Transient blend states and discrete agreement-driven errors in sentence production

Errors in subject-verb agreement are common in everyday language production. This has been studied using a preamble completion task in which a participant hears or reads a preamble containing inflected nouns and forms a complete English sentence (“The key to the cabinets” could be completed as The key to the cabinets is gold. ) Existing work has focused on errors arising in selecting the correct verb form for production in the presence of a more ‘local’ noun with different number features (The key to the cabinets are gold). However, the same paradigm elicits substantial numbers of preamble errors ( The key to the cabinets repeated as The key to the cabinet ) that existing theories have largely failed to address. We propose a Gradient Symbolic Computation (GSC) account of agreement and preamble errors. Sentence processing is modeled as a continuous-time, continuous-state stochastic dynamical system. Within this continuous representational space, a subset of states reflect discrete symbolic structures. The remainder are blend states where multiple symbols are simultaneously partially active. Initial phases of computation prefer blend states; an additional dynamic control parameter, commitment strength, pushes the model to discrete structures. This process, combined with stochastic gradient ascent dynamics respecting grammatical constraints on syntactic structures, yields discrete sentence outputs. We propose that transient blend states allow portions of target and non-target syntactic structures to interact, yielding both verb and preamble errors

Efficiency characterization of a large neuronal network: a causal information approach

When inhibitory neurons constitute about 40% of neurons they could have an important antinociceptive role, as they would easily regulate the level of activity of other neurons. We consider a simple network of cortical spiking neurons with axonal conduction delays and spike timing dependent plasticity, representative of a cortical column or hypercolumn with large proportion of inhibitory neurons. Each neuron fires following a Hodgkin-Huxley like dynamics and it is interconnected randomly to other neurons. The network dynamics is investigated estimating Bandt and Pompe probability distribution function associated to the interspike intervals and taking different degrees of inter-connectivity across neurons. More specifically we take into account the fine temporal ``structures'' of the complex neuronal signals not just by using the probability distributions associated to the inter spike intervals, but instead considering much more subtle measures accounting for their causal information: the Shannon permutation entropy, Fisher permutation information and permutation statistical complexity. This allows us to investigate how the information of the system might saturate to a finite value as the degree of inter-connectivity across neurons grows, inferring the emergent dynamical properties of the system.Comment: 26 pages, 3 Figures; Physica A, in pres