New advances in understanding decisions among multiple alternatives

Abstract

Experimental studies of decision-making have put a strong emphasis on choices between two alternatives. However, real-life decisions often involve multiple alternatives. This article provides an overview of theoretical frameworks that have been proposed to account for behavioral data from both economic and perceptual multialternative decision-making. We further review recent neurophysiological data collected in conjunction with decision-making behavior. These neural recordings provide constraints on putative models of the decision mechanism. For example, the time course of inhibition provides insight into how the competition between alternatives is mediated. Furthermore, whereas decision-related neural activity seems to reach a common threshold at the end of the decision period, the starting point tends to depend systematically on the number of alternatives. We discuss candidate mechanisms that could drive the reduction in firing rates on decisions among multiple alternatives.

Figure 1. Structure of multi-alternative decision mechanisms

Using an example of a decision between three alternatives, the structure of the majority of decision models is illustrated. The decision is based on a competition between three pools of decision-related neurons, one for each alternative (red). These pools of neurons exhibit integrator-like behavior, symbolized as recurrent excitatory feedback. Different models make different assumptions about the integration time constant (leakiness), the existence of a lower reflecting bound, and how the integrator is implemented (attractor dynamics). The simplest decision rule, which has been adopted by most of the models and is consistent with recordings from monkey LIP, is to terminate the decision process when the activity of the most active pool exceeds a decision threshold or bound (represented by high jump bar). More complicated decision rules, like a comparison between the most active and the second-most active pools, are possible. The blue pools of neurons provide sensory evidence for each alternative (in the case of perceptual decision-making) or the desirability or value of each alternative according to a particular attribute (in the case of economic or value-based decision-making). Multiple such representations might exist in the case of multi-attribute decision-making (orange pools). The proposed models for multi-attribute decision-making assume that only a single attribute can influence the decision at any time (open gate) and that the decision mechanism switches randomly between attributes over time. In all of the models, these evidence signals provide feedforward excitation to the decision pools (black arrows). The green arrows indicate feedforward inhibition, meaning that a particular evidence signal cannot only excite one decision pool, but also inhibit other decision pools. These connections are essential in diffusion-like models, present in MDFT, but absent in the standard version of LCA. Recent recordings from monkey LIP [18] suggest that they are present. The purple arrows indicate lateral or feedback inhibition, meaning that the decision pools directly compete with each other by suppressing each other’s activity. Such connections are absent in diffusion-like models, but essential for MDFT and LCA. In the case of MDFT the strength of these inhibitory connections depends on the similarity of choice options, in the case of LCA all inhibitory connections have the same strength. The depicted structure is highly simplified. For example, no interneurons for mediating inhibitory connections are shown.

Figure 2. The addition of more choice alternatives reduces firing rates in LIP and the FEF

a. Mean firing rates of LIP neurons (N=70) recorded while monkeys were engaged in a 2- or 4-choice decision task. Responses are aligned to the onset of the choice targets and end before the onset of the stimulus motion about which the decision is eventually made. Re-plotted with permission from [24]. b. Mean firing rates of FEF neurons (N=71) recorded while monkeys were engaged in a color-to-location choice saccade task. Responses are aligned to the onset of the choice targets and end well before the saccade. Re-plotted with permission from [29]. c. Top: Three target configurations presented to monkeys on a computer monitor during LIP recording. For each configuration, monkeys fixated a central target (cross) and were shown 1–3 peripheral choice targets. A choice target with a fixed value was presented in the response field (RF, schematized by the gray circle) of the neuron under study. Left: 2-choice condition, low value target outside the RF. Middle: 2-choice condition, high value target outside the RF. Right: 3-choice condition; values of the targets outside the RF are as indicated in other panels. Bottom: Mean firing rates of LIP neurons (N=62) for each condition. Responses are aligned to the onset of the choice targets and end well before the saccade. Firing rates depended both on the number of targets and on their value. Responses are consistent with a divisive normalization scheme that is based on response value. Re-plotted with permission from [32].