Evidence Accumulation for Spatial Reasoning.

Author(s):  
T. Matsuyama ◽  
V. S. S. Hwang ◽  
L. S. Davis
1983 ◽  
Author(s):  
V. S. S. Hwang ◽  
T. Matsuyama ◽  
L. S. Davis ◽  
A. Rosenfeld

Author(s):  
Allison J. Jaeger ◽  
Andrew F. Jarosz ◽  
Jennifer Wiley
Keyword(s):  

2001 ◽  
Author(s):  
John R. Rani ◽  
Thomas E. Dawson ◽  
Julia H. Chariker

2020 ◽  
Author(s):  
Lluís Hernández-Navarro ◽  
Ainhoa Hermoso-Mendizabal ◽  
Daniel Duque ◽  
Alexandre Hyafil ◽  
Jaime de la Rocha

It is commonly assumed that, during perceptual decisions, the brain integrates stimulus evidence until reaching a decision, and then performs the response. There are conditions, however (e.g. time pressure), in which the initiation of the response must be prepared in anticipation of the stimulus presentation. It is therefore not clear when the timing and the choice of perceptual responses depend exclusively on evidence accumulation, or when preparatory motor signals may interfere with this process. Here, we find that, in a free reaction time auditory discrimination task in rats, the timing of fast responses does not depend on the stimulus, although the choices do, suggesting a decoupling of the mechanisms of action initiation and choice selection. This behavior is captured by a novel model, the Parallel Sensory Integration and Action Model (PSIAM), in which response execution is triggered whenever one of two processes, Action Initiation or Evidence Accumulation, reaches a bound, while choice category is always set by the latter. Based on this separation, the model accurately predicts the distribution of reaction times when the stimulus is omitted, advanced or delayed. Furthermore, we show that changes in Action Initiation mediates both post-error slowing and a gradual slowing of the responses within each session. Overall, these results extend the standard models of perceptual decision-making, and shed a new light on the interaction between action preparation and evidence accumulation.


2021 ◽  
Vol 5 (1) ◽  
pp. 1-20
Author(s):  
Isabelle Bloch

Abstract In many domains of information processing, such as knowledge representation, preference modeling, argumentation, multi-criteria decision analysis, spatial reasoning, both vagueness, or imprecision, and bipolarity, encompassing positive and negative parts of information, are core features of the information to be modeled and processed. This led to the development of the concept of bipolar fuzzy sets, and of associated models and tools, such as fusion and aggregation, similarity and distances, mathematical morphology. Here we propose to extend these tools by defining algebraic and topological relations between bipolar fuzzy sets, including intersection, inclusion, adjacency and RCC relations widely used in mereotopology, based on bipolar connectives (in a logical sense) and on mathematical morphology operators. These definitions are shown to have the desired properties and to be consistent with existing definitions on sets and fuzzy sets, while providing an additional bipolar feature. The proposed relations can be used for instance for preference modeling or spatial reasoning. They apply more generally to any type of functions taking values in a poset or a complete lattice, such as L-fuzzy sets.


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