Rule-based processes in generalisation and peak shift in human fear conditioning

Transcription

1 _ QJP / The Quarterly Journal of Experimental PsychologyAhmed and Lovibond research-article2018 Special Issue Article Rule-based processes in generalisation and peak shift in human fear conditioning Quarterly Journal of Experimental Psychology 2019, Vol. 72(2) Experimental Psychology Society 2018 Article reuse guidelines: sagepub.com/journals-permissions DOI: qjep.sagepub.com Ola Ahmed and Peter F Lovibond Abstract Two experiments explored the role of verbalisable rules in generalisation of human differential fear conditioning with electric shock as the aversive stimulus. Two circles of different sizes served as conditioned stimuli (CS+ and CS ), before testing with a range of circle sizes. In Experiment 1, shock expectancy ratings followed a peak-shifted unimodal gradient, with maximum ratings at a test value further along the dimension from CS+ in the opposite direction to CS. However, differentiable gradients were observed when participants were divided on the basis of the rules they reported using during the task (linear and similarity). Experiment 2 was designed to counter the contradictory feedback arising from extinction testing by removing the shock electrodes during the test phase. A more linear overall gradient was observed, and sub-groups defined by self-reported rules showed distinct gradients that were congruent with their rules. These results indicate that rule-based processes are influential in generalisation of conditioned fear along simple stimulus dimensions, and may help explain generalisation phenomena that have traditionally been attributed to automatic, similarity-based processes. Keywords Generalisation; fear conditioning; associative learning; rule learning; reasoning; peak shift Received: 9 December 2016; revised: 2 January 2018; accepted: 1 February 2018 Classical or Pavlovian conditioning occurs when an association between some neutral stimulus (the conditioned stimulus [CS+]) and an outcome of interest (the unconditioned stimulus [US]) is learned after exposure to CS-US pairings. Learning of this association is indexed by the occurrence of an anticipatory response (the conditioned response [CR]) when the CS is later presented. In classically conditioned fear, an initially emotionally neutral CS (such as a tone or light) is paired with a biologically significant and aversive US (such as an electric shock). Over the course of conditioning, fear responding increases to the CS+ as its relationship to the US is learned, and in a differential conditioning design, fear decreases to a CS (a second CS not paired with the US) with safety learning. Since naturalistic contexts rarely provide an organism with the opportunity to encounter the exact learning situation on repeated occasions, it becomes essential that a learned association between CS and US can be generalised to novel situations. The phenomenon of generalisation has been central to the empirical and theoretical endeavours of many fields of psychology, with Shepard (1987) famously arguing that the first law of psychology ought to be one of generalisation. In the conditioning literature, generalisation has been a focus of empirical and theoretical efforts for almost 100 years, dating back to Pavlov s (1927) wellknown investigations with dogs. In the context of conditioned learning, generalisation is most often defined as the observation of the CR in response to a stimulus which is similar to the CS+ but has not itself previously been paired with the US (Mackintosh, 1974). Generalisation has often been explored by testing a range of stimuli that fall along a perceptual or physical dimension of change (Moore, 1972). This is achieved by varying a quantitative feature of a CS+, such as the wavelength of light, the intensity of a sound, or the location of a physical feature. Generalisation is tested by plotting responding at several untrained stimulus values along the dimension. A gradient of generalisation is then formed, representing response levels along the stimulus dimension. In their seminal paper, Guttman and Kalish (1956) documented the gradients of generalisation of a key peck School of Psychology, UNSW Sydney, Sydney, NSW, Australia Corresponding author: Peter F Lovibond, School of Psychology, UNSW Sydney, Sydney, NSW 2052, Australia. p.lovibond@unsw.edu.au

2 Ahmed and Lovibond 119 response in pigeons along a wavelength dimension. The pigeons responded most strongly to stimulus values most like the trained value, with reduced responding as stimuli became more unlike the CS+ value in either direction. This kind of gradient (Ghirlanda & Enquist, 2003; Hull, 1943; Shepard, 1987) is often described as a unimodal or peaked gradient. Such unimodal gradients have been observed in a range of species, stimulus dimensions, and conditioning paradigms, including discriminated instrumental conditioning. Several investigations have demonstrated this gradient shape across different paradigms with human participants (Doll & Thomas, 1967; Hovland, 1937; Rosenbaum, 1953). The unimodal generalisation gradient has long been used to argue for the centrality of stimulus similarity in guiding generalisation. In his classic theory of discrimination learning, Spence (1937) argued that stimulus similarity facilitates the spreading of excitatory and inhibitory response tendencies to stimuli resembling the S+ and S (in instrumental conditioning terms), respectively. More recently, theories of generalisation based on error correction have also argued that stimulus similarity mediates the spread of conditioned learning. The Rescorla-Wagner model of classical conditioning (Rescorla Wagner, 1972; Wagner & Rescorla, 1972) posits that learning represents an increase in the associative strength relating a CS with a US over repeated trials. Blough (1975) provided one of the earliest formal attempts to extend the Rescorla Wagner model (Rescorla & Wagner, 1972) to the phenomenon of generalisation. He argued, as did Rescorla (1976) soon after, that the accumulation of associative strength to the individual perceptual elements that make up a trained stimulus (e.g., a visual stimulus that can be broken down into elements of colour) was fundamental to the process of generalisation. Blough proposed that novel stimuli containing perceptual elements in common with the CS would support the activation of the associative learning (and related responding) attached to those common elements. Along a perceptual stimulus dimension, stimuli that are close to the CS are assumed to share the most perceptual elements in common with it, while stimuli further along the dimension share fewer common elements. Responding should generalise most to stimuli close to the CS and progressively less to stimuli that fall further along the dimension. In other words, such an account predicts the unimodal generalisation gradient which is often observed empirically. Similar predictions regarding generalisation gradient shape can be drawn from more recent learning theories which more directly emphasise the role of elemental processing of stimuli in conditioned learning (e.g., McLaren & Mackintosh, 2002). These theoretical accounts are also able to speak to one of the key empirical findings observed in intradimensional differential conditioning, when both a CS+ and CS falling along the same stimulus dimension are trained. Under these conditions, a peak shift effect is often observed, where the highest point of responding at test is not at the original CS+ value but at a value further along the dimension in the direction away from CS (e.g., Ellis, 1970; Hanson, 1959). Spence (1937) attributed peak shift to the interaction of gradients of excitation and inhibition. Subsequent associative accounts (McLaren & Mackintosh, 2002; Rescorla Wagner, 1972) similarly predict that the CS+ and the elements that constitute it will accrue positive or excitatory associative strength, while the elements of the CS will develop negative or inhibitory properties. Since CS+ and CS are likely to have some elements in common, certain CS+ elements will develop inhibitory properties on CS trials, and certain CS elements will develop excitatory properties on CS+ trials. Depending on the distribution of elements, it is possible that a stimulus close to CS+ but further away from CS might have a similar number of excitatory elements to CS+ but fewer inhibitory elements, and hence support the greatest amount of responding at test. In other words, the peak of the generalisation gradient may shift because of the net balance of excitatory and inhibitory conditioning to stimulus elements. A study by Wills and Mackintosh (1998) provides support for these predictions. By creating an artificial stimulus dimension (in which stimuli were composed of a number of smaller abstract symbols), the investigators were able to manipulate the number of elements shared by stimuli along the dimension. A peak shift effect was observed with the strongest generalisation at test falling at the stimulus that had been predicted to accrue the greatest net excitation over the course of conditioning. Similarity-based accounts typically propose that learning generalises in an automatic fashion driven by stimulus perceptual similarity. Perhaps by virtue of the largely nonhuman animal empirical tradition that existed in the early generalisation literature, dominant theoretical accounts like those reviewed here make little mention of symbolic or representational cognitive processes such as reasoning and rule-use. However, symbolic cognitive processes are central to many aspects of human learning and are also likely to play an important role in generalisation of learning. In several studies, Thomas and colleagues demonstrated that human generalisation gradients differed depending upon the verbal labels reported by participants (Thomas & Mitchell, 1962). When participants were explicitly asked to label a trained visual stimulus that fell on a dimension of colour hue between green and blue, Thomas and DeCapito (1966) found that generalisation was biased towards the end of the dimension consistent with the label chosen by participants. Instead of the common symmetrical gradient of generalisation, responding at test was skewed in a linear fashion towards one end of the stimulus dimension in line with the label or rule held by participants.

3 120 Quarterly Journal of Experimental Psychology 72(2) Other investigations in which relational/categorisation rules were explicitly instructed (e.g., Regehr & Brooks, 1993), or where participants were classified according to having shown high mastery over relational rules by the end of training (e.g., Shanks & Darby, 1998), have also shown strong generalisation according to the rule and against the patterns predicted by stimulus similarity. A popular way to accommodate these results has been to propose a dualsystem model in which rule-based generalisation derives from a separate, higher-level cognitive system (e.g., Livesey & McLaren, 2009; McLaren et al., 2014). In particular, it has been proposed that cognitive processes may dominate when a generalisation task involves simple, verbalisable associations, and participants have sufficient time and cognitive capacity to process and apply this learning flexibly (Shanks & Darby, 1998; Smith & Shapiro, 1989; Wills & Mackintosh, 1998). A range of studies have tested this hypothesis by using manipulations intended to limit explicit rule learning such as cognitive load during training (e.g., Smith & Shapiro, 1989), speeded responses at test (Smith & Kemler, 1984), and variations in training contingencies and stimulus properties to reduce training performance (Jones & McLaren, 1999; Livesey & McLaren, 2009). These studies have demonstrated more unimodal gradients, consistent with an automatic, similarity-based system. This pattern points strongly to the involvement of symbolic cognitive processes in human generalisation. However, to our knowledge no investigations have explored the role of similarity and rule-based processes in generalisation of human fear conditioning. Better understanding of the role of symbolic cognitive processes in fear generalisation has important clinical implications. There is growing evidence for over-generalised fear learning in the development and maintenance of certain anxiety disorders. While some generalisation of fear learning is clearly adaptive, excessive generalisation of learned fear may result in false-alarms and heightened threat perception. Over-generalisation has been implicated in posttraumatic stress disorder (PTSD) in particular. In PTSD, the pairing of cues present at the time of the trauma (the CSs) with the traumatic experience itself (the US) is known to be a central feature of the development of the disorder (Ehlers & Clark, 2000; Rothbaum & Davis, 2003). Generalisation of conditioned fear to stimuli that resemble these trauma cues (but were never themselves associated with the traumatic event) is thought to contribute to the heightening of perceived threat characteristic of PTSD (Ehlers & Clark, 2000). Such stimulus generalisation may extend the impact of the trauma on the individual s daily life by producing a proliferation of fear-triggering cues in the environment (Feldner, Monson, & Friedman, 2007). The clinical evidence has been supported by experimental studies demonstrating that anxious individuals over-generalise conditioned fear learning when compared with non-anxious controls (Grillon & Morgan, 1999; Lissek et al., 2010). Despite the clinical significance of the generalisation of fear conditioning, only a limited number of studies exploring generalisation gradients in humans can be found in the conditioning literature. Hovland (1937) reported (one half of) a unimodal gradient reflecting generalisation of fear conditioning along an auditory intensity dimension. However, several subsequent attempts to replicate this finding failed to produce unimodal gradients of generalisation, instead producing flat gradients, or ones in which responding increased linearly towards one extreme of the stimulus dimension (e.g., Epstein, Burstein, & Smith, 1971; Humphreys, 1939; Littman, 1949). Marked inconsistencies in the methodologies of these early studies make it difficult to draw any strong empirical or theoretical conclusions. For example, many studies, including the original demonstration by Hovland (1937), did not include a CS to assess for differential conditioning. The studies also involved significant procedural variations, including within- versus between-subjects testing of generalisation. More recently, Lissek and colleagues (2008) developed a fear conditioning paradigm to investigate the slope of the generalisation gradient obtained between a CS+ (followed by shock) and a CS. They trained CS+ and CS as the extreme values along a circle size dimension and measured fear-potentiated startle responding. At test, they recorded responding at several intermediate circle size values. They showed, perhaps not surprisingly, that responding decreased progressively as test values moved away from CS+ and towards CS. While this design does not allow for any conclusions about overall gradient shape or issues such as peak shift, it provides some evidence of generalisation decrement in line with reduced stimulus similarity in human fear conditioning. We sought to build on this demonstration by Lissek and colleagues (2008) by exploring generalisation of fear conditioning either side of the CS+ along a basic perceptual dimension, with a focus on the direction away from CS. In doing so, we aimed to test the relevance of similaritybased accounts of generalisation in human fear conditioning. Our second aim was to explore the role of symbolic cognitive processes in fear generalisation by investigating the relationship between verbalisable rules and the gradients obtained at test. Experiment 1 The same fear conditioning preparation was employed across both experiments. A differential conditioning design was used with a circle-size dimension similar to that employed by Lissek and colleagues (2008). One of the circle stimuli was trained as a CS+ and a second trained as a CS. A 0.5-s electric-shock stimulus served as the US. Two outcome measures of interest were recorded on all trials of the conditioning and test phases. Changes in participants skin conductance level (SCL) were recorded as a

4 Ahmed and Lovibond 121 physiological measure of arousal associated with anticipation of shock. Participants also rated their expectancy of shock on each trial during the CS presentation period. Associative learning theorists have proposed that selfreported expectancy ratings reflect the learning of an association between CS and US over trials (Dickinson, Shanks, & Evenden, 1984; Shanks, 2007). As such, while expectancy ratings are likely to involve symbolic cognitive processes, they are also taken to reflect associatively guided learning processes. We anticipated some impact of the circle-size dimension on generalisation as a result of the magnitude change that is inherent in manipulating stimulus size. In studies employing magnitude or intensity change, it is not uncommon to note asymmetrical generalisation gradients in which responding is stronger towards the more intense end of the dimension (Hovland, 1937; Pierrel & Sherman, 1960; Razran, 1949; Scavio & Gormezano, 1974). To control for this magnitude effect, CSs were counterbalanced across participants such that a larger circle served as CS+ for some participants (CS+large condition) and a smaller circle served as CS+ for others (CS+small condition). Method Participants. Forty-six first-year psychology students participated in the study in partial fulfilment of course requirements (age M = 19.15, SD = 1.95, 27 female). Participants were randomly allocated to either the CS+large (n = 24) or CS+small (n = 22) condition by the computer software. Materials and apparatus. A circle-size dimension consisting of 9 circles of varying sizes was formed. The smallest circle (stimulus 1) was 2 cm in diameter. Each subsequent circle was 0.5 cm larger in diameter than the previous circle. As such, the largest circle (stimulus 9) was 6 cm in diameter. All circles were represented by a black outline with a white-coloured centre. Stimuli were presented on a white background, centred on the computer screen. A rotary dial was used to measure participants expectancy of shock. The dial was positioned on the side of the desk nearest to the participants preferred hand and moved approximately 180 degrees between 0 and 100 ( certain no shock to certain shock ). Electric shocks of 0.5-s duration were delivered through 2 stainless steel electrodes placed on the proximal and medial segments of the participants index finger on their non-preferred hand. Skin conductance was recorded through a constant voltage preamplifier connected to two electrodes attached to the second and third fingers on the same hand. Procedure. After signing consent forms, participants were fitted with the electrodes and taken through a work-up procedure. Each participant was asked to self-administer shocks and increase the level to a point that was uncomfortable but not painful. It was emphasised that the intensity should be high enough for anticipation of a forthcoming shock to be anxiety-provoking, but not so high as to produce a high level of anxiety for the entire test session. The shock was described as having the potential to vary in duration from 0.5 to 1 s (when in practice the shock was always 0.5 s in duration). The purpose of these instructions was to reduce any potential ceiling to responding at test as a result of the binary (shock/no shock) nature of the US. After determining their individual shock level, participants were seated in the experimental room facing a computer monitor. They were instructed that they would see various figures appear on the screen, and that they should try to learn to predict the occurrence of the shock on the basis of the figures. Participants were then trained in the use of the expectancy dial. All instructions were repeated at the start of the experiment in writing and remained on the monitor for 15 s. On all trials, circle stimuli were presented for 10 s. On shocked trials, the shock was presented during the final 0.5 s of the CS period. Inter-trial intervals ranged between 20 and 40 s, with a mean of 30 s. Within the conditioning and test phases, trial types were presented randomly with the restriction that no more than two trials of the same type would occur consecutively. On each trial, participants were asked to rate their expectancy of shock using the dial while the circle stimulus was on the screen. Expectancy ratings were recorded by the computer at a rate of 5 samples per second. Expectancy values reported here represent the average value recorded during the 2 s before the point at which shock could be delivered (i.e., from 7.5 to 9.5 s after CS onset). Skin conductance was calculated as a difference in SCL between the 10-s stimulus presentation period and the 10-s baseline period prior to stimulus onset. The SCL difference scores were then mean-corrected (Lovibond, 1992). This transformation removes individual differences in overall response magnitude, such that each participant contributes equally to group trends. During the conditioning phase, all participants received 3 CS+ and 3 CS trials. In the CS+large condition, Circle 6 served as the CS+, while Circle 4 served as CS, the stimulus which was never followed by shock. This was reversed for group CS+small, where Circle 4 was the CS+ and Circle 6 was the CS. Generalisation testing immediately followed conditioning (with no obvious separation between the phases) and involved 4 trials in total. Participants received one trial with the CS+, and one with each of stimuli 1, +1, and +3 defined in terms of distance from CS+ in the direction away from CS (positive value) or towards CS (negative value). These values represent different physical stimuli for the two counterbalancing groups. In group CS+large, stimuli 1, CS+, +1 and +3 corresponded to circles 5, 6, 7, and 9 respectively, while in group CS+small, they corresponded

5 122 Quarterly Journal of Experimental Psychology 72(2) across the stimulus dimension. It was expected that traditional unimodal gradients would be reflected in a quadratic trend, while rule-based gradients would be reflected in a linear trend. It is important to note that due to the desire to minimise the number of test trials, and hence extinction of responding, stimulus +3 was tested in the generalisation phase rather than stimulus +2. The more extreme stimulus was included because of the greater potential to see rulebased enhancement of generalisation at such a value. Data are graphed to reflect this greater perceptual step between stimulus +1 and +3 by including the untested +2 position. However, standard linear and quadratic contrast coefficients across the 4 tested stimuli were used to maintain orthogonality of contrasts. Supplementary comparisons of theoretical interest were also planned. To test for peak shift, responding at CS+ was compared to that at stimulus +1. As a test of linear or asymmetrical responding across the gradient, a second comparison between CS+ and stimulus +3 was also planned. Type I errors were controlled using a Bonferroni correction of the critical F value for these comparisons. Analyses comparing responding by post-experimental reports were conducted for sub-groups that included at least 5 participants Figure 1. Mean shock expectancy ratings (top panel) and skin conductance responses (bottom panel) over the three trials of each of CS+ and CS during conditioning in Experiment 1. to circles 5, 4, 3, and 1 respectively. Shock was presented on the CS+ trial of the test phase, to avoid directly contradicting the training contingency and to maintain overall shock expectancy. Following the completion of the Generalisation phase, participants answered a series of written questions aimed at identifying whether they were able to correctly report the conditioning contingencies and whether they inferred and used any relational rules to guide their responding during the test phase. Participants were first asked to answer a series of general questions, such as were you able to predict the occurrence of shock? If so, how? More specific questions were asked on a second page, including what was the relationship between circle size and the occurrence of the shock? Responses to these questions were assessed independently by two raters. Rater 1 first assessed all responses to identify the range of rules reported across participants and decide on a number of rule classifications. Rater 1 then classified each response into one of these categories. Rater 2 then used the classification categories to independently categorise each participant s response. Any differences in classification between the two raters were then discussed, and a final classification was agreed upon. Statistical analysis. The primary analyses of interest for the generalisation data were the linear and quadratic trends Results Participants were included in the final analyses if they showed evidence of acquisition of differential conditioning on both outcome measures. The rationale for this criterion is that it would be hard to interpret generalisation test data for participants who had not learned the training contingencies. Differential conditioning was operationalised as higher responding to CS+ over CS averaged over the final 2 trials of each type. Eleven participants were excluded from the final analyses for not meeting the acquisition criterion. The final analyses included 35 participants, 17 in the CS+large condition and 18 in the CS+small condition. Conditioning phase. There were no significant differences between CS+large and CS+small groups in responding over conditioning on either outcome measure, so the following analyses were conducted on the combined data. Greater mean expectancy ratings were seen to CS+ over CS over the course of conditioning (Figure 1, top panel). This was confirmed by a statistically significant difference between CS+ and CS when ratings were averaged over trials, F(1, 33) = , p <.05. The linear trend across trials (averaged across the two stimuli) was statistically significant, F(1, 33) = 16.67, p <.05. The development of differential ratings over trials was confirmed by a statistically significant interaction in linear trends over trials for CS+ and CS stimuli, F(1, 33) = 51.58, p <.05. Skin conductance responding was also seen to be higher to CS+ over CS averaged over the course of conditioning

6 Ahmed and Lovibond 123 expectancy ratings, with the highest mean rating noted at stimulus +1 before responding declines towards stimulus +3. The linear trend across stimuli was statistically significant, F(1, 33) = 5.29, p <.05, but the quadratic trend was not, F(1, 33) = 2.89, p >.05. Planned comparisons were used to further analyse the gradient. Mean ratings at stimulus +1 were significantly greater than at CS+, F(1, 33) = 7.53, p <.05/2, confirming the visual peak of responding at stimulus +1. No significant difference between ratings at CS+ and +3 was found, F(1, 33) = 0.76, p >.05/2. No statistical comparisons on the skin conductance data were found to be significant, either in overall trends or planned contrasts, maximum F(1, 33) = Figure 2. Mean shock expectancy ratings (top panel) and skin conductance responses (bottom panel) in the generalisation test in Experiment 1. Note that the data have been aligned such that for both counterbalancing groups stimulus +3 represents the end of the circle size dimension in the direction away from CS. (Figure 1, bottom panel). This was confirmed statistically, F(1, 33) = 10.69, p <.05. There was no significant linear trend averaged over CS+ and CS across trials, F(1, 33) = 2.52, p >.05. Although differentiation between CS+ and CS emerged across trials, the interaction in the linear trends for CS+ and CS across trials was also not significant, F(1, 33) = 1.09, p <.05. The conditioning analyses were repeated using all participants to confirm acquisition without the circularity arising from excluding non-learners. These analyses yielded generally smaller F ratios but the same pattern of significant and non-significant effects as described above. Importantly, the main effects for CS+ vs CS remained significant for both measures, F(1, 44) = 79.9, p <.05 for expectancy and F(1, 44) = 10.10, p <.05 for skin conductance. Generalisation phase. Again, data were averaged across CS+large and CS+small groups since no significant statistical differences were noted at test. Figure 2 shows an apparent peak-shifted generalisation gradient on Post-experimental questions. Inter-rater agreement for the classification of the post-experimental responses was assessed using Cohen s kappa. There was very high agreement (Landis & Koch, 1977) between the raters, k =.881, p <.005. Six of the 35 participants included in the final analyses could not report the correct relationship between circle size and shock from the conditioning phase (either reporting no rule or one relating to factors other than circle size). The 29 participants who had been able to report the correct conditioning contingency reported using a range of rules in determining their ratings at test. Five participants reported adopting a similarity rule, whereby they expected that circles close in size to the trained value would continue to predict shock (Similarity sub-group). The majority of participants, 24 in total, reported inferring a linear causal rule, where they expected that increasingly large/ small circles would lead to shock (Linear sub-group). However, at least 11 of these participants reported having considered alternative rules throughout testing when the linear rule did not appear to hold. These included assuming that the size-shock rule had swapped, that a specific size had been predictive of shock, or reporting that things seemed random or uncertain. The generalisation test data are graphed according to the verbal reports of participants in Figure 3. For the expectancy measure, the individual gradients for group Linear and Similarity differed primarily at stimulus +3. While the Linear sub-group produced a relatively flat gradient in which responding did not appear to drop at stimulus +3, the Similarity sub-group produced a gradient with a marked drop in responding from stimulus +1 to +3. The interaction in linear trends between the rule sub-groups was not statistically significant, F(1, 27) = 2.38, p >.05. However, the interaction in quadratic trends across subgroups reached significance, F(1, 27) = 5.35, p <.05. Analysis of data from the Linear sub-group separately indicated that the gradient was significantly linear, F(1, 22) = 5.59, p <.05. In the Similarity sub-group, quadratic trend was significant, F(1, 4) =8.74, p <.05, as was the comparison between CS+ and stimulus +3, F(1, 4) = 7.09, p <.05/2. Although expectancy ratings to stimulus +1

7 124 Quarterly Journal of Experimental Psychology 72(2) Figure 3. Mean shock expectancy ratings (top panel) and skin conductance responses (bottom panel) in the generalisation test for the Linear (n = 24) and Similarity (n = 5) rule subgroups in Experiment 1. were higher than to CS+, indicative of peak shift within this subgroup, this comparison did not reach significance, F(1, 4) = 4.95, p =.09. The skin conductance measure produced gradients that were broadly consistent with shock expectancy, again with apparent peak shift in the Similarity group. However, no interactions between rule subgroups approached significance, largest F(1, 27) = 2.51, p >.05. There were no also no significant effects in the analyses of the individual rule subgroups, largest F(1, 4) = 5.64 for linear trend in the Similarity subgroup, p >.05/2. Discussion The overall generalisation gradient obtained in Experiment 1 was similar to the peak-shifted gradients sometimes reported in the animal generalisation literature. The peak in expectancy ratings in the overall group data was displaced to stimulus +1, and responding reduced further along the dimension at stimulus +3. The unimodal gradient is consistent with similarity-based accounts of generalisation, which predict that generalisation will lessen as novel stimuli become increasingly unlike the CS+ (Blough, 1975; McLaren & Mackintosh, 2002). The results are also consistent with the idea that inhibitory properties associated with the CS displace peak responding further along the test dimension beyond CS+ (McLaren & Mackintosh, 2002; Spence, 1937). To our knowledge, this is one of the few demonstrations of peak shift in human fear generalisation. Peak shift in humans has previously been observed mainly in categorisation studies (Doll & Thomas, 1967; Galizio & Baron, 1979; Wills & Mackintosh, 1998), in which participants are asked to categorise stimuli as belonging to one of two categories. One exception is a study by Struyf, Iberico, and Vervliet (2014) in which peak shift was demonstrated along a circle-size dimension in a predictive learning task. While the overall peak-shifted and unimodal gradient observed in Experiment 1 is consistent with similaritybased generalisation, stimulus similarity cannot easily account for the gradients noted in the Linear rule subgroup. Unlike the Similarity sub-group, which showed a marked decline in responding at the extreme end of the test dimension consistent with similarity-based generalisation, the Linear sub-group showed more sustained response levels at stimulus +3. In these participants, the increasing perceptual distinctiveness of stimulus +3 did not appear to produce a substantial generalisation decrement as similarity-based accounts would predict. Instead, it appears more likely that participants in the Linear sub-group were responding on the basis of a rule of the kind smaller/ larger circles predict the shock, such that even the more extreme stimulus at test (+3) led to an expectation that the shock will occur. Although the small sample sizes in the two rule sub-groups precluded strong statistical conclusions about the distinctiveness of the gradients, the significant linear trend in the Linear sub-group does suggest some influence of rule-based processing. From this perspective, it is interesting to consider why the Linear sub-group did not show a more strongly linear gradient with responding peaking more obviously at stimulus +3. One possible account of this is that participants were reluctant to extrapolate the linear rule to the extreme test stimulus (+3), given how much larger/smaller the stimulus was compared to all of the others experienced by participants. A second and related possibility is that participants became uncertain about their inferred rule when tested under extinction. Several participants reported that they questioned the rule they had inferred from training when they did not receive a shock after a given stimulus at test. As a result, many participants reported modifying their response strategy throughout the test phase, testing alternative rules. This uncertainty hypothesis sits well with the obtained results, particularly for participants reporting a linear causal rule. If participants feel unsure about a linear rule regarding circle size and the occurrence of shock, this is likely to be most apparent at an extreme value (stimulus +3), since a greater commitment to the rule is required at that point. Experiment 2 While the overall generalisation gradient observed in Experiment 1 was generally consistent with similaritybased responding, there was some evidence for rule-based

8 Ahmed and Lovibond 125 responding. Post-experimental reports suggested that participants experienced uncertainty in applying rule learning consistently during the test phase, most likely as a result of extinction. Fear learning in particular is known to extinguish rapidly (Bouton, 2004), which complicates the assessment of generalisation of fear learning. In anticipation of this rapid extinction, Experiment 1 limited testing to the essential 4 stimulus values needed to plot a gradient of generalisation along the circle-size dimension. Furthermore, the CS+ trial was reinforced in an attempt to slow the rate of extinction. Nonetheless, verbal reports of participants indicated that the move to the test phase was quickly noticed, and the absence of the shock on certain trials led them to consider alternative causal rules for the prediction of the shock. The possible impact of testing generalisation under conditions of extinction has been previously raised in the literature. Mackintosh (1974) argued that extinction testing has a direct impact on the gradient obtained by introducing inhibitory properties to the test stimuli that are not followed by the outcome. Empirical evidence for the possible impact of extinction testing on gradient shape can be found when comparing studies employing within- and between-subjects testing phases. In the former, each participant is exposed to the full range of test stimuli under conditions of extinction, while in the latter each participant is tested on a single value at test. These studies suggest that gradients are flatter with strong generalisation across the whole dimension when tested between-subjects, and more peaked and indicative of generalisation decrement when tested within-subjects (Grant & Schiller, 1953; Wickens, Schroder, & Snide, 1954). A more recent study by Vervliet, Iberico, Vervoort, and Baeyens (2011) demonstrated generally lower response levels at test using within-subjects rather than between-subjects testing of the dimension. In Experiment 2, we sought to minimise any impact of extinction during test. We also explored whether extinction testing was responsible for the rule uncertainty reported in Experiment 1. To achieve this, Experiment 2 implemented a no-feedback test phase in which the shock electrodes were removed but participants were asked to continue to make hypothetical expectancy ratings. If extinction testing and related rule uncertainty contributed to the results obtained in Experiment 1, no-feedback testing should produce a gradient which is more reflective of generalisation processes and less reflective of new learning at test. Although removal of the shock electrodes meant that meaningful skin conductance data could not be obtained in the generalisation test, it was hoped that clearer patterns would be observed on the expectancy measure, which was the more sensitive of the two measures in Experiment 1. Method Experiment 2 differed from Experiment 1 in the following respects. Participants. Forty-three first year psychology students participated in the study in partial fulfilment of course requirements (age M = 18.84, SD = 3.27, 25 female). Participants were randomly allocated to either the CS+large (n = 23) or CS+small (n = 20) condition by the computer software. Procedure. Prior to the generalisation phase, the experimenter entered the experimental room, informed participants that they would no longer receive any shock, and removed the shock electrodes from the participants fingers. Participants were asked to continue to make their expectancy ratings, assuming hypothetically that it was still possible for them to receive a shock. As such, only expectancy ratings (and not skin conductance responding) were recorded during the generalisation phase. Results Seven participants were excluded from the final analyses for not meeting the acquisition criterion. The final analyses included 36 participants, 21 in the CS+large condition and 15 in the CS+small condition. Conditioning phase. There were no significant differences between CS+large and CS+small groups in responding over conditioning on the expectancy measure, so the following analyses were conducted on the combined data. Greater mean ratings were seen to CS+ over CS over the course of conditioning (Figure 4, top panel). This was confirmed by a statistically significant difference when ratings were averaged over trials, F(1, 34) = , p <.05. The linear trend across trials (averaged across the two stimuli) was not statistically significant, F(1, 34) = 0.16, p >.05. However, the development of differential ratings over trials was confirmed by a statistically significant interaction in linear trends over trials for CS+ and CS stimuli, F(1, 34) = 49.59, p <.05. There were also no significant differences between CS+large and CS+small groups on the skin conductance measure, so the following analyses were conducted on the combined data. Greater responding was seen to CS+ over CS across the conditioning trials (Figure 4, bottom panel). This was confirmed by a statistically significant difference between CS+ and CS averaged over trials, F(1, 34) = 13.86, p <.05. The linear trend across trials (averaged across the two stimuli) was not statistically significant, F(1, 34)= 0.16, p >.05, and it did not interact with the comparison between CS+ and CS, F(1, 34) = 2.05, p >.05. As in Experiment 1, we repeated the conditioning analyses using all participants. These analyses generated the same pattern of significant and non-significant effects as described above. Generalisation phase. In this experiment, the CS+large and CS+small groups showed somewhat different gradients,

9 126 Quarterly Journal of Experimental Psychology 72(2) Figure 4. Mean shock expectancy ratings (top panel) and skin conductance responses (bottom panel) over the three trials of each of CS+ and CS during conditioning in Experiment 2. Figure 5. Mean shock expectancy ratings in the generalisation test in Experiment 1, graphed separately for CS+Large (n = 21) and CS+Small (n = 15) groups. and there was a trend for higher expectancy ratings in the CS+large group, F(1, 34) = 4.09, p =.051. Therefore, analyses were conducted on both the overall data and separately for each counterbalancing group. As shown in Figure 5, a predominantly linear gradient was observed in the CS+large group, with mean expectancy ratings increasing beyond CS+ and peaking at stimulus +3. The gradient for the CS+small group was more in keeping with that seen in Experiment 1, with a peak in mean ratings at stimulus +1 and a drop in responding towards stimulus +3. The overall linear trend across stimuli, averaged over the counterbalancing groups, was statistically significant, F(1, 34) = 10.75, p <.05. The interaction between linear trend and groups was not significant, F(1, 34) = 1.32, p >.05. Tested separately, linear trend was found to be significant in the CS+large group, F(1, 20) = 12.18, p <.05, but not in the CS+small group, F(1, 14) = 1.85, p >.05. The overall quadratic trend across stimuli, averaged over groups, was not statistically significant, F(1, 34) = 2.51, p >.05, and neither was the interaction between quadratic trend and groups, F(1, 34) = 1.99, p >.05. This was also the case in group CS+large tested separately, F(1, 20) = 0.02, p >.05. However, in group CS+small, the quadratic gradient at test approached significance, F(1, 14) = 4.06, p =.064. An overall comparison between ratings at CS+ and stimulus +1 showed that ratings were significantly higher at stimulus +1, F(1, 34) = 7.66, p <.05/2. The interaction between this comparison and counterbalancing group was not significant, F(1, 34) = 0.44, p >.05/2. However, this difference was significant in group CS+large, F(1, 20) = 7.78, p <.05/2, but not in group CS+small, F(1, 14) = 1.68, p >.05/2. The difference in ratings at stimulus +3 over CS+ averaged across both groups was not found to be significant, F(1, 34) = 2.92, p >.05/2. The interaction between this comparison and groups again failed to reach significance, F(1, 34) = 3.82, p >.05/2. However, this comparison was significant in group CS+large, F(1, 20) = 8.20, p<.05/2, and not significant in group CS+small, F(1, 14) = 0.03, p>.05/2. Taken together, these analyses indicate a peak-shifted overall gradient, similar to that observed in Experiment 1, but with somewhat more evidence of linearity, particularly in the CS+large counterbalancing group. Post-experimental questions. Inter-rater agreement for the classification of the post-experimental responses was assessed using Cohen s kappa. There was almost perfect agreement (Landis & Koch, 1977) between the raters, k =.920, p <.005. Of the 36 participants, 7 (4 from group CS+large, 3 from group CS+small) did not report any relationship between circle size and shock. Eight participants (4 from each group) reported a linear rule relating circle size and shock, but explicitly reported being uncertain about the causal rule at test, particularly when faced with stimulus +3 (Uncertain sub-group). The remaining 21 participants reported either a linear (Linear sub-group, n = 16) or similarity (Similarity sub-group, n = 5) rule. Most linear-rule reporters, 12 of the total 16, were from group CS+large. The 5 similarity-rule reporters included 4 from group CS+small. A chi-square analysis indicated that the proportion of participants who reported a linear rule relative to a similarity rule was higher in group CS+large (12:1) than in group CS+small (4:4), χ 2 (1) = 4.89, p <.05. The expectancy gradients are presented separately for the three rule sub-groups in Figure 6. Visually, the trends are consistent with the rules reported by participants. The

10 Ahmed and Lovibond 127 Figure 6. Mean shock expectancy ratings in the generalisation test for the Linear (n = 16), Similarity (n = 5), and Uncertain (n = 8) rule sub-groups in Experiment 2. gradient for sub-group Uncertain was relatively flat, albeit with a decrease in responding at stimulus +3 (where they had reported being most uncertain). Of particular interest was the comparison between the Linear and Similarity sub-groups. The Linear sub-group produced a steeply linear gradient consistent with the greater expectancy of shock as circles increased/decreased in size. By contrast, the Similarity sub-group produced a unimodal gradient, consistent with lower expectancy of shock as stimuli become increasingly unlike CS+. The linear trend across stimuli was significantly different across the Linear and Similarity sub-groups, F(1, 19)= 7.05, p <.05. In the Linear sub-group, there was a highly significant linear trend across stimuli, F(1, 15) = 32.14, p <.05. In the Similarity sub-group, there was no significant linear trend, F(1, 4) = 0.00, p >.05, but the gradient approached significance for a quadratic trend, F(1, 4) = 4.70, p >.05. The comparison between CS+ and stimulus +3 was significantly different across the Linear and Similarity subgroups, F(1, 19) = 16.82, p <.05/2. In group Linear, mean responding at stimulus +1 was significantly higher than at CS+, F(1, 15) = 14.08, p <.05/2, as was responding at stimulus +3 when compared to responding at CS+, F(1, 15) = 28.44, p <.05/2. In group Similarity, neither of these comparisons was significant, F(1, 4) = 0.05, p >.05/2 and F(1, 4) = 3.17, p >.05/2, respectively. Discussion The overall generalisation gradient obtained in this experiment differed from Experiment 1, particularly for group CS+large. While in Experiment 1 there was a noticeable drop in responding at stimulus +3, a more unambiguously linear gradient was observed across test stimuli in group CS+large. The difference between the counterbalancing groups in overall gradient shape may be the result of several factors, including the potential influence of magnitude properties which might support an intuitive inference that larger circles are more causal (contradictory to the contingencies in group CS+small). The division of participants by post-experimental reports revealed that many more participants in the CS+large condition reported a linear over a similarity causal rule (12 to 1). This is in contrast with the CS+small condition where participants were equally likely to report a similarity or a linear rule (4 of each). This greater representation of certain rule sub-groups across counterbalancing conditions is consistent with the data in which the CS+large condition produced a linear gradient, while the CS+small condition produced a more unimodal gradient with a decline in responding at the extreme stimulus value. Compared to Experiment 1, there was a greater degree of correspondence between verbal reports and generalisation gradients in the present experiment. Most notably, the sub-group which had reported a linear causal rule produced a gradient that was clearly linear, in which responding increased significantly from stimulus +1 to stimulus +3. This pattern of results supports the hypothesis that testing under extinction in the previous experiment may have had an impact on the overall gradient obtained. Removing the impact of feedback during testing produced a pattern more in keeping with the influence of verbalisable rules in guiding generalisation responding. This manipulation may have influenced ratings by reducing the uncertainty and rule-testing which participants reported engaging in during extinction testing in Experiment 1. General discussion The present experiments explored the gradients of generalisation obtained following fear conditioning along a simple circle-size stimulus dimension. Experiment 1 produced an overall gradient similar to the peak-shifted unimodal gradients often observed in non-human animal investigations (Purtle, 1973), and in some previous human investigations (e.g., Doll & Thomas, 1967; Dunsmoor & LaBar, 2013; Struyf et al., 2014; Wills & Mackintosh, 1998). The overall gradient was consistent with a reduction in generalisation as stimuli became increasingly distinct from the CS+, with a bias in peak responding to the side away from CS. In this sense, the results appeared to be highly compatible with models of generalisation based on physical similarity, such as conditioning to overlapping stimulus elements (Blough, 1975) or spread of positive and negative associative strength (Spence, 1937). However, there are several aspects of the current results that are challenging for any theory that involves a single fixed generalisation process. Both experiments demonstrated that an overall generalisation gradient based on group mean data can be de-composed into several quite distinctive gradients. It is hard to see how a fixed process such as one based on physical similarity could account for multiple gradients, especially linear gradients. As noted earlier, one possible account of these data would be a dualsystem model in which unimodal gradients are attributed to an automatic, similarity-based system and all other