Simultaneous integration versus sequential sampling in multiplechoice decision

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 • Sequential sampling dependent upon active changes in attention for choice action

Decision making Simultaneous integration

Decision making Sequential Sampling

Decision making Sequential Sampling

Decision making Sequential Sampling

Decision making Sequential Sampling

Simultaneous Integration

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

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

Generalized N-choice model for perceptual decisions

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

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

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?

Neural data produces variable reaction times and decisions

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

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

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

Sequential Sampling

Neural activity integrates information from each gaze

Neural activity integrates information from each gaze A B

Neural activity integrates information from each gaze A B

Neural activity integrates information from each gaze A B

Neural activity integrates information from each gaze A B

Neural activity integrates information from each gaze A B

Neural activity integrates information from each gaze A B

Neural activity integrates information from each gaze A B

Neural activity integrates information from each gaze A B

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

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

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