scholarly journals Separate neural representations of prediction error valence and surprise: Evidence from an fMRI meta-analysis

2018 ◽  
Vol 39 (7) ◽  
pp. 2887-2906 ◽  
Author(s):  
Elsa Fouragnan ◽  
Chris Retzler ◽  
Marios G. Philiastides
Keyword(s):  
Prediction Error ◽  
Meta Analysis ◽  
Neural Representations

scholarly journals Convergent neural representations of experimentally-induced acute pain in healthy volunteers: A large-scale fMRI meta-analysis

2020 ◽  
Vol 112 ◽  
pp. 300-323 ◽  
Author(s):  
Anna Xu ◽  
Bart Larsen ◽  
Erica B. Baller ◽  
J. Cobb Scott ◽  
Vaishnavi Sharma ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Acute Pain ◽  
Large Scale ◽  
Meta Analysis ◽  
Neural Representations ◽  
Experimentally Induced

scholarly journals Substrates of Human Prediction Error for Incentives, Perception, Cognition, and Action

2021 ◽  
Author(s):  
Philip R. Corlett ◽  
Jessica A Mollick ◽  
Hedy Kober
Keyword(s):  
Artificial Intelligence ◽  
Computational Neuroscience ◽  
Orbitofrontal Cortex ◽  
Prediction Error ◽  
Functional Neuroimaging ◽  
Meta Analysis ◽  
Basic Research ◽  
Preclinical Studies ◽  
Prediction Errors ◽  
Neural Firing

Prediction errors (PEs) are a keystone for computational neuroscience. Their association with midbrain neural firing has been confirmed across species and has inspired the construction of artificial intelligence that can outperform humans. However, there is still much to learn. Here, we leverage the wealth of human PE data acquired in the functional neuroimaging setting in service of a deeper understanding, using meta-analysis. Across 263 PE studies that have focused on reward, punishment, action, cognition, and perception, we found consistent region-PE associations that were posited theoretically or evinced in preclinical studies, but not yet established in humans, including midbrain PE signals during perceptual and Pavlovian tasks. Further, we found evidence for PEs over successor representations in orbitofrontal cortex, and for default mode network PE signals. By combining functional imaging meta-analysis with theory and basic research, we provide new insights into learning in machines, humans, and other animals.

scholarly journals Opinion: Neural differentiation of incorrectly predicted memories

2018 ◽  
Author(s):  
Andrea Greve ◽  
Hunar Abdulrahman ◽  
Richard Henson
Keyword(s):  
Prediction Error ◽  
Neural Differentiation ◽  
Recent Article ◽  
Transitive Inference ◽  
Experimental Conditions ◽  
Neural Representations ◽  
New Information

In their recent article ‘Neural differentiation of incorrectly predicted memories’, Kim et al. (2017) investigate how neural representations of items change when they are incorrectly predicted and subsequently restudied. The authors conclude such items undergo representational differentiation, i.e. a decreased overlap in the representations of an item and its context. We suggest the results are equally compatible with the reverse mechanism of integration, i.e. increased learning of new information and present simulations to demonstrate this. More importantly, we show how new experimental conditions could distinguish integration from differentiation and discuss how the results fit with recent suggestions about prediction-error driven learning and transitive inference.

scholarly journals Just above chance: is it harder to decode information from human prefrontal cortex BOLD signals?

2017 ◽  
Author(s):  
Apoorva Bhandari ◽  
Christopher Gagne ◽  
David Badre
Keyword(s):  
Prefrontal Cortex ◽  
Functional Organization ◽  
Meta Analysis ◽  
Activity Patterns ◽  
Brain Regions ◽  
Base Rate ◽  
Neural Representations ◽  
Important Approach ◽  
Important Open Problem ◽  
Flexible Behavior

AbstractUnderstanding the nature and form of prefrontal cortex representations that support flexible behavior is an important open problem in cognitive neuroscience. In humans, multi-voxel pattern analysis (MVPA) of fMRI BOLD measurements has emerged as an important approach for studying neural representations. An implicit, untested assumption underlying many PFC MVPA studies is that the base rate of decoding information from PFC BOLD activity patterns is similar to that of other brain regions. Here we estimate these base rates from a meta-analysis of published MVPA studies and show that the PFC has a significantly lower base rate for decoding than visual sensory cortex. Our results have implications for the design and interpretation of MVPA studies of prefrontal cortex, and raise important questions about its functional organization.

scholarly journals An improved method for evaluating inverted encoding models

2021 ◽  
Author(s):  
Paul S Scotti ◽  
Jiageng Chen ◽  
Julie D Golomb
Keyword(s):  
Prediction Error ◽  
Statistical Power ◽  
Goodness Of Fit ◽  
Standard Procedure ◽  
Population Level ◽  
Neural Representations ◽  
Trial Stimulus ◽  
Key Issues ◽  
Spurious Results ◽  
Confidence Estimates

Inverted encoding models have recently become popular as a method for decoding stimuli and investigating neural representations. Here we present a novel modification to inverted encoding models that improves the flexibility and interpretability of stimulus reconstructions, addresses some key issues inherent in the standard inverted encoding model procedure, and provides trial-by-trial stimulus predictions and goodness-of-fit estimates. The standard inverted encoding model approach estimates channel responses (or reconstructions), which are averaged and aligned across trials and then typically evaluated using a metric such as slope, amplitude, etc.). We discuss how this standard procedure can produce spurious results and other interpretation issues. Our modifications are not susceptible to these methodological issues and are further advantageous due to our decoding metric taking into account the choice of population-level tuning functions and employing a prediction error-based metric directly comparable across experiments. Our modifications also allow researchers to obtain trial-by-trial confidence estimates independent of prediction error which can be used to threshold reconstructions and increase statistical power. We validate and demonstrate the improved utility of our modified inverted encoding model procedure across three real fMRI datasets, and additionally offer a Python package for easy implementation of our approach.

scholarly journals Rapid computations of spectrotemporal prediction error support perception of degraded speech

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Ediz Sohoglu ◽  
Matthew H Davis
Keyword(s):  
Speech Perception ◽  
Prediction Error ◽  
Predictive Coding ◽  
Signal Quality ◽  
Neural Responses ◽  
Perceptual Inference ◽  
Written Text ◽  
Degraded Speech ◽  
Neural Representations ◽  
Brain Responses

Human speech perception can be described as Bayesian perceptual inference but how are these Bayesian computations instantiated neurally? We used magnetoencephalographic recordings of brain responses to degraded spoken words and experimentally manipulated signal quality and prior knowledge. We first demonstrate that spectrotemporal modulations in speech are more strongly represented in neural responses than alternative speech representations (e.g. spectrogram or articulatory features). Critically, we found an interaction between speech signal quality and expectations from prior written text on the quality of neural representations; increased signal quality enhanced neural representations of speech that mismatched with prior expectations, but led to greater suppression of speech that matched prior expectations. This interaction is a unique neural signature of prediction error computations and is apparent in neural responses within 100 ms of speech input. Our findings contribute to the detailed specification of a computational model of speech perception based on predictive coding frameworks.

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