Modelling Visual Detection: Luminance Response Non-linearity and Internal Noise

Abstract

Two experiments that investigate the effect of various display factors on the detectability of a thin line signal in random visual noise are described. Three statistical decision models are described, together with their ability to account for the results. The first is an "ideal detector" model, the second an "energy integrator" model, and the third a model based upon the operation of retinal ganglion cells which incorporates a gain control mechanism. The ideal detector model fails to give a good account of human performance, whereas the other two models provide a good fit to the data. The digital Laplacian with gain control model has the slight advantage over the energy integrating model in being able to account for a small superiority in the detection of dark as opposed to bright signals. Finally, both models require the inclusion of an estimate of the internal noise of the human visual system to account for the pattern of performance observed under changing conditions of display contrast.

Predicting behavioural responses to novel organisms: state-dependent detection theory

“…Signal detection theory (also known simply as detection theory [23,24]) allows optimal behavioural decisions to be identified in a range of circumstances. The standard model assumes that there are two (mutually exclusive) environmental conditions and two possible actions, with payoffs depending on the condition and the choice of action.…”

“…To predict not just behavioural responses to SBAD, but also the effects of these responses on fitness, we develop a new modelling framework: 'state-dependent detection theory' (SDDT) which makes use of signal detection theory [23,24] in a state-dependent manner [25]. The method assesses: (i) how the optimal response threshold (governing behaviour) in each time unit depends on the organism's state (e.g.…”

What visual illusions tell us about underlying neural mechanisms and observer strategies for tackling the inverse problem of achromatic perception

“…Nearly 150 years ago Mach (1865, in Ratliff, 1965 ) and Hering ( 1874/1964 ) advocated what has come to be called a “low-level” account of simultaneous contrast based on reciprocal interactions between elements in the nervous system. This view was later represented by early models of lateral inhibitory interactions such as those performed by the center-surround receptive fields of neurons in the retina and lateral geniculate nucleus (LGN) of cats and primates (for reviews see: Heinemann, 1972 ; Jameson and Hurvich, 1989 ; Kingdom and Moulden, 1989 ; Fiorentini et al, 1990 ; Kingdom, 1997 , 2003 , 2011 , 2013 ; Fiorentini, 2003 ) and is today represented by modern multiscale filtering models such as the ODOG model (Blakeslee and McCourt, 1999 ; Robinson et al, 2007 ), as well as by models positing the filling-in (Grossberg and Todorovic, 1988 ; Rossi and Paradiso, 2003 ) of the representation of a test patch by information extracted by relatively high-frequency filters (small receptive fields) at the edges of the patch or at multiple edges within the stimulus (Rudd, 2010 , 2013 , 2014 ).…”