Jacqueline Scholl

Rhythmic Fluctuations in Evidence Accumulation during Decision Making in the Human Brain

Categorical choices are preceded by the accumulation of sensory evidence in favor of one action or another. Current models describe evidence accumulation as a continuous process occurring at a constant rate, but this view is inconsistent with accounts of a psychological refractory period during sequential information processing. During multisample perceptual categorization, we found that the neural encoding of momentary evidence in human electrical brain signals and its subsequent impact on choice fluctuated rhythmically according to the phase of ongoing parietal delta oscillations (1-3 Hz). By contrast, lateralized beta-band power (10-30 Hz) overlying human motor cortex encoded the integrated evidence as a response preparation signal. These findings draw a clear distinction between central and motor stages of perceptual decision making, with successive samples of sensory evidence competing to pass through a serial processing bottleneck before being mapped onto action.

Self-Other Mergence in the Frontal Cortex during Cooperation and Competition

Summary To survive, humans must estimate their own ability and the abilities of others. We found that, although people estimated their abilities on the basis of their own performance in a rational manner, their estimates of themselves were partly merged with the performance of others. Reciprocally, their ability estimates for others also reflected their own, as well as the others’, performance. Self-other mergence operated in a context-dependent manner: interacting with high or low performers, respectively, enhanced and diminished own ability estimates in cooperative contexts, but the opposite occurred in competitive contexts. Self-other mergence not only influenced subjective evaluations, it also affected how people subsequently objectively adjusted their performance. Perigenual anterior cingulate cortex tracked one’s own performance. Dorsomedial frontal area 9 tracked others’ performances, but also integrated contextual and self-related information. Self-other mergence increased with the strength of self and other representations in area 9, suggesting it carries interdependent representations of self and other.

A role beyond learning for NMDA receptors in reward-based decision-making - A pharmacological study using d-cycloserine

N-methyl-D-aspartate (NMDA) receptors are known to fulfill crucial functions in many forms of learning and plasticity. More recently, biophysical models, however, have suggested an additional role of NMDA receptors in evidence integration for decision-making, going beyond their role in learning. We designed a task to study the role of NMDA receptors in human reward-guided learning and decision-making. Human participants were assigned to receive either 250 mg of the partial NMDA agonist d-cycloserine (n=20) or matching placebo capsules (n=27). Reward-guided learning and decision-making were assessed using a task in which participants had to integrate learnt and explicitly shown value information to maximize their monetary wins and minimize their losses. To tease apart the effects of NMDA on learning and decision-making we used simple learning models. D-cycloserine shifted decision-making towards a more optimal integration of the learnt and the explicitly shown information, in the absence of a direct learning effect. In conclusion, our results reveal a distinct role for NMDA receptors in reward-guided decision-making. We discuss these findings in the context of NMDA’s roles in neuronal super-additivity and as crucial for evidence integration for decisions.

Beyond negative valence: 2-week administration of a serotonergic antidepressant enhances both reward and effort learning signals.

To make good decisions, humans need to learn about and integrate different sources of appetitive and aversive information. While serotonin has been linked to value-based decision-making, its role in learning is less clear, with acute manipulations often producing inconsistent results. Here, we show that when the effects of a selective serotonin reuptake inhibitor (SSRI, citalopram) are studied over longer timescales, learning is robustly improved. We measured brain activity with functional magnetic resonance imaging (fMRI) in volunteers as they performed a concurrent appetitive (money) and aversive (effort) learning task. We found that 2 weeks of citalopram enhanced reward and effort learning signals in a widespread network of brain regions, including ventromedial prefrontal and anterior cingulate cortex. At a behavioral level, this was accompanied by more robust reward learning. This suggests that serotonin can modulate the ability to learn via a mechanism that is independent of stimulus valence. Such effects may partly underlie SSRIs’ impact in treating psychological illnesses. Our results highlight both a specific function in learning for serotonin and the importance of studying its role across longer timescales.

Excitation and inhibition in anterior cingulate predict use of past experiences

Dorsal anterior cingulate cortex (dACC) mediates updating and maintenance of cognitive models of the world used to drive adaptive reward-guided behavior. We investigated the neurochemical underpinnings of this process. We used magnetic resonance spectroscopy in humans, to measure levels of glutamate and GABA in dACC. We examined their relationship to neural signals in dACC, measured with fMRI, and cognitive task performance. Both inhibitory and excitatory neurotransmitters in dACC were predictive of the strength of neural signals in dACC and behavioral adaptation. Glutamate levels were correlated, first, with stronger neural activity representing information to be learnt about the tasks’ costs and benefits and, second, greater use of this information in the guidance of behavior. GABA levels were negatively correlated with the same neural signals and the same indices of behavioral influence. Our results suggest that glutamate and GABA in dACC affect the encoding and use of past experiences to guide behavior. DOI: http://dx.doi.org/10.7554/eLife.20365.001

Simultaneous representation of a spectrum of dynamically changing value estimates during decision making.

Decisions are based on value expectations derived from experience. We show that dorsal anterior cingulate cortex and three other brain regions hold multiple representations of choice value based on different timescales of experience organized in terms of systematic gradients across the cortex. Some parts of each area represent value estimates based on recent reward experience while others represent value estimates based on experience over the longer term. The value estimates within these areas interact with one another according to their temporal scaling. Some aspects of the representations change dynamically as the environment changes. The spectrum of value estimates may act as a flexible selection mechanism for combining experience-derived value information with other aspects of value to allow flexible and adaptive decisions in changing environments.Activity in dorsal anterior cingulate cortex (dACC) dynamically tracks the value of the choice after every outcome. Here the authors report that dACC represents topographic maps of value estimates for different learning rates and interacts with similar maps in other areas at the time of the decision.

The NMDA receptor partial agonist d-cycloserine does not enhance motor learning.

Rationale: There has recently been increasing interest in pharmacological manipulations that could potentially enhance exposure-based cognitive behaviour therapy for anxiety disorders. One such medication is the partial NMDA agonist d-cycloserine. It has been suggested that d-cycloserine enhances cognitive behaviour therapy by making learning faster. While animal studies have supported this view of the drug accelerating learning, evidence in human studies has been mixed. We therefore designed an experiment to measure the effects of d-cycloserine on human motor learning. Methods: Fifty-four healthy human volunteers were randomly assigned to a single dose of 250mg d-cycloserine versus placebo in a double-blind design. They then performed a motor sequence learning task. Results: D-cycloserine did not increase the speed of motor learning or the overall amount learnt. However, we noted that participants on d-cycloserine tended to respond more carefully (shifting towards slower, but more correct responses). Conclusion: The results suggest that d-cycloserine does not exert beneficial effects on psychological treatments via mechanisms involved in motor learning. Further studies are needed to clarify the influence on other cognitive mechanisms.

Understanding psychiatric disorder by capturing ecologically relevant features of learning and decision-making

HighlightsTasks incorporating ecological features provide insights into learning and decision‐making.Distinct neural processes are recruited depending on the precise nature of the task.Computational modelling can help dissect component processes in complex scenarios.Psychiatric research may benefit from combining modelling with ecological tasks. Abstract Recent research in cognitive neuroscience has begun to uncover the processes underlying increasingly complex voluntary behaviours, including learning and decision‐making. Partly this success has been possible by progressing from simple experimental tasks to paradigms that incorporate more ecological features. More specifically, the premise is that to understand cognitions and brain functions relevant for real life, we need to introduce some of the ecological challenges that we have evolved to solve. This often entails an increase in task complexity, which can be managed by using computational models to help parse complex behaviours into specific component mechanisms. Here we propose that using computational models with tasks that capture ecologically relevant learning and decision‐making processes may provide a critical advantage for capturing the mechanisms underlying symptoms of disorders in psychiatry. As a result, it may help develop mechanistic approaches towards diagnosis and treatment. We begin this review by mapping out the basic concepts and models of learning and decision‐making. We then move on to consider specific challenges that emerge in realistic environments and describe how they can be captured by tasks. These include changes of context, uncertainty, reflexive/emotional biases, cost‐benefit decision‐making, and balancing exploration and exploitation. Where appropriate we highlight future or current links to psychiatry. We particularly draw examples from research on clinical depression, a disorder that greatly compromises motivated behaviours in real‐life, but where simpler paradigms have yielded mixed results. Finally, we highlight several paradigms that could be used to help provide new insights into the mechanisms of psychiatric disorders.

Prospection, Perseverance, and Insight in Sequential Behavior

Summary Real-world decisions have benefits occurring only later and dependent on additional decisions taken in the interim. We investigated this in a novel decision-making task in humans (n = 76) while measuring brain activity with fMRI (n = 24). Modeling revealed that participants computed the prospective value of decisions: they planned their future behavior taking into account how their decisions might affect which states they would encounter and how they themselves might respond in these states. They considered their own likely future behavioral biases (e.g., failure to adapt to changes in prospective value) and avoided situations in which they might be prone to such biases. Three neural networks in adjacent medial frontal regions were linked to distinct components of prospective decision making: activity in dorsal anterior cingulate cortex, area 8 m/9, and perigenual anterior cingulate cortex reflected prospective value, anticipated changes in prospective value, and the degree to which prospective value influenced decisions.

Motor learning shapes temporal activity in human sensorimotor cortex

Although neuroimaging techniques have provided vital insights into the anatomical regions involved in motor learning, the underlying changes in temporal dynamics are not well understood. Using magnetoencephalography and Hidden Markov Modelling to model the dynamics of neural oscillations on data-adaptive time-scales, we detected specific changes in movement-related sensorimotor β-activity during practice of a self-paced sequential visuo-motor task. The behaviourally-relevant neural signature generalised to another motor task, emphasising the centrality of β-activity in motor plasticity.