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Cognitive Systems Research 17–18 (2012) 25–33

Functional activation of the parahippocampal cortex and amygdaladuring social statistical information processing

Action editor: Ron Sun

Mi Li a,b, Ning Zhong a,c,⇑, Kuncheng Li d, Shengfu Lu a,⇑

a The International WIC Institute, Beijing University of Technology, Beijing, Chinab The School of Computer and Communication Engineering, Liaoning ShiHua University, Liaoning, China

c Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi-City, Japand Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China

Available online 31 October 2011

Abstract

Social statistical information can be used to quantitatively describe external events or facts, such as statistics on products, incomes, orsales, which consist of two basic features: associations and sociality. Previous studies in cognitive psychology have investigated statisticalgraph comprehension, but the neural basis of social statistical information processing has not been examined. In our study, 36 subjectswere scanned using functional magnetic resonance imaging (fMRI) while reading statistical information visually presented in one of threebasic forms: as text, as statistical graphs, and as both graphs and text. All three forms consistently activated the right posterior tip of theparahippocampal cortex (PHC) and the left amygdala, suggesting that both regions contribute to social statistical information process-ing, regardless of the presentation form. Previous studies have implicated the posterior tip of the PHC in contextual associations and theamygdala in processing emotion-related events and social cognition. Taken together with previous studies, we proposed that the poster-ior tip of the PHC is more involved in establishing associations during social statistical information processing, while the amygdala ismore related to the social component. This study provides neuroimaging evidence for commonly processing of the two basic featuresof social statistical information by the PHC and amygdala.� 2011 Elsevier B.V. All rights reserved.

Keywords: fMRI; Social statistical information; Sociality; Associations; Parahippocampal cortex (PHC); Amygdala

1. Introduction

The neural substrates of language have been studiedextensively, from the speech recognition to the processingof the semantics of language content. In contrast, little isknown of the brain mechanism of graph analysis andcomprehension beyond the recognition phrase, which isanalogous to face and scene recognition. The neural mech-anisms responsible for understanding the content expressedin graphs are entirely unknown. Most current research is

1389-0417/$ - see front matter � 2011 Elsevier B.V. All rights reserved.doi:10.1016/j.cogsys.2011.10.001

⇑ Corresponding authors at: The International WIC Institute, BeijingUniversity of Technology, Beijing, China (N. Zhong).

E-mail addresses: zhong@maebashi-it.ac.jp (N. Zhong), lusf@bjut.edu.cn (S. Lu).

focused on perception rather than post-perceptional pro-cessing of graphical information and much greater knowl-edge of these higher order phenomena are required forresearch and development of artificial intelligence. In ourstudy, we focused on social statistical information toaddress the brain mechanisms underlying the processingof different visual forms: statistical information in text(ST), statistical graph (SG), and statistical graph with text(SGT).

Social statistical information can be used to quantita-tively describe an event associated with daily life, such asstatistics on products, incomes, or sales. In general, suchstatistical information can be visually presented in threebasic forms: as text, as statistical graphs, and as statisticalgraphs combined with text. The text is a verbal description

26 M. Li et al. / Cognitive Systems Research 17–18 (2012) 25–33

without any spatial association, whereas the statisticalgraph represents the relationships of variables (objects)by their spatial associations (as points or lines) and so con-tains topology (Bastide, 1990; Cleveland & Mcgill, 1984;Feeney & Webber, 2003). Although previous studies incognitive psychology have investigated the statistical graphcomprehension (Carpenter & Shah, 1998; Cheng, Lowe, &Scaife, 2001; Cleveland & Mcgill, 1984; Kosslyn, 1989;Ratwani & Trafton, 2004; Scaife & Rogers, 1996; Zacks& Tversky,1999), little is known of the neural basis of socialstatistical information processing.

In statistics, data is used to describe an event thatinvolves various objects and their relationships. For exam-ple, the 2009 event “the four top countries of the per capitaGross Domestic Product (GDP) are reported: first is USA46,381 dollars; second is France 42,747 dollars; third isGermany 40,848 dollars; Forth is Japan 39,731 dollars”,can be described by either written text or a statistical graph.Both forms can express the same meaning of the event: thetop four countries by per capita GDP in 2009. Four objectsare included (countries), and each object has an associatedobject name and GDP value. Moreover, the statisticalinformation can also show the rankings of countries andthe correlation between the first and last countries in percapita GDP.

Many previous neuroimaging studies have shown thatthe parahippocampal cortex (PHC) is activated duringassociative processing, such as object–location associations(Johnsrude, Owen, Crane, Milner, & Evans, 1999;Sommer, Rose, Weiller, & Buchel, 2005) and contextualassociations (Aminoff, Gronau, & Bar, 2007; Bar &Aminoff, 2003; Bar, Aminoff, & Schacter, 2008b). ThePHC in the medial temporal lobes (MTL) is believed tobe involved in the representation and processing of spatialinformation, and for episodic memory. Previous studies onspatial processing have reported that the PHC is activatedduring tasks involving the processing of environmentallandmarks and visual scenes (Bohbot, Allen, & Nadel,2000; Epstein & Kanwisher, 1998; Levy, Hasson, Avidan,Hendler, & Malach, 2001; Maguire, Frith, Burgess,Donnett, & O’Keefe, 1998; O’Craven & Kanwisher,2000), or tasks requiring spatial navigation (Epstein,2008; Maguire, Frackowiak, & Frith, 1997; Mellet et al.,2000). In parallel, findings from memory studies indicatethat the PHC is involved in episodic memory, sourcememory, and the encoding of novel stimuli (Brewer, Zhao,Desmond, Glover, & Gabrieli, 1998; Davachi, Mitchell, &Wagner, 2003; Gabrieli, Brewer, Desmond, & Glover,1997; Kirwan & Stark, 2004; Schacter & Wagner, 1999;Squire, Stark, & Clark, 2004). These studies indicated thatthe PHC plays a key role in memory formation and visuo-spatial cognition.

Bar et al. found that episodic memory and spatialprocessing are distributed overlapping the PHC along ananterior-posterior hierarchy, which did not show the spa-tial specificity with tasks-related. They suggested that theoverlap is due to properties common to both episodic

memory and spatial processing. Thus, they advanced thatthe PHC was mainly involved in associative informationprocessing; in other words, associative information wasthe bridge among cognitive processing and representationof spatial layout, episodic memory, and spatial navigation(Aminoff et al., 2007; Bar & Aminoff, 2003; Bar, Aminoff,Mason, & Fenske, 2007; Bar et al., 2008b). To supporttheir arguments, Bar et al. tested the involvement of thePHC in spatial and non-spatial contextual tasks, and foundthat spatial contextual tasks activated the posterior PHC(including the PPA, parahippocampal place area) (Aminoffet al., 2007) to a greater degree than non-spatial tasks. Asecond study compared famous faces with unfamiliar faces,and found that famous faces activated the PHC more thandid unfamiliar faces (Bar, Aminoff, & Ishai, 2008a). Manyprevious studies have reported that face recognitionwas related to the face recognition area of the lateral fusi-form gyrus (fusiform face area or FFA) (Chao, Haxby, &Martin, 1999; Kanwisher, McDermott, & Chun,1997;Perani et al., 1995). Familiar famous faces activatedthe PHC more intensely because these faces evoke morecontextual information. Furthermore, studies on informa-tion processing under binding (when associations can bemade or are required) and non-binding conditions showedthat the PHC was more strongly activated under bindingconditions (Goh et al., 2004; Luck et al., 2010).

Social statistical information is widely used to representquantitative data in science, business, and many otherfields. Thus, while reading and comprehending statisticalinformation, people will associate various objects with theirown context, and spontaneously generate self-referencesaccording to their experiences and world knowledge. Forexample, referring to the reason for the highest per capitaGDP in the United States, each person’s response is likelyinfluenced by their own socioeconomic background andnationality (residing in a rich economy or underdevelopedcountry). The event may be consistent or inconsistent withthe readers background and this may evoke a particularemotion (envy, anger, etc.). Therefore, the comprehensionof social statistical information involves generation ofself-references accompanied by emotions that are relatedto social cognition.

A large number of functional neuroimaging studies onsocial cognition have demonstrated that the amygdala isassociated with the processing of sociality-related emo-tional tasks such as self-reference (D’Argembeau et al.,2005; Kelley et al., 2002), theory of mind (Fine, Lumsden,& Blair, 2001; Frith & Frith, 2003; Phan et al., 2004), andempathy (Blair, 2007; Shamay-Tsoory, Tomer, Goldsher,Berger, & Aharon-Peretz, 2004). The amygdala, a small,almond-shaped subcortical structure located deep in theanterior temporal lobe, plays a crucial role in emotionalresponses and emotional memory. Traditionally, the amyg-dala is commonly associated with aversive, negative emo-tional states such as fear (Adolphs, Tranel, Damasio, &Damasio, 1995; Calder, Lawrence, & Young, 2001; Davis& Whalen, 2001). Recent studies, however, have reported

M. Li et al. / Cognitive Systems Research 17–18 (2012) 25–33 27

that this region also responds to happy faces and positivestimuli (Liberzon, Phan, Decker, & Taylor, 2003; Somer-ville, Kim, Johnstone, Alexander, & Whalen, 2004). Theamygdala is also activated by events with emotional sal-ience, and stores these emotional components (emotionalmemories). In general, pleasant or unpleasant events arebetter remembered than neural events. Previous studieswith human and experimental animals have demonstratedthat the amygdala is linked to the emotional enhancementof episodic memory (Cahill & McGaugh, 1998; Hamann,Ely, Grafton, & Kilts, 1999; McGaugh, Cahill, & Roo-zendaal, 1996). Furthermore, some researchers have pro-posed that the amygdala is more involved in processingof emotional information with a social component (joy orsadness) than nonsocial emotional information (appetite).Using fMRI, Britton et al. (2006) directly comparedresponses to social emotions (e.g. joy and sadness) and

Fig. 1. Examples of tasks of text and statistical graph, and their correspondintasks; (b) T1 is an example of written text tasks, which has the same statisticalChinese characters in length (here they are translated into English) (c) L2 is anfrom B1; (d) T2 is the task of written text with the same statistical information asize as that used in the statistical graph tasks. (f) The ST-baseline task is a str

nonsocial emotions (e.g. appetite and disgust), and foundthat social emotions activated the amygdala more intenselythan nonsocial emotions. Similarly, the amygdala wasmore activated when viewing social pictures than nonsocialimages (Harvey, Fossati, & Lepage, 2007).

In addition, the amygdala and MTL memory structures(including the PHC) were more significantly activated byemotional pictures than neutral pictures during successfulencoding, and activity in the two regions were strongly cor-related. Thus, humans show better memory for emotionalevents, possibly because the amygdala enhances the func-tion of the MTL memory system (Dolcos, LaBar, &Cabeza, 2004; Kensinger & Schacter, 2006; Phelps, 2004).

Social statistical information has two basic features:associations and sociality. The main goal of this study isto provide neuroimaging evidence for these two basicfeatures of social statistical information. More specifically,

g baseline tasks used in the experiment. (a) B1 is an example of bar-graphinformation as B1; It is a paragraph ranging between 20 and 30 (mean 25)example of line-graph tasks, which describes another event that is differents L2; (e) The SG-baseline task consists of a blank background of the sameing of stars (*) of similar length to the words used in the text tasks.

Table 1The distribution of the tasks.

Gi B L T BT LT

G1 B1–B10 L11-L20 T21–T40 BT41-BT50 LT51–LT60

G2 B11–B20 L1-L10 T21– T40 BT41–BT50 LT51-LT60

G3 B21–B30 L31-L40 T1–T20 BT51–BT60 LT41-LT50

G4 B31–B40 L21-L30 T1 –T20 BT51–BT60 LT41–LT50

G: group; B: bar graph; L: line graph; T: text; BT: bar graph with text; LT:line graph with text.

28 M. Li et al. / Cognitive Systems Research 17–18 (2012) 25–33

we measured fMRI responses during presentation of socialstatistical information presented in the three basic visualforms. The major hypothesis was that both PHC andamygdala would be more activated during tasks requiringcomprehension of social statistical information, regardlessof presentation form.

2. Methods

2.1. Subjects

After submitting their written informed consent, 36native Chinese speaking, right-handed normal subjects(18 females) with a mean age of 22.5 (SD 1.7) years andmean education level of 15.6 (SD 0.9) years, volunteeredto take part in the study. None of the subjects had previ-ously participated in a similar experiment. The study wasapproved by the Ethical Committee of Xuanwu Hospitalof Capital Medical University.

2.2. Experimental paradigm

Sixty social statistical events that have often occurred inour daily lives were used in the experiment. To eliminatethe effects of other factors, such as the amount and expres-sion of information, each description was standardized into60 written texts (T1–T60). Similarly, each event was alsodescribed by a bar graph for a total of 60 bar graphs(B1–B60). These bar graphs were also described by a linegraph for a total of 60 line graphs (L1–L60). The same indexof each task represents the same content of statistical infor-mation; e.g., Ti, Bi, and Li denote the i-statistics describedby a text, bar graph, and line graph, respectively. In thecurrent study, 40 bar graphs (B1–B40) and 40 line graphs(L1-L40) were selected for separate statistical graph tasks,as shown in Figs. 1a and c. The first 40 texts were selectedas the separate text tasks, as shown in Figs. 1b and d. Theremaining 20 bar graphs, 20 line graphs, and 20 texts werecombined for 20 bar graphs with text tasks (BT41–BT60)and 20 line graphs with text tasks (LT41–LT60).

In the experiment, we presented three types of stimuli tosubjects: statistical graph (bar-graph, line-graph), text, andstatistical graph with text. Considering the effects of thegraphs’ visual characteristics and the graphs’ context onthe comprehension of statistical information, we used twocommon types of statistical graphs: bar graphs and linegraphs. The components of each statistical graph werethe same, including the background, the framework, thespecifier (bar or solid circle), and the labels (X axis’s objectdeclarator and Y axis’s value, the graph label, and thedescription of the specifier) (Kosslyn, 1989). In general, atwo-dimensional statistical graph could have two variables(X,Y) or three variables (X,Y,Z). In the three variablesstatistical graph, it is easy to extract the Y–X relationship,but difficult to obtain the Y–Z relationship (Carpenter &Shah, 1998). Thus, to make statistical graphs with uniformcomplexity, we used statistical graphs with two variables.

Additionally, to keep the amount of statistical informationbalanced, we limited the number of objects in each statisti-cal graph to 4–6. Each statistical graph had the samehorizontal and vertical perspectives (x � y), and the samebrightness, which was presented in gray scale and adjustedto a constant average brightness level. Moreover, we alsotried to make the bars in the bar graph the same width, linesegments the same thickness, and to use the same size andtype of specifier (such as circle, square and triangle).

The text can precisely describe the quantitative data,and so the data can be precisely extracted. The statisticalgraph can also precisely describe the quantitative data,but it is more difficult to precisely extract the data. Tomaintain consistency between these two forms, we labeledthe values on the objects in the statistical graphs. To elim-inate the effect of graph type, the same statistical informa-tion was presented as a bar graph and as a line graph. Theexperimental materials were selected through the evalua-tion of familiarity and difficulty. The familiarity of materi-als was 100%; the accuracy of difficulty was larger than80%. Furthermore, to keep the experimental materialshomogeneous and the difficulty assigned in each sessionbalanced, the accuracy of the same experimental materialsusing different forms (text, statistical graph, and statisticalgraph with text) should have no significant difference.

In our study, all the statistical graphs had the samebackground; thus, we designed the correspondingSG-baseline task to have the same blank background andsize as the statistical graph task. The correspondingST-baseline task was a string of stars (*) of similar lengthto the words used in the text task, as shown in Figs. 1eand f.

2.3. Procedure

Each subject completed 60 tasks consisting of 20 SGtasks (10 bar graphs and 10 line graphs), 20 SGT tasks(10 bar graphs with text and 10 line graphs with text),and 20 ST tasks. The subjects were divided into fourgroups, namely, G1, G2, G3, and G4; the stimuli werecounterbalanced across subjects. The distribution of thetasks is as shown Table 1.

These tasks were assigned in three sessions on average.The inter-session intervals were 120 s. The order of SG,SGT, and ST stimuli was pseudo-randomized in eachsession. Each SG, SGT, and ST stimulus was presented

Table 2Accuracy (%) of behavioral results from fMRI and behavioral experiments(M ± SD).

ST (%) SG (%) SGT (%)

Behavior experiment 80.92 ± 9.01 83.67 ± 9.02 83.22 ± 9.01fMRI experiment 78.62 ± 8.76 78.96 ± 9.29 78.76 ± 10.98

ST: statistical information in text; SG: statistical information in graph;SGT: statistical information in statistical graph with text.

M. Li et al. / Cognitive Systems Research 17–18 (2012) 25–33 29

for a period of 14, 18, and 16 s, respectively. Presentationtime was set according to behavioral experiments prior tothe fMRI study, so the participants could understand fullythe statistical information. Subjects were then required tocomplete another control session (including 10 ST-baselinetasks, 10 SG-baseline tasks, and 10 SGT-baseline tasks).All baseline tasks were presented for 8 s. The participantswere instructed to read the three types of tasks attentively.After a stimulus task disappeared, a question including twooptions was presented, and the subjects pressed a button toselect a response (left button for the first option and rightbutton for the second option). The subjects were limitedto answer the question within 8 s, followed by a 6 s restinterval.

Understanding statistical information entails obtainingthe exact data from the information carrier (text, statisticalgraph, or statistical graph with text) and then performinginformation processing (comparing two items, judgingmaximum, minimum, sorting, etc.). In order to help sub-jects understand social statistical information correctly,our questions were all about the contents relevant to infor-mation processing, and did not involved the value of a sin-gle item data.

Prior to the experiment, the learning tasks of 2 ST tasks,2 SG tasks, and 2 SGT tasks were prepared. To help thesubjects fully understand their participation in the experi-ment, they were instructed to complete the practice tasksduring the fMRI.

2.4. Image acquisition

Scanning was performed on a 3.0 T Siemens systemusing a standard whole-head coil. Functional data wereacquired using a gradient echo planar pulse sequence(TR = 2000 ms, TE = 31 ms, flip angle = 90�, voxel size =3.75 mm � 3.75 mm � 4 mm, 30 slices, slice thickness =0.8 mm, matrix = 64 � 64, FOV = 240 mm � 240 mm).High-resolution T1-weighted anatomical images werecollected in the same plane as the functional image usinga spin echo sequence with the following parameters:(TR = 130 ms, TE = 2.89 ms, flip angle = 70�, voxel size =0.8 mm � 0.8 mm � 4 mm, 30 slices, slice thickness =0.8 mm, matrix = 320 � 320, FOV = 240 mm � 240 mm).Stimulus presentation and data synchronization wereconducted using E-Prime 2.0 (Psychology Software Tools,Pittsburgh, USA). Prior to each run, the first two (10 s) dis-carded volumes were acquired to enable the stabilization ofmagnetization. The scanner was synchronized with the pre-sentation of every trial in each run.

2.5. Data analysis

All pre-processing and analyses of imaging data wasperformed using SPM2 (Wellcome Department of Cogni-tive Neurology, London, UK, http://www.fil.ion.ucl.ac.uk)implemented in Matlab 7.0 (Mathworks, Sherborne, MA,USA). Standard pre-processing of functional images was

performed, including discarding the first two functionalimages to allow scanner equilibrium effects, rigid-bodymotion correction and unwarping, slice timing correction,spatial normalization to the standard MNI template(resampled at 2 mm � 2 mm � 2 mm voxels) and spatialsmoothing (using an 8 mm full-width half maximum isotro-pic Gaussian kernel). Data were high-pass filtered toaccount for low-frequency drift; a cut-off value of 128was used.

In the first level of statistical analyses (single subject),the least squares parameter estimates of height of the bestfitting synthetic HRF for each condition were used inpair-wise contrasts and stored as a separate image for eachsubject. In the second level statistical analyses (group anal-ysis), a random-effects model was used. The images werethen tested against the null hypothesis that there is no dif-ference between conditions using one-sided t-tests. More-over, conjunction analyses were used to examine brainregions co-activated by the three forms (text, statisticalgraph, and statistical graph with text). To obtain an accu-rate result, a region was considered significant if it had 10or more contiguous voxels (80 mm3) and exceeded an alphathreshold of (p < 0.05, corrected). The coordinates given bySPM 2 were corrected to correspond to the atlas ofTalairach and Tournoux (1988).

3. Results

3.1. Behavioral results

As shown in Table 2, we analyzed the behavioral accu-racy from the independent behavioral experiment and thefMRI experiment. The results from the independent behav-ioral experiment revealed no significant difference betweenthe three presentation forms: statistical information in text(ST), statistical graph (SG), and statistical graph with text(SGT) (Analysis of variance between the forms showedthat: ST vs. SG [F(1,60) = 1.44, P = 0.23]; ST vs. SGT[F(1,60) = 1.00, P = 0.32]; SG vs. SGT [F(1,60) = 0.04,P = (0.85)]). Similarly, the accuracy from the fMRI exper-iment also showed that there was no significant differenceamong the three forms (analysis of variance between theforms showed that: ST vs. SG [F(1,71) = 0.03, P = 0.87];ST vs. SGT [F(1, 60) = 0.01, P = 0.90]; SG vs. SGT[F(1,60) = 0.07, P = (0.80)]). These results suggest thatthe three forms of statistical information have no signifi-cant effect on the comprehension of the content.

Fig. 2. Statistical parametric map (SPM) through the subjects normalizedaveraged brains of interesting regions for the conjunction of (ST > ST-baseline) and (SG > SG-baseline) and (SGT > SGT-baseline): (a) Theposterior tip of right PHC (BA 30) was significantly more activated; (b)

30 M. Li et al. / Cognitive Systems Research 17–18 (2012) 25–33

3.2. fMRI results

As shown in Table 3, the text (ST vs. ST-baseline), statis-tical graph (SG vs. SG-baseline), and statistical graph withtext (SGT vs. SGT-baseline) activated the posterior tip ofthe PHC (BA 30) adjacent to the anterior lingual gyrus,namely the PPA (Epstein & Kanwisher, 1998; Epstein,2008) and the amygdala (BA 34). We did conjunction anal-ysis on images obtained with the three presentation forms,and also found that the two regions were activated signifi-cantly above baseline under all three presentation condi-tions (Fig. 2). The results suggest that the two regionsboth contribute to statistical information processing,regardless of the presentation form. In our study, the socialstatistical information had two basic features: associationsand sociality. Bar et al. found that contextual associationprocessing was greater in the left PPA than the right(Aminoff et al., 2007; Bar & Aminoff, 2003). We found thatstatistical information processing activated the right PPAmore than the left, which may be relevant to the tasksinvolving complex information integration (St. George,Kutas, Martinez, & Sereno, 1999). Thus, we proposed thatthe posterior tip of the PHC is more involved in establishingassociations during social statistical information process-ing, and the amygdala is more related to the sociality.

Table 3Brain activations within the PHC and amygdala related to differentcontrasts (p < 0.05, corrected).

Anatomical regions Coordinatesa

x y z t Cluster size (mm3)

ST vs. ST-baseline

Lt. amygdala (BA 34) �30 �1 �20 7.76 2048Lt. arPHC (BA 34) �18 �1 �18 5.90 152Lt. arPHC (BA 34) �16 1 �15 5.87 80Rt. arPHC (BA 34) 16 3 �15 6.54 176Rt. prPHC (BA 30) 20 �46 4 6.44 2040

SG vs. SG-baseline

Lt. amygdala (BA 34) �30 �1 �20 8.94 1992Lt. Hippocampus �24 �9 �21 5.95 256Lt. prPHC (BA 37) �30 �41 �10 8.92 1808Lt. prPHC (BA 36) �20 �41 �10 8.43 648Rt. prPHC (BA 37) 30 �36 �12 7.43 264Rt. prPHC (BA 36) 22 �37 �8 8.47 1920Rt. midPHC (BA 36) 32 �22 �21 6.82 800Rt. prPHC (BA 30) 22 �45 2 7.49 1808

SGT vs. SGT-baseline

Lt. amygdala (BA 34) �30 �1 �20 6.42 794Lt. prPHC (BA 36) �20 �42 �8 8.21 4912Rt. prPHC (BA 36) 20 �39 �6 8.47 1392Rt. prPHC (BA 30) 22 �45 2 8.39 1872

Conjunction analysis

Lt. amygdala (BA 34) �30 �1 �20 6.42 800Rt. prPHC (BA 30) 20 �46 4 6.44 2008

ST: statistical information in text; SG: statistical information in graph;SGT: statistical information in statistical graph with text; Lt: left hemi-sphere; Rt: right hemisphere; ar: anterior portion; pr: posterior portion;PHC: paraphippocampal cortex; BA: Brodmann area.

a The Talairach coordinates of the centroid and associated maximum t

within contiguous regions are reported.

The left amygdala (BA 34) was significantly more activated; ST: statisticalinformation in text; SG: statistical information in statistical graph; SGT:statistical information in statistical graph with text; All of the statisticalparametric mapping t of the contrasts was thresholded att > 5.63(p < 0.05,corrected) and an 80 mm3.

Moreover, understanding the statistical informationfrom the text activated the anterior portion of the PHC,whereas understanding the statistical information fromthe statistical graph activated the posterior portion of thePHC, which suggest that the anterior PHC is moreinvolved in analysis of text, whereas the posterior PHC ismore involved in the analysis of statistical graphs. Theresults are consistent with previous finds showing thatnon-spatial contexts activated the anterior PHC, whereasspatial contexts activated the posterior PHC (Aminoffet al., 2007).

4. Discussion

In our study, fMRI results showed similar regional acti-vation patterns whether the social statistical informationwas described by text, statistical graphs, or statisticalgraphs with text. Conjunction analysis revealed co-acti-vated regions, namely the PHC (BA 30) and amygdala(BA 34), in response to all three presentation forms, consis-tent with our hypothesis. Therefore, the three forms acti-vated the same regions, indicating that the regions arenot related to form (visual, text), but only to the commoncognitive component – comprehension of the informationcontent.

M. Li et al. / Cognitive Systems Research 17–18 (2012) 25–33 31

The activation (BA 30) region at the posterior tip of thePHC is adjacent to the lingual gyrus and retrosplenial cor-tex (RSC). Epstein et al. termed the posterior tip of the PHCand the anterior lingual gyrus as the PPA (Epstein & Kanw-isher, 1998; Epstein, 2008). The posterior PHC and the RSCare involved in spatial processing (Aguirre, Detre, Alsop, &DEsposito, 1996; Cooper & Mizumori, 2001; Maguire et al.,

1997) and episodic memory (Ranganath & D’Esposito,2001), respectively. Furthermore, both the PPA and RSCas a whole system contribute to navigational tasks (Epstein,2008). Aminoff et al., 2007 found that this system also hasan important role in processing spatial and non-spatialassociations. In our study, the activation of the posteriorPHC was independent of the presentation form. Thus, thethree information carriers had a common cognitive compo-nent: social statistical information. The PHC (BA 30) wasactivated by both statistical graphs and text. Social statisti-cal information is typically associative information, thus thePHC (BA 30) activation is associated to associative infor-mation processing.

In addition, the left amygdala (BA 34) was also activated.A large number of previous studies have found that theamygdala is involved in emotional processing (Hamann,Ely, Hoffman, & Kilts, 2002; Wang, McCarthy, Song, &LaBar, 2005), as well as in emotion-related memory encod-ing (Canli, Zhao, Brewer, Gabrieli, & Cahill, 2000). Phelps(2004) found that the amygdala and hippocampal complex,although two independent memory systems, act in concertto promote and complete episodic representations of emo-tionally significant events and interpretation of events.Moreover, neuropsychological studies addressing the roleof the amygdala in spatial processing and episodic memoryfound that the amygdala is involved in episodic memory(Yancey & Phelps, 2001) but not in pure spatial processing(Bohbot et al., 1998). Furthermore, non-associative memo-ries activated a separate area of the right parahippocampalcortex while associative memories activate the left amygdala(Davachi et al., 2003). Kirwan and Stark (2004) demon-strated that the left amygdala and right posterior PHC arerequired for successful encoding of associated information.Furthermore, the amygdala plays a more important role insocial emotional processing than nonsocial. Using fMRI,Britton et al. (2006) directly compared brain activation dur-ing expression of social emotions and nonsocial emotions,and found that the amygdala was more strongly activatedby social emotions. Similarly, the amygdala was morestrongly activated while viewing pictures with a higher socialcontent (Harvey et al., 2007). Consistent with these findings,amygdala-lesioned patients showed greater difficulty recog-nizing social emotions and making trustworthiness judg-ments (Adolphs, Tranel, & Damasio, 1998; Adolphs,Baron-Cohen, & Tranel, 2002). In our experimental design,the three presentation forms (ST, SG, and SGT) did notcontain any emotional information but still activated theamygdala. Thus, the amygdala is not only associated withcognition of external emotional stimuli (bottom-up process-ing), but also with self-generated associations that evoke

emotional cognition (top-down). In bottom-up processing,emotionally salient information first evokes emotional reac-tions before being conveyed to the cortex for processing(and recognition of source and object). For example, fearfulstimuli will first activate the amygdala. In top-down process-ing, however, the emotion is generated during cognitive pro-cessing. For example, when people watch movies, thecerebral cortex first performs story processing, which thengenerate particular emotions through activation of theamygdala. The cognitive processing of social statisticalinformation will lead to self-references that generate socialemotions, leading to activation of the amygdala.

Additionally, the text more strongly activated the ante-rior portion of PHC, whereas the graph more strongly acti-vated the posterior portion of PHC, suggesting a functionaldissociation between anterior and posterior PHC forencoding and processing graphs and text. The anteriorPHC is a part of a verbal information processing network;thus, this region primarily encodes information about theidentities of objects. In contrast, the posterior PHC is adja-cent to the occipital and parietal lobe, which is a part of thevisuospatial processing network; thus, the posterior PHCprimarily encodes information about locations. Aminoffet al. (2007) reported that items from spatial contexts acti-vated the posterior part of the PHC, whereas items fromnon-spatial contexts activated the anterior PHC. The sta-tistical objects described by the form of text have a non-spatial context, while statistical graphs use spatial featuresto describe statistical objects, so the objects have a spatialcontext. Therefore, the anterior PHC is more involved inprocessing statistical information in the form of text,whereas the posterior PHC is more involved in processinginformation in the form of graphs.

In conclusion, both the PHC and amygdala are involvedin the comprehension of social statistical information inde-pendent of the form of presentation. The posterior tip ofthe PHC is more involved in processing associationsbetween social statistical information, while the amygdalais more related to sociality. These data provide the neuro-imaging evidence for these two basic features of social sta-tistical information.

Acknowledgments

This work was partially supported by the National Nat-ural Science Foundation of China (No. 60905027) and thegrant-in-aid for Scientific Research (No. 18300053) fromthe Japanese Ministry of Education, Culture, Sport, Sci-ence and Technology, and the Open Foundation of KeyLaboratory of Multimedia and Intelligent Software Tech-nology (Beijing University of Technology) Beijing.

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