The Brain as Target Image Detector: The Role of Image Category and Presentation Time

Brouwer, A.; Erp, J.B.F. van; Kappé, B.; Urai, Anne E

doi:10.1007/978-3-642-21852-1_1

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…All four electrodes individually show significant differential activity post 250 ms. The ERP plot and the scalp map show the presence of the well known P300 component 40,41 which is known to occur in target detection [42][43][44] . The P300 is elicited when observers respond to a stimulus-driven task and is independent of senosory modality 45 .…”

Section: Erp Analysis Erp Analysis Was Performed On Average Referencmentioning

confidence: 99%

Wisdom of crowds benefits perceptual decision making across difficulty levels

Roy

Mazumder

Das

2021

Sci Rep

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Decades of research on collective decision making has claimed that aggregated judgment of multiple individuals is more accurate than expert individual judgement. A longstanding problem in this regard has been to determine how decisions of individuals can be combined to form intelligent group decisions. Our study consisted of a random target detection task in natural scenes, where human subjects (18 subjects, 7 female) detected the presence or absence of a random target as indicated by the cue word displayed prior to stimulus display. Concurrently the neural activities (EEG signals) were recorded. A separate behavioural experiment was performed by different subjects (20 subjects, 11 female) on the same set of images to categorize the tasks according to their difficulty levels. We demonstrate that the weighted average of individual decision confidence/neural decision variables produces significantly better performance than the frequently used majority pooling algorithm. Further, the classification error rates from individual judgement were found to increase with increasing task difficulty. This error could be significantly reduced upon combining the individual decisions using group aggregation rules. Using statistical tests, we show that combining all available participants is unnecessary to achieve minimum classification error rate. We also try to explore if group aggregation benefits depend on the correlation between the individual judgements of the group and our results seem to suggest that reduced inter-subject correlation can improve collective decision making for a fixed difficulty level.

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