Synthesized clustering: A method for amalgamating alternative clustering bases with differential weighting of variables

Decisions we face in real life are inherently risky and can result in one of many possible outcomes. However, most of what we know about choice under risk is based on studies that use options with only two possible outcomes (simple gambles), so it remains unclear how the brain constructs reward values for more complex risky options faced in real life. To address this question, we combined experimental and modeling approaches to examine choice between pairs of simple gambles and pairs of three-outcome gambles in male and female human subjects. We found that subjects evaluated individual outcomes of three-outcome gambles by multiplying functions of reward magnitude and probability. To construct the overall value of each gamble, however, most subjects differentially weighted possible outcomes based on either reward magnitude or probability. These results reveal a novel dissociation between how reward information is processed when evaluating complex gambles: valuation of each outcome is based on an integrated value whereas combination of possible outcomes relies on a single piece of reward information. We show that differential weighting of possible outcomes enabled subjects to make decisions more easily and quickly. Together, these findings reveal a plausible mechanism for how salience, in terms of possible reward magnitude or probability, can influence the construction of subjective values for complex gambles. They also point to separable neural mechanisms for how reward value controls choice and attention in order to allow for more adaptive decision making.

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Alternative Clustering

Advances in Business Information Systems and Analytics - Applying Predictive Analytics Within the Service Sector ◽

10.4018/978-1-5225-2148-8.ch001 ◽

2017 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Avinash Navlani ◽

V. B. Gupta

Keyword(s):

Research Problem ◽

Research Community ◽

Data Sets ◽

Complex Data ◽

High Quality ◽

Data Set ◽

Alternative Clustering ◽

Complex Data Sets ◽

Data Objects ◽

Community Clustering

In the last couple of decades, clustering has become a very crucial research problem in the data mining research community. Clustering refers to the partitioning of data objects such as records and documents into groups or clusters of similar characteristics. Clustering is unsupervised learning, because of unsupervised nature there is no unique solution for all problems. Most of the time complex data sets require explanation in multiple clustering sets. All the Traditional clustering approaches generate single clustering. There is more than one pattern in a dataset; each of patterns can be interesting in from different perspectives. Alternative clustering intends to find all unlike groupings of the data set such that each grouping has high quality and distinct from each other. This chapter gives you an overall view of alternative clustering; it's various approaches, related work, comparing with various confusing related terms like subspace, multi-view, and ensemble clustering, applications, issues, and challenges.

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Language-dependent changes in pitch-relevant neural activity in the auditory cortex reflect differential weighting of temporal attributes of pitch contours

Journal of Neurolinguistics ◽

10.1016/j.jneuroling.2016.09.005 ◽

2017 ◽

Vol 41 ◽

pp. 38-49 ◽

Cited By ~ 6

Author(s):

Ananthanarayan Krishnan ◽

Jackson T. Gandour ◽

Yi Xu ◽

Chandan H. Suresh

Keyword(s):

Auditory Cortex ◽

Neural Activity ◽

Differential Weighting ◽

Pitch Contours

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Community structure in the World Trade Network based on communicability distances

Journal of Economic Interaction and Coordination ◽

10.1007/s11403-020-00309-y ◽

2020 ◽

Author(s):

Paolo Bartesaghi ◽

Gian Paolo Clemente ◽

Rosanna Grassi

Keyword(s):

Community Structure ◽

Numerical Analysis ◽

World Trade ◽

Metric Spaces ◽

Trade Network ◽

Distance Threshold ◽

Alternative Clustering ◽

Mesoscale Structure ◽

Long Range Interactions ◽

The World

AbstractIn this paper, we investigate the mesoscale structure of the World Trade Network. In this framework, a specific role is assumed by short- and long-range interactions, and hence by any suitably defined network-based distance between countries. Therefore, we identify clusters through a new procedure that exploits Estrada communicability distance and the vibrational communicability distance, which turn out to be particularly suitable for catching the inner structure of the economic network. The proposed methodology aims at finding the distance threshold that maximizes a specific quality function defined for general metric spaces. Main advantages regard the computational efficiency of the procedure as well as the possibility to inspect intercluster and intracluster properties of the resulting communities. The numerical analysis highlights peculiar relationships between countries and provides a rich set of information that can hardly be achieved within alternative clustering approaches.

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Saccadic Localisation of Random Dot Targets

Perception ◽

10.1068/v96l0304 ◽

1996 ◽

Vol 25 (1_suppl) ◽

pp. 101-101 ◽

Cited By ~ 1

Author(s):

J W McGowan ◽

E Kowler ◽

A Sharma ◽

C Chubb

Keyword(s):

Saccadic Eye Movements ◽

Natural Scenes ◽

Landing Position ◽

Local Pattern ◽

Centre Of Gravity ◽

Local Characteristics ◽

Spatial Pooling ◽

Initial Fixation ◽

Single Target ◽

Differential Weighting

Saccadic eye movements land at precise places within simple target forms implying that a spatial pooling process operates over attended regions to determine the saccadic endpoint. To study pooling, we used large, unstructured targets and looked for evidence of differential spatial weighting based on local pattern characteristics. Subjects made a saccade to targets composed of 19 dots scattered randomly within a 4 deg diameter region horizontally displaced 3.8 – 4.2 deg to the left or right of initial fixation. Dot intensity was either uniform or variable. Saccadic landing positions were close to the centre-of-gravity (overshooting or under- shooting by 5% – 10% depending on subject, direction and eccentricity). Precision was excellent (SD=10% ecc), although not as good as with single target points (SD=7% ecc). Correlations between the presence of a dot and saccadic landing position showed that all regions of the pattern contributed. Differential weighting of dots according to location (eg near vs far; central vs boundary) did not yield better predictions of the saccadic landing position. However, predictions of the landing position were improved by assigning more weight to higher-intensity dots. Local dot clusters contributed less than would be expected from the contributions of individual dots. Spatial pooling is highly effective over a large region. Saccadic overshoots or undershoots were not due to differential spatial weighting, and may originate after the centre-of-gravity computation. The differential weighting of high-intensity dots and dot clusters demonstrates sensitivity to local characteristics, and implies that the saccadic endpoint may be determined by pooling the activity of units centred on different subregions of the target. The pooling mechanism supports precise saccadic localisation of large, unstructured targets, and accounts for the ease with which we direct saccades to chosen objects in natural scenes.

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