Population response profiles in early visual cortex are biased in favor of more valuable stimuli

Abstract

Voluntary and stimulus-driven shifts of attention can modulate the representation of behaviorally relevant stimuli in early areas of visual cortex. In turn, attended items are processed faster and more accurately, facilitating the selection of appropriate behavioral responses. Information processing is also strongly influenced by past experience and recent studies indicate that the learned value of a stimulus can influence relatively late stages of decision making such as the process of selecting a motor response. However, the learned value of a stimulus can also influence the magnitude of cortical responses in early sensory areas such as V1 and S1. These early effects of stimulus value are presumed to improve the quality of sensory representations; however, the nature of these modulations is not clear. They could reflect nonspecific changes in response amplitude associated with changes in general arousal or they could reflect a bias in population responses so that high-value features are represented more robustly. To examine this issue, subjects performed a two-alternative forced choice paradigm with a variable-interval payoff schedule to dynamically manipulate the relative value of two stimuli defined by their orientation (one was rotated clockwise from vertical, the other counterclockwise). Activation levels in visual cortex were monitored using functional MRI and feature-selective voxel tuning functions while subjects performed the behavioral task. The results suggest that value not only modulates the relative amplitude of responses in early areas of human visual cortex, but also sharpens the response profile across the populations of feature-selective neurons that encode the critical stimulus feature (orientation). Moreover, changes in space- or feature-based attention cannot easily explain the results because representations of both the selected and the unselected stimuli underwent a similar feature-selective modulation. This sharpening in the population response profile could theoretically improve the probability of correctly discriminating high-value stimuli from low-value alternatives.

Fig. 1.

Depiction of a single trial of the behavioral task used in the scanner. At the beginning of each trial, subjects viewed 2 stimuli, one in each hemifield: one was oriented clockwise (CW) from vertical, the other counterclockwise (CCW). CW and CCW stimuli switched location between hemifields randomly over the course of the experiment. Subjects selected either the CW or CCW stimulus via a button press response and shortly afterward the color of the fixation point changed to indicate whether the choice earned a reward. The probability of receiving a reward for each choice alternative varied unpredictably from block to block. See methods for details.

Fig. 2.

First two panels in first row: average model parameters across all subjects showing influence of prior rewards (solid circles) and choices (dashed triangles) on the current choice (if no data were present at a particular temporal lag for a given subject, those data were treated as missing values and ignored during averaging). Error bars ±1SE across observers. Remaining panels: model parameters for individual subjects. The percentage in the inset of each panel represents the mean leave-one-out accuracy of the model for each subject (see text). Error bars show ±1SE of the parameter estimates.

Fig. 3.

Mean proportion of CW choices as a function of reward probability. Whereas choice ratios generally tracked changes in reward probability, subjects tended to “undermatch” by selecting the more valuable option less often than expected when the reward probabilities were most extreme. Error bars reflect ±1SE across subjects.

Fig. 4.

A: mean peak blood oxygenation level dependent (BOLD) responses from all areas associated with selected (dark bars) and unselected stimuli (light bars) when the value of the selected stimulus was low and (B) when the value of the selected stimulus was high. C: differential response in each visual area to selected and unselected stimuli as a function of value of the selected stimuli. Responses evoked by selected stimuli were larger overall than responses evoked by unselected stimuli and this difference increased when the value of the selected stimulus was high. All error bars reflect ±1SE across subjects.

Fig. 5.

Mean voxel-based tuning functions (VTFs) after recentering each VTF based on its preferred orientation (e.g., shifting VTFs that peak at different orientations so that all VTFs peak at 0°). VTFs associated with low-value stimuli rendered with a solid line; VTFs associated with high-value stimuli rendered with a dashed line. A and B: average VTFs collapsed across all visual areas for selected stimuli (A) and unselected stimuli (B). High-value stimuli evoked larger responses in voxels tuned to the currently presented stimulus compared with low-value stimuli (compare solid and dashed lines at 0° offset). In addition, responses in voxels tuned 20° from a high-value stimulus tended to be relatively attenuated: this was particularly evident in V1. Note that the area under each of the VTFs is 1 because the mean activation level was subtracted during data processing to focus on the shape of the VTFs (see methods and Fig. 4 for data pertaining to value-related changes in mean response amplitude). Error bars reflect ±1SE across subjects.