When more is less: Negative exposure effects in unsupervised learning

Clapper, John P.

doi:10.3758/bf03193435

Cited by 10 publications

(39 citation statements)

References 31 publications

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“…Order effects have been extensively studied in category learning (e.g., Clapper, 2006;Medin & Bettger, 1994;Zaki & Homa, 1999). In fact, rational models have been developed to account for the effects of category drift (i.e., recency effects) for autocorrelated environments that change over time (Elliott & Anderson, 1995).…”

Section: Figure 1 the Design Of Experiments 1 Dark Triangles (L1-l6 mentioning

confidence: 99%

Putting the psychology back into psychological models: Mechanistic versus rational approaches

2008

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Two basic approaches to explaining the nature of the mind are the rational and the mechanistic approaches. Rational analyses attempt to characterize the environment and the behavioral outcomes that humans seek to optimize. The rational approach holds that people are adaptive and learn (at the individual or species level) to b behave optimally given the nature of the environment (i.e., given available information or statistics). The formal p product of a rational analysis is an abstract mathematical model (often Bayesian) that details the behavioral strategies that optimize some cost function, given the environment. Such models do not have recourse to how people actually process and represent information but are, instead, abstract.Considerations of the environment and optimality also resonate with adherents of the mechanistic program, but unlike for a rational model, the main goal of a mechanistic model is to simulate human behavior by using mechanisms (i.e., analogous processes and representations) that are the same as those that support human behavior. The mechanistic program seeks to reverse engineer the human b brain and peer inside the black box. The issues of primary importance to the mechanistic program are how people p p represent and process information.One common criticism of mechanistic approaches is that they lead to ad hoc explanations that lack the elegance and clarity of models derived from rational analysis. To the extent that two models converge on a common set y of predictions, the more transparent and mathematically motivated model should be favored. Echoing these sentiments, Anderson (1991b) stated, "All mechanistic proposals which implement the same rational prescription are the same," and "a rational theory provides a precise characterization and justification of the behavior the mechanistic theory should achieve." These views are seconded by Chater and Oaksford (1999): "The picture that emerges from this focus on mechanistic explanation is of the cognitive system as an assortment of apparently arbitrary mechanisms, subject to equally capricious limitations, with no apparent rationale or purpose."The upshot of these statements is that mechanisms are subservient to rational accounts of thought. Perhaps in a d moment of candor or euphoria, Anderson (1991b) stated that rational models render mechanistic models unnecessary: "One might take the view (and I have so argued in overenthusiastic moments; Anderson, in press) that we do not need a mechanistic theory, that a rational theory of-f f pp p p y fers a more appropriate explanatory level for behavioral Two basic approaches to explaining the nature of the mind are the rational and the mechanistic approaches. Rational analyses attempt to characterize the environment and the behavioral outcomes that humans seek to optimize, whereas mechanistic models attempt to simulate human behavior using processes and representations analogous to those used by humans. We compared these approaches with regard to their accounts of how humans learn the variability...

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Section: Figure 1 the Design Of Experiments 1 Dark Triangles (L1-l6 mentioning

confidence: 99%

Putting the psychology back into psychological models: Mechanistic versus rational approaches

2008

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“…This article investigates the effects of different training sequences on subsequent category formation when an underlying category structure exists. Sequencing (order) effects have been shown to affect both intentional supervised category learning (Goldstone, 1996; Jones, Love, & Maddox, 2006; Jones & Sieck, 2003; Stewart, Brown, & Chater, 2002) and unsupervised incidental category learning (Clapper, 2006; Clapper & Bower, 1994, 2002). However, very little is known about their effects on unsupervised intentional category learning, an everyday form of categorization.…”

Section: Discussionmentioning

confidence: 99%

“…Sequencing (order) effects have been shown to affect both supervised and (incidental) unsupervised learning. While existing studies of supervised category learning addressed primarily the question of how contrast of successive stimuli affects the category assignment for the current stimulus with respect to its usual category assignment (Jones, Love, & Maddox, 2006; Jones & Sieck, 2003; Stewart, Brown, & Chater, 2002), studies of unsupervised learning addressed the question of whether the underlying category structure itself can be learned under particular exemplar sequences (Clapper, 2006; Clapper & Bower, 1994, 2002). As we also aim to address the question of category learnability under unsupervised learning, we first turn to the studies of unsupervised learning…”

Section: Introductionmentioning

confidence: 99%

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Learning mode and exemplar sequencing in unsupervised category learning.

Zeithamová¹,

Maddox²

2009

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Exemplar sequencing effects in incidental and intentional unsupervised category learning were investigated to illuminate how people form categories without an external teacher. Stimuli were perfectly separable into two categories based on one of two dimensions of variation. Sequencing of the first twenty training stimuli was manipulated. In the blocked condition, 10 category A stimuli were followed by 10 category B stimuli. In the intermixed condition, these 20 stimuli were ordered randomly. Experiment 1 revealed an interaction between learning mode and sequence, with better intentional learning for intermixed sequences, but better incidental learning for blocked sequences. Experiment 2 showed that manipulating trial-to-trial variability along each dimension can impact intentional learning. Training sequences that emphasized variation along the category-relevant dimension resulted in better performance than sequences that emphasized variation along the category-irrelevant dimension. The results suggest that unsupervised category learning is influenced by the mode of learning and the order and nature of encountered exemplars.

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“…The lack of a distinct "failure event" means that the leamer is trapped in an overaggregated state in which he or she remains persistently blind to the correlational stmcture of the set. Previous studies have shown strong evidence for this kind of aggregation effect (Clapper, 2006).…”

Section: The Category Invention Theory Of Unsupervised Learningmentioning

confidence: 97%

The effects of prior knowledge on incidental category learning.

Clapper¹

2012

Journal of Experimental Psychology: Learning, Memory, and Cogni

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This article describes 5 experiments investigating the role of prior knowledge in incidental category learning. Experiments 1 to 3 showed that prior knowledge improved learning only if the categories in a given set were related to contrasting themes; there was no consistent knowledge effect if the categories were related to the same theme. Experiments 4 and 5 showed that diagnostic verbal labels facilitated the learning of non-thematic categories but provided no additional benefit when the categories were already related to contrasting themes. In terms of the category invention framework proposed by Clapper (2007), these results imply that prior knowledge provides an effective cue for discovering separate categories, as well as helping people segregate the features of those different categories in memory and so improving their recall. The relevance of these results to other types of category learning tasks is discussed.

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When more is less: Negative exposure effects in unsupervised learning

Cited by 10 publications

References 31 publications

Putting the psychology back into psychological models: Mechanistic versus rational approaches

Putting the psychology back into psychological models: Mechanistic versus rational approaches

Learning mode and exemplar sequencing in unsupervised category learning.

The effects of prior knowledge on incidental category learning.

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