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Whereas the concept of cognitive bias is logical and is empirically supported in many ways, the obstacles involved in confidently assessing the effect of inferred motivational/affective states on cognitive processes are significant. Ideally it would be most useful to utilize a cognitive task to implicitly assess or diagnose motivational issues on an individual basis. However at present we must deal with uncertainties in almost every direction. Inference of motivational state can not be considered a straightforward measure. In the most concrete of assessments we might measure behaviors that are logically related to a concept motivation or emotion. So for example, direct measures of food involvement would be a reasonably defensible way of inferring food-related motivations. While some motivational states may be relatively amenable for direct behavioral assessments, for many motivationally linked behaviors this is problematic. For example, illicit recreational drug taking behaviors as a measure of drug taking motivation would in many cases be difficult for ethical reasons as well as the necessarily covert nature of such activity. On the other hand, it is not unreasonable to accept that clinically anxious individuals are likely to have more intense anxiety-related motivations than the average non-clinical individual. A significant proportion of past studies of cognitive bias have utilized subjects who had clinically diagnosed conditions or syndromes that made them logically predisposed for certain directions and intensities of motivation/affect ( Williams, Mathews and MacLeod, 1996 ). For example a number of studies have reported evidence for cognitive bias for food/hunger or body-image related stimuli in eating disordered subjects (Smeets, Roefs, van Furth, and Jansen, 2008). However there are abundant reasons why accurate assessment of motivation and emotional states in nonclinical populations should be pursued, so the problem of measurement accuracy remains large. This problem is no doubt compounded in that many motivations will be relatively unique to individuals or small subsets of the general population. One way that we may begin to get around this potential problem is to initially target motivational states that are likely to be “common.” The use of the term “common” motivation here refers to the self-evident idea that some motivations are relatively strong and consistently present in almost all healthy adult humans. ( possible refs substantiating the commonality of different motivations?).

DIFFICULTIES WITH SELF-REPORT

Utilization of self-report measures to infer motivation and emotional states is ubiquitous. Unfortunately, and despite intense efforts to validate self-report measures (e.g.. Maisto et al., 1990; Martin et al., 1988), the issue remains at best controversial (Hoffman, Gawronski, Gschwendner, Le, and Schmitt, 2005). It can not be denied that there are a host of factors that may compromise the accuracy of self-report information ( O’Sullivan, 2008; Palen, Smith and Caldwell et al, 2008). Subjects may be defensive (Rosenberg, 1969), reluctant or incapable of reporting accurately for varied reasons particularly in regard to private motivational information (Greenwald et al., 2002). It is clear that self-knowledge may be limited or inaccurate (Epstein, 1994; Fazio, 1990; Greenwald et al., 2002; Wilson, Lindsley, & Schooler, 2000; Nisbett & Wilson, 1977). It is also known that low motivation, demand characteristics (Orne, 1962), social desirability, and faking (Cronbach, 1990; Viswesvaran & Ones, 1999; Holden, Wood, & Tomashewski, 2001; Holden, 2008) impact self-report measures. Consequently assumptions about intensity and type of motivation inferred from self-report are immediate concerns.

Implicit measures of motivational state attempt to assess state in ways that don’t primarily involve subjects responses to motivation questions. A growing number of techniques have been developed for this purpose. For example the Implicit associations test (Nosek, Banaji, & Greenwald, 2002) typically involves measures of reaction times to conditional responses for positively valenced word stimuli associated with target stimuli (like faces of blacks vs whites) compared to RTs for the target stimuli conditionally associated with negatively valenced word stimuli. The idea is that attitudes may be covertly measured by differences in RT since underlying target stimulus valence will have closer associative nodes to either the positive or negative word stimuli. The use if the IAT has proliferated however it is not without controversy. Minimally, the relationship of behavior to IAT measures requires more validation (Arkes and Tetlock, 2004; Rudman, 2004; Fazio and Olson, 2003).

Likewise the modified (emotional) Stroop test is now often used to infer motivation or emotional status based on differences in RT for naming word color associated with relatively neutral words vs target words ( Williams, Mathews and MacLeod, 1996 ). Subjects are typically instructed to ignore the word and respond only to the color of the letter font. Differences in RT across different categories of semantically related word stimuli are taken to reflect the automatic tendency to process word meaning and that words are automatically more salient if they are relevant to the subjects motivational status. The Dot-probe task ( REF), The “dot-probe” task is another measure of attentional bias (Posner, Snyder, and Davidson, 1980) Subjects are required to fixate on a point in the center of a computer screen that is then replaced by a neutral and an “emotional” word, typically 5° above and below the fixation point. Placement of the words was randomly assigned. After a short presentation time (typically 500ms), the word pair disappears and a “dot” probe replaces one of the words. Subjects’ are required to press designated keys in response to the probe location. Their reaction time (RT) is measured as a reflection of cognitive processing time for these stimuli. Theoretically, if subjects were attending to both stimuli equally subjects should respond equally as fast to either position of the probe on average. However, emotionally relevant words have been found to faciliate reaction times. Thus reaction times on the dot-probe task are taken as an index of cognitive bias. Problems: Schmulke, 2005)

the RSVP (ref) the picture-word stroop (REF), and incidental learning tasks have all been utilized by researchers in efforts to measure underlying states in ways that bypass primary dependence on self-report or behavioral observations. In general the influence of motivational state as measured by these implicit techniques is now referred to as cognitive bias (attentional bias, memory bias and report bias are logically incorporated). Again, in clinical samples, the relatedness of cognitive bias with their clinical diagnostic measures are not troubling compared to efforts to correlate these measures in nonclinical samples. Evidence that these covert implicit measures reliably measure motivation or emotional states in nonclinical populations is debated

Rudman (2004),

Greenwald et al. (1998),

REFS on cog bias relatedness to self-report or behavior measures).

POTENTIAL ARTEFACTS IN COGNITIVE BIAS MEASURES

There remain a number of other problems for both RT measures and memory measures of cognitive bias. One common problem exists in the use of words as stimuli. It is quite clear that the majority of studies in the area of cognitive bias have utilized single word stimuli as independent variables. So for example threat words are utilized to assess cognitive bias in anxiety prone individuals. Other studies have utilized images in a similar fashion, and problems associated with image stimuli are in many cases parallel (O'Neill, 2005; Fernández-Rey, 2007; Bellhouse-King, 2007; Forsythe, 2008; Weaver and Stanny, 1984) to the difficulties in use of word stimuli that will be discussed.

Memory for and reaction times to word stimuli ( and probably for image stimuli where applicable (Bradley et al., 1992??) are affected by a number of variables that are independent of specific motivational states. “Imageability” (Paivio et al., 1994; Schwanenflugel et al., 1988), plurality of, and idiosyncratic nature of word meaning or interpretation (perhaps especially in regard to slang words; Braun and Kitziger, 2001); Klepousniotoua and Baum, 2007; Hertel and El-Messidi, 2006; Compeau, Lindsey-mullikin,Grewal, and Petty, 2004; Frisson and Pickering, 2001), changes in word meaning, use and acceptability over time; word length (see Campoy, 2008), part of speech (memory for verbs is not as good as memory for nouns, e.g., Earles & Kersten, 2000; Earles, Kersten, Turner, & McMullen, 1999; Engelkamp, Zimmer, & Mohr, 1990; Reynolds & Flagg, 1976), number of syllables (may affect processing time; Clifton and Tash, 1973), general emotionality (Kensinger and Corkin, 2004; Buchanan and Adolphs, 2004; Buchanan, Etzel, Adolphs and Tranel, 2006), the malleable nature of word emotionality (for example the changes in acceptability of use of a word like “bitch”), frequency of word use (Arndt and Reder,2002; Borowsky and Masson, 1999), and the idiosyncratic and changing nature of word use may all may influence memory and RT measures.

There is ample evidence that sexual motivations are common and potent in college-aged individuals (see Regan & Berscheid, 1999; Beck, Bozman, & Qualtrough, 1991; Gagnon, 1977; Kinsey, Pomeroy, Martin and Gebhard, 1953). Likewise a number of studies have reported cognitive bias for sexual stimuli occurs in non-clinical populations. For example, Geer and Bellard ( 1996) used a speeded lexical decision task and found slower processing of sexual word stimuli as compared to control word stimuli. This effect has been demonstrated in a number of different studies (see Conaglen, 2004) and is referred to as the “sexual content induced delay” (SCID) effect. Similarly, Geer and Melton (1997) presented subjects with sentences that ended in double-entendre words. Subjects were asked to make lexical decisions about these target words. Results showed that RTs were slower when sexual content was salient and supported the hypothesis that sexual words evoke a more complex processing sequence. Bias toward sexual stimuli has also been found in picture categorization tasks involving erotic stimuli (Spiering, 2002; Conaglen, 2006). In Spierings’task (2002), subjects were told either to ignore or focus on a sexual, threatening or neutral prime picture. Subjects were then asked to categorize sexual and neutral target pictures. Experimenters found that subjects were slower to respond in the unprimed condition when making decisions related to erotic stimuli.

It is prudent however to consider the possibility that measures of cognitive bias may be subject to methodological influences. In fact, most studies of cognitive bias for sexual stimuli have utilized lexical stimuli and therefore may be subject to the issues described above. Some studies of cognitive bias for sexual word stimuli have implemented controls across a broader range of these problems. For example Conaglen (2004) assessed bias for sex word stimuli in a rapid lexical decision task that included romantic and household item control words. The study also made efforts to control for word length, frequency of use, and social acceptability. Results from Conaglens study (2004) replicated previous reports that subjects typically have longer RTs to sexual word stimuli (Geer and Bellard, 1996); and in a non-speeded rating task for pictorial stimuli; Conaglen, 2006) and indicate that the sexual content induced delay (SCID) is a reliable phenomena. Although it is tempting and logical to conclude that the SCID reflects cognitive bias related to sexual motivations, there remain a number of ambiguities in such an interpretation. One of the stated goals in Conaglens study (2004) was to replicate the work of Geer and Bellard (1996). This replication utilized most of the sexual word stimuli used in the earlier study, and matched these words with neutral words and “romantic words” along the dimensions of word length, frequency of use ( based on Francis and Kucera, 1982). The study also assessed the potential influence of word familiarity, word emotionality and social acceptability. However, the sexual word stimuli used have a number of potential issues associated with them. Many of the sexual words used had ambiguous meaning so for example “screw,” “balls,” “prick,” and “pussy” are clearly double-entendre and slang terms. Word ambiguity can have effects on perceptual selectivity, paired-associate learning, recognition memory, and language. And concrete words are generally processed faster than ambiguous words in lexical decision tasks (Schwanenflugel, Harnishfeger, & Stowe, 1988; Rodd, Gaskell, & Marslen-Wilson,2002).

The sexual word stimuli used in these studies also had a greater number of syllables than the neutral stimuli which may have contributed to slower responses (Clifton and Tash, 1973). Moreover, the sexual word stimuli were highly overlapping in their primary intended meanings (screw/intercourse, vagina/pussy, testicles/balls) which could produce an unintended priming effect. The same issue applies to the romantic word stimuli (Embrace/hugging/cherish, darling/valentine/beloved, Tender/caring) used in Conaglens’ (2004) study. Similar issues are associated with most cognitive studies that utilize sexual word stimuli. For example Geer and Robertson examined the relationship of self-reported sexuality (sexual opinions survey; Fisher et al., 1988) with a sexually oriented IAT. The sexual words used in part of their assessment were:

Low acceptability sex words

Balls, Cock, Crotch, Cum, Cunt, Fuck, Pussy, Screw, Tits, Whore;

Nonsexual: Burner, Cup, Grate, Meat, Orange, Oven, Plug, Salt, Spoon, Tongs

High Social Acceptability

Sexual words: Clitoris, Ejaculate, Intercourse, Masturbate,

Nipples, Orgasm, Penis, Semen, Testicles, Vagina

Nonsexual: Catalog, Envelope, Microwave, Oven, Recipe,

Restaurant, Salt, Sandwich, Spoon, Telephone

Evaluative Words

Positive: Diamond, Happy, Health, Heaven, Honest,

Laughter, Love, Lucky, Peace, Sunrise

Negative: Abuse, Accident, Ashamed, Hatred, Poison,

Poverty, Rotten, Sickness, Ugly, Vomit

Hakan

brownies dictionary alarm canteen food dishes levitate fixation abundance stomach 72= 7.2 letters per word; possible nouns = 8, possible verbs=3; avg syllables = 2.4; multiple meanings? Brownies, alarm, dishes, stomach

Surgery recovery medication hospital injection pulmonary treatment doctor healing benign 79= 7.9; possible nouns = 7, possible verbs =4; avg syllables =2.8; multiple meanings?

Panties foreplay ejaculate orgasm condom porno masturbate slut nipples lust 66= 6.6; possible nouns = 7, possible verbs = 5; avg syllables = 2.1; multiple meanings? Ejaculate, nipples

Celebrate excitement elation friend vacation love dream compliment birthday hugging 74= 7.4; possible nouns = 8, possible verbs = 6; avg syllables = 2.2; multiple meanings? Vacation, dream, compliment

Argument Betrayal Humiliate Embarrass Criticize Lonely Mucous Cruelty Vomit Gossip 7 .3; possible nouns = 6, possible verbs = 6; avg syllables = 2.6

The purpose of the present studies was to determine if cognitive bias for sexual stimuli could be observed in a lexical task where word stimuli were controlled for word length, part of speech, number of syllables, plurality of meaning, imageability, emotional valence, contemporary familiarity, and current frequency of encounter and use. Many studies utilizing word or pictorial stimuli have relied on previously published lists of words that have been rated on various dimensions such as emotionality, frequency of use, and imageability (Rubin and Friendly; Francis and Kucera, 1982 ).

However, logic dictates that the interpretation of word meaning is likely to be affected by numerous influences ( see for example Braun and Kitziger, 2001; Klepousniotoua and Baum, 2007; Hertel and El-Messidi, 2006; Compeau, Lindsey-mullikin,Grewal, and Petty, 2004; Frisson and Pickering, 2001). For example the offensiveness of words may be particularly sensitive to subject variables and context (e.g., Jay & Janschewitz, 2008; Mabry, 1974; Wells, 1989). Similarly, the use of words changes over time, conceivably over very short periods of time (see Jay, 2009).

STUDY 1

Therefore, to begin this study a “word library” was created. in an attempt to control for factors that may influence recall-ability of words (Rubin & Friendly, 1986). A list of 480 emotionally neutral, emotionally negative, emotionally positive, categorically related and sexual words was created first by rational criteria and then validated based on ratings provided by 113 participants. Words were rated on imageability, emotionality and frequency of use. Ratings were collected using the population of immediate interest, college-aged subjects, thus controlling for cultural or generational differences. The reliability of these ratings was assessed within and between subjects. Additionally, the reliability of these word ratings was cross-validated with the results of other word rating studies (Rubin and Friendly, 1986). Words were then matched on these ratings and utilized in the series of studies described here. Further validation for the characteristics of the word stimuli were assessed following procedures used in study 2 (see below), that also required subjects to rate the selected word stimuli for imageability, emotional valence and frequency of use/encounter.

Subjects

One hundred forty-nine subjects were recruited using a sign-up board for introductory psychology students who received credit for participating. After elimination criteria (see results), 113 subjects were included in analysis (37 males and 76 females). The mean age of the subjects was 18.3, with a standard deviation of 0.8.

Materials

Four hundred eighty words were chosen by rational criteria in a collaborative effort by researchers. Words were generated based on a number of criteria. These included efforts to: minimize social offensiveness; to minimize semantic ambiguity; control for general familiarity, control for word length, and efforts to control for affective intensity and direction were determinants of inclusion in our initial word list. Neutral words for example included “camera” and “number.” Negative emotional words included “criticize” and “humiliate.” Positive emotional words included “vacation” and “birthday.” Sexual words included “orgasm” and “sexual.”

Words were presented on individual slides in a Microsoft Power Point presentation through a classroom multimedia projector. Subjects responded with a numbered answer sheet on three rating scales for each word: imageability, emotionality and frequency of use. Definitions for scales were provided verbally and on the answer sheet. Imageability referred to “how well the word can be concretely visualized or represented by a mental image.” Emotionality was defined as “the positive or negative way you think most people might respond to the word if heard in public.” Frequency of use was defined as “how often you use or encounter the word by hearing, reading, or speaking it.” Each rating was on a 7-point scale, with 1 corresponding to low and 7 to high. Also on the answer sheet was a space to provide a synonym for later assessment of semantic interpretation.

Procedure

The word bank consisted of 480 words, divided into 11 word lists with 60 words in each list. The last five words on each list were randomly selected repeat words included to assess within subject reliability. Subjects rated either one or two word lists depending on number of credits being received. One hundred and twenty-five words were repeated across different lists to assess rating reliability across different groups. Each word was presented individually for 11 seconds.

Results and Discussion

For each subject, the ratings for the last repeated words were compared to the initial ratings of those words. To develop a word library with the most reliable ratings, subjects who deviated in their ratings on these items by more than two rating points on more than two repeat words were eliminated from analysis. After elimination, analysis was conducted for 113 subjects; 37 males and 76 females.

Analysis of inter-rater reliability was conducted using the 125 words repeated across the eleven lists rated by different groups of subjects. Simple regression analysis on these 125 words repeated across the eleven word lists showed that inter-rater ratings of imageability were significantly related [r = .939, r2 = .882, F (1, 123) = 574.225, p< .0001]. Simple regression analysis showed inter-rater ratings of emotionality across these words were significantly related [r = .824, r2 = .679, F (1, 123) = 163.118, p< .0001]. Simple regression analysis for frequency ratings across these words revealed that inter-rater ratings were significantly related [r = .769, r2 = .591, F (1, 123) = 111.114, p< .0001]

Ratings of word emotionality were compared when possible to ratings reported for the same words by Rubin and Friendly (1986). Sixty-six words rated for emotionality in the present study were also rated by subjects in the study conducted by Rubin and Friendly. Both studies used a 7-point rating scale, where 1 corresponded to “not emotional” and 7 to “very emotional.” A simple regression for ratings on these 66 words obtained across both studies revealed a significant positive correlation between ratings of emotionality [r = .889, r2 = .79, F (1, 64) = 240.486, p < .0001].

The purpose of study one was to gain current information about word stimuli that would allow us to better control for factors that might influence performance in lexically based tasks: imageability, emotionality, and frequency of use (Rubin & Friendly, 1986). Analysis of inter-rater reliability and ratings from Rubin and Friendly provide evidence that subjects were able to reliably rate the words in this task. Words were selected from the word library to be implemented in study two. Means and standard deviations were used to match words according to ratings on each of the three scales.

STUDY 2

The purpose of this study was to determine if cognitive bias for sex word stimuli will occur in a

Speeded lexical decision task

RSVP

Incidental learning vs Intentional learning vs paired-associates learning

Bush and Geer (2001) proposed that sexual stimuli have higher saliency and thus tend be more noticeable or conspicuous when presented amongst other stimuli, and that increased saliency for sexual stimuli leads to greater cognitive processing. An initial test of this assumption was reported by Bush and Geer (2001), who presented subjects with a series of words, which were either sexual, negative emotional or neutral. Following presentation of the words, a word stem completion task was given. Sexual words were used more often to complete word stems. This result supports the idea that attentional bias reflects greater cognitive processing, ( Lazarus, Kanner and Folkman, 1980; Geer and Melton, 1997; Wright and Adams, 1999; Bush & Geer, 2001). If RT differences in response to sexual stimuli reflect deeper cognitive processing, then greater memory for these stimuli should result. To determine if cognitive bias for sexual stimuli involves more effective cognitive processing an incidental learning task was used that involved subjects’ ratings of sex word stimuli from the word library along with neutral stimuli and other control word stimuli. We hypothesized that sex word recall would be superior to recall of other word types. We also predicted that compared to explicit test procedures the ILT may be more sensitive to underlying motivational states.

Methods

Participants

One-hundred and seventy undergraduate psychology students were recruited from the University of North Carolina Wilmington via the experimental sign-up board located in the Social and Behavioral Science Building on campus. The sign-up sheet featured a title stating “Word Ratings Experiment” and told potential participants that the study entailed the ratings of various words and where to report for the study. A total of 26 students were recruited for the study, consisting of 17 females and 9 males with a mean age of 18.88 (SD = 2.05).

Materials

To minimize distraction and to ensure proper spacing between subjects, only eight subjects were permitted in the lab at once. Upon entering the lab, subjects were instructed to sit at any computer terminal that was available. Subjects were presented with an informed consent and a blank sheet of paper with a heading for their gender, age, subject code and the date. SuperLab Pro (Cedrus Corp) was used to administer the experiment.

Words used in the experiment were chosen from the word library established in study 1. Words were selected from the word bank based on ratings of emotionality and were matched on ratings of imagery and frequency. Sixty words in total were used. The selected words consisted of 30 neutral words, 10 positive emotional words and 10 negative emotional words and 10 sexual words. Refer to Table 1 for a list of the stimuli used. In addition to emotion, imageability and frequency, word selection was guided by the following criteria: Words were minimally ambiguous in interpretation; Proper nouns were excluded from the study, as well as compound words so as to avoid increased recall due to lexical construction. In addition efforts were made to select words that were equated for length, number of syllables, part of speech and singularity of meaning.

Word stimuli were presented on 17” computer screens and featured 36 point Arial font. Each trial featured text asking the subjects to “Pay attention to the following stimuli.” The next screen presented the stimuli in the center of the screen for a period of three seconds. Following this, the rating scale appeared while the word remained in the middle of the screen. The screen advanced after the subject entered an alphanumeric keypad response . Words were presented to subjects in random order.

Subjects were required to rate each word on three scales: emotionality, imagery and frequency. Each scale was Likert-type, ranging from one to seven. Emotionality was rated from very negative to very positive, imagery from low to high and frequency from rarely to often. Subjects had a maximum of ten seconds per scale to rate each word which resulted in a maximum of 30 seconds per word.

Procedure

Subjects were tested for incidental learning associated with a task where they were instructed to rate computer-presented words. After signing informed consent forms, subjects saw an initial screen on the computer reading “Welcome” and “Press any key when instructed.” The next computer screen provided subjects with an example word to rate. Subjects were then required to rate individually presented words using the alpha-numeric keypads on their computer keyboards.

Following the rating task, a surprise recall task was administered. A screen appeared that instructed subjects to write down every word they could recall from the rating task on the blank sheet of paper provided.

Results

Recall percentages were calculated as the number of words correctly recalled over the total number of words in the category. Total recall percentage was calculated by the number of words correctly recalled over all the categories divided by the total number of words presented. Total recall was 32.78% (SD = 8.70). The mean recall percentage of neutral words was 24.18% (SD = 9.23); of positive emotional words was 29.23% (SD = 18.38); of negative emotional words was 29.23% (SD = 13.83); of sexual words was 60.39% (SD = 16.61). A repeated measures ANOVA found significant differences across all word categories [F(25,3)=38.266, p ................
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