Topic: classification images

Diverse methods have been developed to visualize the representations learnt by artificial neural networks. Such visualization methods however are not easily adapted to biological systems. This prevents direct comparisons between the internal representations of artificial and biological neural systems. One promising approach to visualize sensory representations in biological systems is reverse correlation. Behavioural reverse correlation experiments leverage pareidolia, our tendency to detect spurious signals in noise, such as seeing shapes, objects, or faces in clouds. In these experiments, ob

Spatial frequencies (SF) optimal for word recognition are well established. However, other classes of stimuli demonstrate a rapid temporal evolution of SFs most useful for visual recognition. (Bar,2003,JcogNeurosci) This study aims to assess the time course of SF processing in visual word recognition. Word images were filtered like so: 1.2, 2.4, 4.8, and 9.6 cycles per degree, with 200 ms exposure duration and variyng signal-to-noise ratio (SNR; signal=image; noise=white noise) according to a random sampling function integrating sine waves with 5-55 Hz frequencies in steps of 5 Hz. Classificat