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Simultaneous integration versus sequential sampling in multiple-choice decision making

Simultaneous integration versus sequential sampling in multiple-choice decision making. Nate Smith July 20, 2008. Decision making. A cognitive process of choosing an opinion or action between ≥ 2 choices Simultaneous integration accumulates evidence for both choices

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Simultaneous integration versus sequential sampling in multiple-choice decision making

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  1. Simultaneous integration versus sequential sampling in multiple-choice decision making Nate Smith July 20, 2008

  2. Decision making • A cognitive process of choosing an opinion or action between ≥2 choices • Simultaneous integration accumulates evidence for both choices • Sequential sampling dependent upon active changes in attention for choice action

  3. Decision makingSimultaneous integration

  4. Decision makingSequential Sampling

  5. Decision makingSequential Sampling

  6. Decision makingSequential Sampling

  7. Decision makingSequential Sampling

  8. Simultaneous Integration

  9. Accumulator models used in perceptual decision making Diffusion Model Leaky Competing Accumulator Model • Does not easily extend to N-choice • Does not retain ‘early’ information • Can a network of neurons produce N-choice behavior? Smith and Ratcliffe, 2004

  10. Reduced 2 variable model for perceptual discrimination Mean field approx. Simplified F-I curves Constant NS activity Slow NMDA gating variable Reduced two variable model Wong and Wang, 2006

  11. Generalized N-choice model for perceptual decisions

  12. Multiple alternative simultaneous integration decision making • Similar to previous random-dot motion tasks • Three directions of coherent motion • Subject has to saccade in direction of highest perceived motion (highest coherence) Niwa and Ditterich, 2008

  13. Performance dependent on overall motion Niwa and Ditterich, 2008 • Psychometric and reaction time data are more complex • Simpler mechanism for describing choice behavior?

  14. Research aims • Can a biophysically realistic neural mechanism reproduce results similar to the human psychophysics study? • Investigate whether the psychometric softmax function holds for N-choice tasks • What dynamics underlie N-choice decision making?

  15. Neural data produces variable reaction times and decisions

  16. 3-choice model fits human psychophysics data • Neural model is able to reproduce findings from 3-choice simultaneous integration task

  17. Theoretical psychometric softmax function fits data • Plotting for different coherence values matches up vs. softmax function

  18. Reaction time data Possible lateral inhibition/modulation in area MT responsible for scaling of input with multiple signals?

  19. Sequential Sampling

  20. Neural activity integrates information from each gaze

  21. Neural activity integrates information from each gaze A B

  22. Neural activity integrates information from each gaze A B

  23. Neural activity integrates information from each gaze A B

  24. Neural activity integrates information from each gaze A B

  25. Neural activity integrates information from each gaze A B

  26. Neural activity integrates information from each gaze A B

  27. Neural activity integrates information from each gaze A B

  28. Neural activity integrates information from each gaze A B

  29. First gaze biases selection and reaction time • First gaze increases chance of choosing an option when objects have equivalent value • Reaction time for objects with first gaze faster • Mean reaction time (ms) Probability

  30. Conclusions • Biophysically realistic reduced model replicates experimental data • Softmax function can work as a general underlying framework for decision making in neural circuits • Neural pools can retain and integrate information even in absence of fixation

  31. Acknowledgments Wang Lab Xiao-Jing Wang Alberto Bernacchia Tatiana Engel Morrie Furman John Murray Chung-Chuan Lo Christian Luhmann Jacinto Pereira Dahui Wang

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