Modeling Response Times for Two-Choice Decisions

Ratcliff, Roger; Rouder, Jeffrey N.

doi:10.1111/1467-9280.00067

Cited by 1,426 publications

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References 22 publications

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“…The neurocomputational theory proposed by S.-C. Li and colleagues also posits a direct link between variability and central tendency in cognitive performance, because the model parameter used to simulate age changes in dopaminergic neuromodulation affects both variability and central tendency (S.-C. Li & Lindenberger, 1999;S.-C. Li et al, 2001). Similarly, applications of the diffusion model by Ratcliff and colleagues (e.g., Ratcliff & Rouder, 1998;Smith & Ratcliff, 2004) to behavioral aging highlight neural mechanisms that cause senescent changes in the variability of decision making and predict longitudinal decline in cognitive performance (e.g., Ratcliff, Thapar, Gomez, & McKoon, 2004).…”

Section: Toward Unified Mechanistic Theories Of Behavioral Decline Inmentioning

confidence: 99%

Cognitive and sensory declines in old age: Gauging the evidence for a common cause.

Lindenberger

Ghisletta

2009

Psychology and Aging

219

215

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Resource accounts of behavioral aging postulate that age-associated impairments within and across intellectual and sensory domains reflect, in part, a common set of senescent alterations in the neurochemistry and neuroanatomy of the aging brain. Hence, these accounts predict sizeable correlations of between-person differences in rates of decline, both within and across intellectual and sensory domains. The authors examined reliability-adjusted variances and covariances in longitudinal change for 8 cognitive measures and for close visual acuity, distant visual acuity, and hearing in 516 participants in the Berlin Aging Study (ages 70 to 103 years at 1st measurement). Up to 6 longitudinal measurements were distributed over up to 13 years. Individual differences in rates of cognitive decline were highly correlated, with a single factor accounting for 60% of the variance in cognitive change. This amount increased to 65% when controlling for age at first measurement, distance to death, and risk of dementia. Contrary to expectations, the correlations between cognitive and sensory declines were only moderate in size, underscoring the need to delineate both domain-general and function-specific mechanisms of behavioral senescence.

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Section: Toward Unified Mechanistic Theories Of Behavioral Decline Inmentioning

confidence: 99%

Cognitive and sensory declines in old age: Gauging the evidence for a common cause.

Lindenberger

Ghisletta

2009

Psychology and Aging

219

215

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“…The model draws on an analogy between decision-making and the noisy accumulation of evidence to a decision criterion, formalized as a diffusion to bound 10,17,18 . Models of this variety provide a useful framework to understand a wide range of decision processes 17,[19][20][21] . By invoking a single mechanism to explain which choice is made and when the process terminates, the diffusion-to-bound model provides a unified explanation for choices and RTs.…”

Section: Comparison Of Lip and Mt Microstimulationmentioning

confidence: 99%

Microstimulation of macaque area LIP affects decision-making in a motion discrimination task

2006

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A central goal of cognitive neuroscience is to elucidate the neural mechanisms underlying decisionmaking. Recent physiological studies suggest that neurons in association areas may be involved in this process. To test this, we measured the effects of electrical microstimulation in the lateral intraparietal area (LIP) while monkeys performed a reaction-time motion discrimination task with a saccadic response. In each experiment, we identified a cluster of LIP cells with overlapping response fields (RFs) and sustained activity during memory-guided saccades. Microstimulation of this cluster caused an increase in the proportion of choices to the RF of the stimulated neurons. Choices toward the stimulated RF were faster with microstimulation, while choices in the opposite direction were slower. Microstimulation never directly evoked saccades, nor did it change reaction times in a simple saccade task. These results demonstrate that the discharge of LIP neurons is causally related to decision formation on the discrimination task.Much progress has been made in understanding the neurobiology of decision-making by tracing the neural events that link sensory processing to a choice of action in monkeys 1-4 . For example, to decide whether a pattern of random dots is moving to the left or right, the brain must represent the motion information in the visual cortex, interpret this information as evidence for one or the other direction, and eventually commit to a choice, indicated by some action. Through a combination of recording, lesion, and microstimulation experiments, the activity of directionselective neurons in area MT has been shown to represent an important source of the sensory evidence upon which such a decision about direction is based [5][6][7][8] . This raises the question of how that representation is converted into a categorical decision.When performing the motion discrimination task, monkeys improve in accuracy when given more time to view the stimulus 6,9 . Furthermore, in a reaction-time version of the task, stronger motion leads to both faster and more accurate decisions 10 . These findings can be explained by a simple mechanism whereby momentary sensory evidence is accumulated over time toward a criterion level, which in turn yields a commitment to a proposition and ultimately an action.A neural correlate of this process has been described in the macaque parietal cortex 10,11 . Many LIP neurons respond when visual stimuli are presented at a specific location in space or when monkeys intend to make a saccade to that same location; thus, they have combined sensory and motor RFs 12,13 . When monkeys indicate decisions about direction of motion with an eye movement, LIP neurons increase or decrease their firing as evidence accumulates in favor of or against the choice associated with the target in their RF 10 . This rise and fall in activity

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“…Ratcliff and Rouder (1998) asked participants to classify squares of varying luminance into one of two categories (denoted Category A and Category B). For each trial, luminance of the stimulus was chosen from one of the two categories.…”

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confidence: 99%

“…Figure 1B shows the relative proportion of Category A exemplars as a function of luminance for Ratcliff and Rouder's (1998) design. There are relatively more Category A exemplars for dark stimuli and relatively more Category B exemplars for light stimuli.…”

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confidence: 99%

Comparing categorization models.

Rouder¹,

Ratcliff²

2004

Journal of Experimental Psychology: General

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Four experiments are presented that competitively test rule-and exemplar-based models of human categorization behavior. Participants classified stimuli that varied on a unidimensional axis into 2 categories. The stimuli did not consistently belong to a category; instead, they were probabilistically assigned. By manipulating these assignment probabilities, it was possible to produce stimuli for which exemplar-and rule-based explanations made qualitatively different predictions.F. G. Ashby and J. T. Townsend's (1986) rule-based general recognition theory provided a better account of the data than R. M. Nosofsky's (1986) exemplar-based generalized context model in conditions in which the to-be-classified stimuli were relatively confusable. However, generalized context model provided a better account when the stimuli were relatively few and distinct. These findings are consistent with multiple process accounts of categorization and demonstrate that stimulus confusion is a determining factor as to which process mediates categorization.In this article we present an empirical paradigm to test different theories of categorization behavior. One theory we test is the exemplar-based theory in which categories are represented by sets of stored exemplars. Category membership of a stimulus is determined by similarity of the stimulus to these exemplars (e.g., Medin & Schaffer, 1978;Nosofsky, 1986Nosofsky, , 1987Nosofsky, , 1991). An exemplar-based process relies on retrieval of specific trace-based information without further abstraction; for example, a person is judged as "tall" if he or she is similar in height to others who are considered "tall." The other theory we test is rule-based or decisionbound theory. Decisions are based on an abstracted rule. The relevant space is segmented into regions by bounds, and each region is assigned to a specific category (e.g., Ashby & Gott, 1988;Ashby & Maddox, 1992Ashby & Perrin, 1988;Ashby & Townsend, 1986;Trabasso & Bower, 1968). For example, a person might be considered tall if he or she is perceived as being over 6 ft (1.83 m). The essence of a rule-based process is that processing is based on greatly simplified abstractions or rules but not on the specific trace-based information itself.Both rule-and exemplar-based theories have gained a large degree of support in the experimental literature. There are both exemplar-based models (e.g., generalized context model; Nosofsky, 1986) and rule-based models (e.g., general recognition theory; Ashby & Gott, 1988;Ashby & Townsend, 1986) that can explain a wide array of behavioral data across several domains. Despite the many attempts to discriminate between these two explanations, there have been few decisive tests. Across several paradigms and domains, rule-and exemplarbased predictions often mimic each other.

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Modeling Response Times for Two-Choice Decisions

Cited by 1,426 publications

References 22 publications

Cognitive and sensory declines in old age: Gauging the evidence for a common cause.

Cognitive and sensory declines in old age: Gauging the evidence for a common cause.

Microstimulation of macaque area LIP affects decision-making in a motion discrimination task

Comparing categorization models.

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