Response times seen as decompression times in Boolean concept use

Bradmetz, Joël; Mathy, Fabien

doi:10.1007/s00426-006-0098-7

Cited by 26 publications

(35 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Although our primary interest is in the overt allocation of attention, we note that across our three experiments, participants developed a systematic pattern of accessing stimulus features that is consistent with a serial rule-based approach to categorization (e.g., Bradmetz & Mathy, 2008;Fific, Little, & Nosofsky, 2010;Trabasso, Rollins, & Shaughnessy, 1971). The spatially separated features of our stimuli make this difficult to interpret, however.…”

Section: Discussionmentioning

confidence: 78%

Temporal characteristics of overt attentional behavior during category learning

Chen

Meier

Blair

et al. 2012

Atten Percept Psychophys

View full text Add to dashboard Cite

Many theories of category learning incorporate mechanisms for selective attention, typically implemented as attention weights that change on a trial-by-trial basis. This is because there is relatively little data on within-trial changes in attention. We used eye tracking and mouse tracking as finegrained measures of attention in three complex visual categorization tasks to investigate temporal patterns in overt attentional behavior within individual categorization decisions. In Experiments 1 and 2, we recorded participants' eye movements while they performed three different categorization tasks. We extended previous research by demonstrating that not only are participants less likely to fixate irrelevant features, but also, when they do, these fixations are shorter than fixations to relevant features. We also found that participants' fixation patterns show increasingly consistent temporal patterns. Participants were faster, although no more accurate, when their fixation sequences followed a consistent temporal structure. In Experiment 3, we replicated these findings in a task where participants used mouse movements to uncover features. Overall, we showed that there are important temporal regularities in information sampling during category learning that cannot be accounted for by existing models. These can be used to supplement extant models for richer predictions of how information is attended to during the buildup to a categorization decision.Keywords Attention . Eye tracking . Categorization . Eye movements . Optimization . Learning . Error . Modeling . Temporal regularity . Visual cognition To interact with the world, people must learn how to sort through the vast amount of information it presents. One of the difficulties in doing so is that much of this information is irrelevant to the task at hand. Consequently, it is essential to allocate limited cognitive resources to important aspects of the environment through selective attention. The notion of selective attention is recognized within a broad range of research areas, from working memory (Awh, Anllo-Vento, & Hillyard, 2000) and priming (Tipper & Cranston, 1985) to cognitive development over the human life span (Plude, Enns, & Brodeur, 1994). Category learning, a paradigm that elicits voluntary control of attention, provides a unique opportunity for observing and formally analyzing how attention shifts over time in response to the relevance of available information. The present study uses eye tracking and mouse tracking to document temporal characteristics of overt top-down attentional behavior during categorization tasks and how this changes over the course of learning.One of the most influential models in the categorylearning literature, the generalized context model (GCM;Nosofsky, 1986), models the role of attention in learning as a weighting mechanism for incoming stimulus information. This type of model hypothesizes that relevant information gains more attention weight as a consequence of learning. For example, after learning the task of di...

show abstract

Section: Discussionmentioning

confidence: 78%

Temporal characteristics of overt attentional behavior during category learning

Chen

Meier

Blair

et al. 2012

Atten Percept Psychophys

View full text Add to dashboard Cite

show abstract

“…benefits of different orders may be helpful in deciding among competing rule-based models, which differ in the nature of the rules extracted (Bradmetz & Mathy, 2008;Feldman, 2000;Lafond, Lacouture, & Mineau, 2007;Vigo, 2006; see also Love et al, 2004;. That is, alternative rule-based orders could be devised to match the abstraction or compression techniques entailed by the various theories.…”

Section: Detailed Analysis Of the Progression Of Learningmentioning

confidence: 99%

A rule-based presentation order facilitates category learning

Mathy

Feldman

2009

Psychonomic Bulletin & Review

Self Cite

View full text Add to dashboard Cite

A number of studies have investigated whether category learning is influenced by the order in which examples are presented. Elio and Anderson (1981) found that categories are learned faster when training is blocked into groups of mutually similar examples (see also Elio & Anderson, 1984). More recently, Medin and Bettger (1994) demonstrated a strong learning advantage when training objects were presented in an order that tended to maximize similarity between successive examples. Other studies, such as those of Clapper and Bower (1994) and Goldstone (1996), have focused on the effect of alternation of contrasting categories. Presentation order effects are especially interesting in the light of categorization models that emphasize incremental learning from trial to trial. For example, Sakamoto, Jones, and Love (2008) showed that order can affect the incremental update of both category means and variances (see also Love, Medin, & Gureckis, 2004). Incremental-learning models are naturally susceptible to order effects, whereas other models may be less so, so the manipulation of presentation order is a potentially useful tool for studying the mechanisms of learning.However, previous studies of presentation order were limited in that they used orders based on simple similarity-for example, maximizing or minimizing the similarity between adjacent training examples. Here, we explore a type of presentation order that depends in a more structured way on the nature of the category to be learned. We introduce the notion of a rule-based presentation order, which is one that derives from the internal structure of the training examples. In our rule-based order, objects that are within a rule-that is, that obey the same structured subclass within the category-are presented adjacently in the presentation sequence. Training then moves on to another subclass, and so forth until all the objects have been presented. (Negative instances are randomly interspersed among the positives; only the order of the positives is manipulated.) Below, we will compare subjects' performance with such an order with the similarity-based order found to be advantageous in earlier studies. For comparison, we will also include a dissimilarity-based order, previously found to be disadvantageous. We hypothesize that the rulebased order will facilitate learning, particularly in highly structured concepts (i.e., those containing more clusters), by aiding the subject in mentally organizing what would otherwise appear heterogeneous or chaotic. METHOD SubjectsThe subjects were 96 Rutgers University students who received course credit in exchange for their participation. ProcedureTasks were computer-driven. The subjects learned to sort stimulus objects using two keys, with successful learning encouraged by means of a progress bar. Stimulus objects were presented one at a time in the upper part of the computer screen. After each response, feedback indicating a correct or incorrect classification was provided at the bottom of the screen for 2 sec. The subjects learn...

show abstract

“…This two-factor theory is adapted from the domain of categorization, which has provided a framework for studying category formation in long-term memory, with explanations based on the compressibility of descriptions (Bradmetz & Mathy, 2008; Feldman, 2000, 2003; Goodwin & Johnson-Laird, 2013; Lafond, Lacouture, & Mineau, 2007; Vigo, 2006) using different types of presentation orders (based on rules, similarity, or dissimilarity; see Elio & Anderson, 1981, 1984; Gagné, 1950; Mathy & Feldman, 2009; Medin & Bettger, 1994). This framework nicely accounts for a wide range of categorization performance in long-term memory, but could in principle provide similar predictions for immediate memory.…”

mentioning

confidence: 99%

Chunk formation in immediate memory and how it relates to data compression

2016

Self Cite

View full text Add to dashboard Cite

This paper attempts to evaluate the capacity of immediate memory to cope with new situations in relation to the compressibility of information likely to allow the formation of chunks. We constructed a task in which untrained participants had to immediately recall sequences of stimuli with possible associations between them. Compressibility of information was used to measure the chunkability of each sequence on a single trial. Compressibility refers to the recoding of information in a more compact representation. Although compressibility has almost exclusively been used to study long-term memory, our theory suggests that a compression process relying on redundancies within the structure of the list materials can occur very rapidly in immediate memory. The results indicated a span of about three items when the list had no structure, but increased linearly as structure was added. The amount of information retained in immediate memory was maximal for the most compressible sequences, particularly when information was ordered in a way that facilitated the compression process. We discuss the role of immediate memory in the rapid formation of chunks made up of new associations that did not already exist in long-term memory, and we conclude that immediate memory is the starting place for the reorganization of information.

show abstract

Response times seen as decompression times in Boolean concept use

Cited by 26 publications

References 63 publications

Temporal characteristics of overt attentional behavior during category learning

Temporal characteristics of overt attentional behavior during category learning

A rule-based presentation order facilitates category learning

Chunk formation in immediate memory and how it relates to data compression

Contact Info

Product

Resources

About