Memory processes in frequency judgment: the impact of pre-experimental frequencies and co-occurrences on frequency estimates [Elektronische Ressource] / von Frank Renkewitz

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Sujets

• Psychologie

Informations

• Publié par: technische_universitat_chemnitz
• Publié le: 01 janvier 2004
• Nombre de lectures: 31
• Langue: Deutsch
• Poids de l'ouvrage: 1 Mo

Extrait

Memory Processes in Frequency Judgment:

The impact of preexperimental frequencies and

cooccurrences on frequency estimates. Dissertation zur Erlangung des akademischen Grades doctor rerum naturalium (Dr. rer. nat.) vorgelegt der Philosophischen Fakultät der Technischen Universität Chemnitz von Frank Renkewitz, geboren am 09.10.1970 in Issum Chemnitz, den 4. Februar 2004

Vorwort

Bei der Arbeit an dieser Dissertation unterstützten mich viele Menschen in vielfältiger Weise. Ich möchte nicht versäumen, Ihnen zu danken. Herr Prof. Dr. Peter Sedlmeier und Herr Prof. Dr. Manfred Wettler hatten wesentlichen Anteil daran, dass ich diese Dissertation in einer stets angenehmen Atmosphäre erarbeiten und fertig stellen konnte. Bei inhaltlichen und organisatorischen Problemen, die mich bei dieser Arbeit ereilten, waren sie jederzeit ansprechbar. Petra Seidensticker schulde ich Dank für die Programmierung der Simulationen, die in Kapitel 3 berichtet werden. Alexander Bischof unterstützte mich bei der Programmierung der Versuchssteuerungen zu einigen der Experimente. Bei der Durchführung der Untersuchungen halfen mir Conny Belger, Sonja Kunze, Susann Porstmann und Maya Reimer. Johannes Hönekopp, Andreas Keinath, Martin Baumann und Gesine Heisterkamp danke ich für ihre Bereitschaft zu etlichen und sicher ermüdenden Diskussionen zum Thema Häufigkeitsschätzungen, vor allem aber für ihre äußerst freundschaftliche Unterstützung bei allen Aspekten dieser Arbeit. Von ihren Anregungen, Korrekturen und Aufmunterungen hat diese Dissertation zweifellos sehr profitiert. Schließlich gilt mein Dank Birgit Aufderheide sowie Dieter und Christa Renkewitz, deren Hilfe über meine Mühen mit dieser Dissertation weit hinausreicht und mich stets begleitet.

Contents C HAPTER 1: J UDGMENTS ON F REQUENCIES :I NTRODUCTION AND O VERVIEW …....…... C HAPTER 2: I S THE F AMOUS N AMES E FFECT C AUSED BY D IFFERENCES IN THE P RE E XPERIMENTAL F REQUENCIES OF THE N AMES ? AT EST OF A H YPOTHESIS OF MINERVADM AND PASS……………...

Summary ..….……………...……………………………………………………. Introduction ………………..….………………………………………………… Demonstrations of Faulty Estimates ………………………………….….…..…. The Availability Heuristic as an Explanation for Biased Estimates ….….….….. Memory Models of Frequency Judgment ………………………………………. Biased Estimates Reconsidered ………………………………………………… An Alternative Account for the FamousNames Effect …………………….….. Overview of the Experiments …………………………………………….…….. Experiment 1 ……………………………………………………………………. Method ……………………………………………………………………. Predictions of the Memory Models ………………………………………. Results …………………………………………………………………….. Discussion ………………………………………………………………… Experiment 2 ……………………………………………………………………. Method ……………………………………………………………………. Predictions of the Memory Models ………………………………………. Results …………………………………………………………………….. Discussion ……………………………………………………………..….. Experiment 3 ……………………………………………………………………. Method ……………………………………………………………………. Predictions of the Memory Models ………………………………………. Results …………………………………………………………………….. Discussion ………………………………………………………………… Experiment 4 ………………………………………………………….…..…….. Method ……………………………………………………………………. Predictions of the Memory Models …………………..…………………... Results……………………………………………………………………... Discussion ……………..………………………………….……………….

1 8 8 9 10 12 14 16 18 26 28 28 31 34 40

44 44 46 46 49

53 53 55 57 60

64 64 66 66 69

General Discussion …………………………..……….…….…….…….………. 71 Conclusion ………………………………………………………..…………….. 82 C HAPTER 3: E FFECTS OF ASSOCIATIONS ON F REQUENCY J UDGMENTS : S IMULATIONS WITH PASS AND E MPIRICAL R ESULTS .......……….……... 84 Summary ...……………………………………………………………………… 84 Introduction …………………...…………………………………………………85 What Kinds of Frequency Judgments can be modeled with PASS? ..……. 87 The PASSModel ...……………………………………………….……………..90 FEN – Architecture and Learning Rule .………………………………….. 91 The Generation of Frequency Judgments ...………………………………. 93 Former Simulations with PASS ...………………………………………… 95 Overview of the Simulations and Experiments ....……………………………… 96 Study 1 ...……………………………………..………………………………….100 Method ………………………………………………….………………… 100 Simulation Method ...…………….……………………………………….. 103 Simulation Results ...…..………………………………………………….. 107 Experimental Results ……………...……………………………………… 115 Discussion …….…………………………………………………………... 129 Study 2 …….…………………………………………………………………….137 Method ……………………………………………………………………. 137 Simulation Method and Results ….……………………………………….. 139 Experimental Results …...……………………….………………………... 144 Discussion ………………………………………………………………… 155 General Discussion ……..……………………………………………………….159 The Explanatory Power of PASS ..……….………………………………. 160 What about other Memory Models of Frequency Judgment? ..……...…… 161 What Problems Remain in Predicting Frequency Estimates with PASS? ... 163 Beyond Memory Assessment: Transforming Intuitions into Absolute Frequency Judgments ……..……………………………………………… 165 C HAPTER 4: 168 S UMMARY AND O UTLOOK …….….….………………………………………….. Can Memory Models Account For the FamousNames Effect? …....…………... 169 The Influence of Associations on Frequency Judgments ..….…...……………... 171 R EFERENCES  173

CHAPTER 1

Judgments on Frequencies: Introduction and Overview

Psychologists have shown a longstanding interest in people’s estimates of absolute and relative frequencies and the processes underlying such estimates. One motivation for this interest can be seen in the fact that judgments about frequencies permeate our daily life. For instance, to select the appropriate rate of an Internet provider, we have to assess how often we usually use the Internet. Before we decide to leave the umbrella at home, we may wonder how often it rains in the afternoon although the sky is blue in the morning. Before we take out an insurance, we will probably consider how often the misadventure in question may occur. Judgments on the frequency of certain students’ mistakes may influence the teacher’s lesson plan. A doctor will often ask about the frequency of particular symptoms. Her diagnosis will be influenced by how often the symptoms under consideration have indicated a specific disease in her professional practice (Eraker & Politser, 1988; Weber et. al, 1993). Some of these examples already point to a second motivation for the interest in frequency processing: The experience of frequencies plays a significant role in numerous areas of thinking and judgment. To highlight the diversity of the cognitive processes that are affected by frequencies let us name a few of them. The frequency of

jointly occurring objects or features of objects is the decisive input for categorization achievements and contingency learning (Hintzman, 1988; Shanks, 1995, Smith & Medin, 1981). Probability judgments can be based on the frequency with which certain events occurred in the past (Reyna & Brainerd, 1994; Sedlmeier; 1999; Tversky & Kahneman, 1973). In turn, judgments on probabilities and relative frequencies are an essential determinant of decisions (e.g. Yates, 1990). Valence judgments on a neutral stimulus are influenced by its presentation frequency (Zajonc, 1968; Bornstein,1989).

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The extent of happiness in life depends, among other things, on the perceived frequency of positive and negative life events (Diener, Sandvik & Pavot, 1991). As a last example, frequencies affect various social judgments such as attributions (Kelley, 1973; Cheng, 1997; Cheng & Novick, 1992) or stereotyping (Hamilton & Gifford, 1976; Schaller & Maas, 1989; Schaller et al., 1996). According to the manifold effects of frequencies, the question of how people process frequencies and how they judge them on the basis of this processing was examined in various subdisciplines of psychology. Thus, there are numerous studies on frequency judgments in experimental social psychology (e.g. Betsch et al., 1999; Manis et al., 1993; Schwarz et al., 1991), in developmental psychology (e.g. Fischbein, 1975; Ginsburg & Rapoport, 1967; Huber, 1993; Kuzmak & Gelman, 1986) or in the literature on survey methods (e.g. Blair & Burton, 1987; Conrad, Brown, & Cashman, 1998; Menon, Raghubir, & Schwarz, 1995). However, the longest tradition of research on frequency judgment can be found in the area of judgment and decisionmaking and in the literature on memory and learning. Unfortunately, methods, theoretical assumptions and results from one of those areas have often been neglected in the other. This led to the surprising fact that completely contradictory conclusions have been achieved already with questions the empirical clarification of which may appear simple at first sight. The most prominent example of this kind concerns the question about the accuracy of frequency judgments. A particular interest in the quality of frequency judgments chiefly existed in the area of decisionmaking. Here, research on frequency estimation was heavily influenced by the heuristics and biases program (Kahneman, Slovic, & Tversky, 1982). At the core of this program was the assumption that people use a toolbox of heuristics to solve the complex tasks of generating judgments on frequencies or probabilities and of making predictions on uncertain quantities. In order to explain frequency judgments on serially encoded events, which are in the focus of this thesis, it was assumed for the most part that the availability heuristic is selected from the toolbox (e.g. Tversky & Kahneman, 1973; BeythMarom & Fischhoff, 1977; Lichtenstein et al., 1978). According to the availability heuristic, judgments about the frequency of an event can be based on the ease with which instances of that event can be brought to mind

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(Tversky & Kahneman, 1973). Although it was assumed that the heuristic can lead to reasonable estimates under favorable conditions, the typical procedure to test the availability hypothesis was to create an experimental situation in which the heuristic predicted that the estimates would not conserve the rank ordering of the actual event frequencies and to check whether the predicted error occurs. This procedure produced several impressive demonstrations of biased frequency judgments. However, another consequence of this test strategy was, that evidence for the heuristic almost exclusively stemmed from faulty estimates. Starting from these demonstrations of biases, the conclusion was drawn in the literature on judgment and decisionmaking that frequency estimates were generally errorprone. This conclusion gained enormous popularity far beyond the heuristics and biases program and entered numerous textbooks on cognitive psychology (e.g. Mayer, 1992; Anderson, 1996; Yates, 1990) and social psychology (e.g. Fiske & Taylor, 1991). For a long time, the point of view that frequency judgments are susceptible to biases remained astonishingly unaffected by the fact that in the literature on memory and learning a huge amount of empirical studies appeared in parallel in which the participants showed a remarkable sensitivity to frequencies. Accurate frequency judgments were found for various events from everyday life such as letters, names, words, professions or karate techniques within training sessions (Attneave, 1953; Bedon & Howard, 1992; Shapiro, 1969; Tryk, 1968; Zechmeister et al., 1975). The same result was found to be true in laboratory studies in which, even under aggravated conditions, reasonable estimates were regularly given on the experimentally varied frequencies of stimuli previously presented on lists (e.g. Greene, 1984). From the multitude of corresponding results a consensus arose in the literature on memory and learning which exactly contradicted the conclusion from the research on judgment and

decisionmaking: It was generally assumed that frequency judgments ordinarily reflect

the actual frequencies of the events in question fairly accurately (e.g. Peterson & Beach, 1967; Howell, 1973; Estes, 1976; Jonides & Jones, 1992). Hasher and Zacks (1984) presumably adopted the most explicit point of view of this kind formulating the “automatic coding hypothesis“. In this approach, frequency was regarded as one of the few attributes of events or objects registered automatically. According to this, the

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encoding of frequency information requires little or no attentional capacity and works without any awareness and intention (s. also Hasher& Zacks, 1979; Zacks, Hasher, & Sanft, 1982; Zacks & Hasher, 2002). In this view, people will obviously not have to rely on heuristics to generate frequency judgments since the required information will regularly be stored in memory. Within the memory literature several precise and computational models were suggested that allow the prediction of frequency judgments (e.g. Dougherty et al, 1999; Gillund & Shiffrin, 1984; Hintzman, 1988; Murdock, 1993; Sedlmeier, 1999). All these models incorporate the assumption that every event encoded with a minimum of attention causes a modification of memory representation. Accordingly, in these models the state of the memory representation at the time of judgment can be used to produce a signal, the intensity of which depends on the frequency with which the event in question was encoded. Although the models differ clearly regarding their architecture, their specific representation assumptions and the processes with which the memory representation is accessed, they therefore make the same basic prediction: Frequency judgments will mostly reflect the actual frequencies fairly well. In all models, this prediction is subject to one general restriction. Since the memory representation of an event is not a perfect copy of this event, regression to the mean is expected. According to this, small frequencies should be overestimated and large frequencies underestimated. In fact, regression was observed in numerous studies (e.g. Attneave, 1953; Fiedler, 1991; Hintzman, 1969; Varey et al., 1990). Nowadays there is nearly no dissent any longer that the fundamental predictions of the memory models describe the typical finding to frequency judgments correctly (e.g. Sedlmeier et al., 2002; Fiedler, 1996): estimates are mostly calibrated fairly well, but regressed. However, this does not mean that the models assume that the memory for frequencies is invariant. On the contrary, it is expected that factors affecting the encoding or representation of events also have an effect on frequency judgments on these events. In the research to memory models, recognition judgments have often been regarded as a special case of frequency judgments (in which only the frequencies 0 and not0 are differentiated). Corresponding to the expectation of the models, indeed several factors were identified that exert a reliable influence on both kinds of

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judgments such as item similarity (Hintzman, 1988), depth of encoding (Greene, 1988) or context effects (Brown, 1995). Moreover, recency effects (Lopez et al., 1998; Sedlmeier, 1999), list length and spacing effects (Hintzman, 1988) in frequency judgments were successfully predicted or explained with memory models. Finally, also phenomena beyond simple frequency or recognition judgments could be simulated, such as people’s confidence in their frequency estimates. (Sedlmeier, 1999) All together, research to frequency judgments within the memory paradigm was both empirically and theoretically very fertile. Empirically, researchers succeeded in finding a series of stable phenomena that allow conclusions on how memory processes frequencies.Theoretically, models of memory were developed that are able to account for these phenomena and that have an explanatory scope which is often not confined to frequency estimates. Contemporary research within the memory paradigm primarily aims at the further evaluation of these models. This is mainly evidenced by studies in which new predictions about memory factors influencing frequency estimates are derived and tested (e.g. Murdock, 1999; Hintzman, 2001; Dougherty & FrancoWatkins, 2003). For a long time, however, the theoretical approaches from memory research were not applied to the biases described in the literature on judgment and decision making. This has changed only recently. Later memory models no longer ignored the demonstrations of faulty estimates from the heuristics and biases program. Dougherty, Gettys and Ogden (1999) suggested MINERVADM. The aim of this model is to preserve the advantages of earlier memory models and to explain a multitude of judgmental biases at the same time. Also within the PASS model (Sedlmeier, 1999) attempts have been made to explain some of the demonstrations of faulty frequency estimates on serially encoded events by memory processes. These integrative approaches thus challenge the assumption that the biased frequency estimates observed in the judgment and decisionmaking literature must be ascribed to the use of the availability heuristic. The present dissertation includes two series of studies that fit into the two trends in memory research to frequency estimates described above: In chapter 2 I tested a memorymodelbased explanation of one of the best known and most quoted

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biases, the famousnames effect. Tversky and Kahneman (1973) could demonstrate that after the presentation of a list with a similar number of female and male names, the frequency of that sex is overestimated that was represented on the list with more famous names. Since the more famous names were also recalled better, this effect was usually explained with the availability heuristic An alternative explanation was recently suggested both in MINERVADM (Dougherty et al., 1999) and in PASS (Sedlmeier, 1999). In both models it is assumed that the property of famous names eliciting the bias is their higher preexperimental frequency of occurrence. The memory models suggest furthermore that people are not able to discriminate perfectly between different encoding contexts of the same event. Thus, if the memory representation is accessed to estimate the experimental frequency of famous names, then the resulting memory signal will be influenced by the extraexperimental occurrence of the names. More famous names thus elicit a stronger signal than less famous names. In this view, the assumption that single exemplars are retrieved is not necessary to explain the famousnames effect. An implication of this explanation is that an analogous effect should occur with other stimulus material than famous names: If the frequency of two categories is to be judged, then that category should be overestimated the exemplars of which occur more often outside the target context. I tested this hypothesis in four experiments. The predictions of the memory models were compared with the predictions of a recallestimate version of the availability heuristic. This procedure allows a conclusion whether the occurrence of a bias is indeed caused by the processes described in the memory models or by the use of the availability heuristic. In chapter 3 new predictions about a factor influencing the memory for frequencies are derived from the PASS model. Earlier studies indicate that estimates on the experimental frequency of a word increase with the number of jointly presented associated words (Leicht, 1968; Shaughnessy & Underwood, 1973; Vereb & Voss, 1974). PASS can offer an explanation for the effect of the factor ‘association’. In this memory model the ability to judge the frequencies of serially encoded events is regarded as a byproduct of associative learning. Accordingly, knowledge about the frequencies of events is represented in memory in the form of associative strengths

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between features constituting these events. The acquisition of these associative strengths is modeled by a neural net that successively encodes events. In this process, PASS also acquires associations between events (or objects). Sedlmeier, Wettler and Seidensticker (2004) could now demonstrate that this property of the model can be successfully used to predict human primary associations to stimulus words if the model encoded an adequately large amount of text beforehand. This indicates that PASS can map the associative strengths between words acquired by the participants already before the experiment. Other memory models on frequency judgments do not dispose of this ability. To the best of my knowledge, PASS is thus the only model able to describe the acquisition of associations between words, the experimental encoding of these words and, finally, the generation of frequency estimates on these words. In simulations with PASS, I used this capability of the model to gain precise predictions about the effect of associations between words on frequency estimates. These predictions were tested in two experiments in which the participants were presented with lists containing words that were either strongly or weakly associated among each other. The results indicate that associative learning is more than just a plausible candidate for the memory processes underlying frequency estimates. In chapter 4 the results of both series of studies are summarized briefly and some tentative guidelines for future research are suggested.

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