Thinking about experiments where participants perform visual search tasks, I remember hearing in a Cog Psych lecture that if the instructions of the task were of the form find the element that has property X, the participants would accomplish the task faster and with greater ease than when the instructions were of the form find the element that does not have property Y. It was then discussed that this exemplifies one of the themes in cognitive psych: The cognitive processes handle positive information better than negative information. Is this positivity effect?I wonder if there is something about the second task that is algorithmically more complex than the first. Would a computer show the same slow-down for these tasks? Anyone know of any research exploring this?
Visual search: complexity of positive vs negative search tasks
perception;computational modeling;cognitive modeling
Building on @JohnPick's answer and my comment, but being a little bit more formal. The difference can be explained by the difference between parallel versus sequential processing, and the difficulty of the predicate being evaluated. Your specific question is answered by Treisman (1985) (which I summarize in the second section) but I try to provide a more general explanation.Basic ideaWhenever we say does the element have property $X$, we usually pick a very simple property for $X$, which can be checked quickly. On the other hand a predicate like does not have property $Y$ becomes something like has propert $\neg Y$ and is usually a much more difficult property to evaluate.  Thus, I expect the main problem is not in the negation, but the fact that $X$ is usually much faster to evaluate than $\neg Y$. In general, we would expect similar effects with other experiments that use a very easy property $X$ versus a very difficult but still positive property $Z$.However, if this was the only factor, we would only see a constant-factor slow-down, which would we expect to be interpreted as quantitative but not qualitative slow down. This is where we throw in parallel processing. If we assume that the vision system is capable of highly parallel processing, but the difficulty of the property evaluation involved must be very low or we need to switch to sequential processing. In a computer, think GPU: the GPU can do many many simple calculations in parallel, but we have to use the slower CPU to do difficult computations one at a time.Relevant literature supportClassic work like Treisman (1985) suggests that my assumption of very bounded computations being done in parallel, while more complicated properties require attention and sequential processing is a reasonable one. In particular, Treisman completely answers your question:It suggests that search for the presence of a visual primitive is automatic and parallel, whereas search for the absence of the same feature is serial and requires focused attention.Testing the theory with non-negative propertiesTo test my whole theory we can consider the following experiments. We present the participant with $N$ integers $r_1, ... r_N$ arranged randomly in the visual field, each integer is colored $c_1, ... , c_N$. We ask one of two possible questions:Property $X$: Name a green number, orProperty $Z$: Name a number divisible by $3$.As we increase $N$ from $1$ up to some reasonable limit, we get a function of reaction times $f_X(N)$ and $f_Z(N)$ for the two cases: If both predicates ($X$ and $Z$) are processed in the same way (i.e. ever both parallel or both sequential), then we expect that$\forall N \; \frac{f_X(N)}{f_Z(N)} \approx \frac{f_X(1)}{f_Z(1)}$. In other words, we expect just a constant slow-down from the fact that checking in a single number is green is easier than checking if it is divisible by $3$. If $X$ is processed in parallel, while $Z$ is not, then we expect $\forall N \; \frac{f_X(N)}{f_Z(N)} > \frac{f_X(N + 1)}{f_Z(N + 1)}$. In other words, as we add one more item to the visual field, the parallel processing is slowed down significantly less than the sequential processing.