Social Design Engineering Series

SDES-2020-11

How can neuroscience contribute to the science of intergenerational sustainability?

Ryuta Aoki
Tokyo Metropolitan University

Ayahito Ito
Research Institute for Future Design, Kochi University of Technology

Keise Izuma
School of Economics and Management, Kochi University of Technology
Research Institute for Future Design, Kochi University of Technology

Tatsuyoshi Saijo
Research Institute for Humanity and Nature
Research Institute for Future Design, Kochi University of Technology

3rd August, 2020
School of Economics and Management
Research Institute for Future Design
Kochi University of Technology
KUT-SDE working papers are preliminary research documents published by the School of Economics and Management jointly with the ResearchCenter for Social Design Engineering at Kochi University of Technology. To facilitate prompt distribution, they have not been formally reviewed
and edited. They are circulated in order to stimulate discussion and critical comment and may be revised. The views and interpretations expressedin these papers are those of the author(s). It is expected that most working papers will be published in some other form.

Intergenerational sustainability and the brain

1

How can neuroscience contribute to the science of intergenerational

2sustainability?

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4

Ryuta Aoki1*, Ayahito Ito2, Keise Izuma2,3, Tatsuyoshi Saijo 2,4

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Graduate School of Humanities, Tokyo Metropolitan University, Tokyo, Japan

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2

Research Institute for Future Design, Kochi University of Technology, Kochi, Japan

8

3

School of Economics and Management, Kochi University of Technology, Kochi, Japan

9

4

Research Institute for Humanity and Nature, Kyoto, Japan

10
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12

*

Correspondence should be addressed to: R. Aoki (raoki@tmu.ac.jp)

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Keywords: intergenerational sustainability, neuroscience, transdisciplinary approach

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Author contributions: R.A., A.I., K.I., and T.S. designed the study. R.A., A.I., K.I., and

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T.S. wrote the manuscript.

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Conflict of Interest: The authors declare no competing financial interests.

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Acknowledgments: This work was supported by a Grant-in-Aid for Scientific Research

22(A) to T. Saijo (17H00980).

1Intergenerational sustainability and the brain

23

Abstract

24Intergenerational sustainability is an existential problem for humans, and coping with25

this issue requires large-scale cooperation extended across generations. However, recent

26

empirical evidence suggests that people’s concern for future generations is typically low,27

which is rooted from human’s cognitive biases (e.g., temporal discounting and bounded28

empathy) and possibly exacerbated by modern social systems depreciating future29

generations’ rights and voices. To achieve sustainable society, we need to design and30

implement novel social institutions that leverage our concern for future generations. In

31this paper, we discuss how neuroscience can tackle this fundamental challenge in

32

collaboration with other disciplines. We review psychological factors and neural33

substrates that may underlie decision-making regarding intergenerational sustainability.

34

We also propose empirical approaches to study neural bases of

35intergenerationally-sustainable decision-making. Notably, neuroimaging research has

36potential to unveil “hidden” neurobiological processes that are difficult to identify by

37

behavioral observations alone. In addition, neural data can be used to predict real-world38

outcomes, which complements behavioral and self-report measures that may not always

39

reflect true motives behind decisions. Understanding the neurocognitive mechanisms

40

would provide insights into effective institutions that promote concern for future41

generations. We prospect that future neuroscience research will accumulate evidence

42

from both laboratory and field experiments, thereby contributing to policy making and43

the transformation toward sustainable society.

442Intergenerational sustainability and the brain

45

1. Introduction

46

Intergenerational sustainability is an issue about the very existence of our species. Of

47

particular importance on this topic is climate change, which is now considered as an

48

existential threat for humans (Lenton et al., 2019). Environmental and ecological49

scientists have long been alerting to devastating effects of climate change on future

50

generations (Rockström et al., 2009). Researchers in diverse fields (e.g., biology,

51

economics, and philosophy), along with citizens, have been actively involved in52

collaborative actions to combat this issue. An interest in climate change has also been

53

emerging in neuroscience (Aron, 2019; Aron et al., 2020; Langenbach et al., 2019).

54

However, in the field of neuroscience, this is still sporadic movements by a small

55

number of researchers. To date, there is no coherent trend of incorporating neuroscience

56

into the transdisciplinary framework for sustainability science.

57

This article aims to discuss how neuroscience can contribute to solving this58

fundamentally challenging issue. The problem at the heart of intergenerational

59

sustainability issues is the inescapable conflicts between the current and future

60

generations (Saijo, 2015). We first argue that difficulties associated with

61

intergenerational issues arise from several psychological factors. Next, we review neural62

substrates for these psychological factors, which may provide insight into human63

behavior regarding intergenerational sustainability. Third, we propose empirical

64

approaches to examine brain processes supporting sustainable behavior. Lastly, we

65

present open questions that should be addressed in future research.

66
67
68

2. Climate change as an issue of intergenerational sustainability
3Intergenerational sustainability and the brain

69

2-1.

Why important: impacts on future generations

70

Climate change during the past 50 years is overwhelming (Steffen et al., 2007, 2015).

71

This rapid acceleration in environmental changes is paralleled by explosive increases in72

socioeconomic indices such as global population and real GDP (Fig. 1A). Now we are73

living in an era called Anthropocene (Crutzen, 2002), where we ourselves substantially74

affect the Earth’s ecosystems.

75

Climate change will induce global warming, sea level rise, and other changes

76

in ecosystems (IPCC, 2014). It will also increase the risks of extreme weather events77

and outbreaks of deadly infectious diseases (Shuman, 2010; Stott, 2016; Wu et al.,78

2016). These changes may adversely impact future generations’ life (e.g., industry,

79

agriculture) and well-being. In fact, sea level rise has already affected citizens in island

80

nations (e.g., Tuvalu), and recent extreme weather events (such as heatwaves, hurricanes,

81

floods, droughts and wildfires) have caused huge damages in several countries. If

82

climate change becomes worse, future generations will suffer more frequently and83

severely from these events. Accumulating evidence suggests that global climate change

84

is man-made, meaning that our generation is critically responsible for well-being of85

future generations. Although the precise mechanisms of climate change remain unclear,

86

human activity is the most plausible and parsimonious account, which is widely87

accepted among environmental scientists (IPCC, 2014).

88

The problem is that people often systematically underestimate the importance89

of intergenerational issues, in part due to human’s cognitive biases. However, forecasts

90

on economic impacts due to climate change may urge us to calibrate our perception.

91

According to a recent report by International Labour Organization (Kjellstrom et al.,922019), the annual global economic cost of the productivity loss due to global heating
4Intergenerational sustainability and the brain

93(e.g., heat stress in daylight working) is expected to become ~2.4 trillion USD in 2030,

94

which is a surge from 0.3 trillion USD in 1995. In addition, a recent estimation based on95

an economic growth model predict that the global GDP would reduce, if no effective

96

measure on climate change is taken, by 2.5% in 2050 (roughly corresponding to US$ 597trillion, assuming that global GDP in 2050 is US$ 200 trillion) and by 7.2% in 2100

98

(Kahn et al., 2019). If we consider long-term effects, economic damages induced by99

climate change are likely comparable to or even greater than those by mental disorders

100

or by a pandemic (Bloom et al., 2011; World Bank, 2020). These estimates suggest that101

climate change will become a new plague for future generations, unless our generation

102

takes immediate collective action.

103
104

2-2.

Why challenging: psychological barriers

105

Despite the substantial importance, issues regarding intergenerational sustainability are

106

fundamentally difficult to solve. Voices advocating immediate actions are rapidly

107

glowing across the world, especially among younger generations. However, public

108

opinions on climate change are divided, and attempts of international collaboration

109

often fail (e.g., the US withdrawal from the Paris agreement). These facts show us that110

solving climate issues is quite challenging. Why is it so difficult? We discuss the111

possible reasons in below, focusing on psychological factors hampering our concern for

112

future generation.

113

First, coping with intergenerational issues needs long-horizon goals spanning

114

over a few generations (e.g., by year 2100). This inevitably causes temporal discounting

115(Frederick et al., 2002). If we assume a yearly discount rate of 3%, the subjective value

116

of goods in 2050 is discounted to 41.2% relative to the value at present (in 2020). This
5

Intergenerational sustainability and the brain

117

means that an asset priced at $50 now is preferred over an asset priced at $100 thirty

118

years later. Moreover, if one would not expect to live in 2100, she or he might consider

119

the subjective value of goods in 2100 as zero (according to the homo economicus view).

120

Second, humans have a limited ability to vividly imagine (or prospect) the far future.

121

This

122

naturally-occurring empathy towards future generations. Third, the inherent uncertainty

123of the future may elicit unrealistic optimisms (Sharot et al., 2011), offering excuses of124

not taking actions (e.g., “all problems will disappear by technological innovations”).

125

These psychological factors may also be relevant to intragenerational issues, but likely

126

be exaggerated in intergenerational issues.

perceivedvaguenessandpsychologicaldistance

may

reduceour127

Another critical factor is intergenerational conflict (Kamijo et al., 2017; Saijo,

1282015), which has several common features with intergroup conflict (Fiske, 2002).

129

Intergenerational issues arise from conflicts between the current and future generations

130(e.g., chasing economic growth versus pursuing sustainability). These conflicts may not131

have existed until recently (before the Industrial Revolution), but have become more

132

stark as human activity reaches to the “planetary boundaries” (O’Neill et al., 2018;133

Rockström et al., 2009), i.e., the thresholds that define safe operating space for134

humanity on Earth (Fig. 1). If intergenerational conflict is overly emphasized, for135

instance by mass media messages, it may trigger antagonistic attitudes against future136

generations among a subset of people in the current generation. This response is137

irrational, in light of intergenerational sustainability, but probably congruent with the138

automatic tendency that humans often manifest in (intragenerational) intergroup

139

contexts such as when competing over limited resources and soils. The antagonistic

140

attitudes may further spill over towards members of the current generation who are in6Intergenerational sustainability and the brain141

support of future generations, resulting in ideological polarizations within the current

142

generation.

143

Importantly, the fact that psychological factors limit our concern for future

144

generations suggests that better understandings of these factors offer clues for145

overcoming the limitations. Therefore, we expect that behavioral sciences (e.g.,

146

psychology, experimental economics, and neuroscience) for clarifying the mechanisms

147

of human behavior will provide insights into solutions of intergenerational issues.

148
149

2-3.

What we need: leveraging concern for future generations

150

So far we have argued that solving intergenerational issues is important but challenging,151

and that psychological barriers hamper our motivation to cooperate with future152

generations. What do we need to break the barriers?

153

Unfortunately, relying on “naturally-grown” empathy and self-control is an154

unlikely solution. Empathy is a key ingredient for altruism and prosocial behavior

155(Decety et al., 2016; Klimecki et al., 2016), but it might have evolved in small-size

156

groups, and often is reduced for outgroups compared with ingroups (Cikara & Fiske,1572011). In addition, prosocial motivation (e.g., other-regarding preferences) decays as

158

psychological distance increases

159

greater psychological distance to the future proportional to its temporal distance, the160

spontaneous levels of empathy and prosocial behavior toward future generations are161

likely degraded. Self-control is the ability to resist temptations of sooner-but-smaller162

rewards and prioritize long-term benefits (Frederick et al., 2002). However, behavioral

163

economic studies have clearly shown that humans on average have limited levels of

164

self-control (Thaler & Benartzi, 2004), often resulting in suboptimal choices in term of(Strombach et al., 2015). Because we usually feel a7Intergenerational sustainability and the brain

165

intertemporal rationality in everyday situations (e.g., dietary choices, pension plans).

166

Critically, dilemmas involved in intergenerational-sustainability issues are often much167

harder to resolve compared with those involved in personal intertemporal choices,168

because delay periods are much longer (e.g., over generations) and recipients of

169

long-term benefits (i.e., future generations) are not those who exert patience (i.e., the170

current generations). Because of these reasons, the empathy we naturally feel to future171

generations, as well as the self-control we exert for future generations, might be limited.

172

Consequently,

the

most

important

goal

for

achieving

173

intergenerationally-sustainable society is to develop and implement novel social

174

systems (or “institutions”) that effectively leverage the current generation’s concern for

175

future176

intergenerational common pools and put future generations into a crisis. The role of177

neuroscience, together with other fields in behavioral sciences, is to accumulate178

empirical evidence for effects and mechanisms (e.g., biological underpinnings) of such179

institutions, thereby contributing to policy making for sustainable societies. It is obvious,

180

however, that the entire issues of sustainability cannot be solved by neuroscience alone.

181

We need a transdisciplinary framework that facilitates close collaboration among

182

diverse research fields as well as with citizens and policy makers.

generations.

Otherwisethecurrent

generation

would

overexploit

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184
185

3. Insights from existing neuroscience studies

186

What can neuroscience exactly do? Identifying brain regions involved in

187

intergenerationally-sustainable behavior would be a starting point (if not a goal). To date,188

neural substrates for intergenerationally-sustainable behavior remain poorly understood.
8

Intergenerational sustainability and the brain

189

However, findings from existing neuroscience research may provide useful insights.

190

Here we review past neuroimaging (e.g., functional magnetic resonance imaging

191[fMRI]) studies on related topics. We particularly emphasize that neuroimaging studies,

192

if combined with appropriate experimental designs, may offer useful insights into

193

psychological processes that cannot be obtained by behavioral observations alone.

194
195

3-1.

Prospecting the future

196

The ability to think about future generations relies on our ability to vividly imagine and197

simulate the future with episodic details. This cognitive function is referred to as

198“prospection” or “episodic future thinking” (Gilbert & Wilson, 2007; Schacter et al.,199

2017), and considered as unique to humans (Suddendorf et al., 2018). Previous

200

neuroimaging studies have shown that brain regions such as the medial prefrontal cortex

201(mPFC), precuneus, and temporoparietal junction (TPJ) are involved in prospection,

202

with especially important roles of the anterior PFC (aPFC; also called the frontopolar

203

cortex) in representing future goals (Brown et al., 2016; Doll et al., 2015). These brain

204

regions are overlapping with the so-called default-mode networks (DMN). Interestingly,

205

the DMN is also implicated in creative thinking (Beaty et al., 2018), probably because

206

both prospection and creative thinking requires counterfactual thinking and imagination

207(Hassabis et al., 2007).

208
209

3-2.

Prosocial behavior

210

Intergenerationally-sustainable behavior is by nature prosocial, because it benefits

211

future generations while (typically) imposes costs on the current generation

212(Langenbach et al., 2019; Saijo, 2015). Neuroimaging studies over the past two decades
9Intergenerational sustainability and the brain

213

have revealed sets of brain regions involved in prosocial behavior (J. K. Rilling &214

Sanfey, 2011; Ruff & Fehr, 2014). Intergenerationally-sustainable behavior is likely

215

mediated by similar brain regions, although this should be empirically tested. These216

brain regions include those involved in value-based decision-making (i.e., decisions

217

made on the basis of subjective value—such as when you consider which of “receiving

218$10 now” or “receiving $20 after a month” you prefer), and those involved in social

219

cognition, such as the ability of inferring others’ mental states (Fig. 2).

220

An important issue is whether certain treatments/interventions can leverage

221

prosocial behavior within individuals, so as to know if a given institution can promote

222

prosocial behavior at the societal (collective) level. Past neuroimaging studies have

223

shown that prosocial behavior can be enhanced via multiple distinct neural pathways. A

224

key implication here is that, even if two treatments yield behaviorally similar effects,

225

the neural mechanisms underlying these effects could be distinct. For instance, Hein et226

al. (2016) showed that experimental treatments inducing “empathy-driven altruism” and227“reciprocity-driven altruism” promote prosocial decisions (i.e., giving money to others

228

at the cost of self-interest in a laboratory decision-making task) to an equivalent extent,

229

but their effects are mediated by different brain mechanisms. In their fMRI study,

230“empathy-driven altruism” was operationally defined as increased prosocial decisions

231

after observing another person who received painful electrical shocks (“I help you

232

because you are suffering”). On the other hand, “reciprocity-based altruism” was233

defined as increased prosocial decisions after observing another person who did a kind234

act to the study participant (“I help you because you helped me”). The two treatments

235

elicited distinct patterns of fMRI signals among key brain regions supporting prosocial

236

decisions (the ventral striatum, anterior insula, and dorsal anterior cingulate cortex).
10Intergenerational sustainability and the brain

237

This finding gives an exemplar case where neuroimaging can distinguish different

238

psychological239

indistinguishable prosocial decisions.

motives(i.e.,

empathy

andreciprocity)behindbehaviorally240

It is worth noting that the terms “empathy” and “reciprocity” are multi-facet

241

concepts. Social psychology and neuroscience have investigated how these concepts are

242

comprised of distinct factors, each of which may have different effects on behavior.

243

Empathy can be divided into “affective empathy” and “cognitive empathy,” which are244

subserved by distinct brain systems (Shamay-Tsoory et al., 2009). Affective empathy is245

the ability to share emotion with others (e.g., “emotional contagion”). This function is

246

often automatic and accompanied by visceral responses (Decety et al., 2016). For

247

instance, when we observe another person who is wounded and bleeding, we248

spontaneously feel the pain that the person would feel. Cognitive empathy is our ability

249

to infer others’ mental states (e.g., intentions and beliefs), which is also referred to as

250“theory of mind (ToM)” or “mentalizing” (Frith & Frith, 2003). ToM is critical for251

cooperative behavior (by allowing us to share intentions with others) from hunting in252

human ancestry societies to resolving international conflicts in the modern world, while253

it is also critical for strategic behavior such as bargaining (by enabling us to predict and254

outsmart others’ intentions).

255

Likewise, reciprocity can be divided into several distinct concepts. One basic

256

distinction is between direct reciprocity and indirect reciprocity, with the latter

257

considered to be indispensable for large-scale cooperation among genetically unrelated258

individuals (Rand & Nowak, 2013). Indirect reciprocity can be further divided into

259

upstream (or “pay-it-forward”) reciprocity and downstream (or “reputation-based”)

260

reciprocity. An fMRI study showed that upstream and downstream reciprocities have11

Intergenerational sustainability and the brain

261

different neural substrates (Watanabe et al., 2014), which is another exemplar case262

where neuroimaging provides evidence for dissociations between behaviorally similar

263

concepts. It remains unclear whether the two types of indirect reciprocity are relevant to

264

intergenerationally-sustainable behavior. Intuitively, only upstream reciprocity would

265

contribute to intergenerationally-sustainable behavior, as downstream (reputation-based)

266

reciprocity do not work between (non-overlapping) generations because of the267

asymmetry of time. An intriguing open question is whether an extended version of268

reputational concerns, such as motivations for leaving a legacy (Wade-Benzoni et al.,2692010), plays roles in facilitating intergenerationally-sustainable decisions.

270

Diverse concepts and sub-concepts are relevant to human prosocial behavior,

271

but they may have different effects on intergenerationally-sustainable behavior. Careful

272

distinctions between these concepts are especially important in transdisciplinary273

research, because misuse of the terminology may result in confusion. For example, one274

may want to claim that “empathy promotes intergenerational cooperation.” However, a275

certain kind of empathy may increase cooperation within a small group but may

276

simultaneously enhance aggression toward outgroups (Bernhard et al., 2006; Bruneau et277al., 2017). If this is the case, this type of empathy (i.e., parochial empathy) may not be278

beneficial for (or even backfire) large-scale cooperation required for intergenerational279

sustainability. A more rigorous behavioral and neuroscientific research for clarifying

280

these inter-related concepts to avoid such confusions.

281

Neuroimaging could be useful to predict prosocial behavior especially when282

self-report measures are not reliable predictors of actual behavior. There are several

283

reasons that self-report measures do not necessarily tap into true motives behind284

behavior. Social decisions and evaluations are often influenced by implicit brain
12

Intergenerational sustainability and the brain

285

processes (Stolier & Freeman, 2016). If an individual is not aware of these implicit

286

processes, she or he may not be able to report the true motives behind decisions. In

287

addition, self-report measures are susceptible to reporting biases such as demand288

characteristics. For instance, self-reported intentions of engaging in real-world289

sustainable behavior (e.g., using carpools) may not reflect actual behavior (Kristal &290

Whillans, 2020). In such cases, neural data may outperform self-report measures in291

predicting actual prosocial behavior.

292
293

3-3.

Intertemporal decision-making

294

Neuroimaging studies have revealed brain mechanisms involved in intertemporal choice295(Kable & Glimcher, 2007; McClure, 2004), although these studies mostly deal with296

decisions concerning self alone (e.g., trade-offs between the present self and the future297

self, with no relevance to other persons). A key brain region is the dorsolateral298

prefrontal cortex (dlPFC), a region important for self-control in decision-making (Fig.

2992). Experimentally modulating dlPFC activity by brain stimulation techniques (such as

300transcranial magnetic or current stimulations) influence temporal discounting, such that301

diminished dlPFC activity makes individuals more impulsive (Figner et al., 2010).

302

Another brain region implicated in intertemporal decision-making is the aPFC, a region

303

important for metacognition and prospection (Fleming et al., 2010; Gilbert & Wilson,

3042007). An fMRI study showed that the aPFC is activated when people are aware of the305

temptation of sooner-but-smaller rewards and precommit to restrict the access to the306

tempting options (Crockett et al., 2013). Another line of studies has shown that episodic

307

future thinking (e.g., prompting people vividly imagine future events) can decrease308

temporal discounting (Peters & Büchel, 2010), possibly by reducing perceived distance13

Intergenerational sustainability and the brain

309

and ambiguity of the future. These mechanisms relying on self-control,

310

metacognition/prospection, and imagination may serve as distinct (but inter-related)311

pathways by which people can make intertemporal rational decisions (Bulley &312

Schacter, 2020).

313

As noted above, these findings are derived from studies that examine

314

intertemporal decisions involving rewards for self alone. It remains to be addressed315

whether any of these mechanisms promote intergenerationally rational choice, which316

involved tradeoffs between the present self and the future others. For instance, it could

317

be the case that self-control alone is not sufficient for intergenerationally sustainable318

decisions (Langenbach et al., 2019), but stimulating people’s imagination about the319

future opens the gate for self-control to exert effects on sustainable decisions (i.e., an

320interaction between imagination and self-control). This hypothesis is in line with the321hierarchical organization of the brain, where the aPFC situates the upstream of the322

dlPFC (Koechlin, 2003).

323
324

3-4.

Intergroup conflict

325

Although how the brain react to intergenerational conflict remains unknown,

326

neuroimaging research have studied the mechanisms underlying intergroup conflict.

327

These studies typically focus on racial/ethnic groups (or supporters of different

328

political/sport teams), and examine neural correlates of intergroup behavior (e.g.,329

ingroup favoritism and outgroup hate). Brain regions implicated in automatic emotional

330

responses (e.g., the amygdala), social cognition (e.g., dmPFC), and affective judgment

331(e.g., vmPFC) underpin negative attitudes toward outgroups such as prejudice and332

discrimination (Amodio, 2014). These neural substrates may provide mechanistic14

Intergenerational sustainability and the brain

333

explanations of why people tend to fear outgroups (often automatically) and334

unfavorably evaluate them (e.g., less trustworthy, less competent). For example, the335

amygdala is known to play a key role in fear learning (an association between neutral336

and aversive stimuli), and fear learning is considered as an underlying mechanism of337

how prejudice toward an outgroup is acquired in a real world (Phelps et al., 2000).

338

Consistent with this idea, we recently showed that activation patterns in the left

339

amygdala were significantly associated with implicit evaluations (i.e., prejudice) toward340

an ethnic outgroup (Izuma et al., 2019). Notably, behavioral and neural biases against

341

outgroups emerge even with experimentally created groups (e.g., by minimal group342

procedures). This may raise the possibility that excessively emphasizing the border343

between the current and future generations elicits negative attitudes (e.g., decreased344

empathic care and neglect) toward future generations. Instead, messages emphasizing

345

continuity between the current and future generations (e.g., by emphasizing that the act346

of our generation will be bequeathed to future generations as legacies) may reduce the347

tensions between generations. This idea could be tested in an experiment that contrasts

348

treatments emphasizing competition (vs. continuity) between generations.

349

Intergroup conflicts between races and ethnicities in the real world seem to be

350

harsh and robust. Can we reduce it by interventions? An fMRI study showed that351

receiving helps from outgroup members promotes empathy toward the outgroup, which352

possibly relieves intergroup conflicts (Hein, Engelmann, et al., 2016). This effect was353

underpinned by a prediction error signal (in a reinforcement learning process) observed354

in the anterior insula, consistent with the region’s role in affective empathy. Similar

355

processes likely occur in the real world, for example when immigrants interact with356

local residents. However, the same process may not work in intergenerational situations,15

Intergenerational sustainability and the brain

357

because the current generation has no opportunity to be helped by (remote) future358

generations. Reducing intergenerational conflicts may thus be more difficult compared359

with reducing intergroup conflicts within a generation, and necessitate novel methods of360

interventions.

361
362

3-5.

Connecting the dots363

It is important to note that brain regions work together as distributed systems. For

364

instance, previous work has shown that the TPJ exhibits “functional connectivity” (a365term indicating statistical dependence of fMRI time series between distant brain

366

regions) with regions involved in value-based decision-making such as the striatum and

367

vmPFC (Park et al., 2017), and this functional connectivity underpins prosocial

368

motivation called warm glow (Andreoni, 1990). Functional orchestration among369

different brain regions is critically important for decision making. For instance, the370

vmPFC shows functional connectivity with regions such as the TPJ, dlPFC, and aPFC

371

in context-dependent manners during decision making tasks (Baumgartner et al., 2011;

372

De Martino et al., 2013; Hill et al., 2017). To better understand neural bases of373

intergenerationally-sustainable decisions, we need to examine how multiple brain374

regions work in concert as distributed brain networks.

375
376
377

4. Possible empirical approaches

378

How can we empirically study neural bases supporting intergenerationally-sustainable379

behavior? Here we propose two types of approaches that would be useful in future

380

research.
16

Intergenerational sustainability and the brain

381
382

4-1.

Using economic games: decision-making in laboratory settings

383

The first approach is to use behavioral economic games designed to study

384

integenerationally-sustainable385

neuroimaging experiments. In this approach, participants make decisions regarding

386

intergenerational sustainability while their brain activity is measured using387

neuroimaging techniques. Previous neuroimaging studies have used similar approaches

388

to examine neural bases of social decision-making. For instance, economic games such389

as the trust game, prisoner’s dilemma game, and ultimatum game have been used to390

study trust, cooperation, and inequality aversion, respectively (McCabe et al., 2001; J.

391

Rilling et al., 2002; Sanfey et al., 2003).

decision-making,andincorporate

them

into

392

The advantage of this approach is that it can examine neural responses in393

well-controlled laboratory settings. Behavioral economic games allow researchers to394

systematically manipulate experimental variables, such as the cost and efficiency of395

sustainable decisions, as well as to examine effects of certain treatments versus

396

well-matched control conditions. This is particularly useful when combined with

397

computational modeling (Behrens et al., 2009), which enables to decompose a decision

398

process into distinct components and identify how experimental manipulations (e.g., a399

treatment) affect each component.

400

A few behavioral economic games suitable to study intergenerational

401

sustainability have been proposed (Fischer et al., 2004; Hauser et al., 2014; Kamijo et402al., 2017; Langenbach et al., 2019; Sherstyuk et al., 2016). In these games, a group of403

players (or an individual player in some studies) represents a “generation,” and404

decisions are made successively from one generation to another (Figure 3A). Of note, a17

Intergenerational sustainability and the brain

405

decision made by a generation affects only later generations, but not the other way

406

around. This mirrors the unidirectional nature of intergenerational dependencies

407

between the past and future generations in the real world (i.e., the time flows only from408

the past to the future).

409

The essential characteristic of these games is that they embed intergenerational

410

sustainability dilemmas (i.e., trade-offs between the current and future generations’411

benefits) into the game structure. For instance, in the Intergenerational Goods Game

412

used by Hauser et al. (2014), each generation consisting of five players makes a413

collective decision (on the basis of median voting) as to how much they extract a414

resource from an intergenerational common pool. If the extraction by the current

415

generation is under a predetermined threshold, the pool will be replenished and the next

416

generation will play the game in the same way as the current generation does. On the417

other hand, if the extraction level exceeds the threshold, the pool will be exhausted and418

the following generations will lose the opportunity to play the game. Thus, an419

overexploitation of the intergenerational common pool benefits the current generation,420

but harms the future generations. In the Intergenerational Sustainability Dilemma Game

421

(ISDG) used by Kamijo et al. (2017), each generation consisting of three players makes422

a collective decision (on the basis of conversations among the players within each423

generation) between “Option A” and “Option B” (corresponding to “unsustainable” and424“sustainable” options, respectively). Although the current generation receives a larger

425

payoff by choosing Option A compared with Option B (say, $36 vs. $27), if the current

426

generation chooses Option A, the next generation will face a similar decision between

427

Option A and Option B but with reduced payoffs for both options (e.g., $27 vs. $18). On

428

the other hand, if the current generation chooses Option B, the next generation will face18

Intergenerational sustainability and the brain

429

a binary decision with the payoffs maintained for both options (i.e., $36 vs. $27). This

430

means that the pool gradually decreases and eventually is depleted if many generations

431

choose Option A, whereas it is sustainable as long as all generations choose Option B.

432

Thus, each generation face at a dilemma between self-interest (i.e., choosing Option A)

433

and sustainability (i.e., choosing Option B). The game used by Langenbach et al. (2019)434

also entails a similar feature of intergenerational sustainability dilemma (i.e., forgoing435

self-interest to achieve intergenerational sustainability), although the specific436

implementation differs from the other games.

437

There are some important variations among the games used in the previous

438

studies (Figure 3B). These games typically implement intergenerational dependencies as

439

Markov processes (i.e., the payoff structure of generation t+1 is solely determined by440

the decision of generation t, irrespective of the decisions of generations t–1, t–2, …),

441

and the current generation is not provided with the information about past generations.

442

Therefore, the players’ decisions would be influenced by “prospective” factors (i.e.,443

how their decisions affect the next generation), but not by “retrospective” factors (i.e.,444

how the past generations have made decisions). This simplifies the structure of the445games, but misses the important aspect of the intergenerational decisions in the real446

world—that is, the decisions of the current generation are influenced by the history

447

made by the past generations. A notable exception is the ISDG (Kamijo et al., 2017). In

448

the ISDG, each generation is provided with the full history of the past generations’449

decisions. This allows researchers to examine effects of retrospective factors (i.e., the450

history) in addition to those of prospective factors. For instance, a player’s decision may

451

conform to past generations’ decisions, because the choices made by the past452

generations may serve as a reference point or set a “norm” (Xiang et al., 2013).
19

Intergenerational sustainability and the brain

453

Alternatively, some players may want to “break off bad habits” and behave benevolently454

to the future generations if several past generations have consecutively chosen

455

unsustainable options. Although the effects of past generations’ decisions on the current

456

generation’s decision could be complicated, investigating such retrospective factors

457

would provide important insights into understanding intergenerational decisions in the458real world.

459

Another unique aspect of Kamijo et al. (2017) is that it allowed conversations

460

among the players within each generation (but not between generations) before they

461

make a collective decision. This is unlike the studies done by Hauser et al. (2014) or

462

Langenbach et al. (2019), where participants were prohibited to make communications.

463

Conversations and communications are indispensable parts of policy-making processes464

in the real world (e.g., deliberative democracy and procedural justice). However, their465

effects on collective decisions remain unclear, for instance whether communications

466

among individuals lead to collective wisdom (Bahrami et al., 2010; Navajas et al., 2018)467

or induce phenomena such as risky shift and group polarization (Lord et al., 1979). In

468

particular,

469

intergenerationally-sustainable decisions without additional institutions (Timilsina et al.,470

2017). Thus, how conversations and communications affect collective decisions

471

regarding intergenerational sustainability is worth investigating. Letting participants

472

freely converse with others in the MRI scanner involves technical difficulties, because it

473

induces head motions and decreases fMRI signal quality (if not impossible; e.g., Chen474

et al., 2017). Instead, experimenters can let participants converse with others under475

certain conditions (which serves as experimental treatments) outside the scanner, and476

then let them perform decision-making tasks (without conversations) in the scanner.

itispossiblethat

conversations

alone

cannotfacilitate

20

Intergenerational sustainability and the brain

477

This allows researchers to examine how neural responses during the decision-making

478

task is affected by prior experience of conversations (under a certain condition).

479

A major concern of the approach using economic games is whether behavior

480

observed in laboratory experiments translates to real-world sustainable behavior (from481

purchasing green products and using reusable bags to expressing support for

482

pro-environmental policies). Because the games to study intergenerational sustainability

483

are developed relatively recently, research ensuring ecological validity is still lacking or484

scarce. For other economic games widely used in past research (e.g., the dictator game,485

ultimatum game, and trust game), intensive efforts have been made to ensure their486

ecological validity—that is, decisions in these games reflect real-world behavior

487

regarding generosity, fairness, and trust (Franzen & Pointner, 2013). Similar efforts are488

needed to establish the correspondence between behavior in the laboratory games

489

concerning intergenerational sustainability and real-world sustainable behavior.

490

We also emphasize that a central goal of research is to develop institutions that491

are effective in real-world situations. Some experimental treatments used in laboratory

492

settings may not work in the real world. For instance, Hauser et al. (2014) showed that493

median voting is effective in sustaining intergenerational common pools in a laboratory494

setting. However, median voting may not work if the majority of voters prefer495

self-interest over sustainability and the median exceeds a limit of sustainable resource

496

provision. In fact, when we look around the real world, most countries are not even497

close to heading toward sustainable societies (Wackernagel et al., 2017). As a result,498

agreements reached in international conferences such as the Conference of the Parties to499

the United Nations Framework Convention on Climate Change (COP) and the World

500

Economic Forum (WEF) are often not sufficient to achieve sustainability (Steffen et al.,21

Intergenerational sustainability and the brain

5012018), and criticized as putting future generations still under risk. This may suggest that502

different types of institutions are needed to uplift concerns for future generations.

503
504

4-2.

Using naturalistic stimuli: predicting real-world outcomes505

The second approach focuses more on predicting real-world outcomes from brain data

506

(Fig. 4). This approach uses naturalistic stimuli, such as movies and media articles that507

deliver persuasive messages promoting certain sustainable behaviors (e.g., purchasing

508

green products, using carpools, and reducing air travel). Participants are presented with509

naturalistic stimuli inside the scanner, like when they watch TV commercials or read510

new articles in everyday life. The aim of research is to associate neural responses with

511

behavioral/attitude changes induced by persuasive messages. Similar approaches have512

been used to predict behavioral changes induced by health messages (e.g., quitting

513

smoking, using sunscreens) from neural responses (Chua et al., 2011; Falk et al., 2010).

514

Recent advances in spatiotemporal analysis of fMRI signals have enhanced the utility of

515

naturalistic stimuli. For instance, voxelwise encoding models allow to investigate neural516

representations of various low-level perceptual (e.g., audiovisual) features and semantic

517

contents included in naturalistic stimuli (Huth et al., 2016). In addition, analyses

518

looking at brain synchrony among individuals (e.g., inter-subject correlations of fMRI

519

time series) allow to capture temporal dynamics of neural responses in data-driven

520

manners, without requiring pre-specified stimulus onsets (Sonkusare et al., 2019).

521

This approach can be used in two distinct ways. The first way is to predict

522

individual differences in behavioral changes. The same message may induce different

523

degrees of behavioral changes across individuals, and the inter-individual variations

524

might be associated with neural responses in specific brain regions. Previous studies
22

Intergenerational sustainability and the brain

525

have shown that activation in brain regions processing personal relevance of stimuli

526(e.g., the mPFC and precuneus) is associated with individual differences in behavioral

527

changes (Falk et al., 2010). Using brain data to predict behavioral changes is528

particularly useful when self-reported intentions of changing behavior are biased by529

confounding factors (e.g., social desirability) and not reliable predictors of actual

530

behavioral changes.

531

The second way is to predict collective outcomes in the real world using brain

532

data obtained by laboratory neuroimaging experiments. In other words, using neural533

responses observed in “neural focus group” to forecast population-level behavior in534

large-scale social groups (Falk et al., 2012). Previous studies have shown that brain data535

obtained from small sample-size groups (e.g., around 40 participants) can predict

536

population-level outcomes such as viral sharing of news articles on social networking

537

services and aggregate view frequency of YouTube videos (Scholz et al., 2017; Tong et538al., 2020). These studies suggest that activations in brain regions implicated in539

processing of personal relevance (e.g., the mPFC) and reward values (e.g., the striatum)540

are predictive of population-level outcomes. Such approaches may also be useful to541

examine effects of institutions aiming to promote sustainable behavior at the collective542

level.

543
544
545

5. Future directions

546

In this section, we present important open questions that can be addressed using

547

neuroscientific approaches. We also introduce an emerging transdisciplinary framework548

that can potentially bridge laboratory experiments and practices in the real world.
23

Intergenerational sustainability and the brain

549
550

5-1.

What are neural bases of persistent behavioral changes?

551

For an institution to be effective in the real world, behavioral changes induced by the552

institution has to be persistent (i.e., long-lasting) (van der Linden, 2017). To examine553

long-term effects of experimental treatments on real-world sustainable behavior, we

554

need to longitudinally collect real-world behavioral measures over certain periods of

555

time (e.g., a few months). This can be done by occasional follow-up data collections, for556

instance via online experiments or smartphone apps monitoring daily sustainable557

behavior. An interesting question is what brain regions can predict long-term behavioral

558

changes. If persistent behavioral changes induced by an experimental treatment are

559

supported by implicit brain processes (e.g., emotional processing subserved by brain

560

regions such as the amygdala), brain data could be a better predictor of long-term

561

behavioral changes than self-report measures. It is also possible that some brain regions562

predict both immediate and long-term behavioral changes whereas other regions predict

563

only immediate behavioral changes.

564
565

5-2.

Are macroscopic changes of brain structure involved?

566

Another interesting neuroscientific question is what structural changes of the brain (i.e.,

567

brain plasticity) support behavioral changes related to intergenerational sustainability. If

568

behavioral changes induced by a treatment are long-lasting, they should be accompanied569

by changes in the brain structure, either at a microscopic (e.g., synapses and spines) or

570

macroscopic (e.g., cortical thickness of widespread areas) level. If the structural changes

571

are macroscopic and large enough, they could be detected by structural MRI.

572

Traditionally, intensive training of visuoperceptual or sensorimotor tasks has shown to24

Intergenerational sustainability and the brain573

induce macroscopic changes in task-relevant brain area (Zatorre et al., 2012). Recent

574

studies have further revealed that interventional training aiming at enhancing cognitive575

ability (e.g., attention) and prosocial motivations (e.g., compassion) induces widespread

576

changes in cortical thickness (Valk et al., 2017). In addition, cultural and environmental

577

factors such as socioeconomic status (SES) modulate cortical thickness in widespread

578

areas (even after controlling for genetic factors), which mediates effects of SES on

579

cognitive ability such as executive functions (Noble et al., 2015). This raises the580

possibility that an immersive exposure to interventional training or educational581

programmes aiming at enhancing concern for future generations may induce widespread582

structural changes, particularly in regions implicated in social cognition and/or

583

intertemporal decisions. Testing such possibility would provide useful insight into

584

neural underpinnings underlying sustainable behaviour.

585
586
587

5-3.

Can neuroscience offer better understandings of distinct prosocial
motivations?

588

As we described before, prosocial motivations are multifaceted, and some of them may

589

contribute to intergenerationally-sustainable decisions while others may not. Although590

we have argued that naturally-grown empathy might not be enough for achieving

591

sustainability at the collective level, related (but possibly distinct) prosocial motivations

592

such as compassion and loving-kindness may be enhanced with training (Lutz et al.,593

2008), which may promote concern for future generations by transcending perceived

594

distances. In addition, impartiality (as opposed to parochialism) may play key roles in

595

making intergenerationally-rational decisions (Baumgartner et al., 2013; Everett et al.,596

2018), which may counteract our natural biases toward the current generation.
25

Intergenerational sustainability and the brain

597

Neuroimaging may allow us to clarify common and distinct substrates for these598interrelated prosocial motivations, and help to understand neurocognitive components

599

particularly important for intergenerationally-sustainable decisions.

600
601
602

5-4.

Transdisciplinary research bridging laboratory experiments and real-worldpractices603

Throughout this paper, we have emphasized the importance of developing and604

implementing social institutions to leverage concern for future generations that are

605

effective in real-world situations. To achieve this challenging goal, we need to606

accumulate empirical evidence in both laboratory settings and real-world practices in a607

translatable manner. An emerging transdisciplinary framework, called “Future Design”

608(Saijo, 2015), aims at this goal by facilitating collaborations among researchers and609

citizens (including policy makers). For instance, Kamijo et al. (2017) showed that an

610

institution

611

intergenerationally-sustainable decisions in a laboratory setting (i.e., the ISDG). In an

612

imaginary future generation treatment, some players in the current generation take the613

perspective of future generations, and discuss with other members in the current

614

generation on behalf of future generations. Importantly, the essentially same institution

615

has recently been used in practices in several local governments in Japan (Hara et al.,6162019). This may offer a useful opportunity to examine effects of certain institutions in

617

both well-controlled618

policy-making processes. Future neuroscience research may take the advantage of such

619

situations, for example by inviting the same participants and/or using the same

620

institutions for both laboratory neuroimaging experiments and real-world practices forcalledan“imaginary

laboratory settings

futureandgeneration”

real-worldpractices

promotesin actual26

Intergenerational sustainability and the brain

621

policy making. A key concept in Future Design is “futurability,” which is defined as the622

ability to derive happiness from deciding and acting to forego current benefits in order623

to enrich future generations (Saijo, 2015). Empirical research using neuroimaging may

624

clarify the neurobiological underpinnings of this concept. For the conceptual uniqueness625

and recent progresses in Future Design, see Saijo (2020).

626
627
628

6. Conclusions

629

Intergenerational sustainability dilemmas lie at the heart of pressing issues in the630

contemporary society such as climate change. To solve these dilemmas, we need novel631

social systems to enhance the current generation’s concern for future generations,632

thereby achieving the transformation toward sustainable societies. Neuroscience may

633

play unique roles in advancing the transdisciplinary research for intergenerational634

sustainability.

635

27

Intergenerational sustainability and the brain

636

References

637

Amodio, D. M. (2014). The neuroscience of prejudice and stereotyping. Nature Reviews

638
639

Neuroscience, 15(10), 670–682. https://doi.org/10.1038/nrn3800
Andreoni, J. (1990). Impure Altruism and Donations to Public Goods: A Theory of640

Warm-Glow641

https://doi.org/10.2307/2234133

642

Giving.

The

Economic

Journal,100(401),

464.

Aron, A. R. (2019). The Climate Crisis Needs Attention from Cognitive Scientists.

643

Trendsin

Cognitive

Sciences,644

https://doi.org/10.1016/j.tics.2019.08.001

23(11),

903–906.

645

Aron, A. R., Ivry, R. B., Jeffery, K. J., Poldrack, R. A., Schmidt, R., Summerfield, C., &646

Urai, A. E. (2020). How Can Neuroscientists Respond to the Climate

647

Emergency?

648

https://doi.org/10.1016/j.neuron.2020.02.019

649

Neuron,106(1),

17–20.

Bahrami, B., Olsen, K., Latham, P. E., Roepstorff, A., Rees, G., & Frith, C. D. (2010).

650

Optimally

Interacting

Minds.

651

https://doi.org/10.1126/science.1185718

Science,329(5995),

1081–1085.

652

Bartra, O., McGuire, J. T., & Kable, J. W. (2013). The valuation system: A

653

coordinate-based meta-analysis of BOLD fMRI experiments examining neural654

correlates

655

https://doi.org/10.1016/j.neuroimage.2013.02.063

ofsubjective

value.

NeuroImage,76,

412–427.

656

Baumgartner, T., Knoch, D., Hotz, P., Eisenegger, C., & Fehr, E. (2011). Dorsolateral657

and ventromedial prefrontal cortex orchestrate normative choice. Nature

658

Neuroscience, 14(11), 1468–1474. https://doi.org/10.1038/nn.2933

659

Baumgartner, T., Schiller, B., Hill, C., & Knoch, D. (2013). Impartiality in humans is28

Intergenerational sustainability and the brain

660

predicted by brain structure of dorsomedial prefrontal cortex. NeuroImage, 81,661

317–324. https://doi.org/10.1016/j.neuroimage.2013.05.047

662

Beaty, R. E., Kenett, Y. N., Christensen, A. P., Rosenberg, M. D., Benedek, M., Chen, Q.,

663

Fink, A., Qiu, J., Kwapil, T. R., Kane, M. J., & Silvia, P. J. (2018). Robust

664

prediction of individual creative ability from brain functional connectivity.

665

Proceedings of the National Academy of Sciences of the United States of666

America, 115(5), 1087–1092. https://doi.org/10.1073/pnas.1713532115

667

Behrens, T. E. J., Hunt, L. T., & Rushworth, M. F. S. (2009). The computation of social

668

behavior.

669

https://doi.org/10.1126/science.1169694

670
671

Science

(New

York,

N.Y.),

324(5931),

1160–1164.

Bernhard, H., Fischbacher, U., & Fehr, E. (2006). Parochial altruism in humans. Nature,442(7105), 912–915. https://doi.org/10.1038/nature04981

672

Bloom, D. E., Cafiero, E., Jané-Llopis, E., Abrahams-Gessel, S., Bloom, L. R., Fathima,

673

S., Feigl, A. B., Gaziano, T., Mowafi, M., Pandya, A., Prettner, K., Rosenberg, L.,674

Seligman, B., Stein, A., & Weinstein, C. (2011). The global economic burden of675

noncommunicable diseases. World Economic Forum.

676

Brown, T. I., Carr, V. A., LaRocque, K. F., Favila, S. E., Gordon, A. M., Bowles, B.,

677

Bailenson, J. N., & Wagner, A. D. (2016). Prospective representation of

678

navigational goals in the human hippocampus. Science, 352(6291), 1323–1326.

679

https://doi.org/10.1126/science.aaf0784

680

Bruneau, E. G., Cikara, M., & Saxe, R. (2017). Parochial Empathy Predicts Reduced

681

Altruism and the Endorsement of Passive Harm. Social Psychological and682

Personality Science, 8(8), 934–942. https://doi.org/10.1177/1948550617693064

683

Bulley, A., & Schacter, D. L. (2020). Deliberating trade-offs with the future. Nature
29

Intergenerational sustainability and the brain

684

Human Behaviour, 4(3), 238–247. https://doi.org/10.1038/s41562-020-0834-9

685

Chen, J., Leong, Y. C., Honey, C. J., Yong, C. H., Norman, K. A., & Hasson, U. (2017).

686

Shared memories reveal shared structure in neural activity across individuals.

687

Nature Neuroscience, 20(1), 115–125. https://doi.org/10.1038/nn.4450

688

Chua, H. F., Ho, S. S., Jasinska, A. J., Polk, T. A., Welsh, R. C., Liberzon, I., & Strecher,

689

V. J. (2011). Self-related neural response to tailored smoking-cessation messages

690

predicts

691

https://doi.org/10.1038/nn.2761

quitting.

NatureNeuroscience,

14(4),

426–427.

692

Cikara, M., & Fiske, S. T. (2011). Bounded Empathy: Neural Responses to Outgroup

693

Targets’ (Mis)fortunes. Journal of Cognitive Neuroscience, 23(12), 3791–3803.

694

https://doi.org/10.1162/jocn_a_00069

695

Crockett, M. J., Braams, B. R., Clark, L., Tobler, P. N., Robbins, T. W., & Kalenscher, T.

696(2013). Restricting temptations: Neural mechanisms of precommitment. Neuron,

697

79(2), 391–401. https://doi.org/10.1016/j.neuron.2013.05.028

698

Crutzen, P. (2002). Geology of mankind. Nature, 415(3).

699

De Martino, B., Fleming, S. M., Garrett, N., & Dolan, R. J. (2013). Confidence in

700

value-based

choice.

Nature701

https://doi.org/10.1038/nn.3279

Neuroscience,16(1),

105–110.

702

Decety, J., Bartal, I. B.-A., Uzefovsky, F., & Knafo-Noam, A. (2016). Empathy as a703

driver of prosocial behaviour: Highly conserved neurobehavioural mechanisms

704

across species. Philosophical Transactions of the Royal Society B: Biological705

Sciences, 371(1686). https://doi.org/10.1098/rstb.2015.0077

706

Doll, B. B., Duncan, K. D., Simon, D. A., Shohamy, D., & Daw, N. D. (2015).

707

Model-based choices involve prospective neural activity. Nature Neuroscience,30

Intergenerational sustainability and the brain

708

18(5), 767–772. https://doi.org/10.1038/nn.3981

709

Everett, J. A. C., Faber, N. S., Savulescu, J., & Crockett, M. J. (2018). The costs of710

being consequentialist: Social inference from instrumental harm and impartial

711

beneficence. Journal of Experimental Social Psychology, 79, 200–216.

712

https://doi.org/10.1016/j.jesp.2018.07.004

713

Falk, E. B., Berkman, E. T., & Lieberman, M. D. (2012). From Neural Responses to714

Population Behavior: Neural Focus Group Predicts Population-Level Media

715

Effects.

716

https://doi.org/10.1177/0956797611434964

Psychological

Science,23(5),

439–445.

717

Falk, E. B., Berkman, E. T., Mann, T., Harrison, B., & Lieberman, M. D. (2010).

718

Predicting Persuasion-Induced Behavior Change from the Brain. Journal of

719

Neuroscience,720

https://doi.org/10.1523/JNEUROSCI.0063-10.201030(25),

8421–8424.

721

Figner, B., Knoch, D., Johnson, E. J., Krosch, A. R., Lisanby, S. H., Fehr, E., & Weber,

722

E. U. (2010). Lateral prefrontal cortex and self-control in intertemporal choice.

723

Nature Neuroscience, 13(5), 538–539. https://doi.org/10.1038/nn.2516

724

Fischer, M.-E., Irlenbusch, B., & Sadrieh, A. (2004). An intergenerational common pool725

resource experiment. Journal of Environmental Economics and Management,

726

48(2), 811–836. https://doi.org/10.1016/j.jeem.2003.12.002

727

Fiske, S. T. (2002). What We Know Now About Bias and Intergroup Conflict, the728

Problem of the Century. Current Directions in Psychological Science, 11(4),729

123–128. https://doi.org/10.1111/1467-8721.00183

730

Fleming, S. M., Weil, R. S., Nagy, Z., Dolan, R. J., & Rees, G. (2010). Relating

731

introspective accuracy to individual differences in brain structure. Science,
31Intergenerational sustainability and the brain

732
733

329(5998), 1541–1543. https://doi.org/10.1126/science.1191883
Franzen, A., & Pointner, S. (2013). The external validity of giving in the dictator game.

734

Experimental

735

https://doi.org/10.1007/s10683-012-9337-5

736
737

Economics,

16(2),

155–169.

Frederick, S., Loewenstein, G., & O’donoghue, T. (2002). Time discounting and time
preference: A critical review. Journal of Economic Literature, 40(2), 351–401.

738

Frith, U., & Frith, C. D. (2003). Development and neurophysiology of mentalizing.

739

Philosophical Transactions of the Royal Society of London. Series B: Biological740

Sciences, 358(1431), 459–473. https://doi.org/10.1098/rstb.2002.1218

741

Gilbert, D. T., & Wilson, T. D. (2007). Prospection: Experiencing the future. Science,742

317(5843), 1351–1354.

743

Hara, K., Yoshioka, R., Kuroda, M., Kurimoto, S., & Saijo, T. (2019). Reconciling

744

intergenerational conflicts with imaginary future generations: Evidence from a745

participatory deliberation practice in a municipality in Japan. Sustainability

746

Science, 14(6), 1605–1619. https://doi.org/10.1007/s11625-019-00684-x

747

Hassabis, D., Kumaran, D., & Maguire, E. A. (2007). Using Imagination to Understand748

the Neural Basis of Episodic Memory. Journal of Neuroscience, 27(52),749

14365–14374. https://doi.org/10.1523/JNEUROSCI.4549-07.2007

750

Hauser, O. P., Rand, D. G., Peysakhovich, A., & Nowak, M. A. (2014). Cooperating

751

with

the

future.

752

https://doi.org/10.1038/nature13530

Nature,511(7508),

220–223.

753

Hein, G., Engelmann, J. B., Vollberg, M. C., & Tobler, P. N. (2016). How learning754

shapes the empathic brain. Proceedings of the National Academy of Sciences,755

113(1), 80–85. https://doi.org/10.1073/pnas.1514539112
32

Intergenerational sustainability and the brain

756

Hein, G., Morishima, Y., Leiberg, S., Sul, S., & Fehr, E. (2016). The brain’s functional757

network architecture reveals human motives. Science, 351(6277), 1074–1078.

758

https://doi.org/10.1126/science.aac7992

759

Hill, C. A., Suzuki, S., Polania, R., Moisa, M., O’Doherty, J. P., & Ruff, C. C. (2017). A

760

causal account of the brain network computations underlying strategic social

761

behavior.

762

https://doi.org/10.1038/nn.4602

NatureNeuroscience,20(8),

1142–1149.

763

Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E., & Gallant, J. L. (2016).

764

Natural speech reveals the semantic maps that tile human cerebral cortex. Nature,765

532(7600), 453–458. https://doi.org/10.1038/nature17637

766

IPCC. (2014). Climate change 2014: Synthesis report. Contribution of Working Groups

767

I, II and III to the fifth assessment report of the Intergovernmental Panel on

768

Climate Change. IPCC, Geneva, Switzerland.

769

Izuma, K., Aoki, R., Shibata, K., & Nakahara, K. (2019). Neural signals in amygdala

770

predict implicit prejudice toward an ethnic outgroup. NeuroImage, 189, 341–352.

771

https://doi.org/10.1016/j.neuroimage.2019.01.019

772

Kable, J. W., & Glimcher, P. W. (2007). The neural correlates of subjective value during

773

intertemporalchoice.

Nature774

https://doi.org/10.1038/nn2007

Neuroscience,

10(12),

1625–1633.

775

Kahn, M. E., Mohaddes, K., Ng, R. N., Pesaran, M. H., Raissi, M., & Yang, J.-C. (2019).

776

Long-term macroeconomic effects of climate change: A cross-country analysis

777(No. 0898–2937). National Bureau of Economic Research.

778

Kamijo, Y., Komiya, A., Mifune, N., & Saijo, T. (2017). Negotiating with the future:

779

Incorporating imaginary future generations into negotiations. Sustainability
33

Intergenerational sustainability and the brain

780

Science, 12(3), 409–420. https://doi.org/10.1007/s11625-016-0419-8

781

Kjellstrom, T., Maître, N., Saget, C., Otto, M., & Karimova, T. (2019). Working on a782

warmer planet: The effect of heat stress on productivity and decent work.

783

http://www.ilo.org/global/publications/books/WCMS_711919/lang--en/index.ht

784

m

785

Klimecki, O. M., Mayer, S. V., Jusyte, A., Scheeff, J., & Schönenberg, M. (2016).

786

Empathy promotes altruistic behavior in economic interactions. Scientific

787

Reports, 6(1), 31961. https://doi.org/10.1038/srep31961

788

Koechlin, E. (2003). The Architecture of Cognitive Control in the Human Prefrontal

789

Cortex.

Science,302(5648),

790

https://doi.org/10.1126/science.1088545

1181–1185.

791

Kristal, A. S., & Whillans, A. V. (2020). What we can learn from five naturalistic field

792

experiments that failed to shift commuter behaviour. Nature Human Behaviour,

793

4(2), 169–176. https://doi.org/10.1038/s41562-019-0795-z

794Langenbach, B. P., Baumgartner, T., Cazzoli, D., Müri, R. M., & Knoch, D. (2019).

795

Inhibition of the right dlPFC by theta burst stimulation does not alter sustainable796

decision-making. Scientific Reports, 9(1), 1–8.

797

Lenton, T. M., Rockström, J., Gaffney, O., Rahmstorf, S., Richardson, K., Steffen, W.,

798& Schellnhuber, H. J. (2019). Climate tipping points—Too risky to bet against.

799

Nature, 575(7784), 592–595. https://doi.org/10.1038/d41586-019-03595-0

800

Lord, C. G., Ross, L., & Lepper, M. R. (1979). Biased assimilation and attitude

801

polarization: The effects of prior theories on subsequently considered evidence.

802

Journal803

https://doi.org/10.1037/0022-3514.37.11.2098

ofPersonality

andSocial

Psychology,

37(11),

2098–2109.

34

Intergenerational sustainability and the brain

804

Lutz, A., Brefczynski-Lewis, J., Johnstone, T., & Davidson, R. J. (2008). Regulation of805

the Neural Circuitry of Emotion by Compassion Meditation: Effects of

806

Meditative807

https://doi.org/10.1371/journal.pone.0001897

Expertise.

PLOS

ONE,

3(3),

e1897.

808

McCabe, K., Houser, D., Ryan, L., Smith, V., & Trouard, T. (2001). A functional809

imaging study of cooperation in two-person reciprocal exchange. Proceedings of

810

the

811

https://doi.org/10.1073/pnas.211415698

812

National

Academyof

Sciences,

98(20),

11832–11835.

McClure, S. M. (2004). Separate Neural Systems Value Immediate and Delayed

813

Monetary

Rewards.

Science,814

https://doi.org/10.1126/science.1100907

306(5695),

503–507.

815

Navajas, J., Niella, T., Garbulsky, G., Bahrami, B., & Sigman, M. (2018). Aggregated816

knowledge from a small number of debates outperforms the wisdom of large

817

crowds.

818

https://doi.org/10.1038/s41562-017-0273-4

NatureHuman

Behaviour,2(2),126–132.

819

Noble, K. G., Houston, S. M., Brito, N. H., Bartsch, H., Kan, E., Kuperman, J. M.,

820

Akshoomoff, N., Amaral, D. G., Bloss, C. S., Libiger, O., Schork, N. J., Murray,

821

S. S., Casey, B. J., Chang, L., Ernst, T. M., Frazier, J. A., Gruen, J. R., Kennedy,

822

D. N., Van Zijl, P., … Sowell, E. R. (2015). Family income, parental education823

and brain structure in children and adolescents. Nature Neuroscience, 18(5),824

773–778. https://doi.org/10.1038/nn.3983

825

O’Neill, D. W., Fanning, A. L., Lamb, W. F., & Steinberger, J. K. (2018). A good life for

826

all

withinplanetaryboundaries.

Nature827

https://doi.org/10.1038/s41893-018-0021-4

Sustainability,1(2),88–95.

35

Intergenerational sustainability and the brain

828

Park, S. Q., Kahnt, T., Dogan, A., Strang, S., Fehr, E., & Tobler, P. N. (2017). A neural829

link between generosity and happiness. Nature Communications, 8(1), 15964.

830

https://doi.org/10.1038/ncomms15964

831

Peters, J., & Büchel, C. (2010). Episodic Future Thinking Reduces Reward Delay

832

Discounting through an Enhancement of Prefrontal-Mediotemporal Interactions.

833

Neuron, 66(1), 138–148. https://doi.org/10.1016/j.neuron.2010.03.026

834

Phelps, E. A., O’Connor, K. J., Cunningham, W. A., Funayama, E. S., Gatenby, J. C.,

835

Gore, J. C., & Banaji, M. R. (2000). Performance on Indirect Measures of Race

836

Evaluation Predicts Amygdala Activation. Journal of Cognitive Neuroscience,

837

12(5), 729–738. https://doi.org/10.1162/089892900562552

838
839
840

Rand, D. G., & Nowak, M. A. (2013). Human cooperation. Trends in Cognitive Sciences,17(8), 413–425. https://doi.org/10.1016/j.tics.2013.06.003
Rilling, J., Gutman, D., Zeh, T., Pagnoni, G., Berns, G., & Kilts, C. (2002). A neural

841

basis

842

https://doi.org/10.1016/s0896-6273(02)00755-9

843

for

socialcooperation.

Neuron,35(2),

395–405.

Rilling, J. K., & Sanfey, A. G. (2011). The Neuroscience of Social Decision-Making.

844

AnnualReviewof

Psychology,845

https://doi.org/10.1146/annurev.psych.121208.131647

62(1),

23–48.

846

Rockström, J., Steffen, W., Noone, K., Persson, Å., Chapin, F. S., Lambin, E. F., Lenton,

847

T. M., Scheffer, M., Folke, C., Schellnhuber, H. J., Nykvist, B., de Wit, C. A.,

848

Hughes, T., van der Leeuw, S., Rodhe, H., Sörlin, S., Snyder, P. K., Costanza, R.,

849

Svedin, U., … Foley, J. A. (2009). A safe operating space for humanity. Nature,850

461(7263), 472–475. https://doi.org/10.1038/461472a

851

Ruff, C. C., & Fehr, E. (2014). The neurobiology of rewards and values in social
36

Intergenerational sustainability and the brain

852

decision853

https://doi.org/10.1038/nrn3776

854
855
856

making.

Nature

Reviews

Neuroscience,15(8),

549–562.

Saijo, T. (2015). Future design: Concept for a ministry of the future. Social Design
Engineering Serie.
Saijo, T. (2020). Future Design: Bequeathing Sustainable Natural Environments and857

Sustainable

Societies

toFuture858

http://www.souken.kochi-tech.ac.jp/seido/wp/SDES-2020-5.html

Generations.

859

Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The

860

neural basis of economic decision-making in the Ultimatum Game. Science

861

(New

862

https://doi.org/10.1126/science.1082976

York,N.Y.),

300(5626),

1755–1758.

863

Schacter, D. L., Benoit, R. G., & Szpunar, K. K. (2017). Episodic future thinking:

864

Mechanisms and functions. Current Opinion in Behavioral Sciences, 17, 41–50.

865

https://doi.org/10.1016/j.cobeha.2017.06.002

866

Scholz, C., Baek, E. C., O’Donnell, M. B., Kim, H. S., Cappella, J. N., & Falk, E. B.

867(2017). A neural model of valuation and information virality. Proceedings of the868

National

869

https://doi.org/10.1073/pnas.1615259114

Academyof

Sciences,

114(11),

2881–2886.

870

Schurz, M., Radua, J., Aichhorn, M., Richlan, F., & Perner, J. (2014). Fractionating

871

theory of mind: A meta-analysis of functional brain imaging studies.

872

Neuroscience873

https://doi.org/10.1016/j.neubiorev.2014.01.009&

Biobehavioral

Reviews,

42,

9–34.

874

Shamay-Tsoory, S. G., Aharon-Peretz, J., & Perry, D. (2009). Two systems for empathy:

875

A double dissociation between emotional and cognitive empathy in inferior
37Intergenerational sustainability and the brain

876

frontal gyrus versus ventromedial prefrontal lesions. Brain, 132(3), 617–627.

877

https://doi.org/10.1093/brain/awn279

878

Sharot, T., Korn, C. W., & Dolan, R. J. (2011). How unrealistic optimism is maintained

879

in

the

face

of

reality.

880

https://doi.org/10.1038/nn.2949

NatureNeuroscience,

14(11),

1475–1479.

881

Sherstyuk, K., Tarui, N., Ravago, M.-L. V., & Saijo, T. (2016). Intergenerational Games882

with Dynamic Externalities and Climate Change Experiments. Journal of the883

Association of Environmental and Resource Economists, 3(2), 247–281.

884

https://doi.org/10.1086/684162

885
886
887

Shuman, E. K. (2010). Global climate change and infectious diseases. New England
Journal of Medicine, 362(12), 1061–1063.
Sonkusare, S., Breakspear, M., & Guo, C. (2019). Naturalistic Stimuli in Neuroscience:

888

Critically Acclaimed.

Trends in

Cognitive Sciences,889

https://doi.org/10.1016/j.tics.2019.05.004

23(8), 699–714.

890

Steffen, W., Broadgate, W., Deutsch, L., Gaffney, O., & Ludwig, C. (2015). The

891

trajectory of the Anthropocene: The great acceleration. The Anthropocene

892

Review, 2(1), 81–98.

893

Steffen, W., Crutzen, P. J., & McNeill, J. R. (2007). The Anthropocene: Are humans894

now overwhelming the great forces of nature. AMBIO: A Journal of the Human

895

Environment, 36(8), 614–621.

896

Steffen, W., Rockström, J., Richardson, K., Lenton, T. M., Folke, C., Liverman, D.,

897

Summerhayes, C. P., Barnosky, A. D., Cornell, S. E., Crucifix, M., Donges, J. F.,

898

Fetzer, I., Lade, S. J., Scheffer, M., Winkelmann, R., & Schellnhuber, H. J.

899(2018). Trajectories of the Earth System in the Anthropocene. Proceedings of the38

Intergenerational sustainability and the brain

900

National

Academyof

Sciences,901

https://doi.org/10.1073/pnas.1810141115

115(33),8252–8259.

902

Stolier, R. M., & Freeman, J. B. (2016). Neural pattern similarity reveals the inherent

903

intersection of social categories. Nature Neuroscience, 19(6), 795–797.

904

https://doi.org/10.1038/nn.4296

905
906

Stott, P. (2016). How climate change affects extreme weather events. Science,
352(6293), 1517–1518.

907

Strombach, T., Weber, B., Hangebrauk, Z., Kenning, P., Karipidis, I. I., Tobler, P. N., &908

Kalenscher, T. (2015). Social discounting involves modulation of neural value909

signals by temporoparietal junction. Proceedings of the National Academy of910

Sciences, 112(5), 1619–1624. https://doi.org/10.1073/pnas.1414715112

911

Suddendorf, T., Bulley, A., & Miloyan, B. (2018). Prospection and natural selection.

912

Current

Opinionin

Behavioral

913

https://doi.org/10.1016/j.cobeha.2018.01.019

Sciences,24,

26–31.

914

Thaler, R. H., & Benartzi, S. (2004). Save More Tomorrow TM: Using Behavioral

915

Economics to Increase Employee Saving. Journal of Political Economy, 112(S1),916

S164–S187. JSTOR. https://doi.org/10.1086/380085

917
918

Timilsina, R., Kotani, K., Nakagawa, Y., & Saijo, T. (2017). Can deliberative
democracy resolve intergenerational sustainability dilemma? 35.

919

Tong, L. C., Acikalin, M. Y., Genevsky, A., Shiv, B., & Knutson, B. (2020). Brain920

activity forecasts video engagement in an internet attention market. Proceedings

921

of

922

https://doi.org/10.1073/pnas.1905178117

923

the

National

Academyof

Sciences,

117(12),6936–6941.

Valk, S. L., Bernhardt, B. C., Trautwein, F.-M., Böckler, A., Kanske, P., Guizard, N.,
39

Intergenerational sustainability and the brain

924

Collins, D. L., & Singer, T. (2017). Structural plasticity of the social brain:

925

Differential change after socio-affective and cognitive mental training. Science

926

Advances, 12.

927

van der Linden, S. (2017). The nature of viral altruism and how to make it stick. Nature

928

Human Behaviour, 1(3), 0041. https://doi.org/10.1038/s41562-016-0041

929

Wackernagel, M., Hanscom, L., & Lin, D. (2017). Making the Sustainable Development

930

Goals Consistent with Sustainability. Frontiers in Energy Research, 5.

931

https://doi.org/10.3389/fenrg.2017.00018

932

Wade-Benzoni, K. A., Sondak, H., & Galinsky, A. D. (2010). Leaving a Legacy:

933

Intergenerational Allocations of Benefits and Burdens. Business Ethics

934

Quarterly, 20(1), 7–34.

935

Watanabe, T., Takezawa, M., Nakawake, Y., Kunimatsu, A., Yamasue, H., Nakamura,936

M., Miyashita, Y., & Masuda, N. (2014). Two distinct neural mechanisms

937

underlying indirect reciprocity. Proceedings of the National Academy of938

Sciences, 111(11), 3990–3995. https://doi.org/10.1073/pnas.1318570111

939
940

World

Bank.

(2020).

GlobalEconomic

Prospects,

June

2020.

DOI:

10.1596/978-1-4648-1553-9.

941

Wu, X., Lu, Y., Zhou, S., Chen, L., & Xu, B. (2016). Impact of climate change on942

human infectious diseases: Empirical evidence and human adaptation.

943

Environment

944

https://doi.org/10.1016/j.envint.2015.09.007

International,

86,

14–23.

945

Xiang, T., Lohrenz, T., & Montague, P. R. (2013). Computational substrates of norms

946

and their violations during social exchange. The Journal of Neuroscience: The

947

Official Journal of the Society for Neuroscience, 33(3), 1099–1108a.
40

Intergenerational sustainability and the brain

948

https://doi.org/10.1523/JNEUROSCI.1642-12.2013

949

Zatorre, R. J., Fields, R. D., & Johansen-Berg, H. (2012). Plasticity in gray and white:

950

Neuroimaging changes in brain structure during learning. Nature Neuroscience,951

15(4), 528–536. https://doi.org/10.1038/nn.3045

952
953

41

Intergenerational sustainability and the brain

954

Figure legends

955
A

B

956
957

Figure 1. Scientific facts about climate change

958

A. Great acceleration. Increases in several socioeconomic indices (e.g., global

959

population and real GDP) have rapidly accelerated after 1950 (upper panels). Changes

960

in ecological indices (e.g., carbon dioxide in atmosphere and surface temperature)961

mirror this acceleration (lower panels). For a broader coverage of socioeconomic and962

ecological indices, see Steffen et al. (2015). Climate researchers generally agree with

963

high confidence that the recent climate change is caused by human activity (IPCC,

9642014). B. Planetary boundaries. Crossing certain biophysical thresholds may induce

965

irreversible changes in the Earth’s environment and endanger sustainability of humanity.

966

The green area represents “safe operating space.” The figure illustrates that human967

activity is approaching to or has already crossed the threshold in several domains of the968

Earth’s ecosystem. The figures are adapted with permission from Steffen et al. (2015)

969

and Rockström et al. (2009).

970

42

Intergenerational sustainability and the brain

A

TPJ

IFG

B

Precuneus

Ventral striatum

dmPFC
vmPFC

ATL

C

Somatosensory cortex

ACC

D

Insula

dlPFC

aPFC

vmPFC

Amygdala

971
972

Figure2.

Possibleneuralsubstrates

forintergenerationally-sustainable973

decision-making

974

A. Brain regions implicated in theory of mind (based on Schurz et al., 2014). B. Brain975

regions implicated in value-based decision-making and reward processing (based on

976

Bartra et al., 2013). C. Brain regions implicated in affective empathy (based on Decety

977

et al., 2016). D. Brain regions implicated in intertemporal decision-making and978self-control (based on Crockett et al., 2013). Note that some regions appear in multiple

979

panels (e.g., vmPFC), because a single brain region is often involved in multiple

980

cognitive processes. TPJ: temporoparietal junction; IFG: inferior frontal gyrus; dmPFC:

981

dorsomedial prefrontal cortex; vmPFC: ventromedial prefrontal cortex; ACC: anterior982

cingulate cortex; dlPFC: dorsolateral prefrontal cortex; aPFC: anterior prefrontal cortex.

983

43

Intergenerational sustainability and the brain

A
Generation 2

Generation 3
…

…

…

…

B: $27

B: $18

A: $27

B: $18Sustainable

Unsustainable

A: $36

A: $27

Unsustainable

Pool

Generation 1

?
1

2

3

4 …

Generation

B

984

Study

Prospective factor

Retrospective factor

Conversations

Hauser et al. (2014)

✓

-

-

Kamijo et al. (2017)

✓

✓

✓

Langenbach et al. (2019)

✓

-

-

985

Figure 3. Behavioral economic games

986

A. Illustration of a typical economic game (Kamijo et al., 2017) designed to study

987

integenerationally-sustainable decision-making. A group of players represents a988

generation,

989

intergenerational-sustainability dilemma (e.g., a trade-off between the current and future990

generations’ benefits). The decision made by the current generation influences

991

subsequent (i.e., future) generations but not previous (i.e., past) generations, reflecting

992

the asymmetry of time in the real world. In this example, Generation 2 chooses the993

sustainable option, whereas Generation 1 and 3 choose the unsustainable option (which994

reduces the resources in the intergenerational common pool). B. Comparison among

995

different games (Hauser et al., 2014; Kamijo et al., 2017; Langenbach et al., 2019).

996“Prospective factor” indicates whether players are informed about how their decisions997

affect future generations. “Retrospective factor” indicates whether players are informed998

about the history of decisions made by past generations.

and

makes

a

collective

decisionthat

involves

an

999

44

Intergenerational sustainability and the brain

A

Stimulus 1
Stimulus 2
Stimulus 3

B
Individual
differences

Collective
outcomes

1000
1001

Figure 4. Predicting real-world outcomes from neural data

1002

A. Participants are presented with naturalistic stimuli (e.g., movie clips that deliver

1003

messages promoting sustainable behavior or political announcements on sustainable1004

policies) while their brain activity is measured. B. Patterns of neural responses (e.g.,1005

local activation, functional connectivity, and inter-subject correlation) could be related

1006

to either individual differences (e.g., behavioral changes induced by certain treatments)

1007

or collective outcomes (e.g., population-level responses to certain treatments).

1008

Short-term (immediate) and long-term (persistent) effects of a given treatment could be1009

associated with differential patterns of neural responses.

45