Neural and behavioural correlates of science reasoning during

adolescence.

Jack White-Foy

Birkbeck, University of London

& University College London (Institute of Education)

ABSTRACTInhibitory control is thought to play a role in conceptual change; whereby a

naïve idea is inhibited in favour of the correct scientific one but is never truly

erased. Activation of the anterior cingulate cortex and dorsolateral prefrontal

cortex have been associated with better performance on misconception tasks

in children and adults and could represent a network of error detection and

response inhibition. Adolescence represents a period of development in brain

structure and function but little research into science reasoning and

conceptual change has been undertaken with this age group. Using modified

versions of the Stroop and Go/No-Go tasks with 20 adolescent participants

aged 11-15 years old, semantic and response inhibition performance were

measured. Using functional magnetic resonance imaging data, the left

dorsolateral prefrontal cortex and pre-supplementary motor area showed

greater activation during unique science tasks but was not associated with

better performance on the misconception questions. Whilst there was no

relationship between inhibitory control and performance on misconception

tasks, better verbal IQ, working memory and a larger reaction time cost were

associated with better performance. Implications for teaching and

opportunities for future research are discussed.

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1. INTRODUCTIONHeavier objects fall faster than lighter objects; organisms have chosen their

adaptations; gravity pulls objects downwards (Driver, Guesne, & Tiberghien,

1985). These are just some of the many misconceptions that students can

bring with them when they start formal education (Brault Foisy, Ahr, Masson,

Borst, & Houdé, 2015). Some students go on to demonstrate more

sophisticated and accurate understanding of science concepts, whilst others

enter adulthood still with these naïve and incorrect ideas about the world

(Dunbar, Fugelsang, & Stein, 2007). One of the many challenges faced by a

teacher of science is to ensure that misconceptions are abandoned in favour

of scientific ones (Duit & Treagust, 2003). The purpose is not only to help their

students pass public examinations but also to ensure that they leave school

with the skills to understand future scientific discoveries and theories (e.g.

climate change) (diSessa, 2006). Such a task can be described as a

challenge due to the often prevalent and robust nature of misconceptions and

the abstract features of science concepts to be learned (Chi, 2005). Achieving

conceptual change, the movement from a naïve to an expert understanding of

a concept, has long been the focus of a large and still growing body of

research (Brault Foisy, Ahr, et al., 2015). Understanding how conceptual

change happens is therefore of great importance to educators and society.

1.1 Conceptual changeDifferent teaching methods have been proposed, which highlights that there is

no consensus on how to achieve conceptual change or on the underlying

theoretical framework (Dunbar et al., 2007). Wiser & Carey (1983) suggested

that conceptual change is similar to a scientific revolution, whereby a

paradigm that is no longer capable of satisfactorily explaining evidence is

abandoned in favour of one that provides a better explanation. Although a

scientific revolution at a societal level is likely to differ to one at a personal

level (Karmiloff-Smith, 1988), similar conditions need to be met: a student

must be dissatisfied with their existing concept, the replacement concept must

be understandable, believable and promise to help explain or discover new

ideas (Posner, Strike, Hewson, & Gertzog, 1982).

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To put this within the context of a classroom, teachers would first identify the

naïve ideas their students hold. Then they would present students with a

problem to explain using their ideas. Upon being confronted that their naïve

ideas do not provide a solution, the teacher would introduce the new more

appropriate concept (the scientific one). Following this, students would

explicitly contrast both their naïve and scientific concepts (Chi, 2008). The

hope is that students would recognise that their naïve ideas are not as good

at explaining the problem as the scientific one (Rowell & Dawson, 1985).

Goswami (2008) argued that conceptual change is not static but instead a

continuous cycle between equilibrium (where a concept satisfactorily explains

observations), experience followed by disequilibrium (where the naïve theory

does not explain the new experiences), assimilation of a new concept

followed by the return to equilibrium. In much the same way, Inagaki & Hatano

(2002) suggested that when presented with a scientific theory, a students’

naïve theory is disrupted. To regain stability the student must modify their

concept or replace it altogether. This is not always the case, however, due to

resistance to change; if a student’s naïve concept is robust they may be in

denial that anything requires changing and can instead add to their existing

theory to make it fit better to the perceived disruption (diSessa, 2006).

The difficulty with this explanation is that investigations have found mixed

results when studying conceptual changes. Teaching approaches that work

for some students do not work for others and what may first appear as

conceptual change does not last (Vosniadou, 2002). This throws up the next

major consideration for conceptual change research: when conceptual

change occurs, what happens to the naïve theory? Some argue that for

conceptual change to occur, the naïve theory must be eliminated to make way

for the scientific one, since both cannot be held at the same time; others

argue that the naïve theory and the new scientific one coexist (diSessa,

2006). Evidence from research currently favours the latter argument (Dawson,

2014).

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1.2 Are naïve theories really eliminated?Misconceptions can originate in children from a very young age, when they

begin to observe the world around them and create simple rules about

themselves, others and their environment. For example, by the age of four or

five months, children develop a concept for object permanence (Dunbar et al.,

2007). When an object is shown to be hidden beneath a cover, the infant will

find it, recognising that the object has not vanished even though it is not

visible to them anymore. This concept is not the result of formal training but is

developed from the infant’s own observations and experience (Gropen, Clark-

Chiarelli, Hoisington, & Ehrlich, 2011). Concepts such as these are referred to

as heuristics: simple and quick rules about various phenomena (Brault Foisy,

Potvin, Riopel, & Masson, 2015). Algorithms, on the other hand, are slower

and more accurate logic-based calculations, relying on conscious processing

(Gropen et al., 2011). Adults have developed greater control over which level

of processing to choose, recognising when the experiential approach

(heuristic) is inappropriate and allowing more time for the analytic (algorithm)

(Gropen et al., 2011).

Early on in development, heuristics may help a child to navigate the world

around them but they can become insufficient to explain more complex

phenomena. Heuristics then become misconceptions and may hinder

understanding and problem-solving (Flavell, 1985). This is particularly

important in secondary school, which exposes children to abstract concepts in

biology, chemistry and physics (Driver, Squires, Rushworth, & Wood-

Robinson, 2015). A classic experiment by Piaget (1997) demonstrated the

‘length-equals-number’ misconception. Children were presented with two

parallel lines each with the same number of dots. In one of the lines the dots

were more spread out (Figure 1B). Children between one and seven years of

age incorrectly stated that the longer line contains more dots, demonstrating

that they were relying on their naïve idea of this relationship (Houdé, 2000).

To complete this task successfully and state that the number of dots is equal,

children needed to abandon the heuristic and count each dot (Borst, Simon,

Vidal, & Houdé, 2013).

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Figure 1: (A) Children over two years old correctly stated that there are equal numbers of blue and red dots (B) Children aged two to seven years incorrectly stated that there are more blue dots than red dots.

After seven years of age, typically developing children no longer demonstrate

the misconception that length equals number. This could support the idea that

for conceptual change to occur, the naïve idea is deleted and replaced with a

more accurate one. This theory, however, is inadequate to explain further

investigations. Piaget (1997) investigated development of object permanence

using the ‘A-not-B’ experiment (an extension of the object permanence

experiment). Up to a year old, once an infant has learned that an object is

hidden under cover A, they will continue to search under cover A even when

the object is moved under cover B in full view (Figure 2).

Figure 2: The A-not-B task. Infants aged up to a year still search under cover A (brown) despite having seen the object being moved under cover B (grey) (figure adapted from Marcovitch & Zelazo, 1999).

Piaget (1997) argued this was a violation of object permanence whereby the

infant was unable to coordinate and activate the heuristic that would have

allowed them to find the object. Lewandowsky & Li (1995) suggested instead

that there was competition between two coexisting memory traces: the

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memory of the role of Cover A and the memory of seeing the object move

under Cover B. Diamond (1998) however, argued that both the length-equals-

number and A-not-B tasks show a failure to inhibit the naïve idea, which

would have allowed the algorithm to be processed. Dempster & Brainerd

(1995) supported this conclusion, stating that poor performance on these

tasks is due to the inability to inhibit the interference from the heuristic, rather

than inappropriate selection of the correct conceptual framework to follow.

These two examples of early development highlight the recurring finding that

misconceptions can reappear even after they have supposedly been erased.

Rowell and Dawson (1977) described a series of studies that investigated the

success and duration of conceptual change in adolescents. They found that

some students reverted to naïve ideas after a few weeks or a few months

after demonstrating successful understanding of a new concept. They also

found that that some students correctly answered questions that were

phrased in one context but reverted to misconceptions for another, even if

both questions related to the same concept. This finding is not unique to

children; even professional scientists can demonstrate naïve and incorrect

ideas when reaction time is limited (Masson, Potvin, Riopel, & Brault Foisy,

2014). This adds support to the model that following conceptual change,

naïve and expert concepts can coexist into adulthood.

Further evidence for this model came from studies with Alzheimer’s patients.

Lombrozo, Keleman, & Zaitchik (2007) discovered that participants reverted

back to naïve concepts when their cognitive abilities diminished. If conceptual

change is the deletion and replacement of ideas, either conceptual change did

not take place in these Alzheimer’s patients, or their scientific ideas were

replaced with new naïve ones. Alternatively, their naïve ideas survived

conceptual change and somehow these patients reverted back to them

(diSessa, 2006).

The principles of neural plasticity can go someway to explaining why

memories for naïve concepts can appear to be so robust. Memories are

formed from the strengthening of synapses between neurons within a neural

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network (Gazzaniga, Ivry, & Mangun, 2009). Synapses are strengthened

following repeated activation from experience, observations and interactions

(Huttenlocher, 2002). Whilst memories can be fragile as they move from the

hippocampus (short term) to the cerebral cortex (long term), once established

they only deteriorate slowly after non-use. The idea that conceptual change

occurs by eradicating an entire concept, in a single lesson or even a short

series of lessons, is not supported by the neural basis for memory formation

and forgetting. Mefoh (2010) argued that the appearance of forgetting is

actually due to competition for retrieval between long-term memory traces.

The question becomes whether conceptual change is the appropriate

activation of the scientific concept, the inhibition of the incorrect naïve

concept, or a combination of the two.

1.3 The role of inhibitory controlThere is strong evidence that for a scientific theory to be expressed, the

misconception must be inhibited. As discussed above, heuristics, which form

the basis of misconceptions, are accessed faster than algorithms.

Unfortunately for learners, the more challenging scientific concepts rely on

algorithms in order to solve problems. Lewandowsky & Li (1995) suggested

that there is competition, likened to a race, between memory traces. If it were

just a simple race that determined which concept was expressed, the

heuristic, being faster, would always win. Furthermore, reaction times are

longer for correct expert responses compared to incorrect novices responses,

which suggests that additional processing is taking place (Babai &

Amsterdamer, 2008). This cannot be easily accounted for by the selection-

activation hypothesis alone. If that were the case the same reaction time on

either a naïve or a scientific answer would be expected. The novice who

expresses the misconception has either not learned the correct concept, or is

unable to inhibit their naïve concept and so the heuristic wins the race. An

expert, on the other hand, is able to inhibit the misconception allowing the

correct view to be expressed. The additional processing involved in inhibitory

control could account for the longer time taken to respond. If inhibitory control

plays a role in conceptual change, a question relevant to education is whether

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a student’s ability to inhibit responses makes the process of conceptual

change particularly difficult.

The Stroop task and the Go/No-Go task have been used to investigate

inhibitory control. Different performance on these two tasks suggests that

there may be different aspects of inhibitory control; whereby the Stroop task

targets semantic inhibition (ignoring interference to give a correct response)

whereas the Go/No-Go task targets response inhibition (identifying the correct

stimulus that warrants a response) (Morooka et al., 2014). The original Stroop

task presents a participant with a series of words of colours. The participant

must read the word out loud, and ignore the colour of the letters. In the simple

version of the task, the colour of the word is congruent to the word itself (e.g.

‘Blue’). In the complex version of the task, the colour of the letters is

incongruent to the word itself (e.g. ‘Blue’). To be successful, participants must

inhibit their desire to state the colour of the letters and instead read the word.

Children and adults alike take longer to complete the incongruent than the

congruent trials. Inhibitory control generally improves with age, though when

pressed for time, performance is reduced (Morooka et al., 2014).

In the Go/No-Go task, participants are asked to respond to one stimulus (the

‘Go’ trial) but not another (the ‘No-Go’ trial). Accuracy and reaction time

generally improve with age resulting in fewer incorrect responses for the ‘No-

Go’ trial (Cragg & Nation, 2008). Yet some studies have found that

adolescence sees an increase in reaction times, which can be due to simply

slowing down as well as some responses being initiated and then stopped

(Cragg & Nation, 2008). Accuracy still increases but at the expense of faster

reaction times. Neuroimaging studies on inhibitory control tasks have shown

that, in adults, performance on these tasks was associated with greater

activation of the ventrolateral and dorsolateral prefrontal cortex (VLPFC and

DLPFC respectively) and the anterior cingulate cortex (ACC) (Crone & Dahl,

2012; Durston et al., 2002). An adolescent’s brain is very different to that of a

child or an adult: white matter volumes change significantly during

adolescence (O’Hare & Sowell, 2008) and various structural and functional

changes take place, both in a linear and non-linear trajectory (Dumontheil,

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Houlton, Christoff, & Blakemore, 2010). Mason & Just (2016) have shown that

different brain regions are activated during inhibitory control tasks at different

ages during adolescence. Figure 3 summarises changes in activation through

adolescence during inhibitory control, working memory and switching tasks

(Durston et al., 2002; Velanova, Wheeler, & Luna, 2008; Crone & Dahl, 2012).

Figure 3: Functional changes during adolescence (Crone & Dahl, 2012).

The pattern of changes in activation during inhibitory control tasks is

inconsistent and varies between studies. Several functional magnetic

resonance imaging (fMRI) studies have found an increase in activation of the

prefrontal cortex (PFC) (Adleman et al., 2002; Rubia et al., 2006; Koolschijn,

Schel, de Rooij, Rombouts, & Crone, 2011; Smith, Halari, Giampetro,

Brammer, & Rubia, 2011), the ACC (Koolschijn et al., 2011) and the inferior

frontal gyrus (IFG) (Tamm, Menon, & Reiss, 2002; Smith et al., 2011), relative

to baseline. Others have found a decrease in activation in these regions

(Crone & Dahl, 2012). These findings suggest that adolescence is a period in

development where engagement of the PFC varies in different tasks, which

could represent different strategies to achieve the same goals and

performance as adults (Dumontheil et al., 2010).

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1.4 Neural correlates of conceptual changeFaced with competing and inconsistent approaches to conceptual change,

researchers have turned to neuroimaging to help identify the underlying

neural processes that occur with conceptual change, and whether the

evidence would support a role of inhibitory control in the resolution of

misconceptions. In doing so, it is hoped that a clearer and agreed theoretical

model can be developed, by accessing processes that are unavailable to

behavioural studies alone (Cacioppo, Berntson, & Nusbaum, 2008).

Fugelsang & Dunbar (2005) demonstrated that information is treated

differently depending on whether it agrees or conflicts with existing ideas.

When participants were shown new data that agreed with a learned rule, the

caudate and parahippocampal gyrus were more activated than baseline.

These regions have previously been associated with learning (Durston et al.,

2002). When the data conflicted, ACC, precuneus and DLPFC were activated

more than baseline (Fugelsang & Dunbar, 2005). The ACC is associated with

error detection and conflict monitoring, whilst the DLPFC is associated with

inhibitory control and response selection (Botvinick, 2007). These brain

regions were found to be still differentially activated even after several trials,

suggesting their activation was not related to learning, which had presumably

already taken place.

Dunbar et al. (2007) went on to show that physics experts showed greater

activation of the ACC compared to baseline when viewing two balls of

different sizes hit the ground at different times (a common misconception),

whereas novices showed greater activation of the ACC when viewing the two

balls hit the ground at the same time (the scientifically correct version).

Dunbar et al. (2007) concluded that the experts had undergone conceptual

change and so the ACC detected conflict between the incorrect stimulus and

their correct scientific belief. Interestingly, half of the novices were still able to

give a correct answer, which suggests that whilst they may not have

undergone conceptual change, they were still able to give a correct answer

without understanding why.

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Brault Foisy, Potvin, et al. (2015) found that, compared to experts, novices

showed more activation in the ACC when evaluating non-scientific than

scientific stimuli relative to baseline, despite novices scoring lower than

experts. Brault Foisy, Potvin, et al. (2015) proposed that this was because

novices were at a late stage of conceptual change but not sufficiently

advanced to answer the questions correctly. The authors went on to suggest

that their results showed that the participants were able to recognise when an

intuitive response was given, which made them aware of their potential biases

and hence activated the ACC. De Neys, Vartanian, & Goel (2008) argued that

the ACC can be activated for both correct and incorrect answers but the PFC

is only activated when giving correct answers. Several studies have echoed

these findings with different types of task: electricity (Masson, Potvin, Riopel,

Brault Foisy, & Lafortune, 2012), mechanics (Dunbar et al., 2007) and

chemistry (Nelson, Lizcano, Atkins, & Dunbar, 2007). These studies found

greater activation of the ACC and DLPFC in adults when answering

misconception questions correctly.

Brault Foisy, Potvin, et al. (2015) emphasised the difficulty of measuring

inhibitory control at the precise moment of answering a science task. They

argued that by measuring activity in brain areas associated with inhibitory

control, it would reveal to what extent inhibitory control was being engaged.

One issue with previous neuroimaging studies on conceptual change is that

they rely on reverse inference, namely on concluding that inhibitory control is

recruited when ACC and/or DLPFC show increased activation. However the

ACC and DLPFC show activation in a wide range of tasks beyond inhibitory

control or conflict resolution tasks (Duncan, 2010; Crittenden & Duncan,

2014). Variability in activation during inhibitory control tasks compounds the

difficulty in attempting to interpret and generalise brain activation during

science tasks, particularly when comparing one age group with another.

1.5 The present study The findings from the behavioural and neuroimaging studies discussed here

offer valuable insights into the possible mechanisms behind conceptual

change. A limitation arises by the participants’ ages in these studies. Either

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very young children or adults were used. In terms of conceptual change, there

are very few studies on adolescence, even though as reviewed above it is a

time of development of inhibitory control, in addition to significant changes in

brain structure and brain function more broadly.

The present study followed on from previous behavioural work by the lead

investigator on this project (Brookman, 2015), which investigated in an

adolescent sample the role of inhibitory control in answering questions on

science and mathematics (Brookman, 2015). Results showed that students

who scored higher on inhibitory control tasks were better at answering

science and mathematics misconception questions correctly (Brookman,

2015), suggesting that inhibitory control played a role when answering

questions in science and mathematics, and supporting previous evidence in

children and adults.

The present study is part of a neuroimaging project in adolescents and had

two lines of inquiry. The first was the relationship between inhibitory control

and performance on the science misconception questions. Control tasks were

included which tested working memory, verbal IQ, reasoning IQ and

analogical reasoning. These control tasks were included to assess the

specificity of the relationship between inhibitory control and the science tasks.

Several studies have linked working memory to inhibitory control and

resistance to interference. Engle (2005) argued that working memory is an

attentional control system evolved to prevent interference. Ohlsson (2009)

suggested that conceptual change relies on the ability of learners to recognise

analogies between different domains; whereby understanding can be

transferred from a simple idea to a complex one. The second line of inquiry

investigated activation of the prefrontal cortex, in particular the ACC and

DLPFC as regions of interest, during science misconception questions. The

final line of inquiry was whether activation of these two areas was associated

with higher accuracy and longer reaction times on science misconception

questions. The main hypotheses were:

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i) Better performance on behavioural tasks (inhibitory control, working

memory and analogical reasoning) would be associated with better

accuracy and reaction time on science misconception questions.

ii) The ACC and DLPFC would be activated more when answering

misconception questions relative to baseline.

iii) Activation of the ACC and DLPFC would be associated with better

accuracy and longer reaction times when answering misconception tasks.

2. METHOD2.1 ParticipantsParticipants were recruited from a selective and fee-paying public secondary

school in East London. A total of 20 participants took part, of these 10 were

male, 10 were female. Ages were between 11 and 15 years (mean = 13.7, SD

= 1.2). All participants were screened for learning difficulties, mental health

issues and behavioural problems, none of which were present.

Ethics approval was granted on 28 January 2016 by the local ethics

committee. All participants and parents provided written consent and were

reminded that they had the right to withdraw at any time during or after the

testing session.

2.2 ProcedureTasks measuring inhibitory control and science understanding were

conducted in two stages: the first stage was practice and was carried out on a

laptop outside of the scanner. The second stage was carried out inside the

scanner and constitutes the data used for analysis. The lead investigator

carried out the scanning, whilst I assisted with the practice phase and other

data collection outside the scanner. To measure working memory, we used a

backward digit task and a visuospatial task (VSWM). To measure inhibitory

control we used the Stroop task (congruent and incongruent) and Go/No-Go

tasks (simple and complex).

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2.3 Tasksi) Inhibitory Control

a) Go/No-Go

The Go/No-Go task was adapted from Watanabe et al. (2002). This task

followed a block design, made up of three block types: Go blocks (100% Go

blocks), simple Go/No-Go (50% Go, 50% No-Go) and complex Go/No-Go

(50% Go, 50% No-Go) blocks (Figure 4A). Each trial lasted for 1.1 seconds.

There were a total of 20 trials per block, with four repeats of each block type.

Fixation blocks were presented at 10 seconds at the start, 15 seconds at the

middle of the task and 10 seconds at the end of the task. In each block, 50%

of the trials were Go, which always required a response and 50% were No-Go

where no response was expected.

Figure 4: Go/No-Go task. (A) Timing of task blocks. (B) Example trials of the Go, simple Go/No-Go and complex Go/No-Go blocks.

In Go blocks, participants were asked to press a key to indicate on which side

of the screen a beige square was shown, using their left and right index

fingers (Figure 4B). In simple Go/No-Go blocks, participants were again asked

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to press a key to indicate on which side of the screen the beige square was

shown, while they were told not to press any buttons when they square was

blue (Figure 4B). Complex Go/No-Go blocks followed a 1-back paradigm to

add a working memory load. Participants were asked to press a button using

their index fingers to indicate which side a yellow or pink square was shown

but only if the square was the same colour on the current trial as on the

previous trial (Figure 4B).

Participants were given a practice round before scanning. During this time,

the instructor could explain the instructions, which were presented on screen,

check understanding of the instructions and answer any questions. If the

participant made three or more errors out of 15 trials, the programme would

inform them of such and restart the practice until they made no more than two

errors. Accuracy on Go and No-Go trials were recorded and reaction times

were recorded for Go trials. The test run in the scanner lasted 5.9 min in total.

b) Numerical Stroop task

This variation of the Stroop task was adapted from Brookman (2015), and

followed a block design made up of two alternating block types, which

included either 100% congruent trials or 50% congruent trials and 50%

incongruent trials. Each trial lasted 1.5 seconds. There were a total of 14 trials

per block, with five repeats of each block type (Figure 5A). Fixation baseline

blocks were also presented for 10 seconds at the start; 15 seconds in the

middle of the task and 10 seconds at the end of the task, as in the Go/No-Go

task.

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Figure 5: Numerical Stroop task. (A) Timing of task blocks. (B) Example trials of the congruent and mixed blocks.

Participants were shown one, two, three or four digits in the middle of the

screen, which were formed of the digits 1, 2, 3 or 4. Participants were asked

to press a key to indicate how many of the digits they could see but to ignore

the digit itself. On congruent trials, the number of digits and the digits

themselves matched, for example, ‘3 3 3’ where the correct answer would be

to press the key corresponding to “3”. On incongruent trials, the number of

digits and the digits themselves did not matched, e.g. ‘4 4’, where the correct

answer would be to press the key corresponding to “2”. Participants

responded with their middle and index fingers of both hands, 1 being the left-

most response, 4 the right-most response. Figure 5B shows example

sequences of trials in congruent and mixed (50% congruent, 50%

incongruent) blocks. The test run in the scanner lasted 4.9 min in total.

ii) Science Knowledge and Misconceptions

This task was modified from a previous study carried out by the lead

investigator in 2015. Feedback on these stimuli was sought from specialist

subject teachers in two schools, covering biology, chemistry, physics and

mathematics. Using this feedback, the stimuli were improved and aimed to

test participants’ knowledge and understanding of concepts in science and

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mathematics. Concepts were chosen that were thought to access common

misconceptions that students typically demonstrate in lessons at this age

range. This was confirmed using national curriculum data, research by Driver

et al. (2015), analysis of schemes of work across different GCSE courses

(Edexcel, AQA and OCR exam boards) and consulting subject specialist

teachers. The focus of this dissertation is on scientific misconceptions and so

the data for mathematical performance are not included.

The task comprised four runs alternating between science-related and

mathematics-related questions. Participants were pseudo-randomly assigned

to two alternative sequences: i) science-maths-science-maths or ii) maths-

science-maths-science. There were a total of 96 trials; 48 trials that accessed

a common misconception and 48 control questions that were designed to

relate to the same topic but not to a misconception. An equal number of trials

were presented that related to biology, chemistry and physics. There were

four types of trials: i) a misconception trial that was presented as a false

statement, ii) a misconception trial presented as a true statement, iii) a control

trial presented as a false statement, or iv) a control trial presented as a true

statement. Each slide had a grey background to reduce contrast in the

scanner and therefore eye fatigue, and included a mixture of text and

drawings/schemas to make the task more engaging (Figure 6A). Participants

were asked to select whether they thought the statement was definitely true,

probably true, probably false or definitely false, using their middle and index

fingers. These four options allowed an estimation of how confident

participants were in their answer (Figure 6A).

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Figure 6: Science knowledge and misconception task. (A) Example stimuli. (B) Example sequence of trials.

Each trial lasted for a maximum of 16 seconds. Trials would end upon a

response from a participant, or would proceed automatically after 12 seconds.

Once participant had responded or when 12 seconds had passed the stimulus

disappeared and participants were either presented with a fixation cross or

where asked to press a left/right key with their index fingers to indicate the

direction of arrows presented on the screen, up to 16 seconds after the

science or maths stimulus was initially presented (Figure 6B). Additional

fixation blocks were presented for 10 seconds at the start of run; 15 seconds

in the middle of the run and 10 seconds at the end of the run.

Participants were asked to be as quick as they could without making errors.

Participants were told that the response buttons would be surrounded by a

red box if they had not responded and only had three seconds to do so.

iii) Visuospatial working memory task

To test visuospatial working memory, participants were shown a 4x4 grid on

the screen. A series of dots would appear on the screen one at a time in

different locations in the grid. Participants were asked to recall the positions

and order that the dots appeared in and were then asked to use a computer

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mouse to indicate the sequence by clicking on each location in turn. If the

participant made an error in reproducing the sequence, the message ‘Wrong’

would flash on the screen and the next sequence would be shown. The length

of the sequences increased from three dots per sequence until a maximum of

eight dots per sequence until the participant got three or more sequences

wrong out of four at a given level (Figure 7A). The visuospatial working

memory score is the total number of correctly recalled sequences.

Figure 7: Example stimuli of the (A) Visuospatial working memory task; (B) Matrix reasoning subtest of the WASI (Wechsler, 2011); (C) Analogical reasoning task.

iv) Verbal working memory task

To test participants’ verbal working memory, they were presented with a

sequence of digits between ‘1’ and ‘9’ verbally by the experimenter and were

asked to repeat the sequence back to the experimenter in reverse order.

Trials increased in length by one digit from two digits upwards until the

participant got two or more sequences wrong out of the four trials of a level.

Reaction time was not recorded. The backwards digit score is the total

number of sequences correctly recalled.

v) Wechsler Abbreviated Scale of Intelligence (WASI)

The Vocabulary and Matrix Reasoning subtests of the WASI (Wechsler, 2011)

were used to assess participants’ general ability. In particular it was important

to assess each participant’s level of verbal IQ to give an indication of their

accessibility of the stimuli in both the science and maths questions and the

analogical reasoning tasks. This was to help identify if any participants were

scoring particularly low on any of these tasks simply because they did not

know what the words meant, rather than due to a misconception or

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misunderstanding of the analogical relationship between analogical pairs. In

the Vocabulary subtest participants are asked what a series of words mean.

The Matrix Reasoning subtest is a non-verbal reasoning task, which took the

form of shapes and patterns in a sequence. Participants were asked to select

from a list of potential responses as to which would come next in the

sequence, based on the previous ones (Figure 7B).

vi) Analogical reasoning

Analogies were presented to participants using online Google Forms. They

were told that they would be presented with a series of analogies in the form

of A:B::C:D, whereby A has a relationship to B and C has a relationship to D.

Participants were told that the type of relationship between A and B was

similar to the relationship between C and D. Participants were presented with

A:B and C. They were asked to choose D from a list of potential responses

(Figure 7C).

The stimuli were adapted from a study by Leech, Mareschal, & Cooper

(2007). This study tested analogical reasoning in students of a similar age to

the participants, which meant we were less likely to experience a ceiling or

floor effect on performance. There were 24 questions in total. The questions

were modified to make them more accessible in terms of vocabulary, using

more common alternatives (e.g. unjust instead of bigoted). In addition, some

words were outdated in terms of social experience, and were therefore

replaced with similar but more modern alternatives (e.g. CD player instead of

cassette player).

Participants were first given four practice analogies, during which time they

could ask any questions and the experimenter could explain any incorrect

answers. They then answered the questions at their own pace. Participants

were told to be as quick as possible without making any mistakes. The time to

complete all tasks was recorded using an Apple iPhone. Timing started when

participants scrolled down to the first test question and ended when

participants clicked on the ‘submit’ button at the bottom of the page containing

the questions. The number of correct responses was also recorded.

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2.4 Statistical AnalysisOne-way repeated measures Analysis of Variance (ANOVAs) were performed

to analyse the Go/No-Go task data and paired samples t-tests for the

Numerical Stroop data. Whilst not the main focus of this study, it is useful to

confirm that these tests are presenting a challenge to the participants in line

with previous findings. Due to the sample size, the sample was considered as

a single age group. Whilst it is acknowledged that there may be individual

differences due to age, which is included as a predictor in the regressions,

analysis of performance on the behavioural tests was not carried out with age

as a factor.

Standard analysis of fMRI data was performed by the lead investigator using

Statistical Parametric Mapping software, who provided me with mean

parameter estimates within a pre-supplementary motor area (pre-SMA)

cluster extending into the ACC and a DLPFC cluster corresponding to

activation in each science problem trial type. Scores for each science

(biology, chemistry and physics) were calculated as a percentage, broken

down by question type (control or a misconception) and the format of the

question (true or false). Reaction times were also recorded and presented in

milliseconds. Following statistical analysis of the neuroimaging data

(Appendix 1), regions of interest in the pre-supplementary motor area (pre-

SMA) (cluster extending into the ACC), left DLPFC and the primary and

secondary visual cortex were identified in the contrast for all science trials

versus baseline. For the purposes of this investigation, only the left DLPFC

and pre-SMA were included in the analyses due to their association with

inhibitory control. One-way repeated measures ANOVAs were conducted to

determine whether there was a statistically significant difference in activation

in these regions during different types of science question, whether

misconception (correct or incorrect) or control correct (there were not enough

incorrect control trials for fMRI data analysis of this trial type).

Regression analyses were carried out using the general linear model. The first

of these was to investigate the relationship between the behavioural tasks and

performance (accuracy and reaction time) on the science misconceptions.

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The impact of inhibitory demand was measured using a cost value; calculated

on the Stroop task as the difference in accuracy and reaction times between

the congruent and incongruent trials. For the Go/No-Go, in addition to the

accuracy scores, two costs were calculated each for reaction time. These

were: (a) the difference between simple Go trials and Go Only trials and (b)

the difference between the complex 1-back Go trials and the simple Go trials.

Regression analyses were also carried out to investigate the relationship

between brain activation of the pre-SMA and the left DLPFC on accuracy and

reaction time when answering science misconception tasks.

Tests for collinearity were carried out but none were present. There was no

collinearity as VIF was below 10 and the tolerance value was above .2 on all

analyses. Exclusionary criteria were put in place, to identify outliers further

than 3.29 SD from the mean. In addition, visual examination of boxplots and

multivariate analysis using Cook’s distance, Mahalanobis distance and

standardised DFFIT were used to identify outliers that could be influencing the

data.

Six multiple regression analyses were carried out to examine the relationships

between variables on either accuracy or reaction time when answering

science misconception tasks, entering behavioural task performance, pre-

SMA activation or DLPFC activation as possible predictors. In the first multiple

regressions with behavioural data only, Block 1 contained age, gender and

accuracy/reaction time on control questions; using the enter method. The

latter predictor was included as performance was considered to reflect

general science knowledge and was assumed to be related to performance

on the subsequent misconception tasks that were on the same topic. The

remaining blocks used the stepwise method and included the following

predictors: Go/No-Go accuracy and reaction time costs (simple-Go Only and

complex-simple), Stroop accuracy and reaction time costs (incongruent-

congruent), WASI matrix IQ and verbal IQ (Wechsler, 2011), backward digit

score, visual working memory score and analogical reasoning score and time

taken. The stepwise method was chosen as no specific hypotheses were held

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as to which variables would account for the greatest variance in science

accuracy or reaction times.

For the regressions investigating the associations of the pre-SMA and LDPFC

activations, the same control variables were entered in Block 1 using the enter

method (age, gender, accuracy/reaction time on control science questions).

Activation of the pre-SMA and LDPFC when answering the science control

questions and science misconception questions (correctly and incorrectly)

were entered into Block 2 separately using the stepwise method. Again, there

were no specific hypotheses as to which activation event would have the

greatest association.

Figure 8: Activation during Science Control and Misconception trials (lateral view of

the left hemisphere, medial view of the left hemisphere, lateral view of the right

hemisphere) voxel pFWE <0.05. ROIs used in the analyses are highlighted. NB:

pFWE is the family wise error rate, which is the probability that one of the values will

be greater than the alpha (Friston, Ashburner, Kiebel, Nichols, & Penny, 2007).

3. RESULTS3.1 Stroop taskA paired samples t-test was carried out to determine whether participants

scored significantly better when presented with congruent trials than

incongruent trials. Congruent trials of congruent and mixed blocks were

combined (although see Figure 9A for data split according to block type).

Participants were less accurate on incongruent trials (M = .61, SD = .09) than

congruent trials (M = .91, SD = .06) (t(19) = 20.299, p < .001, d = 4.55).

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Figure 9: Numerical Stroop task behavioural results. (A) Accuracy (M ± SE) as a function of trial and block types. (B) RT (M ± SE) as a function of trial and block types.

Similarly a paired samples t-test was run on Stroop reaction time (RT) data.

Participants responded more slowly on incongruent trials (M = 748, SD = 61

ms) than on congruent trials (M = 641, SD = 48 ms, Figure 9B) (t(19) = -

19.179, p < .001, d = 4.28).

In summary, participants were significantly faster and more accurate on the

congruent compared to the incongruent trials.

3.2Go/No-Go tasksA one-way repeated measures ANOVA was run on the Go/No-Go accuracy

data to compare performance on the five trial types (Go in Go blocks, Go and

No-Go trials in simple blocks, and Go and No-Go trials in complex blocks).

Mauchly's test of sphericity indicated that the assumption of sphericity had

been violated, χ2(9) = 19.27, p = .024. Epsilon (ε) was 0.67 and was used to

correct the ANOVA. Results show a main effect of trial type (F(2.68, 48.22) =

12.79, p = <.001). Planned post-hoc comparisons show that there was no

difference in accuracy between Go trials in Go blocks and in simple blocks (p

> .05) however participants were less accurate in Go trials in complex blocks

than Go trials in Go blocks and in simple blocks (p = <.05). Within the

complex task, accuracy did not differ between Go and No-Go trials (p > 05),

while within the simple task, accuracy was lower in No-Go trials than Go trials

(p = <01) (Figure 10A).

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Figure 10: Go/No-Go behavioural data. (A) Accuracy (M ± SE) as a function of trial and block types. (B) RT (M ± SE) as a function of trial and block types.

A one-way repeated measures ANOVA was carried out to determine the

effect of trial type (Go Only, simple Go and complex Go) on RT. There was a

significant effect of trial type (F(2, 38) = 74.545, p < .001, partial η2 = .80). RT

increased from Go Only trials (M = 423, SD = 35 ms) to simple Go trials (M =

470, SD = 39 ms) and complex Go trials (M = 508, SD = 44 ms), in that order

(Figure 10B). Post hoc analysis with a Bonferroni adjustment revealed that RT

statistically significantly differed between all three trial types (all p’s < .001)

(Figure 10B).

Overall, participants were faster on the Go trials compared with No-Go trials in

both the simple and complex versions of this task. Performance was higher on

the Go trials compared to the No-Go trials only in the simple version.

3.3Science TaskA paired samples t-test was carried out for accuracy on the science control

and misconception questions. Participants were less accurate on the

misconception questions (M = .86, SD = .15) than on the control questions (M

= .67, SD = .11) (t(19) = 6.287, p < .001, d = 1.41, Figure 11A).

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Figure 11: Behavioural results of the Science task. . (A) Accuracy (M ± SE) as a function of trial and block types. (B) RT (M ± SE) as a function of trial and block types.

A paired samples t-test was also carried out for RT on the science control and

misconception questions. Participants took longer to answer the

misconception questions (M = 4946, SD = 735 ms) than the control questions

(M = 6081, SD = 913 ms) (t(19) = 9.24, p < .001, d = 2.07, Figure 11B).

Thus, participants were faster and more accurate when answering control

science questions than the misconception questions.

3.4Brain activations: pre-SMA and DLPFCActivation of the pre-SMA was lowest for misconception incorrect (M = 1.10,

SD = .69) but increased for control correct (M = 1.12, SD = .41) with the

highest activation for misconception correct (M = 1.20, SD = .56) (Figure

12A). However, the question type did not elicit statistically significant changes

in activation of the pre-SMA (F(2, 38) = .424, p = .658, partial η2 = .022).

26

Figure 12: Estimated changes in BOLD as a function of question type in the (A) pre-SMA and (B) DLPFC (M ± SE).

Activation of the left DLPFC was lowest for control correct (M = 1.31, SD

= .41) but increased for misconception incorrect (M = 1.32, SD = .54) with the

highest activation for misconception correct (M = 1.39, SD = .50) (Figure

12B). However, the question type did not elicit statistically significant changes

in activation of the DLPFC (F(2, 38) = .416, p = .663, partial η2 = .021). Thus

neither ROI showed differential activation as a function of trial type.

3.5 Regressions: accuracy on science misconception tasksi) Behavioural tasks

A multiple regression was run to determine whether performance on

behavioural tasks explained variance in accuracy on the science

misconception tasks. In addition to the control variables (Model 1), only WASI

verbal IQ and the two RT costs of the Go/No-Go task were selected in the

final model (Model 4). One score for visuospatial working memory (VSWM)

was missing from the data set and so this participant was not included in the

regression.

Visual examination of boxplots and analysis of Cook’s distance revealed an

outlier that was considerably further away from the other plots. This

participant (number 4) was excluded. As a result, the backward digit score

was found to be significant (Model 5), which improved the overall fit of the

model, increasing R2 by 8.3 %.

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The final model (Model 5) contained age, gender, accuracy on control

questions, verbal IQ, Go/No-Go RT costs and backward digit score as

predictors of accuracy on misconception questions. This was statistically

significant, R2 = .90, F(7,10) = 12.370, p = .001; adjusted R2 = .82. Thus,

Model 5 accounted for 90% of the variance.

Unstandardised Coefficients

Standardised

Coefficients

t pBStd. Error Beta

Model 1R2 = .30,F(3, 14) = 1.993,

(Constant) .153 .305 .500 .625Age -.016 .025 -.176 -.634 .536Gender -.006 .049 -.028 -.125 .902Control Q. Acc. .840 .368 .631 2.281 .039

Model 2R2 = .59,R2 change = .29,F(1, 13) = 9.229,

(Constant) -.637 .355 -1.797 .096Age .004 .021 .040 .175 .864Gender -.002 .039 -.008 -.046 .964Control Q. Acc. .466 .317 .349 1.470 .165Verbal IQ .008 .002 .590 3.049 .009

Model 3R2 = .73,R2 change = .13,F(1, 12) = 5.801,

(Constant) -.561 .305 -1.843 .090Age .013 .018 .138 .683 .507Gender .007 .034 .031 .204 .842Control Q. Acc. .158 .299 .118 .526 .608Verbal IQ .009 .002 .683 4.026 .002RT Cost (comp. – sim.)

-.002 .001 -.408 -2.408 .033

Model 4R2 = .83,R2 change = .10,F(1, 11) = 6.379,

(Constant) -.232 .285 -.814 .433Age .004 .016 .046 .266 .795Gender .011 .028 .051 .396 .700Control Q. Acc. .045 .253 .034 .179 .861Verbal IQ .007 .002 .552 3.666 .004RT Cost (comp. – sim.)

-.002 .001 -.408 -2.897 .015

RT Cost (sim. – Go)

.002 .001 .383 2.526 .028

Model 5R2 = .90,R2 change = .10,F(7,10) = 12.370,Adjusted R2 = .82.

(Constant) -.047 .241 -.194 .850Age -.002 .013 -.022 -.156 .879Gender .013 .023 .059 .572 .580Control Q. Acc. -.117 .213 -.088 -.548 .596Verbal IQ .006 .002 .499 4.048 .002RT Cost (comp. – sim.)

-.002 .000 -.407 -3.573 .005

RT Cost (sim. – Go)

.002 .001 .456 3.622 .005

Back Digit Score .011 .004 .311 2.615 .026Table 1: Regression Model Coefficients – Behavioural Tests as Predictors of Accuracy on Misconceptions (significant predictors are highlighted in bold; RT is

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reaction time).Higher accuracy on the misconception trials were related with higher scores

on the Vocabulary subtest of the WASI, a smaller cost of the 1-back load on

RT in the Go/No-Go tasks, a larger cost (i.e. slowing down) in mixed simple

Go/No-Go blocks compared to Go only blocks, and greater verbal working

memory scores. The standardised betas were similar across these four

variables, suggesting they accounted for a similar proportion of variance in

Science misconception accuracy.

ii) Activation in pre-SMA and DLPFC

A multiple regression was run to determine whether activation of the pre-SMA

brain region (when answering control or misconception questions correctly or

misconceptions incorrectly) had a relationship with accuracy on the science

misconception tasks. In addition to the control variables (Model 1), none of the

pre-SMA measures were selected into the model.

Visual examination of boxplots and analysis of Cook’s distance revealed an

outlier that was considerably further away from the other plots. This

participant (number 4 again) was excluded from the regression, which was

repeated. This revealed a new outlier based on Cook’s distance (number 19),

which was removed and the regression repeated. This did not improve the

model, which was not significant, R2 = .18, F(3,14) = 1.028, p = .410; adjusted

R2 = .01. Similar results were observed in the DLPFC, whereby brain

activations in neither trial type significantly accounted for variance in science

misconception performance, even when possible outliers were excluded.

3.6 Regressions: RT on science misconception tasksi) Behavioural tasks

Following a multiple regression, only reaction time on control questions was

had a significant relationship with reaction time on the misconception tasks.

One outlier was identified from visual examination of boxplots and analysis of

Cook’s distance. This participant (number 10) was excluded from the

regression, which was repeated. This did not find any additional significant

predictors but did improve the overall fit of the model, increasing R2 by 3.4 %.

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See Table 2 for full details on the regression model.

The final model contained age, gender, and reaction time on control questions

as predictors for reaction time on misconception questions (Model 1) and was

statistically significant, R2 = .77, F(3,13) = 14.563, p =<.001; adjusted R2

= .72. Thus, the model accounted for 77% of the variance.

Unstandardised Coefficients

Standardised

Coefficientst pB Std. Error Beta

Model 1R2 = .77,F(3,13) = 14.563,Adj. R2 = .72.

(Constant) 2068.303 2253.591 .918 .375Age (years) -116.277 119.648 -.144 -.972 .349Gender code -51.391 257.543 -.027 -.200 .845Science RT (control Qs)

1.142 .212 .812 5.380 .000

Table 2: Regression Model Coefficients – Predictors of Reaction Time on Misconceptions (significant predictors are highlighted in bold; RT is reaction time).

ii) The relationship between activation in pre-SMA and DLPFC and RT

A multiple regression was run to determine if activation of the pre-SMA brain

region (when answering control or misconception questions correctly or

misconceptions incorrectly), were associated with reaction time on the

science misconception tasks. In addition to the control variables (Model 1),

none of the variables were entered into the model. Thus DLPFC activation

was not associated with RT in science misconception trials either.

In summary, accuracy on the science misconception tasks only had a

relationship with the reaction time cost on the Go/No-Go tasks, verbal IQ and

the backward digit score; whereby a higher cost between the simple Go/No-

Go and Go Only task, a higher verbal IQ score and a higher score on the

backward digit task was associated with better performance on the

misconception tasks. Interestingly, the higher the cost between the complex

and simple Go/No-Go task had a negative relationship with performance on

the misconception tasks. Activation of the regions of interest was not

associated with better performance. There was no relationship between the

behavioural tasks or brain activations and faster reaction times on the

misconception questions. The only variable that was associated with faster

reaction times was reaction times on the control questions.

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4. DISCUSSION4.1 ResultsThe focus of this investigation was the relationship between cognitive skills

(e.g. inhibitory control), brain activation in the DLPFC and ACC and

performance when answering science misconception questions. Few studies

have investigated the neural correlates of inhibitory control in adolescents;

this study being the first one of its kind to investigate this in the context of

conceptual change in science education. The results show that only verbal IQ,

working memory and reaction time on the Go/No-Go task were significantly

associated with performance and only reaction time on control questions was

associated with reaction time on the misconception questions. Therefore, the

first hypothesis was partially accepted but the null hypothesis was accepted

for the second and third hypotheses.

The first hypothesis was that better performance on behavioural tasks

(inhibitory control, working memory and analogical reasoning) would be

associated with better accuracy and reaction time on science misconception

questions. A better verbal IQ, better working memory and longer reaction

times on the inhibitory control component of the Go/No-Go tasks associated

with better performance on the science misconception tasks. The role of

verbal IQ may be in allowing access to the language used in the questions.

Biology, chemistry and physics are dominated by a large number of technical

words that must be known and understood in order to recognise which

concept is being discussed. Science concepts are complex, drawing on laws,

characteristics, behaviours and relationships, which helps to explain why

verbal working memory was also found to be associated with better accuracy.

Solaz-Portolés & Sanjosé-López (2009) suggested that working memory may

be a moderator for inhibitory control, whereby it is responsible for not only

maintaining information but also in selecting what is relevant or not.

Interestingly, visuospatial working memory bore no relationship to improved

accuracy. It is possible that the science tasks did not access that component.

The diagrams that accompanied the trials may have mitigated visuospatial

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working memory demands, possibly allowing participants with poorer VSWM

to not be disadvantaged.

The association of additional processing time with the Go/No-Go task could

reflect extra processing involved in inhibitory control, or that participants

slowed down to improve accuracy. Slowing down could be a distinct cognitive

process in itself, whereby a participant recognised that a task was more

challenging and so, by slowing down, allowed activation of the inhibitory

control mechanism to choose the correct response. Training students to

improve accuracy may involve encouraging them to slow down. This may give

them more time to activate the inhibitory control network. Performance on the

Stroop task, however, was not found to be significantly associated with

accuracy on science misconception problems. As discussed, Morooka et al.

(2014) found differences in performance on the Go/No-Go and Stroop task,

which suggests inhibitory control has distinct components. It is possible that

the phrasing of the misconception questions did not introduce sufficient

interference to discriminate between participants. It would be interesting if

misconception questions were modified to introduce additional interference,

possibly through the use of multiple choice questions. This may be an

interesting avenue to explore since multiple choice questions are often

undervalued by teachers and students alike, who tend to underestimate their

difficulty (Harrington, 2014; Meadows, 2016).

The second and third hypotheses were that the ACC and DLPFC would be

activated more when answering misconception questions than control

questions and that greater activation would be associated with longer reaction

times and improved accuracy. Whilst the left DLPFC was activated more

during the science tasks, the ACC was not. The pre-SMA, whilst not a

planned region of interest, was also found to be significantly activated relative

to baseline. These are interesting findings, since the ACC is associated with

error recognition in children and adults. This suggests that adolescents may

use the ACC differently to children and adults, instead using a different brain

structure for error detection. As described above, the DLPFC is associated

with inhibitory control, specifically in interference or conflict resolution. It may

32

be the case that in adolescents the ACC does not pick up the possible conflict

of misconception problems (considering control and misconception trials in

the same way) and so does not show increased activation in misconception

than control trials. In turn, the ACC does not call for greater activation in the

DLPFC to resolve the conflict, and therefore neither activation in the ACC nor

in the DLPFC correlates with misconception performance.

Brault Foisy, Potvin, et al. (2015) found that novices showed greater activation

of the pre-SMA when looking at scientific stimuli compared to experts.

Activation in this brain region has been associated with organising and

preparing voluntary movement. It is possible that greater effort was needed by

the participants due to unfamiliarity with the task and the additional challenge

of the more complex questions. Criaud & Boulinguez (2013) carried out a

meta-analysis of fMRI studies into the role of the pre-SMA and concluded that

the pre-SMA was mostly involved with working memory and engagement of

attentional control. It could be that the stimuli resulted in a high demand for

resources, particularly from working memory.

In summary, the results of this study suggest that domain general skills

relating to verbal IQ and working memory, as well as possibly slower and

more careful responses, are associated with better accuracy in misconception

science problems. In the brain there is little difference between control and

misconception trials, with general recruitment of DLPFC and pre-SMA,

extending into the ACC, which were not specifically associated with better

performance on misconception trials.

4.2 LimitationsThe sample size in this study was just 20 adolescents. It is common for fMRI

studies to involve such small sizes but it does present difficulty when

interpreting and generalising results, especially when running correlational

analyses investigating individual differences. Whilst outliers were identified

and removed, in such small sample sizes the influence of each individual

result on the mean is greater than when larger samples are used.

Associations between inhibitory control performance and neural activation and

33

science misconception may still be observed in larger samples. As discussed,

a growing body of research is discovering that in adolescents, there are

significant structural and functional changes throughout this stage of

development. Whilst we considered the sample to represent adolescence in

general, the range of ages covered 11 years to 15 years, which could be

associated with considerable changes in the brain. Ideally future studies

would recruit more participants of each year group to identify age-related

overlaps between the neural networks underlying science reasoning and

inhibitory control.

The previous study by Brookman (2015) echoed the findings of the present

study whereby students’ performance was worse on the misconception

questions than the control questions. This would suggest that these questions

were more difficult because they challenged the participants’ misconceptions

and demanding inhibition of these ideas to allow the scientific ones to be

expressed. The limitation of this is that students were not pre-tested to

confirm which misconceptions they held. Indeed much research in conceptual

change presumes that poor performance reflects a student’s misconception,

rather than simply not knowing the answer. diSessa (2006) argued that even

where no prior misconception exists, learning new science concepts is

challenging and takes time. Pre-testing participants to establish which

misconceptions they held prior to completing the science tasks would give

greater insight into the reasons for poor performance and in which situations

inhibitory control could arguably be unnecessary where no prior

misconception existed.

4.3 Implications and Future ResearchMasson et al. (2014) argued that students need to develop skills necessary to

identify when inhibitory control is an appropriate response. Luna & Sweeney

(2004) argued that inhibitory control is not consistently applied until the brain

has fully matured. This presents a challenge for adolescents. They are

entering a period of intense educational demand in preparation for life-

changing examinations. To be successful, they need to develop a range of

cognitive skills that demand the use of different brain regions. Whilst various

34

factors can influence the trajectories of brain and cognitive development,

adolescents are somewhat constrained by the physical structures and neural

pathways of their brain. This presents an incongruity between the expectation

of conceptual change across science subjects and the limitations of

adolescent developmental trajectories. It is possible that too much is expected

at too young an age. Dawson (2014) found that conceptual change is not an

all-or-nothing event and is not always permanent. Conceptual change occurs

faster and with less training for some topics than others. Houdé (2000) argued

that conceptual change was not simply the acquisition of new knowledge but

was also developing metacognitive skills: the awareness of when it was

appropriate to draw on a particular concept over another in a particular

situation. It is possible that some misconceptions are harder to inhibit than

others for certain students at certain ages. Improving our understanding of the

development of conceptual change may allow the development of more

appropriate curricula across the Key Stages that match the cognitive abilities

of adolescents.

A growing body of research has investigated the potential for inhibitory control

training. As is the common theme in conceptual change research, the results

have been inconsistent. Overall, the greatest effect of training has been for

lower ability students, those with learning difficulties or those from a lower

socioeconomic status (Hackman, Gallop, Evans, & Farah, 2015; Neville et al.,

2013). The participants in the current study, and in many others cited in this

dissertation, demonstrated that inhibitory control of misconceptions is not an

all or nothing event. Importantly, other cognitive abilities not assessed in the

current study may play a role, beyond the suggested role of inhibitory control.

If a student does not recognise conflict to begin with then they may not initiate

appropriate inhibitory control. Successful training programmes may well focus

on techniques for coding the correct scientific concept and the associated

metacognitive skills. Once this has been achieved, the task of inhibiting the

misconception is arguably the same for each concept; it is having the

awareness of which is correct and which is the misconception that may be key

to successful conceptual change across all domains (Houdé, 2000).

35

A longitudinal fMRI study with children as they enter adolescence and

adulthood would potentially reveal the minute changes that take place in

structure and function of the brain. Scanning before, during and after a topic is

taught (both for topics that relate to prior misconceptions as well as brand new

concepts) would help reveal how adolescents at different ages achieve

conceptual change and how the neural strategies correlate with the

development of other cognitive processes. Inhibitory control strategies may be

domain-specific and domain-general.

Whilst some work has been carried out to identify when certain

misconceptions are created, very little is still known about what conditions are

needed for a child to develop these and in what conditions they disappear.

What we still do not know is whether neural architecture differs for different

misconceptions; whether there is any qualitative or quantitative difference in

neural activation for successful conceptual change across different concepts

within and between subjects. Rowell and Dawson (1985) were unable to

predict which students would respond to different types of instruction or how

long the effects of any observed conceptual change would last. Combining

such information with neural correlates would shed greater light on the

cognitive abilities of children at different ages and how it relates to specific

contexts of the level of misconception they have. Future studies could

incorporate this by presenting more material that was constrained to a specific

subject. A study such as this could investigate different strategies and/or

activation levels that are required for older misconceptions (e.g. heavier

objects falling faster) than newer ones and for misconceptions from different

domains (e.g. a movement misconception about gravity compared with a

relational misconception about evolution). This could potentially lead to more

specific teaching methods that are more appropriate for each of the three

sciences typically taught during adolescence.

Finally, this study recruited participants from a selective public secondary

school and represented a cohort with a high socioeconomic status (SES).

Though this was not directly measured, SES is a significant factor on

cognitive skills, academic performance and neural development. The

36

consequences of which could mean significantly different approaches to

teaching and training for conceptual change. A second group of lower SES

adolescents is currently being recruited for this study,

4.4 ConclusionBruer (1997) argued that neuroscience is a bridge too far when using

neuroimaging data to inform teaching. As a teacher of science, it is difficult not

to see the immediate benefits of using neuroimaging to help identify which

misconceptions are prevalent at different ages and how they are overcome. A

great deal of intervention in teaching is through trial and error with some

techniques working for some teachers with specific classes but not with

others. Identifying a neural basis for techniques that activate metacognitive

and inhibitory control skills would be invaluable when confronted with a class

of 20 or more individual students, each at a different stage of conceptual

change. Being able to tailor teaching using robust theory-driven practices is

arguably the way forward (Tommerdahl, 2010).

Understanding how students develop, both in terms of their misconceptions

and cognitive skills, will shed greater light on appropriately matched curricula

(Devonshire & Dommett, 2010). The developmental trajectories of

metacognition and inhibitory control skills may in part determine when such

conceptual change is possible and for which scientific concepts. It may be the

case that conceptual change is expected for certain topics that are beyond the

stage of neural development of some students. Not only will greater

understanding of the neural correlates help develop age-appropriate curricula

but it also opens the possibilities of training methods to help students

accelerate such development. This may be particularly beneficial to students

with learning difficulties and those from lower socioeconomic status.

Socioeconomic status remains one of the most significant factors affecting

cognitive development and academic success. Children living in poverty, in

particular, are at greater risk of failing public examinations, leaving school

early and obtaining lower level jobs than those from higher SES backgrounds.

Arguably these students could benefit the most from the fruits of conceptual

37

change research. Improving skills in inhibitory control and metacognition may

be a way of leveling the playing field. This is particularly important at

adolescence, which marks the start of intense public examinations; the results

of which significantly affect the opportunities available to adolescents and

their subsequent experience in adulthood.

38

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6. APPENDIX6.1 MRI data acquisition and analysis (prepared by lead investigator)A 1.5 Tesla Siemens Avanto MRI scanner was used to acquire T1-weighted

structural images and T2-weighted echo-planar volumes with blood-oxygen

level dependent (BOLD) contrast (TR = 1 s, TE = 45 ms) comprising 44 slices,

with a resolution of 3 x 3 x 3 mm. There were six functional runs lasting

between 4.5 and 6.5 min, and one structural scan that lasted 5.5 min, which

typically took place after the first 4 functional runs. The first 4 volumes of each

functional run were discarded to allow for T1 equilibrium effects.

MRI data were preprocessed and analysed using SPM12 (Statistical

Parametric Mapping, Wellcome Trust Centre for Neuroimaging,

www.fil.ion.ucl.ac.uk/spm/ software/spm12/). Functional images were

realigned, spatially normalised and smoothed. Images were realigned using a

second-degree B-spline interpolation for estimation and resliced using a

fourth-degree B-spline interpolation (SMP12 defaults). Realignment estimates

were used to calculate framewise displacement (FD) for each volume which is

a scalar measure of head motion across the six realignment estimates (Siegel

et al., 2014). Volumes with an FD greater than 0.9 mm were censored and

excluded from the general linear model (GLM) estimation, through inclusion of

a regressor of no interest for each censored volume. Scanning runs with more

than 15% of volumes censored were excluded from the analysis (one science

run for one participant). Structural images were coregistered to the mean

realigned functional image, and segmented on the basis of Montreal

Neurological Institute (MNI) registered International Consortium for Brain

Mapping tissue probability maps. Resulting spatial normalisation parameters

were applied to the realigned images to obtain normalised functional images

with a voxel size of 3 x 3 x 3 mm, which were smoothed with an 8-mm full-

width at half maximum Gaussian kernel.

Scanning runs were treated as separate time series and each series was

modelled by a set of regressors in the GLM. Runs of the science and maths

task were each modelled by box-car regressors separately modelling Control

46

Correct, Control Incorrect (if any incorrect responses were given),

Misconception Correct and Misconception Incorrect trials. Durations varied

and corresponded to the RT on each trial. Fixation and the arrows task were

modelled implicitly. All regressors were convolved with a canonical

haemodynamic response function and, together with the separate regressors

representing each censored volume and the mean over scans, comprised the

full model for each session. For the purpose of this research dissertation, two

regions of interest (ROIs) relevant to the science runs of the science and

maths task were identified. A contrast combining the parameter estimates of

all Science trials was run at the first level and entered into a one sample t-test

analysis at the second-level. The resulting SPM map, showing regions of

increased BOLD signal during the correct resolution of science problems, was

family-wise error corrected at p < .05 at the cluster level, with an uncorrected

threshold of p < .001 at the voxel level. The pre-supplementary motor area

(pre-SMA, peak co-ordinates -2, 19, 50, cluster size 212) and the left

dorsolateral prefrontal cortex (DLPFC, peak co-ordinates -45, 13, 32, cluster

size 568) (see Figure 13) were selected as cluster ROIs for further analyses.

Marsbar (http://marsbar.sourceforge.net/) in SPM12 was used to calculate the

mean parameter estimates for the conditions Science Misconception

Incorrect, Science Misconception Correct, and Science Control Correct, in

each ROI, for each participant. These values were copied into SPSS and

analysed as described in the main text.

Figure 13. Clusters identified as ROIs in the science runs of the science and maths task: the left DLPFC (red) and the pre-SMA (blue). ROIs are shown on the average of the 20 participants’ structural scans. Left: x = 30; middle: y = 126; right: z = 119.

47