How Competition between Action Representations Affects Object Perception during Development

Author(s):  
Marc Godard ◽  
Yannick Wamain ◽  
Laurent Ott ◽  
Samuel Delepoulle ◽  
Solène Kalénine
Author(s):  
Sumit Kaur

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 


2021 ◽  
pp. 263497952110276
Author(s):  
Hemangini Gupta

This essay offers a retrospective account of a multimodal public exhibit at the end of a multi-year research project on speculative urbanism. While the registers of speculation are invariably forward-looking, our research presented us with the central place of memory as a frame through which urban residents in Bengaluru, India, negotiate their present and imagine the possibilities of the future. This essay examines four ways in which we created space for memory in our exhibit, understanding our approach as situating an archive-optic, drawing on approaches of critical fabulation, object perception, and submerged perspectives. I suggest that these forms of engagement are multimodal and that they offer feminist and decolonial ways to unmaster linear narratives and situate our research affectively.


2019 ◽  
Vol 31 (6) ◽  
pp. 821-836 ◽  
Author(s):  
Elliot Collins ◽  
Erez Freud ◽  
Jana M. Kainerstorfer ◽  
Jiaming Cao ◽  
Marlene Behrmann

Although shape perception is primarily considered a function of the ventral visual pathway, previous research has shown that both dorsal and ventral pathways represent shape information. Here, we examine whether the shape-selective electrophysiological signals observed in dorsal cortex are a product of the connectivity to ventral cortex or are independently computed. We conducted multiple EEG studies in which we manipulated the input parameters of the stimuli so as to bias processing to either the dorsal or ventral visual pathway. Participants viewed displays of common objects with shape information parametrically degraded across five levels. We measured shape sensitivity by regressing the amplitude of the evoked signal against the degree of stimulus scrambling. Experiment 1, which included grayscale versions of the stimuli, served as a benchmark establishing the temporal pattern of shape processing during typical object perception. These stimuli evoked broad and sustained patterns of shape sensitivity beginning as early as 50 msec after stimulus onset. In Experiments 2 and 3, we calibrated the stimuli such that visual information was delivered primarily through parvocellular inputs, which mainly project to the ventral pathway, or through koniocellular inputs, which mainly project to the dorsal pathway. In the second and third experiments, shape sensitivity was observed, but in distinct spatio-temporal configurations from each other and from that elicited by grayscale inputs. Of particular interest, in the koniocellular condition, shape selectivity emerged earlier than in the parvocellular condition. These findings support the conclusion of distinct dorsal pathway computations of object shape, independent from the ventral pathway.


Open Mind ◽  
2020 ◽  
Vol 4 ◽  
pp. 40-56 ◽  
Author(s):  
Erez Freud ◽  
Marlene Behrmann ◽  
Jacqueline C. Snow

According to the influential “Two Visual Pathways” hypothesis, the cortical visual system is segregated into two pathways, with the ventral, occipitotemporal pathway subserving object perception, and the dorsal, occipitoparietal pathway subserving the visuomotor control of action. However, growing evidence suggests that the dorsal pathway also plays a functional role in object perception. In the current article, we present evidence that the dorsal pathway contributes uniquely to the perception of a range of visuospatial attributes that are not redundant with representations in ventral cortex. We describe how dorsal cortex is recruited automatically during perception, even when no explicit visuomotor response is required. Importantly, we propose that dorsal cortex may selectively process visual attributes that can inform the perception of potential actions on objects and environments, and we consider plausible developmental and cognitive mechanisms that might give rise to these representations. As such, we consider whether naturalistic stimuli, such as real-world solid objects, might engage dorsal cortex more so than simplified or artificial stimuli such as images that do not afford action, and how the use of suboptimal stimuli might limit our understanding of the functional contribution of dorsal cortex to visual perception.


2011 ◽  
Vol 49 (12) ◽  
pp. 3406-3418 ◽  
Author(s):  
Boge Stojanoski ◽  
Matthias Niemeier

2012 ◽  
Vol 35 (1) ◽  
pp. 150-157 ◽  
Author(s):  
Marc H. Bornstein ◽  
Clay Mash ◽  
Martha E. Arterberry ◽  
Nanmathi Manian

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