Perceptual learning of second order cues for layer decomposition

D.N. Dövencioĝlu, A.E. Welchman, A.J. Schofield

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
158 Downloads (Pure)


Luminance variations are ambiguous: they can signal changes in surface reflectance or changes in illumination. Layer decomposition-the process of distinguishing between reflectance and illumination changes-is supported by a range of secondary cues including colour and texture. For an illuminated corrugated, textured surface the shading pattern comprises modulations of luminance (first order, LM) and local luminance amplitude (second-order, AM). The phase relationship between these two signals enables layer decomposition, predicts the perception of reflectance and illumination changes, and has been modelled based on early, fast, feed-forward visual processing (Schofield et al., 2010). However, while inexperienced viewers appreciate this scission at long presentation times, they cannot do so for short presentation durations (250. ms). This might suggest the action of slower, higher-level mechanisms. Here we consider how training attenuates this delay, and whether the resultant learning occurs at a perceptual level. We trained observers to discriminate the components of plaid stimuli that mixed in-phase and anti-phase LM/AM signals over a period of 5. days. After training, the strength of the AM signal needed to differentiate the plaid components fell dramatically, indicating learning. We tested for transfer of learning using stimuli with different spatial frequencies, in-plane orientations, and acutely angled plaids. We report that learning transfers only partially when the stimuli are changed, suggesting that benefits accrue from tuning specific mechanisms, rather than general interpretative processes. We suggest that the mechanisms which support layer decomposition using second-order cues are relatively early, and not inherently slow.
Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalVision Research
Publication statusPublished - 25 Jan 2013


  • Second-order
  • Layer-decomposition
  • Perceptual-learning


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