Examining the Peak-End Effects of Subjective Experience Andy Cockburn1 Philip Quinn1 Carl Gutwin2

1University of Canterbury Christchurch, New Zealand andy@cosc.canterbury.ac.nz

philip.quinn@canterbury.ac.nz

2University of Saskatchewan Saskatoon, Saskatchewan, Canada

gutwin@usask.ca

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Figure 1. A characterisation of hypothetical peak-end effects on a user’s subjective assessment of interactions. We hypothesise that advancing from high to low work on each page (bottom row) will be preferred to advancing from low to high work (top row).

ABSTRACT Psychological research has shown that ‘peak-end’ effects influence people’s retrospective evaluation of hedonic and affective experience. Rather than objectively reviewing the total amount of pleasure or pain during an experience, peo-ple’s evaluation is shaped by the most intense moment (the peak) and the final moment (end). We describe an experi-ment demonstrating that peak-end effects can influence a user’s preference for interaction sequences that are objec-tively identical in their overall requirements. Participants were asked to choose which of two interactive sequences of five pages they preferred. Both sequences required setting a total of 25 sliders to target values, and differed only in the distribution of the sliders across the five pages – with one sequence intended to induce positive peak-end effects, the other negative. The study found that manipulating only the peak or the end of the series did not significantly change preference, but that a combined manipulation of both peak and end did lead to significant differences in preference, even though all series had the same overall effort.

Author Keywords Peak-end rule; subjective preferences; hedonic experience.

ACM Classification Keywords H.5.2. [Information interfaces]: User Interfaces.

INTRODUCTION People’s willingness to use a computing system is strongly influenced by the perceived enjoyment of doing so [2, 4, 11, 17, 26]. Consequently, finding ways to manipulate and improve the hedonic and affective quality of interaction is an important and well-studied objective.

Although rarely examined in Human-Computer Interaction, peak-end effects [14] have been demonstrated to influence hedonic assessment in several settings. These include memory of pain during medical procedures [20], payment sequences and economics [16, 19], and user preferences for progress bars [7, 8]. The theory behind the effect is that the retrospective assessment of an experience is strongly de-pendent on the peak and ending subjective experience – in other words, our memories are substantially influenced by the most intense moment and the terminating moment.

These findings suggest that user interface experiences that finish more pleasantly may be preferred retrospectively to experiences that finish less so. Furthermore, retrospective assessment of experience is important because a user’s willingness to repeat an interaction will be influenced by

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Understanding & Evaluating Performance CHI 2015, Crossings, Seoul, Korea

their memory of it. In short, finding ways to improve users’ hedonic memory of interactions has clear utility.

We examine the presence of peak-end effects in user inter-faces and their utility as a means for influencing user pref-erences. Specifically, we describe an empirical study that validates peak-end effects for completing a series of inter-active pages. Although this represents a relatively simple interaction, it models a common interaction task (e.g., online signup forms, website checkout sequences, or setup ‘wizards’) where the level of enjoyment or annoyance expe-rienced by the user is a critical factor in the application’s success. For example, more than half of online transactions are abandoned by the user, and 21% of these failures are due to “the process taking too long” [24].

Our study involves presenting participants with two se-quences of pages, and having them choose which page series they prefer. Both series involve exactly the same total interactive requirements from the user – moving between five pages and setting 25 sliders to target values. The page series differ only in the distribution of sliders across pages (see characterisation in Figure 1).

By varying the number of sliders on a page, the method manipulates the work required to advance between pages. As the work required on each page involves the mundane task of setting sliders, we intend that pages with few sliders will be subjectively more satisfying than pages with many. Therefore, we hypothesise that page series can be designed to induce positive and negative peak-end experiences – for example, a series that terminates with few sliders should be positive (characterised by the bottom row of Figure 1), and a series that terminates with many sliders should be nega-tive (characterised by the top row of Figure 1). Overall, we predict that participants will prefer the page series designed to induce a positive peak-end experience (over that de-signed to induce a negative peak-end experience).

Experimental results confirm that peak and end effects can influence preference selections. Although only one of three conditions showed a significant effect, the results highlight subtleties in the use of workload as a method for manipulat-ing affective experience. We discuss the design implica-tions and identify directions for further work.

RELATED WORK This research draws on three inter-related areas of prior work, reviewed in the following subsections: (1) peak-end effects and duration neglect, predominantly from the psy-chology literature; (2) HCI research on improving hedonic and affective experience; and (3) factors influencing human perception of the passage of time.

Peak-end Effects and Duration Neglect Kahneman and colleagues introduced the peak-end rule as one of several heuristics and biases that encapsulate human deviations from objective judgment [13, 14]. They note that under a rule of temporal monotonicity, an objective analysis of an experience would be based on the sum of the magni-

tudes of its positive and negative moments, to give a total amount of pleasure or pain. However, it has been frequently observed that people instead remember experiences as mo-mentary ‘snapshots’ [6], with the most intense (peak) and ending moments of positive or negative experience playing a dominant role – a peak-end rule. Duration neglect [6] is a related finding, where judgments of unpleasantness are surprisingly unaffected by their timespan. In a now famous experiment [14], participants engaged in two trials (‘short’ and ‘long’), both of which began with immersing a hand in unpleasantly cold water (14°C) for one minute. In the ‘short’ trials participants then removed their hand from the water, but in the ‘long’ trials they kept their hand sub-merged for an additional 30 seconds while the water was surreptitiously warmed to a less-unpleasant 15°C. When participants were asked which trial they would prefer to repeat, 69% chose the ‘long’ trial: preferring the longer unpleasant experience that had a marginally less unpleasant ending – effectively choosing more pain over less.

A similar effect was observed in the retrospective reports of colonoscopy patients [20, 21], whose experience was domi-nated by their peak discomfort, and their discomfort at the end of the procedure – and was unrelated to the procedure’s duration (which varied between 4 and 69 minutes). Similar peak-end effects have also been observed in article pricing [19] and effortful study [5]. However, the effects are some-times subtle: for example, experiments involving purchase payment sequences [16] found that peak-end effects were not observed when participants were focused on the exper-imental manipulation, but that they became significant when participants were concurrently engaged in a distractor task. Experiments on gastronomic experiences [23] validat-ed duration neglect and demonstrated preference for in-creasing pleasantness across courses, but failed to validate reliable effects of peak or end experience.

Hedonic and Affective Experience of Technology The importance of fun and enjoyment in interaction is widely recognised and studied in HCI (e.g., [2, 17]). For example, in studies using word processing and graphics software, Davis et al. [4] demonstrated that motivation to use technology is positively influenced by both extrinsic factors (task usefulness) and intrinsic ones (enjoyment of use) – usefulness is the primary factor, but enjoyment is important provided task requirements are met. Other studies have analysed the subcomponents of hedonic and affective experience, seeking to model observed effects (e.g., Has-senzahl et al.’s [11] investigation of ergonomic and hedonic quality of interfaces).

Hassenzahl et al. [12] observed a recency effect of mental effort on participants’ summary assessments of perceived usability – high levels of mental effort at the end of the evaluation session had a significant negative effect on per-ceived usability. Their analysis also showed that peak-end measures of mental workload had no effect on perceived usability.

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More comprehensive reviews of affective experience in HCI are given by Venkatesh et al.’s [26] model and review of acceptance of information technology, Hartmann et al.’s [9] analysis of how users make judgments regarding user interface aesthetics and quality, and Reinecke et al.’s [22] review of how such assessments are influenced by culture.

Time Perception Several studies have shown that time perception is subject to a variety of distortions, including: • Vierordt’s Law [27]: The duration of short events tends to

be over-estimated, and long events under-estimated. • Interruption effects [28]: People tend to overestimate the

duration of interrupted simple tasks, and underestimate the duration of uninterrupted ones.

• Rhythm effects: The rhythm and pacing of stimuli has been found to influence time perception (e.g., [18, 25]).

In two studies of progress bars, Harrison et al. [7, 8] asked participants to observe pairs of progress bars (both 5 se-conds long) and choose which appeared to pass more quick-ly. The first study [7] demonstrated that users perceive accelerating rates of progress (i.e., slow start, fast finish) to be faster than several alternatives, including decelerating progress. The second study demonstrated similar effects for progress bar animations [8] – increasing rates of pulsing animation were perceived as faster than decreasing rates. Harrison et al. partially attributed these findings to peak-end effects.

Agarwal [1] used the concept of ‘cognitive absorption’ to explain perceived usefulness and ease of use of software. Their study used factor analysis to validate five dimensions of cognitive absorption: temporal dissociation (losing track of time), focused immersion, and heightened enjoyment, control and curiosity. Their findings on temporal dissocia-tion validate the adage “time flies when you’re having fun.”

These findings show bidirectional relationships between time perception and hedonic experience, motivating our experimental measurement of both.

EXPERIMENT: INTERACTION PEAK-END EFFECTS We conducted an experiment to investigate whether peak-end effects influence users’ preference for interactive tasks. The method compares user preferences and perceptions of page sequences that are identical in the total number of pages and in the total amount of work required across pag-es. Objectively, the total workload of the interactions is identical, with only the distribution of work across pages being varied.

The work required on each page is experimentally manipu-lated by varying the number of sliders that need to be set to target values (see Figure 1). Sliders are used to minimize extraneous factors that might influence other input tasks, such as errors during typed data entry. Varying the amount of work required on each page introduces two possible factors that may influence peak-end effects. First, the work-load per page varies (e.g., a page with many sliders is more

work than one with few). Second, the tempo of movement between pages varies, which may stimulate a rhythm effect.

The primary hypotheses are:

• H1: End-effect for preference. A page series that termi-nates with a low work page will be preferred to a series that terminates with a high work page.

• H2: Peak-effect for preference. A page series that in-cludes a page with notably low work will be preferred to a series that includes a page with notably high work.

• H3: Peak-and-end effect for preference. A page series that includes an internal and terminating page with notably lower work will be preferred to a series that includes an internal and terminating page with notably higher work (a combination of H1 and H2).

Pilot Study We conducted a pilot study with 10 participants to gain initial insights into our experimental method. The pilot method asked participants to state which of two series of six pages they preferred. Each page contained a number of slider widgets that had to be set to a randomly selected value between 1 and 50, shown in red at the right-hand end of the slider (see Figure 1 for an example of the sliders). All sliders were vertically aligned and initially set to ‘0’. Target values turned green when correctly selected, and once all sliders on a page were correctly set a ‘Next’ button at the bottom of the page became active, allowing the user to move to the next page.

Both of the page series contained a total of six pages and 31 sliders, with the series differing only in the order of page presentation. The ‘positive’ series used a decreasing number of sliders on each page: 10, then 8, 6, 4, 2, and 1 slider on the final page. The ‘negative’ series used the opposite or-der, increasing from 1 through 10 page sliders. Odd num-bered participants used the positive series first; even num-bers used the negative series first. Before using the positive or negative series, participants were familiarized with the method using three pages with 5, 5, and 6 sliders.

After using positive and negative series, participants were asked to select their preference for the first or second series.

Pilot study results Seven of the ten participants preferred the positive series, suggesting support for the peak-end prediction. The three participants who selected a preference for the negative series gave interesting explanations for doing so, which influenced our final experimental method.

One participant referred to ‘hitting the values really easily’ while using the negative condition. This can be interpreted as causing an unintended positive peak experience. It oc-curred because the randomly selected targets on one page included three occurrences of the same target value. She noted that selecting slider values was easier when a similar value was already selected because it acted as a landmark in the vertically aligned sliders. The final method balanced the

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variance of the difficulty of selecting slider values in three ways: (1) landmark effects were reduced by randomising each slider’s visual width to be between 175 and 300 pixels; (2) the sliders’ initial value was 25 rather than 0 to balance the amount of slider movement required across target val-ues; and (3) endpoint values (0 and 50) were not used as targets.

A participant who used the positive condition first referred to the ‘shock’ of the first page containing 10 sliders. We interpret this as a peak negative experience due to the fail-ure of the practice trials to expose participants to the num-ber of sliders used in the main trials. We remedied this by ensuring that practice included a page with as many sliders as the maximum encountered in the main conditions.

Experimental Method and Procedure Figure 2 shows the experimental user interface, which used three panes to present a ‘first’ series (left), a ‘second’ series for comparison (middle), and instructions/questions (right).

All pages contained between one and nine sliders. Each page also contained a page counter at the top of the window (e.g., “Page 2 of 5”), a progress bar at the bottom reflecting the same proportion, and a “Next” button that was initially disabled. Sliders were vertically centred and were a random width between 175 and 300 px. They permitted selection of discrete values in the range 0 to 50 and were initially set in the centre: 25. The target value for each slider (randomly chosen in the range 1 to 49, excluding 25) was shown in red to the right of the slider, and changed to green when cor-rectly selected. Once all page sliders were set to their target values, the “Next” button was enabled, allowing the partici-pant to progress to the next page.

Participants initially completed a practice series of three pages containing 7, 7, and 9 sliders. They then completed three pairs of page series, with the pairs representing our manipulation of end, peak, and peak-and-end effects, in that

order. The first sequence of each pair was presented in the left-hand pane, and the second sequence was shown in the middle pane. After each page series, participants were asked to respond to two questions concerning annoy-ance/enjoyment (AE) and estimated time (ET):

AE: “How annoying (-5 max) to enjoyable (+5 max) were those pages?”

ET: “Please estimate how long it took (in seconds) to complete that series of pages.”

Once both page series in a pair were complete, participants answered the main experimental question on preference choice (PC) between page series, and also which series they perceived to be faster (FC).

PC: “Which series did you prefer? First (left), or Second (right)”

FC: “Which series felt faster? First (left), or Second (right).”

They were also asked to comment on their preference.

Page series design The page series for end, peak and peak-and-end conditions were designed to test H1-3 respectively, within the con-straints of using five pages and 25 sliders in total. Table 1 shows the number of sliders in each page for the three con-ditions (e.g., {a, b, c} denotes three pages with a, b, and c number of sliders on each). The symbols ‘+’ and ‘–’ denote which series is intended to give an overall positive or nega-tive subjective experience. All participants completed both series in each condition, with order counterbalanced by participant number (shown by ‘Even/Odd first’).

The +end and –end series focus on the final two pages of the series, and provide either an increasing or a decreasing workload: +end uses three then two sliders on the final pages, contrasting with –end, which uses five then eight.

Figure 2. The experimental interface’s three panes: first & second page series (left & middle); instructions & questions (right).

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Slider counts on the first three pages were then balanced to satisfy a total count of 25 sliders (note that the constraint of an increasing or decreasing end effect led to slightly non-symmetrical workloads in these ‘non-manipulation’ pages).

Condition Even first Odd first end +{7, 7, 6, 3, 2} -{4, 4, 4, 5, 8} peak -{3, 3, 9, 4, 6} +{6, 6, 1, 6, 6} peak-and-end +{7, 2, 7, 6, 3} -{3, 8, 4, 4, 6}

Table 1. Paired page series used for each condition. {1, 2, 3} denotes 3 pages with 1, then 2, then 3 sliders per page; +/–

denote hypothesised +ve and –ve subjective experience.

The +peak and –peak series focus on the third page, which is intended to give positive or negative peak experience in the middle of the series. +Peak uses one slider on the third page, contrasting with –peak, which uses nine. To reduce conflating divergent end experiences within the peak series, both series terminate with the same number of sliders (i.e., six – which again led to a slight asymmetry in workload for the non-manipulation pages). Slider counts were balanced across remaining pages to achieve a total of 25 sliders.

The +peak-and-end and –peak-and-end series focus on the second and final pages. The series are intended to give positive and negative peak experiences at the second page (two versus eight sliders), and also divergent ends on the final page (three versus six). Remaining pages were bal-anced to satisfy 25 sliders in total.

Finally, we chose to compare positive to negative manipu-lations, rather than positive to neutral in order to increase the strength of the effect.

Participants and Apparatus Thirty-two undergraduate computer science students volun-teered to participate (11 female; aged 18-35, mean 21). The study ran on Intel Core i7 computers running Linux Mint 14, with a wired Logitech optical mouse and a 22″ monitor at 1680×1050 px. Experimental software was written in Python, and recorded all user actions and responses.

Design The primary dependent variable is the participants’ prefer-ence choice (PC), separately analysed using binomial sign tests for each of the experimental conditions (end, peak, and peak-and-end). These analyses test hypotheses H1-H3. The participants’ choice for which page series felt faster (FC) is also analysed using sign tests.

Paired t-tests are used for secondary analyses that are in-tended to characterise the participants’ performance and perceptions, as described in the results section.

RESULTS Results are separately presented for each of the experi-mental conditions: end, peak, and peak-and-end.

As intended, there was no significant difference between the actual times taken to complete the 5 pages and 25 slid-ers using the +ve and –ve page series in any of the condi-

tions – e.g., means 81.4 s and 80.8 s respectively in the end condition (t31 = 0.33, p = .74).

Figure 3 shows the mean time taken for pages with differ-ing numbers of sliders, ranging from 4.36 s for pages with one slider to 25.9 s for pages with nine sliders. As expected, the total time spent on each page increased linearly with the number of sliders (R2 = .99). These times provide an indica-tion of the magnitude of work required on different pages.

Figure 3. Mean page time across number of page sliders.

Interestingly (though unimportant for our hypotheses) an effect of Viorordt’s Law was apparent: with participants in all conditions significantly underestimating the time taken to complete each series. For example, in the end condition the mean selected time estimation for the +ve series was 47.6 s, compared to 81 s actual time (t31 = 4.99, p < .001).

The following subsections present results for the main hy-pothesis concerning Preference Choice (PC) for each condi-tion, followed by other measures and participant comments.

Preference Choice

End Condition Twenty participants (62.5%) selected preference (PC) for the +end series, and 12 (37.5%) preferred the –end series. This is not statistically significant (sign test, p=.21), and we therefore reject H1. However, the proportion is not dissimi-lar to Kahneman’s original study, which attained signifi-cance at 22 of 32 participants (69%).

Peak condition Preference choices (PC) for the two peak series were evenly split, regardless of order. Seventeen participants preferred the +peak series and 15 preferred the –peak series (p=.86). We therefore reject H2.

Peak-and-End condition Twenty-three participants (72%) selected the +peak-and-end series {7,2,7,6,3} as preferred over –peak-and-end series {3,8,4,4,6}, giving a significant effect (PC, p = .02). We therefore accept H3.

Order Effects and Enjoyment Other measures provide insight into the factors that influ-enced participants’ preference choices, as follows.

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End condition As Figure 4 indicates, series order appears to have influ-enced preference selections. Participants who used the +end series first were evenly split in their preference choice (8 each), whereas 12 of the 16 participants (75%) who used the +end series second selected it as preferred (p = .07). This may be due to the recency of the terminating positive experience when making the preference selection immedi-ately after completing the +end series.

Nineteen participants (59%) selected the +end series as faster (FC) and twenty participants selected a shorter dura-tion (ET) for the +end series than for the –end series. How-ever, the mean ET values were similar at 47.6 s and 50.2 s (t31 = 0.7, p = .47).Mean enjoyment ratings (AE) were also similar for the two series (0.31 for the +end series, -0.06 for the –end series; t31 = .86, p = .4). Twenty-one participants (65%) rated the +end series higher than for the –end series.

Peak condition Unlike end conditions, order appeared to have little effect on preference choices for peak conditions (Figure 5). Choices for which series was perceived as faster (FC) were tied at 16 each. Means for estimated time (ET) were also tied at 49.3 s. Enjoyment ratings (AE) were -0.03 for the +peak series and -0.44 for –peak, t31 = .9, p = .37).

Peak-and-End condition Series order influenced preferences for peak-and-end condi-tions (Figure 7), with 13 of the 16 participants (81%) who

used +peak-and-end first selecting it as preferred, and only 10 of 16 who used +peak-and-end second.

Twenty-one participants selected the +peak-and-end series as appearing to be faster (FC, p = .11). There was no signif-icant effect of estimated time (ET, means 50.1 s and 53.3 for +peak-and-end and –peak-and-end; t31 = 1.0, p = .33).

Enjoyment ratings (AE) supported the preference selec-tions, with a significantly higher mean of 0.81 for the +peak-and-end series and 0.03 for the –peak-and-end series: t31 = 2.18, p = .037. Twenty-four of the participants (75%) provided a higher AE rating for +peak-and-end.

Figure 6. Peak-and-end (p&e) condition: Percentage and

counts of preference selections for each page series.

In summary, for the peak-and-end condition we accept the primary hypothesis (H3) – a significant majority of partici-pants preferred the +{7,2,7,6,3} series that was designed to induce a positive peak experience (a page with only 2 slid-ers) and a positive end experience (a final page with 3 slid-ers) over the –{3,8,4,4,6} series that contained a peak nega-tive experience (a page with 8 sliders) and a negative end experience (a final page with 6 sliders).

Participant comments Participant comments provide interesting insight into the hypothesised effects. Participants received no instruction on the purpose of the experiment – only that it concerned their preference for user interfaces involving pages of sliders. It is therefore unsurprising that their comments refer to a variety of explanations for their preference selection. Across all conditions, participants’ comments generally referred to five different categories of issues explaining their preference:

• Page series: variations in the number of sliders across pages (our actual manipulation).

• Slider control: differences in the difficulty of selecting target values (not manipulated); e.g., P16 “The bar stayed with the mouse more, so it was easier to control”.

• Visual effects: preferences for screen certain locations. • Perceived time: feeling that one condition was faster. • Learning effects: practice-based improvement.

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Figure 4. End condition: Proportion of preference selec-

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Figure 5. Peak condition: Percentage of preference selec-tions for each page series (actual counts shown).

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Figure 7 shows the proportion of participant comments referring to each of these preference explanations across experimental conditions. Note that nearly half of the com-ments explaining a preference for the +end series do so with respect to the actual experimental manipulation (the page series). Comments include the following:

P10: “the number of sliders decreased on each page, so I [felt] like there was less to do overall (although it may be that the first pages just had more sliders to compensate?).” P23: “decreasing number of sliders seems quicker” P25: “smaller number of sliders to do as time went on.” P27: “Fewer sliders = more enjoyable.”

In contrast, only one participant explained their preference for the –end series with respect to the page series:

P9: “The first lot felt like I was getting more done per page than the second one.”

Instead, the majority of comments explaining preference for the –end series refer to differences in the difficulty of target selection (e.g., P17 “easier to get the slider on the desired point”, P19 “wider sliders making it easier to select the number”, P24 “easier to move the slider”.) These explana-tions are imaginary, or due to random variance.

For the peak series, Figure 7 (middle) shows the proportion of comments to explain the preference choice with refer-ence to each of the five issues described earlier. Unlike the end conditions, a large proportion (45%) of participants explained their preference for the –peak series with refer-ence to properties of the page series, reflecting the failure of our intended manipulation of peak experiences.

Two participants referred to the unintended positive experi-ence of having two pages with only three sliders in the –peak series {3,3,9,4,6}:

P13: “There were a couple pages on the 2nd one with only a few sliders which was nice.” P25: “…had a break where there were only a few sliders rather than heaps on one page”  

Other participants explained their preference for the –peak series by identifying undesired features of the +peak series,

which had six sliders on all but one page {6,6,1,6,6}: P12: “The sudden change in number of sliders in the second one [+peak] was slightly offputting.” P32: “the second set [+peak] seemed to have more sliders on each page except one which only had one meaning that each page seemed to take longer and appeared more daunting.”

However, several comments alluded to our intended manip-ulation. Participant 10, for instance, referred to the positive impact of the page with only one slider:

P10: “right series [+peak] had some pages with very few sliders on, so seemed like fewer sliders overall.”

Three participants explicitly referred to the intended nega-tive impact of the page with nine sliders (–peak):

P5: “the second with heaps of sliders was annoying.” P19: “a page with lots of sliders made me feel like I was going slower.” P23: “the second series felt like it was taking longer especially with the page with a large amount of sliders on it.”

Figure 7 (right) shows that a relatively large proportion of comments (41%) explained preference for the +peak-and-end series with reference to the page series. Several at-tributed their preference to erroneous impressions that there were fewer sliders in the +peak-and-end series. Others stated that the +peak-and-end series had less variance in the number of sliders across pages (the inverse is true, with +peak-and-end having a standard deviation of 2.34 versus 2.0 for –ve series):

P12: “The size of the list didn't seem to vary as much.” P13: “It felt like there was more of an even spread.” P18: “it seemed like there were less sliders.”

Participant 32 explicitly referred to a peak negative experi-ence in the –peak-and-end series: “one page that had a lot more sliders than the others that made it seem more time consuming.” In contrast, only one participant explained their preference of the –peak-and-end series with respect to the page series.

DISCUSSION Our experiment shows limited evidence that interactions which are objectively identical in total work can, in certain situations, induce different subjective experiences through manipulation of peak-end effects. Although the study found mixed results, a combined manipulation of both peak and end saw a significant change in participants’ preferences. In this section, we consider explanations for our main results, discuss issues for further work, and the implications for interface design.

Explanations for Results Of our three hypotheses, only the combination of peak-and-end effects is supported by the experimental data. We con-sider explanations for each of the main outcomes below. First, manipulation of peak alone did not show any overall effect, and participant comments suggest that a markedly different peak experience may not have been perceived as

Figure 7. Percentages (and counts) of preference choice

explanations referring to each of five factors.

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we intended (e.g., some participants saw the change as jarring, rather than beneficial).

Second, the lack of a significant difference for the end ma-nipulations may be due to the small changes that were part of these series (e.g., a difference of only five sliders on the final page). In future studies we will test whether larger differences in the series can engender an effect. It was clear from participant comments that people regularly noticed the difference at the end of the series, and it is likely that with a larger manipulation a measurable difference will become apparent.

Third, we believe that the main reason the combined peak-and-end manipulation showed an effect was the added no-ticeability of both peak and end effects together. Even though neither individual manipulation showed a significant difference, both effects work in the same direction – and so combining them appears to provide an additive effect on people’s recollection of their experience.

Methodological Issues

Rhythm effects Our experimental method manipulated page workload (slid-er count) as a proxy for moment-to-moment subjective experiment (further discussed below). However, this ma-nipulation also influences the rhythm of the user’s transition between pages. Rhythm effects are known to influence user’s perception of time (e.g., [18, 25]) and in HCI they have been demonstrated to influence preference for differ-ent behaviours in observed progress bars [7, 8]. Notably, however, these studies attributed some of the observed preferences to peak-end effects. In other words, even if rhythm caused or contributed to the preference choice, the explanation remains attributable to peak-end effects.

There are also questions for further work around the ele-ments of interaction that contribute to the user’s perception of rhythm. In our experiment, interaction consisted of two types of manipulation – dragging sliders and clicking the next button – and the movement between pages was the primary way in which rhythm could vary substantially. Real interactions, in contrast, are likely to include various inter-active components (typing into text boxes, setting radio-buttons, selecting files, etc.) thereby creating many oppor-tunities for the user to perceive changes in rhythm.

Influence of the terminating page The experimental software used three interface panes to show the first series (left), second series (middle) and in-structions/questions (right). Consequently, while answering questions about the two series, participants could see the terminating page of sliders for both series (e.g., Figure 2). It is therefore possible that the persistent representation of the terminating page may have influenced participants’ prefer-ence selection. Although this is a legitimate concern, none of the participants commented on the issue. Furthermore, 7 of 10 participants in the pilot study preferred the positive

series, using a method that did not show the terminating page of either series.

Influence of order and boredom All participants completed the conditions in the same order: end, peak, and peak-and-end (although series order within each condition was counterbalanced). We suspect that a decrease in the level of the participants’ task focus while progressing through the experiment may have contributed to their preference choices. Previous studies have noted that peak-end experiments are more sensitive when participants are engaged in a concurrent ‘distractor’ task, because the distractor reduces conscious deliberation on the experi-mental manipulation [16]. We therefore suspect that the tedium of previously setting over one hundred sliders may have led participants in the peak-and-end condition to be less focused on the task than those in the end condition.

Implications for Piecemeal Interaction Our evaluation was modelled on collecting data through a series of discrete interactive pages. As discussed earlier, this type of interaction is common in tasks such as signup forms or checkouts, and the user experience in these simple interactions has a strong effect on successful completion. Users may abandon a transaction if they feel it is becoming annoying or taking too much time.

The results of our study are relevant for designers of these kinds of sequences. We found peak-end effects in a generic task involving slider widgets – whereas in real-world tasks where the user is personally involved in the outcome (e.g., making a purchase) there is the potential that the effects will be even greater. As a result, designers of sequence-based tasks have an opportunity to provide a better subjec-tive experience for their users. When designing pages to gather data, designers are often relatively unconstrained regarding (1) the number of pages used to present data items, (2) the number of data items on each page, and (3) the order of pages. Other than optimising the semantic rela-tionship within and between pages (i.e., putting related items on the same page), there is little guidance to suggest that one order might be preferable to another.

Our results suggest that designers should think carefully about peak-end effects when considering the distribution of content across pages. Doing so may improve users’ subjec-tive experience and increase their willingness to use the system again in the future; conversely, failing to consider peak-end effects may cause adverse user reactions.

Other Interface Triggers for Subjective Experience While our method directly examined page-based interac-tions by varying page workload, it is likely that many inter-active experiences can induce peak-end effects in diverse interactive contexts. In general, the peak-end rule is that memory for experiences (such as interactive ones) can be positively influenced by appropriately timing affective and hedonic peaks and troughs.

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Interface designs for influencing hedonic experience of user interfaces are under active research in HCI. A full review is beyond the scope of this paper, but it is clear that some factors are context dependent. For example, research on ‘flow’ and optimal experience [3] shows the importance of balancing task challenge with the user’s skill level. Our experiment used a mundane task of setting sliders to ma-nipulate workload, but other methods for altering workload, such as task complexity, might produce divergent results for users of different skill levels – a challenging task might be positive for a skilled user, but negative for others.

Three interesting questions for further work include (1) what triggered the effects we observed; (2) what other inter-face features can cause related effects; and (3) how strong are the effects?

Regarding (1), while we consciously designed our tasks to influence workload, the induced effect on the user likely includes perceived tedium due to the mundane task. When designing the experiment we considered using other work-load requirements, such arithmetic exercises, but we did not do so due to the lack of control for individual variance in ability (some users might find easy exercises engaging, others tedious). We intend to conduct further studies exam-ining methods for achieving peak-end effects.

Regarding other triggers for hedonic experience (2), we are particularly interested in examining peak-end effects with gamified interaction components. Gamification [15] is under active research, and peak-end effects suggest that there may be benefits in considering the timing at which they are employed. We are also interested in examining the impact of peak-end effects in the retrospective assessment of game enjoyment.

Finally, we are interested in better understanding the strength of peak-end effects (3). Our results demonstrate significant effects on preference choice, but they do not quantify the magnitude of the positive (and negative) sensa-tion. Reliable methods for quantifying hedonic experience are required to do so (for example, physiological sensors), and are under development [10, 29].

Workload Distribution and Subjective Experience In varying workload across pages, the intention of our ex-perimental method was that a page with many sliders would induce a more negative subjective experience (e.g., ‘oh no, look at all these’) than a page with few sliders (e.g., ‘thank goodness, only two this time’).

Many prior studies into peak-end effects have examined strong hedonic stimuli such as pain [6, 14]. Workload ma-nipulation, in contrast, can be considered to be a weak stimulus for peak-end experiments, yet our results still showed significant effects. The use of workload as a ma-nipulation does have precedent in the literature – Finn [5] described an experiment in which participants engaged in an effortful learning exercise (memorising a list of 30 com-plex Spanish-English word translations). Participants

learned a list of 30 complex translations (‘unextended’) and another list that added 15 easier translations to the end of 30 complex translations (‘extended’). Participants significantly preferred the extended condition (attributed to the lower terminating mental workload), despite better test perfor-mance with the unextended list.

Participant comments such as “heaps of sliders was annoy-ing” suggest that our manipulation had the intended effect. However, there were interesting exceptions. Participant 9, for example, explained their preference for the –end series with reference to ‘getting more done’ on pages with many sliders. The subtleties of manipulating workload via number of sliders are also highlighted by the failure of the peak condition, in which several participants perceived the page with a single slider as abrupt.

Further empirical work is needed to better understand the interface conditions that best stimulate the positive mo-ment-to-moment experiences on which peak-end effects depend. This includes issues concerning the granularity of the user’s moment-to-moment experience – e.g., do two ‘easy’ pages form a stronger peak than one easy page.

CONCLUSIONS Peak-end effects concern the substantial influence that the maximum and terminating moments of affective experience have on a person’s overall memory of subjective quality. Given their prevalence in the basic teachings of psycholo-gy, there has been surprisingly little research into peak-end effects in HCI (notable exceptions were reviewed).

We presented experimental results demonstrating that, in certain conditions, peak-end effects can significantly influ-ence user’s preference for interfaces that are otherwise identical in their objective interaction requirements. Design implications of these findings suggest that simply altering the presentation order of interface components can impact remembered subjective experience. While results showed that peak-end effects can influence subjective preferences, they also showed substantial subtleties in their impact, suggesting fruitful opportunities for further research.

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