Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semantic vector space models predict neural responses to complex visual
  stimuli   Encoding models have as their objective to predict neural responses to
naturalistic stimuli with the aim of elucidating how sensory information is
represented in the brain. This prediction is achieved by representing the
stimulus in terms of a suitable feature space and using this feature space to
linearly predict observed neural responses. Here, we investigate to what extent
semantic vector space models can be used to predict neural responses to complex
visual stimuli. We show that these models provide good predictions of neural
responses in downstream visual areas, improving significantly over a low-level
control model based on Gabor wavelet pyramids. The outlined approach provides a
new way to model and map high-level semantic representations across cortex.

28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph