Distractor Inhibition Theory

The first theoretical account in the context of negative priming was the inhibition hypothesis by Neill (1977) and Neill et al. (1990), before Neill and Valdes (1992) began to promote the episodic retrieval theory, see section 2.4.2. Meanwhile Tipper made himself the spokesperson of the dis-tractor inhibition theory (Tipper, 1985; Tipper and Baylis, 1987; Tipper et al., 1988; Tipper and McLaren, 1990; Tipper et al., 1991; Houghton and Tipper, 1994, 1996; Tipper, 2001; Tipper et al., 2002) accompanied by some early questioning work (Tipper and Cranston, 1985).

The basic idea of distractor inhibition theory is that irrelevant stimuli representations are actively suppressed to support the selection of the relevant target stimulus. The inhibition is assumed to persist for some time. If perceptual input is no longer present, the persisting inhibition drives the distractor representation below a baseline activation. The negative priming effect directly results from the time the probe target representation activation needs to reach baseline from below.

There are two complementary processes involved in the attentional selection process: a direct feedforward excitation of the representation of perceived items by the visual pathway and another one that inhibits all irrelevant information. The slowdown of the reaction in the probe trial can be seen as a direct indicator of the amount of activation in the prime display. Distractor inhibition assumes selection to operate on a semantic or postcategorial level (Houghton and Tipper, 1994).

It therefore also explains findings that report negative priming in semantic priming tasks (Tipper and Driver, 1988).

From a modeler’s perspective, the most important contribution to the domain of distractor in-hibition comes from Houghton and Tipper (1994). A computational implementation of an arti-ficial neural network qualitatively explains negative priming by an inhibitory rebound naturally emerging from the network connections between excitatory and inhibitory cells homeostatically balancing the state of a property unit. The initial version of the computational distractor inhibition model is very ambitious, as perception is split into the detection of single features, hardwiredly binding them into objects. The model has a very general connection scheme, to act in a variety of situations. Unfortunately, none of the further projects proposed by Houghton and Tipper (1994) has been realized since then. In order to investigate the time course of negative priming, the role of multiple distractors and different distractor salience, the model is later simplified by looking at only one isolated property unit for the target and one for the distractor, with connections only to their on and off cell (Houghton et al., 1996; Houghton and Tipper, 1998). The aim is to simplify the original model as much as possible while still observing the same dynamics. Regrettably, the generality of the first modeling approach is no longer present. On the one hand, simplifications of complex models are an adequate tool to understand the behavior of the entire system. On the

2 Negative Priming

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Figure 2.5: Schematic view of target and distractor representation during one trial. At stimulus onset both activations rise driven by the input. The two curves diverge due to the inhi-bition the distractor receives. As inhiinhi-bition builds up, it balances the perceptual input to the distractor after some time. If a certain difference between target and distractor representation is reached, the target is assumed to be selected, and an action is taken.

Then the input is switched off as the stimuli disappear. The target activation passively decays to zero, whereas the distractor activation is still subject to persisting inhibi-tion, driving the distractor representation below baseline in the so-called inhibitory rebound, being responsible for the negative priming effect in the next trial. Figure adapted from (Houghton and Tipper, 1994).

other hand, the reintegration of the single units into the bigger network always brings along vari-ous nonlinear effects that are inherent to the model and can not be neglected when deriving system behavior from results of looking at isolated units. The reintegration does not take place, which might be an indicator for too high complexity of the distractor inhibition model to learn something from it.

A strong point of distractor inhibition theory comes from the study of varied distractor saliency.

The negative priming effect increases with growing saliency of the distractor (Lavie and Fox, 2000; Grison and Strayer, 2001; Tipper et al., 2002). This effect can be very well explained in terms of the inhibition model, since a stronger distractor would require more inhibition, causing a stronger inhibitory rebound, and thus leading to a more prolonged reaction time.

Distractor inhibition theory can directly explain the impact of the depth of processing (Craik and Lockhart, 1972; Craik, 2002). Processing on a deep conceptual level produces a bigger negative priming effect. Distractor inhibition theory can explain the results, as deeper processed items have a stronger activation and thus need more inhibition if characterized as distractor. Therefore, more deeply processed stimuli produce larger negative priming.

The original distractor inhibition theory fails to explain the dependency of negative priming on the response stimulus interval. If the representation of a distractor object is inhibited, the impact of inhibition should be strongest immediately after the selection, because the inhibition is assumed to decay to zero with time. Although there is a general trend of negative priming to decay with increasing time between prime and probe (Neill and Valdes, 1992), no negative priming is observed in several studies when the RSI is very short or nonexistent (Lowe, 1985; Houghton et al.,

2.4 Theories of Negative Priming

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Figure 2.6: Alternative view of the distractor inhibition theory accounting for negative priming effects in the absence of a response stimulus interval. If the situation requires very strong inhibition, the activation of the distractor can drop below baseline already be-fore the end of the trial. Sketch adapted from a talk by Christian Frings, May 30th 2007 in Göttingen, on (Frings and Wühr, 2007a).

1996). In the original model the equilibrium between perceptual input and inhibition is tuned such that the activation of the distractor stays positive. If then a new display is shown directly after the response, a facilitatory effect in the DT condition is expected. Unfortunately, already the study that brought negative priming to light of Dalrymple-Alford and Budayr (1966) shows negative priming without any delay between succeeding stimulus pairs, subjects held cards with several colored words which they processed in order. Therefore, Wentura and Rothermund (2003), Frings and Wentura (2006) and Frings and Wühr (2007a) proposed an extension of distractor inhibition by assuming that the amount of inhibition is proportional to task difficulty. In demanding paradigms like the Stroop task, inhibition may exceed excitatory input thus pushing the distractor activation below baseline even before a reaction, see figure 2.6.

Distractor inhibition is incompatible with target only probe displays. In the absence of a dis-tractor priming constellations that usually produce negative priming effects can show facilitatory priming as reported by Moore (1994). A suitable extension of distractor inhibition theory concerns the notion of what is actually inhibited. Neill (1977) suggests that the semantic representations of the distractors themselves are inhibited, which matches with spreading inhibition through se-mantical networks (Quillian, 1966). Tipper and Cranston (1985) propose inhibition to act on the link between semantic representation and the response system. More explicitly, they assume a selection state of the response system in which the time-consuming resolving of the inhibition of the link between representation and response produces negative priming. In situations where no such selection is necessary, the response may still be facilitated because of the residual activation of the distractor representation. Unfortunately, the response inhibition account was not integrated in later papers.

Distractor inhibition theory is also challenged by the empirical finding of long-term negative priming effects (DeSchepper and Treisman, 1996; Grison et al., 2005). Tipper (2001) integrates these findings by emphasizing that different mechanisms might underlie the behaviorally similar effect in different settings. It is also stated that a retrieval of an episode (as postulated by the

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Figure 2.7: Episodic retrieval assumes figuratively a do-not-respond tag that is attached to the prime distractor. If a probe display contains matching information, the former episode is retrieved and with it the tag. Removing this tag in order to respond to the former distractor which has become target in DT trials takes time which is equivalent to the negative priming effect. Disadvantageous for the theoretical discussion, episodic retrieval theory was often reduced to the picture of the tag, which is only introduced as a metaphor in the original work.

episodic retrieval theory, described in the next section) might also retrieve the inhibitory status of the previously ignored distractor.