Abstract
Purpose: Some studies of learning have found that easier tasks enhance the learning process (e.g. Ahissar & Hochstein , Nature 1997). We investigate the influence of signal contrast on very rapid learning effects using an experimental paradigm in which an ideal observer learns from trial to trial. The framework allows us to compare the amount of learning in human observers to maximal learning assessed by the ideal observer across stimulus contrast. Methods: We conducted 8-alternative forced-choice localization experiments over contrasts of 12% to 17%. Each experiment consisted of blocks of four trials in which one of four possible signals (oriented bright or dark Gaussians, embedded in noise) was chosen at random and used throughout the block. Within a block, signal location was randomized, but signal identity was constant, allowing observers (human and ideal) to use previous trials within the block to “learn” the signal profile and thus improve their strategy for localization. The ideal observer allows for the computation of statistical efficiency. In addition, learning efficiency can be determined by comparing contrast thresholds (matched to human observer performance) of the ideal learner with an observer that is ideal except that it does not use information from previous trials (and hence is unable to learn). Results: We observed performance improvements of 3% to 16% in percent correct over the four learning trials. In general, statistical efficiency was relatively constant across learning trials, but as the stimulus contrast increased, it increased by 30% to 50%. Over this same range of contrasts, learning efficiency was highest at low contrast and fell off as contrast increased. Conclusions: Higher signal contrasts - which we have used to manipulate task difficulty - lead to a general improvement in statistical efficiency. However, in terms of learning efficiency, our results indicate that observers learn relatively more in difficult tasks than easy ones.
Support : NIH R01-EY015925.