colleagues asked rugby players to rate their workload on TWLQ items. Subsequent exploratory factor analysis suggested that team workload was best described by three latent factors: Taskwork, the demands for task execution on the individual; Teamwork, the demands for team members to cooperate and coordinate with other teammates; and Team-Task Balancing, the demands associated with the need to manage both taskwork and teamwork – reflective of the dual task nature of working within a team. As with any novel measure of workload, it is important to continue evaluation of the measure’s sensitivity to task demands, diagnosticity regarding sources of task demands, and correlation with performance outcomes. Early research with the TWLQ has demonstrated that the measure is sensitive to changes in team task demands and the effects of training in a team UAV control task (Helton, Epling, de Joux, Funke, & Knott, 2015; Sellers, Helton, Näswall, Funke, & Knott, 2015). An additional, critical component of continued validation of the TWLQ is confirmation of the factor structure initially observed by Sellers and colleagues (2014) with data generated from a novel task. Concerns regarding generalizability are particularly germane because of variability in the nature of tasks that teams engage. Whereas some teams are tasked with executing coordinated physical activities, such as is the case in athletic contests (e.g., rugby), the task of other teams is to talk, plan, and decide (e.g., committees; McGrath, 1984). In the current study, we applied the TWLQ in a collaborative choice task (a personnel hiring decision). This team choice task required a high degree of communication, discussion, and joint decision making – team dynamics that contrast sharply with those required during an execution task. In short, the nature of the teamwork in the current study was significantly different from the teamwork evaluated by Sellers and colleagues (2014) when generating the TWLQ. Our goal in this study was to continue validation of the TWLQ by examining its factor structure with a novel dataset derived from a task requiring qualitatively different team dynamics. Confirmatory factor analysis indicated that the present data (N = 144) were a poor fit for the three-factor structure of the TWLQ. Subsequent exploratory factor analysis revealed a much more interrelated model of team workload with no clear division between the three conceptual factors described in the original validation of the TWLQ. This finding indicates that the factor structure of the TWLQ did not generalize to the present team choice task. Given that the duties of operational teams vary, it is critical that future research examine how the conceptual structure of team workload may be altered by task type.

scholarly journals Brief Report: Preferred Processing of Social Stimuli in Autism: A Perception Task

2021 ◽  
Author(s):  
A. Meermeier ◽  
M. Jording ◽  
Y. Alayoubi ◽  
David H. V. Vogel ◽  
K. Vogeley ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Exploratory Analysis ◽  
Spectrum Disorder ◽  
Social Stimuli ◽  
Future Studies ◽  
Perception Task ◽  
Social Images

AbstractIn this study we investigate whether persons with autism spectrum disorder (ASD) perceive social images differently than control participants (CON) in a graded perception task in which stimuli emerged from noise before dissipating into noise again. We presented either social stimuli (humans) or non-social stimuli (objects or animals). ASD were slower to recognize images during their emergence, but as fast as CON when indicating the dissipation of the image irrespective of its content. Social stimuli were recognized faster and remained discernable longer in both diagnostic groups. Thus, ASD participants show a largely intact preference for the processing of social images. An exploratory analysis of response subsets reveals subtle differences between groups that could be investigated in future studies.

scholarly journals BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition

2020 ◽  
Author(s):  
Usman Naseem ◽  
Matloob Khushi ◽  
Vinay Reddy ◽  
Sakthivel Rajendran ◽  
Imran Razzak ◽  
...  
Keyword(s):  
State Of The Art ◽  
Language Model ◽  
Named Entity Recognition ◽  
Training Data ◽  
Entity Recognition ◽  
Future Research ◽  
Named Entity ◽  
Domain Specific ◽  
Context Dependent ◽  
Biomedical Named Entity Recognition

Abstract Background: In recent years, with the growing amount of biomedical documents, coupled with advancement in natural language processing algorithms, the research on biomedical named entity recognition (BioNER) has increased exponentially. However, BioNER research is challenging as NER in the biomedical domain are: (i) often restricted due to limited amount of training data, (ii) an entity can refer to multiple types and concepts depending on its context and, (iii) heavy reliance on acronyms that are sub-domain specific. Existing BioNER approaches often neglect these issues and directly adopt the state-of-the-art (SOTA) models trained in general corpora which often yields unsatisfactory results. Results: We propose biomedical ALBERT (A Lite Bidirectional Encoder Representations from Transformers for Biomedical Text Mining) - bioALBERT - an effective domain-specific pre-trained language model trained on huge biomedical corpus designed to capture biomedical context-dependent NER. We adopted self-supervised loss function used in ALBERT that targets on modelling inter-sentence coherence to better learn context-dependent representations and incorporated parameter reduction strategies to minimise memory usage and enhance the training time in BioNER. In our experiments, BioALBERT outperformed comparative SOTA BioNER models on eight biomedical NER benchmark datasets with four different entity types. The performance is increased for; (i) disease type corpora by 7.47% (NCBI-disease) and 10.63% (BC5CDR-disease); (ii) drug-chem type corpora by 4.61% (BC5CDR-Chem) and 3.89 (BC4CHEMD); (iii) gene-protein type corpora by 12.25% (BC2GM) and 6.42% (JNLPBA); and (iv) Species type corpora by 6.19% (LINNAEUS) and 23.71% (Species-800) is observed which leads to a state-of-the-art results. Conclusions: The performance of proposed model on four different biomedical entity types shows that our model is robust and generalizable in recognizing biomedical entities in text. We trained four different variants of BioALBERT models which are available for the research community to be used in future research.

Metric Methods in Data Mining

2008 ◽  
pp. 849-879
Author(s):  
Dan A. Simovici
Keyword(s):  
Data Mining ◽  
Metric Space ◽  
Training Data ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Future Research Directions ◽  
Major Data ◽  
Geometric Study

This chapter presents data mining techniques that make use of metrics defined on the set of partitions of finite sets. Partitions are naturally associated with object attributes and major data mining problem such as classification, clustering, and data preparation benefit from an algebraic and geometric study of the metric space of partitions. The metrics we find most useful are derived from a generalization of the entropic metric. We discuss techniques that produce smaller classifiers, allow incremental clustering of categorical data and help user to better prepare training data for constructing classifiers. Finally, we discuss open problems and future research directions.

scholarly journals A study about Explainable Articial Intelligence: using decision tree to explain SVM

2020 ◽  
Vol 12 (1) ◽  
pp. 113-121
Author(s):  
Carla Piazzon Ramos Vieira ◽  
Luciano Antonio Digiampietri
Keyword(s):  
Deep Neural Networks ◽  
Training Data ◽  
Svm Classifier ◽  
Rule Based ◽  
The Past ◽  
Black Boxes ◽  
Inherent Problem ◽  
Self Driving Cars ◽  
Post Hoc ◽  
Health Diagnosis

The technologies supporting Artificial Intelligence (AI) have advanced rapidly over the past few years and AI is becoming a commonplace in every aspect of life like the future of self-driving cars or earlier health diagnosis. For this to occur shortly, the entire community stands in front of the barrier of explainability, an inherent problem of latest models (e.g. Deep Neural Networks) that were not present in the previous hype of AI (linear and rule-based models). Most of these recent models are used as black boxes without understanding partially or even completely how different features influence the model prediction avoiding algorithmic transparency. In this paper, we focus on how much we can understand the decisions made by an SVM Classifier in a post-hoc model agnostic approach. Furthermore, we train a tree-based model (inherently interpretable) using labels from the SVM, called secondary training data to provide explanations and compare permutation importance method to the more commonly used measures such as accuracy and show that our methods are both more reliable and meaningful techniques to use. We also outline the main challenges for such methods and conclude that model-agnostic interpretability is a key component in making machine learning more trustworthy.

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