Altering spatial priority maps via reward-based learning

Abstract

Spatial priority maps are real-time representations of the behavioral salience of locations in the visual field, resulting from the combined influence of stimulus driven activity and top-down signals related to the current goals of the individual. They arbitrate which of a number of (potential) targets in the visual scene will win the competition for attentional resources. As a result, deployment of visual attention to a specific spatial location is determined by the current peak of activation (corresponding to the highest behavioral salience) across the map. Here we report a behavioral study performed on healthy human volunteers, where we demonstrate that spatial priority maps can be shaped via reward-based learning, reflecting long-lasting alterations (biases) in the behavioral salience of specific spatial locations. These biases exert an especially strong influence on performance under conditions where multiple potential targets compete for selection, conferring competitive advantage to targets presented in spatial locations associated with greater reward during learning relative to targets presented in locations associated with lesser reward. Such acquired biases of spatial attention are persistent, are nonstrategic in nature, and generalize across stimuli and task contexts. These results suggest that reward-based attentional learning can induce plastic changes in spatial priority maps, endowing these representations with the "intelligent" capacity to learn from experience.

Keywords: cross-target competition; reward-based learning; spatial attention; spatial priority maps.

Figure 1.

Experimental procedure. A, Illustration of the baseline and test paradigm. At the beginning of each trial, a fixation display appeared on the screen consisting of an iso-eccentric circular array of eight white squares, marking spatial locations for the upcoming stimuli. After 500 ms, the stimulus array was briefly displayed and was immediately followed by a mask. Participants were to identify one or two targets (letters or digits) among seven or six distractors (nonalphanumeric characters), respectively, by pressing the corresponding key on a standard computer keyboard. B, Illustration of the training paradigm. At the beginning of each trial, a fixation display identical to the one used in the baseline/test task appeared on the screen. After 500 ms, the stimulus array was presented on the screen for 300 ms. Participants were asked to discriminate the color of the upper triangle (either black or white) of the target stimulus as quickly and accurately as possible. Correct responses were followed by a reward feedback, which could be high or low, and the amount gained was indicated at the target location. C, Example of reward contingencies for one participant. The probability of receiving high versus low reward was predetermined and systematically biased on the basis of the specific spatial location in the display, such that each location could be assigned to one of four reward categories: 80Hh, 50Hh, 50Lh, and 20Lh. In the specific example presented here, the probability of receiving a high reward was biased in favor of the left visual hemifield, which therefore corresponded to the high-reward hemifield for this example subject.

Figure 2.

General performance effects. A, Training performance. Average reaction times (mean ± SEM; solid black line) and accuracy of report (mean ± SEM; dashed black line) are reported as a function of subsequent blocks of trials during the training sessions. Block 1 and block 2 belong to the first training session, whereas block 3 and block 4 belong to the second and last training session. B, Baseline performance in the single target condition. Average accuracy of report (%) in the single target condition is illustrated in the polar plot as a function of spatial location and target type. C, Baseline and test performance in the double target condition. The average incidence of different responses (double, single, and null report) across participants is illustrated (mean ± SEM), as assessed during the baseline (leftward stacked bar) and during the test (rightward stacked bar) sessions.

Figure 3.

Reward effects. A, Δ probability of report for critical reward-associated spatial locations in the single report condition. The histogram represents the differences in the probability of correctly reporting only the target in a 80Hh location in 80Hh-20Lh pairs (left) and the target in a 50Hh location in 50Hh-50Lh pairs (right) during the test phase with respect to the same probability in the baseline phase. B, Correlation between Δ reward and Δ probability of report. On the x-axis, the differences in the reward value associated with two targets (Δ reward) in a given pair are reported, corresponding to −60, −30, 0, 30, and 60. For illustrative purposes only, data points with a Δ reward of −30, 0, and 30 were relatively displaced from their actual x-axis coordinate in order for SEM bars not to overlap. The y-axis instead represents the difference in the probability of single reports of a given target in the pair during the test and the same probability during the baseline phase (Δ probability, mean ± SEM). Data points represented by filled squares correspond to Δ probabilities of report calculated for the theoretically “prioritized” spatial location in the pair, whereas data points represented by empty squares correspond to Δ probabilities of report calculated for the target location in the pair for which a disadvantage is expected. The red solid line indicates the fitted linear regression. C, Δ probability of a correct response in the single target condition. The difference in the probability of correctly reporting the target during the test versus baseline session (mean ± SEM) is shown as a function of reward level.

Figure 4.

Plasticity of the spatial priority map. The figure illustrates the average priority gain computed for each reward-associated spatial location, both in a 2D plane (middle) and in a 3D representation (top). Bottom, The array of eight spatial locations used in the experiment (Fig. 1A,B), where associated rewards were again spatially arranged on the basis of the example of reward contingencies in Figure 1C. For a given reward level, the average priority gain was calculated by averaging the Δ probability (baseline test difference) of single report for targets at the spatial location associated with that reward level, as separately calculated for all imbalanced-reward pairs containing that reward level. For example, for the two 80H spatial locations, the priority gain, reported in the map with color coding (middle and top) and as z-axis value (top only), corresponds to the average of the Δ probabilities separately calculated in 80Hh-20Lh and 80Hh-50 pairs. In this case, we pooled together 80Hh-50Hh and 80Hh-50Lh pairs corresponding to an identical reward imbalance.