Repetition priming with no antipriming in picture identification

Vision Research 168 (2020) 9–17

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Repetition priming with no antipriming in picture identification Ailsa Humphries , Zhe Chen, Jonathan Wiltshire ⁎

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Department of Psychology, University of Canterbury, Christchurch, New Zealand

ARTICLE INFO

ABSTRACT

Keywords: Antipriming Repetition priming Object recognition Overlapping representations

Previous studies have shown that the processing of a stimulus is facilitated when that stimulus is repeated compared to when it appears the first time, and this phenomenon is called repetition priming (RP). One explanation for RP is that initial processing of a stimulus strengthens connections within the visual representation, enabling subsequent processing of the same stimulus to be more efficient. More recently, it has been reported that presenting an object with features that overlap with those in a subsequent stimulus impairs the latter’s processing, and this cost is termed antipriming (AP). AP is said to be the natural antithesis of RP, and it manifests when two objects share component features, thereby having overlapping representations. In two experiments, we investigated RP and AP in a picture naming task. Following previous research, we used a 4-phase paradigm, in which RP and AP were measured, respectively, by an increase or a decrease in performance for repeated or novel stimuli in Phase 4 compared with the baseline performance in Phase 2. We used a fully randomized design in Experiment 1, and a pseudo-randomized design in stimulus selection but a randomized design in presentation location in Experiment 2. We found robust RP in both experiments, but neither experiment showed any evidence of AP. Our results indicate that RP and AP do not always manifest within the same experiment, and that the relationship between these two effects may be more complex than previously understood.

Repetition priming (RP) refers to improvements in the speed and/or accuracy of processing a stimulus when that stimulus is repeated. Forster and Davis (1984) found faster responses in a lexical decision task when the prime and the subsequent target were the same word compared with different words. Malikovic and Nakayama (1994) also reported faster response times to a target defined by a unique object feature in a visual search task (e.g., a red stimulus among green stimuli or vice versa) when the target on consecutive trials shared the same feature value (e.g., all being red or all being green) compared with different feature values (e.g., a red target followed by a green target). These and other related findings (e.g., Humphries, Chen, & Neumann, 2018; Kersteen-Tucker, 1991; Scheepers & Sturt, 2014; Spataro, Longobardi, Saraulli, & Rossi-Arnaud, 2013; for reviews, see Ásgeirsson, Kristjánsson, & Bundesen, 2015; Henson, Eckstein, Waszak, Frings, & Horner, 2014) indicate that behaviour is not just driven by the current stimulus situation but is also affected by what has been processed in previous stimulus situations (Schacter, 1990). There are two main theoretical perspectives to account for RP. The first perspective explains RP in terms of facilitation in component processes as a result of changes in neural activation patterns. These changes can occur at the level of stimulus, task, and/or response, resulting in improved efficiency of processing when the same stimulus is

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subsequently presented. A number of physiological mechanisms have been proposed to explain how such neural changes could be operationalised, with some mechanisms operating at the level of individual neurons and others at the level of neural networks (see Grill-Spector, Henson, & Martin, 2006, for a review). The second perspective explains RP in terms of encoding and retrieval processes that operate on memory, with the perception of a specific stimulus serving as a retrieval cue for the previous encounter with that same stimulus (Hommel, 1998, 2004; Huang, Holcombe, & Pashler, 2004; Logan, 1988, 1990). Accounts of RP under this perspective assume that representations of familiar stimuli are processed as discrete and non-overlapping units (Grainger & Jacobs, 1996; Logan, 1990; Shiffrin & Steyvers, 1997). It has recently been argued that alongside the benefit of prior processing there is also a cost, and this cost has been termed ‘antipriming’ (AP) (Marsolek, Schnyer, Deason, Ritchey, & Verfaellie, 2006). AP is said to be the natural antithesis of RP and manifests as a detrimental effect on the efficiency of processing an object that shares component features with a previous object (Marsolek, 2008). Underpinning this account is the assumption that RP results from the strengthening of a representation, i.e., connections in the neural pathways used to process a visual image are strengthened when an image is presented. Once neural pathways are strengthened towards one representation, that

Corresponding author at: Department of Psychology, University of Canterbury, Private Bag 4800, Christchurch, New Zealand. E-mail address: [email protected] (A. Humphries).

https://doi.org/10.1016/j.visres.2019.09.011 Received 12 December 2018; Received in revised form 14 September 2019; Accepted 17 September 2019 0042-6989/ © 2020 Elsevier Ltd. All rights reserved.

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representation gains some degree of priority access to those pathways, resulting in easier access on repetition and faster processing for the repeated stimulus, producing RP. However, when an additional stimulus (e.g., a desk), which is not identical to the original stimulus (e.g., a piano) but which shares some component features (e.g., both having legs that support some rectangular components), is subsequently presented, the part of the representational structure that overlaps with the previous stimulus is no longer as easily accessible. Consequently, processing becomes less efficient, producing AP. Hence, AP is regarded as a type of interference that results from competitive interactions between overlapping perceptual representations in the visual modality (Marsolek, 2008). Under the assumption that objects belonging to the same category tend to share more overlapping features than objects belonging to different categories, the finding of AP in behavioural studies is consistent with neuroimaging studies, which show that objects belonging to the same category tend to be processed in the same region and objects belonging to different categories tend to be processed in different regions (Haxby et al., 2001; Kanwisher, McDermott, & Chun, 1997). These results are also consistent with the finding of neurocomputational modelling, which indicates that partially overlapping representations whose connections are continually adjusted based on experience provide an efficient and flexible network for learning about systematic relationships between the visual features of objects and the category the object belongs to (Hinton, McClelland, & Rumelhart, 1986), and for generalising those relationships to novel inputs (McClelland & Rumelhart, 1985). The notion of AP emphasizes the sharing of component features in visual objects. Feature sharing means that the neural pathways utilised for processing one visual object are also utilised for processing other visual objects. As such, it is theoretically plausible that the strengthening of connections between one representation and its component features may render those features less accessible for the processing of other representations. Specifically, when representations have not been recently accessed, their associated networks may be weakened (McClelland, McNaughton, & O'Reilly, 1995). This weakening of un-reinforced connections, relative to the strengthening for repeated representations that manifests as RP, may lead to a loss of efficiency in processing, resulting in AP (Marsolek et al., 2006). Such a cost has been identified in neurocomputational models (e.g., Marsolek et al., 2010; Moldakarimov, Bazhenov, & Sejnowski, 2010; Stark & McClelland, 2000) and in behavioural studies using familiar visual objects (Deason, 2008; Marsolek et al., 2006) and Chinese characters (Zhang, Fairchild, & Li, 2017). If AP is the natural antithesis of RP and a natural consequence of visual processing mechanisms that assist with the learning of visual images (Marsolek et al., 2006), similar processing mechanisms may underlie some other aspects of learning such as the learning of concepts and motor skills, as conceptual and response-related RP have all been reported in previous studies (Giesen & Rothermund, 2016; Kiesel, Kunde, & Hoffmann, 2007; Neely, 1991; Schacter & Buckner, 1998; Schmidt, Haberkamp, & Schmidt, 2011; Tucker & Ellis, 2004). However, before further investigating the source and the nature of AP effects, and extending AP research in areas other than visual object perception, it is important to assess the generality of the effect on visual object perception using similar but not identical methods as those used in the original studies. One specific methodological detail that could potentially affect the manifestation of AP is the use of a full-randomized design versus a pseudo-randomized one. There is evidence in previous research on RP that the two designs can sometimes lead to different patterns of data. Humphries (2017) conducted a series of experiments investigating RP in words and arithmetic equations. When the task was word categorization (animal vs. object) and the identity of the word stimuli changed between the study block and the test block, the average reaction time was slower in the test block compared with the study block, showing an AP like effect. Interestingly, this effect was found only when the words-to-condition was the same for all the participants. The effect was eliminated when the words-to-condition was different for each participant

(i.e., the stimuli were randomly assigned to conditions for each participant). A related effect was also reported by Deason (2008, Experiments 1 and 2), who measured short-term RP and AP in a masked priming paradigm with stimuli that consisted of line drawings of familiar visual objects. To minimize possible stimulus-specific effects, Deason created five lists counterbalanced on variables including object frequency, object typicality, and the semantic category of the object. Among other results, a significant priming by list interaction was found in RT in Experiment 1 and in accuracy in Experiment 2. Further analyses indicated that the main effect of priming was driven primarily by Lists 4 and 5, with AP found in Lists 4 and 5 in Experiment 1, and in List 4 in Experiment 2. These results show that AP is sensitive to stimulus and/or list specific effects. So far, only a few behavioural studies have observed both visual RP and AP within the same experimental paradigm and all have done so utilising pseudo-randomisation and/or counterbalancing designs (Deason, 2008; Marsolek et al., 2006, 2010; Zhang et al., 2017). In the experiments reported here, we tested the generality of AP in a full randomized design in Experiment 1 and a counterbalanced design in stimulus selection but a randomized design in stimulus presentation location in Experiment 2. To forecast our results, we found robust RP with no evidence of AP. 1. Overview of the present study To our knowledge, most studies of AP have used the paradigm developed by Marsolek et al. (2006). A typical experiment consists of four distinct phases. In Phase 1, participants listen to names of common objects presented one at a time while staring at a blank screen, and the task is to judge the likeability of each object.1 As AP is said to manifest when two objects share component features that lead to overlapping representations, Phase 1 would limit, at least to some extent, the influence of pre-experimental processing of visual objects in the environment that may have the same component features as the objects used later in the study, thereby contaminating the performance in the baseline in Phase 2. This in turn would affect AP, which is indicated by the difference in performance between the baseline and the “new” condition in Phase 4. In Phase 2, participants view a set of briefly presented images, each consisted of a single, full colored object, and the task is to name each object as quickly and as accurately as possible. Performance in this phase will serve as a baseline against which AP will be measured. In Phase 3, a new set of objects is shown, each for several seconds, and participants again judge the likeability of each object. Finally, in Phase 4, images of objects are shown very briefly, and the task is to name each object. The type of objects in Phase 4 is manipulated, with half of them old ones that have already been used in Phase 3 and the other half new ones that have not been shown in any of the previous phases. For the “old” objects, performance is expected to be better in Phase 4 compared with Phase 2 due to repetition priming from Phase 3. For the “new” objects, performance is expected to be worse in Phase 4 than in Phase 2 due to the presence of overlapping visual components among different objects. Even though the “new” objects have never been shown in the previous three phases, because objects are constructed from a limited set of geometric shapes such as blocks, cylinders, wedges, and cones (Biederman, 1987), similar component features can be expected to exist in both the “new” objects and the previously shown objects in Phases 2 and 3. Indeed, both RP and AP were found in studies where the primary stimuli were objects (Deason, 2008; Marsolek et al., 2006, 2010) and Chinese characters (Zhang et al., 2017)2. The two experiments reported here were modelled after Marsolek et al. (2006), using the 4-phase paradigm described above. In 1

The stimuli used in McClelland and Rumelhart (2006) and the present study consisted of both objects and animals. For the sake of brevity, we will use the word “object” or “objects” to refer to both types of stimuli. 2 Chinese has a logographic writing system, and visual form plays an important role in character identification. 10

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Experiment 1, we used full randomization in stimulus selection and presentation for each participant. Our decision to use full randomization instead of counterbalanced lists with pseudo-randomized stimulus selection and presentation, as in the original study of Marsolek et al., was based on two considerations. First, it was to minimize the possibility of inadvertently introduced effects such as stimulus and/or list effects (Deason, 2008; Humphries, 2017). Second, the use of probability theory in testing hypotheses is based on the assumption of randomization. When a fully randomized design is not used, it is possible that the results of statistical tests may be spurious (Fisher, 1937, 1955; Neyman & Pearson, 1933; Pearson, 1955). The results of Experiment 1 showed robust RP with no evidence of AP. In Experiment 2, we varied the stimulus presentation duration and used a pseudo-randomized design with counterbalanced lists. Once again, no AP was found even though there was a reliable RP effect. Taken together, these results suggest that RP and AP do not always manifest within the same experiment and that the relationship between RP and AP may be more complex than previously understood.

enrolled in a first year psychology course and participated in return for course credit. 2.1.3. Apparatus and stimuli. The experiment was presented on a PC running Windows 7 with a 50 cm × 30 cm monitor in width and height. E-Prime 2.0 was used to present the stimuli and to collect responses. Participants wore headphones with an integrated microphone in order to receive auditory input and to record verbal output. Manual responses were made on a Chronos Response Box from Psychology Software. Testing was carried out individually in a dimly lit room and the viewing distance was approximately 60 cm. All instructions and fixations were presented in black font on a white background. The fixation was a black cross that extended 0.7 degrees of visual angle in both width and height. The stimuli consisted of 250 familiar objects. They were presented either in visual form, which were full colour images extending 5.4 degrees of visual angle in height, and ranging from 0.9 to 10.9 degrees of visual angle in width, or in auditory form, which were recordings using their common names. To achieve full randomization the stimuli were imported into E-Prime and randomized at the beginning of the experiment for each participant. This ensured that each participant received a unique set both in terms of stimulus selection and presentation sequence in each condition. No stimuli were repeated in any phase except to fulfil the ‘repeated’ condition requirement in Phase 4.

2. Experiment 1 Experiment 1 was modelled after Experiment 1 in Marsolek et al. (2006). The experiment used the same stimuli and 4-phase design as the original study.3 However, unlike Marsolek et al., we used a full randomization design instead of a pseudo-randomization one. AP would be evidenced by a decrease in accuracy for novel stimuli in Phase 4 compared with those in Phase 2, and RP would be evidenced by an increase in accuracy for repeated stimuli in Phase 4 compared with the stimuli in Phase 2.

2.1.4. Design and procedure The experiment used a within subjects design with 3 conditions (baseline vs. repeated vs. novel). Each trial in every phase started with a pre-target fixation for 500 ms and ended with a post-target fixation for 500 ms. Between the two fixations, depending on the phase, participants were required to make a response, within 3000 ms, to either the auditory presentation of a word or the visual presentation of an image (see Fig. 1). Phase 1 consisted of the auditory presentation of 50 words and the task was to rate the likeability of each concept. Responses were made manually with two fingers of the right hand on the basis of a binary like/dislike decision, the first finger was used for a “like” response and the second finger for a “dislike” response. Between the fixations, a blank screen was shown for 3000 ms, during which time the auditory stimulus was presented and the participants were required to make a response. Phase 2 was the baseline phase in which visual objects were briefly presented, and the task was to name the object out loud. The stimulus was flashed for 34 ms followed by a blank screen for 2966 ms.6 The stimuli were presented 4.3° above or below fixation, with location being randomly selected on each trial. The brief stimulus presentation duration and the variation in stimulus presentation location are important features of the AP paradigm to ensure that identification accuracy, which is the primary dependent variable, is not at ceiling. In total, 100 images were shown, with 50 presented above fixation and 50 below fixation. In Phase 3 visual objects were presented centrally for 3000 ms and participants were again required to rate them for likeability by pressing the same two keys as those in Phase 1. As with Phase 1, the rating was based on the concept, rather than the specifics of the image itself, and a binary like/dislike decision was made. All 50 images from Phase 3 were subsequently used as the ‘repeated’ stimuli in Phase 4. In Phase 4 the same task was performed as in Phase 2. Participants again viewed each visual object for 34 ms, followed by a blank screen for 2966 ms, during which time they had to name the object out loud. One hundred objects were presented, 50 were novel and 50 were repeated from Phase 3. The 50 repeated images provided the stimuli for

2.1. Method 2.1.1. Ethics statement This study received prior ethics approval from The University of Canterbury Human Ethics Committee. Written consent was obtained from all the participants. 2.1.2. Participants Twenty participants between the ages of 16 and 49 years (M = 21.6 years, SD = 7.9 years) were recruited from the University of Canterbury (1 male and 19 females)4. This sample size was based on Experiment 1 in Marsolek et al. (2006), in which a main effect of priming with partial η2 = 0.85 was found.5 Assuming a smaller effect with partial η2 = 0.5, we conducted a power analysis with G*Power 3.1 (Faul, Erdfelder, Buchner, & Lang, 2009). For α = 0.05 and 95% power, the recommended sample size was 11. The sample size we used was 20, the same as that used in Marsolek et al. The participants were all 3

We thank Chad Marsolek and Rebecca Deason for sending us the stimuli and the counterbalancing lists. These stimuli were used in both experiments, and the counterbalancing lists were used in Experiment 2. 4 The participants in all the experiments reported here were recruited from the participant pool of the University of Canterbury Psychology Department, which consisted of students from two introductory psychology courses. The wide age range was caused by the age of a small number of mature students enrolled in the courses, and this variability could increase the variability in object recognition performance, which in turn could make the experiments less sensitive. There was also a disproportionate number of female participants in our study. We did not attempt to recruit equal number of male and female participants, as there is no evidence in previous research that RP and/or AP differ as a function of sex or gender. 5 McClelland and Rumelhart (2006) did not report the effect size for the main effect of priming. We calculated the effect size using the method described in Lakens (2013). In hindsight, the sample size used in our study could have been based on the effect size of AP rather than that of the main effect of priming in the original study.

6 The experiment was programmed to have a stimulus duration of 1 refresh cycle, which was 17ms. However, the interface between the E-Prime program and Chronos Response Box led to a duration of 2 refresh cycles.

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Fig. 1. Sequence of events in each phase of Experiments 1 and 2. Excluding the fixation displays, the duration of each trial (stimulus presentation and response) was 3000 ms regardless of phase. The stimuli were equally likely to be above or below fixation in Phases 2 and 4, but were centrally located in Phase 3. In Phase 4, all images from Phase 3 were repeated and interwoven with the same number of novel images. The two types of images were randomly selected on a given trial.

the calculation of RP, while the 50 novel images provided the stimuli for the calculation of AP. The two types of stimuli were randomly selected on a given trial. As in Phase 2, the stimuli were presented randomly at 4.3° above or below the fixation. Because stimuli were shown at fovea in Phase 3 but at parafovea in Phase 4, any RP effect, if found, could not be attributed to the repetition in location. Both speed and accuracy were emphasised. Each participant first completed a brief practice session and no stimuli used in the practice session were used again in the experiment. The total amount of time for the experiment was approximately 40 min. 2.2. Results and discussion Both accuracy and reaction time (RT) were calculated, with accuracy being the primary dependent variable. To be regarded as a correct response, participants had to verbalise either the ‘common name’ for the object or an acceptable synonym. The ‘common name’ was the name that had been identified as correct in the original research (e.g., “jug” for an image of a jug) and a synonym was deemed to be acceptable when the meaning was the same (e.g., “pitcher” instead of “jug”). A name was also deemed to be acceptable when there was a high rate of agreement amongst participants; for example, while one image had been identified as a “teapot” in the original research, over 50% of the participants in the present experiments identified it as a “kettle”. Rather than being an error of naming, these situations are likely due to cultural differences in the common names used for various objects. The analysis of accuracy was carried out in a blinded manner, with the analyst having no knowledge of which condition each trial belonged to. For each participant, the number of correct responses was divided by the total number of trials, and this gave us the percentage of correct responses for each participant. The same procedure was used in calculating the percentage correct for performance in Phase 2, and in the repeated and novel conditions in Phase 4. Fig. 2 shows the mean identification accuracy in the three conditions, and Table 1 shows the RT data.7 A repeated measures 3-way analysis of variance (ANOVA) (baseline

Fig. 2. Mean object identification accuracy in baseline (Phase 2), repeated (Phase 4), and novel (Phase 4) conditions. Error bars represent the standard error of the mean.

Table 1 Mean reaction times as a function of conditions in Experiment 1.

Baseline Repeated Novel

RT (ms)

Diff

SE diff

95% CI diff

886.77 823.73 868.97

62.04 16.79

20.96 20.98

17.60, 106.47 −27.69, 61.68

Note: Diff refers to the mean difference in reaction time between the baseline and the two experimental conditions. SE diff and CI diff refer to the corresponding standard error and confidence intervals.

7 The recording of the RTs relied on voice-key activation through the microphone as participants named the object presented in Phases 2 and 4. Although the participant’s verbalisations were always loud enough to record an auditory response, they were not always loud enough to activate the voice key. Overall, on 2% of trials the voice-key was not activated and for three of the participants there was no RT data to analyse. Hence while the accuracy data is reported for all 20 participants, the RT data is only reported for 17 participants.

vs. repeated vs. novel) was carried out on the error rates. There was a main effect of condition, F(2, 38) = 31.35, p < .001, MSE = 28.70, partial η2 = 0.62, indicating a statistically significant difference in errors between the three conditions. Subsequent one-sample t tests on the difference scores between Phases 2 and 4 further showed no difference 12

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between the baseline and the novel stimuli in Phase 4, t(19) = -0.11, p = 0.91, drm = 0.03,8 but a significant increase in accuracy for the repeated stimuli in Phase 4 compared with the baseline in Phase 2, t (19) = 7.10, p < 0.001, drm = 1.85. Following previous research (e.g., Marsolek et al., 2006), in which AP is indicated by a decrease in accuracy for novel stimuli in Phase 4 compared with those in Phase 2, and RP by an increase in accuracy for repeated stimuli in Phase 4 compared with the stimuli in Phase 2, this pattern of data indicated a robust RP with no AP in accuracy. A repeated measures 3-way ANOVA was also carried out on the mean RTs. There was a main effect of condition, F(2, 32) = 4.96, p = .013, MSE = 3531, partial η2 = 0.24, indicating a statistically significant difference between the three conditions. Further t tests showed a significant decrease in RT from the baseline to the repeated condition in Phase 4, t(16) = 2.96, p = 0.01, drm = 0.48, indicating RP. No difference was found between the baseline and the novel condition in Phase 4, t(16) = 0.80, p = 0.44, drm = 0.13. Once again, the participants showed robust RP with no AP. These results are consistent with those found in accuracy, indicating no speed-accuracy trade-off. The primary goal of Experiment 1 was to generalize the AP effect from a pseudo-randomization design to a full randomization design. We found robust RP but no evidence of AP in error rates or RT. This pattern of data indicates that the visual processing of images in Phase 3 had no measurable impact on the processing of the novel images in Phase 4. At the same time, when visual images were repeated from Phase 3 to Phase 4, accuracy improved compared with that for the novel images in the baseline phase. Why did Experiment 1 not show the AP effect reported in Marsolek et al. (2006)? Is it possible that the difference in results was caused by the higher accuracy in our experiment, which was 77% in Phase 2, than in Marsolek et al., which was 56% in the corresponding condition? Perhaps AP occurs only when the sensory representation of an image is very poor. Although this possibility cannot be ruled out, it is unlikely to be the main cause for two reasons. First, AP is indicated by a reduction in performance in Phase 4 compared with the performance in Phase 2. Relative to lower accuracy, higher accuracy in Phase 2 should provide more room for performance to go down in Phase 4. Consequently, AP should be easier, rather than harder, to manifest when accuracy in Phase 2 is relatively high. Second, Zhang et al. (2017) reported AP in their study despite high accuracy rates in all the experimental conditions (86%, 92%, and 97% for the antipriming, baseline, and repetition priming conditions, respectively), suggesting that AP is not restricted to low performance in the baseline condition. However, it is worth noting that Zhang et al. used a word naming task, which is quite different from the picture naming task used in Experiment 1. In Experiment 2, we used the same picture naming task as that in Experiment 1 and we manipulated stimulus presentation duration to investigate the effect of overall accuracy on the manifestation of AP.

full randomization procedure as in Experiment 1, we used counterbalanced lists from Deason (2008) and Marsolek et al. (2006). As before, the stimuli in Phase 2 and 4 were randomly presented above or below the fixation. We were particularly interested in whether an AP effect would be found in one or both versions of Experiment 2. If the effect in the original research was in some way due primarily to some systematic differences introduced by the counterbalancing and/or pseudo-randomization procedures in stimulus selection, then AP should be observed in both versions, or in one or more lists if AP is sensitive to list-specific effects. If the presentation duration of the objects in Phases 2 and 4 was also a critical factor, then AP should be found only in the 17 ms version. A comparison of results between these two versions would reveal whether randomization and/or stimulus presentation duration could account for the lack of AP in Experiment 1. 3.1. Method The method was the same in Experiment 2 as in Experiment 1 except for the following changes. First, five counterbalanced lists were used across the participants. Within each list, the stimuli were presented in the same sequence for each participant. Second, in Phase 4, the ‘repeated’ and ‘novel’ stimuli were “interwoven”, ensuring there were no sequences of more than three ‘repeated’ or ‘novel’ stimuli in a row, while retaining the presentation sequence within each list. Third, stimulus display duration was varied. In one version the presentation duration was the same as in Experiment 1 (34 ms) and in the other version the duration was halved to 17 ms. The experiment used a 2 (version: 34 ms vs. 17 ms) × 3 (condition: baseline vs. repeated vs. novel) × 5 (list: 1 through 5) mixed design, with version and list being a between-subjects factors. Forty new participants from the same participant pool took part in the experiment (9 males and 31 females). Participants ranged in age from 17 to 35 (M = 19.6 years, SD = 3.1 years). They were randomly and equally assigned to either version, resulting in 6 and 3 males in the 34 ms and the 17 ms version, respectively. Each participant was randomly assigned to each list within a version. 3.2. Results and discussion Results were treated in the same way as in Experiment 1. We report only the accuracy data below because a large proportion of the RT data was not recorded (43% in the 34 ms version and 71% in the 17 ms version) as a result of the voice-key not being activated. Fig. 3 shows the object identification accuracy as a function of stimulus presentation duration. To compare our results with those in Experiment 1, we first conducted a 2 (34 ms vs. 17 ms) × 3 (baseline vs. repeated vs. novel) mixed ANOVA on the error rates. A main effect of version was found, F (1, 38) = 4.22, p = .046, MSE = 120.56, partial η2 = 0.10, indicating a higher overall error rate for the 17 ms version (25%) than the 34 ms version (21%). In addition, there was a main effect of condition, F(1, 38) = 108.27, p < .001, MSE = 22.97, partial η2 = 0.74, indicating a difference in errors between the three conditions. Importantly, condition and version did not interact, F(2, 76) < 1, ns, suggesting that display duration had a negligible effect on RP or AP. Single sample ttests further showed a significant RP, t(39) = 14.43, p < 0.001, drm = 1.94, but no AP t(39) = −0.34, p = 0.74, drm = 0.04. Next, we performed a 2 (34 ms vs. 17 ms) × 3 (baseline vs. repeated vs. novel) × 5 (list 1 through 5) mixed ANOVA to examine the effect of list. In addition to a significant main effect of condition, F(2, 60) = 125.50, p < .001, MSE = 19.82, partial η2 = 0.81, there was a condition by list interaction, F(8, 60) = 3.04, p = .01, MSE = 19.82, partial η2 = 0.29. To clarify the interaction, we conducted two separate ANOVAs, one to examine the effect of list on RP and the other on AP. For the list effect on RP, we performed a 2 (baseline vs. repeated) × 5

3. Experiment 2 Experiment 2 consisted of two versions. In one version, which was completed by one group of participants, visual objects were presented for 34 ms in Phases 2 and 4, the same duration as that in Experiment 1, and we will refer to this version as the 34 ms version. In the other version, which was completed by another group of participants, the stimulus presentation duration was reduced to 17 ms, to more closely match the original research. This version will be referred to as the 17 ms version. Experiment 2 also examined whether random selection of stimulus contributed to the results in Experiment 1. Instead of using a 8 The effect sizes reported here are Cohen’s drm and are calculated as per Lakens (2013). This version of Cohen’s d was chosen as it explicitly takes the correlation between the two conditions into account and it consistently results in more conservative estimates of the effect size.

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What can cause the differences in results between the present study and Marsolek et al. (2006)? One obvious difference between the studies is the participants. We had a much higher proportion of females in our study. However, this difference should not have much influence on the results, as there is no evidence in previous research that the manifestation of RP or AP is affected by the participants’ sex or gender. A large scale recent study has also shown that sample or setting is rarely a factor in the presence or absence of an effect (Klein, Vainello, & Hasselman, 2018). Another difference between the two studies concerns stimulus selection and stimulus-to-location mapping. Whereas care was taken in both studies to minimize stimulus-specific and location-specific effects, different approaches were used. In Marsolek et al. (2006), stimuli were pre-selected to form five counterbalanced versions, and within each version both the sequence of stimuli and the presentation location of stimuli (above or below fixation) in phases 2 and 4 were pseudo-randomized with the constraint that no more than three consecutive trials were of the same type. In our study, we used complete randomization in stimulus selection in Experiment 1 and counterbalanced lists in Experiment 2. In both experiments, stimulus presentation location was randomly selected on each trial. When stimuli are equally likely to appear at one of two locations, the number of location-same trials (i.e., no change in stimulus location from trial n to trial n + 1) approximates the number of location-change trials (i.e., a change in stimulus location from trial n to trial n + 1). When stimulus-to-location mapping is pseudo-randomized with the constraint of no more than three samelocation trials in a row, based on probability theory, there should be more location-change trials than location-same trials. We confirmed this with computer simulations.9 Location is known to play a special role in visual processing. A number of studies have found that spontaneous location processing occurs when attention is paid to an object feature (Cepeda, Cave, Bichot, & Kim, 1998; Chen, 2009; Tsal & Lavie, 1993), and that stimuli presented at the same location have better sensory representation than stimuli presented at different locations (Cave & Chen, 2017; Hawkins et al., 1990; Müller & Findlay, 1987). As there are likely to be more location-change trials in Marsolek et al. (2006) than in the present study, this can explain why the overall performance level was higher in our experiments. However, although the proportion of location-change vs locationsame trials can explain the different level of performance between the present study and Marsolek et al. (2006), it cannot explain the absence of an AP result in our study, because we found no difference in the pattern of data between the location-same and the location-different trials in Experiment 2.10 The robustness of the AP effect may have something to do with the

Fig. 3. Mean object identification accuracy in baseline (Phase 2), repeated (Phase 4), and novel (Phase 4) conditions, for each version of Experiment 2 (34 ms vs. 17 ms). Error bars represent the standard error of the mean.

(list 1 through 5) mixed ANOVA. Both a main effect of RP, F(1, 35) = 356.31, p < .001, MSE = 10.19, partial η2 = 0.91, and a significant priming by list interaction were found, F(4, 35) = 7.91, p < .001, MSE = 10.19, partial η2 = 0.47. The latter result indicates that not all lists were created equal, with some lists showing a larger RP effect than others (e.g., a 19.7% reduction in error rates from Phase 2 to Phase 4 in List 3 but an 8.7% reduction in List 5). To assess the effect of list on AP, we performed a 2 (baseline vs. novel) × 5 (list 1 through 5) mixed ANOVA. The results showed a negligible effect of AP, F(1, 35) < 1, ns., and a trend for an AP by list interaction, F(4, 35) = 2.14, p = .10, MSE = 20.37, partial η2 = 0.20. Of the 5 lists, only List 4 showed an indication of AP (a 5.1% increase in error from Phase 2 to Phase 4). When the data was collapsed across lists, the results of Experiment 2 were remarkably similar to those of Experiment 1. In both experiments, participants showed strong RP with no evidence of AP. This indicates that randomized vs. pseudo-randomized selection of stimulus was unlikely to be a major contributor to the presence or absence of AP. There was an indication of a list by AP interaction, with List 4 showing the most AP. The same pattern of result was found in Deason (2008), even though the paradigm she used was quite different from the one used here. These results suggest that AP may be sensitive to list-specific effects. However, given the small number of participants for each list, these results should be interpreted with caution. Reducing the presentation duration to a single refresh cycle resulted in a decrease in accuracy from 76% (34 ms version) to 71% (17 ms version). This result was expected as naming relies on identification, which was made more difficult when the viewing time was reduced. Importantly, reducing the presentation time did not have an observable effect on the magnitude of RP or AP. This pattern of data is consistent with the notion that performance level is unlikely to be a major factor in the manifestation of AP, at least in the present paradigm. However, it is worth noting that the reduction in presentation time did not bring the accuracy to the same level as was reported in the original AP research (56% in Marsolek et al., 2006, Experiment 1).

9 We performed 2 sets of 100 computer simulations. In one set, each simulation consisted of 100 trials (in line with the number of trials in Phases 2 and 4 of the current experiments) that could take one of two values (with equal probability) with the constraint that no more than 3 trials of either type could be presented in a row. The results show that the proportion of location-change trials was greater than the proportion of location-same trials in 99 of the 100 simulations. On average there were 42.24 (Std. Dev. = 3.56) location-same trials and 56.76 (Std. Dev. = 3.56) location-change trials, and this difference was significant, t(99) = 20.38, p (two-tailed) < 0.001. Conversely, when location selection was done without the constraint (i.e., random stimulus-to-location mapping) in the other set of simulations, the proportion of locationchange trials was greater than location-same trials in 44 of the 100 simulations. On average, there were 50.09 location-same trials (Std. Dev. = 5.13) and 48.91 location-change trials (Std Dev. = 5.13), and this difference was not reliable, t (99) = 1.15, p (two-tailed) = 0.25. 10 To see whether AP is more likely to manifest in the location-change trials compared with the location-same trials in Experiment 2, we conducted a 3 (baseline vs. repeated vs. novel) x 2 (location-same vs location-change) repeated-measures ANOVA while collapsing the data across version and lists. No effects involving location were found.

4. General discussion Using the same stimuli and paradigm as in Marsolek et al. (2006), we conducted two experiments to investigate AP, which is considered as the natural antithesis of RP (Marsolek et al., 2006). However, no AP was found in either experiment despite robust RP in both experiments. This may indicate that the manifestation of AP is sensitive to specific methodological features, and that AP may not be a general phenomenon in visual object perception. Alternatively, it may indicate that the relationship between AP and RP is more complex than originally thought and that AP cannot simply be considered as the flip-side of RP. 14

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way AP is measured in the present paradigm. In both our study and the previous visual AP studies, AP is indicated by decreased performance in the novel condition of Phase 4 compared with the baseline condition in Phase 2. This way of measurement has two inherent issues. First, the level of performance in Phase 2 and the magnitude of RP and/or AP are necessarily correlated. Performance is known to be influenced by two types of processes: a resource-limited process and a data-limited process (Norman & Bobrow, 1975). When the representations of stimuli are poor due to brief display duration or the quality of the stimuli themselves, performance is determined mainly by the data-limited process. Because of individual differences in signal-to-noise sensitivity, some participants will perform better than others. In general, any experimental manipulation designed to improve participants’ performance over time should have a smaller effect on those who initially performed better rather than those who initially performed poorly, due to the ceiling effect. In contrast, any experimental manipulation designed to impair participants’ performance over time should have the opposite effect, due to the floor effect. In terms of the present paradigm, this means that the participants who performed better in Phase 2 would have a smaller RP effect but a larger AP effect if the manipulations in the RP and AP conditions were successful. Examination of the data in the present study showed significant negative correlation in object identification accuracy between the baseline in Phase 2 and RP in both Experiment 1 (r = −0.70, p < .001) and Experiment 2 (r = −0.52, p < .001). A significant positive correlation between the performance in Phase 2 and AP was found in Experiment 1 (r = 0.54, p = .02), but not in Experiment 2 (r = 0.19, p = .25). The second issue concerns the effect of practice.11 Both RP and AP are measured as differences in performance between Phase 2 and Phase 4. If we assume that performance improves with practice overall, using the difference score between the two phases to calculate the degree of priming is likely to inflate the effect of RP, which is indicated by an increase in performance, while masking the effect of AP,12 which is indicated by a decrease in performance. Both issues can distort the results, making them difficult to interpret. Even though we did not find AP in our study, AP may still be a genuine phenomenon. AP has been independently predicted by a ratebased network model of visual processing (Moldakarimov et al., 2010). The model is built on the finding that while repeated exposure of a stimulus decreases the activity of one class of cells in a cortical area, it enhances the activity and selectivity of another class of cells in the same area when the repeated stimulus is a target (Desimone, 1996). However, these mechanisms, which underlie RP, may also lead to AP when

two stimuli share many component features and have overlapping perceptual representations, because the sharpening of the representation of one stimulus could interfere with the representation of the other stimulus. Interestingly, although the rate-based network model predicts a larger AP in behavioural studies when objects share a high degree of visual similarity than when objects share a low degree of visual similarity, no such result was observed in Deason (2008, Experiment 3), who examined the effect of visual similarity on AP, and found no significant difference in either accuracy or RT when the low and the high similarity AP conditions were compared. Previous research has also reported an AP-like cumulative semantic interference effect (Belke, 2008; Howard, Nickels, Coltheart, & ColeVirtue, 2006; Schnur, Schwartz, Brecher, & Hodgson, 2006). Howard et al. showed participants pictures from a large number of semantic categories, with the pictures from the same semantic category intermixed with those from different semantic categories. The task was to name each picture. Naming time increased linearly with the number of preceding pictures that belonged to the same semantic category, and this result was independent of the number of preceding pictures that belonged to different semantic categories. This semantic inhibition effect has also been confirmed in computer simulations (Howard et al., 2006; Oppenheim, Dell, & Schwartz, 2007, 2010). The mechanisms proposed to underpin the semantic interference effect are analogous to those of AP. Namely, semantic interference is thought to be driven by shared representations, competitive selection, and priming, whereas the mechanisms hypothesised to underpin visual AP are overlapping feature representations, competitive utilisation of processing pathways, and priming. Interestingly, the task used in many of the semantic interference studies was picture naming. As pictures that depict objects from the same semantic category (e.g., fruits) may share more overlapping visual features than those from different semantic categories (e.g., fruits vs. zoo animals), part of the semantic interference effect may come from overlapping visual features. The proposal that AP is the natural antithesis of RP is consistent with the continual-adjustment theory. According to the theory, connections between a visual representation and its component features are strengthened when a visual image is processed (e.g., Hinton et al., 1986; McClelland et al., 1995; McClelland & Rumelhart, 1985), resulting in RP. If RP manifests because such strengthening results in more efficient processing for visual input that has recently been experienced, it makes intuitive sense that the same strengthening could render the processed features less accessible when they belong to objects that have very different overall appearances, especially when they belong to different categories (e.g., a piano vs. a desk). While AP may be the natural antithesis of RP, it is possible that they do not have the same characteristics. As we are constantly bombarded with visual input, and the processing of such input uses resources that are finite, it makes sense that input encountered frequently gains a degree of processing efficiency. However, it also makes sense for connections between visual components to retain a degree of flexibility in order to process the constantly changing input received through the visual system. This flexibility fits neatly with the Recognition-byComponents Theory within which it is posited that there are a finite number of geometrical components comprising an ‘alphabet’ of shapes, termed geons, and these geons are able to be combined in an infinite number of ways to aid the recognition of visual objects (e.g., Biederman, 1987; Biederman & Kalocsai, 1997). The flexible recombination of a finite number of geons (approximately 36 were identified by Biederman, 1987) ensures that all processing units and their interconnections are available to combine in novel ways to process the potential infinity of ways in which visual features can be combined into simple and complex objects. While processing can lead to longlasting efficiency gains, it is less likely to lead to long-lasting costs, due to the need to maintain flexibility in visual processing. Hence, the nature of the relationship between RP and AP may be asymmetrical, with RP being a more general effect than AP.

11

We thank Marsolek for pointing out the possible existence of a practice effect in AP. 12 The masking of AP by practice effect is demonstrated in Galazen, Gloston, Archambault, Muncon, & Marsolek (2014), who examined AP in a spoken word identification task using a 4-phase paradigm that consisted of auditory stimuli. No AP was found in accuracy or RTs when the data from Phase 2 and Phase 4 were compared directly. However, when response time residuals were analysed after the speed-up in responses (i.e., the practice effect) was regressed out, significant AP was found in RTs. We also performed a regression analysis on the RT results of Experiment 1 to determine whether practice effects had masked evidence of AP. For each participant, we regressed the RTs (Phases 2 and 4) on trial number and used the best-fit linear model to predict the RT. We then calculated the unstandardized residuals for each trial and ran a 3-way repeated measures ANOVA (baseline vs. repeated vs. novel) with residuals as the dependent variable. There was a main effect of condition, F(2, 32) = 3.85, p = .03, MSE = 2722, partial η2 = 0.19, indicating a statistically significant difference in RT between the three conditions. Subsequent one-sample t tests on the difference scores between Phases 2 and 4 further showed no difference between the baseline and the novel stimuli in Phase 4, t(16) = 0.35, p = 0.732, drm = 0.05, but a significant decrease in RT for the repeated stimuli in Phase 4 compared with the baseline in Phase 2, t(16) = 2.62, p = 0.018, drm = 0.68. These results indicate that the practice effect, if any, cannot explain the absence of AP in our experiment. 15

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Several factors have been found to influence RP. A number of studies have shown that RP is robust and relatively view-invariant when the prime is attended; however, when the prime is unattended, RP appears only when the prime and probe are shown in the same viewpoints (Stankiewicz, Hummel, & Cooper, 1998; Thoma & Davidoff, 2006; Thoma, Hummel, & Davidoff, 2004). Thoma et al. examined the effect of attention and object configuration on short-term priming. Participants saw either an intact object (the intact condition) or an object splitting into two halves (the split condition) in the prime display, with one of them attended and the other one ignored. The task was to name the subsequently presented probe, which was always an intact object. Both attention and viewpoint affected priming. For the attended object, robust priming was found regardless of the configuration of the object in the prime display, although the magnitude of the effect was reduced in the split condition. For the unattended object, a small but significant priming effect was found in the intact condition, but not in the split condition. Further experiments by Thoma and Davidoff showed a similar pattern of data when they manipulated attention and objects rotated in depth. For an attended prime, priming was observed when the probe matched the prime and when they differed in depth-rotation. For an unattended prime, priming was only found when the prime and probe matched in viewpoints. These results indicate that object recognition can be holistic (i.e., viewpoint-dependent) or analytic (i.e., viewpoint-invariant), and that attention plays an important role in the type of representation that mediates object recognition (Hummel & Standiewicz, 1996; Stankiewicz et al., 1998). The finding of RP (not AP) in the attended condition when the prime and probe did not match in viewpoints also suggests that a substantial proportion of the RP effect may come from view-invariant long-term object representations. Depending on the task, manifestation of AP may require objects that share overlapping features that belong to different basiclevel categories (e.g., piano, table) rather than different viewpoints of the same object or different objects within the same basic-level category (e.g., upright piano, grand piano). The studies described above used a relatively short delay between the prime and the probe (i.e., within a few minutes). In general, shortterm RP is more sensitive to viewpoints than long-term RP. In Thoma et al. (2004) and Thoma and Davidoff (2006), who showed viewpoint specific priming, the interval between the prime and probe was within 3 s. In Ellis, Allport, Humphreys, and Collis (1989), who also reported reduced priming when the prime and probe differed in viewpoints compared with when they were identical, used a shorter prime-probe interval (up to 100 ms). Lawson and Humphreys (1996) further showed that the viewpoint specific benefit decreased with an increase in the prime-probe interval. Using a prime-probe delay of approximately 7 min, Biederman and Cooper (1991) observed no evidence of a decrease in priming when prime and probe were shown in mirror-image orientations compared with when they were presented in the identical orientation. The effect of attention on AP is unclear. To date, no research on AP has directly examined the role of attention and no research has investigated AP for ignored objects. For attended objects, AP is typically much smaller than RP (Marsolek et al., 2006; Zhang et al., 2017), and the same pattern of data occurs when the prime is masked and the prime-probe interval is short (Deason, 2008). These results are not surprising in light of previous research on RP, in which the magnitude of priming is also smaller when the prime and probe are not identical (e.g., Thoma et al., 2004; Thoma & Davidoff, 2006). The generally small AP effect when objects are viewed in full attention makes it quite possible that no AP would manifest when a prime is not attended. The effect of delay on AP has not been systematically investigated, either. Although AP has been observed in studies when the delay between the prime and probe was long (e.g., Marsolek et al., 2006; Zhang et al., 2017) and when it was short (e.g., Deason, 2008), the many methodological differences between the studies prevented a clear conclusion. Whether delay would interact with some other factors such as

attention and/or viewpoints will be an interesting topic for future research. In summary, following previous research, we investigated RP and AP in a 4-phase paradigm, and we found robust RP without AP. These results differ from previous research, and they show that RP and AP do not always occur within the same experiment, and that the relationship between RP and AP may be more complex than previously understood. Our study also shows that the ways RP and AP are measured in the present 4-phase paradigm may have unintended consequences such as inflating RP while possibly also masking AP. Our results underscore the importance for future study to take into consideration methodological issues that can affect the manifestation of AP, and to identify the factors that might influence RP and AP differently. 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