The emotional content of visual images can be parameterized along two dimensions: valence (pleasantness) and arousal (intensity of emotion). In this study we ask how these distinct emotional dimensions affect the short-term memory of human observers viewing a rapid stream of images and trying to remember their content. We show that valence and arousal modulate short-term memory as independent factors. Arousal influences dramatically the average speed of data accumulation in memory: Higher arousal results in faster accumulation. Valence has a more interesting effect: While a picture is being viewed, information from positive and neutral scenes accumulates in memory at a constant rate, whereas information from negative scenes is encoded slowly at first, then increasingly faster. We provide evidence showing that neither differences in low-level image properties nor differences in the ability to apprehend the meaning of images at short exposures can account for the observed results, and propose that the effects are specific to the short-term memory mechanism. We interpret this pattern of results to mean that information accumulation in short-term memory is a controlled process, whose gain is modulated by valence and arousal acting as endogenous attentional cues.

*hazard rate*is the most appropriate for this task (Luce, 1986; Ross, 2003). The hazard rate of information accumulation in memory is the conditional probability that a bit of information never sampled previously is sampled and stored in the current unit of time. In the context of the RSVP experiment, the hazard rate can be taken to indicate the probability that a picture will be correctly remembered if seen for an extra instant of time, having failed to be remembered when seen for a shorter time.

*h*(

*t*) can be expressed as , where

*f*(

*t*) is the probability density and

*F*(

*t*) the distribution function [the quantity 1−

*F*(

*t*) is often referred to as the

*survival rate*and its logarithm as the

*log survivor function*]. If the form of the hazard function is known, then the distribution function can be derived analytically by integrating Equation 1: . Consider the case of a constant hazard rate: . In such a case the distribution function becomes . This is the familiar exponential function, which has been used to model the results of short-term memory experiments in many previous studies (Bundesen & Harms, 1999; Lamberts, Brockdorff, & Heit, 2002; Loftus & Ruthruff, 1994; Shibuya & Bundesen, 1988).

*β*< 1,

*β*= 1,

*β*> 1, respectively. The corresponding distribution function , is the well-known two-parameter Weibull function. Note that when expressed as log(

*t*), the function changes scale (shifts laterally on the

*x*-axis) but not shape (steepness) with changes in

*α*, whereas it changes shape but not scale with changes in

*β*. We adopt Equation 6 as our model for the accumulation of data in short-term memory. While our choice is dictated by the necessity to allow for a range of hazard rates and by the ease with which such property is embedded in a single parameter in the case of the Weibull function, such a choice might have a deeper justification if it were possible to demonstrate that data accumulation in memory can be described as an extremal process, in which case the Weibull would be the appropriate limiting distribution (Kotz & Nadarajah, 2000).

*r*

_{arousal}=.72;

*r*

_{valence}=.94). Subjects’ responses from the RSVP task were then parsed into eight arousal categories (arousal ratings 3–3.5, 3.5–4, 4–4.5, 4.5–5, 5–5.5, 5.5–6, 6–6.5, and > 6.5) and seven valence categories (valence ratings 1–2, 2–3, 3–4, 4–5, 5–6, 6–7, and > 7) according to the normative ratings of the IAPS set. Within each category, proportion correct recalls were calculated at each exposure. The scores were corrected for guessing by subtracting the false alarm rate from the hit rate. Scores never reached 100% correct even at the longest exposures: The errors were interpreted as lapses of attention or random errors in motor choices. As none of these factors were the focus of the experiment, they were removed by normalizing the corrected-for-guessing scores to the asymptotic value, taken as the average score at the two longest exposures (in all cases 95% or better). Scores were then fitted by nonlinear regression with a two-parameter Weibull function of the form indicated in Equation 6. All reported fits corresponded to

*R*

^{2}> .96. To obtain non-parametric estimates of hazard rates, probability densities were calculated from the cumulative scores and then divided by the survival rates (as detailed in Results).

*α*) signifying overall improvement in performance, whereas a change from positive to negative valence increases the slope of the curve (increases

*β*), indicating a progressive acceleration of correct response accumulation as exposure increases.

*α*and

*β*values, as shown in Figure 5. The

*α*values show a linear correlation with arousal (

*r*= −.71,

*n*= 8,

*p*< .047), but do not correlate with changes in valence (

*r*= −.00004,

*n*= 7,

*p*< .999). Conversely,

*β*values correlate significantly with valence (

*r*= ?.87,

*n*= 7,

*p*< .01), but are constant as a function of arousal (

*r*= .04,

*n*= 8,

*p*< .92). Notice that there are also clear nonlinear trends in the data: For example, in Figure 5, the

*α*/arousal graph seems to asymptote to a constant level and the

*β*/valence graph might be interpreted as a step function. These higher order trends could be the result of uneven sampling, the nonuniform coverage of all valence and arousal categories in the image set, or more specific factors affecting each particular condition. Nonetheless, the first-order, linear approximation is an important and robust generalization, and overall the pattern of results suggests that arousal and valence operate independently.

*β*= 1.30, CI = 1.13–1.48), whereas positive images do not deviate significantly from unity (

*β*= .96, CI = .88–1.03). This suggests that differences in slope cannot be accounted for by biases in the sampling of arousal levels.

*α*parameter of the Weibull model.

*β*parameter? The results of similar RSVP tasks have been previously interpreted within the framework of a conceptual short-term memory system (Potter, 1976). Current models of conceptual short-term memory (Bundesen & Harms, 1999; Lamberts et al., 2002; Loftus & McLean, 1999; Shibuya & Bundesen, 1988) postulate that the proportion of correct responses at any given image exposure is equivalent to total information acquired in memory. According to this class of models, information accumulates in memory at a constant rate following stimulus presentation, as evidenced by cumulative frequency of response curves that can be fitted with simple exponential functions. Our data for neutral and positive images conform to such models: A

*β*value approaching unity effectively reduces Weibull curves to simple exponentials.

*β*> 1. This effect is best understood by examining the hazard rates of the responses. An increasing hazard rate implies that information accumulation in memory

*accelerates*over time. Neutral and positive images, which elicit constant hazard rates, appear to be processed in a manner where each successive state of the system is independent of the previous states. In probability terms this is equivalent to sampling with replacement and defines a processing mode that is essentially automatic. Negative images may instead trigger a more intelligent processing mode whose current state is influenced by the previous history and thus shows signs of a more controlled activity. The corresponding hazard rate starts lower, but then increases, indicating that memory encoding of information from negative images accelerates, particularly during the first 500 ms of exposure.

*SD*(related to contrast) of the intensity of all pixels across each image were calculated separately for each RGB color component and for their linear sum. The spatial frequency power spectrum was also extracted from a grayscale version of each image, and the obtained series of frequency coefficients was fitted with a power function of the form where

*f*is spatial frequency and

*λ*a coefficient related to the high-frequency cut-off of the spectrum. A lower power coefficient

*λ*indicates that the image contains more high-spatial frequencies relative to an image with a higher exponent.

*SD*, and power coefficients of each image and its arousal and valence normative rating. Only mean intensity and power significantly correlate (

*p*< .05) with valence (

*r*

_{intensity}=.12,

*r*

_{power}=−.16,

*N*= 384), whereas none of the statistics covaries significantly with arousal.

*SD*of the mean. A Weibull model was then fitted to the responses given within the RSVP task to each subset, yielding a comparative estimate of the model’s parameters across dimensions.

*λ*showed no difference in either Weibull’s parameter (

*α*

_{low}= 371, CI 319–423;

*β*

_{low}= 1.08, CI .87–1.29;

*α*

_{high}= 332, CI 287–377;

*β*

_{high}= 1.11, CI .9–1.32). High- and low-brightness images, on the other hand, did produce a difference in the

*α*parameter, brighter images being recognized better at faster exposures than darker images (

*α*

_{bright}= 278, CI 255–301;

*β*

_{bright}= 1.02, CI .91–1.13;

*α*

_{dark}= 424, CI 383–465;

*β*

_{dark}= 1.17, CI 1–1.34). Correlation and RSVP data for the mean intensity partitions are shown in Figure 7.

*α*parameter), but does not significantly affect the steepness of the curves (

*β*parameter).

*t*= 5.39,

*p*< .001,

*df*= 1295) and neutral images rated higher than negative images (

*t*= 7.08,

*p*< .0001,

*df*= 1294). Of specific interest here is the temporal dynamic of the categorization process: Is there a difference in the time-course of the semantic valuation of positive as opposed to negative images? Do negative images take longer to be categorized as such compared to positive images?

*β*parameter) is unity for positive and neutral pictures, corresponding to a constant hazard rate, but it is greater than unity for negative images, indicating that with negative pictures the hazard rate increases with exposure time. The difference in intercept is also dependent only on

*β*, as can be demonstrated by constrained regression with

*α*as a shared parameter. The scale parameter

*α*, indicating the exposure at which 63% of images are remembered, thus does not vary as a function of valence; however, it does vary with arousal. Across arousal levels it can differ by as much as 150 ms, as can be seen in Figure 5, and this range of variation is comparable to what can be obtained with a three-fold difference in the overall brightness of images (see Figure 7). Therefore, the parameters of the Weibull model describing recognition memory performance in the RSVP task correlate orthogonally with valence and arousal: Shape correlates with valence and scale correlates with arousal, but not viceversa. As such, we interpret these results to indicate that valence and arousal affect short-term memory performance independently.

*endogenous*attentional cue, thus leading to a speed-up of information accrual similar in nature to that obtained by exogenous cues. However, unlike Carrasco, who proposes that transient covert attention increases the gain of early detectors (Carrasco, Ling, & Read, 2004), we envisage the effect of negative valence as a later process, acting at the level of short-term memory storage. The time-course of the effect we observe seems too slow to be compatible with a classic gain control mechanism, and no evidence of such effect is observed in tasks where short-term memory involvement is presumed to be minimal (Experiments 2 and 3). Furthermore, the effect appears complex: Recall that the hazard rate for negative images in the RSVP task not only accelerates over time, but also starts from a lower level than for positive or neutral images. Reluctant as we may be to put forward an explanation of such a feature at this stage, it is possible that image features most diagnostic of negative content attract most of the early processing resources at the expense of the wider context, thus hindering recognition memory (conjectures of similar kind have often been advanced in past studies and most recently by Kensinger & Corkin, 2003). Thus, a more appropriate metaphor for the effects of negative valence might be that of a gating system, decreasing then increasing information flow to visual short-term memory. From a teleological standpoint, neutral and positive images do not represent a challenge to the organism, and information accrual from such sources can be let to follow a free-flow, random sampling regime. Conversely, negative images might represent a threat that requires a fast and costly reaction, thus demanding a more accurate and controlled flow of information.