The impact of word frequency in the production effect

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junho de 2016

Marta Alexandra Sousa Borges

The impact of word frequency in the

production effect

Universidade do Minho

Escola de Psicologia

The impact of w ord frequency in t he production ef fect Mar ta Ale xandr a Sousa Bor ges UMinho|20 16

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Dissertação de Mestrado

Mestrado Integrado em Psicologia

Trabalho realizado sob a orientação do

Professor Doutor Pedro Barbas Albuquerque

junho de 2016

Marta Alexandra Sousa Borges

The impact of word frequency in the

production effect

Universidade do Minho

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Declaração

Nome: Marta Alexandra Sousa Borges

Endereço eletrónico: a65528@alunos.uminho.pt; marta-borges28@hotmail.com Número do Cartão de Cidadão: 14228125

Título da Dissertação: The impact of word frequency in the production effect Orientador: Professor Doutor Pedro Barbas Albuquerque

Ano de conclusão: 2016

Designação do Mestrado: Mestrado Integrado em Psicologia

É AUTORIZADA A REPRODUÇÃO INTEGRAL DESTA DISSERTAÇÃO APENAS PARA EFEITOS DE INVESTIGAÇÃO, MEDIANTE DECLARAÇÃO ESCRITA DO INTERESSADO QUE A TAL SE COMPROMETE.

Universidade do Minho, 13/06/16

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II Index Agradecimentos ...III Abstract... IV Resumo ... V Introduction ... 6 Experiment 1... 9 Method ...9 Results ... 10 Discussion ... 11 Experiment 2...12 Method ... 12 Results ... 13 Discussion ... 15 General Discussion ...16 References ...18

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III

Agradecimentos

Antes de mais quero agradecer a todos aqueles que me ajudaram nesta caminhada, tornando-a mais suave e proveitosa.

Ao Professor Doutor Pedro Albuquerque pela exigência e rigor com que sempre pautou o nosso trabalho e pela disponibilidade manifestada em cada uma das etapas.

À minha família, principalmente aos meus pais e avós, que me permitiram tirar mais sentido das pequenas vitórias e desvalorizar as dificuldades que iam surgindo. De forma especial ao meu avô paterno, que entretanto deixou de estar fisicamente entre nós, mas que sempre foi o meu maior defensor.

À minha irmã, companheira de estudo e de todas as horas, pelas conversas, desabafos, e brincadeiras que tornaram tudo mais simples. Ambas conseguimos conviver bem, mesmo quando estávamos simultaneamente a estudar em voz alta.

Às amigas que fiz na universidade por toda a experiência académica partilhada. Obrigada pelas sessões de trabalho de grupo e de estudo coletivo em que não perdíamos o bom humor e conseguíamos juntar o útil ao agradável! Obrigada também pelos momentos de procrastinação em conjunto, quando o cansaço já era muito e não havia outro remédio senão parar por um pouco!

Aos amigos que me conhecem há mais tempo, da escola e do grupo de jovens,

agradeço pela paciência e compreensão por eu não ter tanto tempo disponível como gostaria. Aos colegas do Grupo de Investigação em Memória Humana e aos participantes das experiências pelas sugestões e comentários que fizeram avançar e melhorar o meu trabalho.

A todas as pessoas que conheci durante os anos de universidade: colegas, professores e todos aqueles com quem me cruzei nas várias atividades em que tive oportunidade de me envolver.

Obrigada! De uma forma ou de outra todos contribuíram para que estes cinco anos tenham sido os melhores da minha vida.

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IV

The impact of word frequency in the production effect

Abstract

The production effect refers to better recognition of words read aloud when compared to words read silently (MacLeod, Gopie, Hourihan, Neary, & Ozubko, 2010). Distinctiveness heuristic (Dodson & Schacter, 2001; Schacter & Wiseman, 2006) seems to be the stronger explanation appointed for this effect, saying that words read aloud are more distinctive, in the codification and recuperation, than words read silently. In our study, we manipulated not only production but also another distinctive characteristic of words, like its frequency, with the purpose of testing distinctiveness predictions. Many studies about word frequency in

recognition tests revealed that low-frequency words are better recognized than high-frequency words (Freeman, Heathcote, Chalmers, & Hockley, 2010; Glanzer & Adams, 1985, 1990; Lohnas & Kahana, 2013), a result that is replicated in our first experiment. In the second experiment, with the manipulation of word frequency and production, we found the production effect and the frequency effect, but not an interaction between both effects. All results were framed in the light of distinctiveness heuristic.

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V

O impacto da frequência das palavras no efeito de produção

Resumo

O efeito de produção traduz-se num melhor reconhecimento de palavras lidas em voz alta relativamente a palavras lidas em silêncio (MacLeod et al., 2010). A heurística da

distintividade (Dodson & Schacter, 2001; Schacter & Wiseman, 2006) é apontada como uma das explicações mais robustas deste efeito, sendo referido que as palavras lidas em voz alta tornam-se mais distintivas, tanto na codificação como na recuperação, do que as palavras lidas em silêncio. No nosso estudo, para além da leitura em voz alta manipulámos caraterísticas distintivas das palavras, como a sua frequência na língua, com o objetivo de testar as

previsões da distintividade. Vários estudos têm revelado que palavras de baixa frequência são melhor reconhecidas do que palavras de alta frequência (Freeman et al., 2010; Glanzer & Adams, 1985, 1990; Lohnas & Kahana, 2013), resultado este que é replicado na Experiência 1. Na Experiência 2, onde foi manipulada a frequência das palavras e a sua produção,

verificamos a presença do efeito de produção e de frequência das palavras, mas não uma interação entre ambas. Todos os resultados foram enquadrados na perspetiva da heurística da distintividade.

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Running head: THE IMPACT OF WORD FREQUENCY IN THE PRODUCTION EFFECT

6

Introduction

The production effect, coined by MacLeod and colleagues in 2010, states that reading a word aloud during study improves retrieval when compared to reading a word silently. Before this date there were five isolated studies that gave an important role for pronunciation of material to be remembered (Conway & Gathercole, 1987; Dodson & Schacter, 2001; Gathercole & Conway, 1988; Hopkins & Edwards, 1972; MacDonald & MacLeod, 1998), but none of them was interested solely in show the advantage of reading aloud during study.

After the paper of MacLeod and colleagues (2010), production effect had an exponential growing of studies and it was possible to reach to some characteristics of this effect: production is benefic for memory, particularly in explicit memory tests (MacDonald & MacLeod, 1998); it is mainly found on within-subject designs in which each participant read aloud some words and silently other (for exceptions see Fawcett, 2013; Gathercole &

Conway, 1988, Experiment 5). Besides that, the advantage of read aloud was present in articulated words (MacLeod et al., 2010, Experiment 5); and a meaning of the material is not needed to production have an effect, since MacLeod and colleagues (2010, Experiment 6) revealed that non-words read aloud were better recognized than non-words read silently.

In this seminal paper, MacLeod and colleagues (2010) explain the production effect by distinctiveness, a concept that was a long history in the memory domain (for more details see Hunt, 2006) and was primarily applied to production by Conway and Gathercole (1987). MacLeod and colleagues (2010) introduced this concept in the form of distinctiveness

heuristic (Dodson & Schacter, 2001; Schacter & Wiseman, 2006). This heuristic assumes that words read aloud have more distinctive characteristics, like tone and rhythm, than words read silently, conducting to better recognition for the first. Words read aloud also have better source monitoring than words read silently, once production functions as an additional cue in the recognition test: “If I remember saying aloud this word is because I’ve studied it before”. This better monitoring results in lower false alarm rates and higher discrimination for words read aloud than for words read silently, indicating a distinctive processing for words read aloud, both at coding and at retrieval.

Distinctiveness heuristic is found to be congruent with two relevant characteristics of the production effect. First, production effect occurs mainly in within-subject designs and distinctiveness acts relatively, that is, a word is only distinctive in comparison with a word

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7 that is not distinctive (Hunt, 2006). In fact, this parallel is just possible in the case of the same participant read aloud and silently different words. Beyond that, the advantage of words read aloud is stronger when production is made by the self than by others (MacLeod, 2011). Second, production effect occurs only in explicit memory tests, but not in implicit memory tests, what could indicate that production effect is dependent of the use of an heuristic and that this heuristic can only be applied in explicit tests (MacDonald & MacLeod, 1998).

In spite of the great convergence between distinctiveness heuristic predictions and the results of the studies with production effect (MacLeod et al., 2010; Ozubko & MacLeod, 2010; Quinlan & Taylor, 2013), some problems have appeared: one study found higher false alarm rates for words read aloud than for words read silently (Albuquerque, Gonçalves, & Rosendo, May 2014), a finding that is contrary to what was expected; another study did not find differences in hit rates between words read aloud and silently (Castel, Rhodes, & Friedman, 2013, Experiment 3); and many authors criticized the categorization of words in terms of distinctiveness, a categorization that is based on the number of distinctive

characteristics inferred by investigators, but not in an actual test of the distinctiveness associated to words (Quinlan & Taylor, 2013).

To deal with these limitations of distinctiveness heuristic as explanation for production effect, other alternatives were presented: 1) unusual activities hypothesis – unusual activities are more memorable than usual activities. So read aloud (and sing), as an activity more

unusual than read silently, is more memorable than read silently (Quinlan & Taylor, 2013); 2)

time-effort hypothesis – read aloud takes more time and more effort than read silently, what

means that read aloud have more coding time and, consequently, better results in recognition (Quinlan & Taylor, 2013); 3) strength hypothesis – the specific episodic information about the study of a word is more strong for words read aloud than for words read silently (Ozubko, Gopie, & MacLeod, 2012). In a study where words read silently were repeated to increase its coding time, words read aloud just once continue to have better results, indicating more strength than words read silently (Ozubko, Major, & MacLeod, 2014); 5) activation/

monitoring hypothesis – the study of a word increases false alarm rates for associated words

by the spreading activation of the initial word (Roediger, Balota, & Watson, 2001). For words read aloud this spreading activation is greater than for words read silently. This leads to two opposite results: words read aloud are better recognized and also have more false alarm rates than words read silently (Albuquerque et al., May 2014). 6) double processing – according to Sternberg’s addictive method (1969), memory for words read aloud could be better due to

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8 simultaneous processing of visual (the word written in the monitor) and auditory (listen to reading the word) stimuli; 7) cost to words read silently – production effect does not

necessarily means a memory benefit for words read aloud, it could represent a cost for words read silently (Bodner, Taikh, & Fawcett, 2014; Forrin, Groot, & MacLeod, 2016; Jones & Pyc, 2014).

Most of the production effect explanations suggested that coding is stronger for words read aloud than for words read silently. Coding could be really stronger for words read aloud, but it is insufficient to understand all the characteristics of production effect, like the major occurrence in within-subjects designs. In this sense, despite the limitations described above, distinctiveness heuristic seems to be the more robust explanation for the production effect we have until now. With the purpose of clarify the power of distinctiveness heuristic as an explanation to production effect, we decided to manipulate word variables’ that could have impact on distinctiveness and, consequently, in production effect.

Literature shows that word frequency is a characteristic that produces variation in recognition (Gregg, 1976), resulting in the word frequency effect, with low-frequency words being better recognized than high-frequency words; and with average frequency words being worst recognized than low and high-frequency words (Freeman et al., 2010; Glanzer & Adams, 1985, 1990; Lohnas & Kahana, 2013; Merritt, DeLosh, & McDaniel, 2006). Furthermore, a “mirror effect” (Glanzer & Adams, 1985), is attributed to word frequency. This means that in yes/no recognition tests, false alarm rates for low and high-frequency words mirrors, in reverse order, the hit rates. Later, Glanzer and Adams (1990) found that low-frequency words have more hits and less false alarm rates in comparison with high-frequency words. In this way we can conclude that low-high-frequency words are more distinctive than high-frequency words.

Despite the advantage of low-frequency words in recognition, the previous studies about production effect used only high-frequency words (more than 30 per million in English). Many studies utilized the same list of high-frequency words from MacDonald and MacLeod (1998)’s paper (for example, Bodner, Taikh, & Fawcett, 2014; Bodner & Taikh, 2012; Forrin, MacLeod, & Ozubko, 2012; Lin & MacLeod, 2012; MacLeod et al., 2010; Ozubko & MacLeod, 2010). Only in 2014, Jones and Pyc conducted a study with high and low frequency words and production effect in a free recall test paradigm.

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9 Taking into account that both word frequency and production can promote differences in recognition by differential distinctiveness of stimuli, the present study aims to test

distinctiveness heuristic’s previsions. In Experiment 1, we want to replicate the frequency effect in European Portuguese and in Experiment 2 we manipulate both word frequency and production to evaluate distinctiveness previsions.

Experiment 1

In this first experiment, with the purpose of verify if frequency effect works in European Portuguese creating differences in recognition, we anticipated that we will find an advantage of low-frequency (LF) words over high-frequency (HF) words. This advantage could be found with greater hits and lower false alarm rates for low-frequency than high-frequency words. This pattern of results on hits and false alarms will result on better discrimination for low-frequency than high-frequency words.

Method

Participants.

Forty college students from University of Minho participated in exchange for course credits. There were 35 females and 5 males with ages between 17 and 43 (M = 21.90; SD = 4.78). All participants had normal, or corrected to normal, vision and were informed about the voluntary participation in the study.

Stimulus.

A pool of 108 words was obtained from Soares, Costa, Machado, and Comesaña (in press) lexical database. All words were nouns or adjectives that had four to seven letters, syllabic structure initiated by consonant-vowel, and were controlled for imagery (MLF = 4.81,

SDLF = 0.93; MHF = 4.93, SDHF = 0.73; t(53) = -0.34, p = 0.74, 95% CI [-0.35,0.25]) and

concreteness (MLF = 4.98, SDLF = 1.05; MHF = 5.15, SDHF = 0.68; t(53) = 1.68, p = 0.10, 95%

CI [-0.05,0.52]). We selected 54 low-frequency (less than 10 per million in European

Portuguese) and 54 high-frequency words (more than 75 per million in European Portuguese), following a criterion proposed by Soares and colleagues (2015). From the 54 words of each level of frequency, 36 were used as targets and 18 as distractors in the recognition test.

Materials and Procedure.

The experiment was programmed with Superlab 5 and took place in soundproof booths at the Human Cognition Lab of University of Minho.

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10 Initially there was a training phase constituted by twelve words, 6 LF and 6 HF words. In the presentation phase 72 words, 36 LF and 36 HF, were presented randomly during

2000ms, and each word was succeeded by a mask (500ms) and an inter-stimulus interval (500ms). In the recognition test there were 36 words presented before (18 LF and 18 HF) and 36 new words (again 18 LF and 18 HF). There was not time limit to respond nor feedback, and participants should only press “z”, for words presented before, or “m”, for new words.

All words were read silently and all appeared in black, Times New Roman capitals, size 36, at the center of the screen with white background. The entire experience took, on average, 15 minutes to be completed.

Results

Table 1 presents hit, false alarm and discrimination (d’) rates in function of word frequency. Paired-samples t-tests showed a significant difference considering false alarms, with less false alarms for low-frequency words than for high-frequency words, t (39) = 7.09,

p < .001, Cohen’s d =1.06, 95% CI [0.10, 0.17], but not for hit rates, t (39) = 1.08, p =.30,

Cohen’s d =.15, 95% CI [-0.08, 0.03].

Table 1 – Hit, False Alarm and Discrimination rates in function of word frequency in Experiment 1.

Hit rates

M (SD)

False alarm rates

M(SD)

d’

M(SD)

HF Words 0.66 (0.20) 0.23 (0.15) 1.69 (1.67) LF Words 0.68 (0.19) 0.10 (0.10) 3.53 (2.94)

Since the values of false alarm rates were statistically different, we decided to

calculate an aggregate measure of hits and false alarms - discrimination (Donaldson, 1992) - to had a unique measure to compare low and high-frequency words. We applied a paired-samples t-test to analyze discrimination in function of word frequency, and we found that low-frequency words were better discriminated than high-frequency words, t (39) = 3.78, p < .001, Cohen’s d = .77, 95% CI [-2.82,-0.85].

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Discussion

This experiment replicated the well-known phenomenon labeled frequency effect, with low-frequency words showing an advantage over high-frequency words in discrimination rates. In spite of the statistical difference favoring low-frequency words for false alarms and discrimination, this not happened for hit rates: low-frequency words don’t have, statistically, more hit rates than high-frequency words.

The values of false alarm rates were similar to those of other studies (Glanzer & Adams, 1990 – LF =.23 and HF =.28; Hemmer & Criss, 2013 – LF =.10 and HF =.20). Low-frequency words had less false alarm rates than high-Low-frequency words, a finding that is consistent with the literature (Glanzer & Adams, 1985; Gregg, 1976; Lohnas & Kahana, 2013); also, the “mirror effect” of frequency was reproduced: low-frequency words had less false alarms and more hit rates. These results can be explained considering two perspectives: (1) the traditional account of word frequency in recognition – low-frequency words were better recognized due to more attention during encoding, because participants had less experience with low than high-frequency words (Freeman et al., 2010; Hemmer & Criss, 2013; Merritt et al., 2006); (2) the bias of participants to say that high-frequency words were studied before (Hemmer & Criss, 2013) - the use of high-frequency words in everyday life, causes more activation and worst monitoring for these words, resulting in increased false alarm rates in comparison with low-frequency words.

On the other side, the values of low-frequency words’ hit rates were not significantly superior to high-frequency words’ hit rates, like in other studies (Glanzer & Adams, 1990; Hemmer & Criss, 2013). In spite of the “mirror effect” was present, low-frequency words do not have more hits than high-frequency words. This can be interpreted as a trade-off on hit rates between activation of high-frequency words and attention for low-frequency words.

The values of discrimination showed that low-frequency words were better

discriminated than high-frequency words, a finding that can be explained by distinctiveness’ account. This account postulates that low-frequency words were better discriminated by two mechanisms: they have more distinctive characteristics (like being unusual), and consequently better recognition; and they have better memory monitoring (less uncertainty about the study of a low-frequency word than study of a high-frequency word), what conduces to less false alarm rates in comparison with high-frequency words.

The results of this first study showed that frequency had effect on recognition, in particular, at false alarms rates and discrimination. Resuming, we can conclude that distinctiveness varies in function of frequency, with low-frequency words being more

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12 distinctive than high-frequency words. Taking this into account, in the next experiment, we used frequency to manipulate distinctiveness in order to interact with production and test the predictions of distinctiveness’ heuristic.

Experiment 2

The major aim of Experiment 2 was to evaluate the role of distinctiveness.

Considering the interaction between production and word frequency in free recall tests (Jones & Pyc, 2014), we want to know if in recognition tests more distinctive words, such as low-frequency words or words read aloud, were better recognized than less distinctive words, like high-frequency words or words read silently. To do so, four conditions, in which words of high and low frequency were read aloud or silently, were created.

We expected to find a production effect, with words read aloud being better discriminated than words read silently, and a frequency effect, with low-frequency words being better discriminated than high-frequency words. We also anticipated an interaction effect between production and frequency, with low-frequency words read aloud being the better discriminated and high-frequency words read silently the worst discriminated.

Method

Participants.

Forty students from University of Minho participated in exchange for course credits. There were 34 females and 6 males with ages between 17 and 25 (M = 20.33; SD = 1.56). All participants reported they were not stutterers and had normal, or corrected to normal, vision and none of them participated in Experiment 1. In addition, all participants were informed about the voluntary participation in the study and were instructed about the audio record and the recognition test.

Stimuli.

All words of Experiment 1 were used in this new experiment. From the 54 words of each level of frequency, 36 were used as targets and 18 as distractors in the recognition test. From the 36 target words, 18 were read aloud (RA) and 18 were read silently (RS) in the presentation phase.

Materials and Procedure.

The experiment was programmed with Superlab 5 and took place in soundproof booths at the Human Cognition Lab of the University of Minho.

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13 In the beginning there was a training phase constituted by twelve words, 6

low-frequency and 6 high-low-frequency, half read aloud and half read silently. Then, in the presentation phase, to participants distinguish which words they should read aloud and silently, half of the words were printed in red and another half in blue. To counterbalance, in the presentation phase, half of the participants read aloud blue words and silently red words and the other half do the opposite.

Also to counterbalance there are two lists of words, one in which the word appeared in blue and the other in which the same word appeared in red. Like in the previous experience each word from the presentation phase appeared for 2000 ms and was followed by a mask (500 ms) and an interstimulus interval (500 ms).

In order to assure that participants read aloud the words they should in their condition, all participations were audio recorded. To maintain the instructions present, all screens had a glued paper that tells the participant which color they should read aloud and silently.

Later, in the recognition test all words were presented in black, Times New Roman capitals, size 36, at the center of the screen with white background. There were 36 words presented before (18 LF and 18 HF, 9 of them RA and 9 RS) and 36 new words (18 LF and 18 HF). There was not time limit to respond, participants just have to answer “z” for words presented before or “m” for new words. The total experience took, on average, 15 minutes.

Results

Initially, to verify that all participants read aloud the words that they should read aloud in their condition, all records were listen and codified. All participants read aloud all the words they should, except three participants that read one more word from the words they should read silently. These added words were treated like words read aloud for these participants.

Table 2 presents hit and false alarm rates as a function of production and word frequency. For words studied, aloud or silently, in the presentation phase these were hit rates (words that were studied and participants say that they are studied) and for words not studied in the presentation phase these were false alarm rates (words that were not studied and participants say that they are studied).

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14 Table 2 – Hit and False Alarm rates in function of word frequency in Experiment 2.

Hit rates for words read aloud

M (SD)

Hit rates for words read silently

M (SD)

False alarm rates for words not studied

M(SD)

HF words 0.78 (0.16) 0.51 (0.28) 0.18 (0.13) LF words 0.82 (0.17) 0.54 (0.27) 0.08 (0.08)

We conducted a 2 x 2 repeated measures ANOVA to analyze the effect of production (RA vs. RS) and frequency (LF vs. HF) on hit rates. There was a production effect, F(1, 39) = 77.80, p < .001, ŋ2 = .67, in which words read aloud (M = .80) have higher hit rates than words read silently (M = .53). However, there was not a frequency effect, F(1,39) = 1.25, p = .27, ŋ2 = .03, nor interaction between production and frequency on hit rates, F(1,39) = .13, p = .73, ŋ2 = .003.

To analyze false alarm rates, a paired-samples t-test was conducted for low and high-frequency words. There was a high-frequency effect for false alarm rates with low-high-frequency words having less false alarms than high-frequency words, t(39) = 5.17, p < .001, Cohen’s d = .88, 95% CI [.06, .13].

Since false alarm rates were statistically different, we calculated again discrimination (Donaldson, 1992) in order to analyze the effect of production and frequency in a single measure. Table 3 shows discrimination rates for high and low-frequency words, read aloud and silently.

Table 3 – Discrimination rates in function of production and word frequency in Experiment 2.

Words read aloud M(SD)

Words read silently M(SD) HF Words 3.25 (2.80) 2.70 (3.20) LF Words 5.81 (4.11) 3.84 (3.39)

A 2x2 repeated measures ANOVA was conducted for discrimination. There was a production effect, F(1, 39) = 20.00, p = < .001, ŋ2 =.34, and also a frequency effect on

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15 discrimination, F(1, 39) = 10.82, p = <.01, ŋ2 = .22. Words read aloud were better

discriminated than words read silently and low-frequency words were better discriminated than high-frequency words. Nevertheless, there was not an interaction effect between production and frequency on discrimination, F(1,39) = 3.33, p =.08, ŋ2_{= .08.}

Discussion

In this experiment, the production effect was present in hit and discrimination rates. As in previous studies (MacLeod et al., 2010; Quinlan & Taylor, 2013) there was a robust difference in recognition – of about .27 - between words read aloud and silently in the presentation phase. Considering discrimination, we expected that words read aloud will be better discriminated than words read silently. This expectation happened, reflecting the easiest discrimination for words read aloud, compared to words read silently, postulated by

distinctiveness heuristic.

The production effect was observed both for high and low frequency words, what is consistent with the distinctiveness explanation of production effect: read aloud functions as a source of discrimination that can be used, heuristically, on an explicit test, to determine if the word was studied.

We also expected to find a frequency effect, in which low-frequency words were better recognized than high-frequency words, what could support distinctiveness previsions. This frequency effect, like in Experiment 1, not appeared in hit rates, but it is evident in false alarm rates and discrimination. Low-frequency words had significantly less false alarm rates than high-frequency words, an outcome that could be explained by distinctiveness account, which assumes that monitoring source memory is easier for more distinctive words.

Another expectation we had was the presence of an interaction effect between

production and frequency. This interaction is not statistically significant, what could indicate that the advantage of production functions equally for low and high frequency words.

Although, we can observe that the tendency anticipated is there: low frequency words read aloud as the better discriminated and high frequency read silently as the worst discriminated.

In this study production effect was replicated and extended to the point that it is independent of word frequency. Besides that, our results are similar with the only study about

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16 word frequency and production effect (Jones & Pyc, 2014), showing a production effect, a frequency effect, but not an interaction between both effects.

General Discussion

In these experiments, the power of distinctiveness heuristic as an explanation to production effect was investigated. In the first study we replicated the frequency effect in European Portuguese, seeing its impact on a recognition task. Our interpretation is that monitoring was effective for low-frequency words, leading to better discrimination and a reducing of false alarm rates for these type of words. However better monitoring for low-frequency words was not sufficient to surpass hit rates provoked by the activation of high-frequency words. We concluded that distinctiveness varies in function of high-frequency, with low-frequency words being more distinctive than high-low-frequency words.

In the second study, we found the production effect as a “quite powerful mechanism for improving memory for selected information” (MacLeod et al., 2010, p.671). Words read aloud are consistently better discriminated and with more hit rates than words read silently, a finding totally supported by distinctiveness heuristic. Frequency effect was present again in discrimination and false alarm rates, a result that also could be explained by distinctiveness heuristic. The interaction between both effects was not statistically significant, indicating that production is independent from word frequency.

Comparing the magnitude of both effects we can conclude that the power of production effect is bigger than the power of frequency effect. This outcome is interesting mainly for the implications each one of these effects have in memory: production can be more controllable and applicable in educational settings (for example, times and activities for read aloud can be easily implemented) and is a useful and effective way to improve memory; frequency is not easy controllable as production, because we can’t choose to learn just low frequency words, for example, and it is less effective than production to improve memory.

The production effect could be increased in this study made in laboratory settings due to an amplified attention for words read aloud. In this way, attention for words read silently is diminished, causing a greater retrieval cost for words read silently in laboratory settings than in more naturalistic settings (Bodner et al., 2014; Forrin et al., 2016; Jones & Pyc, 2014).

Moreover, and according to several authors, the calculus of discrimination is “problematic” because false alarm rates in within-subject designs have a different meaning

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17 than they do in between-subject designs. In between-subject designs separate false alarm rates are obtained for words read aloud and for words read silently. These separate rates permit the determination of discrimination values for words read aloud and for words read silently. But, in within-subject designs, it is not possible to obtain separate false alarm rates for words read aloud and silently. This happens because we can not assume that a new word was mistaken with a word read aloud or read silently.

So, to calculate false alarm rates in within-subject designs, like in this study, we used a global value (the mean of false alarm rates for low and high frequency words) that is not correctly representative of words read aloud and silently, but that was also used in previous studies (C. M. MacLeod, personal communication, November 26, 2015). In computing aloud and silently discrimination values in this way we biased the calculus and obtain two

discrimination rates that were not fully independent (Forrin et al., 2016).

This approach considers that false alarm rates were equivalent for the classes of variables tested. If this was the case, individuals would be as likely to say that a new word was read aloud as read silently. However, this assumption is not true: previous studies with modality judgments on recognition tests (classification of test items as “read aloud”, “read silently” or “new”) showed consistently that participants are less likely to misclassify a new word as “read aloud” than as “read silently” (Conway & Gathercole, 1987; Ozubko et al., 2012, 2014). This bias is in line with the predictions from distinctiveness heuristic: more distinctive words have better monitoring and more certainty in case of doubt about the study of a word than less distinctive words.

To solve this limitation in the calculus of discrimination values in within-subject designs, Forrin and colleagues (2016) recently proposed the addition of modality judgments, allowing the calculus of separated false alarm rates. In posterior studies, could be interesting to manipulate word frequency and production together and add modality judgments in the recognition test. In this way, we could explore better the discrimination for words read aloud and silently.

The production effect is part of a conjunct of variables that have impact on

distinctiveness (MacLeod et al., 2010). Consequently, studying some information aloud and other information silently may be a useful strategy to study and highlight important

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18 Since this was the first study combining production effect and word frequency in a recognition task, more investigation is needed to completely understand the role of

distinctiveness on the variables studied.

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