Posts in Category: memory

Paper: Visual working memory resources as item activation

To understand visual intelligence is to understand, in large part, who we are.
Donald D. Hoffmann (1998), p. XII


The body’s movements at this time scale provide an essential link between processes underlying elemental perceptual events
and those involved in symbol manipulation and the organization of complex behaviors.
Ballard et al. (1997), p. 723


Bella Z. Veksler, Rachel Boyd, Christopher W. Myers, Glenn Gunzelmann, Hansjörg Neth, Wayne D. Gray

Visual working memory resources are best characterized as dynamic, quantifiable mnemonic traces

An example stimulus used in the paradigm of repeated serial search.

An example stimulus used in the paradigm of repeated serial search.

Abstract:  Visual working memory (VWM) is a construct hypothesized to store a small amount of accurate perceptual information that can be brought to bear on a task.  Much research concerns the construct’s capacity and the precision of the information stored.  Two prominent theories of VWM representation have emerged: slot-based and continuous-resource mechanisms.  Prior modeling work suggests that a continuous resource that varies over trials with variable capacity and a potential to make localization errors best accounts for the empirical data.  Questions remain regarding the variability in VWM capacity and precision.  Using a novel eye-tracking paradigm, we demonstrate that VWM facilitates search and exhibits effects of fixation frequency and recency, particularly for prior targets.  Whereas slot-based memory models cannot account for the human data, a novel continuous-resource model does capture the behavioral and eye tracking data, and identifies the relevant resource as item activation.

Paper: Ranking LOD data with a cognitive heuristic

Arjon Buikstra, Hansjörg Neth, Lael J. Schooler, Annette ten Teije, Frank van Harmelen

Ranking query results from Linked Open Data using a simple cognitive heuristic

Abstract:  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.

Paper: Discretionary interleaving and cognitive foraging

(…) we make search in our memory for a forgotten idea, just as we rummage our house for a lost object.  In both cases we visit what seems to us the probable neighborhood of that which we miss.  We turn over the things under which, or within which, or alongside of which, it may possibly be;
and if it lies near them, it soon comes to view.
William James (1890), The Principles of Psychology, p. 654

[Copyright, 2007–2014]:

Steve Payne, Geoff Duggan, Hans Neth (2007).

Discretionary task interleaving: Heuristics for time allocation in cognitive foraging.

Journal article in JEP:G.

Stephen J. Payne, Geoffrey B. Duggan, Hansjörg Neth

Discretionary task interleaving: Heuristics for time allocation in cognitive foraging

Abstract:  When participants allocated time across 2 tasks (in which they generated as many words as possible from a fixed set of letters), they made frequent switches.  This allowed them to allocate more time to the more productive task (i.e., the set of letters from which more words could be generated) even though times between the last word and the switch decision (“giving-up times”) were higher in the less productive task.  These findings were reliable across 2 experiments using Scrabble tasks and 1 experiment using word-search puzzles.  Switch decisions appeared relatively unaffected by the ease of the competing task or by explicit information about tasks’ potential gain.  The authors propose that switch decisions reflected a dual orientation to the experimental tasks.  First, there was a sensitivity to continuous rate of return — an information-foraging orientation that produced a tendency to switch in keeping with R. F. Green’s (1984) rule and a tendency to stay longer in more rewarding tasks.  Second, there was a tendency to switch tasks after subgoal completion.  A model combining these tendencies predicted all the reliable effects in the experimental data.

Paper: Dynamic memory updates in TRACS

You can’t play 20 questions with nature and win.
Allen Newell (1973)

[Copyright, 2004]: Hans Neth, Chris Sims, Dan Veksler, Wayne Gray: Dynamic memory updates in TRACS. Paper presented at CogSci 2004.

Hansjörg Neth, Chris R. Sims, Vladislav D. Veksler, Wayne D. Gray

You can’t play straight TRACS and win: Memory updates in a dynamic task environment

Abstract:  To investigate people’s ability to update memory in a dynamic task environment we use the experimental card game TRACS^tm (Burns, 2001).  In many card games card counting is a component of optimal performance. However, for TRACS, Burns (2002a) reported that players exhibited a baseline bias:  rather than basing their choices on the actual number of cards remaining in the deck, they chose cards based on the initial composition of the deck.  Both a task analysis and computer simulation show that a perfectly executed memory update strategy has minimal value in the original game, suggesting that a baseline strategy is a rational adaptation to the demands of the original game.  We then redesign the game to maximize the difference in performance between baseline and update strategies.  An empirical study with the new game shows that players perform much better than could be achieved by a baseline strategy.  Hence, we conclude that people will adopt a memory update strategy when the benefits outweigh the costs.