This general hypothesis Bleomycin purchase has been formalized in a model in which a search salience representation provides evidence that is accumulated by movement neurons to initiate a response (Purcell et al., 2010, 2012). This model utilizes gating inhibition to establish a criterion level of evidence representation necessary to begin response accumulation. It was demonstrated that SAT could be accomplished by elevating this gate to delay RT (Purcell et al., 2012). Our findings of the modulation of the salience representation

in visual neurons and the direction of modulation of movement neuron activity were not anticipated by this or any other stochastic accumulator model. NVP-BGJ398 mw The iA model reconciles the stochastic accumulator model framework with the neural data. The model is inspired by the insight that characteristics of postdecision motor processes constrain the stochastic decision accumulation process and is anchored on invariance at the beginning of the ballistic motor process. Variation in saccade velocity arises

from variation in the magnitude of presaccadic movement activity (van Opstal and Goossens, 2008) and of OPN hyperpolarization (Yoshida et al., 1999). We found no variation of saccade velocity across the large variation of RT across SAT conditions. Hence, the magnitude of neural activity triggering the saccades Oxygenase must be invariant. The iA model achieves that invariance by integrating through time the evidence accumulator. We discovered that the slower accumulation to a lower terminal level in the Accurate condition integrated to the same value as the faster accumulation to a higher terminal level in the Fast condition. This leaky integration is regarded as a proxy for the net hyperpolarization of the OPNs that prevent saccade

generation. The iA model architecture fit the performance measures as well as the typical LBA model while replicating key characteristics of the neural modulation. Recordings of SC and OPNs will be critical tests of this model. The iA model is not proposed as a replacement for conventional accumulator models; it simply proves that the architecture embodied by the model is plausible. In fact, iA and LBA are mirrors of each other that emphasize different assumptions or aspects of the accumulation and response process. The mimicry of computational models with different architectures is well known (Dzhafarov, 1993; Ratcliff et al., 1999; Usher and McClelland, 2001; Ratcliff and Smith, 2004) and represents a fundamental problem of exclusively computational accounts (Moore, 1956). The apparent incompatibility of stochastic accumulator models and the underlying neurophysiology exposes another important theoretical issue.