Inference-driven attention in symbolic and perceptual tasks: Biases toward expected and unexpected inputs

The aims of this paper are (a) to gather support for the hypothesis that some basic mechanisms of attentional deployment (i.e., its high efficiency in dealing with expected and unexpected inputs) meet the requirements of the inferential system and have possibly evolved to support its functioning, and (b) to show that these orienting mechanisms function in very similar ways in two perceptual tasks and in a symbolic task. The general hypothesis and its predictions are sketched in the Introduction, after a discussion of current findings concerning visual attention and the generalities of the inferential system. In the empirical section, three experiments are presented where participants tracked visual trajectories (Experiments 1 and 3) or arithmetic series (Experiments 2 and 3), responding to the onset of a target event (e.g., to a specific number) and to the repetition of an event (e.g., to a number appearing twice consecutively). Target events could be anticipated when they were embedded in regular series/trajectories; they could be anticipated, with the anticipation later disconfirmed, when a regular series/trajectory was abruptly interrupted before the target event occurred; and they could not be anticipated when the series/trajectory was random. Repeated events could not be anticipated. Results show a very similar pattern of allocation in tracking visual trajectories and arithmetic series: Attention is focused on anticipated events; it is defocused and redistributed when an anticipation is not confirmed by ensuing events; however, performance decreases when dealing with random series/trajectory—that is, in the absence of anticipations. In our view, this is due to the fact that confirmed and disconfirmed anticipations are crucial events for “knowledge revision”—that is, the fine tuning of the inferential system to the environment; attentional mechanisms have developed so as to enhance detection of these events, possibly at all levels of inferential processing.