Making robust classification decisions : constructing and evaluating Fast and Frugal Trees (FFTs)
2013, Neth, Hansjörg, Czienskowski, Uwe, Schooler, Lael J., Gluck, Kevin
Fast and Frugal Trees (FFTs) are a quintessential family of simple heuristics that allow effective and efficient binary clas- sification decisions and often perform remarkably well when compared to more complex methods. This half-day tutorial will familiarize participants with examples of FFTs and elu- cidate the theoretical link between FFTs and signal detection theory (SDT). A range of presentations, practical exercises and interactive tools will enable participants to construct and eval- uate FFTs for different data sets.
Ranking query results from Linked Open Data using a simple cognitive heuristic
2011, Buikstra, Arjon, Neth, Hansjörg, Schooler, Lael, Teije, Annette ten, Harmelen, Frank van
We address the problem how to select the correct answers to a query from among the partially incorrect answer sets that result from querying the Web of Data.
Our hypothesis is that cognitively inspired similarity measures can be exploited to filter the correct answers from the full set of answers. These measure are extremely simple and efficient when compared to those proposed in the literature, while still producing good results.
We validate this hypothesis by comparing the performance of our heuristic to human-level performance on a benchmark of queries to Linked Open Data resources. In our experiment, the cognitively inspired similarity heuristic scored within 10% of human performance. This is surprising given the fact that our heuristic is extremely simple and efficient when compared to those proposed in the literature.
A secondary contribution of this work is a freely available benchmark of 47 queries (in both natural language and SPARQL) plus gold standard human answers for each of these and 1896 SPARQL answers that are human-ranked for their quality.
Modeling ancient and modern arithmetic practices : addition and multiplication with Arabic and Roman numerals
2008, Schlimm, Dirk, Neth, Hansjörg
To analyze the task of mental arithmetic with external representations in different number systems we model algorithms for addition and multiplication with Arabic and Roman numerals. This demonstrates that Roman numerals are not only informationally equivalent to Arabic ones but also computationally similar - a claim that is widely disputed. An analysis of our models' elementary processing steps reveals intricate trade-offs between problem representation, algorithm, and interactive resources. Our simulations allow for a more nuanced view of the received wisdom on Roman numerals. While symbolic computation with Roman numerals requires fewer internal resources than with Arabic ones, the large number of needed symbols inflates the number of external processing steps.
Melioration dominates maximization : stable suboptimal performance despite global feedback
2006, Neth, Hansjörg, Sims, Chris R., Gray, Wayne D.
Situations that present individuals with a conflict between local and global gains often evoke a behavioral pattern known as melioration — a preference for immediate rewards over higher long-term gains. Using a variant of a binary forced- choice paradigm by Tunney & Shanks (2002), we explored the potential role of global feedback as a means to reduce this bias. We hypothesized that frequent explicit feedback about future expected and optimal gains might enable decision makers to overcome the documented tendency to meliorate when choices are rewarded probabilistically. Our results suggest that the human tendency to meliorate is tenacious and even prospective normative feedback is insufficient to reliably overcome inefficient choice allocation. We identify human memory limitations as a potential source of this problem and sketch a reinforcement learning model that mimics the effects of a variable feedback horizon on performance. We conclude that melioration is a powerful explanatory mechanism that can account for a wide range of human behavior.
How Healthy Aging and Dementia Impact Memory Search
2013, Morais, Ana Sofia, Neth, Hansjörg, Hills, Thomas
We model the semantic recall sequences of 424 older adults aged between 69 to 103 years in the animal fluency task. Our results suggest that, under normal intellectual functioning, memory search in old age (69–84 years) is consistent with a dynamic process that switches between retrieval probes. With dementia and very old age (85–103 years), however, memory search seems to become more consistent with a static process that activates items in memory as a function of their frequency. The weight that probes have in determining the activation of items in memory seems to be an informative signature of the impact of healthy aging and dementia on memory search. Our results show that, with healthy aging and dementia, the activation of items in memory is increasingly more determined by the frequency of past experience with those items.
Competitive mate choice : how need for speed beats quests for quality and harmony
2011, Neth, Hansjörg, Schächtele, Simeon, Duwal, Sulav, Todd, Peter M.
The choice of a mate is made complicated by the need to search for partners at the same time others are searching. What decision strategies will outcompete others in a population of searchers? We extend previous approaches using computer simulations to study mate search strategies by allowing direct competition between multiple strategies, evaluating success on multiple criteria. In a mixed social environment of searchers of different types, simple strategies can exploit more demanding strategies in unexpected ways. We find that simple strategies that only aim for speed can beat more selective strategies that aim to maximize the quality or harmony of mated pairs.
Interactive coin addition : how hands can help us think
2011, Neth, Hansjörg, Payne, Stephen J.
Does using our hands help us to add the value of a set of coins? We test the benefits and costs of direct interaction with a men- tal arithmetic task in a computerized yoked design in which groups of participants vary in their interactive mode (move vs. look) and the initial configuration of coins (pseudo-random vs. another mover’s final layout). By assessing performance and conducting a microgenetic analysis of the strategies employed we argue that the purpose of movement is the result, rather than the process of moving. Participants move coins in order to sort, rather than to mark, and select them by value, rather than by location. They spontaneously create remarkably smart solutions, thereby incidentally creating physical configurations that can help other problem solvers.
Thinking by doing and doing by thinking : a taxonomy of actions
2008, Neth, Hansjörg, Müller, Thomas
Taking a lead from existing typologies of actions in the philosophical and cognitive science literatures, we present a novel taxonomy of actions. To promote a notion of epistemic agency we distinguish theoretical (mental state-directed) from practical (world-directed) actions. Our basic structural unit is that of a teleological frame, which spans one specific goal of an agent. Relative to a given teleological frame, actions can be classified as focal (directed towards the end) or ancillary (directed towards a means). The framework is applied to further illuminate previous attempts to distinguish between pragmatic and epistemic actions (Kirsh & Maglio, 1994). Physical actions that substitute or support mental processes are reclassified as practical ancillary actions that are strategically contingent alternatives to theoretical actions.
Juggling multiple tasks : a rational analysis of multitasking in a synthetic task environment
2006, Neth, Hansjörg, Khemlani, Sangeet S., Oppermann, Brittney, Gray, Wayne D.
Tardast (Shakeri 2003; Shakeri & Funk, in press) is a new and intriguing paradigm to investigate human multitasking behavior, complex system management, and supervisory control. We present a replication and extension of the original Tardast study that assesses operators’ learning curve and explains gains in performance in terms of increased sensitivity to task parameters and a superior ability of better operators to prioritize tasks. We then compare human performance profiles to various artificial software agents that provide benchmarks of optimal and baseline performance and illustrate the outcomes of simple heuristic strategies. Whereas it is not surprising that human operators fail to achieve an ideal criterion of performance, we demonstrate that humans also fall short of a principally achievable standard. Despite significant improvements with practice, Tardast operators exhibit stable sub-optimal performance in their time-to-task allocations.