Amy Rachel Bland

Uncertainty and cognitive control.

A growing trend of neuroimaging, behavioral, and computational research has investigated the topic of outcome uncertainty in decision-making. Although evidence to date indicates that humans are very effective in learning to adapt to uncertain situations, the nature of the specific cognitive processes involved in the adaptation to uncertainty are still a matter of debate. In this article, we reviewed evidence suggesting that cognitive control processes are at the heart of uncertainty in decision-making contexts. Available evidence suggests that: (1) There is a strong conceptual overlap between the constructs of uncertainty and cognitive control; (2) There is a remarkable overlap between the neural networks associated with uncertainty and the brain networks subserving cognitive control; (3) The perception and estimation of uncertainty might play a key role in monitoring processes and the evaluation of the “need for control”; (4) Potential interactions between uncertainty and cognitive control might play a significant role in several affective disorders.

Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches

A central challenge for neuroscience lies in relating inter-individual variability to the functional properties of specific brain regions. Yet, considerable variability exists in the connectivity patterns between different brain areas, potentially producing reliable group differences. Using sex differences as a motivating example, we examined two separate resting-state datasets comprising a total of 188 human participants. Both datasets were decomposed into resting-state networks (RSNs) using a probabilistic spatial independent component analysis (ICA). We estimated voxel-wise functional connectivity with these networks using a dual-regression analysis, which characterizes the participant-level spatiotemporal dynamics of each network while controlling for (via multiple regression) the influence of other networks and sources of variability. We found that males and females exhibit distinct patterns of connectivity with multiple RSNs, including both visual and auditory networks and the right frontal-parietal network. These results replicated across both datasets and were not explained by differences in head motion, data quality, brain volume, cortisol levels, or testosterone levels. Importantly, we also demonstrate that dual-regression functional connectivity is better at detecting inter-individual variability than traditional seed-based functional connectivity approaches. Our findings characterize robust-yet frequently ignored-neural differences between males and females, pointing to the necessity of controlling for sex in neuroscience studies of individual differences. Moreover, our results highlight the importance of employing network-based models to study variability in functional connectivity.

Electrophysiological correlates of decision making under varying levels of uncertainty.

When making decisions we are often faced with uncertainty about the potential outcomes of a choice. We therefore must rely upon a stimulus-response-outcome (S-R-O) rule learned from previous experiences of gains and losses. Here we report a study that used event-related potentials (ERP) to examine the neural and cognitive mechanisms involved in decision making when S-R-O rules are changing in a volatile manner. Thirty-one participants engaged in a reward-based decision-making task in which two contextual determinants of decision uncertainty were independently manipulated: Volatility (i.e. the frequency of changes in the S-R-O rules) and Feedback validity (i.e. the extent to which an S-R-O rule accurately predicts outcomes). Results of stimulus-locked ERPs showed that volatility of S-R-O rules was associated with two well-known neural signatures of cognitive control processes. First, increased S-R-O volatility in a high FV context was associated with frontally-based N2 (200-350ms) and N400 (350-500ms) components. Second, in a low FV context, volatility was associated with an enhanced late positive complex (LPC, 500-800ms) largest on frontal sites. Feedback-locked ERPs showed an enhanced Feedback-Related Negativity (FRN) and P300 for losses compared to wins as well as a volatility driven FRN. These results suggest that, in a high FV context, coping with volatility might involve conflict monitoring processes. However, in a low FV context, coping with frequent changes in the S-R-O rule might require greater attentional and working memory (WM) resources.

Different varieties of uncertainty in human decision-making

The study of uncertainty in decision-making is receiving greater attention in the fields of cognitive and computational neuroscience. Several lines of evidence are beginning to elucidate different variants of uncertainty. Particularly, risk, ambiguity, and expected and unexpected forms of uncertainty are well articulated in the literature. In this article we review both empirical and theoretical evidence arguing for the potential distinction between three forms of uncertainty; expected uncertainty, unexpected uncertainty, and volatility. Particular attention will be devoted to exploring the distinction between unexpected uncertainty and volatility which has been less appreciated in the literature. This includes evidence mainly from neuroimaging, neuromodulation, and electrophysiological studies. We further address the possible differentiation of cognitive control mechanisms used to deal with these forms of uncertainty. Finally, we explore whether the dual modes of control theory provides a theoretical framework for understanding the distinction between unexpected uncertainty and volatility.

Relative changes from prior reward contingencies can constrain brain correlates of outcome monitoring

It is well-known that the affective value of an environment can be relative to whether it reflects an improvement or a worsening from a previous state. A potential explanation for this phenomenon suggests that relative changes from previous reward contingencies can constrain how brain monitoring systems form predictions about future events. In support of this idea, we found that changes per se relative to previous states of learned reward contingencies modulated the Feedback-Related Negativity (FRN), a human brain potential known to index discrepancies between predictions and affective outcomes. Specifically, we observed that environments with a 50% reward probability yielded different FRN patterns according to whether they reflected an improvement or a worsening from a previous environment. Further, we also found that this pattern of results was driven mainly by variations in the amplitude of ERPs to positive outcomes. Overall, these results suggest that relative changes in reward probability from previous learned environments can constrain how neural systems of outcome monitoring formulate predictions about the likelihood of future rewards and nonrewards.

EMOTICOM: A Neuropsychological Test Battery to Evaluate Emotion, Motivation, Impulsivity, and Social Cognition

In mental health practice, both pharmacological and non-pharmacological treatments are aimed at improving neuropsychological symptoms, including cognitive and emotional impairments. However, at present there is no established neuropsychological test battery that comprehensively covers multiple affective domains relevant in a range of disorders. Our objective was to generate a standardized test battery, comprised of existing, adapted and novel tasks, to assess four core domains of affective cognition (emotion processing, motivation, impulsivity and social cognition) in order to facilitate and enhance treatment development and evaluation in a broad range of neuropsychiatric disorders. The battery was administered to 200 participants aged 18–50 years (50% female), 42 of whom were retested in order to assess reliability. An exploratory factor analysis identified 11 factors with eigenvalues greater than 1, which accounted for over 70% of the variance. Tasks showed moderate to excellent test-retest reliability and were not strongly correlated with demographic factors such as age or IQ. The EMOTICOM test battery is therefore a promising tool for the assessment of affective cognitive function in a range of contexts.

Exploiting Trial-to-Trial Variability in Multimodal Experiments.

Event-related potentials (ERP) observed in the electroencephalogram (EEG) have traditionally provided neural markers for an array of cognitive phenomena through averaging time-locked amplitudes over many trials. However, it is becoming clear that understanding trial-to-trial variability in neural activity and its behavioral consequences is an important venture in cognitive and systems neuroscience. Recent studies have begun to focus on how fluctuations in functional magnetic resonance imaging (fMRI) and electrophysiological (EEG/MEG) signals are correlated with moment-to-moment fluctuations in behavior (e.g., Fox et al., 2005; Pessoa and Padmala, 2005; Mars et al., 2008). Indeed, neural responses can vary in theoretically important ways which may reflect a signature of task-relevant brain-state changes such as a subjects cognitive “context” (Lutz et al., 2002). As such, focusing on single-trial data can provide a more direct link between neural activity and cognitive processes, such as executive function and decision making (Debener et al., 2006).

Differential effects of reward and punishment in decision making under uncertainty: a computational study.

Computational models of learning have proved largely successful in characterizing potential mechanisms which allow humans to make decisions in uncertain and volatile contexts. We report here findings that extend existing knowledge and show that a modified reinforcement learning model, which has separate parameters according to whether the previous trial gave a reward or a punishment, can provide the best fit to human behavior in decision making under uncertainty. More specifically, we examined the fit of our modified reinforcement learning model to human behavioral data in a probabilistic two-alternative decision making task with rule reversals. Our results demonstrate that this model predicted human behavior better than a series of other models based on reinforcement learning or Bayesian reasoning. Unlike the Bayesian models, our modified reinforcement learning model does not include any representation of rule switches. When our task is considered purely as a machine learning task, to gain as many rewards as possible without trying to describe human behavior, the performance of modified reinforcement learning and Bayesian methods is similar. Others have used various computational models to describe human behavior in similar tasks, however, we are not aware of any who have compared Bayesian reasoning with reinforcement learning modified to differentiate rewards and punishments.

Individuals living with lupus: findings from the LUPUS UK Members Survey 2014:

Systemic lupus erythematosus (SLE) is a complex and unpredictable disease which varies greatly among patients and has a significant impact on an individual’s daily living and quality of life. A better understanding of the patients’ experiences with the disease is vital to the effective management of the disease. LUPUS UK, a national UK-registered charity supporting people with systemic and discoid lupus, conducted a UK-wide survey of individuals living with lupus in order to provide foundation information to support and identify gaps needing further research. An anonymous survey was sent to 5660 LUPUS UK members in order to obtain demographic, diagnosis, symptom and treatment information. A total of 2527 surveys were returned by 2371 females (mean age 56.9 years, SD 13.6) and 156 males, (mean age 60.9 years, SD 15.7). Individuals reported a mean (SD) time to diagnosis from the first symptom of 6.4 (9.5) years, with 47% (n = 1186) initially being given a different diagnosis prior to lupus. Fatigue/weakness (91%, n = 2299) and joint pain/swelling (77.4%, n = 1957) were the most common symptoms that interfere with daily activities, while 73% (n = 1836) noted having some problems that make them unable to carry out their usual daily activities. Thirty-two per cent (n = 806) were also seeking support beyond traditional pharmacological treatments, such as acupuncture and massage. This study highlights the range and frequency of symptoms difficult to live with on a daily basis and support areas needing further research to improve patients’ well-being.

Cooperative Behavior in the Ultimatum Game and Prisoner’s Dilemma Depends on Players’ Contributions

Economic games such as the Ultimatum Game (UG) and Prisoner’s Dilemma (PD) are widely used paradigms for studying fairness and cooperation. Monetary versions of these games involve two players splitting an arbitrary sum of money. In real life, however, people’s propensity to engage in cooperative behavior depends on their effort and contribution; factors that are well known to affect perceptions of fairness. We therefore sought to explore the impact of relative monetary contributions by players in the UG and PD. Adapted computerized UG and PD games, in which relative contributions from each player were manipulated, were administered to 200 participants aged 18–50 years old (50% female). We found that players’ contribution had large effects on cooperative behavior. Specifically, cooperation was greater amongst participants when their opponent had contributed more to joint earnings. This was manifested as higher acceptance rates and higher offers in the UG; and fewer defects in the PD compared to when the participant contributed more. Interestingly, equal contributions elicited the greatest sensitivity to fairness in the UG, and least frequent defection in the PD. Acceptance rates correlated positively with anxiety and sex differences were found in defection behavior. This study highlights the feasibility of computerized games to assess cooperative behavior and the importance of considering cooperation within the context of effortful contribution.