Caltech SocDecNeuro

The neurobiology of decision-making is informed by neurons capable of representing information over time scales of seconds. Such neurons were initially characterized in studies of spatial working memory, motor planning (e.g., Richard Andersen lab) and spatial attention. For decision-making, such neurons emit graded spike rates, that represent the accumulated evidence for or against a choice. They establish the conduit between the formation of the decision and its completion, usually in the form of a commitment to an action, even if provisional. Indeed, many decisions appear to arise through an accumulation of noisy samples of evidence to a terminating threshold, or bound. Previous studies show that single neurons in the lateral intraparietal area (LIP) represent the accumulation of evidence when monkeys make decisions about the direction of random dot motion (RDM) and express their decision with a saccade to the neuron’s preferred target. The mechanism of termination (the bound) is elusive. LIP is interconnected with other brain regions that also display decision-related activity. Whether these areas play roles in the decision process that are similar to or fundamentally different from that of LIP is unclear. I will present new unpublished experiments that begin to resolve these issues by recording from populations of neurons simultaneously in LIP and one of its primary targets, the superior colliculus (SC), while monkeys make difficult perceptual decisions.

During primate evolution, prefrontal cortex (PFC) expanded substantially relative to other cortical areas. The expansion of PFC circuits likely supported the increased cognitive abilities of humans and anthropoids to plan, evaluate, and decide between different courses of action. But what do these circuits compute as a decision is being made, and how can they be related to anatomical specialisations within and across PFC? To address this, we recorded PFC activity during value-based decision making using single unit recording in non-human primates and magnetoencephalography in humans. At a macrocircuit level, we found that value correlates differ substantially across PFC subregions. They are heavily shaped by each subregion’s anatomical connections and by the decision-maker’s current locus of attention. At a microcircuit level, we found that the temporal evolution of value correlates can be predicted using cortical recurrent network models that temporally integrate incoming decision evidence. These models reflect the fact that PFC circuits are highly recurrent in nature and have synaptic properties that support persistent activity across temporally extended cognitive tasks. Our findings build upon recent work describing economic decision making as a process of attention-weighted evidence integration across time.

It has generally been presumed that sensory information encoded by a nervous system should be as accurate as its biological limitations allow. However, perhaps counter intuitively, accurate representations of sensory signals do not necessarily maximize the organism’s chances of survival. We show that neural codes that maximize reward expectation—and not accurate sensory representations—account for retinal responses in insects, and retinotopically-specific adaptive codes in humans. Thus, our results provide evidence that fitness-maximizing rules imposed by the environment are applied at the earliest stages of sensory processing.

Investigations of the neural systems mediating the processing of social groups defined by race, specifically Black and White race groups in American participants, reveals significant overlap with brain mechanisms involved in emotion. This talk will provide an overview of research on the neuroscience of race and emotion, focusing on implicit race attitudes. Implicit race attitudes are expressed without conscious effort and control, and contrast with explicit, conscious attitudes. In spite of sharp decline in the expression of explicit, negative attitudes towards outgroup race members over the last half century, negative implicit attitudes persist, even in the face of strong egalitarian goals and beliefs. Early research demonstrated that implicit, but not explicit, negative attitudes towards outgroup race members correlate with blood oxygenation level dependent (BOLD) signal in the amygdala – a region implicated in threat representations, as well as emotion’s influence on cognition. Building on this initial finding, we demonstrate how learning and decisions may be modulated by implicit race attitudes and involve neural systems mediating emotion, learning and choice. Finally, we discuss techniques that may diminish the unintentional expression of negative, implicit race attitudes.

The idea that behavior results from a dynamic interplay between automatic and controlled processing underlies much of decision science, but has also generated considerable controversy. In this talk, I will highlight behavioral and neural data showing how recently-developed computational models of decision making can be used to shed new light on whether, when, and how decisions result from distinct processes operating at different timescales. Across diverse domains ranging from altruism to risky choice biases and self-regulation, our work suggests that a model of prioritized attentional sampling and evidence accumulation may provide an alternative explanation for many phenomena previously interpreted as supporting dual process models of choice. However, I also show how some features of the model might be taken as support for specific aspects of dual-process models, providing a way to reconcile conflicting accounts and generating new predictions and insights along the way.

Risk occupies a central role in both the theory and practice of decision-making. Although it is deeply implicated in many conditions involving dysfunctional behavior and thought, modern theoretical approaches to understanding and mitigating risk in either one-shot or sequential settings, which are derived largely from finance and economics, have yet to permeate fully the fields of neural reinforcement learning and computational psychiatry. I will discuss the use of dynamic and static versions of one prominent approach, namely conditional value-at-risk, to examine both the nature of risk avoidant choices, encompassing such things as justified gambler's fallacies, and the optimal planning that can lead to consideration of such choices, with implications for offline, ruminative, thinking.

The cellular representations and computations that allow rodents to navigate in space have been described with beautiful precision. In this talk, I will show that some of these same computations can be found in humans doing tasks that appear very different from spatial navigation. I will describe some theory that allows us to think about spatial and non-spatial problems in the same framework, and I will try to use this theory to give a new perspective on the beautiful spatial computations that inspired it. The overall goal of this work is to find a framework where we can talk about complicated non-spatial inference problems with the same precision that is only currently available in space.

In many learning and decision scenarios, especially sequential settings like mazes or games, it is easy to state an objective function but difficult to compute it, for instance because this can require enumerating many possible future trajectories. This, in turn, motivates a variety of more tractable approximations which then raise resource-rationality questions about whether and when an efficient agent should invest time or resources in computing decision variables more accurately. Previous work has used a simple all-or-nothing version of this reasoning as a framework to explain many phenomena of automaticity, habits, and compulsion in humans and animals. Here, I present a more finegrained theoretical analysis of deliberation, which attempts to address not just whether to deliberate vs. act, but which of many possible actions and trajectories to consider. Empirically, I first motivate and compare this account to nonlocal representations of spatial trajectories in the rodent place cell system, which are thought to be involved in planning. I also consider its implications, in humans, for variation over time and situations in subjective feelings of mental effort, boredom, and cognitive fatigue. Finally, I present results from a new study using magnetoencephalography in humans to measure subjective consideration of possible trajectories during a sequential learning task, and study its relationship to rational prioritization and to choice behavior.

Classic economists proposed that economic choices rely on the computation and comparison of subjective values. This hypothesis continues to inform economic theory and experimental research, but behavioral measures are ultimately not sufficient to prove the proposal. Consistent with the hypothesis, when agents make choices, neurons in the orbitofrontal cortex (OFC) encode the subjective value of offered and chosen goods. Moreover, neuronal activity in this area suggests the formation of a decision. However, it is unclear whether these neural processes are causally related to choices. More generally, the evidence linking choices to value signals in the brain remains correlational. In my talk, I will present recent results showing that neuronal activity in OFC are causal to economic choices.

From robots to humans, the ability to learn from experience turns a rigid response system into a flexible, adaptive one. In this talk, I will discuss emerging findings regarding the neural and cognitive mechanisms by which learning shapes decisions. The lecture will focus on how multiple brain regions interact to support learning, what this means for how memories are built, and the consequences for how decisions are made. Results emerging from this work challenge the traditional view of separate learning systems and advance understanding of how memory biases decisions in both adaptive and maladaptive ways.

Framing effects in individual decision-making have puzzled economists for decades because they are hard, if at all, to explain with rational choice theories. Why should mere changes in the description of a choice problem affect decision-making? Here, we examine the hypothesis that changes in framing cause changes in the allocation of attention to the different options – measured via eye-tracking – and give rise to changes in decision-making. We document that the framing of a sure alternative as a gain – as opposed to a loss – in a risk-taking task increases the attentional advantage of the sure option and induces a higher choice frequency of that option – a finding that is predicted by the attentional drift-diffusion model (aDDM). The model also correctly predicts other key findings such as that the increased attentional advantage of the sure option in the gain frame should also lead quicker decisions in this frame. In addition, the data reveal that increasing risk aversion at higher stake sizes may also be driven by attentional processes because the sure option receives significantly more attention – regardless of frame – at higher stakes. We also corroborate the causal impact of framing-induced changes of attention on choice with an additional experiment that manipulates attention exogenously. Finally, to study the precise mechanisms underlying the framing effect we structurally estimate an aDDM that allows for frame and option-dependent parameters. The estimation results indicate that – in addition to the direct effects of framing-induced changes in attention on choice – the gain frame also causes (i) an increase in the attentional discount of the gamble and (ii) an increased concavity of utility. Our findings suggest that the traditional explanation of framing effects in risky choice in terms of a more concave value function in the gain domain is seriously incomplete and that attentional mechanisms as hypothesized in the aDDM play a key role.