Age of acquisition and the recognition of brand names: On the importance of being early

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Journal of CONSUMER PSYCHOLOGY

Journal of Consumer Psychology 20 (2010) 43 – 52

Age of acquisition and the recognition of brand names: On the importance of being early Andrew W. Ellis a,⁎, Selina J. Holmes a , Richard L. Wright b a

b

Department of Psychology, University of York, York YO10 5DD, UK Unilever R&D, Port Sunlight, Quarry Road East, Bebington, Wirral CH63 2PP, UK Received 12 September 2008; revised 24 July 2009; accepted 12 August 2009 Available online 29 September 2009

Abstract Research in cognitive psychology has shown that words, objects and faces learned early in life are recognized more fluently than similar items learned later. Experiment 1 shows that early acquired brand names are recognized more quickly than later acquired brands. Experiment 2 shows that the age of acquisition effect extends to accessing semantic knowledge about brands. In Experiment 3, older participants were faster at recognizing early learned brands that are now extinct than more recent, active brand names. Early surviving brands were recognized quickest of all. The significance of these effects for manufacturers and marketing are discussed. © 2009 Society for Consumer Psychology. Published by Elsevier Inc. All rights reserved. Keywords: Age of acquisition; Brand names; Brand recognition; Frequency; Aging

A substantial body of research has established that, all other things being equal, words learned early in life are recognized more quickly and more accurately than words acquired later (Cortese & Khanna, 2007; Johnston & Barry, 2006; Juhasz, 2005). The benefits of early acquisition are not confined to word recognition: objects and faces that are learned early are also recognized and named more quickly than later acquired objects (Cuetos, Ellis, & Alvarez, 1999; Ellis & Morrison, 1998; Holmes & Ellis, 2006; Moore & Valentine, 1998, 1999; Pérez, 2007). The questions we will address in the present paper are the following. (1) Do the benefits of early acquisition extend to the recognition of brand names? (2) Do early acquired brand names enjoy an advantage in recognition speed throughout adulthood simply by virtue of having been learned early in life? (3) If they do, what are the implications for manufacturers and for advertising? The first psychological studies of ‘age of acquisition’ effects mostly employed naming tasks (object naming or ⁎ Corresponding author. Fax: +44 1904 433189. E-mail addresses: [email protected] (A.W. Ellis), [email protected] (S.J. Holmes), [email protected] (R.L. Wright).

reading aloud; e.g., Carroll & White, 1973; Gilhooly & Gilhooly, 1979), and the first theoretical accounts of age of acquisition effects tended to concentrate on how early acquisition might facilitate the retrieval of spoken words from the ‘mental lexicon’ (e.g., Brown & Watson, 1987; Gilhooly & Watson, 1981). More recent research has established that early acquisition benefits performance in tasks that do not require name retrieval, for example, deciding whether pictorial images depict real or imaginary objects (Catling & Johnston, 2006, 2009; Holmes & Ellis, 2006; Moore, Smith-Spark, & Valentine, 2004) or deciding whether pictures of faces depict famous or unknown individuals (Moore & Valentine, 1999; Richards & Ellis, 2008, 2009). We now know that age of acquisition affects object recognition, object naming, face recognition, face classification and face naming, as well as the recognition, classification and reading aloud of both spoken and written words in a wide range of different languages (Johnston & Barry, 2006; Juhasz, 2005). There is growing acceptance, however, that early acquisition facilitates processing in a way that cannot be explained in terms of differences in factors related to age of acquisition, such as the total frequency with which early and late acquired words are experienced over the course of a life.

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The relative importance of age of acquisition and frequency as determinants of processing speed has been extensively debated in the literature on word and object recognition (Johnston & Barry, 2006; Juhasz, 2005). There are actually two aspects of frequency that are relevant to understanding and interpreting age of acquisition effects. Words and objects that have high frequencies in everyday life tend to be learned earlier than items with lower ambient frequencies (Morrison, Chappell, & Ellis, 1997). Many studies have shown that age of acquisition effects remain when frequency is controlled, either experimentally or statistically (see Johnston & Barry, 2006; Juhasz, 2005, for reviews, and Cortese & Khanna, 2007; Pérez, 2007, for recent confirmations of this point). But such control only takes account of current frequency of exposure. Words and objects learned earlier in life will also tend to have been encountered more often in total than items learned more recently; that is, cumulative or lifespan frequency is likely to be higher for early acquired than for later acquired stimuli. Some researchers have asked whether age of acquisition effects might reduce to differences in cumulative frequency across the lifespan (Carroll & White, 1973; Lewis, Gerhand, & Ellis, 2001). Three approaches have been taken to answering the question of whether age of acquisition effects reduce to differences in cumulative frequency of exposure. The first approach involves combining the frequency of an item with its age of acquisition to create a variable designed to capture the possible effects of cumulative frequency. Carroll and White (1973) found that the inclusion of such a variable in a regression analysis of object naming speeds did not eliminate the effect of age of acquisition alone. Similar results have been reported for reading tasks by Bonin, Barry, Méot, and Chalard (2004), Cuetos and Barbón (2006), Gilhooly (1984) and Pérez (2007) using a variety of different methods for estimating cumulative frequency. A second related approach to the question was taken by Ghyselinck, Lewis, and Brysbaert (2004a) who showed mathematically that if the cumulative frequency hypothesis of age of acquisition is correct, then in regression analyses of tasks like object naming or lexical decision, when age of acquisition and frequency measures are log transformed, they should predict the log of the reaction times in such a way that the regression coefficients associated with the two variables should be the same. Across a range of tasks, Ghyselinck et al. (2004a) found that the contribution of age of acquisition to predicting reaction times was consistently greater than that of frequency, indicating that the age of acquisition effect was stronger than would be expected on the basis of cumulative frequency alone and that its influence cannot be reduced to differences in cumulative frequency. Menenti and Burani (2007) found the same pattern for lexical decision and semantic categorization of written words in Dutch and Italian—tasks very similar to those employed in the present experiments. The third approach to evaluating the cumulative frequency account of age of acquisition has been to compare the magnitudes of age of acquisition effects in younger and older participants. The general argument here is that if one word is learned at the age of 2 and another at the age of 5, then if cumulative frequency is important, the difference of 3 years in

age of acquisition might be expected to make a substantial difference to a participant who, like the majority of student participants in cognitive experiments, is 18–21 years old. But by the time participants are 70 years old, the differences in cumulative frequency resulting from a 3-year difference in age of acquisition in childhood will be proportionately much smaller. The cumulative frequency account of age of acquisition therefore predicts that effects will diminish with age while the semantic richness hypothesis and the mapping hypothesis predict that they will remain unchanged. Morrison, Hirsh, Chappell, and Ellis (2002; Experiment 1) measured naming latencies for words matched on word frequency but varying on age of acquisition in groups of younger adults (18–25 years) and older adults (63–86 years). Older adults read the words more slowly than younger adults, but the magnitudes of the age of acquisition effects in the two groups were not significantly different. Experiment 2 of Morrison et al. (2002) compared picture naming latencies for early and late acquired objects in three participant groups aged 18–32, 60–69 and 80–93 years. Older participants named the pictured objects more slowly that younger adults, and there was a strong effect of age of acquisition that did not change across age groups (212 ms for young adults, 150 ms in 60 year olds and 167 ms in the over 80s). Barry, Johnston, and Wood (2006) obtained comparable results in a comparison of picture naming, word reading and lexical decision speed in younger (20–33) and older (80–95) adults. The lack of any sign that the impact of age of acquisition on a variety of tasks diminishes with age is the third line of evidence indicating that age of acquisition effects cannot be explained in terms of differences in cumulative or lifetime frequency of exposure. Frequency of exposure matters (as shown in the present Experiment 3 by the difference in speed of responding to early extinct and early surviving brands), but getting into the system early makes a clear and lasting difference that is distinct from the effects of subsequent exposure. If the consequences of when a word is learned have a different origin from the consequences of how often it is encountered, how are we to explain the impact of age of acquisition? We noted above that the original explanations of age of acquisition effects tended to focus on the retrieval of early learned words from the mental lexicon (Brown & Watson, 1987; Gilhooly & Watson, 1981). More recently, the theoretical emphasis has shifted towards the processes underlying the perceptual identification of familiar stimuli (words, objects or faces) and the nature of the mental representations of early and late acquired concepts and meanings. Brysbaert and colleagues have proposed that faster, more accurate processing of early and late acquired objects or words reflects differences in the quality of their semantic representations (Brysbaert, Van Wijnendaele, & De Deyne, 2000; Ghyselinck, Custers, & Brysbaert, 2004b). A possible mechanism for this was provided by Steyvers and Tenenbaum (2005) who argued that a consequence of the slow, cumulative growth of semantic knowledge is that by the time an individual reaches adulthood, early acquired concepts are more firmly embedded in that person's knowledge system (semantic memory) than are later acquired concepts, which are linked to

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fewer other concepts and are therefore less well supported by the network. A semantic locus for age of acquisition effects is compatible with reports of effects in tasks requiring the classification of objects, faces and words into semantic categories (Brysbaert et al., 2000, Ghyselinck et al., 2004b; De Deyne & Storms, 2007; Johnston & Barry, 2005; Lewis, 1999; Menenti & Burani, 2007; Morrison & Gibbons, 2006). A different but related explanation for age of acquisition effects was put forward by Ellis and Lambon Ralph (2000). They proposed a framework for understanding age of acquisition effects that was based on observing aspects of learning in simple ‘connectionist’ networks (see also Lambon Ralph & Ehsan, 2006). Like real brains, connectionist networks learn by gradually adjusting the strength of the connections between simple processing elements. Ellis and Lambon Ralph (2000) studied learning in such a network when the network was required to learn to associate inputs with different outputs under conditions where some of the items to be learned were trained from the outset while the entry of other items into training was deferred until the early items have been learned. Staggered training of this sort provides a model of the gradual learning of words, objects or faces that may form the basis of age of acquisition effects. Several simulations established that networks show an advantage for early over late acquired items, even after matching the total frequency with which items from the early and late sets are trained. The importance of being early acquired persists even after the later items have been acquired and experienced alongside the early items for extensive periods of time. The reason why the network performed better on items trained from the outset than on items introduced later seems to be that early items seize the opportunity to ‘shape’ the neural network into a form that is optimal for representing those early items. Later items might prefer a somewhat different network structure, but their attempts to reconfigure the network are resisted by the early items that continue to be experienced alongside them. The end result is a network of associations whose intrinsic structure favors the knowledge that was acquired early by the network over the knowledge learned later. We know that children start to learn brands at an early age (Hite & Hite, 1995; Linn & Novosat, 2008). Their ‘vocabulary’ of brand names then grows gradually and cumulatively in a manner that should, according to current theories of age of acquisition, lead to superior processing of early acquired brand names in a variety of situations. So does age of acquisition affect the processing of brand names in the same way that it affects the processing of other types of word and object? Most brand names are acquired after early childhood: if age of acquisition is to apply to brand recognition, it would probably be necessary for those effects not to depend on comparing things learned in early childhood with things learned later, but rather to depend on a comparison between things learned earlier with things learned later at any stage of life. Evidence from recent studies within cognitive psychology suggests that the latter may indeed apply. Izura and Ellis (2002, 2004) and Hirsh, Morrison, Gaset, and Carnicer (2003) found a benefit for recognizing words learned earlier in a second language acquired

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after childhood, while Stewart and Ellis (2008) and Tamminen and Gaskell (2008) found an advantage to being early acquired in tasks in which adult participants were required to learn entirely novel words or visual patterns. Many of the effects of age of acquisition reported in famous face recognition rely on differences in the order of learning celebrity faces through late childhood and adulthood (e.g., Lewis, 1999; Moore & Valentine, 1999; Richards & Ellis, 2008, 2009). There is growing evidence, therefore, that so-called ‘age of acquisition’ effects are not dependent on comparing knowledge acquired in early childhood with knowledge acquired later but can apply to knowledge acquired earlier or later at any point in life. As such, these effects might be better termed ‘order of acquisition’ effects, but the term ‘age of acquisition’ is now deeply entrenched in the literature, having been in use since the 1960s, and it could prove very difficult to re-brand the effect. Nevertheless, the recent studies just mentioned increase the probability that age/order of acquisition effects might be observed in the processing of brands and brand names. The present Experiment 1 tested the prediction that age of acquisition should affect the speed with which brand names can be recognized as familiar when real brand names are mixed together with invented brand names. Experiment 2 looked for age of acquisition effects in a task where participants were required to decide whether or not brand names refer to products from specified categories (e.g., varieties of breakfast cereal or chocolate bars). Experiment 3 represented an extreme test of the notion that early acquisition of brand names will be associated with more fluent recognition in adulthood. Participants aged 50–83 years made familiarity decisions (like those in Experiment 1) to three types of brands: long-established brands that are still in use, early acquired brands that are no longer in circulation, and more recent (and therefore late acquired) brands that are currently available and that have been heavily promoted since their launch. Would older participants recognize the early acquired but defunct brand names more rapidly than the more recently acquired brands, even though many years have elapsed since they last encountered the extinct brand names with any degree of regularity? Experiment 1: familiarity decisions to early and late acquired brand names Young adult participants in Experiment 1 were shown real or invented brand names one at a time on a computer screen. Their task was to press one button on a response box as quickly as possible if they recognized the brand name and another if they thought it was an invented name. This is an experimental analog of the popular ‘lexical decision’ task in which participants decide whether strings of letters form real words or invented non-words, or the ‘familiarity decision’ task in which participants decide whether pictures of objects or faces are familiar or unfamiliar. Those simple tasks show robust effects of age of acquisition on the time to respond to familiar words, objects and faces, with faster reaction times to early than to late acquired items (Holmes & Ellis, 2006; Johnston & Barry, 2006; Juhasz, 2005; Moore & Valentine, 1999; Moore et al., 2004;

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A.W. Ellis et al. / Journal of Consumer Psychology 20 (2010) 43–52

Morrison & Ellis, 1995, 2000; Richards & Ellis, 2008, 2009). The ‘early’ brand names used in the present study had been in existence since before the participants were born, while the ‘late’ brands had been launched more recently. Early and late items covered a similar range of products and were matched on ratings of how often brand names were encountered in the media at the time the experiment was run. Method Participants Twenty participants (10 male and 10 female) took part in the experiment in return for either a course credit or a small payment. All were students at the University of York, UK, aged 19–24. All were raised in the UK. Materials The experimental materials comprised 26 early and 26 late acquired brand names. These were derived from an initial set of 263 brand names, which were either very long-established brands, or brands for which launch dates were available (e.g., from company web sites). Twenty of the 26 early brands were in existence before the 1950s, 1 was launched in the 1950s and 5 were launched in the 1960s. All the late acquired brands had been launched when the participants were at least 5 years old (6 within the past 4 years, 10 between 5 and 9 years previously, and 10 between 10 and 14 years previously). Ratings of the frequency with which the different brands were currently being encountered were obtained from a separate group of 20 different undergraduate students who rated each product on a scale of 0 to 5, which assessed how often they encountered different products in shops, on TV, in newspaper or magazine advertising, etc. The points of the scale were as follows: 0 = not heard of the product, 1 = encountered less than once a year, 2 = encountered more than once a year but less than once a month, 3 = encountered more than once a month but less than once a week, 4 = encountered more than once a week but less than once a day, 5 = encountered more than once a day. Only products that were recognized as familiar by at least 18 of the 20 raters were used in this and the other experiments reported here. The early and late brand names were matched on mean rated frequency of encounter (early: mean = 2.92, SD = 0.46, range = 2.11–3.70; late: mean = 2.98, SD = 0.40, range = 2.10–3.65). Brand names contained either one or two words: early and late sets were matched on the number of words in the brand names (early: mean = 1.38 words; late: mean = 1.42 words) and covered a similar range of product types. Fifty-two plausible but non-existent ‘non-brands’ were created to provide the stimuli for the negative trials. Procedure Brand names and non-brands were presented one at a time in the centre of a computer screen using a large clear font (Geneva 36 point). The screen was positioned approximately 60 cm in front of the participant. Each brand name was preceded by a fixation point for 1000 ms in the centre of the screen followed by a blank screen for 500 ms. A real or invented brand name

was then presented until the participant responded by pressing one of two buttons on a Cedrus 2000 response button box to indicate whether or not the stimulus was a real brand name. Presentation of stimuli and timing of responses were controlled by the Superlab experiment generator package (Haxby, Parasuraman, Lalonde, & Abboud, 1993). After a response had been made, the screen went blank for 500 ms before the next trial began. Participants were encouraged to respond as quickly and as accurately as possible. The experiment proper was preceded by 20 practice trials (10 real brand names and 10 non-brands). Results Participants made fewer errors to early acquired brand names (mean = 5.8%) than to late acquired brand names (mean = 10.8%). A Wilcoxon matched-pairs signed-ranks test comparing the number of errors made in each condition was significant, z = 2.71, p b .01. Only reaction times for correct responses were analyzed. RT data were further trimmed by the removal of 25 individual RTs more than 3 SDs longer than the mean (cut-off = 1296 ms). Familiarity decision responses were faster to early acquired brand names (mean = 632 ms, SD = 85) than to late acquired brand names (mean = 659 ms, SD = 86), t (19) = 2.93, p b .01. The invented brands were rejected with 93.3% accuracy and a mean RT for correct responses of 823 ms. Discussion The results of Experiment 1 established that the age of acquisition effects seen for everyday words, objects and faces in familiarity decision tasks extend to brand names. Early acquired brand names are recognized more quickly and more accurately than later acquired brand names that are encountered with equal frequency in adult life. Early acquisition of a brand name makes for greater perceptual fluency when recognizing that name in adulthood. Experiment 2: semantic category decisions to early and late acquired brand names The familiarity decision task requires participants simply to indicate whether a brand name is or is not known to them. As such, it only taps a basic level of familiarity. Semantic information about the products represented by brand names may be accessed in the course of making familiarity decisions, but the task does not demand that such access be demonstrated or acted upon. Experiment 2 sought to discover whether age of acquisition affects the speed with which participants can access stored knowledge about brands and make decisions based on that knowledge. A number of studies have reported effects of age of acquisition on tasks requiring access to semantic knowledge. For example, Brysbaert et al. (2000) and Ghyselinck et al. (2004a) reported an age of acquisition effect using a semantic task in which participants classified written words as words with definable meanings (e.g., plank and pint) as distinct from peoples' names (e.g., theo and nadia). Morrison and

A.W. Ellis et al. / Journal of Consumer Psychology 20 (2010) 43–52

Gibbons, (2006), De Deyne and Storms (2007) and Menenti and Burani (2007) all found effects of age of acquisition on the speed of deciding whether written object names represent living or non-living things. Johnston and Barry (2005) reported that age of acquisition affects the speed with which participants can classify pictured objects as typically found inside or outside the house, or as being smaller or larger than a loaf of bread. Lewis (1999) found an effect of age of acquisition on the speed of categorizing actors' faces as coming from one of two longrunning TV series. Participants in Experiment 2 saw the name of a type of product, followed by a brand name that either matched or did not match the category label, and was either early or late acquired. For half the participants, the experimental early and late acquired brand names formed the positive set that matched the category labels while filler items were used for the mismatch trials. For the remaining participants, the experimental items occurred on the negative trials, with filler items being used for the positive trials. We are not aware of any previous research investigating the effect of age of acquisition on category– instance verification tasks of precisely this sort, though we have seen above that other semantic tasks have shown effects. Category–instance verification tasks have, however, been used in the study of effects of frequency on word recognition. Forster (2004) reviewed the relevant literature and reported new experiments supporting the claim that large semantic categories produce frequency effects for both Yes decisions (positive trials) and No decisions (negative or mismatch trials), while small semantic categories tend to produce effects for Yes but not No decisions. In comparison with natural categories (e.g., animals and cities), participants know relatively few brands from the commercial product categories used in Experiment 2 (e.g., makes of bread, margarine or shampoo). If age of acquisition behaves like word frequency in category–instance verification, as it does in many tasks (Ghyselinck et al., 2004a; Johnston & Barry, 2006; Juhasz, 2005), then we might expect to see clear effects in positive trials where the brand name matches the category label but weak or no effects in negative (mismatch) trials.

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categories (e.g., chocolate bars, types of aftershave and breakfast cereals). The early and late sets covered the same range of product types and were matched on rated frequency of current encounter using the same ratings as reported for Experiment 1. Early acquired items had a mean rated frequency of encounter of 3.07 (SD = 0.47; range = 2.10–3.80), while late acquired items had a mean rated frequency of encounter of 3.03 (SD = 0.41; range = 2.10–3.65). An additional 50 filler items and 18 practice items were selected on the basis that they too fell easily into product categories. Procedure On each trial, a category label appeared on the screen, followed by a brand name that either did (positive trials) or did not (negative trials) match the product label. For half the participants, the experimental items (25 early; 25 late) were paired with correct category labels to form the positive set. The negative set was created by reshuffling the filler brand names and their category labels so that the brand names no longer matched the category labels. For the other half of the participants, the experimental early and late acquired items and their category labels were reshuffled so that the brand names no longer matched the category labels and therefore formed the negative set. The filler items with their correct category labels formed the positive set. Each trial began with a 500 ms central fixation point, followed by the category label that was presented for 1500 ms. The category label was followed by the brand name after an unfilled interval of 500 ms. The brand name remained on the screen until the participant responded. Participants were instructed to decide whether or not the named brand belonged to the category specified by the preceding label, and to respond as quickly as possible by pressing one of two response buttons. Positive and negative trials were presented in a random order. The experiment began with 18 practice items (9 positive and 9 negative), followed by the 100 trials involving experimental and filler items. Other details of the presentation conditions were the same as in Experiment 1. Results

Method Participants Thirty-two students (15 male and 17 female) from the University of York, UK, took part in Experiment 2 in return for a course credit or a small payment. The participants were aged 19–24 years and were all raised in the UK. Materials Twenty-five early acquired and 25 late acquired were chosen from the pool of available items. Sixteen of the early brands were in existence before the 1950s, 3 were launched in the 1950s and 6 were launched in the 1960s. All the late acquired brands had been launched when the participants were at least 5 years old (6 within the past 4 years, 8 between 5 and 9 years previously and 11 between 10 and 14 years previously). The brands were chosen on the basis that they fell into well-defined

The results of Experiment 2 are shown in Table 1. Error rates were generally low (overall 5.98%). In a two-way ANOVA of error rates with decision type (positive or negative) as a between-groups factor and age of acquisition as a withinsubjects factor, the main effects of decision type and age of Table 1 Mean reaction time (with SD) and percent errors for positive and negative category–instance judgments in Experiment 2.

Positive decisions

Negative decisions

Mean RT SD % error Mean RT SD % error

Early acquired

Late acquired

760 140 4.8 939 238 5.8

873 217 9.5 968 254 3.8

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acquisition on error rates, and the interaction between the two factors were all non-significant. Only correct responses were considered in the reaction time analysis. RTs were trimmed by the removal of responses more than 3 SDs longer than the mean for positive responses (cutoff = 2046 ms; 7 RTs deleted) and for negative responses (cutoff = 2566 ms; 15 RTs deleted). The main effect of decision type was of marginal significance (F(1,30) = 3.37, MSe = 301,517, p = .076), with a trend towards faster positive than negative decisions. The main effect of age of acquisition was significant (F(1,30) = 18.14, MSe = 80,915, p b .001), as was the interaction between age of acquisition and decision type (F(1,30) = 6.38, MSe = 28,428, p b .05). Separate analyses of RTs for positive and negative responses using Bonferroni-corrected t tests (α = .05) found a significant effect of age of acquisition for positive responses (t(15) = 4.21) but not negative responses (t (15) = 1.47). Discussion Positive decisions that brand names belonged to prespecified categories showed a significant effect in favor of the early acquired brands. This is in keeping with reports of advantages for early acquired words, objects and faces in a variety of semantic classification tasks (Brysbaert et al., 2000; De Deyne & Storms, 2007; Ghyselinck et al., 2004a; Johnston & Barry, 2005; Lewis, 1999; Menenti & Burani, 2007; Morrison & Gibbons, 2006). Access to the meaning of a brand name is faster when that brand is early acquired than when it is late acquired. The smaller difference in favor of early brands for negative decisions was not significant. This pattern of results matches those found for the effects of word frequency when the categories employed are relatively small, as they are here (Forster, 2004). One possible explanation for this pattern of results is that more detailed semantic access is required in order to make a positive than a negative decision in this task. For example, fairly detailed semantic access is required in order to judge that Signal™ is a type of toothpaste rather than, say, a brand of soap or shampoo. Less detailed semantic access is required in order to judge that Signal™ is not a type of car. Theories of age of acquisition effects that place those effects either within the semantic system (Brysbaert et al., 2000; Steyvers & Tenenbaum, 2005) or in the process of accessing semantic and other representations (Ellis & Lambon Ralph, 2000) predict that age of acquisition effects will be strongest when semantic access is most detailed. Experiment 3: recognition by older participants of early surviving, early acquired but extinct, and recent brand names New brands are constantly being created, while older brands sometimes disappear from view when the product ceases to be marketed (at least under that label). This makes brand names a good material for studying the relative influence of age of acquisition and frequency of current exposure on recognition speed. In Experiment 3, participants aged 50–83 years

performed a familiarity decision task like that employed in Experiment 1, requiring them to distinguish real from invented brand names. The real brand names were of three types: (1) long-established brands that are still in existence (‘early surviving brands’), (2) long-established brand names that are no longer in use (‘early extinct brands’), and (3) recent (post1970) brands that are in current circulation but would be late acquired for our older participants (‘recent brands’). With an ever-aging population in which older consumers become increasingly important, there is a surprising dearth of research on brand recognition and decision making in older consumers. Yoon, Cole, and Lee (2009) reviewed the extensive literature on aging and performance from both cognitive and social perspectives, considering the potential implications of behavioral changes across the lifespan for consumer decision making. We noted in the Introduction that age of acquisition effects, once established, can be remarkably persistent across the lifespan. Morrison et al. (2002) and Barry et al. (2006) found age of acquisition effects in reading aloud, object naming and lexical decision to be undiminished in 80-year-old participants compared with young adults. If similar results can be found for brand recognition, the implications for our understanding of consumer behavior could be profound. Method Participants Fourteen participants (4 male and 10 female) aged 50–83 years took part in the experiment. Materials Newspaper and magazine archives going back 50 years were trawled to identify brands that were in common use in the 1950s and 1960s and that are either still in use (n = 206) or no longer in general circulation (n = 198). To these were added a list of 119 more recent brands, launched since 1970, which are still in circulation. (Note that a launch date in the 1970s would mean that our participants were adults, or approaching adulthood, when a brand first appeared.) New ratings were provided by two different groups of participants aged 50 or over who were not involved in the main experiment. The instructions given to the first group ( n = 18) asked them to cast their minds back to the 1950s and 1960s and estimate how often they encountered each of the early brands (extinct and surviving) in those decades using a five-point scale from 1 = less than once a year to 5 = more than once a day. In an effort to reinstate the appropriate time period in their minds, the raters were reminded of major events from those decades and were encouraged to recall personal life events from the period. The second group of raters ( n = 9) used the same rating scale to estimate how often they currently encounter different brands. The second set of ratings were obtained for the late acquired (recent) and the early surviving brands. Three sets of 24 brand names were then selected. The early surviving brands were matched to the early extinct brands on estimated frequency of encounter in the 50s and 60s (early surviving: 2.60; early extinct: 2.60). The early surviving brands

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were also matched to the more recent (late acquired) brands on frequency of current encounter (early surviving: 1.84; recent: 1.82). None of the extinct brands had been marketed in the UK since the 1980s. An equal number of invented but plausible brand names were created for use in the experimental task. Procedure Participants were tested in their homes or in other convenient, quiet locations. The three types of real brand names and the invented brand names were presented to participants in a random order for familiarity decisions made by pressing one of two keys on the keyboard of an Apple Macintosh Powerbook computer. The experiment began with 20 practice items (10 real and 10 invented brands). Other conditions of presentation were the same as for Experiment 1. Results Table 2 shows the mean RTs and errors (in percent) for each category of brand name. A one-factor repeated measures ANOVA on the error rates in the different conditions found no significant effect of brand type on the number of errors made (F(2,26) = 1.55). None of the pairwise comparisons was significant on the Wilcoxon matched-pairs signed-ranks test (early surviving vs extinct brands: z = 1.00; early surviving vs recent brands: z = 1.27; extinct vs recent brands: z = 0.42). Only correct responses were used in the reaction time analysis. RT data were trimmed by the removal of 16 individual RTs more than 3 SDs longer than the mean (cut-off = 3590 ms). The overall effect of brand type on reaction times was significant (F(2,26) = 12.47, MSe = 170,800, p b .001). Bonferroni-corrected t tests (α = .05) found all three pairwise comparisons to be significant, with RTs to early surviving brands being significantly faster than to both extinct brands (t (13) = 3.03, p b .01) and recent brands (t(13) = 4.12, p b .01), while RTs to extinct brands were significantly faster than to recent brands (t(13) = 2.76, p b .05). Discussion The familiarity decision times in Experiment 3 were noticeably slower than those in Experiment 1. In part, this may reflect the fact that the items used in Experiment 1 were chosen to be well recognized by student participants, whereas the items used in Experiment 3 included extinct and recent brands that were somewhat less well recognized by the participants. The main reason for the difference in reaction times, though, is almost certainly the difference in age between participants in the two experiments. The participants in Table 2 Mean reaction time (with SD) and percent errors for each brand type in Experiment 3.

Mean RT SD % error

Early surviving

Early extinct

Recent (late acquired)

1213 303 11.3

1315 317 15.2

1433 425 16.7

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Experiment 1 were aged between 18 and 24 years when the experiment was run, while the participants in Experiment 3 were aged between 50 and 83 years. Substantially slower lexical decision speeds in older participants have been reported in several studies (e.g., Bowles & Poon, 1981; Morrison et al., 2002). A basic age of acquisition effect can be seen in Experiment 3 by comparing performance on the early surviving brands and the more recent brands. The early surviving brands have been in use since at least the 1950s and 1960s. Many of them go back much further. The more recent brands have all been launched since 1970 and are still in circulation. For our 50- to 83-year-old participants, the age at which they first encountered the more recent brands will have varied from late teens to around 50 years of age. That is very late acquired by the standards of most studies of age of acquisition. The early surviving and more recent brands were, however, matched on frequency of current exposure based on ratings obtained from a separate group of older people. For brand names correctly recognized as familiar (real), reaction times were 221 ms faster for the early surviving brands than for the recent and, therefore, late acquired brands. Reaction times were also 118 ms faster to early extinct brands than to more recent brands despite the fact that the accuracy rates for the two sets were similar (showing that the participants recognized the more recent brands as well as the early extinct brands) and despite the fact that the participants were being exposed to the recent brands through current advertising and use while everyday exposure to the extinct brand names was minimal. That said, early surviving brands were recognized 102 ms faster than early extinct brands, so everyday exposure counts for something over and above early age of acquisition. As we noted in the Introduction, frequency of exposure affects recognition speed, but that effect is distinct from the effect of age of acquisition. General discussion If two brand names have the same current exposure, the one that was learned early in life will be recognized faster than the one acquired more recently. That applies whether recognition involves simply acknowledging that a brand name is familiar (Experiments 1 and 3) or matching the brand name to a particular type of product (Experiment 2). In the present experiments, those benefits were particularly clear in the reaction time measures. Reaction time is often regarded as a more implicit measure of processing—one that is less affected by deliberation and strategies than explicit measures such as preference ratings or similar judgments. As such, reaction times may be better suited to the investigation of automatic, unbiased responses to brands (Krishnan & Shapiro, 1996; Yoon et al., 2009). We have seen that age of acquisition is not the only factor to influence the speed with which brands are recognized and processed. In Experiment 3, recognition speed was faster for early acquired brands that continue to be experienced on a regular basis than for other brands that were learned equally early but are now extinct and therefore seldom encountered. As

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with word and object recognition, fluency is determined by age of acquisition, frequency of exposure and other factors besides (Cortese & Khanna, 2007, Pérez, 2007). In impulse-buying situations, choice between competing brands may be based in part on simple fluency of recognition (Alba, Hutchinson, & Lynch, 1991; Rook, 1987). The present results suggest that early acquired brands will be favored in such situations. Given the evidence that age of acquisition affects recognition speeds for object and face recognition as well as word recognition, we would expect the benefits of early acquisition to apply to pictorial as well as verbal images of brands (Wyer, Hung, & Jiang, 2008). Greater fluency of processing is also known to be associated with more positive affective judgments (e.g., Bornstein, 1989; Kunst-Wilson & Zajonc, 1980). This relationship between fluency and affect or preference extends to brands (Bohnam et al., 1995; Chung & Szymanski, 1997; Janiszewski & Meyvis, 2001; Lee & Labroo, 2004; Perfect & Heatherley, 1997). We would therefore expect early acquired brands to be associated with more positive affect and evaluation than later acquired brands, purely on the basis of their greater perceptual and conceptual fluency, irrespective of any objective differences between them and later brands. Barone (2005) observed that the majority of new products entering the market represent extensions of existing brand names (see also Keller, 2003). It is important, therefore, to understand the cognitive processes that underlie the assimilation of brand extensions (Fedorkihin, Park, & Thomson, 2008; Zhang & Sood, 2002). We might expect the fluency benefits of an early acquired parent brand to generalize to a subsequent extension of that brand. The network modeling theories imply, however, that there may be respects in which extending early brands could be more difficult than extending later brands. Early brands become deeply entrenched in the network of knowledge that surrounds them (Ellis & Lambon Ralph, 2000; Steyvers & Tenenbaum, 2005). That will facilitate most aspects of processing, but it may also render them more inflexible than later brands, and harder to associate with new product types. Experimental analysis of the sort reported here should be able to determine whether early acquisition makes for easier or more difficult brand extensions. Technological and other advances mean that from time to time entirely new types of product come onto the market. It has long been recognized that there is an enduring advantage to being an ‘early mover’ in such situations (Glazer, 1985; Kerin, Varadarajan, & Peterson, 1992). Early entry into a market is associated with higher market share, greater market penetration and more repeated purchases of frequently purchased consumer goods (Kalyanaram & Urban, 1992). Considerable efforts are required in order to get later entrants established because the pioneering advantage tends to persist even when the costs of switching brands are low (Carpenter & Nakamoto, 1989, 1990; Urban & Houser, 1980). Carpenter and Nakamoto (1989) found that pioneering brands tend to form the prototypes for new categories of brands that are molded around them. Holmes and Ellis (2006) found a significant correlation between the age of acquisition of objects and their rated prototypicality in relation to the category to which they belong. Holmes and Ellis (2006)

argued that early exemplars play an important role in defining and shaping the category to which subsequent objects are added, an argument that is compatible with network accounts of age of acquisition effects (Ellis & Lambon Ralph, 2000; Steyvers & Tenenbaum, 2005). Kardes and Kalyanaram (1992) suggested that the first entrant to a new product category is likely to perceived as more novel and interesting than later entrants. That will attract more attention to the early entrant and lead to better learning of information relating to that pioneering brand (see also Kardes, Kalyanaram, Chandrashekaran, & Dornoff, 1993). Kardes and Kalyanaram (1992) reported two experiments in which participants were exposed to attribute information for three imaginary brands of a novel type of product (microwave popcorn) over a 4-week period. The information supplied about brand C clearly indicated that it was superior to brands A and B. Participants were, however, exposed to information about brand A or B first, followed by brand B or A, with brand C only being introduced after the participants had learned about brands A and B. In subsequent preference and evaluation judgments, the participants responded more positively to the inferior but pioneering brands A and B compared with the superior but later brand C. Recall of both information unique to one or other of the brands and information shared by all three brands was greater for whichever brand was introduced first compared with the second and third brands. These differences held up over a 2week interval to an unexpected final test. When the information about the three brands was provided simultaneously rather than in a staggered fashion, brand C outperformed brands A and B. Those experiments are highly reminiscent of the more recent experiments by Stewart and Ellis (2008) on pattern learning and Tamminen and Gaskell (2008) on word learning that were inspired by age of acquisition research, and the results are fully compatible with semantic and network-based theories of age of acquisition (Brysbaert et al., 2000; Ellis & Lambon Ralph, 2000; Steyvers & Tenenbaum, 2005). We would argue that it is better to have multiple items in the different order of acquisition conditions rather than one item per condition employed by Kardes and Kalyanaram (1992). It is also the case that by the end of training, some of the participants in the Kardes and Kalyanaram (1992) experiments had received more exposure to information about the pioneer brand than the later brands, which confounds the influence of order of acquisition with possible effects of cumulative frequency of exposure to the brands and their attributes. Notwithstanding these minor reservations, we believe that there is much to be learned through the use of cumulative learning experiments, which afford the researcher much tighter control over order of acquisition and frequency of exposure and which would allow other properties of the brands to be manipulated more systematically than is ever possible using naturally occurring brands. The present results clearly imply that new and therefore late brands entering established categories will require high and sustained levels of exposure if they are to compete for familiarity and fluency with established brands. Indeed, it may take a generation before a new brand matches longstanding brands on perceptual and conceptual fluency. If a

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manufacturer decides, for whatever reason, to change a brand name, then it could also take a lifetime before the new brand name achieves the same fluency and familiarity as the name it is replacing. When new product categories are created, the early pioneers will benefit from an age/order of acquisition boost independent of their quality in relation to later entrants. We know that children become aware of brands at a very early age (Hite & Hite, 1995; Linn & Novosat, 2008): the current findings indicate that there should be a considerable advantage to acquainting children with brand names, even if the products in question are ones they will not purchase or be interested in until later in life. Though some may find the conclusion unpalatable, the evidence suggests that mere exposure to brands in childhood will make for more fluent recognition of those brand names in adulthood that will persist through to old age. Acknowledgments This research was supported by an award from Unilever plc. We thank the York City Library for providing access to their archives of newspapers and magazines and York Castle Museum for allowing us access to their collection of products from different historical eras. References Alba, J. W., Hutchinson, J. W., & Lynch, J. G., Jr. (1991). Memory and decision making. In T. S. Robertson, & H. H. Kassarjian (Eds.), Handbook of consumer behavior (pp. 1−49). Eaglewood Cliffs, NJ: Prentice-Hall. Barone, M. J. (2005). The interactive effects of mood and involvement on brand extension evaluations. Journal of Consumer Psychology, 15, 263−270. Barry, C., Johnston, R. A., & Wood, R. F. (2006). Effects of age of acquisition, age, and repetition priming on object naming. Visual Cognition, 13, 911−927. Bohnam, P., Greelee, D., Herbert, C. S., Hruidi, L., Kirby, C., Perkins, A., Salkind, N. J., & Wilfong, R. (1995). Knowledge of brand and preference. Psychological Reports, 76, 1297−1298. Bonin, P., Barry, C., Méot, A., & Chalard, M. (2004). The influence of age of acquisition in word reading and other tasks: A never ending story? Journal of Memory and Language, 50, 456−476. Bornstein, R. (1989). Exposure and affect: Overview and meta-analysis of research, 1968–87. Psychological Bulletin, 106, 265−289. Bowles, N. L., & Poon, L. W. (1981). The effect of age on speed of lexical access. Experimental Aging Research, 7, 417−425. Brown, G. D. A., & Watson, F. L. (1987). First in, first out: Word learning age and spoken word frequency as predictors of word familiarity and word naming latency. Memory & Cognition, 15, 208−216. Brysbaert, M., Van Wijnendaele, I., & De Deyne, S. (2000). Age of acquisition effects in semantic tasks. Acta Psychologica, 104, 215−226. Carpenter, G., & Nakamoto, K. (1989). Consumer preference formation and pioneer advantage. Journal of Marketing Research, 26, 285−298. Carpenter, G., & Nakamoto, K. (1990). Competitive strategies for late entry into a market with a dominant brand. Management Science, 36, 1268−1278. Carroll, J. B., & White, M. N. (1973). Word frequency and age-of-acquisition as determiners of picture-naming latency. Quarterly Journal of Experimental Psychology, 25, 85−95. Catling, J. C., & Johnston, R. A. (2006). The effects of age of acquisition effects on an object classification task. Visual Cognition, 13, 968−980. Catling, J. C., & Johnston, R. A. (2009). The varying effects of age of acquisition. Quarterly Journal of Experimental Psychology, 62, 50−62. Chung, S. W., & Szymanski, K. (1997). Effects of brand name exposure on brand choices: An implicit memory perspective. Advances in Consumer Research, 24, 288−294.

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