Goker Erdogan

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People learn modality-independent, conceptual representations from modality-specific sensory signals. Here, we hypothesize that any system that accomplishes this feat will include three components: a representational language for characterizing modality-independent representations, a set of sensory-specific forward models for mapping from(More)
We investigate the hypothesis that multisensory representations mediate the crossmodal transfer of shape knowledge across visual and haptic modalities. In our experiment, participants rated the similarities of pairs of synthetic 3-D objects in visual, haptic, cross-modal, and multisensory settings. Our results offer two contributions. First, we provide(More)
The format of high-level object representations in temporal-occipital cortex is a fundamental and as yet unresolved issue. Here we use fMRI to show that human lateral occipital cortex (LOC) encodes novel 3-D objects in a multisensory and part-based format. We show that visual and haptic exploration of objects leads to similar patterns of neural activity in(More)
In the past few years, deep convolutional neural networks (CNNs) trained on large image data sets have shown impressive visual object recognition performances. Consequently, these models have attracted the attention of the cognitive science community. Recent studies comparing CNNs with neural data from cortical area IT suggest that CNNs may—in addition to(More)
The world is multimodal.1 We sense our environments using inputs from multiple sensory modalities. Similarly, digital information is increasingly available through multiple media. In this extended abstract, we present a general computational framework for understanding multimodal learning and perception that builds on the analysis-by-synthesis approach [2,(More)
This paper presents a computational model of concept learning using Bayesian inference for a grammatically structured hypothesis space, and test the model on multisensory (visual and haptics) recognition of 3D objects. The study is performed on a set of artificially generated 3D objects known as fribbles, which are complex, multipart objects with(More)
Despite decades of research, little is known about how people visually perceive object shape. We hypothesize that a promising approach to shape perception is provided by a "visual perception as Bayesian inference" framework which augments an emphasis on visual representation with an emphasis on the idea that shape perception is a form of statistical(More)
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