Australasian Mathematical Psychology Conference 2019 - MELBOURNE, AUSTRALIA
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Australasian Mathematical Psychology Conference 2019 MELBOURNE, AUSTRALIA
Welcome
Welcome to Melbourne and welcome to the 2019 Australasian Mathemati-
cal Psychology Conference. The conference is hosted this year by the Mel-
bourne School of Psychological Sciences at the University of Melbourne,
with generous support from the Complex Human Data Hub within the School.
In addition to the main conference programme, the schedule and ab-
stracts of which are included in this document, we also have a conference
dinner (to be held at the Lincoln Hotel) and the traditional soccer game. De-
tails about the events and venues can be found in this document (see p. vii)
and on the website at http://mathpsy.ch/venues.
We would like to give a special thank you to the student volunteers, with-
out whom we wouldnʼt be able to have a conference at all!
We hope that you have a fantastic time during the conference. Please let
us know if we can help you with anything during your time here.
Simon Dennis, Daniel Little, Adam Osth, and Simon Lilburn
AMPC 2019 Organising Committee
We acknowledge the Traditional Owners of the land that this conference is held on,
the Wurundjeri peoples of the Kulin nation, and pay our respects to Elders past
and present.
iiiCode of conduct
All attendees, speakers, sponsors and volunteers at our conference are re-
quired to agree with the following code of conduct. Our conference is ded-
icated to providing a harassment-free conference experience for everyone,
regardless of gender, gender identity and expression, age, sexual orienta-
tion, disability, physical appearance, body size, race, ethnicity, religion (or
lack thereof), or technology choices. We do not tolerate harassment of con-
ference participants in any form. Sexual language and imagery is not appro-
priate for any conference venue, including talks, workshops, parties, Twitter
and other online media.
If you have any concerns during your time at the conference, please con-
tact one of the organisers.
vVenue
The primary location of the conference is the Melbourne Business School,
located at 200 Leicester St. just south of the main Parkville campus of the
University of Melbourne. This is very easily accessible by tram: all north-
bound tram routes from either Flinders St. railway station or Melbourne
Central railway station run through the Melbourne CBD (up Swanston St.)
and pass by the University of Melbourneʼs main tram stop, which is within
easy walking distance of the Business School (the preceding stop at Lincoln
Sq. is even closer to the conference venue).
The soccer game will be held on the Melbourne University Cricket Grounds,
which are a short walk north of the conference venue up Swanston St. and
along Tin Alley (into the main campus). This also passes by the Melbourne
School of Psychological Sciences.
The conference dinner is also located within walking distance of the con-
ference venue, at The Lincoln, on the corner of Queensberry and Cardigan
streets.
These locations are all marked on the accompanying map.
viiviii
Important information
Wi-Fi password
The conference will have free wi-fi access for all attendees. The SSID of the
conference wi-fi is MBS Visitor. When prompted in the portal upon open-
ing a web browser, enter the following details:
Username cmbs
Password 3449
Please let use know if you have any connectivity issues, and we will do
our best to provide technical support.
Nearby lunch venues
The conference is primarily located in the Melbourne Business School, near
to the main Parkville campus of the University of Melbourne, on Leicester
St. The conference venue is close to several very good places to eat break-
fast, lunch, or dinner. In addition to Lygon St., there are a number of very
good lunch venues located within walking distance. Here are a few of our
favourites:
Kaprica Terrific pizza that surpasses anything1 on the traditional Italian
dining strip of Lygon St. Cosy, bustling, but not too cramped or crowded.
Larger groups may be split, however. Although it has vegan and gluten
free options, better vegan and GF options are available slightly farther
afield (Shakahari, across Lygon St. on Faraday St., is a very good op-
tion, although you may be pressed for time in the lunch break). Lo-
cated south of Lincoln Square.
Seven Seeds A coffee roaster located across University Square and slightly
south (on Berkeley St.) with fare and fit-out straight from Melbourne
café central casting, although with the distinction of being one of the
original movers on the scene. The coffee is well regarded, particularly
the blends served with milk, although many prefer that of the also
nearby branch of Everyday Coffee (on Queensberry St.), which is a
strictly coffee-only affair. Seven Seeds is particularly good as a place
to catch breakfast on the way in.
1 Even the lauded DOC Pizza on Drummond St.
ixx
Don Tojo Japanese bento and donburi served with a speed that will make
you start wondering whether maybe Bem was on to something. An
undergraduate favourite for decades, with a small menu of modest
but nourishing options, all for a price that has withstood the tides of
inflation.
Nasi Lemak House Inexpensive Malaysian hawker food in a small restau-
rant. The laksa is good, but the titular nasi lemak is better. Norsiahʼs
Kitchen—across Swanston St. from the conference venue—is another
option for Malaysian (and Indonesian) food, but with a slightly more
no-frills (read: cash only), meat-heavy approach.
Soccer match
A storied tradition of the AMPC is the inclusion of a sporting event. Often
this is cricket but, by request, and following the waning nobility of the Aus-
tralian test cricket team, this year we will host a soccer match. Participation
in the soccer match is by no means required, or even encouraged, but spec-
tators will be treated to a display of majesty and sporting prowess.
The talks will end early on the Friday to allow commencement of the
game on the Melbourne University cricket pitch (marked on the map) at
5:00 pm. The match will last for one hour, allowing sufficient time for con-
valescence before the commencement of the conference dinner at The Lin-
coln at 7:30 pm.
The institution affiliated with the fairest player of the match, in keeping
with tradition, will be awarded the AMPC Ashes. These ashes honour the
staid seat of rationality embodied by mathematical psychology.1
9:00 AM
9:05 AM
9:00 AM. Welcome to AMPC 2019
9:10 AM
9:15 AM
Session
9:10 AM. Smith: Modelling the speed and accuracy of continuous outcome colour decisions:
9:20 AM Metric and categorical effects (p. 56)
9:25 AM
9:30 AM
9:35 AM 9:30 AM. Brown: The role of passing time in decision-making (p. 11)
9:40 AM
9:45 AM
9:40 AM. Ratcliff : Revisiting collapsing boundaries (p. 50)
9:50 AM
9:55 AM
10:00 AM
10:05 AM 10:00 AM. Hawkins: Time-varying cognitive models of decision making (p. 24)
10:10 AM
10:15 AM 10:10 AM. Lerche: The diffusion model provides new insights into the field of motivational psychol-
10:20 AM ogy (p. 35)
10:25 AM
10:30 AM
10:30 AM. Eidels: The cost of errors: Confusion analysis and the mental representation of numer-
10:35 AM
als (p. 18)
10:40 AM
10:45 AM
10:50 AM Morning tea (provided)
10:55 AM
11:00 AM
11:05 AM
11:10 AM
Session
11:05 AM. Heathcote: Control failures in Simon and Flanker Tasks (p. 26)
11:15 AM
11:20 AM
11:25 AM
11:30 AM 11:25 AM. Thorpe: Converting continuous tracking data to response time distributions (p. 59)
11:35 AM
11:40 AM 11:35 AM. Osth: The extralist feature effect in recognition memory: Re-evaluating constraints on
11:45 AM global matching models (p. 46)
11:50 AM
11:55 AM
12:00 PM
11:55 AM. Ballard: The dynamics of decision making during goal pursuit (p. 9)
12:05 PM
12:10 PM
12:15 PM
Break for lunch
Thursday 14/2 (AM)2
13:15 PM
13:20 PM Return from lunch
13:25 PM
13:30 PM
Session
13:25 PM. McCormick: Using the rank order task to estimate discriminability in eyewitness identifi-
13:35 PM cation (p. 42)
13:40 PM
13:45 PM
13:45 PM. Palada: Accumulating evidence about evidence accumulation models in applied con-
13:50 PM
texts (p. 47)
13:55 PM
14:00 PM 13:55 PM. Hotaling: New insights into decisions from experience: Using cognitive models to
14:05 PM understand how value information, outcome order, and salience drive risk taking (p. 27)
14:10 PM
14:15 PM
14:20 PM 14:15 PM. Cavallaro: Consumer choices under time pressure (p. 12)
14:25 PM
14:30 PM 14:25 PM. Cooper: Discriminating shoppers: Applications of SFT to consumer choice (p. 15)
14:35 PM
14:40 PM
14:45 PM
14:50 PM 14:45 PM. Lin: Response times and the exploration-exploitation trade-off (p. 38)
14:55 PM
15:00 PM
14:55 PM. Robins: Causality in social network research (p. 52)
15:05 PM
15:10 PM
15:15 PM
15:20 PM
15:25 PM Afternoon tea (provided)
15:30 PM
15:35 PM
15:40 PM
15:45 PM
Session
15:40 PM. Howe: Evidence for a general conformity mechanism: People follow norms even when
15:50 PM they come from the outgroup (p. 28)
15:55 PM
16:00 PM
16:00 PM. Andreotta: Analysing social media data: A mixed-methods framework combining
16:05 PM
computational and qualitative text analysis (p. 8)
16:10 PM
16:15 PM 16:10 PM. Kashima: Taking an intentional stance in joint action: How can we explain cross-
16:20 PM cultural variability? (p. 30)
16:25 PM
16:30 PM
16:35 PM 16:30 PM. Cavve: Differentiating social preference in hypothetical distributive decisions (p. 13)
16:40 PM
16:45 PM
16:40 PM. Dennis: Privacy versus open science (p. 16)
16:50 PM
16:55 PM
17:00 PM
Thursday 14/2 (PM)3
9:00 AM
9:05 AM
Session
9:00 AM. Perfors: Why do echo chambers form? The role of trust, population heterogeneity, and
9:10 AM objective truth (p. 48)
9:15 AM
9:20 AM
9:25 AM
9:20 AM. Kemp: Season naming and the local environment (p. 31)
9:30 AM
9:35 AM
9:40 AM
9:45 AM
9:40 AM. Shou: Exploring group decision making under ambiguity and risk (p. 55)
9:50 AM
9:55 AM
10:00 AM
10:00 AM. Kuhne: Knowledge is prior: Using past model fits to develpp informative priors in
10:05 AM
model selection. (p. 34)
10:10 AM
10:15 AM 10:10 AM. Yim: Semantic integration of novel words through syntagmatic and paradigmatic associ-
10:20 AM ations (p. 65)
10:25 AM
10:30 AM
10:30 AM. Mason: Distraction and delay: Memory and evaluation of temporal sequences of
10:35 AM
events (p. 41)
10:40 AM
10:45 AM
10:50 AM Morning tea (provided)
10:55 AM
11:00 AM
11:05 AM
11:10 AM
Session
11:05 AM. Newell: Mathematical formalization of Construal Level Theory regarding risk prefer-
11:15 AM ences at different psychological distances (p. 44)
11:20 AM
11:25 AM
11:30 AM 11:25 AM. Goh: The value of predictive information in decision-making under uncertainty (p. 23)
11:35 AM
11:40 AM
11:35 AM. Dunn: Models of risky choice: A signed difference analysis (p. 17)
11:45 AM
11:50 AM
11:55 AM
12:00 PM 11:55 AM. Farrell: Updating judgement contexts with extreme stimuli (p. 19)
12:05 PM
12:10 PM 12:05 PM. Reynolds: I Don’t Know... How to model this. (p. 51)
12:15 PM
Break for lunch
Friday 15/2 (AM)4
13:15 PM
13:20 PM Return from lunch
13:25 PM
13:30 PM
Session
13:25 PM. Sewell: The speed-accuracy tradeoff in probabilistic categorization: Selective influ-
13:35 PM ence? (p. 53)
13:40 PM
13:45 PM
13:45 PM. Turner: Changing our minds about change-of-mind models: Existing models cannot
13:50 PM
account for effects of absolute evidence magnitude (p. 60)
13:55 PM
14:00 PM 13:55 PM. Hayes: The diversity effect in inductive reasoning depends on sampling assump-
14:05 PM tions (p. 25)
14:10 PM
14:15 PM
14:20 PM 14:15 PM. Ransom: When memories fade do sampling effects linger? (p. 49)
14:25 PM
14:30 PM 14:25 PM. Garrett: Estimating multiple item sets: Harder than you think! (p. 22)
14:35 PM
14:40 PM
14:35 PM. Smithson: A new approach to compositional data analysis (p. 57)
14:45 PM
14:50 PM
14:55 PM
14:55 PM. Kennedy: Unrepresentative samples and the quest for generality: Ideas from survey
15:00 PM
statistics (p. 32)
15:05 PM
15:05 PM. Martinie: Using crowd meta-knowledge to identify expertise in the single-question
15:10 PM
domain (p. 40)
15:15 PM
15:20 PM
15:25 PM Afternoon tea (provided)
15:30 PM
15:35 PM
15:40 PM
15:45 PM 15:40 PM. Lilburn: The integration of stimulus information in visual short-term memory (p. 37) Session
15:50 PM
15:50 PM. Taylor: Examination of doubly stochastic processes in a neural model of visual working
15:55 PM
memory. (p. 58)
16:00 PM
16:00 PM. Marris: Modelling human perceptual learning with pre-trained deep convolutional
16:05 PM
neural networks (p. 39)
16:10 PM
16:10 PM. Xie: Sequential testimony is as good as independent testimony in judgments under
16:15 PM
uncertainty (p. 64)
16:20 PM
16:25 PM
16:20 PM. Blaha: Storyline visualizations for eye tracking data (p. 10)
16:30 PM
16:35 PM
Friday 15/2 (PM)
The end of talks will be followed at 5:00 PM by soccer, with the conference dinner
at 7:30 PM in the Main Dining Room of The Lincoln.5
9:00 AM
9:05 AM
9:10 AM
9:15 AM
9:20 AM
9:20 AM. Ngo: The effect of stimulus presentation time on response and stimulus bias: A Session
9:25 AM
diffusion-model based analysis (p. 45)
9:30 AM
9:30 AM. Moneer: The effect of feature separation on processing architecture and implications for
9:35 AM
models of visual attention (p. 43)
9:40 AM
9:45 AM 9:40 AM. Ferdinand: The coevolution of artifacts and ideas: An inference-based model of cultural
9:50 AM evolution (p. 20)
9:55 AM
10:00 AM
10:05 AM 10:00 AM. Zhou: Decision-making in source memory (p. 66)
10:10 AM
10:15 AM 10:10 AM. Fox: Does source memory exist for unrecognized items? (p. 21)
10:20 AM
10:25 AM 10:20 AM. Shabahang: An associative theory of semantic composition (p. 54)
10:30 AM
10:30 AM. Kocsis: The relationship between memory and judgment: Do source memory errors
10:35 AM
influence retrospective evaluation? (p. 33)
10:40 AM
10:45 AM
10:50 AM Morning tea (provided)
10:55 AM
11:00 AM
11:05 AM
11:10 AM
Session
11:05 AM. Vanunu: Rarity vs. extremity and the effect of task complexity in decisions from expe-
11:15 AM rience. (p. 61)
11:20 AM
11:25 AM
11:30 AM 11:25 AM. Walsh: Re-contextualisation of harmonic power for measuring local complexity (Good
11:35 AM wholesome local fun with fractals and eFourier) (p. 62)
11:40 AM
11:45 AM
11:50 AM 11:45 AM. Liew: Distinguishing new categories from not-old categories (p. 36)
11:55 AM
12:00 PM 11:55 AM. Innes: Flying blind: Does adding information really help? (p. 29)
12:05 PM
12:05 PM. Waters: Comparative analysis of search task performance in 2D and 3D environ-
12:10 PM
ments (p. 63)
12:15 PM
12:20 PM 12:15 PM. CoE: Overview of the proposal for the ARC Centre of Excellence for Computational
12:25 PM Behavioural Science (p. 14)
12:30 PM
12:35 PM
12:40 PM Conference conclusion and general meeting
Saturday 16/2Abstracts
Note: The presenting author will be typeset in boldface type.
78
Analysing social media data: A mixed-methods framework combin-
ing computational and qualitative text analysis
Matthew Andreotta School of Psychological Science, University
of Western Australia
Robertus Nugroho Data61, CSIRO
Soegijapranata Catholic University
Mark Hurlstone School of Psychological Science, University
of Western Australia
Fabio Boschetti Ocean & Atmosphere, CSIRO
Simon Farrell School of Psychological Science, University
of Western Australia
Iain Walker School of Psychology and Counselling,
University of Canberra
Cecile Paris Data61, CSIRO
To qualitative researchers, social media offers a novel opportunity to har-
vest a massive and diverse range of content, without the need for intrusive
or intensive data collection procedures. However, performing a qualitative
analysis across a large social media data set is cumbersome and impracti-
cal. Instead, researchers often extract a subset of content to analyse, but a
framework to facilitate this process is currently lacking. We present a four-
phased framework for improving this extraction process, which blends the
capacities of data science techniques to project large data sets into smaller
spaces, with the capabilities of qualitative analysis to address research ques-
tions. We applied the framework to 201,506 Australian tweets on climate
change from 2016. Through combining Non-Negative Matrix inter-joint Fac-
torisation (Nugroho, Zhao, Yang, Paris, & Nepal, 2017) and Topic Alignment
(Chuang et al., 2015) algorithms with the qualitative techniques of Thematic
Analysis, we derived five overarching topics of climate change commentary.
Our approach is useful for researchers seeking to perform qualitative analy-
ses of social media, or researchers wanting to supplement their quantitative
models with a qualitative analysis of broader social context and meaning. A
preprint of this work is available at https://doi.org/10.31234/osf.io/bynz4
Chuang, J., Roberts, M. E., Stewart, B. M., Weiss, R., Tingley, D., Grimmer, J., & Heer, J. (2015).
TopicCheck: Interactive alignment for assessing topic model stability. In Proceedings of the Con-
ference of the North American Chapter of the Association for Computational Linguistics - Human Lan-
guage Technologies (pp. 175–184). Denver, Colorado: Association for Computational Linguistics.
https://doi.org/10.3115/v1/N15-1018
Nugroho, R., Zhao, W., Yang, J., Paris, C., & Nepal, S. (2017). Using time-sensitive interactions
to improve topic derivation in Twitter. World Wide Web, 20(1), 61–87. https://doi.org/10.1007/s11280-
016-0417-x9
The dynamics of decision making during goal pursuit
Tim Ballard Psychology, University of Queensland
Andrew Neal University of Queensland
Simon Farrell University of Western Australia
Andrew Heathcote University of Tasmania
Goal pursuit can be thought as a series of interdependent decisions made in
an attempt to progress towards a performance target. Whilst much is known
about the intra-decision dynamics of single, one-shot decisions, far less is
known about how this process changes over time as people get closer to
achieving their goal and/or as a deadline looms. For example, people may
respond to a looming deadline by either increasing the amount of effort they
apply or by changing strategy. We have developed an extended version of
the linear ballistic accumulator model that accounts for the effects that the
dynamics of goal pursuit exert on the decision process. In this talk, I de-
scribe a series of recent studies that test this model. In each study, partic-
ipants performed a random dot motion discrimination task in which they
gained one point for correct responses and lost one point for incorrect re-
sponses. Their objective was to achieve a certain number of points within a
certain timeframe (e.g., at least 30 points in 40 seconds). Preliminary results
suggest that decision thresholds were highly sensitive to deadline, such that
people prioritised speed over accuracy more strongly as the time remaining
to achieve the goal decreased. The decision process was also sensitive to the
amount of progress that remained before the goal was achieved, the diffi-
culty of the decision, the incentive for goal achievement, and whether the
goal was represented as an approach goal or an avoidance goal. These find-
ings illustrate the sensitivity of decision making to the higher order goals of
the individual, and provides an initial step towards a formal theory of how
these higher level dynamics play out.10
Storyline visualizations for eye tracking data
Leslie Blaha Airman Systems Directorate, Air Force
Research Laboratory
Dustin Arendt Pacific Northwest National Laboratory
Tim Balint TU Delft
Joe Houpt Wright State University
In this talk, Iʼll review work on data driven clustering and visualizing sets
of eye tracking data to capture patterns in dynamic tasks. Storyline visu-
alization is a technique that captures the spatiotemporal characteristics of
individual entities and simultaneously illustrates emerging group behaviors.
We developed a storyline visualization leveraging dynamic time warping to
parse and cluster eye tracking sequences. Visualization of the results cap-
tures the similarities and differences across a group of observers perform-
ing a common task. We applied our storyline approach to gaze patterns of
people watching dynamic movie clips. We use these to illustrate variations
in the spatio-temporal patterns of observers as captured by different data
encoding techniques. We illustrate that storylines further aid in the identi-
fication of modal patterns and noteworthy individual differences within a
corpus of eye tracking data.11
The role of passing time in decision-making
Scott Brown School of Psychology, University of
Newcastle
Nathan Evans University of Amsterdam
Guy Hawkins University of Newcastle
Theories of perceptual decision-making have been dominated by the idea
that evidence accumulates in favour of different alternatives until some
fixed threshold amount is reached, which triggers a decision. Recent the-
ories have suggested that these thresholds may not be fixed during each
decision, but change as time passes. These collapsing thresholds can im-
prove performance in particular decision environments, but reviews of data
from typical decision-making paradigms have failed to support collapsing
thresholds. We designed three experiments to test collapsing threshold as-
sumptions in decision environments specifically tailored to make them opti-
mal. An emphasis on decision speed encouraged the adoption of collapsing
thresholds – most strongly through the use of response deadlines, but also
through instruction to a lesser extent – but setting an explicit goal of reward
rate optimality through both instructions and task design did not.12
Consumer choices under time pressure
Jon-Paul Cavallaro University of Newcastle
Guy Hawkins University of Newcastle
Scott Brown University of Newcastle
Hypothetical consumer choice scenarios provide insight into a consumerʼs
decision-making process when purchasing products or services. The con-
sumerʼs choices elicited in these scenarios are assumed to indicate the con-
sumerʼs subjective value or utility of a product or service. One technique
used to represent hypothetical consumer scenarios is the discrete choice
experiment (DCE). DCEs are a quantitative technique used to capture con-
sumer preferences for multi-attribute products or services. Historically,
DCEs account for choices only. We have extended on DCE research by in-
cluding a response time measure and a time pressure manipulation to evalu-
ate the effect of decision time on the utility inferred from consumersʼ choices.
This extension is motivated by findings from the speeded decision-making
literature that tells us of the importance of decision time and the impact that
time pressure has on choice-related model parameters. Across four hypo-
thetical choice scenarios, we found that the time available to make multi-
attribute decisions impacts the utility that is inferred from those decisions.
The utilities inferred from multi-attribute decisions are inherently tied to
the time taken to make those decisions, which has not been widely acknowl-
edged in the DCE literature.13
Differentiating social preference in hypothetical distributive deci-
sions
Blake Cavve School of Psychology, University of Western
Australia
Mark Hurlstone School of Psychological Science, University
of Western Australia
Simon Farrell School of Psychological Science, University
of Western Australia
Neoclassical economic theory assumes that decision making is primarily
driven by rational material self-interest. A number of more recent psycho-
logical and economic models challenge this assumption, highlighting in-
stead the role of social context in judgement and decision making. Poten-
tial manifestations of social preference span several forms of equality or
fairness, to various forms of competitive status-based concerns (the most
prominent being Brown et al., 2008). Such motivations are assumed to un-
derlie financial choices including support for taxation regimes. Even seem-
ingly similar social preferences generate different implications regarding
distributive and re-distributive decisions of individuals.
In order to differentiate preferences reflecting concern for material self-
interest, equality and competitive status in re-distributive decisions, Social
Utility functions (Loewenstein et al., 1989) were elicited in several (hypothet-
ical) decision making domains (e.g., income, vacation time, attractiveness).
Preferences regarding hypothetical allocations varied by domain, and sub-
stantial discrete individual differences were observed. Overall, Bayesian
model selection indicates prominent fairness-based preferences for re-
source distribution, consistent with Inequality Aversion (Fehr & Schmidt,
1999).
Brown, G. D. A., Gardner, J., Oswald, A. J., & Qian, J. (2008). Does Wage Rank Affect Employeesʼ
Well-being? Industrial Relations, 47(3), 355–389. https://doi.org/10.1111/j.1468-232X.2008.00525.x
Fehr, E., & Schmidt, K. (1999). A Theory of Fairness, Competition, and Cooperation. The Quar-
terly Journal of Economics, 114(3), 817–868.
Loewenstein, G., Thompson, L., & Bazerman, M. (1989). Social utility and decision making in
interpersonal contexts. Journal of Personality and Social Psychology, 57(3), 426–441. https://doi.org/10.1037/0022-
3514.57.3.42614
Overview of the proposal for the ARC Centre of Excellence for Com-
putational Behavioural Science
Yoshihisa Kashima Melbourne School of Psychological
Sciences, University of Melbourne
Simon Dennis Melbourne School of Psychological
Sciences, University of Melbourne
Amy Perfors Melbourne School of Psychological
Sciences, University of Melbourne
Thanks to the participation of many members of the mathematical psychol-
ogy community, we have submitted an invited proposal for the Centre of
Excellence for Computational Behavioural Science. In this discussion, we
will present our motivation for the proposal, provide an overview of the pro-
posed Centre activities including research, capacity building, and outreach,
and seek continuing participation and support of the mathematical psychol-
ogy community. Discussions will surround the lessons learned from our
preparation of the bid, and the opportunities and potential risks of CoE bids
in general.15
Discriminating shoppers: Applications of SFT to consumer choice
Gavin Cooper School of Psychology, University of
Newcastle
Guy Hawkins University of Newcastle
Consumers regularly make multi-alternative, multi-attribute decisions about
products and services. The possible decision strategies used by consumers
that have been proposed in the literature have been problematic to discrim-
inate between in data. These same decision strategies often share higher
level features between them that match the mental architectures that Sys-
tems Factorial Technology (SFT) has been developed to discriminate be-
tween. These higher level features include when a consumer stops process-
ing information and makes a decision (the stopping rule) and whether infor-
mation is processed in serial or parallel. We have aimed to categorise pre-
viously proposed decision strategies by the architecture and stopping rule
they assume, creating sets of strategies that can be ruled in or out through
the use of the methods of SFT, which we support with data from a novel ex-
perimental task. This extension of SFT into the field of consumer choice
presents new opportunities in the discrimination between alternative hy-
potheses of decision strategies.16
Privacy versus open science
Simon Dennis Melbourne School of Psychological
Sciences, University of Melbourne
Paul Garrett University of Newcastle
Hyungwook Yim University of Tasmania
Jihun Hamm Ohio State University
Adam Osth University of Melbourne
Vishnu Sreekumar National Institutes of Health
Ben Stone University of Melbourne
Pervasive internet and sensor technologies promise to revolutionize psy-
chological science. However, the data collected using these technologies is
often very personal - indeed the value of the data is often directly related to
how personal it is. At the same time, driven by the replication crisis, there
is a sustained push to publish data to open repositories. These movements
are in fundamental conflict. In this paper, we propose a way to navigate this
issue. We argue that there are significant advantages to be gained by ceding
the ownership of data to the participants who generate it. Then we provide
desiderata for a privacy preserving platform. In particular, we suggest that
researchers should use an interface to perform experiments and run analy-
ses rather than observing the stimuli themselves. We argue that this method
not only improves privacy, but will also encourage greater compliance with
good research practices than is possible with open repositories.17
Models of risky choice: A signed difference analysis
John Dunn School of Psychological Science, University
of Western Australia
Edith Cowan University
Li-Lin Rao CAS Key Laboratory of Behavioral Science,
Institute of Psychology, Chinese Academy
of Sciences
Signed difference analysis is a methodology that is used to derive a set of or-
dinal predictions from a mathematical model (Dunn & James, 2003; Dunn
& Kalish, 2018). It generalizes state-trace analysis to models with more than
one latent variable (Dunn & Kalish, 2018) where each of two or more depen-
dent variables is an arbitrary monotonic function of a specified function of
the latent variables. This is a property of many models of risky choice in
which the probability of choosing option A over option B is an unknown
monotonic function of a model-specific function of the subjective utilities
of the two options (and potentially additional parameters). We consider two
models of risky choice – the fixed utility model (e.g., cumulative prospect
theory) and the random subjective expected utility (RSEU) model proposed
by Busemeyer and Townsend (1993). The main difference between the two
models is that the predictions of the fixed utility model depend only on the
difference between the utilities of the two options while those of the RSEU
model also the utilities are modified by a term representing their subjective
variance. We derive critical predictions from each of these models and test
them against data from two experiments.
Busemeyer, J. R. & Townsend, J. T. (1993). Decision field theory: A dynamic-cognitive approach
to decision making in an uncertain environment. Psychological Review, 100(3), 432–459.
Dunn, J. C. & Anderson, L. M. (2018). Signed difference analysis: Testing for structure under
monotonicity. Journal of Mathematical Psychology, 85(3), 36–54.
Dunn, J. C. & James, R. N. (2003). Signed difference analysis: Theory and application. Journal of
Mathematical Psychology, 47(4), 389–416.
Dunn, J. C. & Kalish, M. L. (2018). State-trace analysis. Springer.18
The cost of errors: Confusion analysis and the mental representa-
tion of numerals
Ami Eidels University of Newcastle
Murray Bennett University of Newcastle
Paul Garrett University of Newcastle
People express quantities via numbers, using a remarkably small set of only
ten basic units – digits. Confusing digits could be costly, and not all confu-
sions are equal; confusing a price tag of 2 dollars with 9 dollars (or 2 million
vs 9 million, for a more dramatic effect), is naturally more costly than con-
fusing 2 with 3. Confusion patterns are intimately related to the distances
between mental representations, which are hypothetical internal symbols
said to stand for, or represent, ʻrealʼ external stimuli. The distance between
the mental representations of two digits could be determined by their nu-
merical distance. Alternatively, it could be driven by visual similarity (or
by some other properties). We investigated the mental representations of
familiar and unfamiliar numerals (4 sets: Arabic, Chinese, Thai, and non-
symbolic dots) in a set of identification experiments, using Multi Dimen-
sional Scaling and Cluster Analysis. We controlled for undesired effects of
response bias using Luceʼs choice model.19
Updating judgement contexts with extreme stimuli
Simon Farrell School of Psychological Science, University
of Western Australia
Greta Fastrich University of Reading
A number of theories assume that objects are not judged in isolation, but
are compared to other objects. In many experiments the context is the ob-
jects seen in the experiment; for example, if judging the size of squares,
the judgement context would be the set of all (or some of) the squares seen
so far in the experiment. We ask what happens when an extreme stimulus
is only occasionally presented. Does it enter into the judgement context,
or is it effectively discounted? Across two experiments involving magni-
tude judgements on squares and numbers, we find little effect of the out-
lier on following judgements. Nonetheless, we show that people used the
experiment context to form their judgements, by showing sensitivity to the
skew of the distributions. Fitting two models of context-based judgement—
Parducciʼs range-frequency theory, and Haubensakʼs consistency model—
suggests the combined effects of overall context and individual items is chal-
lenging.20
The coevolution of artifacts and ideas: An inference-based model of
cultural evolution
Vanessa Ferdinand Melbourne School of Psychological
Sciences, The University of Melbourne
Learning is rarely, if ever, an unbiased process. As cultural artifacts repli-
cate by being passed from individual to individual, among social groups,
and across generations, the cognitive biases involved in the perception, pro-
cessing, and production of these artifacts can operate as selection pressures
on them, causing certain forms to increase in number at the expense of oth-
ers. Here, I will discuss the similarities between replicator dynamics (a gen-
eral model of evolution) and Bayesian inference (a general model of learn-
ing) and utilize their mathematical equivalence to specify a model where
cultural artifacts and learnersʼ hypotheses about those artifacts co-evolve.
Culture is a special evolutionary system that is composed of two types of
replicators: public structures in the world, such as artifacts and behaviors,
and private structures in the mind, such as brain states or hypotheses (Sper-
ber, 1996). The most interesting part of this model is the interpretation of
fitness for both types of replicators. The fitness of public replicators is given
by their likelihood under the population of hypotheses in learnersʼ minds,
and the fitness of private replicators is dictated by their likelihood under the
population of artifacts in their environment. Both of these replicators can
place constraints on one another as culture evolves and drive the system to
unexpected places when fitness values are asymmetric.21
Does source memory exist for unrecognized items?
Julian Fox University of Melbourne
Adam Osth University of Melbourne
Source memory is memory for the context in which information is pre-
sented. Most models of source memory predict that it is not possible to re-
trieve source information from items that are unrecognized. For example,
multinomial processing-tree models (e.g., Batchelder & Riefer, 1990) and
the bivariate signal detection model of Hautus et al. (2008) predict that when
an item is unrecognized, source retrieval is not performed and a guess re-
sponse is elicited. Empirically, there have been mixed results regarding the
possibility of source discrimination for unrecognized items. Studies that
presented recognition and source judgments for the same item in immedi-
ate succession (i.e., a non-blocked design) revealed chance-level source ac-
curacy for unrecognized items, while studies that presented an initial block
of recognition judgments, followed by a block of source judgments (i.e., a
blocked design), revealed slightly above-chance source accuracy for unrec-
ognized items. A potential explanation for the discrepancy is that source
discrimination is possible for unrecognized items, but that when a negative
recognition judgment is made immediately prior to the source judgment, as
is the case in non-blocked designs, participants are dissuaded from attempt-
ing effortful source retrieval. The present study assessed source memory
for unrecognized items in three conditions: non-blocked, blocked, and ʻre-
verse blockedʼ (where the block of source judgments preceded the recogni-
tion block). It was found that accuracy was significantly above chance in the
blocked and reverse blocked conditions, but consistently at chance in the
non-blocked condition. These results suggest that source discrimination is
above chance for unrecognized items, but that blocked designs are needed
to elucidate the effect as non-blocked designs lead to source guessing.22
Estimating multiple item sets: Harder than you think!
Paul Garrett Psychology, The University of Newcastle
Zachary Howard The University of Newcastle
Joe Houpt Wright State University
David Landy Indiana University
Ami Eidels The University of Newcastle
Like many species, humans can perform non-verbal estimates of quantity
through our innate approximate number system. However, the cognitive
mechanisms that govern how we compare these estimates are not well un-
derstood. Little research has addressed how the human estimation-system
evaluates multiple quantities, and fewer studies have considered the cost to
cognitive workload when undertaking these tasks. Here, we apply the math-
ematical tools of Systems Factorial Technology to a comparative estimation
task. Across a series of experiments, we assess whether quantities, repre-
sented by red and blue discs, are estimated simultaneously (in parallel) or
sequentially (in serial), and under what restrictions to cognitive workload.
Our findings reveal that two item-sets may be estimated simultaneously
through a parallel estimation system, under severe restrictions to cognitive
workload capacity. These restrictions are not due to the estimation process.
The results can be extended to comparisons made with the subitizing range.23
The value of predictive information in decision-making under uncer-
tainty
Ariel Goh School of Psychological Sciences, Monash
Institute of Cognitive and Clinical
Neurosciences
Daniel Bennett Princeton University
Stefan Bode The University of Melbourne
Trevor T-J Chong Monash University
Humans exhibit a drive towards acquiring information. Notably, studies of
humans and non-human animals suggest that information is processed by
similar neural circuits that underlie reward valuation. This project inves-
tigated how humans value information that predicts, but does not change,
the outcome of an upcoming event (non-instrumental information). We con-
ducted two experiments to examine the physical effort costs individuals are
willing to incur for such information. Effort was operationalised as amounts
of force applied to a hand-held force-sensitive dynamometer. In the first
experiment, the amount of information available was held constant, and
participants chose between exerting higher effort levels to obtain predic-
tive information about a lottery outcome, versus exerting minimum effort
and foregoing such information. Results showed that participants willingly
exerted effort to obtain the information, but this effect declined as effort
costs increased. In Experiment 2, we manipulated the amount of informa-
tion provided at the start of each trial, and thus the amount of uncertainty
participants experienced. Results showed that participants invested more
effort for information when prior uncertainty was high (i.e., when the out-
come was ambiguous) compared to when it was low (i.e., when the outcome
was predictable). Bayesian model comparison using the Watanabe-Akaike
Information Criterion revealed that subjective valuation of information was
best modelled as a function of both effort costs and the magnitude of avail-
able information, where information was quantified as the degree to which
information reduced residual uncertainty about the outcome. Model com-
parison also suggested that participantsʼ uncertainty was best modelled by
the Rényi entropy of beliefs (a generalisation of Shannon entropy). Overall,
these results suggest that informationʼs intrinsic value is based on its capac-
ity to reduce uncertainty, and that this valuation is reflected in a willingness
to trade off effort for information. This work helps explain the bias humans
exhibit toward information acquisition, even when this is sub-optimal or
inefficient.24
Time-varying cognitive models of decision making
Guy Hawkins Psychology, University of Newcastle
David Gunawan University of New South Wales
Robert Kohn University of New South Wales
Scott Brown University of New South Wales
Almost all cognitive process models of decision making assume that the la-
tent parameters driving performance are stationary across trials. This con-
flicts with intuition, and data, that performance does not change with in-
creasing exposure to a task. Here, we outline a flexible hierarchical Bayesian
framework that allows for across-trial dynamics in the parameters of de-
cision making models, and thus makes time-varying predictions for be-
haviour. We demonstrate the approach with the Linear Ballistic Accumu-
lator (LBA) model. We show that time-varying LBA models reliably recover
the data-generating model in simulated data, and they are consistently se-
lected over equivalently specified stationary LBA models. Furthermore, the
time-varying LBA model provides a good account of across-trial dynamics
observed in choice and response time data, and time-varying parameter esti-
mates that provide insight into the dynamics of latent cognitive mechanisms
driving observed decision behaviour.25
The diversity effect in inductive reasoning depends on sampling as-
sumptions
Brett Hayes Psychology, University of New South Wales
Danielle Navarro University of New South Wales
Rachel G. Stephens University of New South Wales
Keith Ransom University of Adelaide
Natali Dilevski University of Sydney
A key phenomenon in inductive reasoning is the diversity effect, whereby
a novel property is more likely to be generalized when it is shared by an ev-
idence sample composed of diverse instances than a sample composed of
similar instances. We describe a Bayesian model and an experimental study
that show that the diversity effect depends on a belief that samples of evi-
dence were selected by a helpful agent (strong sampling). Inductive argu-
ments with premises containing either diverse or non-diverse evidence sam-
ples were presented under different sampling conditions, where instruc-
tions and filler items indicated that the samples were selected intentionally
(strong sampling) or randomly (weak sampling). A robust diversity effect
was found under strong sampling but was attenuated under weak sampling.
As predicted by our Bayesian model, the largest effect of sampling was on
arguments with non-diverse evidence, where strong sampling led to more
restricted generalization than weak sampling. These results show that the
characteristics of evidence deemed relevant to an inductive reasoning prob-
lem depend on beliefs about how the evidence was generated.26
Control failures in Simon and Flanker Tasks
Andrew Heathcote Psychology, University of Tasmania
Dora Matzke University of Amsterdam
We examine the complete failure of control within the Conflict LBA model,
which explains conflict in Stroop, Simon and Flanker tasks in terms of prim-
ing and the control deployed to counter the misleading effects of priming.
A key concept for the model is that control is variable, sometimes under-
compensating and sometimes overcompensating for priming. Within the
context of the Conflict LBA model, we extended MacLeod and MacDon-
aldʼs (2000) “inadvertent reading hypothesis” for the Stroop task, that oc-
casional reading rather than colour naming responses explain some por-
tion of incongruent errors and speeding in correct congruent responses,
to Simon task data, collected by Forstman, van den Wildenberg and Rid-
derinkhof (2008), and Flanker data, collected by White, Ratcliff and Starns
(2011). Although the probability of complete failures to exercise any con-
trol on some trials, causing participants to perform the wrong task (e.g.,
responding based on location in the Simon task or to the Flankers in the
Flanker task), was relatively small, such failures were key to explaining the
detailed shapes of conditional-accuracy functions. In the Flanker task, fail-
ure probability was found to increase systematically as the proportion of
incongruent trials in each block decreased. Estimations issues and the rela-
tionships among the parameters of the extended Conflict LBA are discussed.
Forstmann, B. U., van den Wildenberg, W. P., & Ridderinkhof, K. R. (2008). Neural mecha-
nisms, temporal dynamics, and individual differences in interference control. Journal of Cogni-
tive Neuroscience, 20(10), 1854–1865.
MacLeod, C. M., & MacDonald, P. A. (2000). Interdimensional interference in the Stroop effect:
Uncovering the cognitive and neural anatomy of attention. Trends in Cognitive Sciences, 4(10),
383–391.
White, C. N., Ratcliff, R., & Starns, J. J. (2011). Diffusion models of the flanker task: Discrete
versus gradual attentional selection. Cognitive Psychology, 63(4), 210-238.27
New insights into decisions from experience: Using cognitive mod-
els to understand how value information, outcome order, and salience
drive risk taking
Jared M. Hotaling University of New South Wales
Chris Donkin University of New South Wales
Ben R. Newell University of New South Wales
Andreas Jarvstad City, University of London
Many real world decisions must be made on basis of experienced outcomes.
However, little is known about the mechanism by which people make these
decisions from experience. Much of the previous research has focused on
contrasting these decisions with those based on described alternatives. Ob-
servations of a reliable description-experience gap (D-E gap) led Hotaling,
Jarvstad, Donkin, and Newell (under review) to conduct a series of studies
investigating various factors influencing decisions from experience. Criti-
cally, they found that the juncture at which value and probability informa-
tion is provided has a fundamental effect on choice. They also found evi-
dence for the impact of perceptual salience and outcome recency on choice.
To better understand these results and their implications regarding the
mechanisms underlying human decision making, we developed an exemplar-
based cognitive model. It uses a noisy error-prone memory mechanism
to explain how confusions between events give rise to various behavioral
patterns. According to the model, each time an outcome is experienced, a
record is laid down in memory. However, memory traces can be disturbed
in several ways as new information enters the system. We tested several ver-
sions of models within this basic framework, and found that one with mech-
anisms for value-assignment confusions and risk bias provided the best ac-
count. We discuss the implications of these findings on our understanding
of the interplay between attention, memory, and choice, and the psychologi-
cal underpinning of the description-experience gap.28
Evidence for a general conformity mechanism: People follow norms
even when they come from the outgroup
Piers D. L. Howe Melbourne School of Psychological
Sciences, University of Melbourne
Campbell Pryor Melbourne School of Psychological
Sciences, University of Melbourne
Amy Perfors Melbourne School of Psychological
Sciences, University of Melbourne
People are more likely to perform a particular action or hold a particular
opinion when they know that other people have performed similar actions
or have similar opinions, a phenomenon known as the descriptive norm ef-
fect. There are a number of competing accounts of this phenomenon. Our
previous work provided strong evidence against two of these accounts, the
information and social sanctions account, and argued in favour of the ac-
count proposed by self-categorization theory (Pryor, Perfors, Howe, 2019,
Nature Human Behaviour, 3, 57-62). Self-categorization theory makes the
intuitive prediction that people will actively avoid conforming to the norms
of an outgroup in an effort to remain distinct from that outgroup. We tested
this prediction in a series of experiments. By comparing competing Bayesian
models, we showed that people conformed to descriptive norms even when
they came from the outgroup. This result was replicated across multiple def-
initions of ingroups and outgroups, including when the outgroup had oppos-
ing social or political beliefs to the participant, and was robust with respect
to our chosen priors. Additionally, we showed this effect for both meaning-
ful and arbitrary norms, thereby ruling out a number of alternative expla-
nations. These results suggest that a general desire to conform with others
may outpower the common ingroup vs outgroup mentality. We make sug-
gestions as to how this general conformity mechanism may operate.29
Flying blind: Does adding information really help?
Reilly Innes Psychology, University of Newcastle
Zachary Howard University of Newcastle
Alexander Thorpe University of Newcastle
Ami Eidels University of Newcastle
Scott Brown University of Newcastle
In driving and avionics, as well as many other information rich environ-
ments, the user interface is responsible for providing accessible and use-
ful information without making the task more difficult. Adding information
into a display is often viewed as a way to make a task easier and make use
of emerging technology. However, designers often fail to account for the
possible cost this may have on cognitive workload or task performance. In
collaboration with Airbus & Hensoldt, we investigated the effects of new
heads up display technology, which aimed to increase the amount of avail-
able information to pilots. Using the detection response task (DRT), we pro-
vide a measure of cognitive workload during a simulated helicopter flight.
Thirteen pilots completed a 2x2 within-subjects experiment, where visual
environment and level of information was manipulated. Participants re-
sponse times to the DRT provide an index of cognitive workload, and this
measure was analysed alongside flight performance. Results indicated that
increased information improved flight performance. Furthermore, DRT re-
sults indicated that cognitive workload was relatively unaffected by the level
of symbology. This initial experiment is useful but requires a level of ques-
tioning as to possible alternative explanations for results – which should be
addressed in future studies.30
Taking an intentional stance in joint action: How can we explain
cross-cultural variability?
Yoshihisa Kashima Melbourne School of Psychological
Sciences, University of Melbourne
Michael Kirley University of Melbourne
Yuan Sun RMIT
Alex Stivala Swinburne University of Technology
Simon Laham University of Melbourne
Piers Howe University of Melbourne
The concept of intention seems to be central in human sociality. Humans
ascribe intentionality not only to other humans, but also to nonhuman be-
ings and even inanimate objects to understand, explain, and predict their
behaviours. This cognitive practice of taking an intentional stance seems
ubiquitous. Yet, there is also some evidence of cross-cultural and historical
variability in the extent to which people take an intentional stance. This is
puzzling because intentional stance taking (IST) is regarded as a necessary
aspect of joint action. If in fact humanityʼs success is largely due to our abil-
ity to engage in joint actions to achieve a goal unattainable by individuals
alone, and IST is necessary for engaging in a joint action, how can a society
function without engaging in IST? We constructed a cultural evolutionary
model to explain this apparent cross-cultural variability. We incorporate
signalling to model intention-reading as integral to the stag hunt game as a
game theoretic model of joint action, postulate different IST types that vary
from a minimal level of mindreading to a heightened awareness and explicit
consideration of other minds (hyper IST), and show by simulations the envi-
ronmental circumstances in which different IST types become prevalent in
a population. We show that minimal IST becomes a predominant cognitive
practice when a community affords a limited opportunity to interact with
strangers, whereas hyper IST becomes more predominant when society is
highly open and mobile with many chances of interacting with strangers.
Yet, if societal mobility is extremely high, hyper IST cannot sustain coopera-
tive joint actions unless there is an institutional mechanism of social control
to sanction against deception and defection. Implications of this research
are discussed for theory of mind research, moral psychology, and other con-
temporary research in psychology and cognitive science.31
Season naming and the local environment
Charles Kemp University of Melbourne
Alice Gaby Monash University
Terry Regier University of California, Berkeley
Seasonal patterns vary dramatically around the world, and we explore the
extent to which systems of season categories support efficient communica-
tion about the local environment. Our analyses build on a domain-general
information-theoretic model of categorization across languages, and we
identify several qualitative predictions that emerge when this model is ap-
plied to season naming, including the prediction that systems with odd num-
bers of terms should be comparatively rare. We test the model quantitatively
using a collection of season systems drawn from the linguistic and anthro-
pological literature and data specifying temperature and precipitation in
locations associated with these systems. The results include some successes
for the model but also highlight some significant respects in which our ap-
proach falls short of a complete account of season naming.32
Unrepresentative samples and the quest for generality: Ideas from
survey statistics
Lauren Kennedy School of Social Work, Columbia
University
Andrew Gelman Columbia University
Psychology has long been reliant on a variety of convenience samples. Al-
though the source has changed with tools like Amazon Mechanical Turk,
there is still no guarantee that AMT samples are representative of the gen-
eral population on all relevant demographics. Convenience samples can be
a threat to the generality of a study if the effect size differs between demo-
graphic subsets and the sample differs in proportion to those subsets. Tools
from survey statistics, a field which specializes in generalization from non-
representative samples to a wider population, have not previously been ap-
plicable due to the reliance on probability samples. In this talk we consider
multilevel regression and poststratification (MRP), a technique that has
proved useful for non-probability samples. We discuss the potential costs
and benefits of incorporating this technique with psychological datasets.33
The relationship between memory and judgment: Do source mem-
ory errors influence retrospective evaluation?
Marton Kocsis School of Psychological Science, The
University of Western Australia
Simon Farrell The University of Western Australia
We often use summary judgments of our past experiences to inform future
choices, with these evaluations argued to rely on what we can remember
about each experience at time of choice. If we are retrospectively evaluat-
ing multiple experiences from memory facilitate choice, do source mem-
ory errors (e.g., misattributing events from one particular experience to an-
other) influence our evaluation of a target experience? We presented par-
ticipants with 3 interleaved affective word-lists (coded by colour) that on
average were either positive or negatively valenced, and we post-cued one
word-list as the target list for which recall and pleasantness ratings were
captured. While people were able to successfully recall some items from
the target list with minimal non-target intrusions and pleasantness ratings
were consistent with mean valence of the target list, patterns of recall were
inconsistent with the ability of the valence of target list items to predict rat-
ings which appears to argue against evaluation being based on recalled tar-
get list items. To further examine the relationship between memory and
judgment, we compared models based of online evaluation that relies on the
target-list items presented vs. retrieval-based evaluation based on target-list
items recalled, and found that the retrieval-based model of evaluation was
favoured by the data. One reconciliation of these seemingly contradictory
results is that pleasantness ratings relied on the memory for evaluation of
target list items which is retrieved independent of memory for the target list
items themselves.You can also read
