Attribute preference and selection in multi-attribute decision making: implications for unconscious and conscious thought

Unconscious thought theory (UTT) states that all information is taken into account and the attributes are weighted optimally resulting in better decisions in complex decision problems during unconscious thought. Very few studies have investigated the actual amount of information processed in the unconscious thought condition. We hypothesized that only a small subset of information might be considered during unconscious thought (like conscious thought). To test this possibility and to explore the way attribute information is selected and combined, we performed computer simulations on the datasets used by previous researchers. The simulations showed that considering a small subset (3-4) of attributes, yields results comparable to previous studies. There is no need to posit infinite capacity in the unconscious thought condition. The results also suggest that weight information is used for attribute selection that could potentially explain the difficulties in replicating the deliberation-without-attention effect.