Taxonomic relations evoke more fear than thematic relations after fear conditioning: An EEG study

Journal Pre-proofs Taxonomic relations evoke more fear than thematic relations after fear conditioning: An EEG study Yi Lei, Ying Mei, Yuqian Dai, Weiwei Peng PII: DOI: Reference:

S1074-7427(19)30166-2 https://doi.org/10.1016/j.nlm.2019.107099 YNLME 107099

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Neurobiology of Learning and Memory

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11 August 2018 14 August 2019 9 October 2019

Please cite this article as: Lei, Y., Mei, Y., Dai, Y., Peng, W., Taxonomic relations evoke more fear than thematic relations after fear conditioning: An EEG study, Neurobiology of Learning and Memory (2019), doi: https:// doi.org/10.1016/j.nlm.2019.107099

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Taxonomic relations evoke more fear than thematic relations after fear conditioning: an EEG study

Yi Leiabc, Ying Meiabc, Yuqian Daiabc, Weiwei Pengabc*

a

Research Center for Brain Function and Psychological Science, Shenzhen University, Shenzhen,

China b

Shenzhen Key Laboratory of Emotion and Social Cognitive Science, Shenzhen University,

Shenzhen, China c Shenzhen

Institute of Neuroscience, China

* Corresponding author at: Research Center for Brain Function and Psychological Science, Shenzhen University, Shenzhen, China E-mail address: [email protected] 1

ABSTRACT When fear is generalized, knowledge based on concepts is also retrieved. Concepts have two very different relations: thematic relations based on the co-occurrence of events or scenarios, and taxonomic relations based on similarity or shared features. However, it remains unclear whether thematic and taxonomic relationships differentially affect fear generalization. To clarify the underlying cognitive mechanisms of these relations, the current study combined the classical fear conditioning procedure with electroencephalography (EEG). Forty participants were conditioned to a neutral word by pairing the presentation of the word with an unpleasant electrical pulse. A different stimulus was not paired with the electrical pulse. Next, during generalization testing, thematically related or taxonomic-related words were presented. Behavioral responses (shock expectancy and response time) and brain responses (event-related potentials [ERP] and oscillation activity) were recorded. Behavioral results showed that taxonomic relations initiated higher shock expectancy compared with thematic relations, and that conceptual relations did not affect response times. Taxonomic relations induced larger P2 components than thematic relations, and danger generalization stimuli initiated smaller P600 components than safe generalization stimuli. In addition, the magnitudes of alpha and beta oscillations were larger for danger generalization stimuli. These results suggested that taxonomic stimuli generalize broader responses compared with thematic relations after fear conditioning. Therefore, we report a possible new physiological marker for the presentation of fear generalization. These findings aid our understanding of fear generalization at the concept level and have clinical implications for the cognitive treatment of anxiety disorders. Keywords: taxonomic relations, thematic relations, fear generalization, event-related potentials, oscillation activity.

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1. Introduction Generalization of fear memory that involves generalization with related stimuli is a crucial adaptive process for survival. The mechanisms underlying fear generalization in humans have been interpreted as associative learning and inductive reasoning processes driven by within-category similarities and cognitive processing (Dunsmoor & Murphy, 2014; Lee et al., 2019; Wong & Lovibond, 2017). Intuitively, human fear generalization can be associatively facilitated by perceptual and conceptual similarities (Bennett, Vervoort, Boddez, Hermans, & Baeyens, 2015; Dunsmoor, White & Labar, 2011; Lissek et al., 2008; 2010; 2014). On the other hand, higher-level controlled cognitive process such as fear-related inferential reasoning are promoted by the operation of rules (Lee, Hayes, & Lovibond, 2018; Ahmed & Lovibond, 2018; Wong & Lovibond, 2017). However, only few studies have measured the effects of either non-similarity or non-rule based stimuli on fear generalization--the conceptual relation based on co-occurrence of events or scenarios. For example, a passenger who survived a serious car accident may possess extended fear-related responses to traffic lights, highways, and car keys etc. Determining the effects of similarity- and co-occurrence-based knowledge on fear generalization may contribute to an understanding of the mechanisms underlying variations in fear memory and may improve the therapeutic strategies for anxiety disorders. Semantic and conceptual knowledge has been demonstrated to be involved in fear generalization in humans (Boyle, Roche, Dymond & Hermans, 2015; Dunsmoor, Martin & LaBar, 2012; Dunsmoor & Murphy, 2014; Lei et al, 2018). In a semantic fear conditioning paradigm, a neutral word was served as a conditioning stimulus (CS+, e.g., broth) that is repeatedly paired with an aversive unconditioned stimulus (US, e.g., an electric shock) to elicit a conditioned response (CR; e.g., fear) while another word was never (CS-, e.g., assist) paired with US. During a generalization test, words that were semantically related to CS+ (GS+, e.g., soup) or CS- (GS-, e.g., help) were presented, and differential responses (e.g., expectancy ratings) to GS+ and GS- were considered to be represent fear generalization. These studies have highlighted the important role of semantic knowledge (e.g., concept and category) and higher-order cognitive processes (e.g., inductive reasoning) in fear generalization. It is proposed that semantic-based fear generalization is the consequence of the inferences about potential threat stimuli beyond the perceptual dimension due to semantic knowledge expansion (Dymond et al., 2015; Dunsmoor & Murphy, 2015). For 3

instance, posttraumatic stress disorder induced by an experience of being bitten by a ferocious dog, often can be triggered by other canids, dog leash, or a spoken phrase about a dog, due to the knowledge of the conceptual relationships between these instances. There are two different relations in conceptual knowledge: taxonomic and thematic. Taxonomic relation is a similarity-based relationship that involves features that are not necessarily perceptible (e.g., birds-dogs), whereas objects in the thematic relation frequently appear together in the same event or scenario (e.g., birds-bird’s nest) (Mirman et al., 2017). Taxonomic relations are typically organized at three hierarchical levels: subordinate (e.g., sparrow), basic (e.g., bird), and superordinate (e.g., animal). It is assumed that exemplars that pertain to a basic category tend to share more features (Morris & Murphy, 1990; Markman & Wisniewski, 1997). Objects in the thematic relation share few features but they complement each other for specific events or scenarios (e.g., chalk and chalkboards are more easily perceived for the teaching scenario than chalk and pens). In fact, taxonomic and thematic relations are two functionally distinct entities, but complementary conceptual organization modes in long-term semantic memory (Mirman, Landrigan, & Britt, 2017). The distinction between taxonomic and thematic relations is documented in behavioral (Borghi & Caramelli, 2003; Jouravlev & McRae, 2015; Lin & Murphy, 2001; Masuda & Nisbett, 2001; Mirman & Graziano, 2012 ), computational (Maki & Buchanan, 2008; Andrews, Vigliocco, & Vinson, 2009), and neuroscience (Kalénine et al., 2009; Kalénine & Buxbaum, 2016; Sachs, Weis, Huber, & Kircher, 2008) research. It is unknown how taxonomic and thematic relations may modulate the representations of related concepts via fearful experiences, and which conceptual relation is predominant in processing fear generalization. Therefore, in this study, we employed a semantic fear conditioning paradigm to investigate the interaction between conceptual relations and fear generalization. In this paradigm, participants were conditioned to a word (e.g., pencil; CS+) by being paired with an aversive stimulus (e.g., electric shock; US) while another word (e.g., beer; CS-) was explicitly unreinforced. Thereafter, participants performed the generalization test, where they were presented with either thematic words (e.g. book, GS+; cup, GS-) or taxonomical words (e.g., pen, GS+; wine, GS-). The effect of the fearful experience on conceptual inductive reasoning was examined by comparing behavioral responses (response time and shock expectancy) and brain activities to the stimuli conceptually related to the CS+ (thematic GS+ and taxonomic GS+) versus those to the stimuli 4

related to the CS- (thematic GS- and taxonomic GS-). In addition, the effect of the conceptual relation on fear generalization was measured by comparing the effects of the thematic (GS+, GS-) conditions versus the taxonomic (GS+, GS-) conditions on behavioral responses and brain activities. Event-related potential (ERP) and behavioral research on the processing of thematic and taxonomic based inductive reasoning has provided insights into the electrophysiological correlates of conceptual relation-based generalization. Liu and colleagues (2018) conducted a study where participants performed an inductive reasoning task in the thematic or the taxonomic context (e.g., panda has property X, so bamboo/bear has X). Their ERP results showed that in the P2 time window, participants distinguish relation types before integrating the semantic information of the premise and the conclusion. However, the influence of fearful experience on inductive reasoning and the role of conceptual relation in fear generalization has not been sufficiently investigated. The present study aimed to investigate the electrophysiological correlates of taxonomic and thematic based fear generalization by using scalp-recorded EEG. Unlike slow-response acquisition (e.g., Skin Conductance Response; Bach, Friston, & Dolan, 2013) or low temporal resolution (e.g., fMRI; Kim & Jung, 2006), the electro-cortical technique allows for the assessment of time-locked cortical (but not subcortical) changes to conditioned and unconditioned stimuli (Miskovic & Keil, 2012; Lonsdorf et. al., 2017). Neural activities associated with fear conditioning can be indexed by the components of the ERP waveform and the time-frequency-distribution (TFD) magnitude (Miskovic & Keil, 2012; Chien et al., 2017). Event related desynchronisation (ERD) is a reduction in the alpha amplitude after stimuli onset when compared to a baseline level (Pfurtscheller, 1977). Occipital alpha-ERD (8-13Hz) is regarded as an ideal index for monitoring experience in fear conditioning (Miskovic & Keil, 2012; Harris, 2005). Typically, the suppression of the alpha rhythm occurs after the US onset (Miltner et al., 1999; Putney, 1973), and CS+ produces greater alpha suppression than CS- (Harris, 2005). With regard to the time-domain, previous studies demonstrate that CS+ modulation during fear conditioning is related to early ERP correlations (N1, P2) (Flor et al., 2002; Hugdahl & Nordby, 1991; Pizzagalli, Greishchar, & Davison, 2003; Ugland, Dyson & Field, 2013). Based on previous research on fear conditioning and conceptual relations, we predicted that alpha-ERD, N1, P2, N4, would respond to manipulations in the concept-based fear generalization.

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2. Materials and Methods 2.1 Participants Forty right-handed healthy adults (19 women, age range: 18–26 years, mean ± standard error (SE): 20.90 ± 2.26 years) were recruited from Shenzhen University and monetarily compensated for their participation in the study. All participants had normal or corrected-to-normal vision and provided written informed consent before their participation. The investigation was approved by the Medicine Ethics Committee of Shenzhen University and was conducted in accordance with the Declaration of Helsinki. 2.2 Stimulus 2.2.1 Generation of thematic- and taxonomic-related word stimuli To develop the thematic- and taxonomic-related word stimuli for the experiment, another group of 40 participants were recruited from Shenzhen University to complete an online free association task (Jouravlev & McRae, 2016). In this task, the participants first read the definitions of thematic and taxonomic relationships, and then they were given two common objects “pencil” and “beer” and they had to generate nouns of the objects, six thematically-related and six taxonomically-related to each cue word. Therefore, a 40 × 12 word matrix was formed (i.e., 12 columns, 6 for each word, and 40 rows, one for each participant. Thereafter, answers inappropriate on the following basis were excluded: answers that were emotional, non-noun, and not in the required relation. For example, “drunkard”, “drinking,” or “wine” were excluded from the responses to the thematic relation to “beer”. Overlapping responses were then trimmed by retaining the most frequent response. For example, when asked to generate nouns thematically related to “pencil,” twelve participants responded with “sketch,” four “penciling”, and two “painting.” The less frequent responses "penciling" and "painting" were replaced by "sketch". Subsequently, the frequency of a response for each cue word was computed by counting the number of people who gave the same response. The frequencies of a response to a cue word can reflect their strength of semantic connection (McRae, Cree, Seidenberg, & McNorgan, 2005; Nelson et al., 2004). Finally, the seven most frequent responses of each relation to each cue word were selected. Table 1. Results of selected words and the number of responses for the free association task. The words are arranged according to the number of responses. 6

------insert Table 1 here----2.2.2 Fear conditioning stimulus The US was an electric pulse of 100-ms duration, which was generated using a multichannel stimulator (SXC-4A, Sanxia Technique Inc., China). The US was delivered to the subjects’ left wrist through a pair of Ag/AgCl surface electrodes (6 cm distance between electrodes). The intensity of the US was individually calibrated using an ascending staircase procedure, which was set at a level deemed “highly annoying but not painful” (Dunsmoor, Martin, & LaBar, 2012). The ascending staircase procedure started with a low voltage setting near a perceptual threshold and was increased with each shock delivered until the participant requested the procedure be stopped. The average intensity of the US was 5.4 mA ± 1.34 mA.

2.3 Experimental procedure 2.3.1 Participant preparation The participants were invited to the Research Centre of Brain Function and Psychological Science at Shenzhen University where they underwent procedures involving an EEG set-up and experimental instructions. The experiment was conducted in a bright, quiet room with an average temperature of 23oC. All stimuli were black text of font size 240 on a grey background. The stimuli were displayed on a LED computer screen (23.8 inches, Dell, Model: P2314Ht, China) placed 80 cm in front of the subject. The presentation of stimuli and recording of behavioral measures, including subjects’ expectancy ratings and response times (RTs), were controlled using E-Prime 2.0 (Psychology Software Tools). 2.3.2 Fear conditioning procedure The experiment consisted of two sessions (fear conditioning and generalization) as displayed in Fig. 1. In the fear conditioning session, 80 CS were delivered to the participants, with 40 CS defined as CS+, which were paired with the delivery of US, and the other 40 CS defined as CS-, which were never paired with US. The reinforcement rate was 65% (i.e., 26 CS were paired with the US). The order of CS presentation across trials was pseudo-randomized, where there were no more than two CS of the same type presented consecutively. At the start, a fixation cross was presented for 0.8–1.2 s duration followed by a grey blank screen that was presented for 0.8–1.2 s. The CS (e.g., pencil or beer) was then presented for a maximal duration of 5 s. The participants 7

were instructed to judge the likelihood of US delivery by pressing 1 (very unlikely), 2 (moderately likely), or 3 (very likely) on the keyboard with their right hand as quickly as possible. This was done to create an association between the US and the CS. The US expectancy was the likelihood that either CS was paired with the US. The score ranged from 1 for very unlikely to 3 for very likely. The CS disappeared once the judgement was made by the participants. The inter-trail-interval (ITI) was 4–6 s. The definition of CS+ and CS- was counterbalanced across subjects, with pencil defined as the CS+, and beer defined as the CS- for half of the participants (n=20). The order of the definition was reversed for the other half of the participants. 2.3.3 Fear generalization test After a 90-s break, fear generalization was conducted. GS+ and GS- were defined as the thematic- or taxonomic-related CS+ and CS-, respectively. As a result, there were four types of word stimuli, i.e., thematic GS+, taxonomic GS+, thematic GS-, and taxonomic GS-. There were 210 trials in total, with 49 trials for each stimulus type (196 trials in total), which were not followed by the US, as well as seven CS+ (followed by the US, 71% reinforcement rate) and seven CS-, which were not followed by the US trials. The CS+ and CS- trials prevented fear extinction, and these trials were not included in the analysis. Like the fear conditioning session, a fixation cross and short grey blank screen were presented at the start. The word stimuli (thematic GS+, taxonomic GS+, thematic GS-, taxonomic GS-, CS+, or CS-) were then presented. The participants were requested to judge the likelihood of US delivery with an ITI of 2.5–3.5 s . ------insert Figure 1 here----2.4 EEG recording and pre-processing EEG signals were collected using the 64-channel Brain Products system (Brain Products GmbH, Munich, Germany) according to the extended 10-20 system. A ground electrode was placed on the frontal midline. A vertical electrooculogram (EOG) was recorded from an electrode placed approximately 10 mm below the right eye. The difference in activity between the right and left orbital rims was used as the horizontal EOG. The EEG signals were amplified and digitized at a sampling rate of 500 Hz, within the frequency range of 0.01–100 Hz, and all channel impedances were kept below 10 kΩ. 2.5 EEG data analysis 2.5.1 Pre-processing 8

The offline analysis of the EEG data was performed with MATLAB using EEGLAB (Delorme & Makeig, 2004). The EEG data were band-pass filtered between 0.1 and 30 Hz. EEG epochs were segmented using a 1500-ms time window (prestimulus -500 ms and post-stimulus 1000 ms) and baseline corrected using the pre-stimulus time interval. Independent component analysis (ICA) was subsequently performed to correct components associated with eye movements and eye blinks. The ICA-corrected EEG data were re-referenced to the average of the left and right mastoids. 2.5.2 Time-domain analysis For each subject, epochs corresponding to different stimulation conditions (i.e., thematic GS+, thematic GS-, taxonomic GS+, and taxonomic GS-) were individually averaged (49 trials per condition), thus yielding four time-locked single-subject average waveforms. Group-level waveforms were obtained by averaging single-subject average waveforms for each stimulation condition. According to previous studies (Flor et al., 2002; Pizzagalli, Greishchar, & Davison, 2003; Ugland, Dyson & Field, 2013;Cisler & Koster, 2010 ), N1, P2, N4, P6 were respectively measured at electrodes displaying maximal responses based on the respective scalp topographies, and for each ERP component, the mean amplitudes within 20 ms of peak latency (Hu, Peng, Valentini, Zhang, & Hu, 2013; Zhao et al., 2017) were computed from each single-subject average waveform. That is, at (O1+OZ+O2) for N1 (130-150 ms), at (F1+Fz+F2) for P2(210-230 ms), at (F1+Fz+F2) for N4 (300400 ms), and at (C1+Cz+C2) for P6(500-600ms). Spline interpolation was used to compute grouplevel scalp topographies for each ERP component. 2.5.2. Time-frequency-domain analysis The time frequency analysis was performed to explore both phase-locked and non-phaselocked brain responses elicited by GS stimuli. A windowed Fourier transform (WFT) with a fixed 250-ms Hanning window was used to obtain a time-frequency distribution (TFD) of the EEG time course. For each time frequency, there was a complex time-frequency to estimate F(t,f) at each point starting from 500 to 1500 ms (in a 2 ms interval) in latency, and from 1 to 30 Hz (in a 1-Hz interval) in frequency. The spectrogramP (t, f) = |F(t, f)|2 represents the joint function of the power spectral density as the time and frequency at each time frequency point (Peng et al., 2017). The obtained spectrogram was baseline-corrected using the percentage approach, (i.e., ER(t,f)% = [F(t,f)-R(f)]/R(f). According to previous studies (Chien et al., 2017 ； Harris, 2005; Hu, Peng, Valentini, Zhang, & Hu, 2013), three time-frequency regions of interest (ROIs) were defined, 9

including “ERP”: 50–400 ms in latency , 1–10 Hz in frequency; “α-ERD” (alpha event-related desynchronization): 200–900 ms in latency, 8–14 Hz in frequency; and “β-ERD”: 400–900 ms in latency, 15–25 Hz in frequency. Their magnitudes were assessed using the mean response within their respective time-frequency ROIs at electrodes displaying the maximal response. That is, the “ERP” were measured at frontal electrodes (F1, Fz, F2), the “α-ERD” were measured at left parietal electrodes (P1, P3, PO3) and right parietal electrodes (P2, P4, PO4), the “β-ERD” were measured at frontal electrodes (F1, Fz, F2). Mean magnitudes of time-frequency points within ROI were computed for each subject and condition. 2.6. Statistical analysis For the behavioral measures during the fear conditioning session, expectancy to US and RTs to CS were compared between CS+ and CS- conditions, respectively, using paired sample t-tests. For the behavioral and electrophysiological measures during the fear generalization session, twoway repeated measures analysis of variance (ANOVA) with variables “GSs” (GS+ vs. GS-) and “concept relations” (thematic vs. taxonomic) were performed on behavioral ratings (RT and expectancy to GSs), electrophysiological responses (N1, P2, N4, and P6 in the time-domain; ERP; and α-ERD, and β-ERD magnitudes in the time-frequency domain). When their interaction was significant, post hoc pairwise comparisons were performed.

3. Results 3.1 Behavioral results Fear conditioning session: As revealed by the paired-samples t-test, the subjects had a significantly greater expectancy for US (2.36 ± 0.37 vs. 1.32 ± 0.40, p = 0.001, t(39) = 10.62, Cohen’s d = 1.68), and displayed longer RTs (880 ± 281 ms vs. 824 ± 255 ms, p = 0.013, t (39) = 2.61, Cohen’s d = 0.414) in the CS+ condition compared to the CS- condition, indicating successful fear acquisition. Fear generalization session: During the fear generalization session, a two-way repeated measures ANOVA showed that the expectancy for the US was (1) significantly modulated by “GS” type (F(1,39) = 11.474, p = 0.002, η²= 0.227; Fig. 2B) with greater expectancy ratings for GS+ compared to GS(t(39) = 3.387, p = 0.002, Cohen’s d = 0.519), and (2) significantly modulated by “concept relations” (F(1,39) = 13.432, p = 0.001, η² = 0.256), with a higher expectancy for taxonomic relations than for thematic relations (t(39) = 3.665, p = 0.001, Cohen’s d = 0.598). No significant interaction was 10

observed for the expectancy ratings (F (1,39) = 0.067, p = 0.807, η²= 0.002). RTs to GS were significantly influenced by GS type (F (1,39) = 25.486, p< 0.0001, η² = 0.395, Fig. 2D), where subjects showed longer RTs to GS+ compared to GS- stimuli (t (39) = 5.048, p = 0.0001, Cohen’s d = 0.807). RTs to GS were not influenced by concept relations (t (39) = 1.185, p =0.243, Cohen’s d = 0.187) or their interactions (F (1,39) = 1.655, p = 0.206, η² = 0.041). ------insert Figure 2 here----3.2 Electrophysiological results Fig. 3 shows the grand averaged ERP waveforms for the thematic GS+, thematic GS-, taxonomic GS+, and taxonomic GS- conditions. The N1, P2, N4, and P6 components were elicited with optimal scalp topographies at the occipital, frontal, and central regions, respectively. The P2 amplitudes, measured at the F1, FZ, and F2 electrodes, were influenced by concept relations (F(1,39) = 12.403, p = 0.001, η² = 0.241), where taxonomic-related GS elicited larger P2 amplitudes than thematic-related GS (t(39) = 3.522, p = 0.001, Cohen’s d = 0.549), but were not influenced by GS type (t(39) = 0.575, p = 0.569, Cohen’s d = 0.090), or their interactions (F(1,39) = 1.116, p = 0.297, η² = 0.028). The P600 amplitudes, measured at the P3, PZ, P4, and POZ electrodes, were significantly modulated by GS type (F(1,39) = 4.870, p = 0.033, η² = 0.111), where GS- stimuli elicited a larger P600 amplitude compared to GS+ stimuli (t(39) = 2.207, p = 0.033, Cohen’s d = 0.348), but were not modulated by concept relations (t(39) = 0.158, p = 0.876, Cohen’s d = 0.025). The GS type × concept relations interaction was not significant (F(1,39) = 4.870, p = 0.871, η² = 0.001). The N1 amplitudes, measured at the O1, OZ, and O2 electrodes, were not modulated by GS type (t(39) = 0.421 p = 0.676, Cohen’s d = 0.066), concept relations (t(39) = 1.116, p = 0.271, Cohen’s d = 0.220), or their interactions (F(1,39) = 0.174, p = 0.679, η² = 0.004). The N4 amplitudes, measured at the F1, FZ, and F2 electrodes, showed no significant main effect of GS type (t(39) = 0.477, p = 0.636, Cohen’s d = 0.074), no main effect of concept relations (t(39) = 1.106, p = 0.275, Cohen’s d = 0.175), and no interaction effect (F(1,39) = 0.281, p = 0.599, η² = 0.007). ------insert Figure 3 here----Fig. 4 shows the grand averaged TFDs for thematic GS+, thematic GS-, taxonomic GS+, and taxonomic GS- conditions. The ERP, α-ERD, and β-ERD magnitudes were elicited by GS stimuli. The 11

ERP magnitudes, measured at frontal regions (F1, FZ, and F2 electrodes) showed significant modulation by concept relations (Fig. 4; F(1,39) = 9.568, p = 0.004, η² = 0.197)，where taxonomic relationships elicited larger ERP magnitudes compared to thematic relationships (t(39) = 3.093, p = 0.004, Cohen’s d =0.487). There was no significant main effect of GS type (t(39) = 0.201, p = 0.842, Cohen’s d = 0.033), and there was no GS type × concept relations interaction (F(1,39) = 0.741, p = 0.195, η² = 0.043) for ERP magnitudes. The α-ERD magnitudes, measured at the right occipital regions (P2, P4, and PO4 electrodes), were significantly modulated by GS type (F(1,39)= 7.862, p = 0.008, η² =0.168), with greater α-ERD magnitudes for GS+ stimuli than GS- stimuli (t(39) = 2.476, p = 0.018, Cohen’s d = 0.395), though they were not modulated by concept relations (t(39) = 0.606, p = 0.548, Cohen’s d = 0.093), and there was no GFS type × concept relations interaction (F(1,39)= 0.032, p = 0.859, η² =0.001). The β-ERD magnitudes, measured at the frontal regions (F1, FZ, and F2 electrodes), were significantly modulated by GS type (F(1,39)= 7.051, p = 0.011, η² =0.153), with greater β-ERD response magnitudes to GS+ stimuli compared to GS- stimuli (t(39)= 2.706, p = 0.010, Cohen’s d = 0.428), though they were not modulated by concept relations (t(39) = 0.0001, p = 1.000, Cohen’s d = 0.00), and there was no GS type × stimulus category interaction (F(1,39)= 1.231, p =0.274, η² =0.031). ------insert Figure 4 here----4. Discussion This study provided the behavioral and electrophysiological evidence of how thematic and taxonomic relations interact with associative fear-learning. First, the behavioral results showed increased shock expectancy to GS+ related stimuli than GS- related stimuli, suggesting the generalization of fear to stimuli of feature-based and context-based relations that were never directly paired with the US. In addition, taxonomic bias was also observed in shock expectancy: stronger ratings were given in taxonomic conditions than thematic relations across GS types. Moreover, the RTs were only regulated by GS type, being significantly longer for GS+ than GSconditions. Second, in the time domain, the P2 amplitudes were significantly larger for the taxonomic relations, and P6 amplitudes were significantly smaller for the GS+ condition (Fig. 3). Third, in the time frequency domain, the magnitudes of the alpha and beta oscillations were significant for the GS+ conditions (Fig. 4). No significant interaction was found between CS type and stimulus category. Taken together, these findings demonstrated that taxonomic relations are 12

more predominant in fear generalization. 4.1 Behavioral index of fear conditioning and generalization The shock expectancy following the CS+ is higher relative to that following the CS-, indicating a successful learning of fear conditioning. Previous studies demonstrated that the RTs are usually shorter for CS+ than CS- (Dunsmoor, White, & LaBar, 2011; Lissek et al., 2014), reflecting that the identification of threat objects is facilitated (Hauner, Howard, Zelano, & Gottfried, 2013). Conversely, in the present study we observed longer RTs in response to CS+ and GS+ conditions compared with CS- and GS- conditions. Longer RTs in fear conditioning could reflect more uncertainty about the threat properties of the stimuli (Lissek et al., 2014; Dunsmoor & LaBar, 2013). The uncertainty effect in the present study might be caused by the partial reinforcement of CS+. In other words, the participants were uncertain about whether the CS+ or GS+ predicted shocks. Results of the generalization test showed a strong taxonomic bias for GS+ and GS- conditions. The fear conditioning procedure includes fearful learning from the CS+ and safety learning from CS- (Vervliet & Geens, 2014; Dunsmoor & Labar, 2013). In this case, in addition to a border generalization to the learned threaten stimuli (i.e., CS+), taxonomic relations, in relation to thematic relations, also illustrated a fear bias in response to the learned safe stimuli (i.e., CS-). This result might suggest that taxonomy-based inductive reasoning is the predominant strategy of people for fearful predictions about things that have not been reinforced before. Of note, this fearful prediction in taxonomic relation even weakened the learning from CS- to GS-. These findings are consistent with previous findings indicating that the processing of thematic relations requires less cognitive resources than the processing of taxonomic relations (Kalénine et al., 2009; Lewis, Poeppel, & Murphy, 2015; Sachs, Weis, Krings, Huber, & Kircher, 2008). This difference between thematic and taxonomic based inductive reasoning is interpreted as, taxonomic relations seem to be processed semantically by retrieving category memory (relying on static features), whereas thematic relations are more likely to be processed automatically (relying on actions and space processing) (Chen et al., 2014; Kalénine et al., 2009; Kalénine & Buxbaum, 2016). Our results demonstrated that taxonomic knowledge plays a more important role in the processing of fear generalization than thematic relations. 4.2 ERP correlations of generalization

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The ERP correlations that time-locked to GSs provided evidence of cognitive processing during fear generalization. In the P2 time window, there was an effect of concept relations (thematic vs. taxonomic), with a significantly more positive deflection for taxonomic words than thematic words. However, no effect of GS type (GS+ vs. GS-) or interactions were observed. The P2 amplitude is sensitive to allocation of attention (Herbert et al., 2008; Kanske, Plitschka, & Kotz, 2011), perceptual features processing (Chen et al., 2015; Hillyard & Anllo-Vent,1998; Posner, 1980), rudimentary semantic categorization (Kissler, Herbert, winkler, & Junghofer, 2009; Lei et al., 2010). In fear conditioning experiments, the P2 component is associated with threat information or aversive US (Bar-Haim, Lamy, & Glickman, 2005; Carretié, Martin-Loeches, Hinojosa, & Mercado, 2001; Cisler & Koster, 2010; Holmes, Nielsen, & Green, 2008). These early fear effects have often been interpreted as indicating heightened arousal or rapid attention due to fearful stimuli (Ugland, Dyson, & Field, 2013; Weymar, Bradley, Hamm, & Lang, 2013). In the present study, the attenuation of the P2 fear effect may be related to the elimination of fear caused by a large number of stimuli stacks and lack of US reinforcement. Our data may demonstrate that thematic and taxonomic relations receive different extent of attention early in the information processing system, that is, taxonomic-related word captured more attention than thematic relations. Prior studies differentiating thematic and taxonomic relationships suggest that taxonomic relationships may require additional attentional processes, while thematic relationships engage higher level processes, such as memory (Maguire, Brier, & Ferree, 2010; Liu et al., 2018). Our data showed a significant GS- type effect in the P6 component, with a more positive response elicited by GS- stimuli and a more negative response elicited by GS+ stimuli. The P6 component has been found in the domain of language processing, such as syntactic violations, grammatical errors, and semantic anomalies. This positive wave appears approximately 500 ms after the presentation of the stimulus and reaches its peak at around 600 ms in posterior locations. Recent studies have examined the effect of emotional status on language processing, as indexed by P6 (Cisler & Koster, 2010; Vissers, Chwilla, Egger, & Chwilla, 2013). These studies indicate that positive emotions (e.g., happy) lead to flexible thinking and permit the processing of more global, category-level information. On the contrary, negative emotions (e.g., sad) are associated with local, bottom–up, analytic, and systematic information processing (Fredrickson & Branigan, 2005), 14

in which people rely less on heuristics and have a narrowed focus of attention (Gasper & Clore, 2002). Thus, a decrease in P600 amplitude in a negative mood could lead to a reduction in syntactic processing compared to a positive mood. Attention also plays an important role in emotion-related ERP effects on language processing. More cognitive resources attributed to syntactic features may weaken the effect of emotional states on syntactic processing. However, this effect does not remove the tendency of a larger effect in positive than negative moods. Previously, we predicted that the N1 component with respect to occipital electrodes would be modulated by GS type, however, we did not find any significant difference. N1 induced by visual stimuli is related to selective attention processing (Foti, Hajcak, & Dien, 2009). In fear conditioning experiments, N1 is often used to index the degree of the fearful stimuli to capture attention resources (Flor et al., 2002; Rothemund et al., 2012). Typically, CS+ induced larger N1 amplitude than CS-. In the present study, the attenuated N1 effect might be caused by the lack of US reinforcement and fear extinction in the generalization test. In addition, no difference in frontal N400 was observed between thematic and taxonomic conditions. Frontal N400 is often observed in category-based inductive reasoning tasks (e.g., Lei, Liang, & Lin, 2017; Long et al., 2015). Liu and colleagues found that thematic-based reasoning induced frontal-central N400 distance effect, while the distance effect on taxonomic-based reasoning was observed in central-parietal region. In the present study the task was to predict the likelihood of electric shocks. Thus, the insignificant N400 effect in present study may due to non-semantic task demands. 4.3 Oscillatory activity in generalization In the present study, threatening stimuli (GS+) induced greater -ERD in the parietal region compared to safe stimuli (GS-). Alpha-band oscillations have inhibitory functions in the human brain. This oscillation is maximal with eyes closed, and becomes suppressed when eyes are open. There is a general consensus that alpha-band activity is associated with processing sensory information (e.g., visual processing) and motor output (e.g., finger movements) (Pizzagalli, Stancák, & Neuper, 1996). However, more recent evidence suggests that alpha-band activity also plays an important role in basic cognitive processing, such as perception, attention, long-term memory (LTM), and working memory (Klimesch, 2012). For example, in discrimination tasks, predictable stimuli would induce a large pre-stimulus ERD as a function of anticipatory attention (Ergenoglu et al., 2004; Hanslmayr et al., 2007). With regard to differences in processing taxonomic 15

versus thematic relationships, a previous study found that the theta power was increased over right frontal areas for thematic versus taxonomic relationships, and that the alpha power increased over parietal areas for taxonomic versus thematic relationships. In addition, threatening stimuli induce more α-ERD compared to non-threatening stimuli (Vagnoni, Lourenco, & Longo, 2015). This emotional effect on alpha activity begins approximately 200 ms after the stimulus onset and lasts for 600 ms. Together with previous findings, our data suggest that a long-lasting category-level-based fearful state exists when participants perform a generalization task that may involve both conceptual and fear experience memory retrieval. Therefore, this may be new physiological evidence for fear generalization. 4.4 Limitations The current study contained several limitations. First, the taxonomically-related words used from the free association task shared the same subordinate category level with the target word, which led to a high similarity between stimuli (Morris & Murphy, 1990). The Pavlovian conditioning elemental model suggests that when presented with a stimulus that has not been conditioned, generalization is determined by the associative strength of the elements shared with the conditioned stimulus (McLaren & Mackintosh, 2002; Wagner, 2008). The overlapped features in taxonomic relations may result in a high associative strength between the GSs and US (Wagner, 2008), which may induced higher shock expectancy ratings in taxonomic relations. In future studies, the comparison between thematic relations and different hierarchical category levels in taxonomic relations should be examined further. Second, although we believe that the semantic relatedness of the rated words should generalize to a similar group, it is inevitable that the expectancy would be influenced by the subjective experience of participants. This effect is even more obvious in thematic relations (Jouravlev & McRae, 2016), as thematic relations are organized through experiences that may differ across cultures and direct interaction. For example, people who do not enjoy beer may exhibit a weak thematic network with respect to beer. Thus, it could lead to a variety of ratings among participants. To conclude, we need to be prudent about the present results, as this study is a preliminary study to explore the effects of thematic and taxonomic relationship on fear generalization. 4.5 Conclusions

16

In sum, the present experiment provides novel evidence that the conceptual relations of conditional cues influence higher order fear learning in humans. In conclusion, taxonomic relations lead to broader generalizations than thematic relations. Collectively, our P2 findings suggests that taxonomic relations occupied more attention than thematic relations whether in response to danger or safe stimuli. Fear learning also had an impact on semantic processing, as demonstrated by the decreased P600 amplitude towards threatening stimuli compared to safe stimuli. Finally, the alpha-band ERD in our findings may represent a new potential physiological evidence for the presentation of fear generalization. In conclusion, concept level relationships may promote fear generalization via modulation of attention and conceptual knowledge. More importantly, these findings contribute to our understanding of fear generalization in the real world, which is wide and conceptually related. For example, being bitten by a dog could lead one to avoid other category members (e.g., other dogs or similar mammals), and thematic-related instances (e.g., leash, kennel). Hence, understanding the role of thematic and taxonomic relationships in fear generalization would provide indispensable support for cognitive behavioral therapy for anxiety disorders. Acknowledgements We thank Yanjing. Wu and Yuxi. Zhu for providing help with language for this manuscript. This work was supported by the Natural Science Foundation of China (31571153, 31470997, 31500921), University Innovation Team Construction Fund of Guangdong China (2015KCXTD009), Shenzhen basic discipline layout Fund (JCYJ20150729104249783). Author contributions Study design, YL; Data collection, YM, YQD; Data analysis and interpretation, WWP, YM; Writing of the report, YM, YL, WWP; Decision to submit the article for publication, YL. The authors declare no competing financial interests. References Ahmed, O., & Lovibond, P. F. (2019). Rule-based processes in generalisation and peak shift in human fear conditioning. Quarterly Journal of Experimental Psychology, 72(2), 118-131.

Andrews M, Vigliocco G, Vinson DP. (2009) Integrating experiential and distributional data to learn semantic representations. Psychological Review, 116(3), 463–498. Bach, D. R. , Friston, K. J. , & Dolan, R. J. (2013). An improved algorithm for model-based analysis 17

of evoked skin conductance responses. Biological Psychology, 94(3), 490–497. Barhaim, Y., Lamy, D., & Glickman, S. (2005). Attentional bias in anxiety: a behavioral and erp study. Brain & Cognition, 59(1), 11–22. Bennett, M., Vervoort, E., Boddez, Y., Hermans, D., & Baeyens, F. (2015). Perceptual and conceptual similarities facilitate the generalization of instructed fear. Journal of Behavior Therapy and Experimental Psychiatry, 48, 149–155. Boyle, S., Roche, B., Dymond, S., & Hermans, D. (2016). Generalisation of fear and avoidance along a semantic continuum. Cognition and Emotion, 30(2), 340-352. Borghi, A. M., & Caramelli, N. (2003). Situation bounded conceptual organization in children: From action to spatial relations. Cognitive Development, 18, 49–60. Carretié, L., Martín-Loeches, M., Hinojosa, J. A., & Mercado, F. (2001). Emotion and attention interaction

studied

through

event-related

potentials. Journal

of

cognitive

neuroscience, 13(8), 1109-1128.

Chen, Q., Liang, X., Lei, Y., & Li, H. (2015). Electrophysiological difference between the representations of causal judgment and associative judgment in semantic memory. Acta psychologica, 157, 176-184. Chen, Q., Ye, C., Liang, X., Cao, B., Lei, Y., & Li, H. (2014). Automatic processing of taxonomic and thematic relations in semantic priming - Differentiation by early N400 and late frontal negativity. Neuropsychologia, 64, 54–62. Chien, J. H., Colloca, L., Korzeniewska, A., Cheng, J. J., Campbell, C. M., Hillis, A. E., & Lenz, F. A. (2017). Oscillatory EEG activity induced by conditioning stimuli during fear conditioning reflects Salience and Valence of these stimuli more than Expectancy. Neuroscience, 346, 81– 93. Cisler, J. M., & Koster, E. H. W. (2010). Mechanisms of attentional biases towards threat in anxiety disorders: An integrative review. Clinical Psychology Review, 30(2), 203–216. Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21. Dunsmoor, J. E., & LaBar, K. S. (2013). Effects of discrimination training on fear generalization gradients and perceptual classification in humans. Behavioral Neuroscience, 127(3), 350– 18

356. Dunsmoor, J. E., Martin, A., & LaBar, K. S. (2012). Role of conceptual knowledge in learning and retention of conditioned fear. Biological Psychology, 89(2), 300–305. Dunsmoor, J. E., & Murphy, G. L. (2014). Stimulus Typicality Determines How Broadly Fear Is Generalized. Psychological Science, 25(9), 1816–1821. Dunsmoor, J. E., & Murphy, G. L. (2015). Categories, concepts, and conditioning: How humans generalize fear. Trends in Cognitive Sciences, 19(2), 73–77. Dunsmoor, J. E., & Paz, R. (2015). Fear generalization and anxiety: behavioral and neural mechanisms. Biological Psychiatry, 78(5), 336–343. Dunsmoor, J. E. , White, A. J. , & Labar, K. S. . (2011). Conceptual similarity promotes generalization of higher order fear learning. Learning & memory,18(3), 156–160. Dymond, S., Dunsmoor, J. E., Vervliet, B., Roche, B., & Hermans, D. (2015). Fear Generalization in Humans: Systematic Review and Implications for Anxiety Disorder Research. Behavior Therapy, 46(5), 561–582. Ergenoglu, T., Demiralp, T., Bayraktaroglu, Z., Ergen, M., Beydagi, H., & Uresin, Y. (2004). Alpha rhythm of the EEG modulates visual detection performance in humans. Cognitive Brain Research, 20(3), 376–383. Flor, H. , Birbaumer, N. , Hermann, C. , Ziegler, S. , & Patrick, C. J. (2002). Aversive pavlovian conditioning in psychopaths: peripheral and central correlates. Psychophysiology, 39(4), 505-518. Foti, D., Hajcak, G., & Dien, J. (2010). Differentiating neural responses to emotional pictures: evidence from temporal-spatial pca. Psychophysiology, 46(3), 521–530. Fredrickson, B. L., & Branigan, C. (2005). Positive emotions broaden the scope of attention and thought-action repertoires. Cognition and Emotion, 19(3), 313–332. Gasper, K., & Clore, G. L. (2002). Attending to the big picture: Mood and global versus local processing of visual information. Psychological Science, 13(1), 34–40. Hanslmayr, S., Aslan, A., Staudigl, T., Klimesch, W., Herrmann, C. S., & Bäuml, K. H. (2007). Prestimulus oscillations predict visual perception performance between and within subjects. NeuroImage, 37(4), 1465–1473. Harris, J. B. (2005). Differential conditioning of alpha amplitude: a fresh look at an 19

oldphenomenon. Clinical Neurophysiology Official Journal of the International Federation of Clinical Neurophysiology, 116(6), 1433–1443. Hauner, K. K., Howard, J. D., Zelano, C., & Gottfried, J. A. (2013). Stimulus-specific enhancement of fear extinction during slow-wave sleep. Nature Neuroscience, 16(11), 1553–1555. Herbert, C., Kissler, J., Junghöfer, M., Peyk, P., & Rockstroh, B. (2006). Processing of emotional adjectives: Evidence from startle EMG and ERPs. Psychophysiology, 43, 197–206. Hillyard, S. A., & Anllo-Vento, L. (1998). Event-related brain potentials in the study of visual selective attention. Proceedings of the National Academy of Sciences of the United States of America, 95, 781–787. Holmes, A., Nielsen, M. K., & Green, S. (2008). Effects of anxiety on the processing of fearful and happy faces: An event-related potential study. Biological Psychology, 77(2), 159–173. Hu, L., Peng, W., Valentini, E., Zhang, Z., & Hu, Y. (2013). Functional features of nociceptive-induced suppression of alpha band electroencephalographic oscillations. Journal of Pain, 14(1), 89– 99. Hugdahl, K., & Nordby, H. (1991). Hemisphere differences in conditional learning: an erpstudy. Cortex, 27(4), 557–570. Jouravlev, O., & McRae, K. (2016). Thematic relatedness production norms for 100 object concepts. Behavior Research Methods, 48(4), 1349–1357. Kalénine, S., & Buxbaum, L. J. (2016). Thematic knowledge, artifact concepts, and the left posterior temporal lobe: Where action and object semantics converge. Cortex, 82(June), 164–178. Kalénine, S., Peyrin, C., Pichat, C., Segebarth, C., Bonthoux, F., & Baciu, M. (2009). The sensorymotor specificity of taxonomic and thematic conceptual relations: A behavioral and fMRI study. NeuroImage, 44(3), 1152–1162. Kanske, P., Plitschka, J., & Kotz, S. A. (2011). Attentional orienting towards emotion: p2 and n400 erp effects. Neuropsychologia, 49(11), 3121–3129. Kim, J. J. , & Jung, M. W. . (2006). Neural circuits and mechanisms involved in pavlovian fear conditioning: a critical review. Neuroscience & Biobehavioral Reviews, 30(2), 188–202. Kissler, J., Herbert, C., Winkler, I., & Junghofer, M. (2009). Emotion and attention in visual word processing-An ERP study. Biological Psychology, 80(1), 75–83. Klimesch, W. (2012). Alpha-band oscillations, attention, and controlled access to stored 20

information. Trends in Cognitive Sciences, 16(12), 606–617. Lee, J. C., Lovibond, P. F., Hayes, B. K., & Navarro, D. (2019). Negative evidence and inductive reasoning in generalization of associative learning. Journal of Experimental Psychology: General, 148, 289-303. Lee, J. C., Hayes, B. K., & Lovibond, P. F. (2018). Peak shift and rules in human generalization. Journal

of

Experimental

Psychology:

Learning,

Memory,

and

Cognition, 44(12), 1955.

Lei, Y., Li, F., Long, C., Li, P., Chen, Q., Ni, Y., & Li, H. (2010). How does typicality of category members affect the deductive reasoning? An ERP study. Experimental brain research, 204(1), 47-56. Lei, Y., Liang, X., & Lin, C. (2017). How do the hierarchical levels of premises affect category-based induction: diverging effects from the p300 and n400. Scientific Reports, 7(1), 11758. Lei, Y. Wang, J. Dou, H. Qiu, Y. Li, H. (2019). Influence of typicality in category-based fear generalization : Diverging evidence from the P2 and N400 effect. International Journal of Neuroscience, 135, 12–20. Lewis, G. A., Poeppel, D., & Murphy, G. L.（2015） The neural bases of taxonomic and thematic conceptual relations: an MEG study. Neuropsychologia, 68, 176-189. Lin, E. L., & Murphy, G. L. (2001). Thematic relations in adults’ concepts. Journal of Experimental Psychology: General, 130(1), 3–28. Liu, F., Han, J., Zhang, L., & Li, F. (2018). Inductive reasoning differs between taxonomic and thematic contexts: Electrophysiological evidence. BioRxiv, 333211. Lissek, S., Biggs, A. L., Rabin, S. J., Cornwell, B. R., Alvarez, R. P., Pine, D. S., & Grillon, C. (2008). Generalization of conditioned fear-potentiated startle in humans: Experimental validation and clinical relevance. Behaviour Research and Therapy, 46(5), 678–687. Lissek, S., Kaczkurkin, A. N., Rabin, S., Geraci, M., Pine, D. S., & Grillon, C. (2014). Generalized anxiety disorder is associated with overgeneralization of classically conditioned fear. Biological Psychiatry, 75(11), 909–915. Lissek, S., Rabin, S. J., Heller, R. E., Lukenbaugh, D., Geraci, M., Pine, D. S., & Grillon, C. (2010). Overgeneralization of conditioned fear as a pathogenetic marker of panic disorder. American Journal of Psychiatry, 167(1), 47–55. 21

Maki, W. S., & Buchanan, E. (2008). Latent structure in measures of associative, semantic, and thematic knowledge. Psychonomic Bulletin & Review, 15(3), 598-603. Maguire, M. J., Brier, M. R., & Ferree, T. C. (2010). EEG theta and alpha responses reveal qualitative differences in processing taxonomic versus thematic semantic relationships. Brain and Language, 114(1), 16–25. Markman, A. B. , & Wisniewski, E. J. . (1997). Similar and different: the differentiation of basic-level categories. Journal of Experimental Psychology Learning Memory and Cognition,23(1), 54– 70. Masuda, T., & Nisbett, R. E. (2001). Attending holistically versus analytically: Comparing the context sensitivity of Japanese and Americans. Journal of Personality and Social Psychology, 81, 922–934. McLaren, I. P. L., & Mackintosh, N. J. (2002). Associative learning and elemental representation: II. Generalization and discrimination. Animal learning & behavior, 30(3), 177-200. Mcrae, K. , Cree, G. S. , Seidenberg, M. S. , & Mcnorgan, C. (2005). Semantic feature production norms for a large set of living and nonliving things. Behavior Research Methods, 37(4), 547– 559. Miltner, W. H. R. , Braun, C. , Arnold, M. , Witte, H. , & Taub, E. . (1999). Coherence of gamma-band eeg activity as a basis for associate learning. Nature, 397(6718), 434–436. Mirman, D., & Graziano, K. M. (2012). Damage to temporo-parietal cortex decreases incidental activation of thematic relations during spoken word comprehension. Neuropsychologia, 50(8), 1990–1997. Mirman, D., Landrigan, J.-F., & Britt, A. E. (2017). Taxonomic and thematic semantic systems. Psychological Bulletin, 143(5), 499–520. Miskovic, V., & Keil, A. (2012). Acquired fears reflected in cortical sensory processing: a review of electrophysiological studies of human classical conditioning. Psychophysiology, 49(9), 1230– 1241. Morris, M. W. , & Murphy, G. L. . (1990). Converging operations on a basic level in event taxonomies. Memory and Cognition, 18(4), 407-418. Nelson, D. L., McEvoy, C. L., & Schreiber, T. A. (2004). The University of South Florida free association, rhyme, and word fragment norms. Behavior Research Methods, Instruments, & 22

Computers, 36, 402– 407. Pfurtscheller, G. (1977). Graphical display and statistical evaluation of event-related desynchronization (erd). Electroencephalography & Clinical Neurophysiology, 43(5), 757– 760. Pfurtscheller, G., Stancák, A., & Neuper, C. (1996). Event-related synchronization (ERS) in the alpha band - An electrophysiological correlate of cortical idling: A review. International Journal of Psychophysiology, 24(1–2), 39–46. Pizzagalli, D. A. , Greischar, L. L. , & Davidson, R. J. . (2003). Spatio-temporal dynamics of brain mechanisms in aversive classical conditioning: high-density event-related potential and brain electrical tomography analyses. Neuropsychologia, 41(2), 184–194. Putney, R. T. . (1973). Conditioned alpha blocking re-examined with the measurement of individual wave amplitudes. Electroencephalography and Clinical Neurophysiology, 34(5), 485–493. Rothemund, Y. , Ziegler, S. , Hermann, C. , Gruesser, S. M. , Foell, J. , & Patrick, C. J. , et al. (2012). Fear

conditioning

in

psychopaths:

event-related

potentials

and

peripheral

measures. Biological Psychology, 90(1), 50–59. Sachs, O., Weis, S., Zellagui, N., Huber, W., Zvyagintsev, M., Mathiak, K., & Kircher, T. (2008). Automatic processing of semantic relations in fMRI: Neural activation during semantic priming of taxonomic and thematic categories. Brain Research, 1218, 194–205. Ugland, C. C. O., Dyson, B. J., & Field, A. P. (2013b). An ERP study of the interaction between verbal information and conditioning pathways to fear. Biological Psychology, 92(1), 69–81. Vagnoni, E., Lourenco, S. F., & Longo, M. R. (2015). Threat modulates neural responses to looming visual stimuli. European Journal of Neuroscience, 42(5), 2190–2202. Vervliet, B. , & Geens, M. . (2014). Fear generalization in humans: impact of feature learning on conditioning and extinction. Neurobiology of Learning and Memory, 113, 143–148. Vissers, C. T. W. M., Chwilla, U. G., Egger, J. I. M., & Chwilla, D. J. (2013). The interplay between mood and language comprehension: Evidence from P600 to semantic reversal anomalies. Neuropsychologia, 51(6), 1027–1039. Wagner, A. R. (2008). Evolution of an elemental theory of Pavlovian conditioning. Learning & Behavior, 36(3), 253-265. Weymar, M., Bradley, M. M., Hamm, A. O., & Lang, P. J. (2013). When fear forms memories: Threat 23

of shock and brain potentials during encoding and recognition. Cortex, 49(3), 819–826. Wong, A. H. K., & Lovibond, P. F. (2017) Rule-based generalization in single-cue and differential fear conditioning in humans. Biological Psychology, 129, 111- 120. Zhao, K., Tang, Z., Wang, H., Guo, Y., Peng, W., & Hu, L. (2017). Analgesia induced by self-initiated electrotactile sensation is mediated by top-down modulations. Psychophysiology, 54(6), 848–856.

Figure captions

Fig. 1. Illustration of the experimental design. The experimental session included two phases: (A) fear conditioning, and (B) the generalization test. Each trial began with a central crosshair (0.8–1.2 s). After a short interval (0.8–1.2 s), a word was presented for 5 s during which the participants were asked to judge whether the stimulus was paired with the shock by pressing keys denoting 1, 2, or 3 with their right hand as quickly as possible. The words disappeared when participants made the judgment. (A): fear conditioning procedure. The electric shock (US, pictured as a lightning bolt) followed the offset of the CS+ (e.g., pencil), and never followed the CS- (e.g., beer). There was a 4–6 s interval between the offset of the words and the next trial to revert physiological responses to baseline after the electric shock. (B): generalization test procedure. Participants underwent a generalization test for corresponding novel words randomly presented in one of four conditions: thematic GS+ (e.g., eraser), thematic GS- (e.g., cup), taxonomic GS+ (e.g., pen), taxonomic GS- (e.g., liquor). Since there was no electric shock at this stage, the interval between words and the next trial was reduced to 2.5–3.5 s. CS, conditioned stimulus; GS, generalized stimulus; taxo., taxonomic; them., thematic. Fig. 2. Behavioral results. (A) During fear conditioning, participants predicted a higher possibility that stimuli would be paired with the US when they performed the task under CS+ (black bar) compared to CS- (gray bar). (B) The shock ratings during fear generalization revealed broader generalization for GS+ for both 24

thematic and taxonomic relationships, but taxonomic relationships showed more widespread generalization. (C) Participants’ responses were quicker when they were presented with the CS- compared to the CS+ during fear conditioning. (D) As for (C) with shorter response times to GS- in both the thematic and taxonomic relationships. CS, conditioned stimulus; GS, generalized stimulus; taxo., taxonomic; them., thematic.

Fig. 3. ERP results among thematic GS+, thematic GS-, taxonomic GS+, and taxonomic GSconditions. (A): ERP waveforms at frontal, central, and parietal regions for each condition are shown in the left panel. (B): Scalp topographies of the N1 component (O1+OZ+O2)/3, P2 component (F1+Fz+F2)/3, N4 component (F1+Fz+F2)/3, and P6 component (C1+Cz+C2)/3 are shown in the middle panel. (C): The P2 amplitude was significantly larger in the taxonomic conditions than the thematic conditions (5.64± 0.74 μV vs. 4.54 ± 0.69 μV，p<0.01). The P600 amplitude was significantly smaller in the CS+ than the CS- condition. The N1 and N4 amplitudes showed no difference in any condition. CS, conditioned stimulus; GS, generalized stimulus; taxo., taxonomic; them., thematic.

Fig. 4. Grand average TFD of -ERD and -ERD in thematic GS+, thematic GS-, taxonomic GS+ and taxonomic GS- conditions. (A): For group-level TFRs, the x-axis displays the latency (ms), the y-axis displays the frequency (Hz), and the color scale displays the baseline corrected oscillatory magnitude (ER%). (B): Taxonomic relationships elicited significantly larger frontal averaged ERP magnitudes than thematic relationships (0.73 ± 0.12 μV vs. 0.58 ± 0.11 μV, p<0.05). The GS+ condition induced a significantly larger -ERD magnitude than the GS- condition in parietal regions (-0.32 ± 0.07 μV vs. -0.29 ± 0.07 μV, p=0.028). TFD, time-frequency distributions; ERP, event-related potential; GS, generalized stimulus.

25

Highlights



We examine the effect of thematic and taxonomic relations on fear generalization

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Self-reported fear beliefs were stronger for taxonomic than thematic relations

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Conceptual relations heightened attention to stimuli and promoted fear generalization

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Similarity-based categories affect fear learning more than time or space-based stimuli

27