Surprise beyond prediction error.
Ontology highlight
ABSTRACT:
SUBMITTER: Chumbley JR
PROVIDER: S-EPMC4312927 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Through the process of "reconsolidation," reminders can temporarily destabilize memories and render them vulnerable to change. Recent rodent research has proposed that prediction error, or the element of surprise, is a key component of this process; yet, this hypothesis has never before been extended to complex episodic memories in humans. In our novel paradigm, we used naturalistic stimuli to demonstrate that prediction error enables adaptive updating of episodic memories. In Study 1, participants (N = 48) viewed 18 videos, each depicting an action-outcome event. The next day, we reactivated these memories by presenting the videos again. We found that incomplete reminders, which interrupted videos before the outcome, made memories vulnerable to subsequent interference from a new set of videos, producing false memories. In Study 2 (N = 408), an independent sample rated qualities of the stimuli. We found that videos that were more surprising when interrupted produced more false memories. Last, in Study 3 (N = 24), we tested competing predictions of reconsolidation theory and the Temporal Context Model, an alternative account of source confusion. Consistent with the mechanistic time-course of reconsolidation, our effects were crucially time-dependent. Overall, we synthesize prior animal and human research to present compelling evidence that prediction error destabilizes episodic memories and drives dynamic updating in the face of new information.
| S-EPMC6049395 | biostudies-literature
Project description:Some experimental results in science are productively surprising or beautiful. Such results are disruptive in their epistemic nature: by violating epistemic expectations they mark the phenomenon at hand as worthy of further investigation. Could it be that there are emotions beyond these two which are also useful for the epistemic evaluation of scientific experiments? Here, I conduct a structured sociological survey to explore affective experiences in scientific experimental research. I identify that learning the results of an experiment is the high emotional point in the experimenting process. Thus, experimental results can be challenging, beautiful, or boring, and they can worry, amuse, make one sad, and so on. They can also drive meta-cognitive evaluations as well as motivate specific research-related actions. From this, I advance two claims: that emotions beyond surprise and disruptive beauty are epistemically useful in empirical research, and that emotions help to overcome specific challenges of reasoning about new experimental results.Supplementary informationThe online version contains supplementary material available at 10.1007/s13194-023-00536-7.
| S-EPMC10415511 | biostudies-literature
Project description:Prior expectations shape neural responses in sensory regions of the brain, consistent with a Bayesian predictive coding account of perception. Yet, it remains unclear whether such a mechanism is already functional during early stages of development. To address this issue, we study how the infant brain responds to prediction violations using a cross-modal cueing paradigm. We record electroencephalographic responses to expected and unexpected visual events preceded by auditory cues in 12-month-old infants. We find an increased response for unexpected events. However, this effect of prediction error is only observed during late processing stages associated with conscious access mechanisms. In contrast, early perceptual components reveal an amplification of neural responses for predicted relative to surprising events, suggesting that selective attention enhances perceptual processing for expected events. Taken together, these results demonstrate that cross-modal statistical regularities are used to generate predictions that differentially influence early and late neural responses in infants.
| S-EPMC4633815 | biostudies-literature
Project description:To adjust expectations efficiently, prediction errors need to be associated with the precise features that gave rise to the unexpected outcome, but this credit assignment may be problematic if stimuli differ on multiple dimensions and it is ambiguous which feature dimension caused the outcome. Here, we report a potential solution: neurons in four recorded areas of the anterior fronto-striatal networks encode prediction errors that are specific to feature values of different dimensions of attended multidimensional stimuli. The most ubiquitous prediction error occurred for the reward-relevant dimension. Feature-specific prediction error signals a) emerge on average shortly after non-specific prediction error signals, b) arise earliest in the anterior cingulate cortex and later in dorsolateral prefrontal cortex, caudate and ventral striatum, and c) contribute to feature-based stimulus selection after learning. Thus, a widely-distributed feature-specific eligibility trace may be used to update synaptic weights for improved feature-based attention.
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Project description:Studies of event-related potentials have consistently shown that schizophrenia is associated with a blunted error-related negativity (ERN), indicating a deficit in error monitoring. It is unknown whether this deficit is unique to schizophrenia or is common to psychotic disorders more broadly, and its associations with clinical characteristics of the illness are not well understood.The ERN and the error positivity (Pe) were recorded from 33 individuals with schizophrenia, 45 individuals with other psychotic disorders, and 33 healthy control subjects. Patients were drawn from a cohort with psychotic disorders followed since first hospitalization and diagnosed by consensus based on 10 years of observation.The ERN was profoundly blunted in the patient group, regardless of diagnosis, indicating that this deficit is not unique to schizophrenia. The Pe, meanwhile, was blunted only among individuals with schizophrenia, indicating that the ERN and Pe are differentially related to psychotic illnesses. A blunted ERN was associated with more severe negative symptoms and poorer real-world functioning, as indicated by unemployment and re-hospitalization over 10 years of illness. Although reduced compared with control subjects, ERN amplitude was greater in patients with higher neuroticism, indicating that error processing is moderated by personality differences in the same manner as in healthy populations.The current study advances the literature by evaluating diagnostic specificity and functional correlates of impaired error processing in psychosis.
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Project description:Modifying patients' expectations by exposing them to expectation violation situations (thus maximizing the difference between the expected and the actual situational outcome) is proposed to be a crucial mechanism for therapeutic success for a variety of different mental disorders. However, clinical observations suggest that patients often maintain their expectations regardless of experiences contradicting their expectations. It remains unclear which information processing mechanisms lead to modification or persistence of patients' expectations. Insight in the processing could be provided by Neuroimaging studies investigating prediction error (PE, i.e., neuronal reactions to non-expected stimuli). Two methods are often used to investigate the PE: (1) paradigms, in which participants passively observe PEs ("passive" paradigms) and (2) paradigms, which encourage a behavioral adaptation following a PE ("active" paradigms). These paradigms are similar to the methods used to induce expectation violations in clinical settings: (1) the confrontation with an expectation violation situation and (2) an enhanced confrontation in which the patient actively challenges his expectation. We used this similarity to gain insight in the different neuronal processing of the two PE paradigms. We performed a meta-analysis contrasting neuronal activity of PE paradigms encouraging a behavioral adaptation following a PE and paradigms enforcing passiveness following a PE. We found more neuronal activity in the striatum, the insula and the fusiform gyrus in studies encouraging behavioral adaptation following a PE. Due to the involvement of reward assessment and avoidance learning associated with the striatum and the insula we propose that the deliberate execution of action alternatives following a PE is associated with the integration of new information into previously existing expectations, therefore leading to an expectation change. While further research is needed to directly assess expectations of participants, this study provides new insights into the information processing mechanisms following an expectation violation.
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Project description:Animals learn from the past to make predictions. These predictions are adjusted after prediction errors, i.e., after surprising events. Generally, most reward prediction errors models learn the average expected amount of reward. However, here we demonstrate the existence of distinct mechanisms for detecting other types of surprising events. Six macaques learned to respond to visual stimuli to receive varying amounts of juice rewards. Most trials ended with the delivery of either 1 or 3 juice drops so that animals learned to expect 2 juice drops on average even though instances of precisely 2 drops were rare. To encourage learning, we also included sessions during which the ratio between 1 and 3 drops changed. Additionally, in all sessions, the stimulus sometimes appeared in an unexpected location. Thus, 3 types of surprising events could occur: reward amount surprise (i.e., a scalar reward prediction error), rare reward surprise, and visuospatial surprise. Importantly, we can dissociate scalar reward prediction errors-rewards that deviated from the average reward amount expected-and rare reward events-rewards that accorded with the average reward expectation but that rarely occurred. We linked each type of surprise to a distinct pattern of neural activity using functional magnetic resonance imaging. Activity in the vicinity of the dopaminergic midbrain only reflected surprise about the amount of reward. Lateral prefrontal cortex had a more general role in detecting surprising events. Posterior lateral orbitofrontal cortex specifically detected rare reward events regardless of whether they followed average reward amount expectations, but only in learnable reward environments.
| S-EPMC7657565 | biostudies-literature
Project description:Both perceptual inference and motor responses are shaped by learned probabilities. For example, stimulus-induced responses in sensory cortices and preparatory activity in premotor cortex reflect how (un)expected a stimulus is. This is in accordance with predictive coding accounts of brain function, which posit a fundamental role of prediction errors for learning and adaptive behavior. We used functional magnetic resonance imaging and recent advances in computational modeling to investigate how (failures of) learned predictions about visual stimuli influence subsequent motor responses. Healthy volunteers discriminated visual stimuli that were differentially predicted by auditory cues. Critically, the predictive strengths of cues varied over time, requiring subjects to continuously update estimates of stimulus probabilities. This online inference, modeled using a hierarchical Bayesian learner, was reflected behaviorally: speed and accuracy of motor responses increased significantly with predictability of the stimuli. We used nonlinear dynamic causal modeling to demonstrate that striatal prediction errors are used to tune functional coupling in cortical networks during learning. Specifically, the degree of striatal trial-by-trial prediction error activity controls the efficacy of visuomotor connections and thus the influence of surprising stimuli on premotor activity. This finding substantially advances our understanding of striatal function and provides direct empirical evidence for formal learning theories that posit a central role for prediction error-dependent plasticity.
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Project description:Goal-directed human behavior is enabled by hierarchically-organized neural systems that process executive commands associated with higher brain areas in response to sensory and motor signals from lower brain areas. Psychiatric diseases and psychotic conditions are postulated to involve disturbances in these hierarchical network interactions, but the mechanism for how aberrant disease signals are generated in networks, and a systems-level framework linking disease signals to specific psychiatric symptoms remains undetermined. In this study, we show that neural networks containing schizophrenia-like deficits can spontaneously generate uncompensated error signals with properties that explain psychiatric disease symptoms, including fictive perception, altered sense of self, and unpredictable behavior. To distinguish dysfunction at the behavioral versus network level, we monitored the interactive behavior of a humanoid robot driven by the network. Mild perturbations in network connectivity resulted in the spontaneous appearance of uncompensated prediction errors and altered interactions within the network without external changes in behavior, correlating to the fictive sensations and agency experienced by episodic disease patients. In contrast, more severe deficits resulted in unstable network dynamics resulting in overt changes in behavior similar to those observed in chronic disease patients. These findings demonstrate that prediction error disequilibrium may represent an intrinsic property of schizophrenic brain networks reporting the severity and variability of disease symptoms. Moreover, these results support a systems-level model for psychiatric disease that features the spontaneous generation of maladaptive signals in hierarchical neural networks.
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