scholarly journals The Sensory and Cognitive Ecology of Nectar Robbing

2021 ◽  
Vol 9 ◽  
Author(s):  
Sarah K. Richman ◽  
Jessica L. Barker ◽  
Minjung Baek ◽  
Daniel R. Papaj ◽  
Rebecca E. Irwin ◽  
...  

Animals foraging from flowers must assess their environment and make critical decisions about which patches, plants, and flowers to exploit to obtain limiting resources. The cognitive ecology of plant-pollinator interactions explores not only the complex nature of pollinator foraging behavior and decision making, but also how cognition shapes pollination and plant fitness. Floral visitors sometimes depart from what we think of as typical pollinator behavior and instead exploit floral resources by robbing nectar (bypassing the floral opening and instead consuming nectar through holes or perforations made in floral tissue). The impacts of nectar robbing on plant fitness are well-studied; however, there is considerably less understanding, from the animal’s perspective, about the cognitive processes underlying nectar robbing. Examining nectar robbing from the standpoint of animal cognition is important for understanding the evolution of this behavior and its ecological and evolutionary consequences. In this review, we draw on central concepts of foraging ecology and animal cognition to consider nectar robbing behavior either when individuals use robbing as their only foraging strategy or when they switch between robbing and legitimate foraging. We discuss sensory and cognitive biases, learning, and the role of a variable environment in making decisions about robbing vs. foraging legitimately. We also discuss ways in which an understanding of the cognitive processes involved in nectar robbing can address questions about how plant-robber interactions affect patterns of natural selection and floral evolution. We conclude by highlighting future research directions on the sensory and cognitive ecology of nectar robbing.

2016 ◽  
Vol 283 (1825) ◽  
pp. 20152890 ◽  
Author(s):  
John Skelhorn ◽  
Candy Rowe

Camouflage is one of the most widespread forms of anti-predator defence and prevents prey individuals from being detected or correctly recognized by would-be predators. Over the past decade, there has been a resurgence of interest in both the evolution of prey camouflage patterns, and in understanding animal cognition in a more ecological context. However, these fields rarely collide, and the role of cognition in the evolution of camouflage is poorly understood. Here, we review what we currently know about the role of both predator and prey cognition in the evolution of prey camouflage, outline why cognition may be an important selective pressure driving the evolution of camouflage and consider how studying the cognitive processes of animals may prove to be a useful tool to study the evolution of camouflage, and vice versa. In doing so, we highlight that we still have a lot to learn about the role of cognition in the evolution of camouflage and identify a number of avenues for future research.


2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.


2006 ◽  
Vol 152 ◽  
pp. 35-53 ◽  
Author(s):  
Machteld Moonen ◽  
Rick de Graaff ◽  
Gerard Westhoff

Abstract This paper presents a theoretical framework to estimate the effectiveness of second language tasks in which the focus is on the acquisition of new linguistic items, such as vocabulary or grammar, the so-called focused tasks (R. Ellis, 2003). What accounts for the learning impact offocused tasks? We shall argue that the task-based approach (e.g. Skehan, 1998, Robinson, 2001) does not provide an in-depth account of how cognitive processes, elicited by a task, foster the acquisition of new linguistic elements. We shall then review the typologies of cognitive processes derived from research on learning strategies (Chamot & O'Malley, 1994), from the involvement load hypothesis (Laufer & Hulstijn, 2001), from the depth of processing hypothesis (Craik & Lockhart, 1972) and from connectionism (e.g Broeder & Plunkett, 1997; N. Ellis, 2003). The combined insights of these typologies form the basis of the multi-feature hypothesis, which predicts that retention and ease of activation of new linguistic items are improved by mental actions which involve a wide variety of different features, simultaneously and frequently. A number of implications for future research shall be discussed.


2018 ◽  
Vol 31 (2) ◽  
pp. 107-133 ◽  
Author(s):  
Edward J. Lynch ◽  
Lindsay M. Andiola

ABSTRACT Recent advances in technology have increased the accessibility and ease in using eye-tracking as a research tool. These advances have the potential to benefit behavioral accounting researchers' understanding of the cognitive processes underlying individuals' judgments, decisions, and behaviors. However, despite its potential and wide use in other disciplines, few behavioral accounting studies use eye-tracking. The purpose of this paper is to familiarize accounting researchers with eye-tracking, including its advantages and limitations as a research tool. We start by providing an overview of eye-tracking and discussing essential terms and useful metrics, as well as the psychological constructs they proxy. We then summarize eye-tracking research across research domains, review accounting studies that use eye-tracking, and identify future research directions across accounting topics. Finally, we provide an instructional resource to guide those researchers interested in using eye-tracking, including important considerations at each stage of the study. JEL Classifications: M41; C91.


2021 ◽  
Vol 13 (3) ◽  
pp. 1589
Author(s):  
Juan Sánchez-Fernández ◽  
Luis-Alberto Casado-Aranda ◽  
Ana-Belén Bastidas-Manzano

The limitations of self-report techniques (i.e., questionnaires or surveys) in measuring consumer response to advertising stimuli have necessitated more objective and accurate tools from the fields of neuroscience and psychology for the study of consumer behavior, resulting in the creation of consumer neuroscience. This recent marketing sub-field stems from a wide range of disciplines and applies multiple types of techniques to diverse advertising subdomains (e.g., advertising constructs, media elements, or prediction strategies). Due to its complex nature and continuous growth, this area of research calls for a clear understanding of its evolution, current scope, and potential domains in the field of advertising. Thus, this current research is among the first to apply a bibliometric approach to clarify the main research streams analyzing advertising persuasion using neuroimaging. Particularly, this paper combines a comprehensive review with performance analysis tools of 203 papers published between 1986 and 2019 in outlets indexed by the ISI Web of Science database. Our findings describe the research tools, journals, and themes that are worth considering in future research. The current study also provides an agenda for future research and therefore constitutes a starting point for advertising academics and professionals intending to use neuroimaging techniques.


2013 ◽  
Vol 13 (4) ◽  
pp. 663-673 ◽  
Author(s):  
Grażyna Sender ◽  
Agnieszka Korwin-Kossakowska ◽  
Adrianna Pawlik ◽  
Karima Galal Abdel Hameed ◽  
Jolanta Oprządek

Abstract Mastitis is one of the most important mammary gland diseases impacting lactating animals. Resistance to this disease could be improved by breeding. There are several selection methods for mastitis resistance. To improve the natural genetic resistance of cows in succeeding generations, current breeding programmes use somatic cell count and clinical mastitis cases as resistance traits. However, these methods of selection have met with limited success. This is partly due to the complex nature of the disease. The limited progress in improving udder health by conventional selection procedures requires applying information on molecular markers of mastitis susceptibility in marker-assisted selection schemes. Mastitis is under polygenic control, so there are many genes that control this trait in many loci. This review briefly describes genome-wide association studies which have been carried out to identify quantitative trait loci associated with mastitis resistance in dairy cattle worldwide. It also characterizes the candidate gene approach focus on identifying genes that are strong candidates for the mastitis resistance trait. In the conclusion of the paper we focus our attention on future research which should be conducted in the field of the resistance to mastitis.


2021 ◽  
Author(s):  
Hugh McGovern ◽  
Marte Otten

Bayesian processing has become a popular framework by which to understand cognitive processes. However, relatively little has been done to understand how Bayesian processing in the brain can be applied to understanding intergroup cognition. We assess how categorization and evaluation processes unfold based on priors about the ethnic outgroup being perceived. We then consider how the precision of prior knowledge about groups differentially influence perception depending on how the information about that group was learned affects the way in which it is recalled. Finally, we evaluate the mechanisms of how humans learn information about other ethnic groups and assess how the method of learning influences future intergroup perception. We suggest that a predictive processing framework for assessing prejudice could help accounting for seemingly disparate findings on intergroup bias from social neuroscience, social psychology, and evolutionary psychology. Such an integration has important implications for future research on prejudice at the interpersonal, intergroup, and societal levels.


Robotica ◽  
2004 ◽  
Vol 22 (5) ◽  
pp. 533-545 ◽  
Author(s):  
M. Benosman ◽  
G. Le Vey

A survey of the field of control for flexible multi-link robots is presented. This research area has drawn great attention during the last two decades, and seems to be somewhat less “attractive” now, due to the many satisfactory results already obtained, but also because of the complex nature of the remaining open problems. Thus it seems that the time has come to try to deliver a sort of “state of the art” on this subject, although an exhaustive one is out of scope here, because of the great amount of publications. Instead, we survey the most salient progresses – in our opinion – approximately during the last decade, that are representative of the essential different ideas in the field. We proceed along with the exposition of material coming from about 119 included references. We do not pretend to deeply present each of the methods quoted hereafter; however, our goal is to briefly introduce most of the existing methods and to refer the interested reader to more detailed presentations for each scheme. To begin with, a now well-established classification of the flexible arms control goals is given. It is followed by a presentation of different control strategies, indicating in each case whether the approach deals with the one-link case, which can be successfully treated via linear models, or with the multi-link case which necessitates nonlinear, more complex, models. Some possible issues for future research are given in conclusion.


2017 ◽  
Vol 28 (5) ◽  
pp. 998-1023 ◽  
Author(s):  
Ian R. Hodgkinson ◽  
Claire Hannibal ◽  
Byron W. Keating ◽  
Rosamund Chester Buxton ◽  
Nicola Bateman

Purpose In providing a fine-grained analysis of public service management, the purpose of this paper is to make an important contribution to furthering research in service management, a body of literature that has tended to regard public services as homogenous or to neglect the context altogether. Design/methodology/approach Integrating public management and service management literatures, the past and present of public service management are discussed. Future directions for the field are outlined drawing on a service-dominant approach that has the potential to transform public services. Invited commentaries augment the review. Findings The review presents the Public Service Network Framework to capture the public value network in its abstraction and conceptualizes how value is created in public services. The study identifies current shortcomings in the field and offers a series of directions for future research where service management theory can contribute greatly. Research limitations/implications The review encourages service management research to examine the dynamic, diverse, and complex nature of public services and to recognize the importance of this context. The review calls for an interdisciplinary public service management community to develop, and to assist public managers in leveraging service logic. Originality/value The review positions service research in the public sector, makes explicit the role of complex networks in value creation, argues for wider engagement with public service management, and offers future research directions to advance public service management research.


2020 ◽  
Author(s):  
Amir Mosavi

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.


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