scholarly journals Neural structure mapping in human probabilistic reward learning

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Fabrice Luyckx ◽  
Hamed Nili ◽  
Bernhard Spitzer ◽  
Christopher Summerfield
Keyword(s):  
Mechanistic Model ◽  
Structural Alignment ◽  
Learning Task ◽  
Number Line ◽  
Neural Representation ◽  
Structure Mapping ◽  
Neural Signals ◽  
Neural Structure ◽  
Guided Learning ◽  
Network Simulations

Humans can learn abstract concepts that describe invariances over relational patterns in data. One such concept, known as magnitude, allows stimuli to be compactly represented on a single dimension (i.e. on a mental line). Here, we measured representations of magnitude in humans by recording neural signals whilst they viewed symbolic numbers. During a subsequent reward-guided learning task, the neural patterns elicited by novel complex visual images reflected their payout probability in a way that suggested they were encoded onto the same mental number line, with 'bad' bandits sharing neural representation with 'small' numbers and 'good' bandits with 'large' numbers. Using neural network simulations, we provide a mechanistic model that explains our findings and shows how structural alignment can promote transfer learning. Our findings suggest that in humans, learning about reward probability is accompanied by structural alignment of value representations with neural codes for the abstract concept of magnitude.

scholarly journals Neural structure mapping in human probabilistic reward learning

2018 ◽  
Author(s):  
F. Luyckx ◽  
H. Nili ◽  
B. Spitzer ◽  
C. Summerfield
Keyword(s):  
Structural Alignment ◽  
Learning Task ◽  
Number Line ◽  
Visual Images ◽  
Abstract Concepts ◽  
Structure Mapping ◽  
Neural Signals ◽  
Neural Structure ◽  
Relational Patterns ◽  
Guided Learning

AbstractHumans can learn abstract concepts that describe invariances over relational patterns in data. One such concept, known as magnitude, allows stimuli to be compactly represented by a single dimension (i.e. on a mental line), for example according to their cardinality, size or value. Here, we measured representations of magnitude in humans by recording neural signals whilst they viewed symbolic numbers. During a subsequent reward-guided learning task, the neural patterns elicited by novel complex visual images reflected their pay-out probability in a way that suggested they were encoded onto the same mental number line. Our findings suggest that in humans, learning about values is accompanied by structural alignment of value representations with neural codes for the concept of magnitude.

scholarly journals Amphetamine disrupts haemodynamic correlates of prediction errors in nucleus accumbens and orbitofrontal cortex

2019 ◽  
Author(s):  
Emilie Werlen ◽  
Soon-Lim Shin ◽  
Francois Gastambide ◽  
Jennifer Francois ◽  
Mark D Tricklebank ◽  
...  
Keyword(s):  
Nucleus Accumbens ◽  
Orbitofrontal Cortex ◽  
Prediction Error ◽  
Learning Task ◽  
Adaptive Behaviour ◽  
Diagnostic Feature ◽  
Prediction Errors ◽  
Guided Learning ◽  
The Difference ◽  
Precise Relationship

AbstractIn an uncertain world, the ability to predict and update the relationships between environmental cues and outcomes is a fundamental element of adaptive behaviour. This type of learning is typically thought to depend on prediction error, the difference between expected and experienced events, and in the reward domain this has been closely linked to mesolimbic dopamine. There is also increasing behavioural and neuroimaging evidence that disruption to this process may be a cross-diagnostic feature of several neuropsychiatric and neurological disorders in which dopamine is dysregulated. However, the precise relationship between haemodynamic measures, dopamine and reward-guided learning remains unclear. To help address this issue, we used a translational technique, oxygen amperometry, to record haemodynamic signals in the nucleus accumbens (NAc) and orbitofrontal cortex (OFC) while freely-moving rats performed a probabilistic Pavlovian learning task. Using a model-based analysis approach to account for individual variations in learning, we found that the oxygen signal in the NAc correlated with a reward prediction error, whereas in the OFC it correlated with an unsigned prediction error or salience signal. Furthermore, an acute dose of amphetamine, creating a hyperdopaminergic state, disrupted rats’ ability to discriminate between cues associated with either a high or a low probability of reward and concomitantly corrupted prediction error signalling. These results demonstrate parallel but distinct prediction error signals in NAc and OFC during learning, both of which are affected by psychostimulant administration. Furthermore, they establish the viability of tracking and manipulating haemodynamic signatures of reward-guided learning observed in human fMRI studies using a proxy signal for BOLD in a freely behaving rodent.

scholarly journals Alpha/beta power decreases during episodic memory formation predict the magnitude of alpha/beta power decreases during subsequent retrieval

2020 ◽  
Author(s):  
Benjamin J. Griffiths ◽  
María Carmen Martín-Buro ◽  
Bernhard P. Staresina ◽  
Simon Hanslmayr ◽  
Tobias Staudigl
Keyword(s):  
Episodic Memory ◽  
Memory Retrieval ◽  
Spectral Power ◽  
Learning Task ◽  
Neural Representation ◽  
Parameter Estimates ◽  
Beta Activity ◽  
Amount Of Information ◽  
Beta Power ◽  
Alpha Beta

AbstractEpisodic memory retrieval is characterised by the vivid reinstatement of information about a personally-experienced event. Growing evidence suggests that the reinstatement of such information is supported by reductions in the spectral power of alpha/beta activity. Given that the amount of information that can be recalled depends on the amount of information that was originally encoded, information-based accounts of alpha/beta activity would suggest that retrieval-related alpha/beta power decreases similarly depend upon decreases in alpha/beta power during encoding. To test this hypothesis, seventeen human participants completed a sequence-learning task while undergoing concurrent MEG recordings. Regression-based analyses were then used to estimate how alpha/beta power decreases during encoding predicted alpha/beta power decreases during retrieval, on a trial-by-trial basis. When subjecting these parameter estimates to group-level analysis, we find evidence to suggest that retrieval-related alpha/beta (7-15Hz) power decreases fluctuate as a function of encoding-related alpha/beta power decreases. These results suggest that retrieval-related alpha/beta power decreases are contingent on the decrease in alpha/beta power that arose during encoding. Subsequent analysis uncovered no evidence to suggest that these alpha/beta power decreases reflect stimulus identity, indicating that the contingency between encoding- and retrieval-related alpha/beta power reflects the reinstatement of a neurophysiological operation, rather than neural representation, during episodic memory retrieval.

scholarly journals Mutual bootstrapping between language and analogical processing

2010 ◽  
Vol 2 (2) ◽  
pp. 261-283 ◽  
Author(s):  
Dedre Gentner ◽  
Stella Christie
Keyword(s):  
Cognitive Development ◽  
Structural Alignment ◽  
Great Apes ◽  
The Other ◽  
Spatial Language ◽  
Human Cognition ◽  
Symbolic Language ◽  
Structure Mapping ◽  
Symbolic Systems

AbstractWhat makes us so smart as a species, and what makes children such rapid learners? We argue that the answer to both questions lies in a mutual bootstrapping system comprised of (1) our exceptional capacity for relational cognition and (2) symbolic systems that augment this capacity. The ability to carry out structure-mapping processes of alignment and inference is inherent in human cognition. It is arguably the key inherent difference between humans and other great apes. But an equally important difference is that humans possess a symbolic language.The acquisition of language influences cognitive development in many ways. We focus here on the role of language in a mutually facilitating partnership with relational representation and reasoning. We suggest a positive feedback relation in which structural alignment processes support the acquisition of language, and in turn, language—especially relational language—supports structural alignment and reasoning.We review three kinds of evidence (a) evidence that analogical processes support children's learning in a variety of domains; (b) more specifically, evidence that analogical processing fosters the acquisition of language, especially relational language; and (c) in the other direction, evidence that acquiring language fosters children's ability to process analogies, focusing on spatial language and spatial analogies. We conclude with an analysis of the acquisition of cardinality—which we offer as a canonical case of how the combination of language and analogical processing fosters cognitive development.

scholarly journals Author response: Neural structure mapping in human probabilistic reward learning

2019 ◽  
Author(s):  
Fabrice Luyckx ◽  
Hamed Nili ◽  
Bernhard Spitzer ◽  
Christopher Summerfield
Keyword(s):  
Reward Learning ◽  
Author Response ◽  
Structure Mapping ◽  
Neural Structure

scholarly journals “Validating silicon polytrodes with paired juxtacellular recordings: method and dataset”

2016 ◽  
Author(s):  
Joana P. Neto ◽  
Gonçalo Lopes ◽  
João Frazão ◽  
Joana Nogueira ◽  
Pedro Lacerda ◽  
...  
Keyword(s):  
Neural Activity ◽  
Single Neuron ◽  
Cross Validation ◽  
Electrode Materials ◽  
Validation Dataset ◽  
Neural Signals ◽  
Neural Structure ◽  
New Methods ◽  
New Algorithms

AbstractCross-validating new methods for recording neural activity is necessary to accurately interpret and compare the signals they measure. Here we describe a procedure for precisely aligning two probes for in vivo “paired-recordings” such that the spiking activity of a single neuron is monitored with both a dense extracellular silicon polytrode and a juxtacellular micro-pipette. Our new method allows for efficient, reliable, and automated guidance of both probes to the same neural structure with micron resolution. We also describe a new dataset of paired-recordings, which is available online. We propose that our novel targeting system, and ever expanding cross-validation dataset, will be vital to the development of new algorithms for automatically detecting/sorting single-units, characterizing new electrode materials/designs, and resolving nagging questions regarding the origin and nature of extracellular neural signals.

scholarly journals Modeling Circadian Phototransduction: Retinal Neurophysiology and Neuroanatomy

2021 ◽  
Vol 14 ◽  
Author(s):  
Mark S. Rea ◽  
Rohan Nagare ◽  
Mariana G. Figueiro
Keyword(s):  
Optical Radiation ◽  
Neural Circuits ◽  
Brain Structures ◽  
Companion Paper ◽  
High Threshold ◽  
Neural Signals ◽  
Neural Structure ◽  
Psychophysical Data ◽  
Central Pacemaker ◽  
Subadditive Response

The retina is a complex, but well-organized neural structure that converts optical radiation into neural signals that convey photic information to a wide variety of brain structures. The present paper is concerned with the neural circuits underlying phototransduction for the central pacemaker of the human circadian system. The proposed neural framework adheres to orthodox retinal neuroanatomy and neurophysiology. Several postulated mechanisms are also offered to account for the high threshold and for the subadditive response to polychromatic light exhibited by the human circadian phototransduction circuit. A companion paper, modeling circadian phototransduction: Quantitative predictions of psychophysical data, provides a computational model for predicting psychophysical data associated with nocturnal melatonin suppression while staying within the constraints of the neurophysiology and neuroanatomy offered here.

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