Browse
Submit Data
Databases
API
Help

Dataset Information

46 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Neural representation of abstract task structure during generalization.

ABSTRACT: Cognitive models in psychology and neuroscience widely assume that the human brain maintains an abstract representation of tasks. This assumption is fundamental to theories explaining how we learn quickly, think creatively, and act flexibly. However, neural evidence for a verifiably generative abstract task representation has been lacking. Here, we report an experimental paradigm that requires forming such a representation to act adaptively in novel conditions without feedback. Using functional magnetic resonance imaging, we observed that abstract task structure was represented within left mid-lateral prefrontal cortex, bilateral precuneus, and inferior parietal cortex. These results provide support for the neural instantiation of the long-supposed abstract task representation in a setting where we can verify its influence. Such a representation can afford massive expansions of behavioral flexibility without additional experience, a vital characteristic of human cognition.

SUBMITTER: Vaidya AR

PROVIDER: S-EPMC8016482 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Neural representation of abstract task structure during generalization.

Vaidya Avinash R AR Jones Henry M HM Castillo Johanny J Badre David D

eLife 20210317

Cognitive models in psychology and neuroscience widely assume that the human brain maintains an abstract representation of tasks. This assumption is fundamental to theories explaining how we learn quickly, think creatively, and act flexibly. However, neural evidence for a verifiably generative abstract task representation has been lacking. Here, we report an experimental paradigm that requires forming such a representation to act adaptively in novel conditions without feedback. Using functional ...[more]

PMID: 33729156

Similar Datasets

Argument structure and the representation of abstract semantics.

Project description:According to the dual coding theory, differences in the ease of retrieval between concrete and abstract words are related to the exclusive dependence of abstract semantics on linguistic information. Argument structure can be considered a measure of the complexity of the linguistic contexts that accompany a verb. If the retrieval of abstract verbs relies more on the linguistic codes they are associated to, we could expect a larger effect of argument structure for the processing of abstract verbs. In this study, sets of length- and frequency-matched verbs including 40 intransitive verbs, 40 transitive verbs taking simple complements, and 40 transitive verbs taking sentential complements were presented in separate lexical and grammatical decision tasks. Half of the verbs were concrete and half were abstract. Similar results were obtained in the two tasks, with significant effects of imageability and transitivity. However, the interaction between these two variables was not significant. These results conflict with hypotheses assuming a stronger reliance of abstract semantics on linguistic codes. In contrast, our data are in line with theories that link the ease of retrieval with availability and robustness of semantic information.

| S-EPMC4128767 | biostudies-literature

Learning the Abstract General Task Structure in a Rapidly Changing Task Content.

Project description:The ability to learn abstract generalized structures of tasks is crucial for humans to adapt to changing environments and novel tasks. In a series of five experiments, we investigated this ability using a Rapid Instructed Task Learning paradigm (RITL) comprising short miniblocks, each involving two novel stimulus-response rules. Each miniblock included (a) instructions for the novel stimulus-response rules, (b) a NEXT phase involving a constant (familiar) intervening task (0-5 trials), (c) execution of the newly instructed rules (2 trials). The results show that including a NEXT phase (and hence, a prospective memory demand) led to relatively more robust abstract learning as indicated by increasingly faster responses with experiment progress. Multilevel modeling suggests that the prospective memory demand was just another aspect of the abstract task structure which has been learned.

| S-EPMC8269791 | biostudies-literature

Minimal cross-trial generalization in learning the representation of an odor-guided choice task.

Project description:There is no single way to represent a task. Indeed, despite experiencing the same task events and contingencies, different subjects may form distinct task representations. As experimenters, we often assume that subjects represent the task as we envision it. However, such a representation cannot be taken for granted, especially in animal experiments where we cannot deliver explicit instruction regarding the structure of the task. Here, we tested how rats represent an odor-guided choice task in which two odor cues indicated which of two responses would lead to reward, whereas a third odor indicated free choice among the two responses. A parsimonious task representation would allow animals to learn from the forced trials what is the better option to choose in the free-choice trials. However, animals may not necessarily generalize across odors in this way. We fit reinforcement-learning models that use different task representations to trial-by-trial choice behavior of individual rats performing this task, and quantified the degree to which each animal used the more parsimonious representation, generalizing across trial types. Model comparison revealed that most rats did not acquire this representation despite extensive experience. Our results demonstrate the importance of formally testing possible task representations that can afford the observed behavior, rather than assuming that animals' task representations abide by the generative task structure that governs the experimental design.

| S-EPMC8986096 | biostudies-literature

The neural representation of abstract words may arise through grounding word meaning in language itself.

Project description:In order to describe how humans represent meaning in the brain, one must be able to account for not just concrete words but, critically, also abstract words, which lack a physical referent. Hebbian formalism and optimization are basic principles of brain function, and they provide an appealing approach for modeling word meanings based on word co-occurrences. We provide proof of concept that a statistical model of the semantic space can account for neural representations of both concrete and abstract words, using MEG. Here, we built a statistical model using word embeddings extracted from a text corpus. This statistical model was used to train a machine learning algorithm to successfully decode the MEG signals evoked by written words. In the model, word abstractness emerged from the statistical regularities of the language environment. Representational similarity analysis further showed that this salient property of the model co-varies, at 280-420 ms after visual word presentation, with activity in regions that have been previously linked with processing of abstract words, namely the left-hemisphere frontal, anterior temporal and superior parietal cortex. In light of these results, we propose that the neural encoding of word meanings can arise through statistical regularities, that is, through grounding in language itself.

| S-EPMC8449102 | biostudies-literature

Grid-like entorhinal representation of an abstract value space during prospective decision making.

Project description:How valuable a choice option is often changes over time, making the prediction of value changes an important challenge for decision making. Prior studies identified a cognitive map in the hippocampal-entorhinal system that encodes relationships between states and enables prediction of future states, but does not inherently convey value during prospective decision making. In this fMRI study, participants predicted changing values of choice options in a sequence, forming a trajectory through an abstract two-dimensional value space. During this task, the entorhinal cortex exhibited a grid-like representation with an orientation aligned to the axis through the value space most informative for choices. A network of brain regions, including ventromedial prefrontal cortex, tracked the prospective value difference between options. These findings suggest that the entorhinal grid system supports the prediction of future values by representing a cognitive map, which might be used to generate lower-dimensional value signals to guide prospective decision making.

| S-EPMC10858181 | biostudies-literature

Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks.

Project description:An increasingly popular machine learning paradigm is to pretrain a neural network (NN) on many tasks offline, then adapt it to downstream tasks, often by re-training only the last linear layer of the network. This approach yields strong downstream performance in a variety of contexts, demonstrating that multitask pretraining leads to effective feature learning. Although several recent theoretical studies have shown that shallow NNs learn meaningful features when either (i) they are trained on a single task or (ii) they are linear, very little is known about the closer-to-practice case of nonlinear NNs trained on multiple tasks. In this work, we present the first results proving that feature learning occurs during training with a nonlinear model on multiple tasks. Our key insight is that multi-task pretraining induces a pseudo-contrastive loss that favors representations that align points that typically have the same label across tasks. Using this observation, we show that when the tasks are binary classification tasks with labels depending on the projection of the data onto an r -dimensional subspace within the d≫r -dimensional input space, a simple gradient-based multitask learning algorithm on a two-layer ReLU NN recovers this projection, allowing for generalization to downstream tasks with sample and neuron complexity independent of d . In contrast, we show that with high probability over the draw of a single task, training on this single task cannot guarantee to learn all r ground-truth features.

| S-EPMC11486479 | biostudies-literature

Evidence for concrete but not abstract representation of length during spatial learning in rats.

Project description:In 4 experiments, rats had to discriminate between the lengths of 2 objects of the same color, black or white, before a test trial with the same objects but of opposite color. The experiments took place in a pool from which rats had to escape by swimming to 1 of 2 submerged platforms. For Experiments 1 and 2, the platforms were situated near the centers of panels of 1 length, but not another, that were pasted onto the gray walls of a square arena. The acquired preference for the correct length was eliminated by changing the color of the panels. In Experiment 3, the platforms were situated near the middle of the long walls of a rectangular pool, and in Experiment 4 they were situated in 1 pair of diagonally opposite corners of the same pool. Changing the color of the walls markedly disrupted the effects of the original training in both experiments. The results indicate that rats represent the length of objects not by their abstract, geometric attributes but in a more concrete fashion such as by a mental snapshot or by the amount of color stimulation they provide.

| S-EPMC4296930 | biostudies-other

Neural dissociation of food- and money-related reward processing using an abstract incentive delay task.

Project description:Food is an innate reward stimulus related to energy homeostasis and survival, whereas money is considered a more general reward stimulus that gains a rewarding value through learning experiences. Although the underlying neural processing for both modalities of reward has been investigated independently from one another, a more detailed investigation of neural similarities and/or differences between food and monetary reward is still missing. Here, we investigated the neural processing of food compared with monetary-related rewards in 27 healthy, normal-weight women using functional magnetic resonance imaging. We developed a task distinguishing between the anticipation and the receipt of either abstract food or monetary reward. Both tasks activated the ventral striatum during the expectation of a reward. Compared with money, greater food-related activations were observed in prefrontal, parietal and central midline structures during the anticipation and lateral orbitofrontal cortex (lOFC) during the receipt of food reward. Furthermore, during the receipt of food reward, brain activation in the secondary taste cortex was positively related to the body mass index. These results indicate that food-dependent activations encompass to a greater extent brain regions involved in self-control and self-reflection during the anticipation and phylogenetically older parts of the lOFC during the receipt of reward.

| S-EPMC4526483 | biostudies-literature

Homogenization of face neural representation during development.

Project description:Extensive studies have demonstrated that face processing ability develops gradually during development until adolescence. However, the underlying mechanism is unclear. One hypothesis is that children and adults represent faces in qualitatively different fashions with different group templates. An alternative hypothesis emphasizes the development as a quantitative change with a decrease of variation in representations. To test these hypotheses, we used between-participant correlation to measure activation pattern similarity both within and between late-childhood children and adults. We found that activation patterns for faces in the fusiform face area and occipital face area were less similar within the children group than within the adults group, indicating children had a greater variation in representing faces. Interestingly, the activation pattern similarity of children to their own group template was not significantly larger than that to adults' template, suggesting children and adults shared a template in representing faces. Further, the decrease in representation variance was likely a general principle in the ventral visual cortex, as a similar result was observed in a scene-selective region when perceiving scenes. Taken together, our study provides evidence that development of object representation may result from a homogenization process that shifts from greater variance in late-childhood to homogeneity in adults.

| S-EPMC8637318 | biostudies-literature

Abstract coding of audiovisual speech: beyond sensory representation.

Project description:Is there a neural representation of speech that transcends its sensory properties? Using fMRI, we investigated whether there are brain areas where neural activity during observation of sublexical audiovisual input corresponds to a listener's speech percept (what is "heard") independent of the sensory properties of the input. A target audiovisual stimulus was preceded by stimuli that (1) shared the target's auditory features (auditory overlap), (2) shared the target's visual features (visual overlap), or (3) shared neither the target's auditory or visual features but were perceived as the target (perceptual overlap). In two left-hemisphere regions (pars opercularis, planum polare), the target invoked less activity when it was preceded by the perceptually overlapping stimulus than when preceded by stimuli that shared one of its sensory components. This pattern of neural facilitation indicates that these regions code sublexical speech at an abstract level corresponding to that of the speech percept.

| S-EPMC2175551 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data