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Record W2005585020 · doi:10.1371/journal.pbio.1001228

How the Brain Homes in on Valuable Objects

2011· letter· en· W2005585020 on OpenAlexaboutno aff
Janelle Weaver

Bibliographic record

VenuePLoS Biology · 2011
Typeletter
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyEvolutionary biologyComputational biology

Abstract

fetched live from OpenAlex

​OurOur visual surroundings are often crowded with information, and we must focus on the most crucial details to avoid being overwhelmed. The brain automatically detects and biases attention toward important and valuable features, such as the green flash of a traffic light and the shape and color of a dollar bill lying on the sidewalk. Although scientists have identified brain regions involved in either shifting attention or assessing the value of items, relatively little is known about the interplay between these regions. Three major subdivisions (colored) of the primate prefrontal cortex contain neurons guiding the focus of attention. Neurons at the intersection of these subdivisions (shaded) carry information about both the value and location of objects. In a paper published this week in PLoS Biology, a team led by Thilo Womelsdorf of York University in Toronto, Canada, addressed this issue by recording from neurons across a large portion of the monkey brain. They found that two regions—the ventromedial prefrontal cortex (vmPFC) and the anterior cingulate cortex (ACC)—are involved in guiding attention to the most valuable objects in a scene. The findings provide a more complete understanding of the roles of different brain areas and their interactions during this crucial task. In the study, the researchers trained two monkeys to perform a visual attention task. The animals fixated on a gray dot in the center of a screen and saw a circle filled with red and black stripes on one side and another circle with green and black stripes on the other side. Next, the color of the dot changed to red or green to cue the monkeys to pay attention to the corresponding pattern, which then rotated either clockwise or counterclockwise. The animals indicated the direction of motion by moving their eyes up or down, respectively, and they received juice for correct decisions. But the amount of the reward depended on the color of the pattern, and accuracy was higher for stimuli linked to a larger reward than for those associated with less juice. While the monkeys were engaged in the task, the researchers recorded from more than 1,000 neurons in the frontal and cingulate cortex. The activity of some neurons carried precise information about the location of the attended pattern, and these cells were clustered in the ACC, vmPFC, and lateral prefrontal cortex. A subset of these neurons showed transient changes in their firing rates immediately after the dot changed color, suggesting that they help to shift attention to task-relevant stimuli. By contrast, a separate set of ACC neurons showing delayed and prolonged responses could play a role in maintaining attention to relevant locations while filtering out distractions. The research team also examined how neuronal activity was affected by reward magnitude. Neurons that were sensitive to the value of the attended pattern formed a distinct set of clusters in the vmPFC and ACC, but some cells in these two regions responded to both the location and value of the stimulus. Because the ACC is connected with brain areas involved in directing eye gaze and planning other movements, and the vmPFC shares connections with areas that are critical for memory and emotions, these two regions are ideally positioned to integrate information about location and value to focus attention on the most important objects in the environment. Kaping D, Vinck M, Hutchison RM, Everling S, Womelsdorf T (2011) Specific Contributions of Ventromedial, Anterior Cingulate, and Lateral Prefrontal Cortex for Attentional Selection and Stimulus Valuation. doi:10.1371/journal.pbio.1001224

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.219
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.193
GPT teacher head0.296
Teacher spread0.103 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2011
Admission routes1
Has abstractyes

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