Parietal neurons encode expected gains in instrumental information

Abstract

In natural behavior, animals have access to multiple sources of information, but only a few of these sources are relevant for learning and actions. Beyond choosing an appropriate action, making good decisions entails the ability to choose the relevant information, but fundamental questions remain about the brain's information sampling policies. Recent studies described the neural correlates of seeking information about a reward, but it remains unknown whether, and how, neurons encode choices of instrumental information, in contexts in which the information guides subsequent actions. Here we show that parietal cortical neurons involved in oculomotor decisions encode, before an information sampling saccade, the reduction in uncertainty that the saccade is expected to bring for a subsequent action. These responses were distinct from the neurons' visual and saccadic modulations and from signals of expected reward or reward prediction errors. Therefore, even in an instrumental context when information and reward gains are closely correlated, individual cells encode decision variables that are based on informational factors and can guide the active sampling of action-relevant cues.information sampling | saccades | attention | decisions | reward I n natural behavior, animals have access to multiple sources of information, but few of these sources are relevant for learning or action. Making good decisions therefore entails not only the selection of the ultimate action but, more primarily, the decision of which source of information to sample. Decisions about information sampling are central for tasks as diverse as making a medical diagnosis (which is the best test to prescribe?), making categorization decisions (which is the most informative feature?) (1, 2), and guiding skilled actions (what should I keep my eyes on while driving?). Despite the ubiquity and significance of active sampling mechanisms, few studies have been devoted to understanding these mechanisms and their importance for decision theories. Evidence accumulation has been extensively examined in decision research (3) but has been portrayed as a passive process, in the sense that decision makers rely on predetermined (experimenter selected) sources of information but cannot themselves determine which source to consult to guide a future action.Recent studies begin to shed light on this question by showing that animals (including pigeons, monkeys, and humans) prefer to observe cues that are predictive rather than nonpredictive about a future reward, and that the value of informative cues is encoded in the orbital frontal cortex and midbrain dopamine (DA) cells (4, 5). These investigations, however, have been limited to noninstrumental contexts in which animals seek to obtain information about a reward merely in order to know, but cannot act based on the information. Very little is known about the much more common scenario in which animals sample instrumental information to make decisions and guide future actions (6).Understanding instrumental sampling poses...

Induction and Relief of Curiosity Elicit Parietal and Frontal Activity

“…Using functional magnetic resonance imaging (fMRI) we examined which brain areas were modulated by outcome uncertainty and expected value upon lottery presentation, and their updates upon outcome presentation. We hypothesized that during lottery presentation, brain regions associated with uncertainty or risk, such as insular cortex (Critchley et al, 2001;Jepma et al, 2012;Paulus et al, 2003;Preuschoff et al, 2006Preuschoff et al, , 2008 and parietal cortex (Foley et al, 2017;Huettel et al, 2005), would be modulated by outcome uncertainty. During outcome presentation, we expected increased activity in brain regions involved in curiosity relief and information updating, such as the insula (Jepma et al, 2012;Preuschoff et al, 2008), orbitofrontal cortex (Blanchard et al, 2015;Jepma et al, 2012) and ventral striatum (Jepma et al, 2012;Wittmann et al, 2008).…”

The value of what’s to come: neural mechanisms coupling prediction error and reward anticipation

“…This so-called information-prediction-error signal is presumed to arise from the value of information, 9-11, 18, 79 and has been reported in the same midbrain dopaminergic regions as standard reward prediction errors, 9,10 implying that the two signals might be strongly related. 84 Indeed, our model accounts for information prediction errors as a side effect of anticipation-dependent reward predictions, which we show correlates with BOLD signal in dopaminergic midbrain regions 9,10 as well as in the mPPC 36 (see also 110 ).…”

“…Indeed, the model's signal correlated significantly with BOLD in a midbrain dopaminergic region encompassing the ventral tegmental area and substantia nigra (VTA/SN) ( Figure 3A; p < 0.05, small volume FWE correction; 48 [4, −26, 20], t = 3.78). We also found that BOLD in the medial posterior parietal cortex (mPPC) 36,110 correlated significantly with the model's predicted signal ( Figure 3A; p < 0.05, cluster-level whole brain FWE correction with the height threshold , but not with a simpler, so-called state prediction error signal (1−p reward when reward predictive cue was presented, |0 − p reward | when no-reward predictive cue was presented). The differences between the average regression coefficients in the mPPC and in the VTA/SN were significant in the mPPC (p < 0.001, a standard permutation test in which we permuted the average regression coefficients), and the VTA/SN (p < 0.001 permutation test).…”

A neural network for information seeking

“…It remains unknown whether this network could also motivate seeking of other types of information, such as information about instrumental contingencies (i.e. what action will be required to gain the reward 60 ). In natural environments these types of information are likely to synergize with each other: if an agent has a subjective preference to resolve uncertainty in order to better predict future rewards, then the agent will likely be better at learning the objective value of resolving that uncertainty to better take actions and control rewards.…”