EVENT DETAILS AND ABSTRACT

Title: The Morton-Massaro Law of Information Integration: Implications for Models of Perception
Speaker: Javier Movellan
Speaker Info: UCSD
Brief Description:
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Abstract:

Information integration may be studied by analyzing the effect of two or more sources (e.g., auditory and visual) on subjects' responses. Experiments show that ratios of response probabilities often factorize into components selectively influenced by only one source (e.g., one component affected by the acoustic source and another one affected by the visual source). We call this the Morton-Massaro law (MML). We find conditions where the law is optimal and note that it reflects an implicit assumption about the statistics of the environment. Adherence to the MML can be used to assess whether the assumption is being made, and analyses of natural stimuli can be used to determine whether the assumption is reasonable. An important issue raised by the Morton--Massaro law is its potential incompatibility with interactive models of perception, i.e, models in which all the processing units may be coupled via feed-back and lateral connections. We present a class of interactive models governed by stochastic differential equations, and show that the Morton--Massaro law does not rule out either feed-back or lateral connections. Instead it is related to an architectural constraint that we called channel separability. We show how this constraint can be used to analyze how neurons integrate the effects of stimulus (classical receptive field) and context (non-classical receptive field).