Neural Mechanisms for Adaptive Learned Avoidance of Mental Effort.

Humans tend to avoid mental effort. Previous studies have demonstrated this tendency using various demand-selection tasks; participants generally avoid options associated with higher cognitive demand. However, it remains unclear whether humans avoid mental effort adaptively in uncertain and nonstationary environments. If so, it also remains unclear what neural mechanisms underlie such learned avoidance and whether they remain the same regardless of cognitive-demand types. We addressed these issues by developing novel demand-selection tasks where associations between choice options and cognitive-demand levels change over time, with two variations using mental arithmetic and spatial reasoning problems (males/females: 29:4 and 18:2). Most participants showed avoidance, and their choices depended on the demand experienced on multiple preceding trials. We assumed that participants updated the expected cost of mental effort through experience, and fitted their choices by reinforcement learning models, comparing several possibilities. Model-based fMRI analyses revealed that activity in the dorsomedial and lateral frontal cortices was positively correlated with the trial-by-trial expected cost for the chosen option commonly across the different types of cognitive demand. Analyses also revealed a trend of negative correlation in the ventromedial prefrontal cortex. We further identified correlates of cost-prediction error at time of problem presentation or answering the problem, the latter of which partially overlapped with or were proximal to the correlates of expected cost at time of choice cue in the dorsomedial frontal cortex. These results suggest that humans adaptively learn to avoid mental effort, having neural mechanisms to represent expected cost and cost-prediction error, and the same mechanisms operate for various types of cognitive demand.SIGNIFICANCE STATEMENT In daily life, humans encounter various cognitive demands and tend to avoid high-demand options. However, it remains unclear whether humans avoid mental effort adaptively under dynamically changing environments. If so, it also remains unclear what the underlying neural mechanisms are and whether they operate regardless of cognitive-demand types. To address these issues, we developed novel tasks where participants could learn to avoid high-demand options under uncertain and nonstationary environments. Through model-based fMRI analyses, we found regions whose activity was correlated with the expected mental effort cost, or cost-prediction error, regardless of demand type. These regions overlap, or are adjacent with each other, in the dorsomedial frontal cortex. This finding helps clarify the mechanisms for cognitive-demand avoidance, and provides empirical building blocks for the emerging computational theory of mental effort.

Neural Mechanisms of Credit Assignment in a Multicue Environment.

In complex environments, many potential cues can guide a decision or be assigned responsibility for the outcome of the decision. We know little, however, about how humans and animals select relevant information sources that should guide behavior. We show that subjects solve this relevance selection and credit assignment problem by selecting one cue and its association with a particular outcome as the main focus of a hypothesis. To do this, we examined learning while using a task design that allowed us to estimate the focus of each subject's hypotheses on a trial-by-trial basis. When a prediction is confirmed by the outcome, then credit for the outcome is assigned to that cue rather than an alternative. Activity in medial frontal cortex is associated with the assignment of credit to the cue that is the main focus of the hypothesis. However, when the outcome disconfirms a prediction, the focus shifts between cues, and the credit for the outcome is assigned to an alternative cue. This process of reselection for credit assignment to an alternative cue is associated with lateral orbitofrontal cortex. SIGNIFICANCE STATEMENT: Learners should infer which features of environments are predictive of significant events, such as rewards. This "credit assignment" problem is particularly challenging when any of several cues might be predictive. We show that human subjects solve the credit assignment problem by implicitly "hypothesizing" which cue is relevant for predicting subsequent outcomes, and then credit is assigned according to this hypothesis. This process is associated with a distinctive pattern of activity in a part of medial frontal cortex. By contrast, when unexpected outcomes occur, hypotheses are redirected toward alternative cues, and this process is associated with activity in lateral orbitofrontal cortex.

Control without Controllers: Toward a Distributed Neuroscience of Executive Control.

Executive control refers to the regulation of cognition and behavior by mental processes and is a hallmark of higher cognition. Most approaches to understanding its mechanisms begin with the assumption that our brains have anatomically segregated and functionally specialized control modules. The modular approach is intuitive: Control is conceptually distinct from basic mental processing, so an organization that reifies that distinction makes sense. An alternative approach sees executive control as self-organizing principles of a distributed organization. In distributed systems, control and controlled processes are colocalized within large numbers of dispersed computational agents. Control then is often an emergent consequence of simple rules governing the interaction between agents. Because these systems are unfamiliar and unintuitive, here we review several well-understood examples of distributed control systems, group living insects and social animals, and emphasize their parallels with neural systems. We then reexamine the cognitive neuroscience literature on executive control for evidence that its neural control systems may be distributed.

Perceptions of social rigidity predict loneliness across the Japanese population.

Loneliness is associated with mental and physical health problems and elevated suicide risk, and is increasingly widespread in modern societies. However, identifying the primary factors underlying loneliness remains a major public health challenge. Historically, loneliness was thought to result from a lack of high-quality social connections, but broader cultural factors (e.g. social norms) are increasingly recognized to also influence loneliness. Here, we used a large-scale survey (N = 4977) to assess to what degree the loneliness epidemic in Japan is associated with traditional measures of social isolation (number of close friends), cultural factors (perceptions of social rigidity, as measured by relational mobility), and socioeconomic factors (e.g. income). We confirmed that a lack of close friends is a dominant factor underlying loneliness in Japan. We also found that perceptions of the social rigidity in one's environment was a major correlate of loneliness. Subjects who perceived lower levels of rigidity in their social environments felt significantly less lonely than those who perceived higher levels of social rigidity, though the association was weak in low income males. Thus, Japanese society and other high social rigidity cultures may need to reflect on the possibility that inflexible traditional norms of socialization are exacerbating loneliness.

Trust in Institutions, Not in Political Leaders, Determines Compliance in COVID-19 Prevention Measures within Societies across the Globe.

A core assumption often heard in public health discourse is that increasing trust in national political leaders is essential for securing public health compliance during crises such as the COVID-19 pandemic (2019-ongoing). However, studies of national government trust are typically too coarse-grained to differentiate between trust in institutions versus more interpersonal trust in political leaders. Here, we present multiscale trust measurements for twelve countries and territories across the West, Oceania and East Asia. These trust results were used to identify which specific domains of government and social trust were most crucial for securing public health compliance (frequency of mask wearing and social distancing) and understanding the reasons for following health measures (belief in effectiveness of public health measures). Through the use of linear regression and structural equation modeling, our cross-cultural survey-based analysis (N = 3369 subjects) revealed that higher trust in national and local public health institutions was a universally consistent predictor of public health compliance, while trust in national political leaders was not predictive of compliance across cultures and geographical regions. Institutional trust was mediated by multiple types of transparency, including providing rationale, securing public feedback, and honestly expressing uncertainty. These results highlight the importance of distinguishing between components of government trust, to better understand which entities the public gives the most attention to during crises.

The neuroanatomy of social trust predicts depression vulnerability.

Trust attitude is a social personality trait linked with the estimation of others' trustworthiness. Trusting others, however, can have substantial negative effects on mental health, such as the development of depression. Despite significant progress in understanding the neurobiology of trust, whether the neuroanatomy of trust is linked with depression vulnerability remains unknown. To investigate a link between the neuroanatomy of trust and depression vulnerability, we assessed trust and depressive symptoms and employed neuroimaging to acquire brain structure data of healthy participants. A high depressive symptom score was used as an indicator of depression vulnerability. The neuroanatomical results observed with the healthy sample were validated in a sample of clinically diagnosed depressive patients. We found significantly higher depressive symptoms among low trusters than among high trusters. Neuroanatomically, low trusters and depressive patients showed similar volume reduction in brain regions implicated in social cognition, including the dorsolateral prefrontal cortex (DLPFC), dorsomedial PFC, posterior cingulate, precuneus, and angular gyrus. Furthermore, the reduced volume of the DLPFC and precuneus mediated the relationship between trust and depressive symptoms. These findings contribute to understanding social- and neural-markers of depression vulnerability and may inform the development of social interventions to prevent pathological depression.

Stimulation of the frontal eye field reveals persistent effective connectivity after controlled behavior.

Our ability to choose nonhabitual controlled behavior instead of habitual automatic behavior is based on a flexible control mechanism subserved by neural activity representing the behavior-guiding rule. However, it has been shown that the behavior slows down more when switching from controlled to automatic behavior than vice versa. Here we show that persistent effective connectivity of the neural network after execution of controlled behavior is responsible for the behavioral slowing on a subsequent trial. We asked normal human subjects to perform a prosaccade or antisaccade task based on a cue and examined the effective connectivity of the neural network based on the pattern of neural impulse transmission induced by stimulation of the frontal eye field (FEF). Effective connectivity during the task preparation period was dependent on the task that subjects had performed on the previous trial, regardless of the upcoming task. The strength of this persistent effective connectivity was associated with saccade slowing especially on trials after controlled antisaccade. In contrast, the pattern of regional activation changed depending on the upcoming task regardless of the previous task and the decrease in activation was associated with errors in upcoming antisaccade task. These results suggest that the effective connectivity examined by FEF stimulation reflects a residual functional state of the network involved in performance of controlled antisaccade and its persistence may account for the behavioral slowing on the subsequent trial.

Multiscale Computation and Dynamic Attention in Biological and Artificial Intelligence.

Biological and artificial intelligence (AI) are often defined by their capacity to achieve a hierarchy of short-term and long-term goals that require incorporating information over time and space at both local and global scales. More advanced forms of this capacity involve the adaptive modulation of integration across scales, which resolve computational inefficiency and explore-exploit dilemmas at the same time. Research in neuroscience and AI have both made progress towards understanding architectures that achieve this. Insight into biological computations come from phenomena such as decision inertia, habit formation, information search, risky choices and foraging. Across these domains, the brain is equipped with mechanisms (such as the dorsal anterior cingulate and dorsolateral prefrontal cortex) that can represent and modulate across scales, both with top-down control processes and by local to global consolidation as information progresses from sensory to prefrontal areas. Paralleling these biological architectures, progress in AI is marked by innovations in dynamic multiscale modulation, moving from recurrent and convolutional neural networks-with fixed scalings-to attention, transformers, dynamic convolutions, and consciousness priors-which modulate scale to input and increase scale breadth. The use and development of these multiscale innovations in robotic agents, game AI, and natural language processing (NLP) are pushing the boundaries of AI achievements. By juxtaposing biological and artificial intelligence, the present work underscores the critical importance of multiscale processing to general intelligence, as well as highlighting innovations and differences between the future of biological and artificial intelligence.

Predictive and postdictive mechanisms jointly contribute to visual awareness.

One of the fundamental issues in visual awareness is how we are able to perceive the scene in front of our eyes on time despite the delay in processing visual information. The prediction theory postulates that our visual system predicts the future to compensate for such delays. On the other hand, the postdiction theory postulates that our visual awareness is inevitably a delayed product. In the present study we used flash-lag paradigms in motion and color domains and examined how the perception of visual information at the time of flash is influenced by prior and subsequent visual events. We found that both types of event additively influence the perception of the present visual image, suggesting that our visual awareness results from joint contribution of predictive and postdictive mechanisms.

Impacts of switching antidepressants after successful electroconvulsive therapy on the maintenance of clinical remission in patients with treatment-resistant depression: a chart review.

INTRODUCTION: There is no consensus regarding whether a previously prescribed, that is, failed, antidepressant should be continued or switched after a successful electroconvulsive therapy (ECT) for the maintenance of clinical remission in patients with treatment-resistant depression (TRD). In this study, we conducted a chart review to examine impacts of the antidepressant switch after the successful ECT on 1-year outcome in patients with TRD. MATERIALS AND METHODS: This retrospective chart review included inpatients with TRD (ie, those who failed to respond to adequate trials of 2 distinctly different classes of antidepressants) who showed clinical remission after ECT. Readmission rate and social functioning 6 months and 1 year after the successful ECT were compared between patients who experienced an antidepressant switch and those who continued prior regimen. RESULTS: Twenty-eight patients (mean age, 59 years; 9 men) were followed-up for 1 year. The patients who changed antidepressants after ECT (n = 7) experienced a readmission significantly less frequent than the others (n = 21) in 1 year (0% vs 43%, P = 0.043). In addition, the former showed significantly better social contacts at 6 months (P = 0.022) and 1 year (P = 0.015). There were no significant differences in baseline characteristics between the 2 groups. CONCLUSIONS: The patients who experienced an antidepressant switch after ECT required a readmission less frequently in 1 year than those who were maintained with the same antidepressant. The findings of this preliminary study suggest that a switch to another antidepressant after successful ECT may be encouraged for the maintenance of clinical remission in patients with TRD.

A Spotlight on Reward.

Research published in this issue of Neuron from McGinty et al. (2016) suggests that attention may help bind information about value to specific options in economic choice. Responses of orbitofrontal neurons are strongly modulated by the distance from gaze to the position of a reward-predicting target.

Predictive decision making driven by multiple time-linked reward representations in the anterior cingulate cortex.

In many natural environments the value of a choice gradually gets better or worse as circumstances change. Discerning such trends makes predicting future choice values possible. We show that humans track such trends by comparing estimates of recent and past reward rates, which they are able to hold simultaneously in the dorsal anterior cingulate cortex (dACC). Comparison of recent and past reward rates with positive and negative decision weights is reflected by opposing dACC signals indexing these quantities. The relative strengths of time-linked reward representations in dACC predict whether subjects persist in their current behaviour or switch to an alternative. Computationally, trend-guided choice can be modelled by using a reinforcement-learning mechanism that computes a longer-term estimate (or expectation) of prediction errors. Using such a model, we find a relative predominance of expected prediction errors in dACC, instantaneous prediction errors in the ventral striatum and choice signals in the ventromedial prefrontal cortex.

Autonomous mechanism of internal choice estimate underlies decision inertia.

Our choice is influenced by choices we made in the past, but the mechanism responsible for the choice bias remains elusive. Here we show that the history-dependent choice bias can be explained by an autonomous learning rule whereby an estimate of the likelihood of a choice to be made is updated in each trial by comparing between the actual and expected choices. We found that in perceptual decision making without performance feedback, a decision on an ambiguous stimulus is repeated on the subsequent trial more often than a decision on a salient stimulus. This inertia of decision was not accounted for by biases in motor response, sensory processing, or attention. The posterior cingulate cortex and frontal eye field represent choice prediction error and choice estimate in the learning algorithm, respectively. Interactions between the two regions during the intertrial interval are associated with decision inertia on a subsequent trial.

Task-related modulation of effective connectivity during perceptual decision making: dissociation between dorsal and ventral prefrontal cortex.

The dorsal and ventral parts of the lateral prefrontal cortex have been thought to play distinct roles in decision making. Although its dorsal part such as the frontal eye field (FEF) is shown to play roles in accumulation of sensory information during perceptual decision making, the role of the ventral prefrontal cortex (PFv) is not well-documented. Previous studies have suggested that the PFv is involved in selective attention to the task-relevant information and is associated with accuracy of the behavioral performance. It is unknown, however, whether the accumulation and selection processes are anatomically dissociated between the FEF and PFv. Here we show that, by using concurrent TMS and EEG recording, the short-latency (20-40 ms) TMS-evoked potentials after stimulation of the FEF change as a function of the time to behavioral response, whereas those after stimulation of the PFv change depending on whether the response is correct or not. The potentials after stimulation of either region did not show significant interaction between time to response and performance accuracy, suggesting dissociation between the processes subserved by the FEF and PFv networks. The results are consistent with the idea that the network involving the FEF plays a role in information accumulation, whereas the network involving the PFv plays a role in selecting task relevant information. In addition, stimulation of the FEF and PFv induced activation in common regions in the dorsolateral and medial frontal cortices, suggesting convergence of information processed in the two regions. Taken together, the results suggest dissociation between the FEF and PFv networks for their computational roles in perceptual decision making. The study also highlights the advantage of TMS-EEG technique in investigating the computational processes subserved by the neural network in the human brain with a high temporal resolution.

Task-specific signal transmission from prefrontal cortex in visual selective attention.

Our voluntary behaviors are thought to be controlled by top-down signals from the prefrontal cortex that modulate neural processing in the posterior cortices according to the behavioral goal. However, we have insufficient evidence for the causal effect of the top-down signals. We applied a single-pulse transcranial magnetic stimulation over the human prefrontal cortex and measured the strength of the top-down signals as an increase in the efficiency of neural impulse transmission. The impulse induced by the stimulation transmitted to different posterior visual areas depending on the domain of visual features to which subjects attended. We also found that the amount of impulse transmission was associated with the level of attentional preparation and the performance of visual selective-attention tasks, consistent with the causal role of prefrontal top-down signals.