The Neural Mechanisms that Underlie Decision Making

Gallistel, C. R.

doi:10.1016/b978-0-12-374176-9.00027-0

Cited by 7 publications

(6 citation statements)

References 15 publications

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“…The calculation of rate of reward (perceived number of rewards divided by the temporal interval) in a foraging scenario predicts animal behavior in both naturalistic (Harper, 1982) and laboratory experiments (Gallistel, Mark, King & Latham, 2001, Leon & Gallistel, 1998). These calculations are also proposed to underlie a spontaneous appreciation of risk and optimal switching behavior (Balci, Freestone & Gallistel, 2009; Gallistel, 2008), and are found in experimentally naïve mice (Gallistel, King, Gottlieb, Balci, Papachristos, Szalecki & Carbone, 2007) – bolstering the claim that complex scaling abilities are innately present. In their review on the topic, Jacob et al .…”

Section: Discussionmentioning

confidence: 85%

Non‐symbolic halving in an Amazonian indigene group

McCrink

Spelke

Dehaene

et al. 2013

Developmental Science

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Much research supports the existence of an Approximate Number System (ANS) that is recruited by infants, children, adults, and non-human animals to generate coarse, non-symbolic representations of number. This system supports simple arithmetic operations such as addition, subtraction, and ordering of amounts. The current study tests whether an intuition of a more complex calculation, division, exists in an indigene group in the Amazon, the Mundurucu, whose language includes no words for large numbers. Mundurucu children were presented with a video event depicting a division transformation of halving, in which pairs of objects turned into single objects, reducing the array's numerical magnitude. Then they were tested on their ability to calculate the outcome of this division transformation with other large-number arrays. The Mundurucu children effected this transformation even when non-numerical variables were controlled, performed above chance levels on the very first set of test trials, and exhibited performance similar to urban children who had access to precise number words and a surrounding symbolic culture. We conclude that a halving calculation is part of the suite of intuitive operations supported by the ANS.

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Section: Discussionmentioning

confidence: 85%

Non‐symbolic halving in an Amazonian indigene group

McCrink

Spelke

Dehaene

et al. 2013

Developmental Science

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“…At first, we need a theoretical model of human behavior. Such a model should include the following factors ( Hofstadter, 1979 ; Gallistel, 2009 ):…”

Section: Computational Meaningfulness As the Core Of The Human Ration...mentioning

confidence: 99%

“…When we take understanding human behavior in natural environments as a criterion to build a theory of behavior, it means that we are better able to describe, explain and predict human behavior ( Gallistel, 2009 , 2020 ; Yarkoni and Westfall, 2017 ; Jolly and Chang, 2019 ).…”

Section: Computational Meaningfulness As the Core Of The Human Ration...mentioning

confidence: 99%

Computational meaningfulness as the source of beneficial cognitive biases

Suomala

Kauttonen

2023

Front. Psychol.

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The human brain has evolved to solve the problems it encounters in multiple environments. In solving these challenges, it forms mental simulations about multidimensional information about the world. These processes produce context-dependent behaviors. The brain as overparameterized modeling organ is an evolutionary solution for producing behavior in a complex world. One of the most essential characteristics of living creatures is that they compute the values of information they receive from external and internal contexts. As a result of this computation, the creature can behave in optimal ways in each environment. Whereas most other living creatures compute almost exclusively biological values (e.g., how to get food), the human as a cultural creature computes meaningfulness from the perspective of one’s activity. The computational meaningfulness means the process of the human brain, with the help of which an individual tries to make the respective situation comprehensible to herself to know how to behave optimally. This paper challenges the bias-centric approach of behavioral economics by exploring different possibilities opened up by computational meaningfulness with insight into wider perspectives. We concentrate on confirmation bias and framing effect as behavioral economics examples of cognitive biases. We conclude that from the computational meaningfulness perspective of the brain, the use of these biases are indispensable property of an optimally designed computational system of what the human brain is like. From this perspective, cognitive biases can be rational under some conditions. Whereas the bias-centric approach relies on small-scale interpretable models which include only a few explanatory variables, the computational meaningfulness perspective emphasizes the behavioral models, which allow multiple variables in these models. People are used to working in multidimensional and varying environments. The human brain is at its best in such an environment and scientific study should increasingly take place in such situations simulating the real environment. By using naturalistic stimuli (e.g., videos and VR) we can create more realistic, life-like contexts for research purposes and analyze resulting data using machine learning algorithms. In this manner, we can better explain, understand and predict human behavior and choice in different contexts.

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“…Reductionists tend to stress the remarkable successes in discovering neural mechanisms of sensory systems, such as early vision, pain, taste, and other basic sensations (Bickle 2003;Kim 2006). Antireductionists, in contrast, emphasize that comparable achievements cannot be claimed for language processing, decision making, and other functions of higher cognition and, consequently, deem the pursuit of project (ii) hopeless (Fodor 1999) or, at best, drastically premature when applied to the more central cognitive systems (Gallistel 2009;Coltheart 2013). This view of the psycho-neural interface, assumed by reductionists and functionalists alike, is too restrictive.…”

Section: Bridge Laws In Theory Reductionmentioning

confidence: 99%

Mapping the mind: bridge laws and the psycho-neural interface

Nathan

Pinal

2015

Synthese

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Recent advancements in the brain sciences have enabled researchers to determine, with increasing accuracy, patterns and locations of neural activation associated with various psychological functions. These techniques have revived a longstanding debate regarding the relation between the mind and the brain: while many authors claim that neuroscientific data can be employed to advance theories of higher cognition, others defend the so-called 'autonomy' of psychology. Settling this significant issue requires understanding the nature of the bridge laws used at the psycho-neural interface. While these laws have been the topic of extensive discussion, such debates have mostly focused on a particular type of link: reductive laws. Reductive laws are problematic: they face notorious philosophical objections and they are too scarce to substantiate current research at the intersection of psychology and neuroscience. The aim of this article is to provide a systematic analysis of a different kind of bridge laws-associative laws-which play a central, albeit overlooked role in scientific practice.

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The Neural Mechanisms that Underlie Decision Making

Cited by 7 publications

References 15 publications

Non‐symbolic halving in an Amazonian indigene group

Non‐symbolic halving in an Amazonian indigene group

Computational meaningfulness as the source of beneficial cognitive biases

Mapping the mind: bridge laws and the psycho-neural interface

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