Trying Harder: How Cognitive Effort Sculpts Neural Representations during Working Memory.

While the exertion of mental effort improves performance on cognitive tasks, the neural mechanisms by which motivational factors impact cognition remain unknown. Here, we used fMRI to test how changes in cognitive effort, induced by changes in task difficulty, impact neural representations of working memory (WM). Participants (both sexes) were precued whether WM difficulty would be hard or easy. We hypothesized that hard trials demanded more effort as a later decision required finer mnemonic precision. Behaviorally, pupil size was larger and response times were slower on hard compared with easy trials suggesting our manipulation of effort succeeded. Neurally, we observed robust persistent activity during delay periods in the prefrontal cortex (PFC), especially during hard trials. Yet, details of the memoranda could not be decoded from patterns in prefrontal activity. In the patterns of activity in the visual cortex, however, we found strong decoding of memorized targets, where accuracy was higher on hard trials. To potentially link these across-region effects, we hypothesized that effort, carried by persistent activity in the PFC, impacts the quality of WM representations encoded in the visual cortex. Indeed, we found that the amplitude of delay period activity in the frontal cortex predicted decoded accuracy in the visual cortex on a trial-wise basis. These results indicate that effort-related feedback signals sculpt population activity in the visual cortex, improving mnemonic fidelity.

Wagers for work: Decomposing the costs of cognitive effort.

Some aspects of cognition are more taxing than others. Accordingly, many people will avoid cognitively demanding tasks in favor of simpler alternatives. Which components of these tasks are costly, and how much, remains unknown. Here, we use a novel task design in which subjects request wages for completing cognitive tasks and a computational modeling procedure that decomposes their wages into the costs driving them. Using working memory as a test case, our approach revealed that gating new information into memory and protecting against interference are costly. Critically, other factors, like memory load, appeared less costly. Other key factors which may drive effort costs, such as error avoidance, had minimal influence on wage requests. Our approach is sensitive to individual differences, and could be used in psychiatric populations to understand the true underlying nature of apparent cognitive deficits.

Trying harder: how cognitive effort sculpts neural representations during working memory.

The neural mechanisms by which motivational factors influence cognition remain unknown. Using fMRI, we tested how cognitive effort impacts working memory (WM). Participants were precued whether WM difficulty would be hard or easy. Hard trials demanded more effort as a later decision required finer mnemonic precision. Behaviorally, pupil size was larger and response times were slower on hard trials suggesting our manipulation of effort succeeded. Neurally, we observed robust persistent activity in prefrontal cortex, especially during hard trials. We found strong decoding of location in visual cortex, where accuracy was higher on hard trials. Connecting these across-region effects, we found that the amplitude of delay period activity in frontal cortex predicted decoded accuracy in visual cortex on a trial-wise basis. We conclude that the gain of persistent activity in frontal cortex may be the source of effort-related feedback signals that improve the quality of WM representations stored in visual cortex.

The interpretation of computational model parameters depends on the context.

Reinforcement Learning (RL) models have revolutionized the cognitive and brain sciences, promising to explain behavior from simple conditioning to complex problem solving, to shed light on developmental and individual differences, and to anchor cognitive processes in specific brain mechanisms. However, the RL literature increasingly reveals contradictory results, which might cast doubt on these claims. We hypothesized that many contradictions arise from two commonly-held assumptions about computational model parameters that are actually often invalid: That parameters generalize between contexts (e.g. tasks, models) and that they capture interpretable (i.e. unique, distinctive) neurocognitive processes. To test this, we asked 291 participants aged 8-30 years to complete three learning tasks in one experimental session, and fitted RL models to each. We found that some parameters (exploration / decision noise) showed significant generalization: they followed similar developmental trajectories, and were reciprocally predictive between tasks. Still, generalization was significantly below the methodological ceiling. Furthermore, other parameters (learning rates, forgetting) did not show evidence of generalization, and sometimes even opposite developmental trajectories. Interpretability was low for all parameters. We conclude that the systematic study of context factors (e.g. reward stochasticity; task volatility) will be necessary to enhance the generalizability and interpretability of computational cognitive models.

Reinforcement learning and Bayesian inference provide complementary models for the unique advantage of adolescents in stochastic reversal.

During adolescence, youth venture out, explore the wider world, and are challenged to learn how to navigate novel and uncertain environments. We investigated how performance changes across adolescent development in a stochastic, volatile reversal-learning task that uniquely taxes the balance of persistence and flexibility. In a sample of 291 participants aged 8-30, we found that in the mid-teen years, adolescents outperformed both younger and older participants. We developed two independent cognitive models, based on Reinforcement learning (RL) and Bayesian inference (BI). The RL parameter for learning from negative outcomes and the BI parameters specifying participants' mental models were closest to optimal in mid-teen adolescents, suggesting a central role in adolescent cognitive processing. By contrast, persistence and noise parameters improved monotonically with age. We distilled the insights of RL and BI using principal component analysis and found that three shared components interacted to form the adolescent performance peak: adult-like behavioral quality, child-like time scales, and developmentally-unique processing of positive feedback. This research highlights adolescence as a neurodevelopmental window that can create performance advantages in volatile and uncertain environments. It also shows how detailed insights can be gleaned by using cognitive models in new ways.

Modeling changes in probabilistic reinforcement learning during adolescence.

In the real world, many relationships between events are uncertain and probabilistic. Uncertainty is also likely to be a more common feature of daily experience for youth because they have less experience to draw from than adults. Some studies suggest probabilistic learning may be inefficient in youths compared to adults, while others suggest it may be more efficient in youths in mid adolescence. Here we used a probabilistic reinforcement learning task to test how youth age 8-17 (N = 187) and adults age 18-30 (N = 110) learn about stable probabilistic contingencies. Performance increased with age through early-twenties, then stabilized. Using hierarchical Bayesian methods to fit computational reinforcement learning models, we show that all participants' performance was better explained by models in which negative outcomes had minimal to no impact on learning. The performance increase over age was driven by 1) an increase in learning rate (i.e. decrease in integration time scale); 2) a decrease in noisy/exploratory choices. In mid-adolescence age 13-15, salivary testosterone and learning rate were positively related. We discuss our findings in the context of other studies and hypotheses about adolescent brain development.

Resting-state brain oscillations predict cognitive function in psychiatric disorders: A transdiagnostic machine learning approach.

BACKGROUND: Cognitive dysfunction is widespread in psychiatric disorders and can significantly impact quality of life. Deficits cut across traditional diagnostic boundaries, necessitating new approaches to understand how cognitive function relates to large-scale brain activity and psychiatric symptoms across the diagnostic spectrum. OBJECTIVE: Using random forest regression, we aimed to identify transdiagnostic patterns linking cognitive function to resting-state EEG oscillations. METHODS: 216 participants recruited through an outpatient psychiatric clinic completed the Cambridge Neuropsychological Test Automated Battery and underwent a 5-minute eyes-closed resting state EEG recording. We built random forest regression models to predict performance on each cognitive test using the resting-state EEG power spectrum as input, and we compared model performance to a sampling distribution constructed with random permutations. For models that performed significantly better than chance, we used feature importance estimates to identify features of the EEG power spectrum that are predictive of cognitive functioning. RESULTS: Random forest models successfully predicted performance on measures of episodic memory and associative learning (Paired Associates Learning, PAL), information processing speed (Choice Reaction Time, CRT), and attentional set-shifting and executive function (Intra-Extra Dimensional Set Shift, IED). Oscillatory power in the upper alpha range was associated with better performance on PAL and CRT, while low alpha power was associated with worse CRT performance. Beta power predicted poor performance on all three tests. Theta power was associated with good performance on PAL, and delta and theta oscillations were identified as predictors of good performance on IED. No differences in cognitive performance were found between diagnostic categories. CONCLUSION: Resting oscillations are predictive of certain dimensions of cognitive function across various psychiatric disorders. These findings may inform treatment development to improve cognition.

Distentangling the systems contributing to changes in learning during adolescence.

Multiple neurocognitive systems contribute simultaneously to learning. For example, dopamine and basal ganglia (BG) systems are thought to support reinforcement learning (RL) by incrementally updating the value of choices, while the prefrontal cortex (PFC) contributes different computations, such as actively maintaining precise information in working memory (WM). It is commonly thought that WM and PFC show more protracted development than RL and BG systems, yet their contributions are rarely assessed in tandem. Here, we used a simple learning task to test how RL and WM contribute to changes in learning across adolescence. We tested 187 subjects ages 8 to 17 and 53 adults (25-30). Participants learned stimulus-action associations from feedback; the learning load was varied to be within or exceed WM capacity. Participants age 8-12 learned slower than participants age 13-17, and were more sensitive to load. We used computational modeling to estimate subjects' use of WM and RL processes. Surprisingly, we found more protracted changes in RL than WM during development. RL learning rate increased with age until age 18 and WM parameters showed more subtle, gender- and puberty-dependent changes early in adolescence. These results can inform education and intervention strategies based on the developmental science of learning.

Attachment figures activate a safety signal-related neural region and reduce pain experience.

Although it has long been hypothesized that attachment figures provide individuals with a sense of safety and security, the neural mechanisms underlying attachment-induced safety have not been explored. Here, we investigated whether an attachment figure acts as a safety signal by exploring whether viewing an attachment figure during a threatening experience (physical pain) led to increased activity in a neural region associated with safety signaling, the ventromedial prefrontal cortex (VMPFC), and corresponding reductions in pain. Female participants in long-term romantic relationships were scanned as they received painful stimuli while viewing pictures of their partner and control images (stranger, object). Consistent with the idea that the attachment figure may signal safety, results revealed that viewing partner pictures while receiving painful stimulation led to reductions in self-reported pain ratings, reductions in pain-related neural activity (dorsal anterior cingulate cortex, anterior insula), and increased activity in the VMPFC. Moreover, greater VMPFC activity in response to partner pictures was associated with longer relationship lengths and greater perceived partner support, further highlighting a role for the VMPFC in responding to the safety value of the partner. Last, greater VMPFC activity while viewing partner pictures was associated with reduced pain ratings and reduced pain-related neural activity. An implication of these findings is that, in the same way that stimuli that historically have threatened survival (e.g., snakes, spiders) are considered to be prepared fear stimuli, attachment figures, who have historically benefited survival, may serve as prepared safety stimuli, reducing threat- or distress-related responding in their presence.

Neurobiological correlates of coping through emotional approach.

This investigation considered possible health-related neurobiological processes associated with "emotional approach coping" (EAC), or intentional efforts to identify, process, and express emotions surrounding stressors. It was hypothesized that higher dispositional use of EAC strategies would be related to neural activity indicative of greater trait approach motivational orientation and to lower proinflammatory cytokine and cortisol responses to stress. To assess these relationships, 46 healthy participants completed a questionnaire assessing the two components of EAC (i.e., emotional processing and emotional expression), and their resting frontal cortical asymmetry was measured using electroencephalography (EEG). A subset (N=22) of these participants' levels of the soluble receptor for tumor necrosis factor-alpha (sTNFalphaRII), interleukin-6 (IL-6), and cortisol (all obtained from oral fluids) were also assessed before and after exposure to an acute laboratory stressor. Consistent with predictions, higher reported levels of emotional expression were significantly associated with greater relative left-sided frontal EEG asymmetry, indicative of greater trait approach motivation. Additionally, people who scored higher on EAC, particularly the emotional processing component, tended to show a less-pronounced TNF-alpha stress response. EAC was unrelated to levels of IL-6 and cortisol. Greater left-sided frontal EEG asymmetry was significantly related to lower baseline levels of IL-6 and to lower stress-related levels of sTNFalphaRII, and was marginally related to lower stress-related levels of IL-6. The findings suggest that the salubrious effects of EAC strategies for managing stress may be linked to an approach-oriented neurocognitive profile and to well-regulated proinflammatory cytokine responses to stress.

Neurocognitive components of the behavioral inhibition and activation systems: implications for theories of self-regulation.

We examined the neurocognitive correlates of the Behavioral Inhibition and Behavioral Activation Systems (BIS/BAS) in an effort to clarify ambiguities concerning interpretations of BIS as reflecting inhibition versus avoidance. We hypothesized that self-reported BIS should relate to neural mechanisms associated with conflict monitoring, whereas self-reported BAS should be associated with neural correlates of approach motivation. Consistent with these predictions, higher self-reported BIS was uniquely related to the N2 event-related potential on No-Go trials of a Go/No-Go task, linking BIS with conflict monitoring and sensitivity to No-Go cues. Higher BAS was uniquely related to greater left-sided baseline frontal cortical asymmetry associated with approach orientation. Implications for theories of self-regulation involving conflict monitoring, cognitive control, and approach/avoidance motivation are discussed.

Neurocognitive correlates of liberalism and conservatism.

Political scientists and psychologists have noted that, on average, conservatives show more structured and persistent cognitive styles, whereas liberals are more responsive to informational complexity, ambiguity and novelty. We tested the hypothesis that these profiles relate to differences in general neurocognitive functioning using event-related potentials, and found that greater liberalism was associated with stronger conflict-related anterior cingulate activity, suggesting greater neurocognitive sensitivity to cues for altering a habitual response pattern.

Some evidence about character and mate selection.

The authors conducted four studies (total N = 292) about character and mate desirability. In Study 1, undergraduates judged stimuli for attractiveness-physically and as a casual or longterm date. The target was described as faithful, having cheated but stayed with mates, or having cheated and left. Contrary to the hypothesis, men and women were equally affected by both kinds of cheaters. Study 2 replicated Study 1 with nonstudent adults. In Study 3, undergraduates rated a stimulus on the same attractiveness variables. This target had $14 million from winning a lottery or selling a dot-com company. Women, but not men, found the dot-com creator to be more physically attractive than the lottery winner. In Study 4, undergraduates rated someone who sold a cookie-making company or profited from a lucky real estate transaction. Both men and women preferred the cookie-company seller on all three measures of attractiveness.