Es: the amount of points they obtained relative for the quantity they could have harvested had they 
chosen the criterion optimally primarily based on their stimulus sensitivity at each and every time point. As with all the monkeys 
in, all 4 participants harvested greater than in the points for extended delay conditions. For the two SB-366791 manufacturer longest delay circumstances their harvest prices are: :,:,:,:. On the other hand, for the two shortest delay circumstances, the rates are :,:,:,: indicating that they are considerably underbiased under these circumstances. We consider probable reasons for this underbias within the Discussion.with two altertives, the accumulation dymics is described by dy ({cy {bf z zI )dtz^dv, e dy ({cy {bf z zI )dtz^dv; eDymical ModelsMotivated by the dymics of the stimulus sensitivity and reward bias, we now explore a possible mechanism underlying the effect of reward on the decisionmaking process within the context of the leaky competing accumulator (LCA) model. We review the LCA model first and then implement and test the three hypotheses raised in the Introduction. PubMed ID:http://jpet.aspetjournals.org/content/141/2/161 This leads to several altertive accounts of the underbiasing of performance on trials at short delays.The Leaky Competing Accumulator Model and Its OneDimensiol ReductionIn the leaky competing accumulator model, noisy evidence for each altertive is accumulated over time in each accumulator. The accumulators compete with each other through mutual inhibition, and the accumulated evidence in each is subject to “leakage” or decay. To model our experiment in which participants have to respond promptly after a go cue, we assume that the go cue triggers a comparison of the activation of the two accumulators, and the response associated with the highest value is emitted, subject to a possible offset as discussed below. For our case ONE one.orgwhere y,y represent the activations of the accumulators, c,b are leak and inhibition strengths respectively, I,I are stimulus inputs to the two accumulators, dv,dv denote independent white noise with strength ^, and f z is a nonlinear inputoutput function e arising from the neural inspiration for the model. A neuron does not send outputs to other neurons when its activation goes below a certain level; above this level, its output can be approximated with a linear function of its activation. Motivated by this fact, we follow in using the threshold linear function. The value of the function f z is equal to its argument when the argument is above zero, but is equal to zero when the argument is below zero. By convention, we treat altertive as the positive altertive (associated with the high reward), and altertive as the negative altertive (associated with the low reward). The assumption that the participant chooses the response associated with the accumulator with the largest activation is equivalent to the assumption that the choice is determined by the sign of the activity FT011 custom synthesis difference y y {y at the moment the accumulators are interrogated. If yw(y wy ), the positive altertive is chosen, otherwise the negative altertive is chosen. Therefore, we only need to track the difference between the two accumulators y, hereafter referred to as the activation difference variable. Note that this variable is similar to the normalized evidence variable x from our alysis using sigl detection theory, but is not the same as that variable since it is not scaled in the units of its standard deviation. As long as the activities of the two units stay above zero, f z i yi, we can subtract Equation from, yi.Es: the number of points they obtained relative towards the quantity they could have harvested had they chosen the criterion optimally based on their stimulus sensitivity at each time point. As using the monkeys in, all 4 participants harvested more than in the points for long delay circumstances. For the two longest delay situations their harvest prices are: :,:,:,:. Nonetheless, for the two shortest delay circumstances, the rates are :,:,:,: indicating that they are considerably underbiased below these circumstances. We consider feasible reasons for this underbias in the Discussion.with two altertives, the accumulation dymics is described by dy ({cy {bf z zI )dtz^dv, e dy ({cy {bf z zI )dtz^dv; eDymical ModelsMotivated by the dymics of the stimulus sensitivity and reward bias, we now explore a possible mechanism underlying the effect of reward on the decisionmaking process within the context of the leaky competing accumulator (LCA) model. We review the LCA model first and then implement and test the three hypotheses raised in the Introduction. PubMed ID:http://jpet.aspetjournals.org/content/141/2/161 This leads to several altertive accounts of the underbiasing of performance on trials at short delays.The Leaky Competing Accumulator Model and Its OneDimensiol ReductionIn the leaky competing accumulator model, noisy evidence for each altertive is accumulated over time in each accumulator. The accumulators compete with each other through mutual inhibition, and the accumulated evidence in each is subject to “leakage” or decay. To model our experiment in which participants have to respond promptly after a go cue, we assume that the go cue triggers a comparison of the activation of the two accumulators, and the response associated with the highest value is emitted, subject to a possible offset as discussed below. For our case ONE one.orgwhere y,y represent the activations of the accumulators, c,b are leak and inhibition strengths respectively, I,I are stimulus inputs to the two accumulators, dv,dv denote independent white noise with strength ^, and f z is a nonlinear inputoutput function e arising from the neural inspiration for the model. A neuron does not send outputs to other neurons when its activation goes below a certain level; above this level, its output can be approximated with a linear function of its activation. Motivated by this fact, we follow in using the threshold linear function. The value of the function f z is equal to its argument when the argument is above zero, but is equal to zero when the argument is below zero. By convention, we treat altertive as the positive altertive (associated with the high reward), and altertive as the negative altertive (associated with the low reward). The assumption that the participant chooses the response associated with the accumulator with the largest activation is equivalent to the assumption that the choice is determined by the sign of the activity difference y y {y at the moment the accumulators are interrogated. If yw(y wy ), the positive altertive is chosen, otherwise the negative altertive is chosen. Therefore, we only need to track the difference between the two accumulators y, hereafter referred to as the activation difference variable. Note that this variable is similar to the normalized evidence variable x from our alysis using sigl detection theory, but is not the same as that variable since it is not scaled in the units of its standard deviation. As long as the activities of the two units stay above zero, f z i yi, we can subtract Equation from, yi.
