Prediction error determines how memories are organized in the brain.

How is new information organized in memory? According to latent state theories, this is determined by the level of surprise, or prediction error, generated by the new information: a small prediction error leads to the updating of existing memory, large prediction error leads to encoding of a new memory. We tested this idea using a protocol in which rats were first conditioned to fear a stimulus paired with shock. The stimulus was then gradually extinguished by progressively reducing the shock intensity until the stimulus was presented alone. Consistent with latent state theories, this gradual extinction protocol (small prediction errors) was better than standard extinction (large prediction errors) in producing long-term suppression of fear responses, and the benefit of gradual extinction was due to updating of the conditioning memory with information about extinction. Thus, prediction error determines how new information is organized in memory, and latent state theories adequately describe the ways in which this occurs.

Perceptual error based on Bayesian cue combination drives implicit motor adaptation.

The sensorimotor system can recalibrate itself without our conscious awareness, a type of procedural learning whose computational mechanism remains undefined. Recent findings on implicit motor adaptation, such as over-learning from small perturbations and fast saturation for increasing perturbation size, challenge existing theories based on sensory errors. We argue that perceptual error, arising from the optimal combination of movement-related cues, is the primary driver of implicit adaptation. Central to our theory is the increasing sensory uncertainty of visual cues with increasing perturbations, which was validated through perceptual psychophysics (Experiment 1). Our theory predicts the learning dynamics of implicit adaptation across a spectrum of perturbation sizes on a trial-by-trial basis (Experiment 2). It explains proprioception changes and their relation to visual perturbation (Experiment 3). By modulating visual uncertainty in perturbation, we induced unique adaptation responses in line with our model predictions (Experiment 4). Overall, our perceptual error framework outperforms existing models based on sensory errors, suggesting that perceptual error in locating one's effector, supported by Bayesian cue integration, underpins the sensorimotor system's implicit adaptation.

Comparing algorithms to approximate accuracies for single-step genomic best linear unbiased predictor.

The exact accuracy of estimated breeding values can be calculated based on the prediction error variances obtained from the diagonal of the inverse of the left-hand side (LHS) of the mixed model equations (MME). However, inverting the LHS is not computationally feasible for large datasets, especially if genomic information is available. Thus, different algorithms have been proposed to approximate accuracies. This study aimed to: 1) compare the approximated accuracies from two algorithms implemented in the BLUPF90 suite of programs; 2) compare the approximated accuracies from the two algorithms against the exact accuracy based on the inversion of the LHS of MME; 3) evaluate the impact of adding genotyped animals with and without phenotypes on the exact and approximated accuracies. Algorithm 1 approximates accuracies based on the diagonal of the genomic relationship matrix (G). In turn, Algorithm 2 combines accuracies with and without genomic information through effective record contributions. The data were provided by the American Angus Association and included three datasets of growth, carcass, and marbling traits. The genotype file contained 1,235,930 animals, and the pedigree file contained 12,492,581 animals. For the genomic evaluation, a multi-trait model was applied to the datasets. To ensure the feasibility of inverting the LHS of the MME, a subset of data under single-trait models was used to compare approximated and exact accuracies. The correlations between exact and approximated accuracies from Algorithms 1 and 2 of genotyped animals ranged from 0.87 to 0.90 and 0.98 to 0.99, respectively. The intercept and slope of the regression of exact on approximated accuracies from Algorithm 2 ranged from 0.00 to 0.01 and 0.82 to 0.87, respectively. However, the intercept and the slope for Algorithm 1 ranged from -0.10 to 0.05 and 0.98 to 1.10, respectively. In more than 80% of the traits, Algorithm 2 exhibited a smaller mean square error than Algorithm 1. The correlation between the approximated accuracies obtained from Algorithms 1 and 2 ranged from 0.56 to 0.74, 0.38 to 0.71, and 0.71 to 0.97 in the groups of genotyped animals, genotyped animals without phenotype, and proven genotyped sires, respectively. The approximated accuracy from Algorithm 2 showed a closer behavior to the exact accuracy when including genotyped animals in the analysis. According to the results, Algorithm 2 is recommended for genetic evaluations since it proved more precise.

Accuracy of intraocular lens power formulas in patients with average keratometry greater than 46 diopters.

OBJECTIVE: To compare the accuracy of Kane, Barrett Universal II, Haigis, and SRK-T formulas in eyes with average keratometry greater than 46 diopters (D). METHODS: A retrospective analysis was conducted on 101 eyes of 101 patients with average keratometry greater than 46 D. The absolute prediction error (EA) was obtained for each patient one month after surgery. The mean absolute prediction error (MEA), median absolute prediction error (MedEA) and the percentage of patients with absolute refractive error less than 0.25 D, 0.50 D, and 1.00 D were calculated for each formula analyzed. RESULTS: The Kane formula achieved the lowest MEA (0.53 ±â€¯0.43) and the lowest MedEA (0.41), followed by Barrett Universal II (MEA: 0.56 ±â€¯0.42, MedEA: 0.49), SRK-T (MEA: 0.59 ±â€¯0.44, MedEA: 0.54), and Haigis (MEA: 0.77 ±â€¯0.47, MedEA: 0.69), showing a significant difference in the results. It was also observed that the Kane formula was the most accurate, with the highest percentage of patients, with EA less than 0.25 D, 0.50 D, and 1.00 D (30.7%, 54.4%, and 86.1%, respectively), while the Haigis formula was the least accurate (12.9%, 33.7%, and 69.3%, respectively). CONCLUSION: In eyes with corneas having average keratometry greater than 46 D, the Kane formula proves to be a useful tool in intraocular lens (IOL) power calculation and demonstrates higher precision compared to the Barrett Universal II, SRK-T, and Haigis formulas.

Repetition-Dependent Adaptation and Prediction Error Signalling in Schizophrenia Patients With Auditory Hallucinations: A Roving Mismatch Negativity Study.

BACKGROUND: Dysregulated prediction error-signalling may explain auditory hallucinations in schizophrenia (SZ-AH). Roving mismatch negativity (rMMN) is an event-related potential (ERP) index where the deviant tone becomes the new standard with repetitions. Longer repetitions of standard stimuli yield a more positive sensory-adaptation response (Repetition Positivity-RP), elicit a stronger deviance-detection when interrupted (deviant negativity-DN), and the difference waveform between them reflects the strength of prediction-error signalling (mismatch negativity-MMN). METHODS: Twenty-three SZ-AH patients and twenty-three healthy controls (HC) underwent rMMN assessment. Various standard stimuli were repeated in sets of 3, 8 and 33 yielding three components for RP (RP3, RP8, RP33), DN (DN3, DN8, DN33), and MMN (MMN3, MMN8, MMN33). Amplitudes and latencies were compared across groups. Correlation between (a) rMMN amplitudes and latencies, and clinical variables in SZ-AH, and (b) the RP-DN amplitude pair for all three repetition sets (3, 8, 33) were also examined. RESULTS: All DN and MMN33 amplitudes were significantly suppressed in SZ-AH, while RP amplitudes were not. MMN33 latency was significantly longer in SZ-AH than HC. A few amplitudes and latencies significantly correlated with the frequency of AH. HC showed a significant positive correlation between RP-DN amplitude pairs for sets of 3 and 8 but not for 33; SZ-AH group's correlation profile was opposite to this. DISCUSSION: The link between repetition-dependent sensory-adaptation and deviance-detection is perturbed in SZ-AH. The unimpaired RP profile in SZ-AH is due to potential interference of AH with auditory information processing, and does not indicate a preserved short-term plasticity of the echoic memory trace.

Evaluation of the Accuracy of Intraocular Lens Power Calculation Formulas in Phacovitrectomy.

INTRODUCTION: The aim of this study was to evaluate the refractive error in patients undergoing combined phacovitrectomy with and without gas tamponade. METHODS: This was a retrospective chart review including patients undergoing phacoemulsification alone (Group 1), combined phacovitrectomy for epiretinal membrane (Group 2), and combined phacovitrectomy with gas tamponade for rhegmatogenous retinal detachment (RRD) (Group 3). Axial length and keratometry were measured using an optical biometric system (Argos, Alcon Laboratories. Inc.), and a three-piece intraocular lens (IOL; NX-70S) was implanted in all groups. In each group, the prediction error at 3 months was calculated using IOL power calculation formulas (SRK/T, Hill-RBF, Kane, and Barrett Universal II) for each eye. Outcome measures included the mean prediction error (MPE), its standard deviation (SD), and the mean absolute error (MAE). The change in IOL position at 3 months was also assessed using anterior segment optical coherence tomography. RESULTS: A total of 104 eyes were included (Group 1: 30; Group 2: 34; Group 3: 40 eyes). The MPE was -0.08 ± 0.37 diopters (D), -0.26 ± 0.32 D, and -0.59 ± 0.34 D in Group 1, Group 2, and Group 3, respectively, using the Barrett Universal II formula (P < 0.01, ANOVA). The movement forward in the IOL position was 0.95 ± 0.16 mm, 0.94 ± 0.12 mm, and 1.07 ± 0.20 mm in Group 1, Group 2, and Group 3, respectively (P < 0.01). No significant difference was shown in MPE among the four formulas after combined phacovitrectomy with gas (P = 0.531). CONCLUSIONS: Phacovitrectomy in RRD induced a significant myopic shift using any of the clinically available formulas. This suggests that myopic shift should be taken into consideration for better refractive outcomes in phacovitrectomy with gas tamponade in RRD.

Anterior Cingulate Cortex Causally Supports Meta-Learning.

In dynamic environments with volatile rewards the anterior cingulate cortex (ACC) is believed to determine whether a visual object is relevant and should be chosen. The ACC may achieve this by integrating reward information over time to estimate which objects are worth to explore and which objects should be avoided. Such a higher-order meta-awareness about which objects should be explored predicts that the ACC causally contributes to choices when the reward values of objects are unknown and must be inferred from ongoing exploration. We tested this suggestion in nonhuman primates using a learning task that varied the number of object features that could be relevant, and by controlling the motivational value of choosing objects. During learning the ACC was transiently micro-stimulated when subjects foveated the to-be-chosen stimulus. We found that stimulation selectively impaired learning when feature uncertainty and motivational value of choices were high, which was linked to a deficit in using reward outcomes for feature-specific credit assignment. Application of an adaptive reinforcement learning model confirmed a primary deficit in weighting prediction errors that led to a meta-learning impairment to adaptively increase exploration during learning and to an impaired use of working memory to support learning. These findings provide causal evidence that the reward history traces in ACC are essential for meta-adjusting the exploration-exploitation balance and the strength of working memory of object values during adaptive behavior.

Dorsal raphe neurons integrate the values of reward amount, delay, and uncertainty in multi-attribute decision-making.

The dorsal raphe nucleus (DRN) is implicated in psychiatric disorders that feature impaired sensitivity to reward amount, impulsivity when facing reward delays, and risk-seeking when confronting reward uncertainty. However, it has been unclear whether and how DRN neurons signal reward amount, reward delay, and reward uncertainty during multi-attribute value-based decision-making, where subjects consider these attributes to make a choice. We recorded DRN neurons as monkeys chose between offers whose attributes, namely expected reward amount, reward delay, and reward uncertainty, varied independently. Many DRN neurons signaled offer attributes, and this population tended to integrate the attributes in a manner that reflected monkeys' preferences for amount, delay, and uncertainty. After decision-making, in response to post-decision feedback, these same neurons signaled signed reward prediction errors, suggesting a broader role in tracking value across task epochs and behavioral contexts. Our data illustrate how the DRN participates in value computations, guiding theories about the role of the DRN in decision-making and psychiatric disease.

The Causes and Consequences of Drifting Expectations.

Awaiting news of uncertain outcomes is distressing because the news might be disappointing. To prevent such disappointments, people often "brace for the worst," pessimistically lowering expectations before news arrives to decrease the possibility of surprising disappointment (a negative prediction error, or PE). Computational decision-making research commonly assumes that expectations do not drift within trials, yet it is unclear whether expectations pessimistically drift in real-world, high-stakes settings, what factors influence expectation drift, and whether it effectively buffers emotional responses to goal-relevant outcomes. Moreover, individuals learn from PEs to accurately anticipate future outcomes, but it is unknown whether expectation drift also impedes PE-based learning. In a sample of students awaiting exam grades (N = 625), we found that expectations often drift and tend to drift pessimistically. We demonstrate that bracing is preferentially modulated by uncertainty; it transiently buffers the initial emotional impact of negative PEs but impairs PE-based learning, counterintuitively sustaining uncertainty into the future.

Unsupervised Characterization of Prediction Error Markers in Unisensory and Multisensory Streams Reveal the Spatiotemporal Hierarchy of Cortical Information Processing.

Elicited upon violation of regularity in stimulus presentation, mismatch negativity (MMN) reflects the brain's ability to perform automatic comparisons between consecutive stimuli and provides an electrophysiological index of sensory error detection whereas P300 is associated with cognitive processes such as updating of the working memory. To date, there has been extensive research on the roles of MMN and P300 individually, because of their potential to be used as clinical markers of consciousness and attention, respectively. Here, we intend to explore with an unsupervised and rigorous source estimation approach, the underlying cortical generators of MMN and P300, in the context of prediction error propagation along the hierarchies of brain information processing in healthy human participants. The existing methods of characterizing the two ERPs involve only approximate estimations of their amplitudes and latencies based on specific sensors of interest. Our objective is twofold: first, we introduce a novel data-driven unsupervised approach to compute latencies and amplitude of ERP components accurately on an individual-subject basis and reconfirm earlier findings. Second, we demonstrate that in multisensory environments, MMN generators seem to reflect a significant overlap of "modality-specific" and "modality-independent" information processing while P300 generators mark a shift toward completely "modality-independent" processing. Advancing earlier understanding that multisensory contexts speed up early sensory processing, our study reveals that temporal facilitation extends to even the later components of prediction error processing, using EEG experiments. Such knowledge can be of value to clinical research for characterizing the key developmental stages of lifespan aging, schizophrenia, and depression.

Surprise!-Clarifying the link between insight and prediction error.

The AHA experience, a moment of deep understanding during insightful problem-solving involving feelings of certainty, pleasure, and surprise, has captivated psychologists for more than a century. Recently, a new theoretical framework has proposed a link between the AHA experience and prediction error (PE), a popular concept in decision-making and reinforcement learning. This framework suggests that participants maintain a meta-cognitive prediction about the time it takes to solve a problem and the AHA experience arises when the problem is solved earlier than expected, resulting in a meta-cognitive PE. In our preregistered online study, we delved deeper into this idea, investigating whether prediction errors also pertain to participants' predictions regarding the solvability of the problem itself, and which dimension of the AHA experience aligns with the meta-cognitive PE. Utilizing verbal insight problems, we found a positive association between the AHA experience and the meta-cognitive PE, specifically in regards to problem solvability. Specifically, the element of surprise, a critical AHA dimension, emerged as a key indicator of the meta-cognitive PE, while other dimensions-such as pleasure, certainty, and suddenness-showed no signs for similar relationships, with suddenness exhibiting a negative correlation with meta-cognitive PE. This new finding provides further evidence that aspects of the AHA experience, surprise in particular, correspond to a meta-cognitive PE. The finding also underscores the multifaceted nature of this phenomenon, linking insights with learning theories and enhancing our understanding of this intriguing phenomenon.

Investigating sensitivity to multi-domain prediction errors in chronic auditory phantom perception.

The perception of a continuous phantom in a sensory domain in the absence of an external stimulus is explained as a maladaptive compensation of aberrant predictive coding, a proposed unified theory of brain functioning. If this were true, these changes would occur not only in the domain of the phantom percept but in other sensory domains as well. We confirm this hypothesis by using tinnitus (continuous phantom sound) as a model and probe the predictive coding mechanism using the established local-global oddball paradigm in both the auditory and visual domains. We observe that tinnitus patients are sensitive to changes in predictive coding not only in the auditory but also in the visual domain. We report changes in well-established components of event-related EEG such as the mismatch negativity. Furthermore, deviations in stimulus characteristics were correlated with the subjective tinnitus distress. These results provide an empirical confirmation that aberrant perceptions are a symptom of a higher-order systemic disorder transcending the domain of the percept.

The cardiac cycle modulates learning-related interoception.

Behavior is guided by the compatibility of expectations based on past experience and the outcome. In a recent study, Fouragnan and colleagues report that absolute prediction error (PE)-related heart-evoked potentials (HEPs) differ according to the cardiac cycle phase at outcome, and that the magnitude of this effect positively correlates with reward learning in healthy adults.

What do you learn from a single cue? Dimensional reweighting and cue reassociation from experience with a newly unreliable phonetic cue.

In language comprehension, we use perceptual cues to infer meanings. Some of these cues reside on perceptual dimensions. For example, the difference between bear and pear is cued by a difference in voice onset time (VOT), which is a continuous perceptual dimension. The present paper asks whether, and when, experience with a single value on a dimension behaving unexpectedly is used by the learner to reweight the whole dimension. We show that learners reweight the whole VOT dimension when exposed to a single VOT value (e.g., 45 ms) and provided with feedback indicating that the speaker intended to produce a /b/ 50% of the time and a /p/ the other 50% of the time. Importantly, dimensional reweighting occurs only if 1) the 50/50 feedback is unexpected for the VOT value, and 2) there is another dimension that is predictive of feedback. When no predictive dimension is available, listeners reassociate the experienced VOT value with the more surprising outcome but do not downweight the entire VOT dimension. These results provide support for perceptual representations of speech sounds that combine cues and dimensions, for viewing perceptual learning in speech as a combination of error-driven cue reassociation and dimensional reweighting, and for considering dimensional reweighting to be reallocation of attention that occurs only when there is evidence that reallocating attention would improve prediction accuracy (Harmon, Z., Idemaru, K., & Kapatsinski, V. 2019. Learning mechanisms in cue reweighting. Cognition, 189, 76-88.).

A dopamine mechanism for reward maximization.

Individual survival and evolutionary selection require biological organisms to maximize reward. Economic choice theories define the necessary and sufficient conditions, and neuronal signals of decision variables provide mechanistic explanations. Reinforcement learning (RL) formalisms use predictions, actions, and policies to maximize reward. Midbrain dopamine neurons code reward prediction errors (RPE) of subjective reward value suitable for RL. Electrical and optogenetic self-stimulation experiments demonstrate that monkeys and rodents repeat behaviors that result in dopamine excitation. Dopamine excitations reflect positive RPEs that increase reward predictions via RL; against increasing predictions, obtaining similar dopamine RPE signals again requires better rewards than before. The positive RPEs drive predictions higher again and thus advance a recursive reward-RPE-prediction iteration toward better and better rewards. Agents also avoid dopamine inhibitions that lower reward prediction via RL, which allows smaller rewards than before to elicit positive dopamine RPE signals and resume the iteration toward better rewards. In this way, dopamine RPE signals serve a causal mechanism that attracts agents via RL to the best rewards. The mechanism improves daily life and benefits evolutionary selection but may also induce restlessness and greed.

Striatal Dopamine Contributions to Skilled Motor Learning.

Coordinated multijoint limb and digit movements-"manual dexterity"-underlie both specialized skills (e.g., playing the piano) and more mundane tasks (e.g., tying shoelaces). Impairments in dexterous skill cause significant disability, as occurs with motor cortical injury, Parkinson's disease, and a range of other pathologies. Clinical observations, as well as basic investigations, suggest that corticostriatal circuits play a critical role in learning and performing dexterous skills. Furthermore, dopaminergic signaling in these regions is implicated in synaptic plasticity and motor learning. Nonetheless, the role of striatal dopamine signaling in skilled motor learning remains poorly understood. Here, we use fiber photometry paired with a genetically encoded dopamine sensor to investigate striatal dopamine release in both male and female mice as they learn and perform a skilled reaching task. Dopamine rapidly increases during a skilled reach and peaks near pellet consumption. In the dorsolateral striatum, dopamine dynamics are faster than in the dorsomedial and ventral striatum. Across training, as reaching performance improves, dopamine signaling shifts from pellet consumption to cues that predict pellet availability, particularly in medial and ventral areas of the striatum. Furthermore, performance prediction errors are present across the striatum, with reduced dopamine release after an unsuccessful reach. These findings show that dopamine dynamics during skilled motor behaviors change with learning and are differentially regulated across striatal subregions.

Continuous-Time Model Identification of the Subglottal System.

Mathematical models that accurately simulate the physiological systems of the human body serve as cornerstone instruments for advancing medical science and facilitating innovative clinical interventions. One application is the modeling of the subglottal tract and neck skin properties for its use in the ambulatory assessment of vocal function, by enabling non-invasive monitoring of glottal airflow via a neck surface accelerometer. For the technique to be effective, the development of an accurate building block model for the subglottal tract is required. Such a model is expected to utilize glottal volume velocity as the input parameter and yield neck skin acceleration as the corresponding output. In contrast to preceding efforts that employed frequency-domain methods, the present paper leverages system identification techniques to derive a parsimonious continuous-time model of the subglottal tract using time-domain data samples. Additionally, an examination of the model order is conducted through the application of various information criteria. Once a low-order model is successfully fitted, an inverse filter based on a Kalman smoother is utilized for the estimation of glottal volume velocity and related aerodynamic metrics, thereby constituting the most efficient execution of these estimates thus far. Anticipated reductions in computational time and complexity due to the lower order of the subglottal model hold particular relevance for real-time monitoring. Simultaneously, the methodology proves efficient in generating a spectrum of aerodynamic features essential for ambulatory vocal function assessment.

Prediction-error-dependent processing of immediate and delayed positive feedback.

Learning often involves trial-and-error, i.e. repeating behaviours that lead to desired outcomes, and adjusting behaviour when outcomes do not meet our expectations and thus lead to prediction errors (PEs). PEs have been shown to be reflected in the reward positivity (RewP), an event-related potential (ERP) component between 200 and 350 ms after performance feedback which is linked to striatal processing and assessed via electroencephalography (EEG). Here we show that this is also true for delayed feedback processing, for which a critical role of the hippocampus has been suggested. We found a general reduction of the RewP for delayed feedback, but the PE was similarly reflected in the RewP and the later P300 for immediate and delayed positive feedback, while no effect was found for negative feedback. Our results suggest that, despite processing differences between immediate and delayed feedback, positive PEs drive feedback processing and learning irrespective of delay.

Cellular mechanisms of cooperative context-sensitive predictive inference.

We argue that prediction success maximization is a basic objective of cognition and cortex, that it is compatible with but distinct from prediction error minimization, that neither objective requires subtractive coding, that there is clear neurobiological evidence for the amplification of predicted signals, and that we are unconvinced by evidence proposed in support of subtractive coding. We outline recent discoveries showing that pyramidal cells on which our cognitive capabilities depend usually transmit information about input to their basal dendrites and amplify that transmission when input to their distal apical dendrites provides a context that agrees with the feedforward basal input in that both are depolarizing, i.e., both are excitatory rather than inhibitory. Though these intracellular discoveries require a level of technical expertise that is beyond the current abilities of most neuroscience labs, they are not controversial and acclaimed as groundbreaking. We note that this cellular cooperative context-sensitivity greatly enhances the cognitive capabilities of the mammalian neocortex, and that much remains to be discovered concerning its evolution, development, and pathology.