Rhesus macaques (macaca mulatta) monitor uncertainty during numerosity judgments.

Beran, Michael J.; Smith, J. David; Redford, Joshua S.; Washburn, David A.

doi:10.1037/0097-7403.32.2.111

Cited by 83 publications

(88 citation statements)

References 37 publications

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“…In order to test animals in this task, the different options were linked with different reward contingencies: correct choices were rewarded with one unit of food reward, uncertain responses were rewarded with a smaller amount of food while incorrect choices lead to omission of reward and a time-out punishment. Smith and colleagues studied monkeys, dolphins and rats and compared their performance with humans [27][28][29][30][31]. Under these conditions, monkeys and dolphins showed a qualitative similarity in response strategies as well as a quantitative agreement in the response distributions of animals and humans.…”

Section: Behavioural Reports Of Confidence In Humans and Other Animalsmentioning

confidence: 99%

A computational framework for the study of confidence in humans and animals

Kepecs

Mainen

2012

Phil. Trans. R. Soc. B

268

289

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Confidence judgements, self-assessments about the quality of a subject's knowledge, are considered a central example of metacognition. Prima facie, introspection and self-report appear the only way to access the subjective sense of confidence or uncertainty. Contrary to this notion, overt behavioural measures can be used to study confidence judgements by animals trained in decision-making tasks with perceptual or mnemonic uncertainty. Here, we suggest that a computational approach can clarify the issues involved in interpreting these tasks and provide a much needed springboard for advancing the scientific understanding of confidence. We first review relevant theories of probabilistic inference and decision-making. We then critically discuss behavioural tasks employed to measure confidence in animals and show how quantitative models can help to constrain the computational strategies underlying confidence-reporting behaviours. In our view, post-decision wagering tasks with continuous measures of confidence appear to offer the best available metrics of confidence. Since behavioural reports alone provide a limited window into mechanism, we argue that progress calls for measuring the neural representations and identifying the computations underlying confidence reports. We present a case study using such a computational approach to study the neural correlates of decision confidence in rats. This work shows that confidence assessments may be considered higher order, but can be generated using elementary neural computations that are available to a wide range of species. Finally, we discuss the relationship of confidence judgements to the wider behavioural uses of confidence and uncertainty.

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Section: Behavioural Reports Of Confidence In Humans and Other Animalsmentioning

confidence: 99%

A computational framework for the study of confidence in humans and animals

Kepecs

Mainen

2012

Phil. Trans. R. Soc. B

268

289

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“…Hence, using complex procedures, such as the one developed by Smith et al (2006) or Beran et al (2006), will just make the life of the researchers unnecessarily complicated. First, it may not be possible to use such procedures with other species.…”

mentioning

confidence: 99%

Reinforcement and Metacognition

Jozefowiez¹,

Staddon²,

Cerutti³

2009

CCBR

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We return to the issue of reinforcement addressed by Smith, Beran, Couchman, Coutinho & Boomer. We argue that their concerns are unfounded because (a) reinforcing the 'uncertain' response need not undermine an otherwise convincing demonstration of animal metacognition, even for skeptics, (b) with or without metacognition, in the absence of the appropriate reinforcement contingencies, animals will not choose at all.

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“…In one case [22], macaques judged whether arrays were less or more numerous than a session-specific numerosity. Numerosities nearer the boundary value were more difficult to classify.…”

Section: Testing Low-level Interpretations Of Animal Metacognitionmentioning

confidence: 99%

“…To address this concern, researchers removed the reward contingency for that response [19,22,35]. In one case [22], macaques judged whether arrays were less or more numerous than a session-specific numerosity.…”

Section: Testing Low-level Interpretations Of Animal Metacognitionmentioning

confidence: 99%

The Highs and Lows of Theoretical Interpretation in Animal-Metacognition Research

Smith¹,

Couchman²,

Beran³

2014

The Cognitive Neuroscience of Metacognition

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Humans feel uncertain. They know when they do not know. These feelings and the responses to them ground the research literature on metacognition. It is a natural question whether animals share this cognitive capacity, and thus animal metacognition has become an influential research area within comparative psychology. Researchers have explored this question by testing many species using perception and memory paradigms. There is an emerging consensus that animals share functional parallels with humans' conscious metacognition. Of course, this research area poses difficult issues of scientific inference. How firmly should we hold the line in insisting that animals' performances are low-level and associative? How high should we set the bar for concluding that animals share metacognitive capacities with humans? This area offers a constructive case study for considering theoretical problems that often confront comparative psychologists. The authors present this case study and address diverse issues of scientific judgement and interpretation within comparative psychology.

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Rhesus macaques (macaca mulatta) monitor uncertainty during numerosity judgments.

Cited by 83 publications

References 37 publications

A computational framework for the study of confidence in humans and animals

A computational framework for the study of confidence in humans and animals

Reinforcement and Metacognition

The Highs and Lows of Theoretical Interpretation in Animal-Metacognition Research

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