Modeling the emergence of informational content by adaptive networks for temporal factorisation and criterial causation

Propagated activation of neurons through their network is an important process in the brain. Another crucial part of neural processing concerns adaptation over time of characteristics of this network such as connection strengths or excitability thresholds. This adaptation can be slow, as in learning from a multiple experiences, or it can be fast, as in memory formation. These adaptive network characteristics can be considered informational criteria for activation of a neuron. This then is viewed as a form of emergent information formation. Activation of neurons is determined by such information via a process termed criterial causation. In the current paper, the relationship of criterial causation with the principle of temporal factorisation for the dynamics of the world in general is explored. Temporal factorisation describes how the world represents information about its past in its present state, which then in turn determines the world’s future. In the paper, it is shown how these processes are analysed in more detail and modeled by (adaptive) network models.     

The Learning Signal in Perceptual Tuning of Speech: Bottom Up Versus Top-Down Information

Cognitive systems face a tension between stability and plasticity. The maintenance of long-term representations that reflect the global regularities of the environment is often at odds with pressure to flexibly adjust to short-term input regularities that may deviate from the norm. This tension is abundantly clear in speech communication when talkers with accents or dialects produce input that deviates from a listener's language community norms. Prior research demonstrates that when bottom-up acoustic information or top-down word knowledge is available to disambiguate speech input, there is short-term adaptive plasticity such that subsequent speech perception is shifted even in the absence of the disambiguating information. Although such effects are well-documented, it is not yet known whether bottom-up and top-down resolution of ambiguity may operate through common processes, or how these information sources may interact in guiding the adaptive plasticity of speech perception. The present study investigates the joint contributions of bottom-up information from the acoustic signal and top-down information from lexical knowledge in the adaptive plasticity of speech categorization according to short-term input regularities. The results implicate speech category activation, whether from top-down or bottom-up sources, in driving rapid adjustment of listeners' reliance on acoustic dimensions in speech categorization. Broadly, this pattern of perception is consistent with dynamic mapping of input to category representations that is flexibly tuned according to interactive processing accommodating both lexical knowledge and idiosyncrasies of the acoustic input.