Bayesian multi-level modelling for predicting single and double feature visual search

Performance in visual search tasks is frequently summarised by “search slopes”- the additional cost in reaction time for each additional distractor.  While searchtasks with a shallow search slopes are termed efficient (pop-out, parallel, feature),there is no clear dichotomy between efficient and inefficient (serial, conjunction)search. Indeed, a range of search slopes are observed in empirical data. The Tar-get Contrast Signal (TCS) Theory is a rare example of quantitative model thatattempts  to  predict  search  slopes  for  efficient  visual  search.   One  study  usingthe TCS framework has shown that the search slope in a double-feature search(where the target differs in both colour and shape from the distractors) can be es-timated from the slopes of the associated single-feature searches. This estimationis done using a contrast combination model, and a collinear contrast integrationmodel was shown to outperform other options.  In our work, we extend TCS toa Bayesian multi-level framework.  We investigate modelling using normal andshifted-lognormal distributions, and show that the latter allows for a better fit topreviously published data.  We run a new fully within-subjects experiment to at-tempt to replicate the key original findings,  and show that overall,  TCS does agood job of predicting the data. However, we do not replicate the finding that thecollinear combination model outperforms the other contrast combination models,instead finding that it may be difficult to conclusively distinguish between them.