Ed by free of charge parameter Ir. The initial situation hypothesis, HIC, assumes that reward info affects the initial situation or starting point in the course of action by the amount Yr. Within the complete framework of your LCA model, this could result from a greater starting point of accumulator, or a reduced starting point of accumulator, or both. This hypothesis differs in the initial a single in that reward info enters the Larotrectinib sulfate MedChemExpress THS-044 accumulation procedure only at or ahead of the stimulus onset. Mechanistically the reward effect might be thought of as obtaining been topic to integration before the stimulus onset with all the integration termiting when the stimulus turns on, or possibly 
before that time. This impact then follows the dymics of your system in this hypothesis. Mathematically, the imply of the activation distinction variable is changed 
to m(t) aS ({e{l(t{T ) )zYr e{l(t{T ), l where the dymic effect of the reward is represented by Yr e{l(t{T ). The value of the parameter Yr denotes the overall strength of the reward effect.Integration of Reward and PubMed ID:http://jpet.aspetjournals.org/content/141/2/161 Stimulus InformationIn the fixed offset hypothesis, HFO, reward affects the decision independently of the sensory accumulation process. The reward effect is therefore treated as a constant offset of the activation difference variable whose mean value is changed to: m(t) aS ({e{l(t{T ) )zCr : l According to this hypothesis, the accumulators only accumulate evidence from the stimulus, and the reward information is essentially processed separately, without interacting with the dymics of stimulus integration. This is quite different from the situation in the other two hypotheses, where decisions are completely determined by the activity of the accumulator, and reward and stimulus both influence the processing dymics. So far, we have quantified the reward effect on the mean of the activation difference variable averaged across trials m(t). However, response probability is determined by variability s(t) as well as by the mean, as previously discussed. One source of noise is variability in the initial state of the activation difference variable, with standard deviation s. The other source is the noise intrinsic to the dymics of the process itself, with standard deviation e. The absolute noise level is not measurable in the current experiment because response probability results from the sigl to noise ratio. For this reason, we can fix the strength of the intrinsic noise at a specific value, and we set e without loss of generality. The fitted values for other free parameters can therefore be viewed as relative to the value of the intrinsic noise level e. We now summarize the predictions of the three hypotheses on response probabilities. The probability of choosing the higher reward is determined by the ratio between the mean and the standard deviation of the activation difference variable, which both evolve with time. Thanks to the linearity of the OU model, it is a linear combition of a stimulus term and a reward term. These hypotheses share the same stimulus term, Equation, and they have their unique reward terms Ir ({e{l(t{T z:) ) l : rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi; s e{l(t{T ) z ({e{l(t{T ) ) leach participant. Four of them are shared across the three hypotheses: l which determines the dymics of the system (the sign of l determines whether the process is l.Ed by absolutely free parameter Ir. The initial situation hypothesis, HIC, assumes that reward information and facts impacts the initial situation or starting point from the course of action by the quantity Yr. In the complete framework of the LCA model, this could outcome from a higher beginning point of accumulator, or a reduced beginning point of accumulator, or each. This hypothesis differs from the 1st one in that reward facts enters the accumulation process only at or before the stimulus onset. Mechanistically the reward effect may be believed of as obtaining been subject to integration before the stimulus onset using the integration termiting when the stimulus turns on, or possibly just before that time. This impact then follows the dymics from the system within this hypothesis. Mathematically, the imply from the activation difference variable is changed to m(t) aS ({e{l(t{T ) )zYr e{l(t{T ), l where the dymic effect of the reward is represented by Yr e{l(t{T ). The value of the parameter Yr denotes the overall strength of the reward effect.Integration of Reward and PubMed ID:http://jpet.aspetjournals.org/content/141/2/161 Stimulus InformationIn the fixed offset hypothesis, HFO, reward affects the decision independently of the sensory accumulation process. The reward effect is therefore treated as a constant offset of the activation difference variable whose mean value is changed to: m(t) aS ({e{l(t{T ) )zCr : l According to this hypothesis, the accumulators only accumulate evidence from the stimulus, and the reward information is essentially processed separately, without interacting with the dymics of stimulus integration. This is quite different from the situation in the other two hypotheses, where decisions are completely determined by the activity of the accumulator, and reward and stimulus both influence the processing dymics. So far, we have quantified the reward effect on the mean of the activation difference variable averaged across trials m(t). However, response probability is determined by variability s(t) as well as by the mean, as previously discussed. One source of noise is variability in the initial state of the activation difference variable, with standard deviation s. The other source is the noise intrinsic to the dymics of the process itself, with standard deviation e. The absolute noise level is not measurable in the current experiment because response probability results from the sigl to noise ratio. For this reason, we can fix the strength of the intrinsic noise at a specific value, and we set e without loss of generality. The fitted values for other free parameters can therefore be viewed as relative to the value of the intrinsic noise level e. We now summarize the predictions of the three hypotheses on response probabilities. The probability of choosing the higher reward is determined by the ratio between the mean and the standard deviation of the activation difference variable, which both evolve with time. Thanks to the linearity of the OU model, it is a linear combition of a stimulus term and a reward term. These hypotheses share the same stimulus term, Equation, and they have their unique reward terms Ir ({e{l(t{T z:) ) l : rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi; s e{l(t{T ) z ({e{l(t{T ) ) leach participant. Four of them are shared across the three hypotheses: l which determines the dymics of the system (the sign of l determines whether the process is l.