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A lower bound on the number of mechanisms for discriminating fourth and higher order spatial correlations

The human visual system must employ mechanisms to minimize informational redundancy whilst maximizing dynamic range and maintaining that which is behaviorally relevant [1, 2]. Previous research has concentrated on two-point correlation properties, as captured by spatial frequency and orientation tuning. There has been less research into higher-order correlations although they may inform us about cortical functioning [3]. Isotrigon textures can be used to probe the sensitivity of the human visual system. The obvious structure in isotrigons is exclusively due to 4th and higher-order spatial correlations [4]. Thus, in order to discriminate isotrigons from noise, it is necessary to identify higher-order structure. Although artificially generated, the same structural features that give isotrigons salience also create salience in natural images [2].

Factor analysis can be used to infer the number of underlying independent neurological mechanisms which govern isotrigon discrimination. In this study, mean performance functions were calculated for two subjects using ten new isotrigons (VnL2) (Figure 1A). Two forms of factor analysis identified 3 principal factors (Figure 1B) [5]. Previous studies support that the number of mechanisms is less than 10 [6], and more likely 2-4 [7, 8]. Such mechanisms may represent some combination of recursive or rectifying processes. Simple models of cortical processing, based on recursion, can discriminate isotrigons [9]. The formation of recursively applied products is physiologically plausible and can occur via dendritic back-propagation or dendritic spiking [10].

Figure 1
figure1

A: Mean performance functions for all subjects by texture type, presented separately according to subject and texture size. For All16 (16x16 textures) and All32 (32x32) error bars are SE for n = 6 subjects. Glider shapes are shown in the bottom panel. B: Communalities for 5 different factor models. As the number of factors grows, the profile of bars becomes flatter indicating that the models progressively account for the data in a more balanced way. After nf = 3, the improvement in the reconstruction is marginal.

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Correspondence to John WG Seamons.

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Keywords

  • Structural Feature
  • Simple Model
  • Spatial Frequency
  • Spatial Correlation
  • Performance Function