Modeling Probabilistic Categorization Data: Exemplar Memory and Connectionist Nets

Myers, Jerome L.; Lohmeier, Jill H.; Well, Arnold D.

doi:10.1111/j.1467-9280.1994.tb00635.x

Cited by 10 publications

(14 citation statements)

References 15 publications

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“…Estes et al, 1989;Friedman and Massaro, 1998;Friedman et al, 1995;Kitzis et al, 1998;Myers et al, 1994;Nosofsky et al, 1992;Shanks, 1990Shanks, , 1991. These studies have uniformly documented sub-optimal responding and in many cases probability matching.…”

Section: Multiple-cue Choice Tasksmentioning

confidence: 95%

A re‐examination of probability matching and rational choice

Shanks

Tunney

McCarthy

2002

Behavioral Decision Making

241

230

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In a typical probability learning task participants are presented with a repeated choice between two response alternatives, one of which has a higher payoff probability than the other. Rational choice theory requires that participants should eventually allocate all their responses to the high-payoff alternative, but previous research has found that people fail to maximize their payoffs. Instead, it is commonly observed that people match their response probabilities to the payoff probabilities. We report three experiments on this choice anomaly using a simple probability learning task in which participants were provided with (i) large financial incentives, (ii) meaningful and regular feedback, and (iii) extensive training. In each experiment large proportions of participants adopted the optimal response strategy and all three of the factors mentioned above contributed to this. The results are supportive of rational choice theory. Copyright # 2002 John Wiley & Sons, Ltd.key words probability matching; maximization; choice; rationality; feedback; payoffs; learning; reinforcement A striking violation of rational choice theory is commonly observed in simple repeated binary choice tasks in which a payoff is available with higher probability given one response than another. In such tasks people often tend to 'match' probabilities: That is, they allocate their responses to the two options in proportion to their relative payoff probabilities. Thus suppose that a monetary payoff of fixed size is given with probability p ¼ 0.7 for choosing left and with probability 1 À p ¼ 0.3 for choosing right. Probability matching refers to behavior in which left is chosen on about 70% of trials and right on 30%. Such responding violates rational choice theory because the optimal strategy in such tasks, after an initial period of experimentation and assuming that the payoff probabilities are stationary, is always to select the option associated with the higher probability of payoff. On any trial, the expected payoff for choosing left is higher than the expected payoff for choosing right.

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Section: Multiple-cue Choice Tasksmentioning

confidence: 95%

A re‐examination of probability matching and rational choice

Shanks

Tunney

McCarthy

2002

Behavioral Decision Making

241

230

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“…Of primary interest was the result that, when presented with symptom s1 alone, subjects tended to choose the rare disease significantly more often than 50%. That result has been replicated several times (Cobos, López, Rando, Fernández, & Almaraz, 1993;Estes, Campbell, Hatsopoulos, & Hurwitz, 1989;Myers, Lohmeier, & Well, 1994;Nosofsky, Kruschke, & McKinley, 1992;Shanks, 1990b).…”

Section: Experiments 3: Apparent Base Rate Neglect Is An Attenuated Camentioning

confidence: 78%

“…Kruschke, 1993b). Myers et al (1994) recently proposed an exemplar-based model that accounts for some aspects of the inverse baserate effect and apparent base-rate neglect. Their model is an extension of Chumbley's (1986) CLEM (concept learning by exemplar memorization) model.…”

Section: Rational Modelmentioning

confidence: 99%

“…(The interested reader can consult the original article for details.) Myers et al (1994) showed that a revised version of CLEM can account for some aspects of results from the inverse base-rate paradigm; in particular, for test cases I, PC+PR, and I+PC+PR. The key aspect of CLEM that causes the inverse base-rate effect is greater forgetting of PC →C traces than of PR →R traces.…”

Section: Rational Modelmentioning

confidence: 99%

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The Role of Base Rates in Category Learning

Kruschke¹

1994

PsycEXTRA Dataset

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Previous researchers have discovered perplexing inconsistencies in how human subjects appear to utilize knowledge of category base rates when making category judgments. In particular, Medin and Edelson (1988) found an "inverse base rate effect" in which subjects tended to select a rare category when tested with a combination of conflicting cues, and Gluck and Bower (1988) reported apparent "base rate neglect" in which subjects tended to select a rare category when tested with a single symptom whose objective diagnosticity was equal for all categories. In this article I suggest that two principles underlie those effects: First, base rate information is learned and consistently deployed during all training and testing cases. Second, the dominant effect of base rates is to cause frequent categories to be learned before rare categories, so that the common categories are encoded in terms of their typical features, and the rare categories are encoded by whichever features distinguish them relative to the already-learned, common categories. Four new experiments are reported which confirm those principles. Experiment 1 replicates and extends the basic inverse base rate effect and establishes a standard for subsequent experiments and modeling. Experiment 2 shows that pre-training on a subset of categories has comparable effects as giving those categories high base rates during training. Experiment 3 shows that apparent "base rate neglect" is simply an attenuated case of the "inverse base rate effect," hence any model of one effect should account for the other. Experiment 4 uses probabilistic categories, and shows that apparent "base rate neglect" can be obtained even for cues that occur in less than 50% of the rare category's exemplars, and that cues that are not associated with any category decrease the base-rate bias. The principles are formalized in a new connectionist model that modifies attention to stimulus features based on the network's current knowledge. Quantitative fits to the empirical data are reported, and provide additional support for the principles.

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“…A clear indication of the theoretical difficulty and importance of the IBRE can be had from the fact that there have been as many attempts to model it (Anderson, 1990;Gluck, 1992;Juslin et al, 2001;Kalish, 2001; Kruschke, 1996Kruschke, , 2001Kruschke, , 2003Markman, 1989;Medin & Edelson, 1988;Myers, Lohmeier, & Well, 1994;Shanks, 1992;Winman et al, 2003) as to empirically clarify it (Bohil et al, 2005;Juslin et al, 2001;Kalish, 2001; Kruschke, 1996Kruschke, , 2001Medin & Bettger, 1991;Medin & Edelson, 1988;Nelson, 1993;Shanks, 1992;Winman et al, 2005). This is particularly telling since the difficulty of accounting for the IBRE essentially reduces to handling a single problematic data point (PC PR) without creating an entire theoretical structure that has no application beyond that data point.…”

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confidence: 99%

Paradoxical effects of base rates and representation in category learning

Johansen¹,

Fouquet

Shanks

2007

Memory & Cognition

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The inverse base rate effect (IBRE) continues to be a puzzling case of decision making on the basis of conflicting information in human category learning. After being trained via feedback over trials to assign combinations of cues to high- and low-frequency categories, participants tend to respond with the low-frequency category to an otherwise perfectly conflicting pair of test cues, contrary to the category base rates. Our Experiment 1 demonstrated that decision making on the basis of an explicit summary of the cue-outcome and outcome base rate information from the standard learning task does not result in the effect. The remaining experimental conditions evaluated the necessary and sufficient conditions for the effect by systematically exploring experimental deviations between the standard learning task and the pure decision-making task. In partial disagreement with both recent theoretical accounts of the effect, these experiments indicate that asymmetric outcome representation and profound base rate neglect are individually necessary and jointly sufficient conditions. Broader theoretical implications are discussed.

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Modeling Probabilistic Categorization Data: Exemplar Memory and Connectionist Nets

Cited by 10 publications

References 15 publications

A re‐examination of probability matching and rational choice

A re‐examination of probability matching and rational choice

The Role of Base Rates in Category Learning

Paradoxical effects of base rates and representation in category learning

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