=Paper=
{{Paper
|id=Vol-1278/paper2
|storemode=property
|title=Memory and Decision Making: From Basic Cognitive Research to Design Issues
|pdfUrl=https://ceur-ws.org/Vol-1278/paper2.pdf
|volume=Vol-1278
|dblpUrl=https://dblp.org/rec/conf/dmrs/Missier14
}}
==Memory and Decision Making: From Basic Cognitive Research to Design Issues==
https://ceur-ws.org/Vol-1278/paper2.pdf
Memory and Decision Making: From Basic Cognitive
Research to Design Issues
Fabio Del Missier1,2
1
Department of Life Sciences, University of Trieste, Trieste, Italy
2
Department of Psychology, Stockholm University, Stockholm, Sweden
delmisfa@units.it
Abstract. This abstract summarizes the talk given at the International Work-
shop on Decision Making and Recommender Systems 2014. The talk discussed
ways to bridge cognitive research and recommender system research by focus-
ing, in particular, on human memory and decision-making processes.
Keywords: memory, decision making, recommender systems.
1 Introduction
Recommender systems researchers are becoming more and more aware of the im-
portance of designing user-interaction by relying on cognitive research. They are also
becoming more sensitive to the need of designing their systems by taking into account
theories and findings on human decision making. However, there is still a large gap
between basic research in cognitive psychology and recommender systems research.
There are multiple reasons for this state of affairs, including insufficient communica-
tion between research fields, fragmentation of cognitive theories, diversity of recom-
mender technologies and aids, and specific difficulties in the empirical evaluation of
complex systems also including human components.
A productive interchange between cognitive research and recommender systems re-
search can be fostered by focusing on some empirical generalizations coming from
cognitive research, which may be helpful to inform recommender system design. This
may involve not only ‘traditional’ aspects of human-computer interaction and inter-
face design, but also the entire decision-making course. The workshop talk focused, in
particular, on empirical generalizations coming from memory and decision-making
research, and it was shaped as an introductory lecture for a relatively unskilled audi-
ence in psychology and cognition. It ranged from high-level aspects of the choice
process to more specific aspects of the interface and user interaction, because research
implications encompass different levels of analysis. Some key findings in human
memory research relevant for recommender design and their theoretical background
were initially discussed, followed by some key findings in the psychology of decision
making. After that, some reflections were proposed on how recommender technology
1
International Workshop on Decision Making and Recommender Systems 2014
18-19 September 2014, Bolzano - Bozen, Italy
is changing the way in which we decide. The final part of the talk dealt with opportu-
nities and challenges related to bridging cognitive research and research on recom-
mender systems. Due to space constraints, only a short summary is presented here.
2 Memory and Recommender Systems
In the first part of the talk, two related issues were dealt with: (1) when do we use
memory when interacting with recommender systems? (2) how could we support
memory during interaction with recommender systems? Answering the first question
produces to a rather long list of situations, because different memory processes can
contribute to the interaction (see Table 1). These processes have been functionally and
neurally dissociated in memory research, but debates are still ongoing on their struc-
tural dissociation and, partly, on their neural dissociation [1, 2]. Moreover, significant
individual and age-related differences exist in some of these processes, affecting per-
formance in decision making and in other complex cognitive tasks [3, 4, 5].
Table 1. Memory processes in the interaction with recommender systems.
Memory Processes Examples of interaction with Recommender Systems
• Keep in mind sequences of numbers or codes
• Keep in mind and integrate information to compare recommend-
Short-term memory ed options and their features (e.g. books, movies)
Working memory • Formulate evaluations based on information integration (i.e.,
book price, author, delivery time)
• Apply rather complex choice strategies to select one option
• Retrieve specific episodes to decide whether to buy a product
from a vendor, trust system recommendations, use a service, or
Episodic memory appraise whether a certain product price is cheap or expensive.
• Rely on recognition to navigate within a system to find a given
product or service, or to understand where you are.
• Accesses semantic knowledge to understand features of the
Semantic memory options, scenario descriptions, option descriptions, and reviews.
• Make knowledge-based inferences on options.
• Use semantic knowledge to select links and navigate.
• Navigate and complete tasks effectively after initial learning
Procedural memory • Learn to operate on similar systems (but learned procedures may
also create problems when switching to a new system with in-
consistent situation-response mapping - i.e., negative transfer).
Given that different memory processes seem to have different functional roles in the
interaction with recommender system, they may need to be supported in specific
ways. Table 2 presents some potential suggestions (see also [3, 4, 6, 7, 8, 9, 10, 11,
12, 13, 14]), which cannot be further discussed here due to space limitations.
2
Table 2. Potential ways to support memory in the interaction with recommender systems
Memory Processes Potential Support
Short-term memory Reduce STM load
Working memory • Chunk information (visually, spatially, and semantically)
Don’t ask users to keep • Use easy to remember groups of digits and letters
too many things in Reduce working memory load
mind and do complex • Display close together pieces of information that need to be
computations or infor- processed sequentially (e.g. features of one option)
mation integration • Provide results of already-made computations (e.g. unit prices)
or computation tools
• Provide comparison matrices and external memory tools
• Summarize complex information with user-centered infor-
mation displays and visual representations
Episodic memory Anticipate retrieval attempts by providing their potential targets
Don’t ask users to • Present factual info where needed (cognitive task analysis and
retrieve, or at least help tests with users help to understand what is needed and where)
them to retrieve using Transform retrieval in external search
appropriate cues • Provide access to previously visited pages or options (e.g.
history, past searches) or transactions (e.g. orders)
Prefer recognition over recall
• Transform a recall task into a recognition one (e.g., autocom-
plete search forms and preview search results)
Semantic memory Provide knowledge
Provide knowledge • Provide knowledge that helps to understand product features
whenever needed. or information (wherever user may need it)
Users’ semantic Design building on users’ semantic knowledge
knowledge can be a • Take into account user semantic representation of the domain
design tool in planning information architecture (e.g., use cart sorting and
knowledge elicitation methods)
• Design user-centered links and labels by maximizing the asso-
ciation strength between words in links and labels and key-
words in the target content
Procedural memory • Be consistent with (good) interface standards and within your
Users apply learned system to benefit from positive transfer and learned habits
procedure; familiar • Remember that what can’t be seen or can’t be reached can’t be
and simple things are used
easier to learn • Test with users, appraise learning and flux experience
3 Decision Making and Recommender Systems
Starting from theories of decision-making competence and recent neuropsychological
research [15, 16, 17, 18], the second part of the talk traced a distinction between dif-
ferent decision-making processes. These processes are decision structuring, infor-
mation integration, and information evaluation. We also considered post-choice pro-
3
cesses, for their influence on future decisions. Illustrative examples of suboptimal
decision behaviors related to these processes have been described (Table 3), as well as
some proposed workarounds, even if research on debiasing is rather scarce due to the
historical focus on biases or anomalies rather than on ways to avoid them [16, 19].
Table 3. Decision processes and related problems in the interaction with recommender systems
Decision Processes Potential Problems Potential
Workarounds
Decision structuring Too narrow representation • Suggest good options or
Define objectives and al- and search (e.g., availability, important attributes missed
ternatives, estimate uncer- focusing) and estimation • Support representation with
tain quantities, collect biases (e.g., anchoring) external memories
information, … • Help users to estimate uncer-
tain quantities
Information integration Unintentional misweighting • Decrease time costs of ex-
Process and integrate of evidence (e.g., order ef- ternal information access.
information about options fects, frequency-related bias- • Summarize search and navi-
and attributes to reach a es, salience effects) gation results using external
decision (comparisons, memories and aggregation
computations, weighting, tools
integration)
Information evaluation Biases in valuation processes • Teach users to recognize
Evaluate options and their or emotion-related biases specific situations potential-
features according to per- (e.g., framing, sunk cost, ly biasing and provide con-
sonal preferences, criteria, improper influence of inci- crete examples of actions to
and values dental affect) take
• Present information using
bias preventing formats or
displays
Post-choice processes Distortion/reconstruction, • Bias-specific interventions
selective retrieval, reappraisal (as before)
processes (e.g. hindsight and • Provide an external history
positivity biases) of past choices and related
information
4 The Impact of Recommender Systems on Decision Making
A fundamental way in which recommender technology can shape decision processes
is through the provision of potentially good and interesting options (e.g., books, mov-
ies, songs, etc.). After all, this is exactly what recommenders are made for, and con-
sidering that the users’ representation of the decision problem is usually rather narrow
(e.g., [20, 21]), especially if the problem is ill-structured and the domain complex and
4
not very familiar, recommender technology has the potential to overcome a potential
weakness. However, providing more options and attributes may imply placing a
greater burden on integration and evaluation processes. Thus, also these processes
may need to be properly supported, via external memories, interaction design, and
decision aids that can ease information integration and evaluation (e.g., [6, 14, 22]). In
this regard, several (still largely unresolved) design issues may need to be considered
in order to provide tools that are, at the same time, prescriptively defensible, easy to
use, and effortless for the user. These problems may be also exacerbated by the diffu-
sion of mobile devices, which introduces rather tight screen constraints.
Moreover, considering that users are generally able to figure out some good options in
reasonably familiar domains, recommended options need to be clearly better (and
perceived as such) in order to make a difference. Thus, in order to be appreciated,
recommendation technologies should increase significantly choice quality and users’
satisfaction, but keep low the information integration and evaluation load.
Another way in which recommender technology can change our choices is through
the provision of knowledge about options, attributes, and the decision domain. For
instance, providing knowledge on the reasons why a given attribute is important for a
choice and helping users to make sense of attribute values is an important aspect,
especially for nonexperts in the domain. This can contribute to more aware choices.
Recommender technology can also change the way in which we use episodic
memory, by replacing memory retrieval with external browsing (assuming that the
access cost of external information is lower and accuracy higher than retrieval) or
turning retrieval into recognition. Thus, new memory problems may not reside no
more in retrieving information, but in filtering and combining it, and in handling in-
terference.
Recommender technologies may also have a potential ‘dark side’ when deployed as
commercial services. Besides the important issue of personal data protection and user
rights, these technologies have the potential to affect user behavior in rather subtle
ways, ranking options according to sponsors’ contributions (without providing a bold
warning), enabling by default fast shortcuts to purchase, or influencing users’ prefer-
ences even outside their awareness via mere exposure, priming, framing, or anchor-
ing. In this regard, it is always worth remembering that decision technology should
ideally help the users to choose with full awareness and in their best interests.
References
1. Baddeley, A., Eysenck, M. W., & Anderson, M. C. (2009). Memory. Hove: Psychology
Press.
2. Roediger, H. L. (Ed.). (2008). Cognitive psychology of memory. Vol. 2 of Learning and
memory: A comprehensive reference (J. Byrne, Ed.). Oxford: Elsevier.
3. Del Missier, F., Mäntylä, T., Hansson, P., Bruine de Bruin, W., Parker, A., & Nilsson, L-
G. (2013). The multifold relationship between memory and decision making: An individu-
al-differences study. Journal of Experimental Psychology: Learning, Memory, and Cogni-
tion, 39, 1344-1364.
5
4. Del Missier, F., Mäntylä, T., & Nilsson, L. G. (2014). Aging, memory, and decision mak-
ing. In T. M. Hess, C. E. Loeckenhoff & J.-N. Strough (Eds.), Aging and decision-making:
Empirical and applied perspectives (in press). Elsevier Inc.
5. Sharit, J., Hernández, M. A., Czaja, S. J., & Pirolli, P. (2008). Investigating the roles of
knowledge and cognitive abilities in older adult information seeking on the web. ACM
Transactions on Computer-Human Interaction (TOCHI), 15, 3.
6. Hollands, J. G., & Wickens, C. D. (1999). Engineering psychology and human perfor-
mance. New Jersey: Prentice Hall.
7. Budiu, R. (2014). Memory recognition and recall in user interfaces. Retrieved at
http://www.nngroup.com/articles/recognition-and-recall/
8. Pirolli, P. L., Chi, E. H., & Pitkow, J. E. (2003). U.S. Patent No. 6,671,711. Washington,
DC: U.S. Patent and Trademark Office.
9. Pereira, R. E. (2000). Optimizing human-computer interaction for the electronic commerce
environment. Journal of Electronic Commerce Research, 1, 23-44.
10. Larson, K., & Czerwinski, M. (1998). Web page design: Implications of memory, structu-
re, and scent for information retrieval. Proceedings of CHI ’98, Human Factors in Compu-
ting Systems, 25-32. ACM press.
11. Mayhew, D. J. (1991). Principles and guidelines in software user interface design. Prenti-
ce-Hall, Inc.
12. Johnson, E. J., Bellman, S., & Lohse, G. L. (2003). Cognitive lock-in and the power law of
practice. Journal of Marketing, 67, 62-75.
13. Murray, K. B., & Häubl, G. (2002). The fiction of no friction: A user skills approach to
cognitive lock-in. Advances in Consumer Research, 29, 11–18.
14. Del Missier, F., & Ferrante, D. (2007). Presentazione dell'informazione e scelta. In. R. Mi-
suraca, B. Fasolo & M. Cardaci (Eds.), I processi decisionali: Sfide, paradossi e supporti
(pp. 69-114). Bologna: Il Mulino.
15. Finucane, M. L., & Lees, N. B. (2005, November). Decision-making competence of older
adults: Models and methods. In Workshop on Decision Making Needs of Older Adults, the
National Academies, Washington, DC.
16. Bruine de Bruin, W., Parker, A. M., & Fischhoff, B. (2007). Individual differences in adult
decision-making competence. Journal of Personality and Social Psychology, 92, 938–956.
17. Goel, V. (2010). Neural basis of thinking: laboratory problems versus real-world problems.
Wiley Interdisciplinary Reviews: Cognitive Science, 1, 613-621.
18. Goel, V. & Grafman, J. (2000). Role of the right prefrontal cortex in ill-structured plan-
ning. Cognitive Neuropsychology, 17, 415-436.
19. Koehler, D. J., & Harvey, N. (Eds.). (2008). Blackwell handbook of judgment and decision
making. John Wiley & Sons.
20. Gettys, C. F., Pliske, R. M., Manning, C., & Casey, J. T. (1987). An evaluation of human
act generation performance. Organizational Behavior and Human Decision Processes, 39,
23-51.
21. Del Missier, F., Visentini, M., & Mäntylä, T., (2014). Option generation in decision mak-
ing: Ideation beyond memory retrieval. Manuscript submitted for publication
22. Edwards, W., & Fasolo, B. (2001). Decision technology. Annual Review of Psychology,
52, 581-606.
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