Choice latency times as determinants of post-decisional confidence

Choice latency times as determinants of post-decisionalcon®dence

Dan Zakay *, Ra® Tuvia

Department of Psychology, Tel-Aviv University, 69978 Ramat-Aviv, Israel

Received 24 September 1996; received in revised form 16 May 1997; accepted 5 June 1997

Abstract

In three experiments the relationship between choice latency times and post-decisional con-

®dence was examined. In the ®rst experiment 81 participants answered a general knowledge

questionnaire and rated their degree of con®dence in the correctness of each choice they made.

Choice latency times were also measured. A negative correlation between choice latency times

and con®dence ratings was obtained, especially when very high con®dence ratings were report-

ed. This relationship, however, was not compatible with that between choice latency times and

choice accuracy, which was very low. These results were replicated in the third experiment in

which 81 participants performed a perceptual learning task. In the second experiment another

group of 81 participants answered the same questionnaire, but this time they were asked ± af-

ter seeing a question and before making a choice ± to state their level of prospective con®dence

regarding their ability to make a correct choice. Results indicated that when prospective con-

®dence was high, the following choice latency times were signi®cantly shorter than when pro-

spective con®dence was either low or moderate. This ®nding indicates that prospective con®-

dence partly determines choice latency times. It was suggested that choice latency times might

be a misleading cue for feelings of con®dence. Implications of these ®ndings for understanding

the overcon®dence (OC) phenomenon were discussed. Ó 1997 Elsevier Science B.V. All

rights reserved.

PsycINFO classi®cation: 3040

Keywords: Overcon®dence; Choice latency time; Post-decisional con®dence

Acta Psychologica 98 (1998) 103±115

* Corresponding author. Fax: +972 3 640 9547; e-mail: [email protected].

S0001-6918/98/$19.00 Ó 1998 Elsevier Science B.V. All rights reserved.

PII S 0 0 0 1 - 6 9 1 8 ( 9 7 ) 0 0 0 3 7 - 1

1. Introduction

Retrospective con®dence (RC) is a feeling which emerges after mental activities,such as knowledge retrieval or decision making, have been completed. RC re¯ectshow con®dent one is regarding the optimality of one's past performance (Petersonand Pitz, 1988). Many empirical ®ndings, however (e.g., Thompson and Mason,1996; Zakay, 1996) indicate that feelings of con®dence are not su�ciently sensitiveto the actual optimality of performance. This lack of sensitivity has been found toexist in various ®elds, including eyewitness testimony (e.g., Sporer et al., 1995; Wein-gardt et al., 1994), forecasting (e.g., Russo and Schoemaker, 1989) and decision mak-ing (e.g., Wickens et al., 1993; Zakay, 1985).

One of the best examples of the lack of sensitivity of feelings of con®dence is theovercon®dence (OC) phenomenon (e.g., Allwood and Granhag, 1996; Koriat et al.,1980). Fischho� and MacGregor (1982) cite an impressive body of research demon-strating how pervasive OC is in a variety of ®elds. OC is not con®ned to novices only,but is also found among experts, including lawyers (Loftus and Wagenaar, 1988) andchemical-industry managers (Lichtenstein et al., 1982) in the domain of their own ex-pertise.

Various hypotheses and models have been suggested in an attempt to account forthe OC phenomenon. Koriat et al. (1980) argue that OC is a product of a con®rma-tion bias: people are inclined to support their hypotheses and therefore look only forsupportive evidence and neglect contradictory evidence. Gri�n and Tversky (1992)hypothesized that OC and undercon®dence are the result of a tendency of people tofocus on the strength or extremity of the available evidence with insu�cient regardfor the credibility of the source of the evidence. Wagenaar (1988) suggests that whenpeople do not know the correct response required in a speci®c task, they attempt toinfer it, and therefore it is possible that although a response is incorrect, a respondentwill feel very con®dent in his or her inferential reasoning, a contradiction leading toOC. Despite this wealth of hypotheses, a comprehensive explanation of the OC phe-nomenon is still lacking. This is partly evident in the ®nding that the debiasing meth-ods based on these hypotheses, do not e�ectively reduce OC (Granhag, 1996; Tra®-mow and Sniezek, 1994).

Some scholars (e.g., Gigerenzer et al., 1991; Keren, 1991) argue that the OC phe-nomenon is an artifact caused by a lack of ecological validity due to biased selectionof questions. These researchers claim that if participants are presented with a set ofquestions forming a representative sample of a knowledge domain, OC disappears(e.g., Juslin, 1994). Without entering into the depth of the ecological dispute, it is ar-gued that understanding the lack of sensitivity of feelings of con®dence in general isstill worthwhile because everyday situations in which one is forced to make choicesare not always ``ecologically valid'' in the strict sense of this term (Granhag, 1996).Sometimes life events are unique and have never been encountered before.

In this paper it is argued that the lack of sensitivity of feelings of con®dence ispartly due to factors which have a high impact on feelings of con®dence, but arenot related to choice processes which determine choice accuracy. One example isthe nature of available information at the time of the choice. Kahneman and Tversky

104 D. Zakay, R. Tuvia / Acta Psychologica 98 (1998) 103±115

(1973) have shown that people are most con®dent in their judgments when informa-tion is consistent and/or extreme, even though these factors should induce them todecrease their con®dence. The quantity of available information has a similar e�ect.Russo and Schoemaker (1989) demonstrated that analysts may feel more con®dentwith more information even if the additional information does not lead to any in-crease in accuracy. When factors such as those described above increase feelingsof con®dence without a parallel e�ect on accuracy, OC is inevitable.

The present study focuses on choice latency time. It is argued that choice latencytime (latency time) is another ambiguous determinant of post-decisional con®dence(con®dence). While we expect to ®nd a signi®cant negative relationship between con-®dence and latency time, we anticipate a much lower relationship between latencytime and choice accuracy. The rationale for these hypotheses is that reaction timesare positively associated with perceived mental e�ort (Wickens, 1992), and decisionmakers tend to report higher con®dence levels when the perceived mental load dur-ing the choice process is lower (Zakay and Tsal, 1993). The case of guessing might besomewhat di�erent since it seems reasonable that there are two kinds of ``guessing'',one with slow latency times, where the subject tries to ®nd an answer without feelingsuccessful, and one where the subject at once ``realizes'' that he knows nothing. Thehypothesized negative relationship between latency time and con®dence is strength-ened by the similar case of feeling of knowing, where a negative relationship betweenresponse times and the level of feelings of knowing has been observed (e.g., Koriat,1993). Indeed, Allwood and Montgomery (1987) suggest that the feeling of knowingphenomenon can be considered a parallel to judgments of con®dence in one's know-ledge. Nevertheless, it is clear that low perceived mental load and past latency timesare not necessarily associated with better decisions.

Three experiments were designed to test the hypotheses.

2. Experiment 1

2.1. Method

Participants. Eighty-one ®rst-year social science students at Tel-Aviv University,with ages ranging between 19 and 31 years, participated in the experiment in partialful®llment of course requirements. Hebrew was the mother tongue of all partici-pants.

Instruments. A Compaq 486 DX-33 MHz PC was utilized. All the instructions andstimuli were presented on the computer screen. Responses were entered directly intothe computer and recorded automatically.

General knowledge questionnaire. A general knowledge questionnaire composed of20 binary questions was prepared. The questionnaire was similar to questionnairesused in OC research (e.g., Allwood and Granhag, 1996). Examples of questionsare: ``What is the capital of Romania?'' (a) Bucharest; (b) Sophia. ``Which islandis larger in area?'' (a) Corsica; (b) Sardinia. The distribution of correct responses be-tween (a) and (b) was counterbalanced.

D. Zakay, R. Tuvia / Acta Psychologica 98 (1998) 103±115 105

Latency time measurement. Choice latency times were measured automatically bythe computer from the presentation of a question until a key on the computer's key-board was pressed. The ``4'' and ``6'' keys were used for indicating whether the ``a''or the ``b'' response was selected as the correct choice, respectively. The labels a andb were glued on the keys.

Post-decisional con®dence measurement. Following Allwood and Granhag (1996),participants were asked to rate their degree of con®dence in the correctness of theirchoice on a 50-point scale ranging between 50% and 100%, where 50% meant thatthey could just as well have chosen the other alternative answer, and 100% meantthat they were absolutely sure that the alternative chosen was correct.

2.2. Procedure

Participants were tested individually. First, the task and the use of the computerwere explained until it was ensured that participants mastered both. Questions werepresented one by one in random order and participants were told that the correctanswer should be chosen by pressing either the a or the b key. In the event of uncer-tainty as to the correct answer, the answer perceived as having a higher probabilityof being correct was to be chosen. Participants were asked to press a key immediatelyafter reaching a choice. Following that, con®dence was reported. Participants werenot told about the measurement of choice latency times.

2.3. Results and discussion

The average percentage of correct choices across all participants was 75.10%(SD� 10.7), and the average con®dence reported by participants was 78.50%(SD� 6.1). The calibration curve is presented in Fig. 1.

The average OC across participants was 3.40% (SD� 9.10) and the di�erence be-tween average con®dence and average percentage of correct choices was signi®cant(t(79)� 3.35; p < 0.01). As can be seen in Fig. 1, the only deviation from a typicalcalibration curve is in the 71±80 con®dence category, in which participants were,on the average, calibrated. Average choice latency times was computed for eachone of the six con®dence categories. The data are presented in Table 1.

The con®dence category of 50 was considered to be a unique one because of theunique meaning attached to it in the instructions given to participants. Choice laten-cy times were analyzed by a one-way ANOVA, which revealed signi®cant di�erencesbetween con®dence categories (F(5,1614)� 3.40; p < 0.001). Duncan multiple rangetests, however, revealed that speci®c signi®cant contrasts exist only between the high-est con®dence category (91±100) and all other con®dence categories (p < 0.01). Theoverall correlation between con®dence and choice latency time was )0.39(d.f.� 1618; p < 0.01).

With the exception of the two lowest con®dence categories, the shorter the choicelatency time was, the higher the reported con®dence. This tendency is more empha-sized when con®dence scores are divided into the following three categories: 50±60;61±90; 91±100. Choice latency times in these categories are 12.5 s (SD� 8.3); 11.3 s


(SD� 7.0); and 7.6 s (SD� 5.7). In most cases, longer latency times are associatedwith lower percentages of correct choices. The overall correlation between choice la-tency times and percentage of correct responses is )0.19 (d.f.� 1618; p < 0.01). Thecorrelations between latency times and accuracy were computed within each con®-dence category and are presented in Table 1. The highest correlation coe�cient is)0.29. The overall correlation between con®dence and percentage of correct choices

Table 1

Data obtained in Experiment 1

Con®dence category No. of

choices

Average CLT (s) % Correct responses Correlation

between CLT

and accuracymean (SD)

50 147 12.3 (8.5) 53.00 )0.05

51±60 54 14.1 (5.8) 52.00 )0.29

61±70 158 12.1 (7.5) 64.00 )0.13

71±80 184 11.4 (6.3) 74.00 0.17

81±90 435 10.5 (7.2) 68.00 )0.14

91±100 642 7.6 (5.7) 90.00 )0.19

Fig. 1. Calibration curves ± Experiments 1 and 2 (ÐhÐ, Experiment 1; ÐdÐ, Experiment 2; - - -m- - -,

Identity line). Each con®dence category is presented by its midpoint (e.g., the 61±70 category is presented

by 65), except for 50% which stands for itself.


is 0.20 (d.f.� 1618; p < 0.01). The partial correlation of con®dence with percentageof correct choices when the respective correlation with choice latency times is partial-led out, is 0.13 (p < 0.01). The partial correlation of con®dence with latency times,when accuracy is partialled out, is )0.36. Since the regular respective correlation is)0.39, it can be concluded that accuracy is not in¯uencing the con®dence±latencytime relationship.

The ®ndings obtained in the ®rst experiment supported the tested hypotheses. Anegative correlation explaining about 15% of the variance exists between latencytime and con®dence. The correlation between latency time and accuracy is lowand across all participants and choices explains only about 4% of the variance. Thisis also true for each con®dence category by itself. The correlation between con®denceand accuracy explains only about 4% of the variance and after the correlation be-tween latency time and accuracy is partialled out, only about 2% of the varianceare explained. The ®ndings obtained in this experiment, however, do not enable aconclusion to be made about the causal relationships between latency time and con-®dence.

3. Experiment 2

The ®ndings of the ®rst experiment revealed the existence of a negative relation-ship between latency time and con®dence. Kruglanski et al. (1991) found that initiallevels of con®dence determine the motivation for conducting memory search. Thiswas evident because subjects having higher levels of initial con®dence spent moretime in memory search than subjects with low level of initial con®dence. Therefore,it is plausible that whenever a general knowledge question is presented, an initial lev-el of con®dence emerges and in¯uences the duration of memory search processes be-fore a response is chosen. If this, indeed, is the case, then latency time partly re¯ectsthe initial level of con®dence, but afterwards it is one of the cues which determinecon®dence. We call the initial level of con®dence prospective con®dence. In orderto measure it, a method similar to a method employed by Allwood and Granhag(1996) was adopted. Allwood and Granhag asked participants, before seeing a ques-tion, to state their level of knowledge in the knowledge domain to which the questionbelongs. In the present experiment participants were asked to state their feeling ofprospective con®dence that they would be able to correctly answer a question afterbeing presented with the question, but before seeing the potential responses.

3.1. Method

Participants. A second group of 81 ®rst-year social science students at Tel-AvivUniversity participated in the second experiment in partial ful®llment of course re-quirements. Their ages were similar to those of the ®rst experiment's participants.

Instruments and procedure. The instruments and procedure used in the second ex-periment were identical to those used in Experiment 1, with the following exception:each question was ®rst presented on the computer screen by itself without the two


response options. Participants were asked to state the level of their prospective con-®dence by selecting one of the following three responses: No, Maybe, and Certainly.Only three levels of prospective con®dence were used because Allwood and Granhag(1996) found that when such a scale was used no signi®cant e�ects of the knowledge-area assessments were found on calibration. Next, the two response options ap-peared on the screen and the procedure continued exactly as in Experiment 1. Laten-cy times were measured from the appearance of the response options until a responsekey was pressed.

3.2. Results and discussion

The average percentage of correct choices across all participants was 72.5%(SD� 7.30), and the average con®dence reported by participants was 79.00%(SD� 9.85). The calibration curve is presented in Fig. 1. The average OC across par-ticipants was 6.5% (SD� 11.4) and the di�erence between average con®dence andaverage percentage of correct choices was signi®cant (t(79)� 5.06; p < 0.01). Aver-age choice latency time, average percentage of correct choices, and average con®-dence scores were computed for each one of the prospective con®dence categories,and are presented in Table 2.

A series of one-way ANOVA revealed signi®cant e�ects for percentage of correctchoices (F(2,1617)� 9.10; p < 0.001); con®dence scores (F(2,1617)� 28.73;p < 0.001), and choice latency times (F(2,1617)� 6.01; p < 0.001). Accuracy washighest when prospective con®dence was ``certain'' and lowest and it was rated aseither ``no'' or ``maybe''. Con®dence was positively correlated with prospective con-®dence and choice times were shortest when prospective con®dence was certain andhighest when it was rated as either no or maybe. Latency times are shorter than thoseobtained in the ®rst experiment because they were measured from the appearance ofthe response options only, and not from the appearance of the question, as in the®rst experiment.

The ®ndings indicate that whereas prospective con®dence is positively correlatedwith later con®dence levels and speed of following choices, prospective con®dence, aswell as RC, is not highly sensitive to accuracy since percentages of correct responsesare the same in the two lower con®dence categories. Latency times were shortest in

Table 2


Prospective con®dence

category

No. of

choices

Average PDC % Correct

responses

Average

CLT (s)

No 240 53.00 58.00 10.50

(7.80) (49.00) (7.00)

Maybe 595 68.00 59.00 10.60

(11.90) (49.00) (9.50)

Certainly 785 95.00 87.00 6.70

(7.60) (33.00) (5.10)

Standard deviations appear in brackets.


the highest con®dence category which is also associated with highest accuracy. How-ever, the correlation between latency times and accuracy within this con®dence cat-egory is, similar to what was found in the former experiment, only )0.19, and ex-plains less than 4% of the variance.

4. Experiment 3

The aim of Experiment 3 was to replicate the ®ndings obtained in the ®rst exper-iment in the context of a di�erent type of knowledge. We attempted to determinewhether it is possible to generalize the pattern of relationships between latency time,con®dence and accuracy of knowledge retrieval beyond the context of general know-ledge questions. In order to achieve this goal, a perceptual learning task was selectedin which the knowledge to be retrieved was nonverbal. A second reason for this se-lection was that in perceptual tasks undercon®dence is a more typical ®nding thanOC (e.g., Keren, 1988).

The same pattern of relationships as found in the ®rst experiment would indicatethat this pattern is not con®ned to cases in which OC prevails. The third reason forthe selection of this task was to control the level of relevant previous knowledge ofparticipants, as is not the case when general knowledge questions are used. In thepresent task, participants did not have any previous relevant knowledge.

4.1. Method

Participants. A third group of 81 social science students similar in the distributionof demographic data to the former two groups, participated in the experiment in par-tial ful®llment of course requirements.

Instruments. The same computer used in the former experiments was utilized here,but the knowledge retrieval task was changed in order to enable control over formerlevel of relevant knowledge of participants.

Knowledge retrieval task. Twenty-seven pairs of characters (e.g., +; / ) appeared onthe screen. Each pair appeared on the screen twice for 4 s and participants' task wasto learn the two characters and the association between them. No character appearedin more than one pair. The two presentations of each pair appeared in random po-sitions in the sequence of pairs but not consecutively. After the learning phase 20 testpairs selected at random from the learned set, were presented. One character, chosenat random from a pair, appeared on top and two characters marked as a and b ap-peared below the chosen character (see Fig. 2).

One of these characters was the correct member of the pair and the second char-acter was chosen at random by the computer from the whole set of characters includ-ed in all other pairs. Pairs were chosen at random and each pair appeared only once.The placement of the correct response as either a or b was counterbalanced. Partic-ipants' task was to indicate, by pressing either a or b on the keyboard, which char-acter was the correct member of the pair.


4.2. Procedure

The procedure was identical to that of Experiment 1.

4.3. Results

The average percentage of correct choices across all participants was 77.8%(SD� 15.5), and the average con®dence reported by participants was 74.8%(SD� 12.9). The calibration curve is presented in Fig. 3.

In this case participants were, on the average, undercon®dent ()3.00%, SD� 8.65),a typical ®nding in perceptual learning tasks (e.g., Keren, 1988). The di�erence be-tween average con®dence and average percentage of correct choices was signi®cant(t(79)�)2.47; p < 0.05). Average choice latency times was computed for each oneof the six con®dence categories. These averages are presented in Table 3.

A one-way ANOVA did not reveal a signi®cant e�ect of con®dence categories onlatency time, but the contrast between average choice latency time in the 91±100 con-®dence category and the rest of the categories was signi®cant (Duncan's multiplerange test, p < 0.05). The overall correlation between con®dence and choice latencytime was )0.32 (d.f.� 1618; p < 0.01). The correlations between choice latency timesand percentage of correct choices were computed within each one of the six con®-dence categories. These correlation coe�cients are presented in Table 3, and the cor-relations' magnitude is similar to what was found in Experiment 1. The overall cor-relation between con®dence level and accuracy was 0.18, and between latency timeand accuracy it was )0.15. The partial correlation between con®dence and accuracywhen the respective correlation with latency time is partialled out, is 0.14.

5. General discussion

The ®ndings of Experiment 1 indicate that whereas a signi®cant negative correla-tion explaining about 10% of the variance in the relationship between latency time

Fig. 2. A typical display employed in Experiment 2.


and con®dence ratings, the correlation between accuracy and latency time across allitems is very low. This ®nding indicates that the relationship between con®dence andlatency time might re¯ect the use of a heuristic by which con®dence level is deter-mined: the faster the decisions are made, the more con®dent people feel about its cor-rectness. Such a heuristic, however, might be somewhat misleading. While it is truethat the shortest latency times were associated with highest con®dence levels andwith highest accuracy levels, this was not the case for low and intermediate con®-

Fig. 3. Calibration curves ± Experiment 3 (- - -, Identity line). Each con®dence category is presented by its

midpoint (e.g., the 61±70 category is presented by 65), except for 50% which stands for itself.

Table 3


Con®dence

category

No. of choices Average CLT (s) % Correct

responses

Correlation

between CLT

and accuracymean (SD)

50 471 7.10 (4.80) 60.00 )0.08

51±60 46 8.70 (4.40) 58.00 )0.15

61±70 182 7.70 (4.20) 63.00 )0.17

71±80 140 6.80 (2.80) 72.00 )0.11

81±90 136 6.90 (3.30) 77.00 )0.17

91±100 645 5.20 (2.30) 97.00 )0.21


dence levels where the correlations between latency times and accuracy are very low.Therefore, relying too heavily on latency time in determining con®dence levels mightcause malcalibration.

The second experiment revealed another potential hazard of using the choice la-tency heuristic. Prospective feelings of con®dence which emerge before a choice iseven made and before one can be aware of the speed in which a choice was made,in¯uence the following latency times. When one has a very high feeling of prospec-tive con®dence, latency times become shorter compared to when prospective con®-dence is moderate or low. This e�ect might be explained by motivational factors(Mayseless and Kruglanski, 1987). Kruglanski et al. (1991) demonstrated that whenthe initial level of con®dence in a given hypothesis was high, the motivation to investmental energy for acquiring more knowledge in order to test a hypothesis was lowerthan when the initial con®dence was low. Since knowledge acquisition requires time,shorter latency times are predicted when the initial level of con®dence is high thanwhen it is low.

The ®ndings obtained in the ®rst two experiments are similar to those obtained byWright and Ayton (1988), who report that decision time and con®dence are di�erentin terms of their relationship with accuracy. However, they also obtained a negativerelationship between decision time and con®dence in the frame of general knowledgequestions. The authors conclude that decision time may not be a valid index of con-®dence.

The third experiment, in which participants performed a perceptual learning task,indicated that the pattern of the relationships between con®dence, accuracy and la-tency time is a consistent one, since the patterns obtained in Experiments 1 and 3 arevery similar, despite the di�erences in the nature of the knowledge retrieval tasks andthe existence of OC in the ®rst experiment and of undercon®dence in the third one.

A general ®nding of the present study is that, similar to what is reported in theliterature (e.g., Thompson and Mason, 1996), the correlation between con®dencelevels and accuracy is low. The generalization of the above ®nding is strengthenedby similar magnitudes of con®dence±accuracy correlations obtained in the domainof eyewitness testimony: 0.07 (Wells and Murray, 1984) and 0.25 (Sporer et al.,1995). The partly misleading in¯uence of latency times on con®dence level mightbe one reason for this insensitivity of con®dence to real accuracy levels. Many otherfactors, including perceived mental e�ort (Zakay and Tsal, 1993), structure andquantity of available information (Kahneman and Tversky, 1973), and level of per-ceived internal con¯ict while making a choice (Adams and Adams, 1961) most prob-ably add their impact to increase or decrease con®dence levels without having a cor-ollary impact on choice accuracy. These arguments correspond to those suggested byArkes et al. (1987), Keren (1987), and Tra®mow and Sniezek (1994), that feelings ofcon®dence are strongly in¯uenced by general and peripheral information as well asby general considerations.

Further research is required to identify all of the factors that in¯uence feelings ofcon®dence. Identifying these factors is a prerequisite for the development of e�ectivedebiasing methods. The ``choice latency heuristic'', which was suggested here as onepotential determinant of feelings of con®dence and of OC is most probably activated


only when certain conditions prevail. Further research should also de®ne these con-ditions.

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Choice latency times as determinants of post-decisional confidence