Learning time-varying categories

Navarro, Daniel J.; Perfors, Amy; Vong, Wai Keen

doi:10.3758/s13421-013-0309-6

Cited by 9 publications

(8 citation statements)

References 25 publications

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“…Also, we should be able to find the evidence for the use of short-term representation in other experiments, as long as more one test trial is adopted. In fact, Navarro et al ( 2013 ) recently ask the participants to learn the category structure, which varies along with learning trials. The task is not easy to learn, yet the participants' performance is above the chance level.…”

Section: Discussionmentioning

confidence: 99%

Category variability effect in category learning with auditory stimuli

Yang

2014

Front. Psychol.

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The category variability effect refers to that people tend to classify the midpoint item between two categories as the category more variable. This effect is regarded as evidence against the exemplar model, such as GCM (Generalized Context Model) and favoring the rule model, such as GRT (i.e., the decision bound model). Although this effect has been found in conceptual category learning, it is not often observed in perceptual category learning. To figure out why the category variability effect is seldom reported in the past studies, we propose two hypotheses. First, due to sequence effect, the midpoint item would be classified as different categories, when following different items. When we combine these inconsistent responses for the midpoint item, no category variability effect occurs. Second, instead of the combination of sequence effect in different categorization conditions, the combination of different categorization strategies conceals the category variability effect. One experiment is conducted with single tones of different frequencies as stimuli. The collected data reveal sequence effect. However, the modeling results with the MAC model and the decision bound model support that the existence of individual differences is the reason for why no category variability effect occurs. Three groups are identified by their categorization strategy. Group 1 is rule user, placing the category boundary close to the low-variability category, hence inducing category variability effect. Group 2 takes the MAC strategy and classifies the midpoint item as different categories, depending on its preceding item. Group 3 classifies the midpoint item as the low-variability category, which is consistent with the prediction of the decision bound model as well as GCM. Nonetheless, our conclusion is that category variability effect can be found in perceptual category learning, but might be concealed by the averaged data.

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Section: Discussionmentioning

confidence: 99%

Category variability effect in category learning with auditory stimuli

Yang

2014

Front. Psychol.

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“…16). This independence of cues between stimuli satisfies a crucial assumption of the CRP mixture model known as exchangeability-all permutations of the same set of stimuli are identical to the model (17). Contrary to the effect of stimulus order that we wish to capture, the exchangeability assumption implies that the order of stimuli should not have influenced the outcome of an inference, since it precludes the possibility that stimulus order can serve as a cue to a task environment.…”

Section: Significancementioning

confidence: 99%

Learning bundles of stimuli renders stimulus order as a cue, not a confound

Qian

Aslin

2014

Proc. Natl. Acad. Sci. U.S.A.

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The order in which stimuli are presented in an experiment has long been recognized to influence behavior. Previous accounts have often attributed the effect of stimulus order to the mechanisms with which people process information. We propose that stimulus order influences cognition because it is an important cue for learning the underlying structure of a task environment. In particular, stimulus order can be used to infer a "stimulus bundle"-a sequence of consecutive stimuli that share the same underlying latent cluster. We describe a clustering model that successfully explains the perception of streak shooting in basketball games, along with two other cognitive phenomena, as the outcome of finding the statistically optimal bundle representation. We argue that the perspective of viewing stimulus order as a cue may hold the key to explaining behaviors that seemingly deviate from normative theories of cognition and that in task domains where the assumption of stimulus bundles is intuitively appropriate, it can improve the explanatory power of existing models.H uman cognition typically takes place in an environment where information becomes available over time. The sequential nature of information availability implies that stimulus order-the order in which a sequence of stimuli is presentedmay strongly influence the interpretation of a task environment. Consider the weather patterns in Fig. 1 as an example. If we were to record the dominant weather phenomenon in each month of a year, order 1, as shown in Fig. 1, would likely represent a typical city in upstate New York, such as Rochester. Given this sequence of stimuli, it is relatively easy for an observer to infer the existence of "seasons" underlying the weather pattern-that is, winter seems to be in effect from November to April, followed by spring/summer, and then by fall. However, if we reorder this set of stimuli from order 1 to order 2 as shown in Fig. 1, the inference of seasons becomes much less apparent. Even when the exact same set of stimuli is presented in both sequences, the difference in stimulus order has led to a dramatically different interpretation about its underlying structure. Why does stimulus order affect human cognition in this interesting way?The effect of stimulus order on cognition has often been viewed as a consequence of the way that the human mind processes information (1-8). For example, Elliott and Anderson (5) accounted for the effect of stimulus order on category learning by incorporating the mechanisms of memory decay into a categorization model. Memory decay intuitively captures the assumption that the human mind tends to be influenced by nearby stimuli more than by distant ones. It has also been hypothesized that when a sequence of stimuli is ordered in such a way that the similarity between successive stimuli is maximized, it offers a processing benefit because the stimuli being kept in working memory will be similar to each other and thus allow for easier abstraction of concepts (4). In addition, the effects of stimulus ord...

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“…Human behavior in dynamic environments has been studied with respect to several problems, including bandit tasks (Daw et al, 2006;Speekenbrink & Konstantinidis, 2014, decision making biases (Ayton & Fischer, 2004), prediction tasks (Brown & Steyvers, 2009;Osman & Speekenbrink, 2012), risky choice (Rakow & Miler, 2009), sequential effects (Yu & Cohen, 2008), probability matching behavior (Green et al, 2010) and categorization (Navarro et al, 2013), but to our knowledge there are no studies investigating how genuinely dynamic environments affect how people approach problems -such as the observe or bet task -in which information-generating actions are separated from reward-generating ones. 1 Nevertheless, the relevance seems obvious.…”

Section: Introductionmentioning

confidence: 99%

Learning and choosing in an uncertain world: An investigation of the explore–exploit dilemma in static and dynamic environments

Navarro

Newell

Schulze

2016

Cognitive Psychology

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How do people solve the explore-exploit trade-off in a changing environment? In this paper we present experimental evidence from an "observe or bet" task, in which people have to determine when to engage in information-seeking behavior and when to switch to reward-taking actions. In particular we focus on the comparison between people's behavior in a changing environment and their behavior in an unchanging one. Our experimental work is motivated by rational analysis of the problem that makes strong predictions about information search and reward seeking in static and changeable environments. Our results show a striking agreement between human behavior and the optimal policy, but also highlight a number of systematic differences. In particular, we find that while people often employ suboptimal strategies the first time they encounter the learning problem, most people are able to approximate the correct strategy after minimal experience. In order to describe both the manner in which people's choices are similar to but slightly different from an optimal standard, we introduce four process models for the observe or bet task and evaluate them as potential theories of human behavior.

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Learning time-varying categories

Cited by 9 publications

References 25 publications

Category variability effect in category learning with auditory stimuli

Category variability effect in category learning with auditory stimuli

Learning bundles of stimuli renders stimulus order as a cue, not a confound

Learning and choosing in an uncertain world: An investigation of the explore–exploit dilemma in static and dynamic environments

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