Neural Mechanisms That Make Perceptual Decisions Flexible

Abstract

Neural mechanisms of perceptual decision making have been extensively studied in experimental settings that mimic stable environments with repeating stimuli, fixed rules, and payoffs. In contrast, we live in an ever-changing environment and have varying goals and behavioral demands. To accommodate variability, our brain flexibly adjusts decision-making processes depending on context. Here, we review a growing body of research that explores the neural mechanisms underlying this flexibility. We highlight diverse forms of context dependency in decision making implemented through a variety of neural computations. Context-dependent neural activity is observed in a distributed network of brain structures, including posterior parietal, sensory, motor, and subcortical regions, as well as the prefrontal areas classically implicated in cognitive control. We propose that investigating the distributed network underlying flexible decisions is key to advancing our understanding and discuss a path forward for experimental and theoretical investigations.

Keywords: decision policy; distributed neural networks; flexible decision making; sensory-guided behavior; stimulus-action mapping; task switch.

Figure 1

Perceptual decision making in a stationary context. (a) Design of a typical visual discrimination task. Subjects report the net direction of random-dots motion (left or right) by making a saccadic eye movement. The percentage of coherently moving dots varies across trials. Neural recording is typically made from neurons whose response field (RF, gray shading) overlaps with one of the targets. (b) A bounded accumulation model accounts for the behavior. The model accumulates noisy sensory evidence to form a decision variable (DV) and commits to a choice when the DV reaches a bound. (c) Neural activity similar to the DV can be found in diverse brain regions involved in oculomotor control. For example, neurons in LIP increase their firing rates when the stimulus supports a saccade to the target in their RFs. T_in, target in neuron RF; T_out, target outside RF. Right panel, adapted with permission from Reference 8; copyright 2002 Society for Neuroscience. (d) Circuit models. In bistable attractor dynamics models (left; 19, 20), two pools of neurons, each supporting one of the choices, compete until one pool dominates. In probabilistic population codes, networks that integrate neural activity can perform optimal evidence accumulation (right; adapted with permission from Reference 21; copyright 2008 Elsevier). dlPFC, dorsolateral prefrontal cortex; FEF, frontal eye field; LIP, lateral intraparietal area; MT, middle temporal area.

Figure 2

Diverse forms of context dependency in perceptual decision making. (a) Decision making is hierarchical in nature. Even for the simplest decision, the brain first chooses relevant stimuli, actions, solutions, and policies. (b) Adjustments in decision policy are often explained as changes in the parameters of decision-making models. Adjustable parameters of the bounded accumulation model are shown in red. (c) Post-error slowing is an example of policy adjustment. Slower reaction times after error trials (left panel) are explained by reduced sensory sensitivity and lower urgency. Correspondingly, LIP buildup activity decreases after error trials (right panel). Adapted with permission from Reference 50; copyright 2016 Elsevier. (d) Common task designs that test flexibility in stimulus-action mapping: (1) Change in relevant sensory modality or feature. (2) Change in effectors (e.g., saccade and reach; top) or reversal of stimulus-action mapping (bottom). (3) Change in categorization boundary. (e) Flexibility to adopt different solutions. For example, the same stimulus could be integrated, differentiated, matched to a template, and so on (top). More complex tasks that involve hierarchical inference, offer a rich ground for studying flexibility of strategy (bottom; adapted with permission from Reference 51; copyright 2021 Ariel Zylberberg).

Figure 3

Diverse brain regions are implicated in context-dependent decision making. Dark red circles on the brain indicate the site recorded in the study highlighted in each panel. We depicted a monkey brain for illustrative purposes, but several results are from rodents. (a) Medial intraparietal (MIP) neurons encode the decision variable with different strengths when monkeys report their decisions through reaching or eye movements. Adapted with permission from Reference 85; copyright 2015 Society for Neuroscience. (b) Lateral intraparietal (LIP) neurons encode the decision variable for their preferred saccade targets along curved manifolds in population state space that are distinct for motion and face discrimination tasks. Adapted with permission from Reference 69; copyright 2021 Elsevier. (c) Monkey V1 neurons show distinct patterns of noise correlations depending on the category boundary in an orientation discrimination task. Adapted with permission from Reference 106; copyright 2018 Springer Nature. (d) When mice report if two sequentially presented odors (sample and test stimuli) match, premotor (PM) neurons encode sample odor during the delay period before any motor plan can be made. Adapted with permission from Reference 102; copyright 2020 Elsevier. (e) A1 neurons encode task rules before stimulus presentation when rats report either the location or the pitch of the same auditory stimulus. Adapted with permission from Reference 75; copyright 2014 Elsevier. (f) Superior colliculus (SC) neurons encode task rules before stimulus presentation when rats switch between pro (orienting toward a stimulus) and anti (orienting away) stimulus-action associations. Adapted with permission from Reference 97; copyright 2021 Springer Nature.

Figure 4

Mechanisms of deciding to switch task rules proposed in Purcell et al. (137). (a) Subjects report motion direction using either the upper or lower pair of direction targets depending on the context. (b) The context is not cued and must be inferred from feedback. Subjects switch context after errors (open circles; color indicates motion coherence) depending on the history of feedback and motion coherence on the previous trials (top). Behavior could be modeled as the accumulation of switch evidence to a bound (bottom). Switch evidence is formed by combining feedback and the certainty of the previous trial. Adapted with permission from Reference 137; the authors hold the copyright.

Figure 5

Schematic of network architectures proposed to explain flexible perceptual decision-making. (a) A prevalent theory is that the prefrontal cortex or a frontoparietal network operates as a central hub that gates relevant sensory information and generates appropriate motor plans. Its internal circuits flexibly adjust computations applied to sensory inputs, sending final decisions to appropriate motor regions for execution. (b) A distributed architecture consistent with recent experimental findings. There are no distinct central controls, but multiple brain regions have the capacity to maintain task contexts and flexibly modulate behavior. Dynamics of activity in these brain regions flexibly form decisions. There are gradients within the network such that regions deeper in the sensorimotor hierarchy play more prominent roles in creating flexible behavior.

Figure 6

Example circuit motifs proposed to model decision-making and its flexible adjustments. S1 and S2 are inputs from sensory neurons selective to the discriminated stimuli (e.g., left and right motion directions). A1 and A2 are drives for the two actions (e.g., leftward and rightward saccade). (a) When a decision is made through competition of choice-selective neural modules, changes in self-excitation (red plus signs) alter speed-accuracy trade-off. (b) To achieve flexible stimulus-action mapping, a control module could switch the routing of sensory information depending on contextual signals. (c) More recent models implement similar computations by training recurrent neural networks. (d) Embracing the distributed nature of neural processing and interactions in the actual brain can yield mechanistic models that better explain the neural responses. Multi-module RNNs are a fruitful step in that direction.