First-Pass Processing of Value Cues in the Ventral Visual Pathway

Abstract

Real-world value often depends on subtle, continuously variable visual cues specific to particular object categories, like the tailoring of a suit, the condition of an automobile, or the construction of a house. Here, we used microelectrode recording in behaving monkeys to test two possible mechanisms for category-specific value-cue processing: (1) previous findings suggest that prefrontal cortex (PFC) identifies object categories, and based on category identity, PFC could use top-down attentional modulation to enhance visual processing of category-specific value cues, providing signals to PFC for calculating value, and (2) a faster mechanism would be first-pass visual processing of category-specific value cues, immediately providing the necessary visual information to PFC. This, however, would require learned mechanisms for processing the appropriate cues in a given object category. To test these hypotheses, we trained monkeys to discriminate value in four letter-like stimulus categories. Each category had a different, continuously variable shape cue that signified value (liquid reward amount) as well as other cues that were irrelevant. Monkeys chose between stimuli of different reward values. Consistent with the first-pass hypothesis, we found early signals for category-specific value cues in area TE (the final stage in monkey ventral visual pathway) beginning 81 ms after stimulus onset-essentially at the start of TE responses. Task-related activity emerged in lateral PFC approximately 40 ms later and consisted mainly of category-invariant value tuning. Our results show that, for familiar, behaviorally relevant object categories, high-level ventral pathway cortex can implement rapid, first-pass processing of category-specific value cues.

Keywords: IT; decision; monkey; object; prefrontal; value; visual.

Figure 1. Proposed mechanisms for category-specific value processing

(A) Here, the hypothetical category is “fox terrier”, for which category diagnostic shape cues might include the sharp beard, eyebrows, flopped ears, and short docked tail (blue). Desirable shape qualities (value cues) for a fox terrier include a long snout, a curved neck, and a short back (yellow). (B) Top-down. This strategy would concatenate known neural mechanisms of visual processing, category recognition, and top-down modulation. Category-diagnostic visual information from TE, such as the full beard and short tail, could provide inputs necessary for categorical recognition in PFC. Category recognition could invoke top-down control mechanisms for selective processing of snout length, neck curvature, and back length. The resulting category-specific information about fox terrier conformation could then pass to PFC and/or other structures involved in calculating value. A long snout, curved neck, and short back would produce a high value signal. (C) First-pass. This strategy would depend on learning-induced changes in neural tuning to process all the necessary information during the first pass of visual information through TE. Specifically, neurons that respond to category-diagnostic cues (beard, tail) would also be tuned for snout length, neck curvature, back length, and or other value-diagnostic shape dimensions. Based on such information from TE, value information could be generated immediately in PFC.

Figure 2. Behavioral task

(A) Stimuli comprised four categories (rows). In each category, liquid reward value (symbolized by blue dots) was proportional to either the distance between limbs or the curvature at limb junctions. These were the value-relevant cues, shown on the left. In each category, the other cue varied but was irrelevant, as shown on the right. (B) Task sequence. After the appearance of a 0.2° diameter fixation point, the monkey was required to fixate within a 1° radius window throughout the trial. After initiation of fixation, the task stages were: stimulus 1 presentation (600 ms), delay (600 ms), stimulus 2 presentation (600 ms), delay (600 ms), simultaneous offset of fixation point and onset of saccade targets. The monkey was required to saccade to a target within 1 s to receive the liquid reward amount indicated by either stimulus 1 (blue target) or stimulus 2 (yellow target). Stimuli were generated randomly but balanced for category and reward level. Targets appeared on opposite sides of the fixation point at a randomized angle. (C) Behavioral performance. Probability of choosing the reward indicated by stimulus 1 (see color scale) was averaged across 90 training sessions for the first monkey and 75 training sessions for the second monkey. Reward values are shown in arbitrary units, which equated to a variable amount across sessions, ranging from 15 to 20 μl. The range of values for stimulus 2 was actually larger (0–12 units, data not shown) to prevent early decisions based on maximum or minimum values for stimulus 1 (3–9 units). Thus, the maximum reward ranged from 0.18 to 0.24 ml. See also Figure S1.

Figure 3. Responses of example TE neurons

(A) TE neuron tuned for large limb distance in category II. Each stimulus used to study this neuron is represented by a white icon. Background color indicates the time-averaged response rate to the stimulus (see scale bar) based on five repetitions including presentation as both object 1 and object 2. Stimuli are grouped into categories and arranged from lowest reward value (upper left) to highest (lower right). The shape cue indicating reward value is diagrammed beside each category. This neuron’s tuning provided potentially useful information about low reward values in category II. (B) TE neuron tuned for broad curvature in category IV. This tuning provided potentially useful information about high reward values in category IV. See also Figures S2 and S4.

Figure 4. Probability distributions for value cue tuning in TE

Tuning was measured by the linear correlation (r) between value cue level and response. Results are shown as absolute r values. Only significant (p < 0.05, t-test) r values are plotted. Frequencies of tuning for limb distance and curvature are combined. The frequency scale (y axis) shows fraction of tests out of 310 (2 categories X 155 neurons) for both relevant and irrelevant cues in each bin. Tuning was stronger in categories where the cue was relevant (black) in comparison to categories where the cue was irrelevant (gray).

Figure 5. Responses of example LPFC neuron

In each category, this neuron responded more strongly to high value stimuli. In this earlier experiment, category IV stimuli had a simpler shape, and limb thicknesses varied, but the value cues were the same. See also Figure S2.

Figure 6. Category specificity of value/value cue tuning in TE (A) and LPFC (B)

For each neuron, correlation between response strength and value/value cue level (r) was calculated for each of the four categories using linear regression. For each neuron, the largest absolute r value (r_max) out of the four categories is plotted on the x axis. The same linear model was then used to predict responses in the other three categories, in order to test for consistency of value tuning across categories. The most different r value (r_min, correlation between predicted and observed responses) among the other three categories is plotted on the y axis. Thus, positive values on the vertical axis represent consistent value tuning across all four categories. Filled circles indicate significant r_min (p < 0.05). See also Figure S3.

Figure 7. Temporal profiles of TE and LPFC tuning

To perform these analyses, spike trains were smoothed by convolution with an asymmetrical product of exponents filter to prevent backward biasing of response energy in time [–64]. Tuning was evaluated for each 1 ms time bin. (A) Temporal tuning profiles for individual neurons with significant (p < 0.05) value-related tuning in at least one category for a continuous period of at least 50 ms. For each neuron, brightness corresponds to tuning strength (response/value correlation) in the category where its tuning strength was greatest (i.e., the category with maximum time-averaged response/value correlation, r_max, as in Figure 6, horizontal axis). Thus, brightness is analogous to curve height in a peri-stimulus time histogram. Color indicates consistency of value tuning across categories, in terms of minimum cross-predicted correlation among the other three categories (r_min, as in Figure 6, y axis). Neurons are rank-ordered from top to bottom within each subpopulation (TE and LPFC) according to value-tuning consistency. (B) Temporal profiles for category-specific value cue tuning in TE (green, averaged across 50 TE neurons for which r_min had a different sign or was not significant, t-test, p < 0.05) and category-invariant value tuning in LPFC (red, averaged across 20 neurons for which r_min had the same sign and was significant). See also Figures S4, S5 and S6.