scholarly journals How usefulness shapes neural representations during goal-directed behavior

2021 ◽  
Vol 7 (15) ◽  
pp. eabd5363
Author(s):  
G. Castegnetti ◽  
M. Zurita ◽  
B. De Martino
Keyword(s):  
Prefrontal Cortex ◽  
Neural Representation ◽  
Coding Scheme ◽  
Neural Representations ◽  
Specific Goal ◽  
Sensory Cortices ◽  
Flexible Behavior ◽  
Goal Directed Behavior ◽  
The Brain

Value is often associated with reward, emphasizing its hedonic aspects. However, when circumstances change, value must also change (a compass outvalues gold, if you are lost). How are value representations in the brain reshaped under different behavioral goals? To answer this question, we devised a new task that decouples usefulness from its hedonic attributes, allowing us to study flexible goal-dependent mapping. Here, we show that, unlike sensory cortices, regions in the prefrontal cortex (PFC)—usually associated with value computation—remap their representation of perceptually identical items according to how useful the item has been to achieve a specific goal. Furthermore, we identify a coding scheme in the PFC that represents value regardless of the goal, thus supporting generalization across contexts. Our work questions the dominant view that equates value with reward, showing how a change in goals triggers a reorganization of the neural representation of value, enabling flexible behavior.

scholarly journals The dimensionality of neural representations for control

2020 ◽  
Author(s):  
David Badre ◽  
Apoorva Bhandari ◽  
Haley Keglovits ◽  
Atsushi Kikumoto
Keyword(s):  
Prefrontal Cortex ◽  
Cognitive Control ◽  
Neural Computation ◽  
Neural Representation ◽  
Future Research ◽  
Trade Off ◽  
Neural Representations ◽  
Open Questions ◽  
The Brain ◽  
Review Current

Cognitive control allows us to think and behave flexibly based on our context and goals. At the heart of theories of cognitive control is a control representation that enables the same input to produce different outputs contingent on contextual factors. In this review, we focus on an important property of the control representation’s neural code: its representational dimensionality. Dimensionality of a neural representation balances a basic separability/generalizability trade-off in neural computation. We will discuss the implications of this trade-off for cognitive control. We will then briefly review current neuroscience findings regarding the dimensionality of control representations in the brain, particularly the prefrontal cortex. We conclude by highlighting open questions and crucial directions for future research.

scholarly journals Parallel representation of context and multiple context-dependent values in ventro-medial prefrontal cortex

2021 ◽  
Author(s):  
Nir Moneta ◽  
Mona M. Garvert ◽  
Hauke R. Heekeren ◽  
Nicolas W Schuck
Keyword(s):  
Prefrontal Cortex ◽  
Competing Values ◽  
Task Goal ◽  
Neural Representations ◽  
Multiple Context ◽  
Co Activation ◽  
Current Task ◽  
Goal Directed Behavior ◽  
Multiple Task ◽  
Influence Behavior

Value representations in ventromedial prefrontal-cortex (vmPFC) are known to guide decisions. But how preferable available options are depends on one's current task. Goal-directed behavior, which involves changing between different task-contexts, therefore requires to know how valuable the same options will be in different contexts. We tested whether multiple task-dependent values influence behavior and asked if they are integrated into a single value representation or are co-represented in parallel within vmPFC signals. Thirty five participants alternated between tasks in which stimulus color or motion predicted rewards. Our results provide behavioral and neural evidence for co-activation of both contextually-relevant and -irrelevant values, and suggest a link between multivariate neural representations and the influence of the irrelevant context and its associated value on behavior. Importantly, current task context could be decoded from the same region, and better context-decodability was associated with stronger (relevant-)value representations. Evidence for choice conflicts was found only in the motor cortex, where the competing values are likely resolved into action.

scholarly journals Subjective value and decision entropy are jointly encoded by aligned gradients across the human brain

2020 ◽  
Author(s):  
Sebastian Bobadilla-Suarez ◽  
Olivia Guest ◽  
Bradley C. Love
Keyword(s):  
Prefrontal Cortex ◽  
Human Brain ◽  
Ventromedial Prefrontal Cortex ◽  
Neural Representation ◽  
Retrospective Evaluation ◽  
Systematic Fashion ◽  
Subjective Value ◽  
Fmri Analysis ◽  
The Relationship ◽  
The Brain

AbstractRecent work has considered the relationship between value and confidence in both behavior and neural representation. Here we evaluated whether the brain organizes value and confidence signals in a systematic fashion that reflects the overall desirability of options. If so, regions that respond to either increases or decreases in both value and confidence should be widespread. We strongly confirmed these predictions through a model-based fMRI analysis of a mixed gambles task that assessed subjective value (SV) and inverse decision entropy (iDE), which is related to confidence. Purported value areas more strongly signalled iDE than SV, underscoring how intertwined value and confidence are. A gradient tied to the desirability of actions transitioned from positive SV and iDE in ventromedial prefrontal cortex to negative SV and iDE in dorsal medial prefrontal cortex. This alignment of SV and iDE signals could support retrospective evaluation to guide learning and subsequent decisions.

scholarly journals Task dependence of neural representations of global scene properties but not scene categories in the prefrontal cortex

2020 ◽  
Author(s):  
Yaelan Jung ◽  
Dirk B. Walther
Keyword(s):  
Prefrontal Cortex ◽  
Visual Cortex ◽  
Scene Perception ◽  
Sound Level ◽  
Task Context ◽  
Complex Scene ◽  
Neural Representations ◽  
Task Conditions ◽  
Scene Content ◽  
The Brain

AbstractNatural scenes deliver rich sensory information about the world. Decades of research has shown that the scene-selective network in the visual cortex represents various aspects of scenes. It is, however, unknown how such complex scene information is processed beyond the visual cortex, such as in the prefrontal cortex. It is also unknown how task context impacts the process of scene perception, modulating which scene content is represented in the brain. In this study, we investigate these questions using scene images from four natural scene categories, which also depict two types of global scene properties, temperature (warm or cold), and sound-level (noisy or quiet). A group of healthy human subjects from both sexes participated in the present study using fMRI. In the study, participants viewed scene images under two different task conditions; temperature judgment and sound-level judgment. We analyzed how different scene attributes (scene categories, temperature, and sound-level information) are represented across the brain under these task conditions. Our findings show that global scene properties are only represented in the brain, especially in the prefrontal cortex, when they are task-relevant. However, scene categories are represented in the brain, in both the parahippocampal place area and the prefrontal cortex, regardless of task context. These findings suggest that the prefrontal cortex selectively represents scene content according to task demands, but this task selectivity depends on the types of scene content; task modulates neural representations of global scene properties but not of scene categories.

scholarly journals Schema support for forming inferences in the human brain

2021 ◽  
Author(s):  
John Philippe Paulus ◽  
Carlo Vignali ◽  
Marc N Coutanche
Keyword(s):  
Prefrontal Cortex ◽  
Human Brain ◽  
Medial Prefrontal Cortex ◽  
Neural Representations ◽  
Memory Integration ◽  
New Information ◽  
Neural Integration ◽  
Two Factors ◽  
Memory Schema ◽  
The Brain

Associative inference, the process of drawing novel links between existing knowledge to rapidly integrate associated information, is supported by the hippocampus and neocortex. Within the neocortex, the medial prefrontal cortex (mPFC) has been implicated in the rapid cortical learning of new information that is congruent with an existing framework of knowledge, or schema. How the brain integrates associations to form inferences, specifically how inferences are represented, is not well understood. In this study, we investigate how the brain uses schemas to facilitate memory integration in an associative inference paradigm (A-B-C-D). We conducted two event-related fMRI experiments in which participants retrieved previously learned direct (AB, BC, CD) and inferred (AC, AD) associations between word pairs for items that are schema congruent or incongruent. Additionally, we investigated how two factors known to affect memory, a delay with sleep, and reward, modulate the neural integration of associations within, and between, schema. Schema congruency was found to benefit the integration of associates, but only when retrieval immediately follows learning. RSA revealed that neural patterns of inferred pairs (AC) in the PHc, mPFC, and posHPC were more similar to their constituents (AB and BC) when the items were schema congruent, suggesting that schema facilitates the assimilation of paired items into a single inferred unit containing all associated elements. Furthermore, a delay with sleep, but not reward, impacted the assimilation of inferred pairs. Our findings reveal that the neural representations of overlapping associations are integrated into novel representations through the support of memory schema.

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 Insights into Early Word Comprehension: Tracking the Neural Representations of Word Semantics in Infants.

2021 ◽  
Author(s):  
Rohan Saha ◽  
Jennifer Campbell ◽  
Janet F. Werker ◽  
Alona Fyshe
Keyword(s):  
Brain Activity ◽  
Word Meaning ◽  
Age Groups ◽  
Event Related Potential ◽  
Neural Representation ◽  
Machine Learning Techniques ◽  
Semantic Representations ◽  
Word Comprehension ◽  
Neural Representations ◽  
The Brain

Infants start developing rudimentary language skills and can start understanding simple words well before their first birthday. This development has also been shown primarily using Event Related Potential (ERP) techniques to find evidence of word comprehension in the infant brain. While these works validate the presence of semantic representations of words (word meaning) in infants, they do not tell us about the mental processes involved in the manifestation of these semantic representations or the content of the representations. To this end, we use a decoding approach where we employ machine learning techniques on Electroencephalography (EEG) data to predict the semantic representations of words found in the brain activity of infants. We perform multiple analyses to explore word semantic representations in two groups of infants (9-month-old and 12-month-old). Our analyses show significantly above chance decodability of overall word semantics, word animacy, and word phonetics. As we analyze brain activity, we observe that participants in both age groups show signs of word comprehension immediately after word onset, marked by our model's significantly above chance word prediction accuracy. We also observed strong neural representations of word phonetics in the brain data for both age groups, some likely correlated to word decoding accuracy and others not. Lastly, we discover that the neural representations of word semantics are similar in both infant age groups. Our results on word semantics, phonetics, and animacy decodability, give us insights into the evolution of neural representation of word meaning in infants.

scholarly journals How the Brain Converts Negative Evaluation into Performance Facilitation

2017 ◽  
Vol 28 (2) ◽  
pp. 602-611 ◽  
Author(s):  
Charlotte Prévost ◽  
Hakwan Lau ◽  
Dean Mobbs
Keyword(s):  
Brain Regions ◽  
Negative Evaluation ◽  
Neural Representation ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Success Stories ◽  
Goal Directed Behavior ◽  
The Brain ◽  
Insight Into ◽  
Recurrent Theme

Abstract Surpassing negative evaluation is a recurrent theme of success stories. Yet, there is little evidence supporting the counterintuitive idea that negative evaluation might not only motivate people, but also enhance performance. To address this question, we designed a task that required participants to decide whether taking up a risky challenge after receiving positive or negative evaluations from independent judges. Participants believed that these evaluations were based on their prior performance on a related task. Results showed that negative evaluation caused a facilitation in performance. Concurrent functional magnetic resonance imaging revealed that the motivating effect of negative evaluation was represented in the insula and striatum, while the performance boost was associated with functional positive connectivity between the insula and a set of brain regions involved in goal-directed behavior and the orienting of attention. These findings provide new insight into the neural representation of negative evaluation-induced facilitation.

scholarly journals Attentionally modulated subjective images reconstructed from brain activity

2020 ◽  
Author(s):  
Tomoyasu Horikawa ◽  
Yukiyasu Kamitani
Keyword(s):  
Image Reconstruction ◽  
Visual Experience ◽  
Brain Activity ◽  
Visual Image ◽  
Neural Representation ◽  
Visual Images ◽  
Top Down ◽  
Neural Representations ◽  
Deep Image ◽  
The Brain

SummaryVisual image reconstruction from brain activity produces images whose features are consistent with the neural representations in the visual cortex given arbitrary visual instances [1–3], presumably reflecting the person’s visual experience. Previous reconstruction studies have been concerned either with how stimulus images are faithfully reconstructed or with whether mentally imagined contents can be reconstructed in the absence of external stimuli. However, many lines of vision research have demonstrated that even stimulus perception is shaped both by stimulus-induced processes and top-down processes. In particular, attention (or the lack of it) is known to profoundly affect visual experience [4–8] and brain activity [9–21]. Here, to investigate how top-down attention impacts the neural representation of visual images and the reconstructions, we use a state-of-the-art method (deep image reconstruction [3]) to reconstruct visual images from fMRI activity measured while subjects attend to one of two images superimposed with equally weighted contrasts. Deep image reconstruction exploits the hierarchical correspondence between the brain and a deep neural network (DNN) to translate (decode) brain activity into DNN features of multiple layers, and then create images that are consistent with the decoded DNN features [3, 22, 23]. Using the deep image reconstruction model trained on fMRI responses to single natural images, we decode brain activity during the attention trials. Behavioral evaluations show that the reconstructions resemble the attended rather than the unattended images. The reconstructions can be modeled by superimposed images with contrasts biased to the attended one, which are comparable to the appearance of the stimuli under attention measured in a separate session. Attentional modulations are found in a broad range of hierarchical visual representations and mirror the brain–DNN correspondence. Our results demonstrate that top-down attention counters stimulus-induced responses and modulate neural representations to render reconstructions in accordance with subjective appearance. The reconstructions appear to reflect the content of visual experience and volitional control, opening a new possibility of brain-based communication and creation.

scholarly journals Decoding the Brain: Neural Representation and the Limits of Multivariate Pattern Analysis in Cognitive Neuroscience

2017 ◽  
Author(s):  
J. Brendan Ritchie ◽  
David Michael Kaplan ◽  
Colin Klein
Keyword(s):  
Cognitive Neuroscience ◽  
Strong Evidence ◽  
Neural Activity ◽  
Pattern Analysis ◽  
Neural Representation ◽  
Neural Decoding ◽  
Multivariate Pattern Analysis ◽  
Neural Representations ◽  
Multivariate Pattern ◽  
The Brain

AbstractSince its introduction, multivariate pattern analysis (MVPA), or “neural decoding”, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the Decoder’s Dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the Dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the Dictum, arguing that it is false: decodability is a poor guide for revealing the content of neural representations. However, we also suggest how the Dictum can be improved on, in order to better justify inferences about neural representation using MVPA.

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