Responses of human frontal cortex to surprising events are predicted by formal associative learning theory

Liraglutide restores impaired associative learning in individuals with obesity

Survival under selective pressure is driven by the ability of our brain to use sensory information to our advantage to control physiological needs. To that end, neural circuits receive and integrate external environmental cues and internal metabolic signals to form learned sensory associations, consequently motivating and adapting our behaviour. The dopaminergic midbrain plays a crucial role in learning adaptive behaviour and is particularly sensitive to peripheral metabolic signals, including intestinal peptides, such as glucagon-like peptide 1 (GLP-1). In a single-blinded, randomized, controlled, crossover basic human functional magnetic resonance imaging study relying on a computational model of the adaptive learning process underlying behavioural responses, we show that adaptive learning is reduced when metabolic sensing is impaired in obesity, as indexed by reduced insulin sensitivity (participants: N = 30 with normal insulin sensitivity; N = 24 with impaired insulin sensitivity). Treatment with the GLP-1 receptor agonist liraglutide normalizes impaired learning of sensory associations in men and women with obesity. Collectively, our findings reveal that GLP-1 receptor activation modulates associative learning in people with obesity via its central effects within the mesoaccumbens pathway. These findings provide evidence for how metabolic signals can act as neuromodulators to adapt our behaviour to our body's internal state and how GLP-1 receptor agonists work in clinics.

Investigating the role of the right inferior frontal gyrus in control perception: A double-blind cross-over study using ultrasonic neuromodulation

Being able to control inner and environmental states is a basic need of living creatures. The perception of such control is based on the perceived ratio of outcome probabilities given the presence and the absence of agentic behavior. If an organism believes that options exist to change the probability of a given outcome, control perception (CP) may emerge. Nonetheless, regarding this model, not much is known about how the brain processes CP from this information. This study uses low-intensity transcranial focused ultrasound neuromodulation in a randomized-controlled double blind cross-over design to investigate the impact of the right inferior frontal gyrus of the lateral prefrontal cortex on this process. 39 healthy participants visited the laboratory twice (once in a sham, once in a neuromodulation condition) and rated their control perception regarding a classical control illusion task. EEG alpha and theta power density were analyzed in a hierarchical single trial-based mixed modeling approach. Results indicate that the litFUS neuromodulation changed the processing of stimulus probability without changing CP Furthermore, neuromodulation of the right lPFC was found to modulate mid-frontal theta by altering its relationship with self-reported effort and worrying. While these data indicate lateral prefrontal sensitivity to stimulus probability, no evidence emerged for the dependency of CP on this processing.

Transcranial direct current stimulation impairs updating of avoidance-based associative learning

Introduction

Exposure-based psychotherapies for the treatment of anxiety- and fear-based disorders rely on "corrective" associative learning. Namely the repeated confrontation with feared stimuli in the absence of negative outcomes allows the formation of new, corrected associations of safety, indicating that such stimuli no longer need to be avoided. Unfortunately, exposure-facilitated corrective learning tends to be bound by context and often poorly generalizes. One brain structure, the prefrontal cortex, is implicated in context-guided behavior and may be a relevant target for improving generalization of safety learning. Here, we tested whether inhibition of the left prefrontal cortex causally impaired updating of context-bound associations specifically or, alternatively, impaired updating of learned associations irrespective of contextual changes. Additionally, we tested whether prefrontal inhibition during corrective learning influenced subsequent generalization of associations to a novel context.

Methods

In two separate experiments, participants received either 10 min of 2 mA cathodal transcranial direct current stimulation (tDCS) over EEG coordinate F3 (Experiment 1 n = 9, Experiment 2 n = 22) or sham stimulation (Experiment 1 n = 10, Experiment 2 n = 22) while previously learned associations were reversed in the same or a different context from initial learning. Next, to assess generalization of learning, participants were asked to indicate which of the previously seen images they preferred in a novel, never seen before context.

Results

Results indicate that tDCS significantly impaired reversal irrespective of context in Experiment 2 only. When taking learning rate across trials into account, both experiments suggest that participants who received sham had the greatest learning rate when reversal occurred in a different context, as expected, whereas participants who received active tDCS in this condition had the lowest learning rate. However, active tDCS was associated with preferring the originally disadvantageous, but then neural stimulus after stimulus after reversal occurred in a different context in Experiment 1 only.

Discussion

These results support a causal role for the left prefrontal cortex in the updating of avoidance-based associations and encourage further inquiry investigating the use of non-invasive brain stimulation on flexible updating of learned associations.

Irrelevant Threats Linger and Affect Behavior in High Anxiety

Threat-related information attracts attention and disrupts ongoing behavior, and particularly so for more anxious individuals. Yet, it is unknown how and to what extent threat-related information leave lingering influences on behavior (e.g., by impeding ongoing learning processes). Here, human male and female participants (N = 47) performed probabilistic reinforcement learning tasks where irrelevant distracting faces (neutral, happy, or fearful) were presented together with relevant monetary feedback. Behavioral modeling was combined with fMRI data (N = 27) to explore the neurocomputational bases of learning relevant and irrelevant information. In two separate studies, individuals with high trait anxiety showed increased avoidance of objects previously paired with the combination of neutral monetary feedback and fearful faces (but not neutral or happy faces). Behavioral modeling revealed that high anxiety increased the integration of fearful faces during feedback learning, and fMRI results (regarded as provisional, because of a relatively small sample size) further showed that variance in the prediction error signal, uniquely accounted for by fearful faces, correlated more strongly with activity in the right DLPFC for more anxious individuals. Behavioral and neuronal dissociations indicated that the threat-related distractors did not simply disrupt learning processes. By showing that irrelevant threats exert long-lasting influences on behavior, our results extend previous research that separately showed that anxiety increases learning from aversive feedbacks and distractibility by threat-related information. Our behavioral results, combined with the proposed neurocomputational mechanism, may help explain how increased exposure to irrelevant affective information contributes to the acquisition of maladaptive behaviors in more anxious individuals.SIGNIFICANCE STATEMENT In modern-day society, people are increasingly exposed to various types of irrelevant information (e.g., intruding social media announcements). Yet, the neurocomputational mechanisms influenced by irrelevant information during learning, and their interactions with increasingly distracted personality types are largely unknown. Using a reinforcement learning task, where relevant feedback is presented together with irrelevant distractors (emotional faces), we reveal an interaction between irrelevant threat-related information (fearful faces) and interindividual anxiety levels. fMRI shows provisional evidence for an interaction between anxiety levels and the coupling between activity in the DLPFC and learning signals specifically elicited by fearful faces. Our study reveals how irrelevant threat-related information may become entrenched in the anxious psyche and contribute to long-lasting abnormal behaviors.

Changes in both top-down and bottom-up effective connectivity drive visual hallucinations in Parkinson's disease

Visual hallucinations are common in Parkinson's disease and are associated with a poorer quality of life and a higher risk of dementia. An important and influential model that is widely accepted as an explanation for the mechanism of visual hallucinations in Parkinson's disease and other Lewy body diseases is that these arise due to aberrant hierarchical processing, with impaired bottom-up integration of sensory information and overweighting of top-down perceptual priors within the visual system. This hypothesis has been driven by behavioural data and supported indirectly by observations derived from regional activation and correlational measures using neuroimaging. However, until now, there was no evidence from neuroimaging for differences in causal influences between brain regions measured in patients with Parkinson's hallucinations. This is in part because previous resting-state studies focused on functional connectivity, which is inherently undirected in nature and cannot test hypotheses about the directionality of connectivity. Spectral dynamic causal modelling is a Bayesian framework that allows the inference of effective connectivity-defined as the directed (causal) influence that one region exerts on another region-from resting-state functional MRI data. In the current study, we utilize spectral dynamic causal modelling to estimate effective connectivity within the resting-state visual network in our cohort of 15 Parkinson's disease visual hallucinators and 75 Parkinson's disease non-visual hallucinators. We find that visual hallucinators display decreased bottom-up effective connectivity from the lateral geniculate nucleus to primary visual cortex and increased top-down effective connectivity from the left prefrontal cortex to primary visual cortex and the medial thalamus, as compared with non-visual hallucinators. Importantly, we find that the pattern of effective connectivity is predictive of the presence of visual hallucinations and associated with their severity within the hallucinating group. This is the first study to provide evidence, using resting-state effective connectivity, to support a model of aberrant hierarchical predictive processing as the mechanism for visual hallucinations in Parkinson's disease.

Blunted reward prediction error signals in internet gaming disorder

BACKGROUND

Internet gaming disorder (IGD) is a type of behavioural addictions. One of the key features of addiction is the excessive exposure to addictive objectives (e.g. drugs) reduces the sensitivity of the brain reward system to daily rewards (e.g. money). This is thought to be mediated via the signals expressed as dopaminergic reward prediction error (RPE). Emerging evidence highlights blunted RPE signals in drug addictions. However, no study has examined whether IGD also involves alterations in RPE signals that are observed in other types of addictions.

METHODS

To fill this gap, we used functional magnetic resonance imaging data from 45 IGD and 42 healthy controls (HCs) during a reward-related prediction-error task and utilised a psychophysiological interaction (PPI) analysis to characterise the underlying neural correlates of RPE and related functional connectivity.

RESULTS

Relative to HCs, IGD individuals showed impaired reinforcement learning, blunted RPE signals in multiple regions of the brain reward system, including the right caudate, left orbitofrontal cortex (OFC), and right dorsolateral prefrontal cortex (DLPFC). Moreover, the PPI analysis revealed a pattern of hyperconnectivity between the right caudate, right putamen, bilateral DLPFC, and right dorsal anterior cingulate cortex (dACC) in the IGD group. Finally, linear regression suggested that the connection between the right DLPFC and right dACC could significantly predict the variation of RPE signals in the left OFC.

CONCLUSIONS

These results highlight disrupted RPE signalling and hyperconnectivity between regions of the brain reward system in IGD. Reinforcement learning deficits may be crucial underlying characteristics of IGD pathophysiology.

Behavioral and neural responses during fear conditioning and extinction in a large transdiagnostic sample

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Behavioral and neural responses during Pavlovian fear learning were examined in a large sample of healthy and individuals with anxiety and depression.

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Latent profile models to threat were derived from behavioral and neural data.

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Demographic, cognitive, and psychological variables did not robustly characterize latent profiles.

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Neuroimaging data did not evidence functional role of amygdala in fear learning.

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Human fear learning recruited a distributed network of regions involved in interoceptive, cognitive, motivational, and psychomotor processes.

Background

Dysregulation of fear learning has been associated with psychiatric disorders that have altered positive and negative valence domain function. While amygdala-insula-prefrontal circuitry is considered important for fear learning, there have been inconsistencies in neural findings in healthy and clinical human samples. This study aimed to delineate the neural substrates and behavioral responses during fear learning in a large, transdiagnostic sample with predominantly depressive and/or anxious dysfunction.

Methods

Two-hundred and eighty-two individuals (52 healthy participants; 230 participants with depression and/or anxiety-related problems) from the Tulsa 1000 study, an ongoing, naturalistic longitudinal study based on a dimensional psychopathological framework, completed a Pavlovian fear learning task during functional magnetic resonance imaging. Linear mixed-effects analyses examined condition-by-time effects on brain activation (CS+, CS- across familiarization, conditioning, and extinction trials). A data-driven latent profile analysis (LPA) examined distinct patterns of behavioral and neural responses to threat across fear conditioning and extinction, while logistic regression analyses evaluated cognitive-affective predictors of latent profiles.

Results

Whole-brain analyses revealed a condition-by-time interaction in the anterior insula, postcentral gyrus, superior temporal gyrus, middle frontal gyrus, and cerebellum but not amygdala. The LPA identified distinct latent profiles across subjective and neural levels of measurement. Anterior insula profiles were characterized by marginal differences in age and state anxiety.

Conclusions

Our findings demonstrate that human fear learning recruits a distributed network of regions involved in interoceptive, cognitive, motivational, and psychomotor processes. Data-driven analyses identified distinct profiles of subjective and neural responses during fear learning that transcended clinical diagnoses, but no robust relationships to demographic or cognitive-affective variable were identified.

Spatial and Temporal Context Jointly Modulate the Sensory Response within the Ventral Visual Stream

Both spatial and temporal context play an important role in visual perception and behavior. Humans can extract statistical regularities from both forms of context to help process the present and to construct expectations about the future. Numerous studies have found reduced neural responses to expected stimuli compared with unexpected stimuli, for both spatial and temporal regularities. However, it is largely unclear whether and how these forms of context interact. In the current fMRI study, 33 human volunteers were exposed to pairs of object stimuli that could be expected or surprising in terms of their spatial and temporal context. We found reliable independent contributions of both spatial and temporal context in modulating the neural response. Specifically, neural responses to stimuli in expected compared with unexpected contexts were suppressed throughout the ventral visual stream. These results suggest that both spatial and temporal context may aid sensory processing in a similar fashion, providing evidence on how different types of context jointly modulate perceptual processing.

Neural correlates of the inverse base rate effect

The inverse base rate effect (IBRE) is a nonrational behavioral phenomenon in predictive learning. Canonically, participants learn that the AB stimulus compound leads to one outcome and that AC leads to another outcome, with AB being presented three times as often as AC. When subsequently presented with BC, the outcome associated with AC is preferentially selected, in opposition to the underlying base rates of the outcomes. The current leading explanation is based on error‐driven learning. A key component of this account is prediction error, a concept previously linked to a number of brain areas including the anterior cingulate, the striatum, and the dorsolateral prefrontal cortex. The present work is the first fMRI study to directly examine the IBRE. Activations were noted in brain areas linked to prediction error, including the caudate body, the anterior cingulate, the ventromedial prefrontal cortex, and the right dorsolateral prefrontal cortex. Analyzing the difference in activations for singular key stimuli (B and C), as well as frequency matched controls, supports the predictions made by the error‐driven learning account.

Failure of hypothesis evaluation as a factor in delusional belief

INTRODUCTION

In accounts of the two-factor theory of delusional belief, the second factor in this theory has been referred to only in the most general terms, as a failure in the processes of hypothesis evaluation, with no attempt to characterise those processes in any detail. Coltheart and Davies ([2021]. How unexpected observations lead to new beliefs: A Peircean pathway. Consciousness and Cognition, 87, 103037. https://doi.org/10.1016/j.concog.2020.103037) attempted such a characterisation, proposing a detailed eight-step model of how unexpected observations lead to new beliefs based on the concept of abductive inference as introduced by Charles Sanders Peirce.

METHODS

In this paper, we apply that model to the explanation of various forms of delusional belief.

RESULTS

We provide evidence that in cases of delusion there is a specific failure of the seventh step in our model: the step at which predictions from (delusional) hypotheses are considered in the light of relevant evidence.

CONCLUSIONS

In the two-factor theory of delusional belief, the second factor consists of a failure to reject hypotheses in the face of disconfirmatory evidence.

Magic, Bayes and wows: A Bayesian account of magic tricks

Magic tricks have enjoyed an increasing interest by scientists. However, most research in magic focused on isolated aspects of it and a conceptual understanding of magic, encompassing its distinct components and varieties, is missing. Here, we present an account of magic within the theory of Bayesian predictive coding. We present the "wow" effect of magic as an increase in surprise evoked by the prediction error between expected and observed data. We take into account prior knowledge of the observer, attention, and (mis-)direction of perception and beliefs by the magician to bias the observer's predictions and present a simple example for the modelling of the evoked surprise. The role of misdirection is described as everything that aims to maximize the surprise a trick evokes by the generation of novel beliefs, the exploitation of background knowledge and attentional control of the incoming information. Understanding magic within Bayesian predictive coding allows unifying all aspects of magic tricks within one framework, making it tractable, comparable and unifiable with other models in psychology and neuroscience.

The Versatile Wayfinder: Prefrontal Contributions to Spatial Navigation

The prefrontal cortex (PFC) supports decision-making, goal tracking, and planning. Spatial navigation is a behavior that taxes these cognitive processes, yet the role of the PFC in models of navigation has been largely overlooked. In humans, activity in dorsolateral PFC (dlPFC) and ventrolateral PFC (vlPFC) during detours, reveal a role in inhibition and replanning. Dorsal anterior cingulate cortex (dACC) is implicated in planning and spontaneous internally-generated changes of route. Orbitofrontal cortex (OFC) integrates representations of the environment with the value of actions, providing a 'map' of possible decisions. In rodents, medial frontal areas interact with hippocampus during spatial decisions and switching between navigation strategies. In reviewing these advances, we provide a framework for how different prefrontal regions may contribute to different stages of navigation.

Dopamine, Prediction Error and Beyond

A large body of work has linked dopaminergic signaling to learning and reward processing. It stresses the role of dopamine in reward prediction error signaling, a key neural signal that allows us to learn from past experiences, and that facilitates optimal choice behavior. Latterly, it has become clear that dopamine does not merely code prediction error size but also signals the difference between the expected value of rewards, and the value of rewards actually received, which is obtained through the integration of reward attributes such as the type, amount, probability and delay. More recent work has posited a role of dopamine in learning beyond rewards. These theories suggest that dopamine codes absolute or unsigned prediction errors, playing a key role in how the brain models associative regularities within its environment, while incorporating critical information about the reliability of those regularities. Work is emerging supporting this perspective and, it has inspired theoretical models of how certain forms of mental pathology may emerge in relation to dopamine function. Such pathology is frequently related to disturbed inferences leading to altered internal models of the environment. Thus, it is critical to understand the role of dopamine in error-related learning and inference.

Precision weighting of cortical unsigned prediction error signals benefits learning, is mediated by dopamine, and is impaired in psychosis

Recent theories of cortical function construe the brain as performing hierarchical Bayesian inference. According to these theories, the precision of prediction errors plays a key role in learning and decision-making, is controlled by dopamine and contributes to the pathogenesis of psychosis. To test these hypotheses, we studied learning with variable outcome-precision in healthy individuals after dopaminergic modulation with a placebo, a dopamine receptor agonist bromocriptine or a dopamine receptor antagonist sulpiride (dopamine study n = 59) and in patients with early psychosis (psychosis study n = 74: 20 participants with first-episode psychosis, 30 healthy controls and 24 participants with at-risk mental state attenuated psychotic symptoms). Behavioural computational modelling indicated that precision weighting of prediction errors benefits learning in health and is impaired in psychosis. FMRI revealed coding of unsigned prediction errors, which signal surprise, relative to their precision in superior frontal cortex (replicated across studies, combined n = 133), which was perturbed by dopaminergic modulation, impaired in psychosis and associated with task performance and schizotypy (schizotypy correlation in 86 healthy volunteers). In contrast to our previous work, we did not observe significant precision-weighting of signed prediction errors, which signal valence, in the midbrain and ventral striatum in the healthy controls (or patients) in the psychosis study. We conclude that healthy people, but not patients with first-episode psychosis, take into account the precision of the environment when updating beliefs. Precision weighting of cortical prediction error signals is a key mechanism through which dopamine modulates inference and contributes to the pathogenesis of psychosis.

Learning in Infancy Is Active, Endogenously Motivated, and Depends on the Prefrontal Cortices

A common view of learning in infancy emphasizes the role of incidental sensory experiences from which increasingly abstract statistical regularities are extracted. In this view, infant brains initially support basic sensory and motor functions, followed by maturation of higher-level association cortex. Here, we critique this view and posit that, by contrast and more like adults, infants are active, endogenously motivated learners who structure their own learning through flexible selection of attentional targets and active interventions on their environment. We further argue that the infant brain, and particularly the prefrontal cortex (PFC), is well equipped to support these learning behaviors. We review recent progress in characterizing the function of the infant PFC, which suggests that, as in adults, the PFC is functionally specialized and highly connected. Together, we present an integrative account of infant minds and brains, in which the infant PFC represents multiple intrinsic motivations, which are leveraged for active learning.

Exposure to information increases motivation to learn more

Low motivation to learn in undergraduate general education courses hinders learning. Research has identified strategies that increase motivation to learn before students are asked to learn content (e.g., outlining the utility of the course material for future careers). Extending this work, we propose that learning any material in a course may beget motivation to learn more, in line with the notion that the process of learning itself may spark curiosity and interest. Across three populations (college-aged individuals on mTurk, large public university students, and small private university students), we found that watching a TED Talk video (i.e., exposure to new information, learning) pertaining to any topic led to an increase in motivation to continue learning about that topic and other topics more generally. These results reveal a need to broaden models of motivation to consider the importance of exposure to content to increase motivation to learn.

Predictive coding: Neuroscience and art

This paper examines the applicability of predictive coding as an explanatory model for perception. This is carried out from two perspectives. First, the central assumptions of the model are re-examined in light of the neuroscientific evidence for the structure and functioning of key brain areas involved in perception. The inferential processes involved in predictive coding are then investigated in the context of ambiguous stimuli. This showed that while predictive coding may provide an accurate explanation for our perceptual experiences in some cases, there are also several instances where the picture is not as clear cut. Following on from this, particular emphasis is placed on ambiguous art in order to examine the psychological and cognitive implications of predictive coding in affective states. This not only sheds light on the impact of predictive coding for cognition and emotion, but also helps clarify the nature of ambiguous art.

Taking another's perspective promotes right parieto-frontal activity that reflects open-minded thought

Critical thinking (CT) is important for consensus building. Although the practice of CT using debate is widely used to improve open-minded thought, the cognitive processes underlying this improvement remain poorly understood. Here, functional magnetic resonance imaging (fMRI) was employed to assess how neural responses while considering another's opinion are changed by CT practice and to determine whether the cortical network showing activation changes related to personality traits is relevant to consensus building. A total of 52 healthy participants were divided into three groups for an intervention; the participants read another's reasoning regarding a controversial issue and judged whether this person's viewpoint was affirmative during fMRI measurements. The intervention required them to prepare speech texts from a designated viewpoint based on both themselves and others. Compared to the control group, the group who took another's perspective showed enhanced activation of the right parieto-frontal network, that has been implicated in belief update, cognitive reappraisal of emotion, and self-perspective inhibition. Additionally, activation of the orbitofrontal cortex was negatively correlated with a stubbornness index. The results provide the first neural evidence of the effects of CT practice and support the notion that open-minded thought underlies the benefits of this practice.

Word Recall is Affected by Surrounding Metrical Context

It has been claimed that English has a metrical structure, or rhythm, in which stressed and unstressed syllables alternate. In previous research regular, alternating patterns have been shown to facilitate online language comprehension. Expanding these findings to downstream processing would lead to the prediction that metrical regularity enhances memory. Research from the memory literature, however, indicates that regular patterns are less salient and therefore less well remembered, and also that strings of similar sounds are harder to remember. This work suggests that, like lists of words with similar sounds, lists of words with similar metrical patterns are less accurately remembered than comparable metrically irregular patterns. The present study tests these conflicting predictions by examining the effects of metrical regularity in a recall task. We find that words are better recalled when they do not match their metrical context, suggesting that a regular metrical structure may not be beneficial in all contexts.

Significant repetition probability effects in schizophrenia

BACKGROUND

A growing body of evidence suggests that the comparison of expected and incoming sensory stimuli (the prediction-error (ε) processing) is impaired in schizophrenia patients (SZ). For example, in studies of mismatch negativity, an ERP component that signals ε, SZ patients show deficits in the auditory and visual modalities. To test the role of impaired ε processing further in SZ, using neuroimaging methods, we applied a repetition-suppression (RS) paradigm.

METHODS

Patients diagnosed with SZ (n = 17) as well as age- and sex- matched healthy control subjects (HC, n = 17) were presented with pairs of faces, which could either repeat or alternate. Additionally, the likelihood of repetition/alternation trials was modulated in individual blocks of fMRI recordings, testing the effects of repetition probability (P(rep)) on RS.

RESULTS

We found a significant RS in the fusiform and occipital face areas as well as in the lateral occipital cortex that was similar in healthy controls and SZ patients SZ. More importantly, we observed similar P(rep) effects (larger RS in blocks with high frequency of repetitions than in blocks with low repetition likelihood) in both the control and the patient group.

CONCLUSION

Our findings suggest that repetition_probability modulations affect the neural responses in schizophrenia patients and healthy participants similarly. This suggests that the neural mechanisms determining perceptual inferences based on stimulus probabilities remain unimpaired in schizophrenia.

Beyond the "Conceptual Nervous System": Can computational cognitive neuroscience transform learning theory?

In the last century, learning theory has been dominated by an approach assuming that associations between hypothetical representational nodes can support the acquisition of knowledge about the environment. The similarities between this approach and connectionism did not go unnoticed to learning theorists, with many of them explicitly adopting a neural network approach in the modeling of learning phenomena. Skinner famously criticized such use of hypothetical neural structures for the explanation of behavior (the "Conceptual Nervous System"), and one aspect of his criticism has proven to be correct: theory underdetermination is a pervasive problem in cognitive modeling in general, and in associationist and connectionist models in particular. That is, models implementing two very different cognitive processes often make the exact same behavioral predictions, meaning that important theoretical questions posed by contrasting the two models remain unanswered. We show through several examples that theory underdetermination is common in the learning theory literature, affecting the solvability of some of the most important theoretical problems that have been posed in the last decades. Computational cognitive neuroscience (CCN) offers a solution to this problem, by including neurobiological constraints in computational models of behavior and cognition. Rather than simply being inspired by neural computation, CCN models are built to reflect as much as possible about the actual neural structures thought to underlie a particular behavior. They go beyond the "Conceptual Nervous System" and offer a true integration of behavioral and neural levels of analysis.

Frontal cortex tracks surprise separately for different sensory modalities but engages a common inhibitory control mechanism

The brain constantly generates predictions about the environment to guide action. Unexpected events lead to surprise and can necessitate the modification of ongoing behavior. Surprise can occur for any sensory domain, but it is not clear how these separate surprise signals are integrated to affect motor output. By applying a trial-to-trial Bayesian surprise model to human electroencephalography data recorded during a cross-modal oddball task, we tested whether there are separate predictive models for different sensory modalities (visual, auditory), or whether expectations are integrated across modalities such that surprise in one modality decreases surprise for a subsequent unexpected event in the other modality. We found that while surprise was represented in a common frontal signature across sensory modalities (the fronto-central P3 event-related potential), the single-trial amplitudes of this signature more closely conformed to a model with separate surprise terms for each sensory domain. We then investigated whether surprise-related fronto-central P3 activity indexes the rapid inhibitory control of ongoing behavior after surprise, as suggested by recent theories. Confirming this prediction, the fronto-central P3 amplitude after both auditory and visual unexpected events was highly correlated with the fronto-central P3 found after stop-signals (measured in a separate stop-signal task). Moreover, surprise-related and stopping-related activity loaded onto the same component in a cross-task independent components analysis. Together, these findings suggest that medial frontal cortex maintains separate predictive models for different sensory domains, but engages a common mechanism for inhibitory control of behavior regardless of the source of surprise.

Surprise is an elementary cognitive computation that the brain performs to guide behavior. We investigated how the brain tracks surprise across different senses: Do unexpected sounds make subsequent unexpected visual stimuli less surprising? Or does the brain maintain separate expectations of environmental regularities for different senses? We found that the latter is the case. However, even though surprise was separately tracked for auditory and visual events, it elicited a common signature over frontal cortex in both sensory domains. Importantly, we observed the same neural signature when actions had to be stopped after non-surprising stop-signals in a motor inhibition task. This suggests that this signature reflects a rapid interruption of ongoing behavior when our surroundings do not conform to our expectations.

Encoding prediction signals during appetitive and aversive Pavlovian conditioning in the primate lateral hypothalamus

The lateral hypothalamus (LH), which plays a role in homeostatic functions such as appetite regulation, is also linked to arousal and motivational behavior. However, little is known about how these components are encoded in the LH. Thus cynomolgus monkeys were conditioned with two distinct contexts, i.e., an appetitive context with available rewards and an aversive context with predicted air puffs. Different LH neuron groups encoded different degrees of expectation, predictability, and risks of rewards in a specific manner. A nearly equal number of one-third of the recorded LH neurons showed a positive or negative correlation between their response to visual conditioned stimuli (CS) that predicted the probabilistic delivery of rewards (0%, 50%, and 100%) and the associative values. For another one-third of recorded neurons, a nearly equal number showed a positive or negative correlation between their responses to rewards [appetitive unconditioned stimulus (US)] and reward predictability. Some neurons exhibited their highest or lowest trace-period responses in the 50% reward trials. These response modulations were represented independently and overlaid on a consistent excitatory or inhibitory response across the conditioning events. LH neurons also showed consistent responses in the aversive context. However, the responses to aversive conditioning events depending on the air puff value and predictability were less common. The multifaceted modulation of consistent activity related to outcome predictions may reflect motivational and arousal signals. Furthermore, it may underlie the role the LH plays in the integration and relay of signals to cortices for adaptive and goal-directed physiological and behavioral responses to environmental changes.

NEW & NOTEWORTHY The lateral hypothalamus (LH) is implicated in motivational and arousal behavior; however, the detailed information carried by single LH neurons remains unclear. We demonstrate that primate LH neurons encode multiple combinations of signals concerning different degrees of expectation, appreciation, and uncertainty of rewards in consistent responses across conditioning events and between different contexts. This multifaceted modulation of activity may underlie the role of the LH as a critical node integrating motivational signals with arousal signals.

Crayfish Learning: Addiction and the Ganglionic Brain

Recognizing addiction as a phenomenon with deep evolutionary roots grants valuable new perspectives into understanding its behavioral features, as well as its underlying neural mechanisms and genetic architecture. Although now generally misbranded as "human drugs of abuse," addictive plant alkaloids originally arose as potent chemical defenses against insect herbivory. The products of this evolutionary arms race, compounds such as nicotine, cathinone, or morphine, target essential biological mechanisms for motivation and learning and act as weaponized disruptors. Human vulnerabilities to these addictive drugs may thus represent little more than collateral damage arising from deep homology, i.e., shared biological implementation of behavioral functions with taxa that trace back to the early divergence of bilateral metazoans. Consistent with such a view, invertebrate preparations exhibit a rich spectrum of behavioral and neural consequences in response to drug exposure. Although there is certainly evidence for addiction-like phenomena in many invertebrate lineages, the present review focuses attention primarily on our recent work in crayfish. Using this decapod crustacean model, we have characterized a range of amphetamines, cathinones, and opioids for evidence of unconditioned intoxication, sympathomimetic properties, psychostimulant sensitization, conditioned cue learning, and operant self-administration. Overall, our findings on drug-sensitive reward in crayfish bear striking similarities to equivalent phenomena illustrated in mammals. Experimentally tractable invertebrate models may thus provide fundamental insights into the homo- and paralogous mechanisms mediating responses to addictive drugs, while illuminating the limits of such contrasts.

A Neuroeconomic Theory of Mental Time Travel

We propose a theoretical model that places attention at the center of mental time travel (MTT) ability. This theory predicts that attention promotes a memory-based process that encodes memories of unexpected events, facilitates accurate recollection of information of such events during MTT, and optimizes subsequent decision-making. This process coexists with a habitual process that governs all other events and treats them equally. Our theory demonstrates that the memory-based process is useful when the environment features novel experiences that are likely to be relevant in future decision-making, hence worth remembering accurately. By contrast, the habitual process is optimal in environments that either do not change significantly, or have a small chance of being repeated in the future. This may explain why the ability to mentally travel in time has developed differently in humans than in other species. Implications are discussed in the context of decision-making.

Violations of Core Knowledge Shape Early Learning

Research on cognitive development has revealed that even the youngest minds detect and respond to events that adults find surprising. These surprise responses suggest that infants have a basic set of "core" expectations about the world that are shared with adults and other species. However, little work has asked what purpose these surprise responses serve. Here we discuss recent evidence that violations of core knowledge offer special opportunities for learning. Infants and young children make predictions about the world on the basis of their core knowledge of objects, quantities, and social entities. We argue that when these predictions fail to match the observed data, infants and children experience an enhanced drive to seek and retain new information. This impact of surprise on learning is not equipotent. Instead, it is directed to entities that are relevant to the surprise itself; this drive propels children-even infants-to form and test new hypotheses about surprising aspects of the world. We briefly consider similarities and differences between these recent findings with infants and children, on the one hand, and findings on prediction errors in humans and non-human animals, on the other. These comparisons raise open questions that require continued inquiry, but suggest that considering phenomena across species, ages, kinds of surprise, and types of learning will ultimately help to clarify how surprise shapes thought.

Executive Functions Brain System: An Activation Likelihood Estimation Meta-analytic Study

Background and objective

To characterize commonalities and differences between two executive functions: reasoning and inhibitory control.

Methods

A total of 5,974 participants in 346 fMRI experiments of inhibition or reasoning were selected. First level analysis consisted of Analysis of Likelihood Estimation (ALE) studies performed in two pooled data groups: (a) brain areas involved in reasoning and (b) brain areas involved in inhibition. Second level analysis consisted of two contrasts: (i) brain areas involved in reasoning but not in inhibition and (ii) brain areas involved in inhibition but not in reasoning. Lateralization Indexes were calculated.

Results

Four brain areas appear as the most critical: the dorsolateral aspect of the frontal lobes, the superior parietal lobules, the mesial aspect of the premotor area (supplementary motor area), and some subcortical areas, particularly the putamen and the thalamus. ALE contrasts showed significant differentiation of the networks, with the reasoning > inhibition-contrast showing a predominantly leftward participation, and the inhibition > reasoning-contrast, a clear right advantage.

Conclusion

Executive functions are mediated by sizable brain areas including not only cortical, but also involving subcortical areas in both hemispheres. The strength of activation shows dissociation between the hemispheres for inhibition (rightward) and reasoning (leftward) functions.

How Do Expectations Shape Perception?

Perception and perceptual decision-making are strongly facilitated by prior knowledge about the probabilistic structure of the world. While the computational benefits of using prior expectation in perception are clear, there are myriad ways in which this computation can be realized. We review here recent advances in our understanding of the neural sources and targets of expectations in perception. Furthermore, we discuss Bayesian theories of perception that prescribe how an agent should integrate prior knowledge and sensory information, and investigate how current and future empirical data can inform and constrain computational frameworks that implement such probabilistic integration in perception.

Interpretation of Social Interactions: Functional Imaging of Cognitive-Semiotic Categories During Naturalistic Viewing

Social interactions arise from patterns of communicative signs, whose perception and interpretation require a multitude of cognitive functions. The semiotic framework of Peirce's Universal Categories (UCs) laid ground for a novel cognitive-semiotic typology of social interactions. During functional magnetic resonance imaging (fMRI), 16 volunteers watched a movie narrative encompassing verbal and non-verbal social interactions. Three types of non-verbal interactions were coded ("unresolved," "non-habitual," and "habitual") based on a typology reflecting Peirce's UCs. As expected, the auditory cortex responded to verbal interactions, but non-verbal interactions modulated temporal areas as well. Conceivably, when speech was lacking, ambiguous visual information (unresolved interactions) primed auditory processing in contrast to learned behavioral patterns (habitual interactions). The latter recruited a parahippocampal-occipital network supporting conceptual processing and associative memory retrieval. Requesting semiotic contextualization, non-habitual interactions activated visuo-spatial and contextual rule-learning areas such as the temporo-parietal junction and right lateral prefrontal cortex. In summary, the cognitive-semiotic typology reflected distinct sensory and association networks underlying the interpretation of observed non-verbal social interactions.

Abnormal reward prediction-error signalling in antipsychotic naive individuals with first-episode psychosis or clinical risk for psychosis

Ongoing research suggests preliminary, though not entirely consistent, evidence of neural abnormalities in signalling prediction errors in schizophrenia. Supporting theories suggest mechanistic links between the disruption of these processes and the generation of psychotic symptoms. However, it is unknown at what stage in the pathogenesis of psychosis these impairments in prediction-error signalling develop. One major confound in prior studies is the use of medicated patients with strongly varying disease durations. Our study aims to investigate the involvement of the meso-cortico-striatal circuitry during reward prediction-error signalling in earliest stages of psychosis. We studied patients with first-episode psychosis (FEP) and help-seeking individuals at-risk for psychosis due to sub-threshold prodromal psychotic symptoms. Patients with either FEP (n = 14), or at-risk for developing psychosis (n = 30), and healthy volunteers (n = 39) performed a reinforcement learning task during fMRI scanning. ANOVA revealed significant (p < 0.05 family-wise error corrected) prediction-error signalling differences between groups in the dopaminergic midbrain and right middle frontal gyrus (dorsolateral prefrontal cortex, DLPFC). FEP patients showed disrupted reward prediction-error signalling compared to controls in both regions. At-risk patients showed intermediate activation in the midbrain that significantly differed from controls and from FEP patients, but DLPFC activation that did not differ from controls. Our study confirms that FEP patients have abnormal meso-cortical signalling of reward-prediction errors, whereas reward-prediction-error dysfunction in the at-risk patients appears to show a more nuanced pattern of activation with a degree of midbrain impairment but preserved cortical function.

Rapid Infant Prefrontal Cortex Development and Sensitivity to Early Environmental Experience

Over the last fifteen years, the emerging field of developmental cognitive neuroscience has described the relatively late development of prefrontal cortex in children and the relation between gradual structural changes and children's protracted development of prefrontal-dependent skills. Widespread recognition by the broader scientific community of the extended development of prefrontal cortex has led to the overwhelming perception of prefrontal cortex as a "late developing" region of the brain. However, despite its supposedly protracted development, multiple lines of research have converged to suggest that prefrontal cortex development may be particularly susceptible to individual differences in children's early environments. Recent studies demonstrate that the impacts of early adverse environments on prefrontal cortex are present very early in development: within the first year of life. This review provides a comprehensive overview of new neuroimaging evidence demonstrating that prefrontal cortex should be characterized as a "rapidly developing" region of the brain, discusses the converging impacts of early adversity on prefrontal circuits, and presents potential mechanisms via which adverse environments shape both concurrent and long-term measures of prefrontal cortex development. Given that environmentally-induced disparities are present in prefrontal cortex development within the first year of life, translational work in intervention and/or prevention science should focus on intervening early in development to take advantages of this early period of rapid prefrontal development and heightened plasticity.

Organic psychosis: The pathobiology and treatment of delusions

Organic or secondary psychosis can be seen in diverse conditions such as toxic/metabolic disorders, neurodegenerative disease, and stroke. Poststroke psychosis is a rare phenomenon, but its study has significantly contributed to the understanding of delusion formation. The evidence from case studies of patients with focal strokes shows that delusions develop following unilateral damage of the right hemisphere. The majority of patients with right hemisphere stroke do not develop delusions however, and advanced neuroimaging analysis has elucidated why this symptom develops in only a small proportion. Lesions of the right lateral prefrontal cortex or lesions with connectivity to this area correlate with delusional beliefs in this subgroup. Studies of patients with primary psychosis, for example schizophrenia, or under the influence of the psychotogenic drug ketamine, also show abnormal function of this area in relation to the severity of their abnormal beliefs. The conclusion of these studies is that the right lateral prefrontal cortex is 1 hub in a neural network which includes the basal ganglia and limbic system and receives inputs from midbrain dopamine neurones. In patients with schizophrenia, or at risk of psychosis, dopamine is dysregulated and evidence suggests that faulty dopamine signaling is the precursor of delusion formation. It is therefore likely that the mechanism of delusion formation is the same in both primary and secondary psychosis. This is consistent with the mainstay of treatment of both conditions being antipsychotic medication. However, antipsychotic medication in people with cerebrovascular disease should be avoided if at all possible. This is because epidemiological studies have found that antipsychotic use is associated with an increased risk of stroke and will thus compound the possibility of a further cerebrovascular accident.

Identity prediction errors in the human midbrain update reward-identity expectations in the orbitofrontal cortex

There is general consensus that dopaminergic midbrain neurons signal reward prediction errors, computed as the difference between expected and received reward value. However, recent work in rodents shows that these neurons also respond to errors related to inferred value and sensory features, indicating an expanded role for dopamine beyond learning cached values. Here we utilize a transreinforcer reversal learning task and functional magnetic resonance imaging (fMRI) to test whether prediction error signals in the human midbrain are evoked when the expected identity of an appetitive food odor reward is violated, while leaving value matched. We found that midbrain fMRI responses to identity and value errors are correlated, suggesting a common neural origin for these error signals. Moreover, changes in reward-identity expectations, encoded in the orbitofrontal cortex (OFC), are directly related to midbrain activity, demonstrating that identity-based error signals in the midbrain support the formation of outcome identity expectations in OFC.

Responses in the dopaminergic midbrain are known to signal prediction errors for reward value. Here, the authors show that the human midbrain also encodes errors in predicted reward identity, and that these signals update expectations of reward identity in the orbitofrontal cortex.

Associationism and cognition: Human contingency learning at 25

A major topic within human learning, the field of contingency judgement, began to emerge about 25 years ago following publication of an article on depressive realism by Alloy and Abramson (1979). Subsequently, associationism has been the dominant theoretical framework for understanding contingency learning but this has been challenged in recent years by an alternative cognitive or inferential approach. This article outlines the key conceptual differences between these approaches and summarizes some of the main methods that have been employed to distinguish between them.

Posterior and prefrontal contributions to the development posttraumatic stress disorder symptom severity: an fMRI study of symptom provocation in acute stress disorder

Acute stress disorder (ASD) is predictive of the development of posttraumatic stress disorder (PTSD). In response to symptom provocation, the exposure to trauma-related pictures, ASD patients showed increased activation of the medial posterior areas of precuneus and posterior cingulate cortex as well as of superior prefrontal cortex in a previous study. The current study aimed at investigating which activated areas are predictive of the development of PTSD. Nineteen ASD patients took part in an fMRI study in which they were shown personalized trauma-related and neutral pictures within 4 weeks of the traumatic event. They were assessed for severity of PTSD 4 weeks later. Activation contrasts between trauma-related and neutral pictures were correlated with subsequent PTSD symptom severity. Greater activation in, among others, right medial precuneus, left retrosplenial cortex, precentral and right superior temporal gyrus as well as less activation in lateral, superior prefrontal and left fusiform gyrus was related to subsequently increased PTSD severity. The results are broadly in line with neural areas related to etiological models of PTSD, namely multisensory associative learning recruiting posterior regions on the one hand and failure to reappraise maladaptive cognitions, thought to involve prefrontal areas, on the other.

Dorsolateral prefrontal cortex contributes to the impaired behavioral adaptation in alcohol dependence

Substance-dependent individuals often lack the ability to adjust decisions flexibly in response to the changes in reward contingencies. Prediction errors (PEs) are thought to mediate flexible decision-making by updating the reward values associated with available actions. In this study, we explored whether the neurobiological correlates of PEs are altered in alcohol dependence. Behavioral, and functional magnetic resonance imaging (fMRI) data were simultaneously acquired from 34 abstinent alcohol-dependent patients (ADP) and 26 healthy controls (HC) during a probabilistic reward-guided decision-making task with dynamically changing reinforcement contingencies. A hierarchical Bayesian inference method was used to fit and compare learning models with different assumptions about the amount of task-related information subjects may have inferred during the experiment. Here, we observed that the best-fitting model was a modified Rescorla-Wagner type model, the “double-update” model, which assumes that subjects infer the knowledge that reward contingencies are anti-correlated, and integrate both actual and hypothetical outcomes into their decisions. Moreover, comparison of the best-fitting model's parameters showed that ADP were less sensitive to punishments compared to HC. Hence, decisions of ADP after punishments were loosely coupled with the expected reward values assigned to them. A correlation analysis between the model-generated PEs and the fMRI data revealed a reduced association between these PEs and the BOLD activity in the dorsolateral prefrontal cortex (DLPFC) of ADP. A hemispheric asymmetry was observed in the DLPFC when positive and negative PE signals were analyzed separately. The right DLPFC activity in ADP showed a reduced correlation with positive PEs. On the other hand, ADP, particularly the patients with high dependence severity, recruited the left DLPFC to a lesser extent than HC for processing negative PE signals. These results suggest that the DLPFC, which has been linked to adaptive control of action selection, may play an important role in cognitive inflexibility observed in alcohol dependence when reinforcement contingencies change. Particularly, the left DLPFC may contribute to this impaired behavioral adaptation, possibly by impeding the extinction of the actions that no longer lead to a reward.

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Alcohol-dependent patients (ADP) had difficulty adapting to the reversals.

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The impaired adaptation was associated with a decrease in punishment sensitivity.

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The dorsolateral prefrontal cortex (DLPFC) of ADP failed to track prediction errors.

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A reduced tracking of the negative prediction error was present in the left DLPFC.

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The clinical severity of dependence was correlated with abnormal DLPFC activity.

Positive affect, surprise, and fatigue are correlates of network flexibility

Advances in neuroimaging have made it possible to reconstruct functional networks from the activity patterns of brain regions distributed across the cerebral cortex. Recent work has shown that flexible reconfiguration of human brain networks over short timescales supports cognitive flexibility and learning. However, modulating network flexibility to enhance learning requires an understanding of an as-yet unknown relationship between flexibility and brain state. Here, we investigate the relationship between network flexibility and affect, leveraging an unprecedented longitudinal data set. We demonstrate that indices associated with positive mood and surprise are both associated with network flexibility - positive mood portends a more flexible brain while increased levels of surprise portend a less flexible brain. In both cases, these relationships are driven predominantly by a subset of brain regions comprising the somatomotor system. Our results simultaneously suggest a network-level mechanism underlying learning deficits in mood disorders as well as a potential target - altering an individual's mood or task novelty - to improve learning.

Prediction error, ketamine and psychosis: An updated model

In 2007, we proposed an explanation of delusion formation as aberrant prediction error-driven associative learning. Further, we argued that the NMDA receptor antagonist ketamine provided a good model for this process. Subsequently, we validated the model in patients with psychosis, relating aberrant prediction error signals to delusion severity. During the ensuing period, we have developed these ideas, drawing on the simple principle that brains build a model of the world and refine it by minimising prediction errors, as well as using it to guide perceptual inferences. While previously we focused on the prediction error signal per se, an updated view takes into account its precision, as well as the precision of prior expectations. With this expanded perspective, we see several possible routes to psychotic symptoms - which may explain the heterogeneity of psychotic illness, as well as the fact that other drugs, with different pharmacological actions, can produce psychotomimetic effects. In this article, we review the basic principles of this model and highlight specific ways in which prediction errors can be perturbed, in particular considering the reliability and uncertainty of predictions. The expanded model explains hallucinations as perturbations of the uncertainty mediated balance between expectation and prediction error. Here, expectations dominate and create perceptions by suppressing or ignoring actual inputs. Negative symptoms may arise due to poor reliability of predictions in service of action. By mapping from biology to belief and perception, the account proffers new explanations of psychosis. However, challenges remain. We attempt to address some of these concerns and suggest future directions, incorporating other symptoms into the model, building towards better understanding of psychosis.

Adolescent Maturational Transitions in the Prefrontal Cortex and Dopamine Signaling as a Risk Factor for the Development of Obesity and High Fat/High Sugar Diet Induced Cognitive Deficits

Adolescence poses as both a transitional period in neurodevelopment and lifestyle practices. In particular, the developmental trajectory of the prefrontal cortex (PFC), a critical region for behavioral control and self-regulation, is enduring, not reaching functional maturity until the early 20 s in humans. Furthermore, the neurotransmitter dopamine is particularly abundant during adolescence, tuning the brain to rapidly learn about rewards and regulating aspects of neuroplasticity. Thus, adolescence is proposed to represent a period of vulnerability towards reward-driven behaviors such as the consumption of palatable high fat and high sugar diets. This is reflected in the increasing prevalence of obesity in children and adolescents as they are the greatest consumers of “junk foods”. Excessive consumption of diets laden in saturated fat and refined sugars not only leads to weight gain and the development of obesity, but experimental studies with rodents indicate they evoke cognitive deficits in learning and memory process by disrupting neuroplasticity and altering reward processing neurocircuitry. Consumption of these high fat and high sugar diets have been reported to have a particularly pronounced impact on cognition when consumed during adolescence, demonstrating a susceptibility of the adolescent brain to enduring cognitive deficits. The adolescent brain, with heightened reward sensitivity and diminished behavioral control compared to the mature adult brain, appears to be a risk for aberrant eating behaviors that may underpin the development of obesity. This review explores the neurodevelopmental changes in the PFC and mesocortical dopamine signaling that occur during adolescence, and how these potentially underpin the overconsumption of palatable food and development of obesogenic diet-induced cognitive deficits.

Neurocomputational mechanisms of prosocial learning and links to empathy

Reinforcement learning theory powerfully characterizes how we learn to benefit ourselves. In this theory, prediction errors-the difference between a predicted and actual outcome of a choice-drive learning. However, we do not operate in a social vacuum. To behave prosocially we must learn the consequences of our actions for other people. Empathy, the ability to vicariously experience and understand the affect of others, is hypothesized to be a critical facilitator of prosocial behaviors, but the link between empathy and prosocial behavior is still unclear. During functional magnetic resonance imaging (fMRI) participants chose between different stimuli that were probabilistically associated with rewards for themselves (self), another person (prosocial), or no one (control). Using computational modeling, we show that people can learn to obtain rewards for others but do so more slowly than when learning to obtain rewards for themselves. fMRI revealed that activity in a posterior portion of the subgenual anterior cingulate cortex/basal forebrain (sgACC) drives learning only when we are acting in a prosocial context and signals a prosocial prediction error conforming to classical principles of reinforcement learning theory. However, there is also substantial variability in the neural and behavioral efficiency of prosocial learning, which is predicted by trait empathy. More empathic people learn more quickly when benefitting others, and their sgACC response is the most selective for prosocial learning. We thus reveal a computational mechanism driving prosocial learning in humans. This framework could provide insights into atypical prosocial behavior in those with disorders of social cognition.

Developmental Changes in Cross-Situational Word Learning: The Inverse Effect of Initial Accuracy

Intuitively, the accuracy of initial word-referent mappings should be positively correlated with the outcome of learning. Yet recent evidence suggests an inverse effect of initial accuracy in adults, whereby greater accuracy of initial mappings is associated with poorer outcomes in a cross-situational learning task. Here, we examine the impact of initial accuracy on 4-year-olds, 10-year-olds, and adults. For half of the participants most word-referent mappings were initially correct and for the other half most mappings were initially incorrect. Initial accuracy was positively related to learning outcomes in 4-year-olds, had no effect on 10-year-olds' learning, and was inversely related to learning outcomes in adults. Examination of item learning patterns revealed item interdependence for adults and 4-year-olds but not 10-year-olds. These findings point to a qualitative change in language learning processes over development.

Neural Mechanism for Mirrored Self-face Recognition

Self-face recognition in the mirror is considered to involve multiple processes that integrate 2 perceptual cues: temporal contingency of the visual feedback on one's action (contingency cue) and matching with self-face representation in long-term memory (figurative cue). The aim of this study was to examine the neural bases of these processes by manipulating 2 perceptual cues using a "virtual mirror" system. This system allowed online dynamic presentations of real-time and delayed self- or other facial actions. Perception-level processes were identified as responses to only a single perceptual cue. The effect of the contingency cue was identified in the cuneus. The regions sensitive to the figurative cue were subdivided by the response to a static self-face, which was identified in the right temporal, parietal, and frontal regions, but not in the bilateral occipitoparietal regions. Semantic- or integration-level processes, including amodal self-representation and belief validation, which allow modality-independent self-recognition and the resolution of potential conflicts between perceptual cues, respectively, were identified in distinct regions in the right frontal and insular cortices. The results are supportive of the multicomponent notion of self-recognition and suggest a critical role for contingency detection in the co-emergence of self-recognition and empathy in infants.