Monkey Prefrontal Cortex Learns to Minimize Sequence Prediction Error.

In this study, we develop a novel recurrent neural network (RNN) model of pre-frontal cortex that predicts sensory inputs, actions, and outcomes at the next time step. Synaptic weights in the model are adjusted to minimize sequence prediction error, adapting a deep learning rule similar to those of large language models. The model, called Sequence Prediction Error Learning (SPEL), is a simple RNN that predicts world state at the next time step, but that differs from standard RNNs by using its own prediction errors from the previous state predictions as inputs to the hidden units of the network. We show that the time course of sequence prediction errors generated by the model closely matched the activity time courses of populations of neurons in macaque prefrontal cortex. Hidden units in the model responded to combinations of task variables and exhibited sensitivity to changing stimulus probability in ways that closely resembled monkey prefrontal neurons. Moreover, the model generated prolonged response times to infrequent, unexpected events as did monkeys. The results suggest that prefrontal cortex may generate internal models of the temporal structure of the world even during tasks that do not explicitly depend on temporal expectation, using a sequence prediction error minimization learning rule to do so. As such, the SPEL model provides a unified, general-purpose theoretical framework for modeling the lateral prefrontal cortex.

Transcranial Electrical Neurostimulation as a Potential Addiction Treatment.

Addiction remains difficult to treat, but non-invasive transcranial electrical and magnetic neurostimulation methods may provide promising and cost-effective treatment approaches. We provide a narrative review of recent developments and evidence of effectiveness and consider newer technology that may yield improved treatment approaches. In particular, we review temporal interference electrical neurostimulation, which allows non-invasive and focal stimulation of deep brain regions. This provides a promising new potential approach to treat addiction, because many of the brain regions that seem most important for addiction are deeper in the brain, out of reach of existing technologies such as transcranial direct current stimulation.

Neural evidence of switch processes during semantic and phonetic foraging in human memory.

Humans may retrieve words from memory by exploring and exploiting in "semantic space" similar to how nonhuman animals forage for resources in physical space. This has been studied using the verbal fluency test (VFT), in which participants generate words belonging to a semantic or phonetic category in a limited time. People produce bursts of related items during VFT, referred to as "clustering" and "switching." The strategic foraging model posits that cognitive search behavior is guided by a monitoring process which detects relevant declines in performance and then triggers the searcher to seek a new patch or cluster in memory after the current patch has been depleted. An alternative body of research proposes that this behavior can be explained by an undirected rather than strategic search process, such as random walks with or without random jumps to new parts of semantic space. This study contributes to this theoretical debate by testing for neural evidence of strategically timed switches during memory search. Thirty participants performed category and letter VFT during functional MRI. Responses were classified as cluster or switch events based on computational metrics of similarity and participant evaluations. Results showed greater hippocampal and posterior cerebellar activation during switching than clustering, even while controlling for interresponse times and linguistic distance. Furthermore, these regions exhibited ramping activity which increased during within-patch search leading up to switches. Findings support the strategic foraging model, clarifying how neural switch processes may guide memory search in a manner akin to foraging in patchy spatial environments.

Insular and Striatal Correlates of Uncertain Risky Reward Pursuit in Schizophrenia.

BACKGROUND AND HYPOTHESIS: Risk-taking in specific contexts can be beneficial, leading to rewarding outcomes. Schizophrenia is associated with disadvantageous decision-making, as subjects pursue uncertain risky rewards less than controls. However, it is unclear whether this behavior is associated with more risk sensitivity or less reward incentivization. Matching on demographics and intelligence quotient (IQ), we determined whether risk-taking was more associated with brain activation in regions affiliated with risk evaluation or reward processing. STUDY DESIGN: Subjects (30 schizophrenia/schizoaffective disorder, 30 controls) completed a modified, fMRI Balloon Analogue Risk Task. Brain activation was modeled during decisions to pursue risky rewards and parametrically modeled according to risk level. STUDY RESULTS: The schizophrenia group exhibited less risky-reward pursuit despite previous adverse outcomes (Average Explosions; F(1,59) = 4.06, P = .048) but the comparable point at which risk-taking was volitionally discontinued (Adjusted Pumps; F(1,59) = 2.65, P = .11). Less activation was found in schizophrenia via whole brain and region of interest (ROI) analyses in the right (F(1,59) = 14.91, P < 0.001) and left (F(1,59) = 16.34, P < 0.001) nucleus accumbens (NAcc) during decisions to pursue rewards relative to riskiness. Risk-taking correlated with IQ in schizophrenia, but not controls. Path analyses of average ROI activation revealed less statistically determined influence of anterior insula upon dorsal anterior cingulate bilaterally (left: χ2 = 12.73, P < .001; right: χ2 = 9.54, P = .002) during risky reward pursuit in schizophrenia. CONCLUSIONS: NAcc activation in schizophrenia varied less according to the relative riskiness of uncertain rewards compared to controls, suggesting aberrations in reward processing. The lack of activation differences in other regions suggests similar risk evaluation. Less insular influence on the anterior cingulate may relate to attenuated salience attribution or inability for risk-related brain region collaboration to sufficiently perceive situational risk.

Foundations of human spatial problem solving.

Despite great strides in both machine learning and neuroscience, we do not know how the human brain solves problems in the general sense. We approach this question by drawing on the framework of engineering control theory. We demonstrate a computational neural model with only localist learning laws that is able to find solutions to arbitrary problems. The model and humans perform a multi-step task with arbitrary and changing starting and desired ending states. Using a combination of computational neural modeling, human fMRI, and representational similarity analysis, we show here that the roles of a number of brain regions can be reinterpreted as interacting mechanisms of a control theoretic system. The results suggest a new set of functional perspectives on the orbitofrontal cortex, hippocampus, basal ganglia, anterior temporal lobe, lateral prefrontal cortex, and visual cortex, as well as a new path toward artificial general intelligence.

The PRO model accounts for the anterior cingulate cortex role in risky decision-making and monitoring.

The anterior cingulate cortex (ACC) has been implicated in a number of functions, including performance monitoring and decision-making involving effort. The prediction of responses and outcomes (PRO) model has provided a unified account of much human and monkey ACC data involving anatomy, neurophysiology, EEG, fMRI, and behavior. We explored the computational nature of ACC with the PRO model, extending it to account specifically for both human and macaque monkey decision-making under risk, including both behavioral and neural data. We show that the PRO model can account for a number of additional effects related to outcome prediction, decision-making under risk, gambling behavior. In particular, we show that the ACC represents the variance of uncertain outcomes, suggesting a link between ACC function and mean-variance theories of decision making. The PRO model provides a unified account of a large set of data regarding the ACC.

A computational examination of the two-streams hypothesis: which pathway needs a longer memory?

The two visual streams hypothesis is a robust example of neural functional specialization that has inspired countless studies over the past four decades. According to one prominent version of the theory, the fundamental goal of the dorsal visual pathway is the transformation of retinal information for visually-guided motor behavior. To that end, the dorsal stream processes input using absolute (or veridical) metrics only when the movement is initiated, necessitating very little, or no, memory. Conversely, because the ventral visual pathway does not involve motor behavior (its output does not influence the real world), the ventral stream processes input using relative (or illusory) metrics and can accumulate or integrate sensory evidence over long time constants, which provides a substantial capacity for memory. In this study, we tested these relations between functional specialization, processing metrics, and memory by training identical recurrent neural networks to perform either a viewpoint-invariant object classification task or an orientation/size determination task. The former task relies on relative metrics, benefits from accumulating sensory evidence, and is usually attributed to the ventral stream. The latter task relies on absolute metrics, can be computed accurately in the moment, and is usually attributed to the dorsal stream. To quantify the amount of memory required for each task, we chose two types of neural network models. Using a long-short-term memory (LSTM) recurrent network, we found that viewpoint-invariant object categorization (object task) required a longer memory than orientation/size determination (orientation task). Additionally, to dissect this memory effect, we considered factors that contributed to longer memory in object tasks. First, we used two different sets of objects, one with self-occlusion of features and one without. Second, we defined object classes either strictly by visual feature similarity or (more liberally) by semantic label. The models required greater memory when features were self-occluded and when object classes were defined by visual feature similarity, showing that self-occlusion and visual similarity among object task samples are contributing to having a long memory. The same set of tasks modeled using modified leaky-integrator echo state recurrent networks (LiESN), however, did not replicate the results, except under some conditions. This may be because LiESNs cannot perform fine-grained memory adjustments due to their network-wide memory coefficient and fixed recurrent weights. In sum, the LSTM simulations suggest that longer memory is advantageous for performing viewpoint-invariant object classification (a putative ventral stream function) because it allows for interpolation of features across viewpoints. The results further suggest that orientation/size determination (a putative dorsal stream function) does not benefit from longer memory. These findings are consistent with the two visual streams theory of functional specialization. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-021-09703-z.

A review of risky decision-making in psychosis-spectrum disorders.

The investigation of risky decision-making has a prominent place in clinical science, with sundry behavioral tasks aimed at empirically quantifying the psychological construct of risk-taking. However, use of differing behavioral tasks has resulted in lack of agreement on risky decision-making within psychosis-spectrum disorders, as findings fail to converge upon the typical, binary conceptualization of increased risk-seeking or risk-aversion. The current review synthesizes the behavioral, risky decision-making literature to elucidate how specific task parameters may contribute to differences in task performance, and their associations with psychosis symptomatology and cognitive functioning. A paring of the literature suggests that: 1) Explicit risk-taking may be characterized by risk imperception, evidenced by less discrimination between choices of varying degrees of risk, potentially secondary to cognitive deficits. 2) Ambiguous risk-taking findings are inconclusive with few published studies. 3) Uncertain risk-taking findings, consistently interpreted as more risk-averse, have not parsed risk attitudes from confounding processes that may impact decision-making (e.g. risk imperception, reward processing, motivation). Thus, overgeneralized interpretations of task-specific risk-seeking/aversion should be curtailed, as they may fail to appropriately characterize decision-making phenomena. Future research in psychosis-spectrum disorders would benefit from empirically isolating contributions of specific processes during risky decision-making, including the newly hypothesized risk imperception.

Neural bases of risky decisions involving nicotine vapor versus monetary reward.

Substantial effort has gone into neuroimaging studies of neural mechanisms underlying addiction. Human studies of smoking typically either give monetary reward during an fMRI task or else allow subjects to smoke outside the scanner, after the session. This raises a fundamental issue of construct validity, as it is unclear whether the same neural mechanisms process decisions about nicotine that process decisions about money. To address this, we developed a novel MR-compatible nicotine vaping device, such that access to nicotine vapor could be controlled and monitored. We recruited heavy smokers (Money: 45 subjects, 13 females and 32 males; Nicotine: 21 subjects, 4 females and 17 males) to perform a gambling task with nicotine and monetary reward on separate days. We collected BOLD fMRI data while they performed the task inside the scanner and analyzed it using general linear modeling, with inference based on cluster-size correction. This allowed a direct comparison between the neural mechanisms of choosing and receiving immediate drug vs. monetary reward. We found substantial differences in the neural mechanisms that underlie risky choices about money vs. drug reward, including a reversal of the well-known error effects in the medial prefrontal cortex.

Neural correlates of visual attention during risky decision evidence integration.

Value-based decision-making is presumed to involve a dynamic integration process that supports assessing the potential outcomes of different choice options. Decision frameworks assume the value of a decision rests on both the desirability and risk surrounding an outcome. Previous work has highlighted neural representations of risk in the human brain, and their relation to decision choice. Key neural regions including the insula and anterior cingulate cortex (ACC) have been implicated in encoding the effects of risk on decision outcomes, including approach and avoidance. Yet, it remains unknown whether these regions are involved in the dynamic integration processes that precede and drive choice, and their relationship with ongoing attention. Here, we used concurrent fMRI and eye-tracking to discern neural activation related to visual attention preceding choice between sure-thing (i.e. safe) and risky gamble options. We found activation in both dorsal ACC (dACC) and posterior insula (PI) scaled in opposite directions with the difference in attention to risky rewards relative to risky losses. PI activation also differentiated foveations on both risky options (rewards and losses) relative to a sure-thing option. These findings point to ACC involvement in ongoing evaluation of risky but higher value options. The role of PI in risky outcomes points to a more general evaluative role in the decision-making that compares both safe and risky outcomes, irrespective of potential for gains or losses.

Case report of instantaneous resolution of juvenile macular degeneration blindness after proximal intercessory prayer.

An 18-year-old female lost the majority of her central vision over the course of three months in 1959. Medical records from 1960 indicate visual acuities (VA) of less than 20/400 for both eyes corresponding to legal blindness. On fundus examination of the eye there were dense yellowish-white areas of atrophy in each fovea and the individual was diagnosed with juvenile macular degeneration (JMD). In 1971, another examination recorded her uncorrected VA as finger counting on the right and hand motion on the left. She was diagnosed with macular degeneration (MD) and declared legally blind. In 1972, having been blind for over 12 years, the individual reportedly regained her vision instantaneously after receiving proximal-intercessory-prayer (PIP). Subsequent medical records document repeated substantial improvement; including uncorrected VA of 20/100 in each eye in 1974 and corrected VAs of 20/30 to 20/40 were recorded from 2001 to 2017. To date, her eyesight has remained intact for forty-seven years.

Does Chronic Cannabis Use Impact Risky Decision-Making: An Examination of fMRI Activation and Effective Connectivity?

With the increase in use of cannabis and its shifting legal status in the United States, cannabis use has become an important research focus. While studies of other drug populations have shown marked increases in risky decision-making, the literature on cannabis users is not as clear. The current study examined the performance of 17 cannabis users and 14 non-users on the Balloon Analog Risk Task (BART) using behavioral, fMRI and effective connectivity methods. Significant attenuation was found in a functional pathway projecting from the dorsal anterior cingulate cortex (dACC) to the nucleus accumbens (NAc) in cannabis users compared to non-using controls as well as decreases in risk-taking behaviors. These findings suggest that cannabis users may process and evaluate risks and rewards differently than non-users.

Case Report of gastroparesis healing: 16 years of a chronic syndrome resolved after proximal intercessory prayer.

A male infant at two weeks of age was hospitalized vomiting forcefully. He had a pyloromyotomy. He did not improve with medical therapy. The diagnosis of gastroparesis was made after a nuclear medicine gastric emptying study and intestinal manometry. He required a gastrostomy tube (g-tube) and a jejunostomy tube (j-tube) for feeding. At 11 months of age, the j-tube was converted into a feeding jejunostomy with Roux-en-Y limb. For 16 years he was completely dependent on j-tube feeding. In November 2011, he experienced proximal-intercessory-prayer (PIP) at a church and felt an electric shock starting from his shoulder and going through his stomach. After the prayer experience, he was unexpectedly able to tolerate oral feedings. The g- and j-tube were removed four months later and he did not require any further special treatments for his condition as all symptoms had resolved. Over seven years later, he has been free from symptoms. This article investigates a case of PIP as an alternative intervention for resolving severe idiopathic gastroparesis when maximal medical management is not effective.

A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task.

Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.

The Role of the Anterior Cingulate Cortex in Prediction Error and Signaling Surprise.

In the past two decades, reinforcement learning (RL) has become a popular framework for understanding brain function. A key component of RL models, prediction error, has been associated with neural signals throughout the brain, including subcortical nuclei, primary sensory cortices, and prefrontal cortex. Depending on the location in which activity is observed, the functional interpretation of prediction error may change: Prediction errors may reflect a discrepancy in the anticipated and actual value of reward, a signal indicating the salience or novelty of a stimulus, and many other interpretations. Anterior cingulate cortex (ACC) has long been recognized as a region involved in processing behavioral error, and recent computational models of the region have expanded this interpretation to include a more general role for the region in predicting likely events, broadly construed, and signaling deviations between expected and observed events. Ongoing modeling work investigating the interaction between ACC and additional regions involved in cognitive control suggests an even broader role for cingulate in computing a hierarchically structured surprise signal critical for learning models of the environment. The result is a predictive coding model of the frontal lobes, suggesting that predictive coding may be a unifying computational principle across the neocortex.

Discriminating Formal Representations of Risk in Anterior Cingulate Cortex and Inferior Frontal Gyrus.

Considerable debate persists around the definition of risk. Depending on the area of study, the concept of risk may be defined as the variance of the possible outcomes, the probability of a loss, or a combination of the loss probability and its maximum possible loss. Mounting evidence suggests the anterior cingulate cortex (ACC), including the surrounding medial prefrontal cortex (mPFC), and the anterior insula/inferior frontal gyrus (IFG) are key neural regions that represent perceived risks. Yet it remains unclear which of these formalisms best accounts for the pattern of activation in brain regions representing risk, and it is also difficult to disentangle risk from value, as both contribute to perceived utility. To adjudicate among the possible definitions, we used fMRI with a novel gambling task that orthogonalized the variance, loss probability, and maximum possible loss among the risky options, while maintaining a constant expected value across all monetary gambles to isolate the impact of risk rather than value. Here we show that when expected value is controlled for ACC and IFG activation reflect variance, but neither loss probability nor maximum possible loss. Across subjects, variance-related activation within the ACC correlates indirectly with risk aversion. Our results highlight the variance of the prospective outcomes as a formal representation of risk that is reflected both in brain activity and behavior, thus suggestive of a stronger link among formal economic theories of financial risk, naturalistic risk taking, and neural representations of risk.

Frontal cortex function as derived from hierarchical predictive coding.

The frontal lobes are essential for human volition and goal-directed behavior, yet their function remains unclear. While various models have highlighted working memory, reinforcement learning, and cognitive control as key functions, a single framework for interpreting the range of effects observed in prefrontal cortex has yet to emerge. Here we show that a simple computational motif based on predictive coding can be stacked hierarchically to learn and perform arbitrarily complex goal-directed behavior. The resulting Hierarchical Error Representation (HER) model simulates a wide array of findings from fMRI, ERP, single-units, and neuropsychological studies of both lateral and medial prefrontal cortex. By reconceptualizing lateral prefrontal activity as anticipating prediction errors, the HER model provides a novel unifying account of prefrontal cortex function with broad implications for understanding the frontal cortex across multiple levels of description, from the level of single neurons to behavior.

Prefrontal Cortex in Control: Broadening the Scope to Identify Mechanisms.

Sometime in the past two decades, neuroimaging and behavioral research converged on pFC as an important locus of cognitive control and decision-making, and that seems to be the last thing anyone has agreed on since. Every year sees an increase in the number of roles and functions attributed to distinct subregions within pFC, roles that may explain behavior and neural activity in one context but might fail to generalize across the many behaviors in which each region is implicated. Emblematic of this ongoing proliferation of functions is dorsal ACC (dACC). Novel tasks that activate dACC are followed by novel interpretations of dACC function, and each new interpretation adds to the number of functionally specific processes contained within the region. This state of affairs, a recurrent and persistent behavior followed by an illusory and transient relief, can be likened to behavioral pathology. In Journal of Cognitive Neuroscience, 29:10 we collect contributed articles that seek to move the conversation beyond specific functions of subregions of pFC, focusing instead on general roles that support pFC involvement in a wide variety of behaviors and across a variety of experimental paradigms.

Does gratitude writing improve the mental health of psychotherapy clients? Evidence from a randomized controlled trial.

Although the past decade has witnessed growing research interest in positive psychological interventions (PPIs), their potential as adjunctive interventions for psychotherapy remains relatively unexplored. Therefore, this article expands the frontiers of PPI research by reporting the first randomized controlled trial to test a gratitude writing adjunctive intervention for psychotherapy clients. Participants were 293 adults seeking university-based psychotherapy services. Participants were randomly assigned to one of three conditions: (a) control (psychotherapy only), (b) a psychotherapy plus expressive writing, and (c) a psychotherapy plus gratitude writing. Participants in the gratitude condition wrote letters expressing gratitude to others, whereas those in the expressive writing condition wrote about their deepest thoughts and feelings about stressful experiences. About 4 weeks as well as 12 weeks after the conclusion of the writing intervention, participants in the gratitude condition reported significantly better mental health than those in the expressive and control conditions, whereas those in the expressive and control conditions did not differ significantly. Moreover, lower proportions of negative emotion words in participants' writing mediated the positive effect of condition (gratitude versus expressive writing) on mental health. These findings are discussed in light of the use of gratitude interventions as adjunctive interventions for psychotherapy clients.

Foraging Value, Risk Avoidance, and Multiple Control Signals: How the Anterior Cingulate Cortex Controls Value-based Decision-making.

Recent work on the role of the ACC in cognition has focused on choice difficulty, action value, risk avoidance, conflict resolution, and the value of exerting control among other factors. A main underlying question is what are the output signals of ACC, and relatedly, what is their effect on downstream cognitive processes? Here we propose a model of how ACC influences cognitive processing in other brain regions that choose actions. The model builds on the earlier Predicted Response Outcome model and suggests that ACC learns to represent specifically the states in which the potential costs or risks of an action are high, on both short and long timescales. It then uses those cost signals as a basis to bias decisions to minimize losses while maximizing gains. The model simulates both proactive and reactive control signals and accounts for a variety of empirical findings regarding value-based decision-making.