Nucleus accumbens shell moderates preference bias during voluntary choice behavior

The nucleus accumbens (NAc) shell lies anatomically at a critical intersection within the brain's reward system circuitry, however, its role in voluntary choice behavior remains unclear. Rats with electrolytic lesions in the NAc shell were tested in a novel foraging paradigm. Over a continuous two-week period they freely chose among four nutritionally identical but differently flavored food pellets by pressing corresponding levers. We examined the lesion's effects on three behavioral dynamics components: motivation (when to eat), preference bias (what to choose) and persistence (how long to repeat the same choice). The lesion led to a marked increase in the preference bias: i.e., increased selection of the most-preferred choice option, and decreased selection of the others. We found no effects on any other behavioral measures, suggesting no effect on motivation or choice persistence. The results implicate the NAc shell in moderating the instrumental valuation process by inhibiting excessive bias toward preferred choice options.11Ysciessciscopu

Choice order and preference in Experiment 1.

<p>(A) The choice rate of food items for a given choice order averaged over all monkeys in Condition 1; peanut half (PN), yellow fruity gem (FG), pellet (PL), and krisp (KR). (B) The winning percentage of different food items in Condition 2 (binary choice). (C) The winning percentage of different food items in Condition 2 (binary choice) sorted by individual rank order, showing a significant preference towards the favorite options. (D) The relationship between choice order and winning percentage reflecting the degree of preference among alternatives<b>.</b> Error bars depict standard error of the mean (SEM).</p

Choice order and preference in Experiment 2.

<p>(A) The choice order matrix representing the choice percentages of different colored items for given choice order averaged across all monkeys in Condition 1. (B) The winning percentage of different color items in Condition 2 (binary choice). (C) The winning percentage of different color items in Condition 2 (binary choice) sorted by individual rank order, revealing a significant individual preference among the colors. (D) The relationship between choice order and winning percentage that reflects the degree of preference among alternatives<b>.</b> Error bars depict standard error of the mean (SEM).</p

An example of the empirical choice patterns and the mean choice percentage of consumed pellets by food location, flavor, and rank.

<p>(A) The foraging behavior of a representative rat for two days, day 5 and day 14, illustrating the choice dynamics. The ordinate represents each food location/type and the abscissa represents the hour of the day. The light and dark cycles are denoted as yellow and black bars above each day's choice plot, with overall choice plotted per hour below the choice plot. The histogram to the right shows the total choices for the entire experiment. For subject 2, the rank 1 flavor (red color) was chocolate, located at the far right [RR]; the rank 2 (orange color) was coffee, middle left [ML]; the rank 3 (green color) was banana, middle right [MR]; finally, the rank 4 (blue color) was cinnamon, at the far left [LL]. (B) Entropy changes of representative data over trials. Black and red solid lines represent the entropy changes of the empirical and randomly shuffled data, respectively. (C) Mean choice percentage for specific food locations (LL, ML, MR, and RR) across subjects. (D) Mean choice percentage by flavor across subjects. (E) The mean choice percentage across subjects for each rank is shown in a log-linear scale. Choice percentage linearly decreases as a function of log(rank order). The dotted line is the log-linear fit (the slope  = −70.7±4.95 [mean ± s.e.m], adj. <i>R²</i> = 0.994). For all figures, error bars are standard errors of the mean (s.e.m). In C, D and E, a Dunnett-T3 post hoc test was conducted: *<i>p</i><0.05, **<i>p</i><0.01, ***<i>p</i><0.001.</p

Comparison of a choice sequence generated from the dual-control model with the empirical data from two representative rats.

<p>(A–B) Cumulative run distributions of the empirical data for the two representative rats and the simulated data in a log-log scale. The black squares denote the empirical data and the blue circles the simulated data. (C–D) Cumulative choice frequency graphs for each rank for both the empirical data (solid lines) and simulation (dashed lines). Red, orange, green, and blue represent the rank order from rank 1 to rank 4, respectively.</p

Bursts and Heavy Tails in Temporal and Sequential Dynamics of Foraging Decisions

<div><p>A fundamental understanding of behavior requires predicting <i>when</i> and <i>what</i> an individual will choose. However, the actual temporal and sequential dynamics of successive choices made among multiple alternatives remain unclear. In the current study, we tested the hypothesis that there is a general bursting property in both the timing and sequential patterns of foraging decisions. We conducted a foraging experiment in which rats chose among four different foods over a continuous two-week time period. Regarding <i>when</i> choices were made, we found bursts of rapidly occurring actions, separated by time-varying inactive periods, partially based on a circadian rhythm. Regarding <i>what</i> was chosen, we found sequential dynamics in affective choices characterized by two key features: (a) a highly biased choice distribution; and (b) preferential attachment, in which the animals were more likely to choose what they had previously chosen. To capture the temporal dynamics, we propose a dual-state model consisting of active and inactive states. We also introduce a satiation-attainment process for bursty activity, and a non-homogeneous Poisson process for longer inactivity between bursts. For the sequential dynamics, we propose a dual-control model consisting of goal-directed and habit systems, based on outcome valuation and choice history, respectively. This study provides insights into how the bursty nature of behavior emerges from the interaction of different underlying systems, leading to heavy tails in the distribution of behavior over time and choices.</p></div

Comparison of the simulation of the dual-state model with the empirical data.

<p>(A) Schematic diagram for the dual-state model. (B–C) Cumulative ICI distributions of the empirical data (black squares) from two example rats and the simulated data from the dual-state model (red circles) in a log-log scale. (D) Autocorrelograms of the empirical and the simulated data averaged across rats. The black and red lines denote the empirical and the simulated data, respectively. The time interval between peaks of the simulated data is 24 hours, which is consistent with that of empirical data.</p

Parameter estimates of the bimodal ICI distributions.

<p>The estimated parameters of the bimodal ICI distributions from 12 subjects. Values are given as mean (s.e.m). Overall: Overall ICI distribution; Light: ICI distribution in the light cycle; Dark: ICI distribution in the dark cycle. See text for parameter definitions.</p

Sequential features of the empirical choice patterns.

<p>(A) A trial-dependent change of run lengths for one example rat is shown both for all runs together and separated by rank. Short runs are frequent while a few long runs are intermittently observed. (B) Cumulative distribution of runs longer than a given length of run in a log-log scale for one example rat (subject 5). The cumulative run distribution of the empirical data compared to randomly shuffled data with no trial-by-trial dependencies. (C) Cumulative run distribution of each rank for the same rat (subject 5). (D) The hazard rate for ending a run with respect to the number of preceding choices in a run averaged over all rats.</p