Balancing true and false detection of intermittent sensory targets by adjusting the inputs to the evidence accumulation process
Decisions about noisy stimuli are widely understood to be made by accumulating evidence up to a decision bound that can be adjusted according to task demands. However, relatively little is known about how such mechanisms operate in continuous monitoring contexts requiring intermittent target detecti...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2023-08-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/83025 |
| _version_ | 1827801003359993856 |
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| author | Anna C Geuzebroek Hannah Craddock Redmond G O'Connell Simon P Kelly |
| author_facet | Anna C Geuzebroek Hannah Craddock Redmond G O'Connell Simon P Kelly |
| author_sort | Anna C Geuzebroek |
| collection | DOAJ |
| description | Decisions about noisy stimuli are widely understood to be made by accumulating evidence up to a decision bound that can be adjusted according to task demands. However, relatively little is known about how such mechanisms operate in continuous monitoring contexts requiring intermittent target detection. Here, we examined neural decision processes underlying detection of 1 s coherence targets within continuous random dot motion, and how they are adjusted across contexts with weak, strong, or randomly mixed weak/strong targets. Our prediction was that decision bounds would be set lower when weak targets are more prevalent. Behavioural hit and false alarm rate patterns were consistent with this, and were well captured by a bound-adjustable leaky accumulator model. However, beta-band EEG signatures of motor preparation contradicted this, instead indicating lower bounds in the strong-target context. We thus tested two alternative models in which decision-bound dynamics were constrained directly by beta measurements, respectively, featuring leaky accumulation with adjustable leak, and non-leaky accumulation of evidence referenced to an adjustable sensory-level criterion. We found that the latter model best explained both behaviour and neural dynamics, highlighting novel means of decision policy regulation and the value of neurally informed modelling. |
| first_indexed | 2024-03-11T20:18:43Z |
| format | Article |
| id | doaj.art-ef9da00187a8444daefc47a0489c6910 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-03-11T20:18:43Z |
| publishDate | 2023-08-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-ef9da00187a8444daefc47a0489c69102023-10-03T09:56:18ZengeLife Sciences Publications LtdeLife2050-084X2023-08-011210.7554/eLife.83025Balancing true and false detection of intermittent sensory targets by adjusting the inputs to the evidence accumulation processAnna C Geuzebroek0https://orcid.org/0000-0002-8287-2990Hannah Craddock1Redmond G O'Connell2https://orcid.org/0000-0001-6949-2793Simon P Kelly3https://orcid.org/0000-0001-9983-3595School of Electrical and Electronic Engineering and UCD Centre for Biomedical Engineering, University College Dublin, Dublin, IrelandSchool of Electrical and Electronic Engineering and UCD Centre for Biomedical Engineering, University College Dublin, Dublin, Ireland; Department of Statistics, University of Warwick, Warwick, United KingdomTrinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, Dublin, IrelandSchool of Electrical and Electronic Engineering and UCD Centre for Biomedical Engineering, University College Dublin, Dublin, IrelandDecisions about noisy stimuli are widely understood to be made by accumulating evidence up to a decision bound that can be adjusted according to task demands. However, relatively little is known about how such mechanisms operate in continuous monitoring contexts requiring intermittent target detection. Here, we examined neural decision processes underlying detection of 1 s coherence targets within continuous random dot motion, and how they are adjusted across contexts with weak, strong, or randomly mixed weak/strong targets. Our prediction was that decision bounds would be set lower when weak targets are more prevalent. Behavioural hit and false alarm rate patterns were consistent with this, and were well captured by a bound-adjustable leaky accumulator model. However, beta-band EEG signatures of motor preparation contradicted this, instead indicating lower bounds in the strong-target context. We thus tested two alternative models in which decision-bound dynamics were constrained directly by beta measurements, respectively, featuring leaky accumulation with adjustable leak, and non-leaky accumulation of evidence referenced to an adjustable sensory-level criterion. We found that the latter model best explained both behaviour and neural dynamics, highlighting novel means of decision policy regulation and the value of neurally informed modelling.https://elifesciences.org/articles/83025decision makingdetectionEEGneurally informed modelling |
| spellingShingle | Anna C Geuzebroek Hannah Craddock Redmond G O'Connell Simon P Kelly Balancing true and false detection of intermittent sensory targets by adjusting the inputs to the evidence accumulation process eLife decision making detection EEG neurally informed modelling |
| title | Balancing true and false detection of intermittent sensory targets by adjusting the inputs to the evidence accumulation process |
| title_full | Balancing true and false detection of intermittent sensory targets by adjusting the inputs to the evidence accumulation process |
| title_fullStr | Balancing true and false detection of intermittent sensory targets by adjusting the inputs to the evidence accumulation process |
| title_full_unstemmed | Balancing true and false detection of intermittent sensory targets by adjusting the inputs to the evidence accumulation process |
| title_short | Balancing true and false detection of intermittent sensory targets by adjusting the inputs to the evidence accumulation process |
| title_sort | balancing true and false detection of intermittent sensory targets by adjusting the inputs to the evidence accumulation process |
| topic | decision making detection EEG neurally informed modelling |
| url | https://elifesciences.org/articles/83025 |
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