Content uploaded by John D Ragland
Author content
All content in this area was uploaded by John D Ragland on Nov 15, 2017
Content may be subject to copyright.
Atypical Learning in Autism Spectrum Disorders: A Functional
Magnetic Resonance Imaging Study of Transitive Inference
Marjorie Solomon, PhD, J. Daniel Ragland, PhD, Tara A. Niendam, PhD, Tyler A. Lesh, PhD,
Jonathan S. Beck, BS, John C. Matter, BS, Michael J. Frank, PhD, and Cameron S. Carter,
MD
Drs. Solomon, Ragland, Niendam, Lesh, and Carter and Mssrs. Beck and Matter are at the
University of California, Davis (UC Davis). Drs. Solomon, Ragland, Niendam, Lesh, and Carter
are also with the UC Davis Imaging Research Center, Davis, CA. Drs. Solomon and Carter are
also with the MIND Institute, Davis. Dr. Carter is also with the Center for Neuroscience of UC
Davis. Dr. Frank is with Brown University, Cognitive, Linguistic, and Psychological Sciences,
Providence, RI.
Abstract
Objective—To investigate the neural mechanisms underlying impairments in generalizing
learning shown by adolescents with autism spectrum disorder (ASD).
Method—Twenty-one high-functioning individuals with ASD aged 12–18 years, and 23 gender,
IQ, and age-matched adolescents with typical development (TYP) completed a transitive inference
(TI) task implemented using rapid event-related functional magnetic resonance imaging (fMRI).
They were trained on overlapping pairs in a stimulus hierarchy of colored ovals where
A>B>C>D>E>F and then tested on generalizing this training to new stimulus pairings (AF, BD,
BE) in a “Big Game.” Whole-brain univariate, region of interest, and functional connectivity
analyses were used.
Results—During training, TYP exhibited increased recruitment of the prefrontal cortex (PFC),
while the group with ASD showed greater functional connectivity between the PFC and the
anterior cingulate cortex (ACC). Both groups recruited the hippocampus and caudate comparably;
however, functional connectivity between these regions was positively associated with TI
performance for only the group with ASD. During the Big Game, TYP showed greater recruitment
Correspondence to Dr. Marjorie Solomon, UC Davis Health System, MIND Institute, 2825 50th Street, Sacramento, CA;
marjorie.solomon@ucdmc.ucdavis.edu.
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our
customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of
the resulting proof before it is published in its final citable form. Please note that during the production process errors may be
discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Authors’ contributions: Dr. Solomon designed the study, oversaw all aspects of data collection and analysis, and wrote the manuscript.
Drs. Carter, Ragland, Frank, Niendam, and Lesh, and Mr. Beck and Mr. Matter made substantial contributions to study design, data
analysis, and interpretation. All co-authors read all drafts of the manuscript, and approved the final version.
Disclosure: Drs. Solomon, Ragland, Niendam, Lesh, Frank, Carter, and Messrs. Beck and Matter report no biomedical financial
interests or potential conflicts of interest.
Supplemental material cited in this article is available online.
HHS Public Access
Author manuscript
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November
01.
Published in final edited form as:
J Am Acad Child Adolesc Psychiatry. 2015 November ; 54(11): 947–955. doi:10.1016/j.jaac.2015.08.010.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
of the PFC, parietal cortex, and the ACC. Recruitment of these regions increased with age in the
group with ASD.
Conclusion—During TI, TYP recruited cognitive control-related brain regions implicated in
mature problem solving/reasoning including the PFC, parietal cortex, and ACC, while the group
with ASD showed functional connectivity of the hippocampus and the caudate that was associated
with task performance. Failure to reliably engage cognitive control-related brain regions may
produce less integrated flexible learning in those with ASD unless they are provided with task
support that in essence provides them with cognitive control, but this pattern may normalize with
age.
Keywords
learning; fMRI; adolescents; reasoning; problem solving
INTRODUCTION
Individuals with autism spectrum disorders (ASD) learn facts, details, and routines 1–6
relatively well but exhibit impairments in generalizing learning from one context to
another 7,8. This situation-focused learning profile may help explain their characteristic
behavioral inflexibility 9, which has a profound impact on their academic, social, and
adaptive functioning.
Transitive inference (TI) is a form of relational reasoning where training on adjacent pairs in
a hierarchy in which A>B>C>D>E>F produces generalization in the form of associations
between untrained novel pairs (e.g. B>D, B>E, and A>F). Extensive rodent 10,11, non-
human primate 12, computational modeling, and human neuroimaging literature about the
hippocampus 13–16, the striatum 17, and the PFC 18–21 have advanced understanding of the
neural substrates of this form of generalization, leading to the development of several
mechanistic models that can be used to derive testable hypotheses22.
One of these mechanistic models suggests that TI is the result of conjunctive encoding by
the hippocampus, which is thought to store memories of elements of different experiences
and to flexibly compare and recombine them to permit generalization of learning 22,23. A
second one of these models suggests that TI emerges due to the development of associative
strength-based reinforcement histories of stimuli, meaning that stimuli that are more
frequently reinforced develop stronger memory traces that support inferences based on their
relative values 24–26. Such reward-based working memories are thought to be produced by
the act of interworking of the striatum 27,28 and the prefrontal cortex (PFC)20,29–34. The
formation of a U-shaped serial position curve, whereby the outer end-item pairs, which have
higher relative values, show greater accuracy, has been taken as evidence for this view. 35,36
The more explicit and hippocampally-mediated learning suggested by the first model and
the more striatally-mediated learning suggested by the second one are thought to be
competitive35,37 in that they cannot occur simultaneously.
Findings of a recent behavioral study of TI suggest that young adults with ASD are poorer at
the generalization of learning assessed by TI, and may rely on a strategy involving
Solomon et al. Page 2
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
conjunctive representations by the hippocampus with less evidence of the beneficial
influences of striatally mediated associative strengths governing typical behavior 38. The
goal of the current study was to test this hypothesis using functional magnetic resonance
imaging (fMRI) in adolescents with ASD and TYP. We predicted that the group with ASD
would perform more poorly than TYP and use a conjunctive strategy evidenced by greater
hippocampal involvement, whereas the TYP group would use a more associative strategy as
evidenced by greater prefrontal, parietal, and striatal recruitment and functional
connectivity. Finally, we predicted that both groups would show a lack of simultaneous
hippocampal and striatal recruitment with no functional connectivity between these regions,
given that the neural substrates of conjunctive versus associative learning are thought to
operate competitively 35,37.
METHOD
Participants
Thirty individuals with ASD and 27 typically developing individuals were recruited through
psychiatrists, psychologists, speech and language pathologists, advocacy groups, state-
funded centers for persons with developmental disabilities, and MIND Institute’s Subject
Tracking System database and were enrolled in the study. The groups were matched for age,
gender, and IQ. One individual with ASD was removed due to less than chance performance
during training. Four individuals with ASD and 2 with TYP were excluded because they
showed root mean square motion (RMS) greater than 1mm. None were outliers based on
percent signal change (as calculated by the art_groupoutlier function from the ArtRepair
toolbox [http://cibsr.stanford.edu/tools/human-brain-project/artrepair-software.html] for
SPM8). Two additional individuals with TYP were excluded because their IQs were greater
than 2 standard deviations above the mean. Four with ASD were excluded because their IQ
scores were at the very low end of the borderline range and produced a sample unmatched
on IQ. The final sample included 21 adolescents with ASD (mean age = 15 years; SD = 1.9;
range = 12.2–17.9) and 23 with typical development (mean age = 14.8 years; SD = 1.9;
range = 12.3–17.8), who were matched on age, gender, RMS motion, and IQ. Four to five
women were enrolled in each group 39. See Table 1.
All participants had a Full Scale IQ > 70 on the Wechsler Abbreviated Scales of
Intelligence 40. Participants with ASD had scores in the autism spectrum range on the
Autism Diagnostic Observation Schedule-2 41 (ADOS-2), the Social Communication
Questionnaire42 (SCQ), and met diagnostic criteria based on a checklist of items from the
DSM-5 43. Exclusion criteria for participants with ASD included diagnoses with known
genetic etiologies, and current parent-reported diagnoses of depression, anxiety disorders, or
psychosis. Participants taking antipsychotic medications were excluded. The 1 participant
taking psychostimulants (from the group with ASD) was asked to stop for 48 hours prior to
the study. All other participants were psychotrophic medication free. After receiving a
complete description, parents of all participants gave written consent, and their minor
children gave assent to participate in the study, which was approved by the University of
California, Davis Institutional Review Board.
Solomon et al. Page 3
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Measures
Descriptions of standard measures used to diagnose ASD are included in Supplement 1,
available online.
Ovals TI Task 44 was adapted from Townsend, Richmond, Vogel-Farley, and Thomas
(2010) 45. Participants were trained on a hierarchy of six colored ovals where
(A>B>C>D>E>F) through presentations of 5 “premise” or trained pairs (AB, BC, CD, DE,
EF; see Figure 1[a]). After two training sessions, TI was tested through the presentation of 3
novel inference pairs (BD, BE, AF) without feedback during a “Big Game”. See Figure 1(b).
Timing for the task is shown in Figure 1(c). Jittering schedules were devised using Optseq 46
and ranged between 2–4 s for the inter-stimulus interval (ISI) and 2–8.5 seconds for the
inter-trial interval (ITI). See Figure 2. As more thoroughly described in Supplement 1,
available online, the task was designed to optimize participant performance. We used the
social stories technique that provided participants with simple scripts about events they
would encounter during testing; a graphic representation of the entire task with an indication
of where the participant was in the task at that point; frequent positive performance updates;
and prizes for good performance. Upon task completion, participants we assessed for
awareness of the hierarchy (the percentage of stimuli for which the correct position in the
hierarchy was reported) as awareness can be an important contributor to
performance 24,47,48. A chi-square test of independence was conducted and revealed no
significant difference in awareness between the ASD and TYP group (X2 = 7.27, p = .201).
Only participants with better than chance performance after training session 2 were retained
(1 participant with ASD was excluded).
Behavioral Data Analysis
To account for the repeated nature of the behavioral data in training sessions as well as for
the heterogeneity of variances across block, pairs, and diagnoses, between group differences
in stimulus pair accuracy was examined using linear mixed models implemented in SAS.
Data was transformed using a square root transformation to better approximate a normal
distribution.
fMRI Analyses
Information about imaging data acquisition and preprocessing can be found in Supplement
1, available online.
Imaging Data Analysis—We first report whole brain analyses followed by region of
interest (ROI) and functional connectivity analyses. In the whole brain analysis, at the first
level, regressors were included for each run and each pair type for both training sessions 1
and 2. Two sets of 2-way analyses of variance (ANOVAs) were performed at the second
level, which included contrast images of the stimulus or feedback phase as dependent
variables, both versus implicit baseline. For both ANOVAs, the between-subject factor was
diagnosis, and the within-subjects factor was block. Since the structure of the task during
training and test was not parallel (i.e. there was no feedback in the Big Game), we separately
examined group inference pair performance during the Big Game using the same approach.
Although the groups were matched, given the significant cognitive development occurring
Solomon et al. Page 4
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
during adolescence, we used age as a covariate. We report all positive effects. Analyses of
both the stimulus and feedback epochs included only correct trials to ensure that group
comparisons include only trials where participants are engaged in the task as recommended
by best practice parameters 49,50. There were no between group differences in numbers of
trials included in analyses ( t(42) = 0.91, p = .37). We thresholded random effects analyses
at a voxel-wise height threshold of t = 3.19 for a p < .001 and report clusters that are Family-
wise error-corrected (FWE) at p < .05 across the whole brain based on recent
recommendations for cluster-extent – based thresholding 51. Given that our task did not
utilize an implicit baseline, to give us greater confidence that our task was assessing TI
learning, versus lower level cognitive processes, we constructed Bayesian state-space
learning curves for each phase of the task for each individual participant, which were used
as parametric modulators in the general linear model (GLM). See Supplement 1, available
online, for within-group analyses, which demonstrate that our task captured higher level
learning processes in both groups.
To test hypotheses about the hippocampus and the caudate, we employed ROI and
functional connectivity analyses. We produced unbiased bilateral ROIs using the AAL
Atlas 52 for both the hippocampus (546 voxels) and the caudate (546 voxels). Parameter
estimates extracted from these regions during the feedback phases of the task were subjected
to t-tests and correlations with Bonferroni correction.
To test hypotheses about functional connectivity with other brain regions, we used cognitive
control related seed regions in the PFC, ACC, and parietal cortex, and the putamen, for
which there were group differences in whole-brain analyses during training sessions 1 and 2,
and/or the Big Game. These functional seeds were prepared by using a 5mm sphere around
the peak of each seed (Brodmann area [BA]40 [−48 −37 31], BA9 [−30 20 40] BA24 [−6
−19 46]). Functional connectivity analyses were conducted using the beta series correlation
method 53 with custom-written Matlab 54 scripts. See Supplement 1, available online, for a
more extensive discussion of this method and motion scrubbing 55,56.
RESULTS
Behavioral Results
There were significant fixed effects of session (F(1,42) = 19.07, p < .001); individual pair
type (F(1,284) = 71.35, p < .001); and a session by individual pair type interaction (F(1,284)
= 67.90, p < .001). However, there were no significant interactions with diagnosis. Mean
accuracy rates for both groups were lower in the second, more challenging, session where
trials were presented in a mixed versus sequential order. Overall, performance on end item
pairs was better. The session by pair type interaction was driven by the fact that accuracy
rates for inner pairs during the second session were significantly different from those in the
first session (t(81.5) = 8.21, p < .001), whereas this was not the case for the outer pairs ((t) =
1.52, p=.13), suggesting that both groups showed a more characteristic U-shaped serial
position curve whereby outer pair (AB, EF) accuracy was higher than inner pair accuracy
(BC, CD, DE) by the second training session. This pattern is characteristic of associative
learning.35,37 During the Big Game, Student’s t-tests showed there were no group
differences in premise or inference pair performance (all p’s > .3). However, for the group
Solomon et al. Page 5
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
with ASD, there was a significant negative correlation between inference performance and
SCQ scores that remained after co-varying age (r = −.60, p = .004).
Whole-Brain Analyses
A 2×2 ANOVA was conducted using age in months as a covariate to investigate neural
recruitment during the stimulus phase of training sessions 1 and 2, which revealed a positive
effect of the task in both groups involving recruitment of regions involved in relational
reasoning19 including bilateral cerebellum ([−24, −64, −17], [−36, −49, −32], and [27, −70, -
17]); right occipito-temporal cortex (RBA 37 [42, −67, −2]; left ACC (BA 32 [−3, 5, 43]);
and left premotor regions (BA 4 [−39, −25, 64] and [−30 −28 70]). Consistent with our
hypothesis, there was a main effect of diagnosis such that the TYP group showed greater
recruitment of the left dorsolateral PFC (BA 9 [−30, 20, 40]. They also showed greater
recruitment in left sensory cortex (BA 2 [−54,-31,37]). See Figure 3 and Table S1, available
online.
A second 2×2 ANOVA using age as a covariate was conducted to investigate neural
recruitment in the feedback phase of training sessions 1 and 2, which revealed a positive
effect of task, with both groups showing elevated activity in the body of the caudate
bilaterally ([−18, −10, 31], [18, 17, 19]), and in the right tail of the caudate ([24, −43, 16]).
There were no significant group differences or interactions in the feedback phase.
During the Big Game, there was greater recruitment of the left inferior lateral parietal lobe
(BA40 [−48 −37 31]), the left anterior cingulate (BA24 [−6 −19 46]), and the left putamen
([−27 −13 1]) in the TYP group with no other significant main effects or interactions. There
was also an effect of age in the group with ASD revealing greater recruitment of the right
dorsolateral PFC (RBA9 [24 32 28]), the bilateral posterior cingulate (RBA31 [15 −64 16],
LBA31[−6 −28 40], [0 −37 40], [0 −19 46], RBA23[6 −61 16]), the bilateral extrastriate
cortex (RBA19[36 −79 22], [36 −79 13], LBA19[−36 −82 19]), the left anterior cingulate
(LBA32[−3 44 16], [0 35 22], LBA24[−6 11 31]), the left superior temporal sulcus
(LBA39[−45 −52 7]), the left superior temporal gyrus(LBA22[−51 −61 16]), and the
anterior portion of the right premotor cortex (RBA8[24 35 43], [18 38 52]).
ROI Analyses
Counter to hypotheses, ANOVAs using parameter estimates averaged over the hippocampal
ROI showed there were no significant group differences in the recruitment of the
hippocampus throughout training (all p's > .14). There also were strong positive associations
between recruitment of the hippocampus and the caudate for both groups (ASD: r =.651, p=.
001; TYP: r =.455, p=.003) during training. See Figure 4.
Functional Connectivity Analyses
There were no group differences in the whole-brain functional connectivity analyses
conducted with the bilateral Atlas-derived seeds in the hippocampus and caudate. During the
feedback phase of Training Block 2, there was greater functional connectivity in the ASD
versus the TYP group between the left dorsolateral PFC seed (BA9 [−30, 20, 40], BA 8 [24,
14, 40]), and the dorsal ACC (BA 32 [18, 8, 49]). There were no significant differences for
Solomon et al. Page 6
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
the PFC, parietal cortex, and ACC, derived from areas of group difference in whole-brain
analyses for training sessions 1 and 2 and the Big Game.
Brain Function and Big Game Performance
An ANOVA was conducted to examine the effects of hippocampus and caudate functional
connectivity on task performance in individuals with ASD and TYP. While there were no
main effects of functional connectivity strength or group, there was a significant interaction
of functional connectivity strength and group (F(1,40)=12.06, p=.001). Functional
connectivity between the hippocampus and the caudate during training was positively
associated with Big Game performance for the group with ASD, and negatively associated
with performance for TYP at a trend level (ASD: r = .645 p = .001; TYP: r = −.347, p = .
105). See Figure 4.
DISCUSSION
We used fMRI and a newly adapted child and ASD-friendly TI paradigm to investigate
whether the neural substrates of learning in adolescents with ASD and TYP was more
consistent with a conjunctive or an associative learning strategy. Contrary to hypotheses, the
group with ASD showed comparable task performance to TYP, and incorporated elements
of both conjunctive and associative learning strategies when completing the task. Supportive
of the contention that they used associative learning, the group with ASD showed a U-
shaped serial position curve by the end of training and recruitment of the striatum during
feedback processing that was comparable to TYP. Furthermore, they exhibited functional
connectivity between the hippocampus and the caudate that was positively associated with
Big Game performance. The TYP group also evidenced associative learning in their
recruitment of the caudate during feedback processing. However, compared to individuals
with ASD, they showed greater recruitment of cognitive control-related brain regions in the
PFC, parietal cortex, and ACC during learning and the Big Game. The group with ASD
appeared to “catch up” to TYP in their recruitment of these brain regions during the BIG
Game. Unexpectedly, there also was strong functional connectivity between the
hippocampus and caudate during learning in both groups, although it was positively
associated with task performance in those with ASD and negatively associated with
performance in those with TYP.
As is commonly found in studies of individuals with ASD 57, affected adolescents used
alternative task strategies. Recently, it has been suggested that when the PFC cannot be
brought online “proactively” to sustain task-based working memories due to patients’
cognitive control deficits 58,59, they may engage in a less efficient strategy where rules and
task memories are retrieved from the hippocampus “reactively” on a trial-by-trial basis
engendering greater response conflict involving the ACC 60. Findings of the current study
for the group with ASD (reductions in PFC recruitment during training; greater PFC/ACC
functional connectivity; and the relationship between hippocampal connectivity and Big
Game performance) are reminiscent of this pattern. Interestingly, the lack of group
differences in the training session feedback phase suggests that those with ASD are able to
process feedback comparably to TYP, and that it is the inability to represent, versus process
Solomon et al. Page 7
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
this feedback, that is impaired in ASD. Perhaps because the participants with ASD relied
more on hippocampal conjunctive encoding during learning, the extent to which they made
successful inferences depended on additional connectivity of the hippocampus with the
caudate, allowing their associative learning abilities to contribute to TI performance. In fact,
the prevailing view is that implicit learning, which is reliant on the striatum, is relatively
intact in ASD 38,61,62 (but see 63–65 for examples showing impairments in learning related to
motor tasks and to 6 for a study showing slower implicit learning). Recently, it also has been
suggested that the hippocampus and the caudate interact cooperatively during spatial
information processing such as that involved in conceptualizing a stimulus hierarchy 66,67,
especially in cases where environments share elements like the hierarchy we employed 68.
This raises the possibility that such spatial information processing mechanisms may be used
by those with ASD to compensate for PFC impairments.
While hypotheses about the TYP group were not entirely confirmed, the brain regions used
by this group were consistent with reasoning/problem solving research that views TI as a
form of deductive reasoning subserved by a network that also includes occipital, parietal,
temporal, and anterior prefrontal regions, in addition to the striatum 69. According to this
view, occipito-temporal cortex and visual cortical brain regions permit premise pair
processing, with information integration recruiting the PFC and the ACC 19.
Previously our group found interesting group differences in performance on end-item pairs38
that were not replicated in the current study. This may have been a consequence of the
highly ASD- and child-friendly task design, which included frequent instructions and
progress reports presented visually as is recommended by ASD clinical experts 70. Few were
unable to learn the task, suggesting we successfully ameliorated the generalized deficits
observed in patients71. Another possible explanation for the failure in replication is that
current study participants were adolescents versus the adults from the prior one. TYP adults
may show continued cognitive development into adulthood72, which produces performance
on end item pairs that is superior to same-aged adults with ASD. While our findings of
increased recruitment of the brain regions associated with mature problem solving in the
group with ASD with age would argue against this interpretation, the prevalence and extent
of this catch up and its relationship to behavior remain unclear.
The current study is limited in several respects. Although it met benchmarks for adequate
fMRI sample size 49, recent criticisms about relatively small n’s in such studies (e.g. 73) are
well-taken. Given the heterogeneity present in ASD and the variable cognitive strategies
affected individuals are known to utilize, a larger study including a wider cognitive ability
range would permit better exploration of potential ASD learning phenotypes. Finally,
although the use of Bayesian state space learning curves in both groups provided confidence
that our task assessed learning versus lower level perceptual and motor processes, it was
designed without an explicit baseline condition. Future studies should include a clearer
baseline and/or more trials to increase the power of learning curve-based analyses to detect
group differences.
In conclusion, the current study suggests interesting directions for future research with
implications for educational and psychosocial intervention. For example, studies that
Solomon et al. Page 8
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
manipulate the task supports provided during learning -- such as we did somewhat
inadvertently with our ASD- and child-friendly new paradigm – can be used to investigate
the mechanisms by which learning and problem solving in those with ASD can be made
more flexible and integrative of contextual information, and the degree to which such
supports attenuate group performance differences. This is consistent with both the social
stories approach mentioned above, which provides students with clear and explicit scripting
about what to expect and with an extensive body of work suggesting that learning and
memory can be enhanced when task support is provided at the time of testing 74. The study
of the relationship between such experimental studies and real world behavior at school and
other environments holds the potential to motivate new interventions that optimize learning,
promote more flexible attention allocation, and improve daily adaptive functioning.
Furthermore, the study was conducted as follow-up to our prior behavioral study of
transitive inference, which was provocative in demonstrating that young adults with ASD
showed an AE pair versus a BD pair deficit as in common in groups with psychopathology
including persons with schizophrenia (e.g.75). Although the current study was not
longitudinal, and did not include a second comparison group, it was designed as a necessary
first step towards understanding the neural mechanisms underlying our provocative findings
about TI in young adults with ASD, and a precursor to a larger developmental study of
adolescents and young adults with ASD and schizophrenia that would investigate
dissociations between the development of prefrontal and hippocampal neural mechanisms of
learning and memory in these two patient groups. Such a study also could help us further
investigate whether individuals with ASD increasingly recruit brain regions involved in
mature problem solving as they become young adults, and whether this maturation
influences the cognitive strategies they employ during daily living.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
During this work, Dr. Solomon was supported by an R21 from the National Institute of Mental Health (1R21
MH099250-01). Dr. Carter was supported by the National Institute of Mental Health (2R01 MH059883-05A1 and
1R24MH081807). Dr. Niendam was supported by the National Institute of Mental Health (K23MH087708). Dr.
Ragland was supported by the National Institute of Mental Health (R01MH084895, Ragland, Principle Investigator
[PI]). Dr. Frank was supported by the National Science Foundation (Proposal 1125788, Frank, PI) and the National
Institute of Mental Health (R01 MH080066-01, J.M. Gold, PI). Dr. Ana-Maria Iosif, IDDRC Biostatistics Core
MIND Institute (grant #U54 HD079125), served as the statistical expert for this research.
The authors acknowledge Edward Owens, BA, of the University of California, Davis, for his assistance with data
analysis during his time as a paid research assistant. The authors also would like to thank Kathleen Thomas, PhD,
Associate Professor of the University of Minnesota, and Elise Townsend, DPT, PhD, PCS, Associate Professor of
the MGH Institute of Health Professionals, for sharing the behavioral version of the TI Ovals Task used in the
study. They also would like to thank the participants and their families.
REFERENCES
1. Toichi M, Kamio Y. Long-term memory and levels-of-processing in autism. Neuropsychologia.
2002; 40(7):964–969. [PubMed: 11900748]
2. Hermelin, B.; O’Connor, N. Psychological Experiments with Autistic Children. Oxford: Pergamon
Press; 1970.
Solomon et al. Page 9
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
3. Walenski M, Mostofsky S, Gidley-Larson J, Ullman M. Brief Report: Enhanced picture naming in
Autism. Journal of Autism and Developmental Disorders. 2008; 38(7):1395–1399. [PubMed:
18163206]
4. Loth E, Happe F, Gomez JC. Variety is not the spice of life for people with autism spectrum
disorders: frequency ratings of central, variable and inappropriate aspects of common real-life
events. J Autism Dev Disord. 2010; 40(6):730–742. [PubMed: 20066484]
5. Happe F, Vital P. What aspects of autism predispose to talent? Philos Trans R Soc Lond B Biol Sci.
2009; 364(1522):1369–1375. [PubMed: 19528019]
6. Solomon M, Smith AC, Frank MJ, Ly S, Carter CS. Probabilistic reinforcement learning in adults
with autism spectrum disorders. Autism Res. Apr. 2011; 4(2):109–120.
7. Stokes TF, Baer DM. An implicit technology of generalization. J Appl Behav Anal. Summer. 1977;
10(2):349–367.
8. Plaisted, D. Reduced Generalization in Autism: An Alternative to Weak Central Coherence. In:
Burack, JA.; Charman, T.; Yirmiya, N.; Zelazo, PR., editors. The Development of Autism:
Perspectives From Theory and Research. Mahwah, NJ: Lawrence Erlbaum Associates, Inc; 2001. p.
149-169.
9. Geurts HM, Corbett B, Solomon M. The paradox of cognitive flexibility in autism. Trends Cogn
Sci. Feb. 2009; 13(2):74–82.
10. Devito LM, Kanter BR, Eichenbaum H. The hippocampus contributes to memory expression
during transitive inference in mice. Hippocampus. Jan. 2010; 20(1):208–217.
11. DeVito LM, Lykken C, Kanter BR, Eichenbaum H. Prefrontal cortex: role in acquisition of
overlapping associations and transitive inference. Learn Mem. Mar. 2010; 17(3):161–167.
12. Buckmaster CA, Eichenbaum H, Amaral DG, Suzuki WA, Rapp PR. Entorhinal Cortex Lesions
Disrupt the Relational Organization of Memory in Monkeys. The Journal of Neuroscience. 2004;
24(44):9811–9825. [PubMed: 15525766]
13. Greene AJ, Gross WL, Elsinger CL, Rao SM. An FMRI analysis of the human hippocampus:
inference, context, and task awareness. J Cogn Neurosci. Jul. 2006; 18(7):1156–1173.
14. Heckers S, Zalesak M, Weiss AP, Ditman T, Titone D. Hippocampal activation during transitive
inference in humans. Hippocampus. 2004; 14(2):153–162. [PubMed: 15098721]
15. Zalesak M, Heckers S. The role of the hippocampus in transitive inference. Psychiatry Res. 2009
Apr 30; 172(1):24–30. [PubMed: 19216061]
16. Nagode JC, Pardo JV. Human hippocampal activation during transitive inference. Neuroreport.
2002 May 24; 13(7):939–944. [PubMed: 12004195]
17. Prado J, Chadha A, Booth JR. The brain network for deductive reasoning: a quantitative meta-
analysis of 28 neuroimaging studies. Journal of cognitive neuroscience. 2011; 23(11):3483–3497.
[PubMed: 21568632]
18. Acuna BD, Eliassen JC, Donoghue JP, Sanes JN. Frontal and parietal lobe activation during
transitive inference in humans. Cereb Cortex. Dec. 2002; 12(12):1312–1321.
19. Fangmeier T, Knauff M, Ruff CC, Sloutsky V. FMRI evidence for a three-stage model of
deductive reasoning. J Cogn Neurosci. Mar. 2006; 18(3):320–334.
20. Kumaran D, Summerfield JJ, Hassabis D, Maguire EA. Tracking the emergence of conceptual
knowledge during human decision making. Neuron. 2009; 63(6):889–901. [PubMed: 19778516]
21. Wendelken C, Bunge SA. Transitive inference: distinct contributions of rostrolateral prefrontal
cortex and the hippocampus. J Cogn Neurosci. 2009; 22(5):837–847. [PubMed: 19320546]
22. Libben M, Titone D. The role of awareness and working memory in human transitive inference.
Behav Processes. 2008; 77(1):43–54. [PubMed: 17703897]
23. Smith C, Squire LR. Declarative Memory, Awareness, and Transitive Inference. The Journal of
Neuroscience. 2005; 25(44):10138–10146. [PubMed: 16267221]
24. Frank MJ, Rudy JW, Levy WB, O’Reilly RC. When logic fails: implicit transitive inference in
humans. Mem Cognit. Jun. 2005; 33(4):742–750.
25. Wynne CDL. Reinforcement Accounts for Transitive Inference Performance. Animal Learning &
Behavior. 1995; 23(2):207–217.
Solomon et al. Page 10
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
26. Frank MJ, Seeberger L, O’Reilly R. By carrot or by stick: Cognitive reinforcement learning in
Parkinsonism. Science. 2004; 306:1940–1943. [PubMed: 15528409]
27. Graybiel AM. Habits, rituals, and the evaluative brain. Annu Rev Neurosci. 2008; 31:359–387.
[PubMed: 18558860]
28. Jog MS, Kubota Y, Connolly CI, Hillegaart V, Graybiel AM. Building neural representations of
habits. Science. 1999 Nov 26; 286(5445):1745–1749. [PubMed: 10576743]
29. Badre D, Frank MJ. Mechanisms of hierarchical reinforcement learning in cortico-striatal circuits
2: evidence from fMRI. Cereb Cortex. 2012 Mar; 22(3):527–536. [PubMed: 21693491]
30. Frank MJ, Badre D. Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1:
computational analysis. Cereb Cortex. 2012 Mar; 22(3):509–526. [PubMed: 21693490]
31. Wendelken C, Chung D, Bunge SA. Rostrolateral prefrontal cortex: domain-general or domain-
sensitive? Hum Brain Mapp. 2012 Aug; 33(8):1952–1963. [PubMed: 21834102]
32. Nee DE, Brown JW. Rostral-caudal gradients of abstraction revealed by multi-variate pattern
analysis of working memory. Neuroimage. 2012 Nov 15; 63(3):1285–1294. [PubMed: 22992491]
33. Badre D, D’Esposito M. Is the rostro-caudal axis of the frontal lobe hierarchical? Nature Reviews
Neuroscience. 2009 Sep; 10(9):659–669. [PubMed: 19672274]
34. Koscik TR, Tranel D. The human ventromedial prefrontal cortex is critical for transitive inference.
J Cogn Neurosci. 2012 May; 24(5):1191–1204. [PubMed: 22288395]
35. Frank MJ, O’Reilly R, Curran T. When memory fails, intuition reigns: Midazolam enhances
implicit inference in humans. Psychological Science. 2006; 17:700–707. [PubMed: 16913953]
36. Vasconcelos M. Transitive inference in non-human animals: an empirical and theoretical analysis.
Behav Processes. 2008 Jul; 78(3):313–334. [PubMed: 18423898]
37. Poldrack RA, Packard MG. Competition among multiple memory systems: converging evidence
from animal and human brain studies. Neuropsychologia. 2003; 41(3):245–251. [PubMed:
12457750]
38. Solomon M, Frank MJ, Smith AC, Ly S, Carter CS. Transitive inference in adults with autism
spectrum disorders. Cogn Affect Behav Neurosci. 2011 Sep; 11(3):437–449. [PubMed: 21656344]
39. Nyden A, Hjelmquist E, Gillberg C. Autism spectrum and attention-deficit disorders in girls. Some
neuropsychological aspects. Eur Child Adolesc Psychiatry. 2000; 9:180–185. [PubMed:
11095040]
40. Wechsler, D. Wechsler Abbreviated Scale of Intelligence - Second Edition: Manual. Pearson;
2011.
41. Lord C, Risi S, Lambrecht L, et al. The autism diagnostic observation schedule-generic: a standard
measure of social and communication deficits associated with the spectrum of autism. Journal of
Autism and Developmental Disorders. 2000 Jun; 30(3):205–223. [PubMed: 11055457]
42. Rutter, M.; Bailey, A.; Lord, C. SCQ: Social communication questionnaire. Los Angeles, CA:
Western Psychological Services; 2003.
43. APA. DSM-5. Washington DC: American Psychiatric Association; 2013.
44. Solomon, M.; Carter, CS. Ovals Transitive Inference Task. Davis: University of California; 2014.
45. Townsend EL, Richmond JL, Vogel-Farley VK, Thomas K. Medial temporal lobe memory in
childhood: developmental transitions. Dev Sci. 2010 Sep 1; 13(5):738–751. [PubMed: 20712740]
46. Optseq [computer program]. Boston, MA: Harvard University; 2006.
47. Moses SN, Villate C, Ryan JD. An investigation of learning strategy supporting transitive
inference performance in humans compared to other species. Neuropsychologia. 2006; 44(8):
1370–1387. [PubMed: 16503340]
48. Kumaran D, Melo HL, Duzel E. The emergence and representation of knowledge about social and
nonsocial hierarchies. Neuron. 2012 Nov 8; 76(3):653–666. [PubMed: 23141075]
49. Carter CS, Heckers S, Nichols TE, Pine DS, Strother S. Optimizing the design and analysis of
clinical functional magnetic resonance imaging research studies. Biological Psychiatry. 2008;
64(10):842–849. [PubMed: 18718572]
50. Carter CS, Pine DS. Polishing the windows of the mind. American Journal of Psychiatry. 2006
May; 163(5):761–763. [PubMed: 16648308]
Solomon et al. Page 11
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
51. Woo CW, Krishnan A, Wager TD. Cluster-extent based thresholding in fMRI analyses: pitfalls and
recommendations. Neuroimage. 2014 May 1.91:412–419. [PubMed: 24412399]
52. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labeling of
activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject
brain. Neuroimage. 2002 Jan; 15(1):273–289. [PubMed: 11771995]
53. Rissman J, Gazzaley A, D’Esposito M. Measuring functional connectivity during distinct stages of
a cognitive task. NeuroImage. 2004; 23(2):752–763. [PubMed: 15488425]
54. Natick M. The Math Works, MATLAB. 2000
55. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic
correlations in functional connectivity MRI networks arise from subject motion. Neuroimage.
2012 Feb 1; 59(3):2142–2154. [PubMed: 22019881]
56. Van Dijk KR, Sabuncu MR, Buckner RL. The influence of head motion on intrinsic functional
connectivity MRI. Neuroimage. 2012 Jan 2; 59(1):431–438. [PubMed: 21810475]
57. Sohn MH, Goode A, Koedinger KR, et al. Behavioral equivalence, but not neural equivalence--
neural evidence of alternative strategies in mathematical thinking. Nat Neurosci. Nov. 2004; 7(11):
1193–1194.
58. Solomon M, Ozonoff SJ, Ursu S, et al. The neural substrates of cognitive control deficits in autism
spectrum disorders. Neuropsychologia. 2009 Oct; 47(12):2515–2526. [PubMed: 19410583]
59. Solomon M, Yoon JH, Ragland JD, et al. The development of the neural substrates of cognitive
control in adolescents with autism spectrum disorders. Biological psychiatry. 2014; 76(5):412–
421. [PubMed: 24209777]
60. Braver TS. The variable nature of cognitive control: a dual mechanisms framework. Trends Cogn
Sci. 2012 Feb; 16(2):106–113. [PubMed: 22245618]
61. Barnes K, Howard J, Howard D, et al. Intact implicit learning of spatial context and temporal
sequences in childhood autism spectrum disorder. Neuropsychology. 2008 Sep; 22(5):563–570.
2008. [PubMed: 18763876]
62. Barnes SJ, Finnerty GT. Sensory experience and cortical rewiring. Neuroscientist. 2010 Apr;
16(2):186–198. [PubMed: 19801372]
63. Gidley Larson JC, Mostofsky SH. Evidence that the pattern of visuomotor sequence learning is
altered in children with autism. Autism Research. 2008; 1(6):341–353. [PubMed: 19360689]
64. Mostofsky SH, Goldberg MC, Landa RJ, Denckla MB. Evidence for a deficit in procedural
learning in children and adolescents with autism: implications for cerebellar contribution. Journal
of the International Neuropsychological Society. 2000; 6:752–759. [PubMed: 11105465]
65. Travers BG, Kana RK, Klinger LG, Klein CL, Klinger MR. Motor Learning in Individuals With
Autism Spectrum Disorder: Activation in Superior Parietal Lobule Related to Learning and
Repetitive Behaviors. Autism Research. 2015; 8(1):38–51. [PubMed: 25258047]
66. Dahmani L, Bohbot VD. Dissociable contributions of the prefrontal cortex to hippocampus- and
caudate nucleus-dependent virtual navigation strategies. Neurobiol Learn Mem. 2015 Jan.117:42–
50. [PubMed: 25038426]
67. Woolley DG, Mantini D, Coxon JP, D’Hooge R, Swinnen SP, Wenderoth N. Virtual water maze
learning in human increases functional connectivity between posterior hippocampus and dorsal
caudate. Human brain mapping. 2015; 36(4):1265–1277. [PubMed: 25418860]
68. Brown TI, Ross RS, Tobyne SM, Stern CE. Cooperative interactions between hippocampal and
striatal systems support flexible navigation. Neuroimage. 2012 Apr 2; 60(2):1316–1330. [PubMed:
22266411]
69. Goel V. Anatomy of deductive reasoning. Trends in cognitive sciences. 2007; 11:435–41.
[PubMed: 17913567]
70. Karkhaneh M, Clark B, Ospina MB, Seida JC, Smith V, Hartling L. Social StoriesTM to improve
social skills in children with autism spectrum disorder: A systematic review. Autism. 2010 Oct 5;
14(6):641–662. [PubMed: 20923896]
71. Chapman LJ, Chapman JP. The measurement of differential deficit. J Psychiatr Res. 1978; 14(1–
4):303–311. [PubMed: 722633]
Solomon et al. Page 12
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
72. Dumontheil I, Burgess PW, Blakemore SJ. Development of rostral prefrontal cortex and cognitive
and behavioural disorders. Dev Med Child Neurol. 2008 Mar; 50(3):168–181. [PubMed:
18190537]
73. Button KS, Ioannidis JP, Mokrysz C, et al. Power failure: why small sample size undermines the
reliability of neuroscience. Nat Rev Neurosci. 2013 May; 14(5):365–376. [PubMed: 23571845]
74. Bowler DM, Gardiner JM, Berthollier N. Source memory in adolescents and adults with
Asperger’s syndrome. Journal of autism and developmental disorders. 2004; 34(5):533–542.
[PubMed: 15628607]
75. Titone D, Ditman T, Holzman PS, Eichenbaum H, Levy DL. Transitive inference in schizophrenia:
impairments in relational memory organization. Schizophr Res. 2004 Jun 1; 68(2–3):235–247.
[PubMed: 15099606]
Solomon et al. Page 13
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Figure 1.
Transitive inference task. (A) A 5-pair hierarchy of colored ovals is presented in the task.
There were 6 different oval orders administered to reduce the potential for confounding by
individual stimuli. Ovals constitute a stimulus hierarchy in which A>B>C>D>E>F. (B)
Schedule shown to participants at the beginning of the task and at the beginning of training
sessions 1 and 2 and the Big Game. It shows that training occurs in brief sessions after
which participants are shown their performance, and that training sessions conclude with
Solomon et al. Page 14
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
several practice trials. It also shows that after the Big Game, participants can pick a prize
based on their earnings from the task. (C) Timing of the task.
Solomon et al. Page 15
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Figure 2.
Accuracy rates on the Ovals Transitive Inference Task during training and the Big Game.
Note: Both groups show the formation of a serial position curve by the end of Training
Block 2, suggesting they both use associative learning. There are no group differences in
inference performance. ASD = autism spectrum disorder; TYP = typically developing.
Solomon et al. Page 16
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Figure 3.
Significant group differences in neural recruitment during training and the Big Game. Note:
The typically developing (TYP) group shows greater activation in brain regions including
the left prefrontal cortex and the left superior temporal sulcus during the stimulus phase of
training than the group with autism spectrum disorder (ASD). During the Big Game, the
TYP group shows greater recruitment of the posterior cingulate and pre-motor areas than the
group with ASD. Both groups show activation in the caudate bilaterally during the feedback
phase of training. These are not shown since there were no group differences. LBA = left
Brodmann area.
Solomon et al. Page 17
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Figure 4.
Competition or cooperation between the hippocampus and the caudate. (A) Top two graphs
show that neural activity in the hippocampus as operationalized by parameter estimates is
positively correlated in both the autism spectrum disorder (ASD) and typically developing
(TYP) groups. (B) The bottom graph shows that functional connectivity between the
hippocampus and the caudate is positively related to Big Game inference performance in the
group with ASD.
Solomon et al. Page 18
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Solomon et al. Page 19
Table 1
Participant Characteristics
ASD TYP
N 21 23
Gender (M:F) 17:4 18:5
M(SD) M(SD)
Age (years) 15(1.9) 14.8(1.9)
FSIQ-4 100.9(14.3) 104.5(7.5)
VCI 99.9(14.5) 105.3(8.2)
PRI 103(15.9) 102.7(10.4)
ADOS 6.8(1.5) --
ADOS: Severity 6.3(1.8) --
SCQ 23.6(4.4) 3.3(2)
SRS 73.1(9.7) 43.4(8.2)
RMS Motion 0.36(0.23) 0.26(0.18)
Training 1 0.86(0.09) 0.89(0.06)
Training 2 0.73(0.17) 0.76(0.11)
All Pairs 0.68(0.25) 0.73(0.28)
Training 1 RT 788.35(134.63) 765.92(93.48)
Training 2 RT 848.29(155.14) 887.30(116.07)
Big Game RT 773.88(134.30) 828.19(135.75)
Awareness 0.48%(0.41%) 0.38%(0.35%)
Note: ADOS = Autism Diagnostic Observation Schedule; ASD = autism spectrum disorder; FSIQ-4 = Full Scale IQ on the Wechsler Abbreviated
Scale of Intelligence (which consists of 4 subscales); PRI = Perceptual Reasoning Index; RMS = root mean square motion; RT = reaction time;
SCQ = Social Communication Questionnaire; SRS = Social Responsiveness Scale; TYP = typically developing; VCI = Verbal Comprehension
Index.
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2016 November 01.
