Perceptual learning reconfigures the effects of visual adaptation

Abstract

Our sensory experiences over a range of different timescales shape our perception of the environment. Two particularly striking short-term forms of plasticity with manifestly different time courses and perceptual consequences are those caused by visual adaptation and perceptual learning. Although conventionally treated as distinct forms of experience-dependent plasticity, their neural mechanisms and perceptual consequences have become increasingly blurred, raising the possibility that they might interact. To optimize our chances of finding a functionally meaningful interaction between learning and adaptation, we examined in humans the perceptual consequences of learning a fine discrimination task while adapting the neurons that carry most information for performing this task. Learning improved discriminative accuracy to a level that ultimately surpassed that in an unadapted state. This remarkable improvement came at a price: adapting directions that before learning had little effect elevated discrimination thresholds afterward. The improvements in discriminative accuracy grew quickly and surpassed unadapted levels within the first few training sessions, whereas the deterioration in discriminative accuracy had a different time course. This learned reconfiguration of adapted discriminative accuracy occurred without a concomitant change to the characteristic perceptual biases induced by adaptation, suggesting that the system was still in an adapted state. Our results point to a functionally meaningful push-pull interaction between learning and adaptation in which a gain in sensitivity in one adapted state is balanced by a loss of sensitivity in other adapted states.

Figure 1.

Fisher information carried by a homogeneous population of neurons performing a fine discrimination task. a, Tuning functions of direction-selective neurons responding to upward motion (black) and directions offset symmetrically ±20° (dark gray) and ±40° (light gray) from upward. b, Fisher information for performing this task is highest for neurons tuned to directions ±20° from upward (dark gray circles) because small deviations from upward produce the largest differential firing rate. Neurons tuned to upward (black circle) and directions ±40° from upward (light gray circles) convey no or very little information because their differential firing rates to small deviations from upward are zero or negligible, respectively. Vertical dashed line indicates the boundary around which neurons discriminate whether a stimulus was moving in a direction clockwise (CW) or counterclockwise (CCW) from upward. Fisher information was calculated as follows: FI = f′_i(θ)²/n_i(θ), where f′_i(θ) is the differential firing rate to small deviations from upward, and n_i(θ) is the variance of the Poisson distributed response.

Figure 2.

Perceptual adaptation and training procedures. a, Adaptation procedure. Subjects judged whether a field of dots was moving in a direction clockwise or counterclockwise from upward. The change in discrimination performance caused by adapting to motion in an upward direction (0°) or two directions offset symmetrically (±10 to ±50°) from upward was measured. Adapting directions were presented initially for 30 s with 4 s top-ups between each trial. Test stimuli were presented for 0.5 s, separated by a 0.5 s interval containing a fixation cross on a uniform luminance background. b, Training procedure. Before and after training, we measured the change in baseline direction discrimination performance caused by adapting to motion in an upward direction (0°) or two directions offset symmetrically (±10 to ±50°) from upward. During training, the same group of subjects repeatedly practiced the direction discrimination task while adapted to directions ±20° from upward.

Figure 3.

Learning turns the cost of adaptation into a benefit for discriminative accuracy. a, Learning curve from an individual subject who repeatedly practiced the discrimination task for nine sessions while adapting to directions (±20°) that before training caused the largest elevation in discrimination thresholds. Upward arrowheads show unadapted discrimination thresholds before (white) and after (black) training, respectively. Circles show adapted discrimination thresholds before (white), during (gray), and after (black) training. Error bars indicate 1 SEM. b, Symbols show direction discrimination thresholds of individual subjects in an unadapted state plotted as a function of those obtained in an adapted state (±20° adapter) before (white) and after (black) learning. Marginal histogram shows the ratio of adapted to unadapted direction discrimination thresholds. Black and white represent the relationship between unadapted and adapted discrimination performance before and after learning in an adapted state, respectively.

Figure 4.

Learning reconfigures adapted discriminative accuracy. a, White circles show the mean change in direction discrimination thresholds caused by adaptation to visual motion in an upward direction or directions offset symmetrically from upward. Black circles show the same function after the same group of subjects had repeatedly practiced the direction discrimination task while adapted to directions offset ±20° from upward. Symbols are the geometric mean discrimination thresholds of 20 subjects; error bars are ±95% CIs estimated from the intersubject variability of log-transformed threshold ratios. b, In each panel, the ratio of individual subjects' adapted and unadapted discrimination thresholds before training are plotted as a function of those obtained after training. Panels correspond to different adapting directions.

Figure 5.

Learned reconfiguration of adapted discriminative accuracy is robust across different methods of threshold estimation. a, Psychometric functions for one subject obtained in the upward (0°) and ±20° from upward adapting conditions before (open circles) and after (filled circles) training. b, Ratio of individual subjects' adapted and unadapted discrimination thresholds before training are plotted as a function of those obtained after training. Thresholds were estimated from either the slope of the psychometric function (circles) or the deviation from upward yielding 75% correct performance (stars).

Figure 6.

Time courses of the opposing outcomes of learning in an adapted state. a, Panels show individual threshold ratios before training plotted as a function of those obtained after training (as in Fig. 4b). Columns show groups that trained for different numbers of days. Rows show different adapting conditions. b, Bars show geometric mean threshold ratios for groups that trained for different numbers of days. White and black bars show these data before and after training. Left and right panels correspond to different adapting conditions. Error bars are ± 95% CIs, calculated as in Figure 4a.

Figure 7.

Learning while adapted does not alter adaptation-induced perceptual biases. a, White circles show the mean duration of motion aftereffects (MAE) caused by adaptation to visual motion in an upward direction or directions offset symmetrically from upward. Black circles show the same function after the same group of subjects had repeatedly practiced the direction discrimination task while adapted to directions offset ±20° from upward. Symbols are the arithmetic mean of six subjects. b, Each panel shows the mean duration of motion aftereffects for individual subjects before training and are plotted as a function of those obtained after training. Panels correspond to different adapting directions. Error bars are ±95% CIs calculated from either the intersubject variability in mean duration estimates (a) or trial-by-trial variability in individual duration estimates (b).