A computational approach to negative priming

Abstract

Priming is characterized by a sensitivity of reaction times to the sequence of stimuli in psychophysical experiments. The reduction of the reaction time observed in positive priming is well-known and experimentally understood [Scarborough et al., 1977]. Negative primingthe opposite effect -is experimentally less tangible [Fox, 1995]. The dependence on subtle parameter changes (such as response-stimulus interval) usually varies. The sensitivity of the negative priming effect bears great potential for applications in research in fields such as memory, selective attention, and aging effects.We develop and analyze a computational realization, CISAM, of a recent psychological model for action decision making, the ISAM [Kabisch, 2003], which is sensitive to priming conditions. With the dynamical systems approach of the CISAM, we show that a single adaptive threshold mechanism is sufficient to explain both positive and negative priming effects. This is achieved by comparing results obtained by the computational modeling with experimental data from our lab. The implementation provides a rich base from which testable predictions can be derived, e.g. with respect to hitherto untested stimulus-combinations (e.g. single-object trials).

Inhibition in the Dynamics of Selective Attention: An Integrative Model for Negative Priming

“…The threshold variable itself obeys an exponential fixed-point dynamics on the basis of a scaled average of activation in the semantic layer. This is done similarly to the threshold behavior in Schrobsdorff et al (2007b). The scaling of the average ${\nu }_{s^{\theta }}$ is dependent on the paradigm and should be set such that the fixed-point of the threshold is between the highest two semantic activations.…”

“…If the particular feature instance defines the object to be target or distractor, an additional input, excitatory or inhibitory, respectively, is applied to the corresponding feature variable. In case of feature perception, $F_i^j$ is set to a generic input strength $\stackrel{MathClass-op^}{F}$ plus the current value of the variable accounting for the reception of input by only a subset of neurons in one assembly, similar to residual activations introduced in Schrobsdorff et al (2007b). The residual overshoot of the input decays to the maximum input in the same way that would feature activation.…”

Tomatoes and Apples or Red and Green Lines: Are Age-Related Interference Effects Based on Competition Among Concepts or Percepts?

“…We also included these so called positive priming (PP) trials. PP trials were used to collect data for computational modeling of repetition effects (e.g., Schrobsdorff et al, 2007). We will, however, not report in detail on PP trials further, because the focus of the present article is on distractor interference effects.…”

Computational modeling of the negative priming effect based on inhibition patterns and working memory

“…Over the past two decades, theoretical accounts of NP have flourished e.g., the distractor-inhibition model (Houghton and Tipper, 1994 ), the episodic memory retrieval model (Neill and Valdes, 1992 ; Frings et al, 2007 ), temporal discrimination model (Milliken et al, 1998 ), the dual-mechanism hypothesis (May et al, 1995 ), and the computational imago-semantic action model (CISAM) (Schrobsdorff et al, 2007 ), but these theories remain controversial (Kane et al, 1997 ; Tipper, 2001 ; Grison et al, 2005a , b ). This lack of consensus results not only from individual differences depend on demographics [age (Gamboz et al, 2002 ), sex (Bermeitinger et al, 2008 ), and neuropsychiatric disorders (Ungar et al, 2010 )], but also from the complexity of the stimuli combinations in NP; the NP effect depends on subtle experimental parameters such as inter-stimulus time intervals, and various stimulus combinations within NP may represent different degrees of inhibition.…”

“…This lack of consensus results not only from individual differences depend on demographics [age (Gamboz et al, 2002 ), sex (Bermeitinger et al, 2008 ), and neuropsychiatric disorders (Ungar et al, 2010 )], but also from the complexity of the stimuli combinations in NP; the NP effect depends on subtle experimental parameters such as inter-stimulus time intervals, and various stimulus combinations within NP may represent different degrees of inhibition. Moreover, the dearth of predictive computational formulations is a substantial reason for this lacuna (Schrobsdorff et al, 2007 ). Thus, an integrated theoretical explanation that accounts for the effects of such parameters has been elusive [c.f., see Schrobsdorff et al ( 2012a ) for the general model for Negative priming (GMNP model)].…”