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Cue repetition increases inhibition of return
Neuroscience Letters 448 (2008) 231–235
Contents lists available at ScienceDirect
Neuroscience Letters journal homepage: www.elsevier.com/locate/neulet
Cue repetition increases inhibition of return Kristie R. Dukewich a,∗ , Susan E. Boehnke b,∗∗ a b
Department of Psychology, Dalhousie University, Life Sciences Centre, Halifax, Nova Scotia, Canada B3H 4J1 Centre for Neuroscience Studies, Queen’s University, Kingston, ON, Canada, K7L 3N6
a r t i c l e
i n f o
Article history: Received 1 August 2008 Received in revised form 14 September 2008 Accepted 21 October 2008 Keywords: Attention Inhibition of return Habituation Adaptation
a b s t r a c t Inhibition of return (IOR) refers to slowed responses to targets presented at the same location as a preceding stimulus. We explored whether the IOR effect would increase with the number of cues preceding the target (a ‘cue’). Subjects performed a Posner cueing task with 1–5 cue presentations prior to the target, to which they made either a manual localization (Experiment 1) or target discrimination response (Experiment 2). The cues could be the same as (Experiment 1), or differ in shape from (Experiment 2), the target. The results showed that regardless of cue-target congruency the IOR effect increased dramatically with the number of preceding cues. This increase was driven mostly by a linear slowing of reaction times to targets presented on the same side as the cue(s), suggesting that a process such as sensory adaptation and/or habituation may be a contributing mechanism to the IOR effect. © 2008 Elsevier Ireland Ltd. All rights reserved.
People are slower to respond to a target when it is preceded by a stimulus presented at the same location (for a review see [10]). This effect is thought of as an after-effect of visuospatial orienting, referred to as “inhibition of return” (IOR). The IOR paradigm introduced by Posner and Cohen [17] has received enormous study in the past two decades [13], but the underlying mechanisms that produce IOR are still not well understood. The zeitgeist among researchers is that IOR is a consequence of attentional orienting in space; however, there is a surprising amount of evidence that IOR involves changes in sensory processing, inconsistent with the prevailing view of the effect [3]. In all likelihood, there are several processes that contribute to the IOR effect–attention-based, motorbased and sensory-based processes. Here we attempt to explore the contribution of the sensory-based process. Intuitively, the fact that reaction times (RTs) are increased when a stimulus is repeated at the same location suggests a process such as adaptation or habituation may be at work. Visual adaptation is the process by which the visual system alters its responsiveness based on recent stimulus history, often by reducing its response to unchanging stimuli [4], while habituation is a non-associative learning mechanism by which an organism stops responding to a repeatedly presented irrelevant stimulus [20]. These processes are difficult to dissociate, and can both result in a decrement in responding to a continuous or repetitive stimulus. Two early IOR
∗ Corresponding author. Tel.: +1 902 494 6551; fax: +1 902 494 6585. ∗∗ Corresponding author. E-mail addresses: [email protected] (K.R. Dukewich), [email protected] (S.E. Boehnke). 0304-3940/$ – see front matter © 2008 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.neulet.2008.10.063
studies have been invoked to suggest that IOR is not a product of adaptation and/or habituation in the visual system. First, IOR was shown to occur at the environmental rather than the retinotopic location of the cue [16]. However, IOR has now been shown in retinotopic co-ordinates for saccades [1] and to occur in both frames of reference for manual responses [19]. Secondly, when cue location was held constant across runs of trials, presumably leading to habituation to the cue, no difference in the magnitude of IOR was observed [15]. However, the time interval between these cues across trials was probably longer than the timecourse of an adaptation process, and may have been outside the range that would produce IOR. Finally, recent data obtained from visuomotor neurons in the superior colliculus of monkeys performing a Posner cueing task have shown that IOR behavior correlates specifically with changes in the sensory-related processing, but not responserelated neural activity [2,5–7]. The reduced sensory response to the target after previously responding to the cue led to a delay in the time for the neurons to reach a threshold firing rate to generate a saccade eye movement response. Taken together, these findings suggest that revisiting the sensory basis of IOR is warranted. If part of the IOR effect is an expression of adaptation and/or habituation, then the more times the cue is presented at the same location, the more the visual response to the target will be reduced, and the slower the subject will be to initiate a response to that target leading to an increase in the IOR effect. In addition, because increasing the rate of stimulus presentation has been shown to produce greater adaptation and faster habituation [20] increasing the stimulus presentation rate, which would reduce stimulus onset asynchrony (SOA) between repeating cues, should also lead to greater IOR. We tested these predictions in two experiments in
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which multiple cues were presented at several SOAs prior to a target requiring either a manual target localization (Experiment 1) or a target discrimination response (Experiment 2). The aim of Experiment 1 was to determine the effect of the number of cues preceding the target and the time between cues (SOA) on IOR using a simple cueing task with a localization response. We used a modified Posner cueing paradigm in which 1–5 cues were presented repeatedly at one location prior to a target presented at that same or opposite location to which participants made a localization response. If IOR is due to adaptation or habituation, the magnitude of the IOR effect should grow as the number of stimulations at the cued location increases. We used a paradigm similar to that used in neural recording studies [2,5,6]. The target and cues were the same—a filled disc. When the target stimulus appeared in the opposite location to the cue(s) it was obvious that that stimulus was the ‘target’, as the location changed relative to the location of the cues; however, when the cue and the target were at the same location, it could be difficult to tell if the target was another cue or in fact the target. This potential problem was ameliorated by turning the fixation point off concurrently with turning the target on, and presenting blocks of trials with a set ‘number of cues’. We also chose to keep the SOA constant between the multiple cues in a given trial, but vary the SOA on a trial-by-trial basis. These latter features ensured 100% temporal predictability of target appearance when there were multiple cues, but no spatial predictability. Sixteen observers with normal or corrected-to-normal vision participated in Exp. 1. Subjects provided informed consent and were compensated with payment of $10/h or course credit, in accordance with the Tri-Council Ethics Policy Statement. The experiment was run on an AMD Athalon XP1600+ computer with a Radeon 7500 graphics card. Stimuli were white, presented on a black background at a distance of 57 cm from the observer. All stimuli were presented using E-Prime experiment software. The central fixation cross was 0.8◦ VA. Cues consisted of a white disc 2.6◦ VA in diameter that appeared 10.5◦ VA from the central fixation on either the left or the right side of the screen from the center of the fixation cross to the center of the disc. Participants were asked to report which side the target was presented on as fast as possible by pressing the ‘z’ key on a keyboard for a left and the ‘?/’ key for a right-located target. Each participant began the session with a block of 34 practice trials, followed by 5 blocks (120 trials each, counterbalanced for order) of experimental trials at a given number of cues. Prior to each block, a screen was presented that informed them of the number of cues per trial that would be present in the upcoming block. Within each block (counterbalanced for order), 20 repetitions of each combination of Target Location (‘Same’ or ‘Opposite’ to the cue) and SOA (200, 250 and 350 ms) were presented in random order. Participants were given a rest between each block. Each trial began with the presentation of the central fixation cross for a variable duration chosen randomly from 300 to 1100 ms. This was followed by the presentation of 1–5 cues (100 ms each) at one of the peripheral locations and then by the presentation of the target (100 ms) at the same or opposite location to those cues. The central fixation cross disappeared simultaneously with the presentation of the target. Three SOAs were pseudo-randomly dispersed across trials in a block, while the stimulus presentation rate was always constant between all stimuli in a given trial; on onecue trials, the SOA represents the cue-target onset asynchrony. For example, on a two-cue trial with an SOA of 350 ms, the time interval between the onset of the first cue and the onset of the target would be 700 ms (see Fig. 1A). The SOAs were chosen based on typical SOAs employed in target detection or localization tasks using a single cue and single target [14]. Participants were told to fixate at center, and respond to the target stimulus as fast and accurately as possible.
Fig. 1. Methods and results from E1 which used a Posner cueing task with a target localization manual response. (A) An example of a single Opposite condition trial during the two-cue block of trials using a 350 ms SOA. (B). Mean RTs for the Same and Opposite trials plotted as a function of the number of cues presented. The conditions labels indicate the trial type and SOA (ms). (C) Cueing effect (the difference between mean RTs for the Opposite and Same conditions) as a function of the number of cues. Each curve represents a different SOA. Error bars for both graphs represent the within-subject confidence intervals [12].
Intertrial interval (ITI), the interval from the button press to the time of the fixation presentation, was held constant at 500 ms. Fig. 1A illustrates an example trial procedure for the two-cue condition. We excluded 4.17% of trials because RTs were either under 100 ms after target onset (anticipations, 0.9%) or RTs were over 1200 ms (1.06%) or they were incorrect responses made with RTs of 100–1200 ms (direction errors, 2.21%). Mean RTs (see Fig. 1B) were analyzed using a 2 × 5 × 3 repeated measures ANOVA (all analyses HF-corrected for sphericity violations), revealing a main effect of Target Location [F(1, 15) = 29.4, p < .01], demonstrating that overall participants are faster to respond to targets presented at the opposite location of the cue(s) than to targets presented at the same location as the cue(s), i.e. we observed IOR. There were also significant effects of Number of Cues [F(4, 60) = 4.2, p < .01] and SOA [F(2, 30) = 16.6, p < .01] and significant interactions between Target Location and Number of Cues [F(4,
K.R. Dukewich, S.E. Boehnke / Neuroscience Letters 448 (2008) 231–235
60) = 16.4, p < .01], Target Location and SOA [F(2, 30) = 3.7, p < .05], and Number of Cues and SOA [F(8, 120) = 2.13, p < .05]. The threeway interaction was not significant [F(8, 120) = 1.3 ns]. The two-way interactions involving Target Location are easier to interpret when cueing effects (Opposite minus Same RTs) are plotted as a function of Number of Cues and SOA as illustrated in Fig. 1C. As the number of cues increased, the IOR effect increased. This was confirmed by a significant linear contrast [F(1, 60) = 44.12, p < .01] which accounted for 67% of the sums of squares in a trend analysis. A significant quadratic effect accounted for the remainder [32%, F(1, 60) = 21.4, p < .01], explaining the plateau in the curve between 3 and 5 cues. Critically, this increase in IOR with number of cues was determined largely by increases in RT for the ‘Same’ condition (38 ms RT increase) rather than changes in the RTs for the ‘Opposite’ condition (which initially decrease from 1 to 2 cues, then slowly increase). When RTs were split by Target Location and analyzed with separate two-way ANOVAs, there was a significant linear contrast across Number of Cues for the ‘Same’ RTs [F(1, 60) = 20.11, p < .01], but not for the ‘Opposite’ RTs [F(1, 60) = 1.0 ns]. Reflecting the two-way interaction between Target Location and SOA, the cueing effect decreased linearly as the SOA became longer (67, 57 and 47 ms IOR for 200, 250 and 350 ms SOA, respectively). There was a significant linear contrast of cueing effect with SOA [F(1, 30) = 7.33, p < .02] which accounted for all the sums of squares in the ANOVA. Mean error rates (incorrect responses made with RTs from 100 to 1200 ms/total trials made within that RT range) were analyzed using a 2 (Target Location) × 5 (Number of Cues) × 3 (SOA) ANOVA, revealing a significant main effect of Target Location [F(1, 15) = 42.73, p < .01]. There were no other significant main effects or interactions (all ps > .17). Mean direction error rates for ‘Opposite’ and ‘Same’ conditions were 3.6% and 1.3%, respectively. In accordance with our predictions, participants showed increasingly more IOR as the number of cues presented increased and as the within-trial SOA decreased. The difference in error rates suggests that overall participants were more conservative about initiating responses to cued targets. This pattern is consistent with Klein and Taylor’s [11] proposal that IOR can manifest as a reluctance to respond to or toward targets at the cued location, and converges with Ivanoff and Klein’s observation [8] of a speedaccuracy tradeoff in two different tasks across eight different IOR conditions. Thus, we have replicated this known motor component of IOR. However, this component does not seem to contribute to the increase in IOR that was observed as we increased the number of cues or decreased the SOA, since the error rates remained constant across conditions. In Exp. 2 we sought to extend the results of Exp. 1 by testing the effect of number of cues and SOA on a target identification task that employed cues that were perceptually different from each other and from the targets. We added several other visual features so this experiment would be more similar to typical human IOR studies (square placeholders; constant central fixation mark). We also adjusted stimulus duration and SOAs so that the SOA between the cue and target on a one-cue trial was in the range typically used in a target-identification IOR experiment [14]. Methods were the same as in Exp. 1, except for the following details. Twenty observers participated in Exp. 2. Cues were presented in rectangular outlines that were 8.8◦ × 6.7◦ centered 11.5◦ VA to the left and right of the central fixation cross. The cues ($, %, &, ! and ?) and targets (# and @) were symbols created in E-prime using courier new bold font in a 35-point font size (subtending approximately 2.5◦ VA in height), and appeared in the center of the peripheral rectangles. Exp. 2 was a speeded target-identification task in which participants were required to report which of two possible targets
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Fig. 2. Methods and results from E2 which used a Posner cueing task with a target identification manual response. (A) An example of a single Same condition trial from E2 during the two-cue block of trials, using a 600 ms SOA (see text for details). (B and C) As in caption for Fig. 1.
appeared. Target identities were mapped on to the response buttons (‘B’ and ‘N’), with the target identity that corresponded to each button counterbalanced across participants. Each participant began the session with a block of 24 practice trials, followed by 5 blocks of experimental trials (96 trials/block). Within each block, 16 repetitions of each combination of Target Location (‘Same’ or ‘Opposite’ to the cue) and SOA (300, 450 and 600 ms) were presented in random order. On each trial the cue(s) were randomly chosen without replacement from the list of possible cues. Cue and target durations were reduced to 50 ms, and SOAs were set to 300, 450 and 600 ms. Rates were adjusted to reflect SOAs in the range typically employed in target discrimination cueing experiments that use one cue and one target [14]. Fig. 2A illustrates an example trial procedure for the three-cue condition. We used a shorter cue duration in order to make each stimulus appear more discrete, as the goal was to generate repetitive stimuli. At the longer duration, the characters
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comprising the cues seemed to morph from one to the other at the shortest SOA, perhaps due to object review processes prolonging perceptual processing [9] which was not required for E1 which employed simple flashes of light. The results from E2 were very similar to E1, and were analyzed in the exact same way. We excluded 4.37% of trials because RTs were under 100 ms after target onset (anticipation errors, 0.06%) or RTs were over 1200 ms (timing errors, 0.76%) or they were incorrect responses made in the correct allotted time (direction errors, 3.55%). Mean RTs (see Fig. 2B) were analyzed using a 2 (Target Location) × 5 (Number of Cues) × 3 (SOA) repeated measures ANOVA revealing a main effect of Target Location [F(1, 19) = 45.6, p < .01], demonstrating that overall participants are faster to respond to targets presented at the opposite location of the cue(s) than to targets presented at the same location as the cue(s), i.e. we observed IOR. There were also significant effects of Number of Cues [F(4, 76) = 8.20, p < .01] and SOA [F(2, 38) = 4.09, p < .03] and significant interactions between Target Location and Number of Cues [F(4, 76) = 9.47, p < .01] and Number of Cues and SOA [F(8, 152) = 2.38, p < .05], but not Target Location and SOA [F(2, 38) = 2.29, p = .11]. The three-way interaction was not significant [F(8, 152) < 1]. Although the interaction between target location and SOA was not significant, this was probably due to the range of SOA being too narrow to produce a large enough effect to produce such an interaction. The two-way interactions involving Target Location are easier to interpret when cueing effects (Opposite minus Same RTs) are plotted as a function of the Number of Cues and SOA as illustrated in Fig. 2C. As the number of cues increased, the IOR effect increased. This was confirmed by a significant linear contrast [F(1, 76) = 35.75, p < .01] which accounted for 94% of the sums of squares in a trend analysis, with no significant remainder. Critically, this increase in IOR with number of cues was determined largely by increases in RT for the ‘Same’ condition (65 ms increase from 1 to 5 cues) rather than changes in the RTs for the ‘Opposite’ condition (13 ms increase). When RTs were split by Target Location and analyzed with separate two-way ANOVAs, there was a significant linear contrast across number of cues for the ‘Same’ RTs [F(1, 76) = 41.80, p < .01] that accounted for 85% of the sums of squares. Unlike E1, there was a linear trend for the ‘Opposite’ RTs [F(1, 76) = 5.28, p < .03], but it was far weaker—accounting for less than 50% of the sums of squares. Mean direction errors were analyzed using a 2 (Target Location) × 5 (Number of Cues) × 3 (SOA) ANOVA, revealing a significant main effect of target location [F(1, 15) = 12.8, p < .01] but no other significant main effects or interactions (all ps > .1). Mean error rates for Opposite and Same conditions were 5.0% and 2.7%, respectively. As in Exp. 1, we see a speed-accuracy tradeoff. Again, however, the difference in error rates for the same and opposite target locations does not seem related to the increase in IOR observed with repetition of cues, since the error rates remained relatively constant across conditions (i.e., there was no interaction between Number of Cues and Location, or SOA and Location). Two experiments investigated predictions from the hypothesis that adaptation and/or habituation contribute to the IOR effect. We predicted that IOR will increase with cue repetition and repetition rate, and this increase will largely be driven by increases in RTs when the target is presented at the same side as the cue. This is because the neural response to the visual stimulus will decrease with repeated stimulation via processes of adaptation (or habituation), delaying when action can be initiated [2,5,6]. The results of Exp. 1, where identical cues and targets (simple spots of light) were used in a target-localization task, supported these predictions. Participants were slower to respond when the target appeared at the cued location compared to when the target appeared at the
opposite location to the cue(s), and were progressively slower as the number of cues increased and the rate of their presentation increased (shorter SOAs). In Exp. 2, we extended these findings to show that the same effects can be observed in a target discrimination task where the cues and targets had different identities, thus showing the effect of cue repetition is robust. Importantly, the pattern of results obtained in both experiments was predicted by an adaptation/habituation account of IOR. Previously IOR has been characterized as a speed-accuracy tradeoff [8,11]; indeed, we observed evidence to support this conclusion by finding that participants were faster but less accurate in the Opposite condition than in the Same condition in both experiments. However, the tradeoff between speed and accuracy could not account for the increase in IOR as a function of the number of cues presented because the error pattern was constant across number of cues. This finding suggests there are two mechanisms driving the IOR effect in our experiments, one that produces a speed-accuracy tradeoff, and one that produces an increase in the IOR effect as the number of cues increases. The mechanism that drives the speed-accuracy tradeoff is likely related to Klein and Taylor’s [11] hypothesis that IOR is a reluctance to respond to a previously stimulated location, which Ivanoff and Klein [8] later demonstrated was action or motor-based. We suggest that the second mechanism involved in our observations of IOR is a sensorybased process–adaptation and/or habituation. The proposal that there is a sensory basis for IOR is as old as the effect itself (see [17]); however, following the publication of Maylor and Hockey [16], Posner and many other subsequent researchers adopted a more attentional interpretation of the effect [18]. This explanation is not necessarily inconsistent with the results from the current experiments. An attention-based theory might state that each presentation of the irrelevant cue lays down stronger IOR at the cued location, hence IOR increases with cue repetition. However, it is not clear what predictions would be made regarding SOA. Adaptation and habituation both predict a greater decrement in responding to ‘Same’ targets as the rate of presentation speeds up [20]. This means that the condition that typically produces the greatest behavioral inhibition in a standard onecue paradigm (i.e., the longest SOA condition) would map on to the condition in which the least amount of behavioral inhibition was observed overall when multiple cues are used. Because “traditional” attentional accounts of IOR have evolved to explain the timecourse in the one-cue paradigm, it is not clear what predictions would be made regarding the effect of SOA. Importantly, all of the patterns we have obtained here were predicted from the adaptation/habituation hypothesis. Acknowledgements We thank Ray Klein and two anonymous reviewers for comments on earlier drafts of this manuscript, Doug Munoz for support of author SB, and NSERC and Killam scholarships to author KD. References [1] R.A. Abrams, J. Pratt, Oculocentric coding of inhibited eye movements to recently attended locations, J. Exp. Psychol. Human 26 (2000) 776–788. [2] A.H. Bell, J.H. Fecteau, D.P. Munoz, Using auditory and visual stimuli to investigate the behavioral and neuronal consequences of reflexive covert orienting, J. Neurophysiol. 91 (2004) 2172–2184. [3] G. Berlucchi, Inhibition of return: a phenomenon in search of a mechanism and a better name, Cogn. Neuropsychol. 23 (2006) 1065–1074. [4] C.W. Clifford, M.A. Webster, G.B. Stanley, A.A. Stocker, A. Kohn, T.O. Sharpee, O. Schwartz, Visual adaptation: neural, psychological and computational aspects, Vision Res. 47 (2007) 3125–3131. [5] M.C. Dorris, R.M. Klein, S. Everling, D.P. Munoz, Contribution of the primate superior colliculus to inhibition of return, J. Cogn. Neurosci. 14 (2002) 1256–1263.
K.R. Dukewich, S.E. Boehnke / Neuroscience Letters 448 (2008) 231–235 [6] J.H. Fecteau, A.H. Bell, D.P. Munoz, Neural correlates of the automatic and goal-driven biases in orienting spatial attention, J. Neurophysiol. 92 (2004) 1728–1737. [7] J.H. Fecteau, D.P. Munoz, Correlates of capture of attention and inhibition of return across stages of visual processing, J. Cogn. Neurosci. 17 (2005) 1714–1727. [8] J. Ivanoff, R.M. Klein, Attending, intending and the importance of task settings, Behav. Brain Sci. 24 (2001) 889–890. [9] D. Kahneman, A. Treisman, B. Gibbs, The reviewing of object files: objectspecific integration of information, Cogn. Psychol. 24 (1992) 175–219. [10] R.M. Klein, Inhibition of return, Trends Cogn. Sci. 4 (2000) 138–147. [11] R.M. Klein, T.L. Taylor, Categories of cognitive inhibition with reference to attention, in: D. Dagenbach, T.H. Carr (Eds.), Inhibitory Processes in Attention, Memory, and Language, Academic Press, New York, 1994, pp. 113–150. [12] G.R. Loftus, M.E. Masson, Using confidence intervals in within-subject designs, Psychon. Bull. Rev. 1 (1994) 476–490. [13] J. Lupianez, R.M. Klein, P. Bartolomeo, Inhibition of return: twenty years after, Cogn. Neuropsychol. 23 (2006) 1003–1014.
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[14] J. Lupianez, E.G. Milan, F.J. Tornay, E. Madrid, P.o. Tudela, Does IOR occur in discrimination tasks? Yes, it does, but later, Percept. Psychophys. 59 (1997) 1241–1254. [15] E.A. Maylor, R. Hockey, Effects of repetition on the facilitatory and inhibitory components of orienting in visual space, Neuropsychologia 25 (1987) 41–54. [16] E.A. Maylor, R. Hockey, Inhibitory component of externally controlled covert orienting in visual space, J. Exp. Psychol. Human 11 (1985) 777–787. [17] M.I. Posner, Y. Cohen, Components of visual orienting, in: H. Bouma, D. Bonwhuis (Eds.), Attention and Performance X: Control of Language Processes, Erlbaum, Hillsdale, NJ, 1984, pp. 551–556. [18] M.I. Posner, R.D. Rafal, L.S. Choate, J. Vaughan, Inhibition of return: neural basis and function, Cogn. Neuropsychol. 2 (1985) 211–228. [19] A. Sapir, A. Hayes, A. Henik, S. Danziger, R. Rafal, Parietal lobe lesions disrupt saccadic remapping of inhibitory location tagging, J. Cogn. Neurosci. 16 (2004) 503–509. [20] R.F. Thompson, W.A. Spencer, Habituation:, A model phenomenon for the study of neuronal substrates of behavior, Psychol. Rev. 73 (1966) 16–43.
Contents lists available at ScienceDirect
Neuroscience Letters journal homepage: www.elsevier.com/locate/neulet
Cue repetition increases inhibition of return Kristie R. Dukewich a,∗ , Susan E. Boehnke b,∗∗ a b
Department of Psychology, Dalhousie University, Life Sciences Centre, Halifax, Nova Scotia, Canada B3H 4J1 Centre for Neuroscience Studies, Queen’s University, Kingston, ON, Canada, K7L 3N6
a r t i c l e
i n f o
Article history: Received 1 August 2008 Received in revised form 14 September 2008 Accepted 21 October 2008 Keywords: Attention Inhibition of return Habituation Adaptation
a b s t r a c t Inhibition of return (IOR) refers to slowed responses to targets presented at the same location as a preceding stimulus. We explored whether the IOR effect would increase with the number of cues preceding the target (a ‘cue’). Subjects performed a Posner cueing task with 1–5 cue presentations prior to the target, to which they made either a manual localization (Experiment 1) or target discrimination response (Experiment 2). The cues could be the same as (Experiment 1), or differ in shape from (Experiment 2), the target. The results showed that regardless of cue-target congruency the IOR effect increased dramatically with the number of preceding cues. This increase was driven mostly by a linear slowing of reaction times to targets presented on the same side as the cue(s), suggesting that a process such as sensory adaptation and/or habituation may be a contributing mechanism to the IOR effect. © 2008 Elsevier Ireland Ltd. All rights reserved.
People are slower to respond to a target when it is preceded by a stimulus presented at the same location (for a review see [10]). This effect is thought of as an after-effect of visuospatial orienting, referred to as “inhibition of return” (IOR). The IOR paradigm introduced by Posner and Cohen [17] has received enormous study in the past two decades [13], but the underlying mechanisms that produce IOR are still not well understood. The zeitgeist among researchers is that IOR is a consequence of attentional orienting in space; however, there is a surprising amount of evidence that IOR involves changes in sensory processing, inconsistent with the prevailing view of the effect [3]. In all likelihood, there are several processes that contribute to the IOR effect–attention-based, motorbased and sensory-based processes. Here we attempt to explore the contribution of the sensory-based process. Intuitively, the fact that reaction times (RTs) are increased when a stimulus is repeated at the same location suggests a process such as adaptation or habituation may be at work. Visual adaptation is the process by which the visual system alters its responsiveness based on recent stimulus history, often by reducing its response to unchanging stimuli [4], while habituation is a non-associative learning mechanism by which an organism stops responding to a repeatedly presented irrelevant stimulus [20]. These processes are difficult to dissociate, and can both result in a decrement in responding to a continuous or repetitive stimulus. Two early IOR
∗ Corresponding author. Tel.: +1 902 494 6551; fax: +1 902 494 6585. ∗∗ Corresponding author. E-mail addresses: [email protected] (K.R. Dukewich), [email protected] (S.E. Boehnke). 0304-3940/$ – see front matter © 2008 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.neulet.2008.10.063
studies have been invoked to suggest that IOR is not a product of adaptation and/or habituation in the visual system. First, IOR was shown to occur at the environmental rather than the retinotopic location of the cue [16]. However, IOR has now been shown in retinotopic co-ordinates for saccades [1] and to occur in both frames of reference for manual responses [19]. Secondly, when cue location was held constant across runs of trials, presumably leading to habituation to the cue, no difference in the magnitude of IOR was observed [15]. However, the time interval between these cues across trials was probably longer than the timecourse of an adaptation process, and may have been outside the range that would produce IOR. Finally, recent data obtained from visuomotor neurons in the superior colliculus of monkeys performing a Posner cueing task have shown that IOR behavior correlates specifically with changes in the sensory-related processing, but not responserelated neural activity [2,5–7]. The reduced sensory response to the target after previously responding to the cue led to a delay in the time for the neurons to reach a threshold firing rate to generate a saccade eye movement response. Taken together, these findings suggest that revisiting the sensory basis of IOR is warranted. If part of the IOR effect is an expression of adaptation and/or habituation, then the more times the cue is presented at the same location, the more the visual response to the target will be reduced, and the slower the subject will be to initiate a response to that target leading to an increase in the IOR effect. In addition, because increasing the rate of stimulus presentation has been shown to produce greater adaptation and faster habituation [20] increasing the stimulus presentation rate, which would reduce stimulus onset asynchrony (SOA) between repeating cues, should also lead to greater IOR. We tested these predictions in two experiments in
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which multiple cues were presented at several SOAs prior to a target requiring either a manual target localization (Experiment 1) or a target discrimination response (Experiment 2). The aim of Experiment 1 was to determine the effect of the number of cues preceding the target and the time between cues (SOA) on IOR using a simple cueing task with a localization response. We used a modified Posner cueing paradigm in which 1–5 cues were presented repeatedly at one location prior to a target presented at that same or opposite location to which participants made a localization response. If IOR is due to adaptation or habituation, the magnitude of the IOR effect should grow as the number of stimulations at the cued location increases. We used a paradigm similar to that used in neural recording studies [2,5,6]. The target and cues were the same—a filled disc. When the target stimulus appeared in the opposite location to the cue(s) it was obvious that that stimulus was the ‘target’, as the location changed relative to the location of the cues; however, when the cue and the target were at the same location, it could be difficult to tell if the target was another cue or in fact the target. This potential problem was ameliorated by turning the fixation point off concurrently with turning the target on, and presenting blocks of trials with a set ‘number of cues’. We also chose to keep the SOA constant between the multiple cues in a given trial, but vary the SOA on a trial-by-trial basis. These latter features ensured 100% temporal predictability of target appearance when there were multiple cues, but no spatial predictability. Sixteen observers with normal or corrected-to-normal vision participated in Exp. 1. Subjects provided informed consent and were compensated with payment of $10/h or course credit, in accordance with the Tri-Council Ethics Policy Statement. The experiment was run on an AMD Athalon XP1600+ computer with a Radeon 7500 graphics card. Stimuli were white, presented on a black background at a distance of 57 cm from the observer. All stimuli were presented using E-Prime experiment software. The central fixation cross was 0.8◦ VA. Cues consisted of a white disc 2.6◦ VA in diameter that appeared 10.5◦ VA from the central fixation on either the left or the right side of the screen from the center of the fixation cross to the center of the disc. Participants were asked to report which side the target was presented on as fast as possible by pressing the ‘z’ key on a keyboard for a left and the ‘?/’ key for a right-located target. Each participant began the session with a block of 34 practice trials, followed by 5 blocks (120 trials each, counterbalanced for order) of experimental trials at a given number of cues. Prior to each block, a screen was presented that informed them of the number of cues per trial that would be present in the upcoming block. Within each block (counterbalanced for order), 20 repetitions of each combination of Target Location (‘Same’ or ‘Opposite’ to the cue) and SOA (200, 250 and 350 ms) were presented in random order. Participants were given a rest between each block. Each trial began with the presentation of the central fixation cross for a variable duration chosen randomly from 300 to 1100 ms. This was followed by the presentation of 1–5 cues (100 ms each) at one of the peripheral locations and then by the presentation of the target (100 ms) at the same or opposite location to those cues. The central fixation cross disappeared simultaneously with the presentation of the target. Three SOAs were pseudo-randomly dispersed across trials in a block, while the stimulus presentation rate was always constant between all stimuli in a given trial; on onecue trials, the SOA represents the cue-target onset asynchrony. For example, on a two-cue trial with an SOA of 350 ms, the time interval between the onset of the first cue and the onset of the target would be 700 ms (see Fig. 1A). The SOAs were chosen based on typical SOAs employed in target detection or localization tasks using a single cue and single target [14]. Participants were told to fixate at center, and respond to the target stimulus as fast and accurately as possible.
Fig. 1. Methods and results from E1 which used a Posner cueing task with a target localization manual response. (A) An example of a single Opposite condition trial during the two-cue block of trials using a 350 ms SOA. (B). Mean RTs for the Same and Opposite trials plotted as a function of the number of cues presented. The conditions labels indicate the trial type and SOA (ms). (C) Cueing effect (the difference between mean RTs for the Opposite and Same conditions) as a function of the number of cues. Each curve represents a different SOA. Error bars for both graphs represent the within-subject confidence intervals [12].
Intertrial interval (ITI), the interval from the button press to the time of the fixation presentation, was held constant at 500 ms. Fig. 1A illustrates an example trial procedure for the two-cue condition. We excluded 4.17% of trials because RTs were either under 100 ms after target onset (anticipations, 0.9%) or RTs were over 1200 ms (1.06%) or they were incorrect responses made with RTs of 100–1200 ms (direction errors, 2.21%). Mean RTs (see Fig. 1B) were analyzed using a 2 × 5 × 3 repeated measures ANOVA (all analyses HF-corrected for sphericity violations), revealing a main effect of Target Location [F(1, 15) = 29.4, p < .01], demonstrating that overall participants are faster to respond to targets presented at the opposite location of the cue(s) than to targets presented at the same location as the cue(s), i.e. we observed IOR. There were also significant effects of Number of Cues [F(4, 60) = 4.2, p < .01] and SOA [F(2, 30) = 16.6, p < .01] and significant interactions between Target Location and Number of Cues [F(4,
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60) = 16.4, p < .01], Target Location and SOA [F(2, 30) = 3.7, p < .05], and Number of Cues and SOA [F(8, 120) = 2.13, p < .05]. The threeway interaction was not significant [F(8, 120) = 1.3 ns]. The two-way interactions involving Target Location are easier to interpret when cueing effects (Opposite minus Same RTs) are plotted as a function of Number of Cues and SOA as illustrated in Fig. 1C. As the number of cues increased, the IOR effect increased. This was confirmed by a significant linear contrast [F(1, 60) = 44.12, p < .01] which accounted for 67% of the sums of squares in a trend analysis. A significant quadratic effect accounted for the remainder [32%, F(1, 60) = 21.4, p < .01], explaining the plateau in the curve between 3 and 5 cues. Critically, this increase in IOR with number of cues was determined largely by increases in RT for the ‘Same’ condition (38 ms RT increase) rather than changes in the RTs for the ‘Opposite’ condition (which initially decrease from 1 to 2 cues, then slowly increase). When RTs were split by Target Location and analyzed with separate two-way ANOVAs, there was a significant linear contrast across Number of Cues for the ‘Same’ RTs [F(1, 60) = 20.11, p < .01], but not for the ‘Opposite’ RTs [F(1, 60) = 1.0 ns]. Reflecting the two-way interaction between Target Location and SOA, the cueing effect decreased linearly as the SOA became longer (67, 57 and 47 ms IOR for 200, 250 and 350 ms SOA, respectively). There was a significant linear contrast of cueing effect with SOA [F(1, 30) = 7.33, p < .02] which accounted for all the sums of squares in the ANOVA. Mean error rates (incorrect responses made with RTs from 100 to 1200 ms/total trials made within that RT range) were analyzed using a 2 (Target Location) × 5 (Number of Cues) × 3 (SOA) ANOVA, revealing a significant main effect of Target Location [F(1, 15) = 42.73, p < .01]. There were no other significant main effects or interactions (all ps > .17). Mean direction error rates for ‘Opposite’ and ‘Same’ conditions were 3.6% and 1.3%, respectively. In accordance with our predictions, participants showed increasingly more IOR as the number of cues presented increased and as the within-trial SOA decreased. The difference in error rates suggests that overall participants were more conservative about initiating responses to cued targets. This pattern is consistent with Klein and Taylor’s [11] proposal that IOR can manifest as a reluctance to respond to or toward targets at the cued location, and converges with Ivanoff and Klein’s observation [8] of a speedaccuracy tradeoff in two different tasks across eight different IOR conditions. Thus, we have replicated this known motor component of IOR. However, this component does not seem to contribute to the increase in IOR that was observed as we increased the number of cues or decreased the SOA, since the error rates remained constant across conditions. In Exp. 2 we sought to extend the results of Exp. 1 by testing the effect of number of cues and SOA on a target identification task that employed cues that were perceptually different from each other and from the targets. We added several other visual features so this experiment would be more similar to typical human IOR studies (square placeholders; constant central fixation mark). We also adjusted stimulus duration and SOAs so that the SOA between the cue and target on a one-cue trial was in the range typically used in a target-identification IOR experiment [14]. Methods were the same as in Exp. 1, except for the following details. Twenty observers participated in Exp. 2. Cues were presented in rectangular outlines that were 8.8◦ × 6.7◦ centered 11.5◦ VA to the left and right of the central fixation cross. The cues ($, %, &, ! and ?) and targets (# and @) were symbols created in E-prime using courier new bold font in a 35-point font size (subtending approximately 2.5◦ VA in height), and appeared in the center of the peripheral rectangles. Exp. 2 was a speeded target-identification task in which participants were required to report which of two possible targets
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Fig. 2. Methods and results from E2 which used a Posner cueing task with a target identification manual response. (A) An example of a single Same condition trial from E2 during the two-cue block of trials, using a 600 ms SOA (see text for details). (B and C) As in caption for Fig. 1.
appeared. Target identities were mapped on to the response buttons (‘B’ and ‘N’), with the target identity that corresponded to each button counterbalanced across participants. Each participant began the session with a block of 24 practice trials, followed by 5 blocks of experimental trials (96 trials/block). Within each block, 16 repetitions of each combination of Target Location (‘Same’ or ‘Opposite’ to the cue) and SOA (300, 450 and 600 ms) were presented in random order. On each trial the cue(s) were randomly chosen without replacement from the list of possible cues. Cue and target durations were reduced to 50 ms, and SOAs were set to 300, 450 and 600 ms. Rates were adjusted to reflect SOAs in the range typically employed in target discrimination cueing experiments that use one cue and one target [14]. Fig. 2A illustrates an example trial procedure for the three-cue condition. We used a shorter cue duration in order to make each stimulus appear more discrete, as the goal was to generate repetitive stimuli. At the longer duration, the characters
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comprising the cues seemed to morph from one to the other at the shortest SOA, perhaps due to object review processes prolonging perceptual processing [9] which was not required for E1 which employed simple flashes of light. The results from E2 were very similar to E1, and were analyzed in the exact same way. We excluded 4.37% of trials because RTs were under 100 ms after target onset (anticipation errors, 0.06%) or RTs were over 1200 ms (timing errors, 0.76%) or they were incorrect responses made in the correct allotted time (direction errors, 3.55%). Mean RTs (see Fig. 2B) were analyzed using a 2 (Target Location) × 5 (Number of Cues) × 3 (SOA) repeated measures ANOVA revealing a main effect of Target Location [F(1, 19) = 45.6, p < .01], demonstrating that overall participants are faster to respond to targets presented at the opposite location of the cue(s) than to targets presented at the same location as the cue(s), i.e. we observed IOR. There were also significant effects of Number of Cues [F(4, 76) = 8.20, p < .01] and SOA [F(2, 38) = 4.09, p < .03] and significant interactions between Target Location and Number of Cues [F(4, 76) = 9.47, p < .01] and Number of Cues and SOA [F(8, 152) = 2.38, p < .05], but not Target Location and SOA [F(2, 38) = 2.29, p = .11]. The three-way interaction was not significant [F(8, 152) < 1]. Although the interaction between target location and SOA was not significant, this was probably due to the range of SOA being too narrow to produce a large enough effect to produce such an interaction. The two-way interactions involving Target Location are easier to interpret when cueing effects (Opposite minus Same RTs) are plotted as a function of the Number of Cues and SOA as illustrated in Fig. 2C. As the number of cues increased, the IOR effect increased. This was confirmed by a significant linear contrast [F(1, 76) = 35.75, p < .01] which accounted for 94% of the sums of squares in a trend analysis, with no significant remainder. Critically, this increase in IOR with number of cues was determined largely by increases in RT for the ‘Same’ condition (65 ms increase from 1 to 5 cues) rather than changes in the RTs for the ‘Opposite’ condition (13 ms increase). When RTs were split by Target Location and analyzed with separate two-way ANOVAs, there was a significant linear contrast across number of cues for the ‘Same’ RTs [F(1, 76) = 41.80, p < .01] that accounted for 85% of the sums of squares. Unlike E1, there was a linear trend for the ‘Opposite’ RTs [F(1, 76) = 5.28, p < .03], but it was far weaker—accounting for less than 50% of the sums of squares. Mean direction errors were analyzed using a 2 (Target Location) × 5 (Number of Cues) × 3 (SOA) ANOVA, revealing a significant main effect of target location [F(1, 15) = 12.8, p < .01] but no other significant main effects or interactions (all ps > .1). Mean error rates for Opposite and Same conditions were 5.0% and 2.7%, respectively. As in Exp. 1, we see a speed-accuracy tradeoff. Again, however, the difference in error rates for the same and opposite target locations does not seem related to the increase in IOR observed with repetition of cues, since the error rates remained relatively constant across conditions (i.e., there was no interaction between Number of Cues and Location, or SOA and Location). Two experiments investigated predictions from the hypothesis that adaptation and/or habituation contribute to the IOR effect. We predicted that IOR will increase with cue repetition and repetition rate, and this increase will largely be driven by increases in RTs when the target is presented at the same side as the cue. This is because the neural response to the visual stimulus will decrease with repeated stimulation via processes of adaptation (or habituation), delaying when action can be initiated [2,5,6]. The results of Exp. 1, where identical cues and targets (simple spots of light) were used in a target-localization task, supported these predictions. Participants were slower to respond when the target appeared at the cued location compared to when the target appeared at the
opposite location to the cue(s), and were progressively slower as the number of cues increased and the rate of their presentation increased (shorter SOAs). In Exp. 2, we extended these findings to show that the same effects can be observed in a target discrimination task where the cues and targets had different identities, thus showing the effect of cue repetition is robust. Importantly, the pattern of results obtained in both experiments was predicted by an adaptation/habituation account of IOR. Previously IOR has been characterized as a speed-accuracy tradeoff [8,11]; indeed, we observed evidence to support this conclusion by finding that participants were faster but less accurate in the Opposite condition than in the Same condition in both experiments. However, the tradeoff between speed and accuracy could not account for the increase in IOR as a function of the number of cues presented because the error pattern was constant across number of cues. This finding suggests there are two mechanisms driving the IOR effect in our experiments, one that produces a speed-accuracy tradeoff, and one that produces an increase in the IOR effect as the number of cues increases. The mechanism that drives the speed-accuracy tradeoff is likely related to Klein and Taylor’s [11] hypothesis that IOR is a reluctance to respond to a previously stimulated location, which Ivanoff and Klein [8] later demonstrated was action or motor-based. We suggest that the second mechanism involved in our observations of IOR is a sensorybased process–adaptation and/or habituation. The proposal that there is a sensory basis for IOR is as old as the effect itself (see [17]); however, following the publication of Maylor and Hockey [16], Posner and many other subsequent researchers adopted a more attentional interpretation of the effect [18]. This explanation is not necessarily inconsistent with the results from the current experiments. An attention-based theory might state that each presentation of the irrelevant cue lays down stronger IOR at the cued location, hence IOR increases with cue repetition. However, it is not clear what predictions would be made regarding SOA. Adaptation and habituation both predict a greater decrement in responding to ‘Same’ targets as the rate of presentation speeds up [20]. This means that the condition that typically produces the greatest behavioral inhibition in a standard onecue paradigm (i.e., the longest SOA condition) would map on to the condition in which the least amount of behavioral inhibition was observed overall when multiple cues are used. Because “traditional” attentional accounts of IOR have evolved to explain the timecourse in the one-cue paradigm, it is not clear what predictions would be made regarding the effect of SOA. Importantly, all of the patterns we have obtained here were predicted from the adaptation/habituation hypothesis. Acknowledgements We thank Ray Klein and two anonymous reviewers for comments on earlier drafts of this manuscript, Doug Munoz for support of author SB, and NSERC and Killam scholarships to author KD. References [1] R.A. Abrams, J. Pratt, Oculocentric coding of inhibited eye movements to recently attended locations, J. Exp. Psychol. Human 26 (2000) 776–788. [2] A.H. Bell, J.H. Fecteau, D.P. Munoz, Using auditory and visual stimuli to investigate the behavioral and neuronal consequences of reflexive covert orienting, J. Neurophysiol. 91 (2004) 2172–2184. [3] G. Berlucchi, Inhibition of return: a phenomenon in search of a mechanism and a better name, Cogn. Neuropsychol. 23 (2006) 1065–1074. [4] C.W. Clifford, M.A. Webster, G.B. Stanley, A.A. Stocker, A. Kohn, T.O. Sharpee, O. Schwartz, Visual adaptation: neural, psychological and computational aspects, Vision Res. 47 (2007) 3125–3131. [5] M.C. Dorris, R.M. Klein, S. Everling, D.P. Munoz, Contribution of the primate superior colliculus to inhibition of return, J. Cogn. Neurosci. 14 (2002) 1256–1263.
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