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The gut chooses faster than the mind: Alatency advantage of affective over cognitivedecisionsTom S. Saunders a & Marc J. Buehner aa School of Psychology, Cardiff University, Cardiff, UKAccepted author version posted online: 16 Jul 2012.Version of record firstpublished: 06 Sep 2012.

To cite this article: Tom S. Saunders & Marc J. Buehner (2013): The gut chooses faster than the mind: Alatency advantage of affective over cognitive decisions, The Quarterly Journal of Experimental Psychology,66:2, 381-388

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The gut chooses faster than the mind: A latency advantageof affective over cognitive decisions

Tom S. Saunders and Marc J. Buehner

School of Psychology, Cardiff University, Cardiff, UK

Dual-process theories often cite that affective processing occurs more rapidly than cognitive processing.A wide range of evidence seems to support this notion; however, little research exists in the context ofdecision making. We tested the hypothesis that affective decisions would be performed faster than cog-nitive decisions. Forty-nine students completed a series of forced-choice tasks involving well-knownconsumer brands, focusing on either emotionally or cognitively relevant aspects of the products. Theresults revealed a significant latency advantage for affective processing compared to cognitive processing.

Keywords: Decision making; Affect; Cognition; Consumer preferences; Dual-process theory.

A substantial bulk of decision research assumes thathumans always think about choices. Indeed, botheconomic (e.g., expected utility theory; vonNeumann & Morgenstern, 1944) and psychologi-cal (e.g., prospect theory; Kahneman & Tversky,1979) theories focus exclusively on what role cogni-tive processes have in decisions. Until fairlyrecently, the role of emotion in decision makinghas been largely neglected. One important excep-tion to this trend comes from a landmark paperby Zajonc (1980) who proposed that “preferencesneed no inferences” (p. 151): They are not alwaysqualified by prior cognitive processing. Indeed,Zajonc thought that affective processing is likelyto precede cognitive processing in many cases. Infact, he argued that affect plays a key role ininitial judgement. For example, he suggests that

we may primarily choose an option because welike it and later rationalize the choice cognitively.

Investigators have since made substantial effortsto clarify the role of emotion in judgement anddecision making. These efforts have been promptedin part by advances in neuroimaging techniques(Dolan, 2002), the conception of the interdisciplin-ary field of neuroeconomics (e.g., Sanfey, 2007),and the development of several influential dual-process theories and hypotheses that make abroad distinction between affect and cognition(e.g., Bechara & Damasio, 2005; Loewenstein,Weber, Hsee, & Welch, 2001; Slovic, Finucane,Peters, & MacGregor, 2004). Many of these the-ories and hypotheses concur with Zajonc (1980),in that they posit that affective processing is fasterthan cognitive processing.

Correspondence should be addressed to Marc J. Buehner, School of Psychology, Cardiff University, Cardiff, CF10 3AT, UK.

E-mail: BuehnerM@Cardiff.ac.uk

This article is based on an undergraduate final year research project carried out by the first author under the supervision of the

second author.

# 2013 The Experimental Psychology Society 381

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Vol. 66, No. 2, 381–388, http://dx.doi.org/10.1080/17470218.2012.712541

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Judgement goes beyond the processing of optionsin that it involves the evaluation of an option.Verplanken, Hofstee, and Janssen (1998) con-ducted a series of studies in which they asked par-ticipants to judge brand names and countries witheither a cognitive or an affective focus. Morespecifically, participants indicated which one oftwo bipolar adjectives (e.g., good–bad) bestreflected their attitudes towards the stimuli.Participants utilizing an affective focus reportedtheir judgements significantly faster than partici-pants utilizing a cognitive focus, therefore support-ing the notion that the affective processingadvantage extends into the domain of judgement.Subsequent research has supported Verplankenet al.’s findings in the form of replications byPham, Cohen, Pracejus, and Hughes (2001)using magazine pictures and television commer-cials, and Huskinson and Haddock (2006) usingcountries, blood donation, capital punishment,the British monarchy and Tony Blair. The broadrange of stimuli and methodologies employed inthese studies suggests that the affective processingadvantage in judgement is robust.

Given that decision making involves the judge-ment and evaluation of at least two options—inaddition to other processes—it is reasonable to con-jecture that affective decision making should also befaster than cognitive decision making. Yet, thereappears to be surprisingly little research addressingthis issue. Zajonc (1980) hinted that research hadalready been conducted in the context of decisions(p. 151):

A number of experimental results on preferences, attitudes,

impression formation, and decision making . . . suggest that

affective judgments may be fairly independent of, and precede

in time, the sorts of perceptual and cognitive operations com-

monly assumed to be the basis of these affective judgments.

Unfortunately, Zajonc did not cite any evidence inhis paper that directly addressed the issue ofresponse latencies in decision making, suggestingthat the research had simply not been conducted.As far as we are aware, no research has so far com-pared processing speed in cognitive- versus affec-tive-based decisions.

One exception was reported by Lee, Amir, andAriely (2009), in an experiment that required

participants to choose between two novel pro-ducts; choice trials involved pairs of either black-and-white or colour photographs. The authorsargued that, on the basis of previous research,colour photographs would provide more affect-rich detail than black-and-white photographs.The researchers found a slight (nonsignificant)latency advantage for decisions between thecolour stimuli compared to decisions betweenthe black-and-white stimuli, therefore providingtentative support for the notion that affectivelyenhanced stimuli should evoke faster processing.Crucially, however, since Lee et al. were notprimarily concerned with response latencies, par-ticipants were not instructed to respond asquickly as possible in the decision task. Inaddition, participants were allowed to study thematerials for an unlimited time period before theactual decision task, meaning that participantscould have already made their judgements aboutthe stimuli in advance. Both aspects potentiallydiminished any latency effect in the subsequentdecision task.

It appears then that very little research has inves-tigated in a direct and systematic way whetheraffective processing is temporally advantageous inthe context of decision making. Thus, althoughthere is fairly consensual agreement that affectiveprocessing precedes cognitive processing, thisassertion does not appear to have been appropri-ately tested in the context of decisions. We setout to close this gap.

In summary, the present research sought toinvestigate whether affective processing wouldyield faster response latencies than cognitive pro-cessing in the context of a decision task in whichtwo brand name stimuli were presented simul-taneously. By manipulating (within subjects)whether decisions were approached with a focuson cognitive versus emotional aspects of thedecision, we could determine whether there wasan advantage for an affective focus, compared to acognitive focus, within the same categories ofbrands. We hypothesized that participants wouldrespond faster in the affective focus conditionthan in the cognitive focus condition. In addition,we also explored the extent to which decision

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latencies are impacted by individual differences.Specifically, we investigated the roles of need forcognition (NFC; Cacioppo, Petty, & Kao, 1984)and need for affect (NFA; Maio & Esses, 2001).These scales assess individual tendencies regardingapproach and avoidance to cognition and affect,which might, perhaps via attention, mediatedecision latencies. However, we did not have direc-tional hypotheses in relation to the NFA and NFCscales, because it is conceivable that both approachand avoidance towards affect and cognition couldfacilitate or hinder response latencies: Highapproach scores might facilitate faster processingbecause of familiarity with such a processing strat-egy; however, high approach scores might also becharacteristic of more exhaustive processing, there-fore slowing response latencies.

Method

ParticipantsForty-nine participants were recruited via theonline Human Participant Panel System of theCardiff School of Psychology in exchange forcourse credit or £2 payment. The age range of par-ticipants was 18 to 25 years (mean = 19.63, SD =1.24); there were 44 females and 5 males.

Design, materials, and apparatusCognitive versus affective focus was manipulatedwithin subjects. Participants completed all choicesper focus condition before making choices in theother condition. The order of focus conditionswas randomized. The decision making task wasimplemented in DirectRT (Jarvis, 2008a). TheNFA (Maio & Esses, 2001) and the short formof the NFC (Cacioppo et al., 1984) scales wereimplemented in MediaLab (Jarvis, 2008b).Participants completed all tasks in a computerresearch laboratory. This experiment and another

task not reported here were completed in a counter-balanced order.

The decision task involved indicating a prefer-ence for one of two simultaneously presented con-sumer brands. There were a total of 76 binarychoices (38 each under affective versus cognitivefocus) drawn from 152 UK brand names (listed inAppendix A), which were obtained from Mintel(2011): a UK-based online market intelligenceresource. Potential brands were screened usingselection criteria (Appendix B) to increase theprobability that brands would be familiar to awide range of consumers.1 Brand names were pre-sented as opposed to brand logos to ensure the con-sistency of the presentation format. Bothconditions were organized into 14 blocks accordingto product category (e.g., bottled water, chocolate,detergent, etc.), with at least two decisions perblock for each condition. Different brands wereused within each block, meaning participantsnever judged the same brands from both a cognitiveand an affective perspective.

In the affect condition, participants wereinstructed to focus on their affective reactions(emotions and feelings) towards the brands whenmaking their choice and, in the cognition condition,their cognitive reactions (thoughts and beliefs)towards the brands. Additionally, each block waspreceded by a unique set of instructions, whichencouraged focusing on either the emotional orthe cognitive qualities of the relevant product cat-egory that followed. For example, in the affect con-dition, participants were asked to base decisionsbetween brands of bottled water “entirely on youremotions & feelings about each brand of bottledwater. For example, you might base your choiceon the purity of the water of each brand, or therefreshing quality of each brand”. In contrast, inthe cognition condition, participants were askedto decide based on their thoughts and beliefsabout the brands and to focus on the source of

1 Market share and number of outlet data may not necessarily correspond to consumer (participant) awareness of the brands

because, amongst other reasons, certain categories may only penetrate particular market segments, the number of consumers respon-

sible for differing proportions of market share may be different for different markets, and the size of the customer base was different for

each of the selected markets. Nevertheless, we felt that using market share data provided a reasonable estimate of brand awareness and

was also a readily available source of information.

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the water of each brand, or the size of the bottle ofeach brand. Appendix C lists the specific instruc-tions for each block.

Within each condition, blocks were presented inone of two counterbalanced orders. Choice pro-blems were made of brands paired according tothe market share and number of outlets data: Thetop brand was always paired with the second, thethird always with the fourth, and so on. Brandswere coupled using this method to try to matchthe level of familiarity within any given choice.Since all the brand pairings were held constant,the left–right positioning of individual brandswithin a choice problem was also counterbalanced.Choice problems were displayed vertically centredwith one stimulus left and one stimulus right ofthe horizontal midpoint. Participants indicatedtheir choice with either the “\” key (left) or the “/”key (right). The order of choice problems withineach block was randomized

The NFA and NFC questionnaires were admi-nistered in a randomized order. It is worth notingthat the NFA and NFC scales have been foundto correlate only to a small degree (r = .21; Maio& Esses, 2001), meaning the shared variance isonly 4.4%. Thus, using both scales is not a redun-dant approach as they appear to tap distinctconstructs.

ProcedureAfter participants gave their consent, they readgeneral task instructions outlining the differencebetween cognitive and affective reactions, usingthe example of blood donation to illustrate howthe two components might diverge (Verplankenet al., 1998). Participants were also instructed toimagine they were making each choice in real life.Then, a set of condition-specific instructions fol-lowed, outlining which condition the participantwas in, followed by block-specific instructions(Appendix C) as outlined above. These instructionsinformed the participant of the upcoming brandcategory and provided two examples of cognitive

or affective aspects of the category. Two exampleswere given to prevent participants from solelyfocusing on one aspect of each brand. It was alsoexplained that participants could include other cog-nitive or affective aspects of the brands whenmaking a decision. These instructions allowed usto make salient to participants the affect or cogni-tion focus that they should be taking, as well asmaking clear exactly what was meant by an affectiveor cognitive focus.2 Then the first trial started,which consisted of a central fixation cross presentedfor 200 ms, immediately followed by the stimulibrand pairs, which remained on screen until theparticipant responded. An intertrial interval (ITI)of 1,500 ms ensued, followed by the next trial.This procedure repeated until all the choice pro-blems for that block had been completed, atwhich point the next block started. Thus, a newset of block-specific instructions followed, and theabove procedure was rerun with the new blockstimuli. When all 14 blocks had been presented,participants switched conditions and were pre-sented with a new set of condition-specific instruc-tions, block-specific instructions, and the novel halfof stimuli brand pairs in the same nature as thatdescribed above. The interblock and interconditionintervals were 1,500 ms. At the end of the session,participants completed the NFC and NFA ques-tionnaires. The total session lasted approximately17 minutes.

Results

One participant’s data were removed due to a com-puter error. Reaction times (RTs) for each choiceproblem were log-transformed to remove a positiveskew. The units of analysis were the median(logged) RTs that each participant returned forthe cognitive and affective conditions, which aredisplayed in Table 1. Inspection of Table 1 suggestsan 80-ms latency advantage for affective decisionsover cognitive decisions. Statistical analysis con-firmed that this difference is significant, t(48) =

2 Care was taken over all instructions in the brands task to ensure that the aims of the experiment were not subtly conveyed to

participants. For example, although the phrase “affective reaction” and “cognitive evaluation” often appear in the literature, only the

phrases “affective reaction” and “cognitive reaction” appeared in the instructions, thus not connoting different temporal meanings.

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2.10, p < .05. Individual differences were examinedvia correlating NFA and NFC with decisionlatencies. We found weak, insignificant correlationsbetween the two personality constructs andlatencies (rs ranging from .02 to .18; all ps > .2).Not surprisingly, cognitive and affective decisionlatencies were highly positively correlated, r = .76,p < .01.

Discussion

The results revealed a significant processing latencyadvantage for affect-based over cognition-baseddecisions between consumer brands. This providesfurther support for the hypothesis that affectiveprocessing is faster than cognitive processing,perhaps acquiring greater processing resources, uti-lizing cruder processing methods, or simply occur-ring earlier than cognitive processing. Thus,Zajonc’s (1980) original conjecture and relevantdual-process theories that posit a processing advan-tage for affect (e.g., Bechara & Damasio, 2005;Loewenstein et al., 2001; Slovic et al., 2004) havebeen corroborated. Importantly, we were able toextend earlier findings (e.g., Huskinson &Haddock, 2006; Pham et al., 2001; Verplankenet al., 1998) of an affective processing advantagein judgement to the context of decision making.This not only contributes to the theoretical under-pinning of cognitive and affective processing butalso allows for an understanding of how some jud-gement processes may transfer to decision making.Of course, further research would need to elucidatethese similarities. The use of consumer brandsallows for the potential generalization of our find-ings and points to some intriguing practical

implications (discussed below). Overall, thepresent study demonstrates a novel application—in terms of the use of consumer stimuli and thedecisional context—of the widely held hypothesisfor an affective processing advantage. The evidenceconcerning individual differences in processingadvantage was less clear. NFC and NFA did notsignificantly correlate with decision latencies.

It is important at this point to address and ruleout various potential procedural artefacts that mayhave influenced our results. Selecting and rankingstimuli based on theMintel database does not guar-antee identical product familiarity within choicepairs. While we agree that pilot testing couldhave resulted in an even better familiarity match,we would argue that due to the random assignmentof product pairs to conditions, any systematiceffects of product familiarity on decision latencywill have averaged out. Inspection of the block-specific instructions (Appendix C) might suggesta certain element of subjectivity in interpretation.For example, the fair trading of a brand maysimply be a cognitive feature for some participantsbut evoke an affective reaction for others. Wewould argue that the wide range of differentinstructional examples used across blocks suffi-ciently addresses this concern. Furthermore, anyambiguity of instructional interpretation wouldhave worked against the hypothesis of an affectiveprocessing advantage because it would have led toan evening out of choice latencies between con-ditions. Finally, the possibility that the individualbrand pairings in each condition could accountfor the difference between the conditions is elimi-nated by the fact that brand pairings were randomlyassigned to either the affective or the cognitivecondition.

The current results are also of practical impor-tance, especially given that our stimuli focused onconsumer brands. For example, the fact that anaffective focus elicits faster processing may haveimplications for marketers and product packagingdesigners wishing to manipulate decision times.Information likely to evoke affective reactionscould be selectively utilized on packaging and inenvironments where it is of interest to elicit fasterconsumer choice (e.g., in fast food restaurants) and

Table 1. Decision latencies for affective and cognitive decisions

Log RT Raw RT

Condition Mean SD Mean SD

Affect 7.14 0.24 1,297.65 310.96

Cognition 7.19 0.27 1,377.61 348.35

Note: N = 49. RT = reaction time, in ms. Means were calculated

by averaging over participants’ median returned RTs.

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ultimately improved consumer throughput.Conversely, cognitive information could be utilizedin circumstances where it is of interest to increaseconsumer decision time, perhaps in order to allowconsumers to take in more detail (e.g., in choosingfinancial products). Such strategies have potentiallyvery important topical implications for a societystruggling to deal with rising numbers of obesityand for the controversial selling of financial productsand services. An interesting investigation for futureresearch would be to consider whethermanipulatingdecision times influences use of affective or cognitiveprocessing strategies. Such research would greatlycomplement the present findings both theoreticallyand in practical relevance.

In conclusion, we have provided evidence thatthe gut is indeed faster than the mind, not onlyin terms of appraising options, but also when itcomes to choosing between alternatives. Whatremains to be seen is whether the temporal advan-tage of gut-based decisions leads to qualitativebenefits or drawbacks (i.e., better or worsedecisions) and how it impacts satisfaction withthe chosen option.

Original manuscript received 29 September 2011

Accepted revision received 10 July 2012

First published online 6 September 2012

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APPENDIX A

Stimuli brandsNote: Stimuli are listed by category. Brand pairings are separated

by semicolon.

Bottled water: Volvic, Evian; Highland Spring, Buxton;

Robinsons Fruit Shoot H20, Perfectly Clear; San Pallegrino, Vittel.

Bread: Warburtons, Hovis; Kingsmill, Braces; Roberts, Weight

Watchers; Allinson, Mothers Pride.

Cars: Ford, Vauxhall; Volkswagen, Toyota; Audi, Honda; BMW,

Peugeot.

Cereal:Weetabix, Special K; Crunchy Nut, Corn Flakes; Coco Pops,

Cheerios; Rice Krispies, Oatso Simple; Shreddies, Frosties; All-Bran

Flakes, Shredded Wheat; Alpen, Fruit 'n Fibre; Sugar Puffs,

Kellogg's Variety; Crunchy Nut Clusters, Weetos; Country Crisp,

Shredded Wheat Bitesize.

Cheese: Cathedral City, Dairylea; Philadelphia, Cheestrings;

McLelland Seriously Strong, Pilgrim's Choice; Babybel,Wyke Farms.

Chocolate: Dairy Milk, Galaxy; Maltesers, Mars; KitKat, Aero;

Wispa, Snickers; Milky Bar, Twirl; Twix, Green & Black's.

Coffee & tea:Nescafe, Kenco; Douwe Egberts, Carte Noire; Tetley,

PG Tips; Twinnings, Typhoo.

Crisps:Walkers, Pringles; Doritos, McCoy's; Kettle Chips, Quavers;

Hula HoopsMini Cheddars;MonsterMunch, Wotsits; French Fries,

Seabrook; Squares, Skips; Tyrells, Red Sky.

Detergents: Persil, Ariel; Bold 2-in-1, Surf; Fairy, Daz; Vanish,

Febreeze; Calgon, OxiClean; Comfort Refresh, Ace.

Digital cameras: Canon, Fuji; Kodak, Sony; Nikon, Panasonic;

Olympus, Casio.

Hotels: Premier Inn, Travelodge; Holiday Inn, Innkeeper's Lodge;

Hilton, Ramada; Ibis, Marriott.

Pizza:PizzaHut,Domino's Pizza; PizzaExpress, Ask; Zizzi, Papa

John's; Prezzo, Perfect Pizza; Pizza GoGo, Bella Italia; Strada,

Jamie's Italian.

Sauces:Dolmio, Loyd Grossman; Knorr, Colman's; Schwartz, Uncle

Ben's; Sharwood's, Homepride; Patak's, Blue Dragon; Sacla, Bertolli;

Seeds of Change, Old El Paso; Discovery, Amoy.

Soft drinks: Coca-Cola, Pepsi; Fanta, Schweppes; Irn-Bru, Dr

Pepper; Sprite, 7-Up.

APPENDIX B

Selection criteria for brand namesBrands were obtained fromMintel Market Intelligence (Mintel,

2011)market share and number of outlets data. Market share rank-

ings were based on the most recent of actual or estimated percen-

tages of market value. The data were fairly up to date (year range:

2008–2010; modal year: 2009), and where the values were esti-

mated, they were derived (byMintel) from brands’ previous years

actual market performance. The following criteria allowed for a

range of brands that were likely to be familiar to participants.3

The criteria for searching for potential markets and brand

names were as follows.

1. Markets and brands were selected if they were considered to

be at least fairly accessible and familiar to a wide range of

consumers and, therefore, prospective participants.

Consequently, markets and brands were excluded if:

• They were clearly age or gender specific; or

• They were only available in a small subsection of stores or

regions.

2. To increase participants' brand accessibility, actual brand

names were utilized rather than umbrella brands or manu-

facturers names (i.e., those that include more than one of

the brands for the industry in question).

3. A heterogeneous set of stimuli was aimed for to improve the

generalizability of responses and to reduce redundancy in the

data. Consequently:

• Repeats of brand names across markets were excluded;

and

• Only the largest of very similar markets were included (e.

g., breakfast cereal, not cereal bars).

4. Own-label and other brands were excluded for lack of

specificity.

5. There must have been at least eight brands in the market

share data for that market to allow for at least two binary

choices (per participant) to be performed for each block in

each of the affect and cognition conditions. To meet this cri-

terion, the tea and coffee categories were combined, as were

detergents and laundry aids. Pairings were never made across

these combined categories.

6. As many as possible of the brand names in the market share

data were included; however, the rudimentary requirement

for pairs of brands meant that the brand with the least

market share in an odd-numbered dataset was excluded,

which was in keeping with utilizing the most familiar

brands.

7. For ethical reasons, cigarette- and alcohol-related markets

were excluded.

The above criteria may have resulted in the exclusion of certain

markets, such as those in the technology industry. However, it is

argued that the exclusion of particular markets would be out-

weighed by the range of industry types that were selected.

APPENDIX C

Block-specific instructionsThe two examples used in each condition of the experiment for

each block were as follows. Examples are italicized and separated

by comma.

Bottled water: Affect: purity of the water, refreshing qualities;

cognition: source, size of the bottle.

3 The term market is used here synonymously with the terms brand category and block that appear in the main body of this report.

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Bread: Affect: softness, fresh smell; cognition: wholemeal content,

shelf-life.

Cars: Affect: feelings of freedom, feelings of prestige; cognition: fuel

efficiency, price range.

Cereal: Affect: flavour, feelings of being full; cognition: texture,

energy-providing qualities.

Cheese: Affect: strength of flavour, smell; cognition: shelf life,

packet size.

Chocolate: Affect: flavour, fair trading; cognition: cocoa content,

portion size.

Coffee & tea: Affect: smell, fair trading; cognition: health qual-

ities, source of ingredients.

Crisps: Affect: flavour, smell; cognition: texture, shape of crisps.

Detergents:Affect: fresh feel of clothes, fresh smell of clothes; cogni-

tion: stain-removing capabilities, colour-preserving qualities.

Digital cameras: Affect: feelings of pleasure from capturing photos,

feelings of status; cognition: quality of photos, portability of the

cameras.

Hotels: Affect: feelings of luxury, feelings of relaxation; cognition:

location, price range.

Pizza: Affect: freshness of the taste, feelings of being full; cognition:

range of available toppings, delivery availability.

Sauces: Affect: flavour, smell; cognition: number of possible differ-

ent uses, size of the container.

Soft drinks: Affect: revitalizing qualities, taste; cognition: health

qualities, price.

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