Switch-Independent Task Representations in Frontal and Parietal Cortex

Lasse S. Loose, David Wisniewski, Marco Rusconi, Thomas Goschke and John-Dylan Haynes
Journal of Neuroscience 16 August 2017, 37 (33) 8033-8042; DOI: https://doi.org/10.1523/JNEUROSCI.3656-16.2017

Alternating between two tasks is effortful and impairs performance. Previous fMRI studies have found increased activity in frontoparietal cortex when task switching is required. One possibility is that the additional control demands for switch trials are met by strengthening task representations in the human brain. Alternatively, on switch trials, the residual representation of the previous task might impede the buildup of a neural task representation. This would predict weaker task representations on switch trials, thus also explaining the performance costs. To test this, male and female participants were cued to perform one of two similar tasks, with the task being repeated or switched between successive trials. Multivoxel pattern analysis was used to test which regions encode the tasks and whether this encoding differs between switch and repeat trials. As expected, we found information about task representations in frontal and parietal cortex, but there was no difference in the decoding accuracy of task-related information between switch and repeat trials. Using cross-classification, we found that the frontoparietal cortex encodes tasks using a generalizable spatial pattern in switch and repeat trials. Therefore, task representations in frontal and parietal cortex are largely switch independent. We found no evidence that neural information about task representations in these regions can explain behavioral costs usually associated with task switching.


When Brain Beats Behavior: Neuroforecasting Crowdfunding Outcomes

Alexander Genevsky, Carolyn Yoon and Brian Knutson
The Journal of Neuroscience, 6 September 2017, 37(36):8625-8634

Although traditional economic and psychological theories imply that individual choice best scales to aggregate choice, primary components of choice reflected in neural activity may support even more generalizable forecasts. Crowdfunding represents a significant and growing platform for funding new and unique projects, causes, and products. To test whether neural activity could forecast market-level crowdfunding outcomes weeks later, 30 human subjects (14 female) decided whether to fund proposed projects described on an Internet crowdfunding website while undergoing scanning with functional magnetic resonance imaging. Although activity in both the nucleus accumbens (NAcc) and medial prefrontal cortex predicted individual choices to fund on a trial-to-trial basis in the neuroimaging sample, only NAcc activity generalized to forecast market funding outcomes weeks later on the Internet. Behavioral measures from the neuroimaging sample, however, did not forecast market funding outcomes. This pattern of associations was replicated in a second study. These findings demonstrate that a subset of the neural predictors of individual choice can generalize to forecast market-level crowdfunding outcomes—even better than choice itself.


Ventromedial Prefrontal Cortex Encodes a Latent Estimate of Cumulative Reward

Keno Juechems, Jan Balaguer, Maria Ruz, Christopher Summerfield
Neuron, Volume 93, Issue 3, p705–714.e4, 8 February 2017

Humans and other animals accumulate resources, or wealth, by making successive risky decisions. If and how risk attitudes vary with wealth remains an open question. Here humans accumulated reward by accepting or rejecting successive monetary gambles within arbitrarily defined temporal contexts. Risk preferences changed substantially toward risk aversion as reward accumulated within a context, and blood oxygen level dependent (BOLD) signals in the ventromedial prefrontal cortex (PFC) tracked the latent growth of cumulative economic outcomes. Risky behavior was captured by a computational model in which reward prompts an adaptive update to the function that links utilities to choices. These findings can be understood if humans have evolved economic decision policies that fail to maximize overall expected value but reduce variance in cumulative outcomes, thereby ensuring that resources remain above a critical survival threshold.


Suppression of Ventral Hippocampal Output Impairs Integrated Orbitofrontal Encoding of Task Structure

Andrew M. Wikenheiser, Yasmin Marrero-Garcia, Geoffrey Schoenbaum
Neuron, Volume 95, Issue 5, p1197–1207.e3, 30 August 2017

The hippocampus and orbitofrontal cortex (OFC) both make important contributions to decision making and other cognitive processes. However, despite anatomical links between the two, few studies have tested the importance of hippocampal–OFC interactions. Here, we recorded OFC neurons in rats performing a decision making task while suppressing activity in a key hippocampal output region, the ventral subiculum. OFC neurons encoded information about expected outcomes and rats’ responses. With hippocampal output suppressed, rats were slower to adapt to changes in reward contingency, and OFC encoding of response information was strongly attenuated. In addition, ventral subiculum inactivation prevented OFC neurons from integrating information about features of outcomes to form holistic representations of the outcomes available in specific trial blocks. These data suggest that the hippocampus contributes to OFC encoding of both concrete, low-level features of expected outcomes, and abstract, inferred properties of the structure of the world, such as task state.


Specialized Representations of Value in the Orbital and Ventrolateral Prefrontal Cortex: Desirability versus Availability of Outcomes

Peter H. Rudebeck, Richard C. Saunders, Dawn A. Lundgren, Elisabeth A. Murray
Neuron, Volume 95, Issue 5, p1208–1220.e5, 30 August 2017

Advantageous foraging choices benefit from an estimation of two aspects of a resource’s value: its current desirability and availability. Both orbitofrontal and ventrolateral prefrontal areas contribute to updating these valuations, but their precise roles remain unclear. To explore their specializations, we trained macaque monkeys on two tasks: one required updating representations of a predicted outcome’s desirability, as adjusted by selective satiation, and the other required updating representations of an outcome’s availability, as indexed by its probability. We evaluated performance on both tasks in three groups of monkeys: unoperated controls and those with selective, fiber-sparing lesions of either the OFC or VLPFC. Representations that depend on the VLPFC but not the OFC play a necessary role in choices based on outcome availability; in contrast, representations that depend on the OFC but not the VLPFC play a necessary role in choices based on outcome desirability.


Learning Predictive Statistics: Strategies and Brain Mechanisms

Rui Wang, Yuan Shen, Peter Tino, Andrew E. Welchman and Zoe Kourtzi
Journal of Neuroscience 30 August 2017, 37 (35) 8412-8427; DOI: https://doi.org/10.1523/JNEUROSCI.0144-17.2017

When immersed in a new environment, we are challenged to decipher initially incomprehensible streams of sensory information. However, quite rapidly, the brain finds structure and meaning in these incoming signals, helping us to predict and prepare ourselves for future actions. This skill relies on extracting the statistics of event streams in the environment that contain regularities of variable complexity from simple repetitive patterns to complex probabilistic combinations. Here, we test the brain mechanisms that mediate our ability to adapt to the environment's statistics and predict upcoming events. By combining behavioral training and multisession fMRI in human participants (male and female), we track the corticostriatal mechanisms that mediate learning of temporal sequences as they change in structure complexity. We show that learning of predictive structures relates to individual decision strategy; that is, selecting the most probable outcome in a given context (maximizing) versus matching the exact sequence statistics. These strategies engage distinct human brain regions: maximizing engages dorsolateral prefrontal, cingulate, sensory–motor regions, and basal ganglia (dorsal caudate, putamen), whereas matching engages occipitotemporal regions (including the hippocampus) and basal ganglia (ventral caudate). Our findings provide evidence for distinct corticostriatal mechanisms that facilitate our ability to extract behaviorally relevant statistics to make predictions.


Adaptive Encoding of Outcome Prediction by Prefrontal Cortex Ensembles Supports Behavioral Flexibility

Alberto Del Arco, Junchol Park, Jesse Wood, Yunbok Kim and Bita Moghaddam
Journal of Neuroscience 30 August 2017, 37 (35) 8363-8373; DOI: https://doi.org/10.1523/JNEUROSCI.0450-17.2017

The prefrontal cortex (PFC) is thought to play a critical role in behavioral flexibility by monitoring action–outcome contingencies. How PFC ensembles represent shifts in behavior in response to changes in these contingencies remains unclear. We recorded single-unit activity and local field potentials in the dorsomedial PFC (dmPFC) of male rats during a set-shifting task that required them to update their behavior, among competing options, in response to changes in action–outcome contingencies. As behavior was updated, a subset of PFC ensembles encoded the current trial outcome before the outcome was presented. This novel outcome-prediction encoding was absent in a control task, in which actions were rewarded pseudorandomly, indicating that PFC neurons are not merely providing an expectancy signal. In both control and set-shifting tasks, dmPFC neurons displayed postoutcome discrimination activity, indicating that these neurons also monitor whether a behavior is successful in generating rewards. Gamma-power oscillatory activity increased before the outcome in both tasks but did not differentiate between expected outcomes, suggesting that this measure is not related to set-shifting behavior but reflects expectation of an outcome after action execution. These results demonstrate that PFC neurons support flexible rule-based action selection by predicting outcomes that follow a particular action.