A Neural Mechanism for Reward Discounting: Insights from Modeling Hippocampal–Striatal Interactions

Abstract

Decision-making often requires taking into consideration immediate gains as well as delayed rewards. Studies of behavior have established that anticipated rewards are discounted according to a decreasing hyperbolic function. Although mathematical explanations for reward delay discounting have been offered, little has been proposed in terms of neural network mechanisms underlying discounting. There has been much recent interest in the potential role of the hippocampus. Here we demonstrate that a previously-established neural network model of hippocampal region CA3 contains a mechanism that could explain discounting in downstream reward-prediction systems (e.g., basal ganglia). As part of its normal function, the model forms codes for stimuli that are similar to future, predicted stimuli. This similarity provides a means for reward predictions associated with future stimuli to influence current decision-making. Simulations show that this “predictive similarity” decreases as the stimuli are separated in time, at a rate that is consistent with hyperbolic discounting.

Cueing the personal future to reduce discounting in intertemporal choice: Is episodic prospection necessary?

“…Boyer () proposed that imagining future experiences provides a motivational “brake” that steers people away from short‐term, “myopic” decisions and toward decisions that give more weight to long‐term outcomes. A related suggestion regarding the neural mechanism involved is that hippocampally mediated representations of decision outcomes evoke anticipatory reward‐related activity in downstream reward‐prediction systems (e.g., basal ganglia; Laurent, ). However, the hypothesized role of episodic prospection in making future‐oriented choices is called into question by the finding that hippocampal amnesic individuals with impaired episodic prospection nevertheless value and discount future rewards to the same degree as do healthy controls (Kwan et al, ; Palombo et al, ).…”

Linguistic Interval Hesitant Fuzzy Sets and Their Application in Decision Making

“…Step 1 Because all attributes are benefit, according to Table 4, we derive According to model (30), we construct the following model for the optimal fuzzy measure v on the attribute set C: min À0:04ðvðc 1 Þ À vðc 2 ; c 3 ÞÞ À 0:062ðvðc 2 Þ À vðc 1 ; c 3 ÞÞ þ 0:103ðvðc 3 Þ À vðc 1 ; c 2 ÞÞ þ 1:943 s:t: Step 2 According to model (31), we build the following model for the optimal fuzzy measure l on the ordered set N:…”

“…Decision making is one of the most significant and omnipresent human activities that has been applied in many fields [3,30,44,45]. There are many researches [4, 5, 11-13, 25-27, 41, 50, 68] about how the decision makers make judgments according to the cognitive information.…”

“…Step 2 When the weight information of the attributes is not exactly known, we apply model (28) [or (30)] to calculate the optimal weight vector x (or the optimal fuzzy measure v); otherwise, turn to Step 3.…”

“…When the fuzzy measures l and v are both 2-additive measures, according to models (30) and (31) With respect to the GLIHFH2SA operator and different values of c, ranking order is obtained as shown in Table 7.…”