
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39289409
68024
10.1038/s41598-024-68024-3
Article
A spiking neuron model of moral judgment in trolley dilemmas
Gothard Timothy timothycgothard@gmail.com

Davies Jim
https://ror.org/02qtvee93 grid.34428.39 0000 0004 1936 893X Department of Cognitive Science, Carleton University, Ottawa, K1S 5B6 Canada
17 9 2024
17 9 2024
2024
14 217339 1 2023
18 7 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
People will make different moral judgments in similar moral dilemmas where one can act to sacrifice some number of lives to save several more. Research has shown that although people can reason that an action would save more lives, automatic processes can overwrite deliberate reasoning. Having participants imagine hypothetical moral dilemmas, researchers have discovered that factors such as action/omission, means/side-effect, and personal/impersonal can affect judgment. Joshua Greene suggests that these features do not affect people’s judgment because they are morally relevant but are instead a result of the myopic nature of the automatic moral process. Greene hypothesizes that there is some myopic module or domain-general process that attaches a negative emotional response to an action when one is contemplating violent actions. In the present research a model of this myopic automatic process is paired with an analytic system to replicate deontological and utilitarian responses to moral dilemmas. Our system, MERDJ, models this in simulated spiking neurons. The system takes in representations of specific moral dilemmas as inputs and outputs judgments of appropriate or inappropriate.

Keywords

Morality
Ethics
Computational neuroscience
Neural modelling
Subject terms

Cognitive neuroscience
Computational neuroscience
Emotion
issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

What produces moral judgments has been a central question to moral psychology throughout its history. When Freudian psychology was the dominant view, emotional internalization was often believed to produce moral judgments. As behaviourists became leading voices in psychology it was believed moral judgment came about through conditioning and reinforcement1. During the cognitive revolution of the 1950s and 1960s the focus shifted toward reasoning with Kohlberg’s2 six-stage model of the development of moral reasoning, built on earlier work of Piaget3, which suggested that through taking other perspectives one improves one’s moral reasoning. By the 1980s research in evolutionary psychology and primatology suggested that the importance of moral emotions had been ignored. In the 1990s the research focused on automaticity, and how unconscious processing plays an important role in moral judgment, while reasoning was believed to be used only for justifying conclusions arrived at unconsciously1. With the focus on automatic processes dominating moral judgment, Joshua Greene proposed a hypothesis of emotion-reason conflict for moral judgment4.

May one innocent life be sacrificed to save five others? This is studied using a family of ethical dilemmas from contemporary moral philosophy. Two examples of these dilemmas have a similar structure, but provoke different distributions of moral judgments. The first is the trolley dilemma from Phillippa Foot5, which we will call the switch dilemma. In this thought experiment, a runaway train trolley is going to hit and kill five people if it continues on its course. There is a switch nearby that can be activated to put the trolley on a side track, where it would kill only one person. Most participants (though not all) respond that it is morally appropriate to pull the switch4.

One finds different results in the footbridge dilemma6. where the trolley once again is threatening five lives, but this time there is a footbridge over the track with a bystander who, if pushed off the footbridge in front of the trolley, will save the five but die in the process. In contrast with the switch dilemma, in this version, most people do not consider it appropriate for someone to push the bystander in front of the trolley4.

What makes it morally acceptable to sacrifice one life to save five others in the switch but not the footbridge dilemma? A common answer to this puzzle is to take a Kantian perspective and suggest that what differentiates these two dilemmas is the doctrine of double effect (DDE), which holds that it is morally permissible to harm as an unintended side effect to achieve a good end, but it is not morally permissible to harm as an intended means to achieve a good end. In the switch dilemma, someone dies because they happen to be on the side track, but their death is not what saves the five others. In the footbridge dilemma, on the other hand, the pushed person’s death is the causal reason that the five are saved.

This hypothesis was tested with the loop dilemma, which is similar to the switch dilemma but instead the side track loops back onto the main track before reaching the five people, and the person on this side track, when killed, will stop the trolley. What the loop dilemma does is it makes the bystander on the side track causally necessary. Killing this bystander is required to save the five others, and as such is used as a means to an end, like the pushed person in the footbridge problem. However, the empirical investigation did not support this hypothesis: Participants judge the loop dilemma more similarly to the switch than the footbridge dilemma7,8. If the DDE was the most impactful factor for moral judgment than we would see participants’ judgment of the loop and footbridge dilemma to be more alike, as they both violate the DDE, unlike the switch dilemma.

So why do people respond to the loop dilemma similarly to how they respond to the switch dilemma even though they are using the bystander as a means to an end? In his first attempt to solve this puzzle Greene4 proposes an emotion-reason conflict model to predict differences in dilemma responses.

Greene argued that moral judgments are formed as a result of two separate, sometimes competing processes. Our deliberate moral reasoning typically reflects utilitarian morals, that is, that the morally better choice is the one that produces more good. In contrast, our emotional moral system is an automatic process that implements deontological morality: a moral framework that uses rules of how one ought to act to determine right from wrong. Greene conducted a series of experiments to test his emotion-reason conflict model of moral judgment. First, by using brain imaging, Greene was able to show that brain regions often associated with deliberate reasoning (i.e., the dorsolateral prefrontal cortex (DLPFC)) had greater activation when participants responded to dilemmas that often yield a utilitarian response, while brain region associated with emotions (i.e., ventromedial prefrontal cortex (VMPFC)) were more active during dilemmas that typically elicited deontological judgments4,1,9. Second, lesion patients with missing or damaged VMPFC consistently chose utilitarian responses to dilemmas that typically elicit deontological responses10. This supports the claim that deontological judgments are formed from emotional processes. Finally, Greene and his colleagues found that utilitarian responses take longer than deontological responses under load, supporting the claim that utilitarian judgments require slower deliberate processing where deontological ones are automatic11.

In early studies, Greene4 proposed that what differentiated the responses to these dilemmas was ‘personalness.’ What this means is that dilemmas like the footbridge were personal because the actor would need to use force initiated from the body, such as pushing someone with your hands or with an object they are holding. In contrast, dilemmas like the switch and loop were deemed impersonal because the actor was interacting with a switch or some other mechanism. However, Greene only proposed this as a placeholder to begin his research, hoping it would lead to the discovery of better dimension(s) to dissociate the dilemmas16,12. Since proposing the personal/impersonal distinction, studies have shown evidence of other potential factors, often in conjunction with the personal/impersonal distinction: the DDE13,14, the difference between an action and omission13, and the difference between introducing a new threat and redirecting an existing threat14. With more supporting evidence for the DDE, Greene investigated this and found an interaction between personalness and the DDE on moral judgment12.

Taking into account all of these experiments Greene presented a new explanation called the myopic module hypothesis16. It builds off his emotion-reason dual-process competition evidence, adds elements from the universal moral grammar (UMG) hypothesis7,15 and addresses evolutionary constraints imposed upon automatic processing. The UMG hypothesis is a theory of how our minds represent hypothetical actions as act-tokens. These form causal chains that culminate in a goal being reached. They can be modelled in branching tree diagrams16,7 (See Fig. 1) As an example, under the switch section, we can see a main trunk which includes the action and its direct entailments. The agent moves their hands which moves the switch that aligns the track to the second line which turns the trolley saving the five people. Additionally, there is a branch from the main trunk that occurs when the trolley is diverted off the main track, in this branch one person is being hit by the trolley on the side track. By looking at the responses to a larger set of trolley dilemmas Greene revisits the DDE and the personal/impersonal distinction, finding that most deontological responses resemble what DDE predicts, but with an interaction with the personal/impersonal distinction. Once the DDE has been violated then the personal/impersonal distinction affects how common and how salient the deontological response is, where more personal dilemmas seem to have increased deontological responses and emotional salience (Greene acknowledges there is some uncertainty around the role of ‘personalness’)16. The important issue here is that the loop dilemma, as well as a few other trolley-type dilemmas, that use the bystander as a means to an end. Using the UMG hypothesis and the evolutionary constraints of automatic processing, Greene creates an account for the loop dilemma and others like it. By representing the action with a causal chain of events, where one event causes multiple effects, you can divide the main trunk of the tree diagram and the branches into primary and secondary causal chains. The primary causal chain contains only that which is causally necessary to reach the goal state, while the secondary causal chain contains side effects.

In the case of the loop dilemma, one is imagining a trolley travelling down a track bound to hit five people. When the trolley is switched onto the loop track a second causal chain is constructed, which represents what would happen on the sidetrack. The primary chain’s goal state is reached by changing the tracks as this now alters the trolley from a direct collision course with the five people. However, this starts a new and parallel secondary causal chain that starts when the trolley switches tracks. The goal state, here, is reached when the trolley is stopped by hitting the person on the secondary track16. Greene argues that the automatic emotional response is only capable of attending to the primary causal chain, meaning that our automatic response can only respond to harm as a means (as in the case of the DDE) that occurs in the primary causal chain. Greene comes to this conclusion using the data from the set of trolley problems mentioned earlier, with particular focus on the switch, footbridge, and loop dilemmas. These three dilemmas are presented as branching causal chains in Fig. 1. In this figure under the switch section, we can see a main trunk which includes the action and its direct entailments. The agent moves their hands which moves the switch that aligns the track to the second line which turns the trolley saving the five people. Additionally, there is a branch from the main trunk that occurs when the trolley is diverted off the main track. On this branch the one person is being hit by the trolley on the second line. This is because automatic responses are evolved to be fast-acting heuristics, taking in small a amount of information, and responding quickly. Because the secondary causal chain may contain unnecessary side effects or events that are causally necessary, for things that are unusually complex, such as the switch case, our automatic emotional response is unable to detect the violation of the DDE.Fig. 1 These are the switch, footbridge, and loop dilemmas displayed as branching causal chains. The main trunk represents the causal processes that cause the desired outcome, and the branches off of the main trunk represent side effects. These figures are based on similar figures from Green’s 2013 paper16.

Any additional causal chains (secondary, tertiary, etc.) will contain the unnecessary side effects, as is found in the switch dilemma, or the additional causal chains will contain acts that are causally necessary but are unusually complex, as is found in the loop dilemma. These causally necessary acts in the additional causal chains are considered unusually complex because even if they are simple acts, for them to occur they require the existence of a primary causal chain that they branch off of and no longer interact with. As stated previously, automatic responses take in little information and respond quickly, thus tracking diverging causal chains is unlikely to be done by an automatic response. This is how Greene got the name ‘myopic module:’ to describe an emotional module that is too nearsighted to see beyond the primary causal chain.

Furthermore, Greene explains why our moral intuition can also reflect the doctrine of doing and allowing (DDA), which holds that it is morally worse to act and harm someone than it is to allow harm to happen to someone through inaction (this is also known as omission bias that results from the distinction between acts of commission and omission). Although this is not fleshed out in the trolley dilemmas, other contrasting dilemmas have shown that people’s intuitions tend to follow the DDA as people have deontological responses to actions but rarely for omissions16. Greene explains that the myopic constraints of the automatic processes are incapable of predicting what one could allow to happen. This is because there are countless things that one is not doing and thus allowing to happen, whereas there are far fewer things someone can actually be doing at a given time.

At present, there are no software models of moral judgment implemented in simulations of spiking neurons. Ideally, developing models of competing moral judgment theories and comparing them to participant responses and response times would yield an understanding of what theory is most neurally plausible. The present research aims to make preliminary steps toward this analysis by modelling important aspects Greene’s myopic module hypothesis. The objective of this study is to build a simple model of Greene’s myopic module hypothesis using simulated spiking neurons. This work is described as a simple model because it only models the most common judgment to a given trolley problem rather than both judgments. This is because divergent judgments from the norm are hypothesized to involve additional brain regions serving as control mechanisms9, or cases of atypical damage such as lesions to important brain regions10. Specifically, to build this simple model, what is needed is an emotion-reason competition built on top of Mikhail’s action representation model, while being sensitive to the distinctions between the switch, footbridge, and loop dilemmas. When presented with any of the three dilemmas the model will represent the action one might take and the action’s subsequent causal chains. Then the model will identify the morally relevant information for the utilitarian and deontological judgments, when present, and make the final judgment predicted by the myopic module hypothesis for the given dilemma.

Model

The neural engineering framework (NEF) and semantic pointer architecture (SPA)

The present model was implemented in the previously developed Neural Engineering Framework (NEF) and Semantic Pointer Architecture17,18. The NEF is a general framework for building scalable, biologically plausible neural software models of cognition. The NEF acts as a neural compiler. The programmer specifies the properties of neurons, the values to be represented, and the functions to be computed. The NEF calculates appropriate connection weights between its simulated neurons so the desired functions can be approximated19. There are two main purposes of the NEF. First, by using realistic neuron simulations, one can better evaluate cognitive theories not just by producing the correct behavior but also by producing it in a way that is consistent with neuroscience constraints. Second, the NEF suggests new types of algorithms to be used. This is because the NEF does not perfectly implement the algorithm one specifies, rather, the neurons approximate the algorithm described by the modeller. The accuracy of these approximations is determined by both the neural properties and the functions being computed–generally, the more neurons are in the simulation, the better the input algorithm is approximated. This is because the NEF only allows one to use algorithms that are available to neurons (such as addition, subtraction, and circular convolution), which in turn allows one to make strong claims regarding what algorithms are being used in the human brain19.

The Semantic Pointer Architecture (SPA) is a framework built in the NEF for modelling cognitive phenomena in a neurally plausible way. The SPA has been used to build Spaun, a large-scale model of the brain that is capable of performing different cognitive functions without being reprogrammed20. Before describing the model, a short description of the SPA is provided based on previous work21.

Using the SPA, all components of the model can be built using biologically realistic simulated spiking neurons (The model in this paper uses a Leaky Integrate-and-Fire (LIF) neuron model). Each subsystem in the SPA is typically used to correspond to a particular brain region, with the synaptic connections between them optimized to compute some function. A group of neurons performing some function is known as an “ensemble.”

For example, a common function for these ensembles is a buffer. The purpose of a buffer is to store a value over time. A buffer is an ensemble of neurons that will represent some value even if the ensemble is receiving no new input. The SPA accomplishes this by expressing it as a differential equation: the value represented is x, with the input to the group of neurons as u. For the neurons to function as a buffer, they should continue to store x even after u is equal to zero. In the presence of input to the ensemble the stored value will change proportional to the input, but once a value is being stored it should not change, even after the input has stopped. This can be expressed mathematically as (dx/dt)=u where (dx/dt) represents the difference d of x over the difference in time t.

Representation as a differential equation allows x to be approximated by any group of spiking Neurons using the NEF17. This is done through the generation of random ‘tuning curves’ for each neuron, each of which specifies its spike rate for a given value of x. For example, a neuron might fire slowly when x is close to zero and speed up as the value of x increases. The random generation of these ‘tuning curves’ are consistent with empirical data of neuron firing patterns.

Suppose we want to approximate a function in neurons, say, to transform value x into value y, such as y=2x. We create two neuron ensembles, one to represent x and one to represent y. These ensembles are connected with synapses. Given an input function (y=2x), the NEF will generate a set of connection weights between ensembles such that given some value of x, the second ensemble will represent 2x. Thus the neurons will approximate the computation y=f(x). To store a value in a buffer, the NEF allows for the approximation of any function with recurrent connections by computing the function with dx/dt=f(x,u). What this means is that an ensemble of neurons can have a set of connection weights from its output to its own input such that it can compute functions on its own values.

Since x can be a vector (where each dimension of the vector corresponds to the activation level of a neuron in the ensemble) the NEF allows for the implementation of a Vector Symbolic Architectures (VSAs)22 to allow its neuron models to manipulate vectors. VSAs are a set of algorithms that allow for structured and/or unstructured symbols to be represented as high-dimensional vectors. For example, a trolley could be represented as a randomly assigned vector with no relation to other vectors in the VSA or similar terms, such as “train,” which also runs on tracks and can be used to transport people and could have a similar vector (the similarity of vectors can be calculated with a cosine similarity). These vectors are called semantic pointers in the SPA, the name comes from an analogy to the computer science data structure of a pointer. Where a vector of neuron activation points to a symbol that is being represented by that ensemble. Additionally, the operations mentioned earlier (addition, subtraction, and circular convolution) can be used on both structured and unstructured vectors to manipulate the information in functionally useful ways.

To associate two things, such as “brown” and “dog” to get a combined concept of “brown dog,” the two vectors can be combined to create a new vector. VSAs use binding operations to combine vectors in a structured way. If two features are bound to the same object, they are combined with circular convolution, represented by the symbol ⨂, meaning the VSA used is a holographic reduced representation.23, HRR This is typically used to bind a slot (such as colour) and a value (such as blue.)

Further, two concepts can be combined in a single vector by adding them together, represented with the + symbol, which simply adds two vectors together. This is used when you want to combine aspects of two different objects in the same scene. For example, imagine we want to represent the morally relevant information of the initial description of a trolley problem where five people get killed but the bystander is unharmed due to omission of action. We can represent this as runaway_trolley = lives_lost ⨂FIVE + lives_saved ⨂ one.

Importantly, VSAs also define an operation to unbind vectors that have been combined together into new vector, as mentioned earlier. This can be done by binding the combined vector with the inverse of one of the vectors it was combined with. This is denoted as x-1, where x can represent any semantic pointer. This can be used for things such as determining associated representations. As an example say you have a green⨂leaf+red⨂strawberry, and you want to isolate the colour of the strawberry, then you can do an inverse circular convolution of the strawberry SP. This would look like strawberry-1⨂(green⨂leaf+red⨂strawberry)=red+noise.

The SPA uses vectors as a generic representation for passing information between different subsystems. This is because any ensemble of neurons can be used to approximate the vectors corresponding to any given semantic pointer. Thus sensory modules can convert stimuli into vectors, and then send the vectors to working memory buffers, which can perform some set of functions on them, changing the vectors, before sending them to a motor module, which can convert the vectors into muscle movements. However, modelling this also requires an additional system to control the flow of information. To do this effectively, a method modelling the cortex-basal ganglia-thalamus loop controls the flow of information between the subsystems24. This loop acts as an action selection and execution system. Neural connections from all over the brain connect into the basal ganglia, which then selects which action to take. This can be done with conditional statements in the form of if-then rules or it can compute the utility of each action and then execute production rules. Once an action is selected this information is passed onto the thalamus, which suppresses the neurons for every action except the action that is selected. An action is selected every 50 ms. The information flow control system can also be used for gating the flow of information between two subsystems. This occurs by sending an inhibitory signal to every neuron in the subsystem that you are looking to gate the flow of information too25.

Myopic emotion-reason dilemma judgment (MERDJ) model

Overview

Creating a complete model of human moral judgment would require modelling many aspects of human cognition, including emotional, sensory, and perceptual functions, which are beyond the scope of the current research. We propose a model that takes as input descriptions of any of the three dilemmas (switch, footbridge, and loop). The model converts them into an action representation resembling Mikhail’s act-tokens and their causal chains7. Then it identifies the morally relevant information for both a utilitarian judgment and a myopically-constrained deontological judgment. These judgments compete for affecting the utility estimates of the available actions (to sacrifice one person to save five, or to do nothing.) Our Myopic Emotion-Reason Dilemma Judgment (MERDJ) model uses Nengo (a software architecture that uses the NEF and SPA) to implement this model in simulated spiking neurons.

The MERDJ model has five main processes: the conversion of the input dilemma descriptions into high-level representations of act-tokens (a representation of an action as performed by a particular agent at a particular time), the construction of causal chains of act-tokens and the effects these actions have on the world, a utilitarian calculus, a myopic emotional response system, and a judgment formed on reason-emotion competition.

The five processes are built using five components of SPA and the NEF: a working memory system that stores the act-tokens and current hypothetical circumstances as semantic pointers (SPs) (see Appendix A), an associative memory that matches act-tokens and circumstance to the next act-token (see Appendix B), a set of Nengo neuron ensembles that performs utilitarian calculus, an action selection mechanism used to control flow of information between different states, and finally an emotional response that alters information flow depending on the emotional state (Fig. 2). Additionally, Fig. 3 offers a high-level description from the model input, the information being processed, to the model output.Fig. 2 This is a functional visualization of important aspects of the model. Areas surrounded by black outlines refer to specific subsystems that perform different processes or act as memory buffers. Sections with a green background are working memory sates, blue background corresponds to associative memory, and red corresponds to subsystems important for the emotional response. The arrows between subsystems show important information flow between subsystems through binding operations and action selection mechanisms.

Fig. 3 A High-level description of the processing in the model from input to output. It begins with a Semantic Pointer being input to the model to inform which dilemma is being presented. Next, the primary causal chain of the action is processed for any intentional harm in the action and the number of lives saved and lost as a result of the action. If the causal chain branches off then a secondary causal chain of the branched action is processed, only for lives saved and lost as a result of the action. Next the difference between lives saved and lives lost is calculated. Finally, both the difference between lives saved and lives lost and the detection of intentional harm is used to form a judgment that will be output.

Input

In terms of functionality, the SPs that represent the inputs are randomly generated vectors and have no structured relation to any information that is present in the dilemmas as presented to experimental participants. Although this is unrealistic, for trolley problems making this more realistic would require a conversion from written language and diagrams (which is typically how they are presented in experiments) into neurally plausible simple and complex act-token representations7. Doing this would require natural language processing and visual sensory processing that is beyond the scope of the current model.

The MERDJ takes four different SPs as inputs. The first, switch_d, represents the switch trolley dilemma where a person may pull a switch to divert a trolley bound to run over a group of people, but in doing so the trolley kills someone else5. The second, footbridge_d, represents the footbridge trolley dilemma, where one has the option to push someone in front of the trolley to stop it before it kills the person on the tracks6. The third, loop_d, represents a similar scenario to the switch case, but the sidetrack loops back onto the main track before the group of five people, and the trolley gets stopped when it hits and kills the bystander on the second.track6 This is important because it creates causal necessity for the death of the person on the sidetrack to save the larger group16. Finally, trolley_scene represents the initial scene of the runaway trolley, which comes at the beginning of every dilemma and is the same for each of them. The reason we chose to keep this separate from the dilemma representation is that when trolley problems are presented to the participants they all start with the same simple description of circumstances (there is a runaway trolley and it is going to hit five people) before diverging into the specifics of each individual dilemma. Thus, evaluations of the circumstances are likely being made before they are given any action to judge. Furthermore, to properly model the effects predicted in Greene’s myopic module hypothesis, one must model how the myopic emotional response is only sensitive to actions and does not respond to omissions, as is the case when the initial trolley scene is described. The input function presents the trolley_scene SP before presenting each dilemma to show this effect.

Conversion rules are used to transform inputs into high-level representations of the circumstances, and actions in the case of the dilemmas. This is accomplished using if-then rules via the cortex-basal ganglia-thalamus loop. The four if-then rules are:If (input = trolley_scene)

Then (Circumstance = runaway_trolley)

If (input = switch_d)

Then ((Circumstance = runaway_trolley_on_side_track) &

(Primary Causal Chain = pull_switch))

If (input = footbridge_d)

Then ((Circumstance = runaway_trolley_under_footbridge) &

(Primary Causal Chain = push_bystander))

If (input = loop_d)

Then ((Circumstance = runaway_trolley_on_loop) &

(Primary Causal Chain = pull_switch))

Action representation and causal chains

The MERDJ model represents actions based on the act-token representation structure described by Mikhail7, which built on the previous works of Goldman26 and Katz27. In Mikhail’s hypothesis, visual or descriptive stimuli go through conversion rules to become act-tokens, that take the general form of:[S'sV-ingatta]C→[S'sU-ingattb]

Which is to say Agent (S) doing something (V-ing) at a given time (ta) in a particular circumstance (C) causes the agent to do something else (U-ing) at a different time (tb) (circumstances may be affected by the action as well, although that does not occur in this model because the circumstances of a given dilemma are represented by a single SP). These act-tokens chain together to represent the action as a whole. In Mikhail’s work he shows that this action representation can be modelled by tree diagrams of the causal chain, which he calls action trees. The act-tokens used in MERDJ are structured using the SPA architecture at a high-level, only containing the morally relevant information for the utilitarian calculus and myopic automatic response in a 

slot ⨂ value structure.

Act-tokens are represented with three buffers: two working memory buffers (Circumstance Buffer and Primary Causal Chain), and one associative memory (Primary Entailments). Random vectors are generated to function as the SPs for the current circumstance and action. The SPs that represent the circumstance are stored in the Circumstance Buffer. The SPs that represent the current action are stored in the Primary Causal Chain. Next the SPs in the Circumstance Buffer and Primary Causal Chain are bound together (with circular convolution) into a new vector and projected to the Entailments. This reflects the first part of Mikhail’s act-token ([S’s V-ing at ta] C), as it takes the current action and binds it to the circumstance, thus representing the agent doing something at a particular instance in time. The associative memory matches the bound vector to the next act-token (see Appendix B). This association fulfils the causal necessity of action (→ [S’s U-ing at tb]), how the agent doing something at a particular time causes the agent to be doing something at the next time step.

The difference in time steps between act-tokens is accounted for by the simulated time it takes for the association to occur, caused by the NEF’s simulation of neurons and the synapses between them. Once a match is made in Primary Entailments the associated SP (the next act-token) is projected into Primary Causal Chain.

With a new SP in the Primary Causal Chain, that represents the next causally necessary entailment of an action, that SP is once again bound to the SP in the Circumstance Buffer and the bound vector is projected to the Primary Entailments again. This loop continues until there are no more act-tokens associated by the binding of the current circumstance to the act-token in the Primary Causal Chain.

Put simply, this loop has the act-tokens appear in causal order within the Primary Causal Chain at different times. Thus, the act-tokens in the Primary Causal Chain can be modelled as a tree diagram the same as they would in Greene’s myopic module hypothesis. For example, in this model, when the Circumstance Buffer contains the SP runaway_trolley (the scenario found in both the switch and loop dilemmas) and the Primary Causal Chain contains the SP pull_switch (first action in both switch and loop dilemmas) the two are then bound into a single vector represented as runaway_trolley ⨂ pull_switch. The associative memory, Primary Entailments, then matches to another act-token, in this case align_track, representing the effect of pulling the switch and realigning the track from the main track to the sidetrack. Primary Entailments then sends the new act-token to the Primary Causal Chain.

In Greene’s theory, as causal chains become more complex and actions cause multiple things to occur at the same time, such as putting the trolley on a sidetrack (which saves the five people along with changing its track), a secondary causal chain branches off the primary chain to represent these more complex act-tokens along with their unintended side effects16. There are two condition statements used to start the secondary causal chains, that take the form of:If (Primary Causal Chain = turn_trolley)

Then (Secondary Causal Chain = trolley_on_second_track)

If (Primary Causal Chain = stop_trolley_with_bystander)

Then (Secondary Causal Chain = upset_family)

The first condition statement represents the act of turning the trolley onto the side-track in both the switch and loop dilemmas. The second of these rules represents a possible side effect that one might predict in the case of the footbridge trolley problem. Greene16 uses the example of an upset family as a possible side effect of stopping the trolley with a bystander. Once the Secondary causal chain is started it uses the same system to run through causal chains as the Primary Causal Chain), by binding the SPs of the Secondary Causal Chain and the SP in the circumstance state with its own Secondary Entailments associative memory that the newly bound vector get projected to.

Utilitarian calculus

Throughout Greene’s work on moral judgment, he has provided ample evidence that deliberate moral reasoning tends to reflect utilitarian calculus and result in utilitarian responses4,11,1,12. Greene16 argues that unlike the myopic functionality in the automatic emotional response characteristic of deontological judgments, deliberate reasoning has full access to everything from omissions of actions, side effects and of course the causally necessary act-tokens in the Primary Causal Chain. To functionally implement this into the model synapses perform an inverse binding of the SP lives_lost and lives_saved from all of the working memory states (Circumstance), Primary Causal Chain, Secondary Causal Chain into there own states (one for the lives lost and lives saved). Due to the slot ⨂ value structure used this works to unbind the relevant number, as a SP, from the act-token it is stored in. From there they are converted to numeric representation using the NEF, where the lives lost are subtracted from the lives saved. Then, if the number of lives saved is greater than the number of lives lost, then the information is converted back to a SP called good_result. If the number of lives lost is greater, the information will be converted back to a SP called bad_result, if the subtraction is between zero and one life saved or lost no information is converted back into SP as there is no morally relevant result in the utilitarian calculus. Once the information is converted back it is sent to the predicted outcome state.

Myopic emotional response

Before discussing how the myopic emotional response is built into the model, it is important to address how emotions are represented. The POinters EMotions (POEM) theory was developed by Thagard and Schöder28 and further expanded in Kajic et al.29 to show how many aspects of emotions can be represented in spiking neurons as semantic pointers. The POEM theory represents 23 different emotions as semantic pointers on a three dimensional vector. The three dimensions are evaluation (goodness versus badness), the potency (how powerful the emotion is), and activity (liveliness versus torpidity) making the 23 emotions modelled by POEM exist in what is called the EPA space. In the myopic module hypothesis there is no claim made as to what specific emotion is driving the override of deliberate reasoning, only that one dimension of emotions, a negative emotional response to violence has this ability16. For the purpose of this model the focus is put specifically on the first of these dimensions, evaluation, and only two semantic pointers are made, negative and neutral_or_positive. For ease of modelling both of these SPs exist in the same dimensional space as the other semantic pointers, and are randomly generated, rather than having their own separate representational space related to cultural effects of language and embodied effects on emotions as they do in POEM.

The myopic automatic emotional response can be broken down into two smaller processes. First is the myopic harm detection. As Greene’s hypothesis suggests we have a harm detection module16 or domain general process30 that identifies prototypical violent actions taking place in the Primary Causal Chain as described in more detail earlier. This myopic detection is modelled by a synapse projecting from the Primary Causal Chain to the harm detection state that performs an inverse binding of the SP slot HARM. When the value of prototypical_violence is bound to the HARM slot in the Primary Causal Chain the information is sent to the harm detector state where it then triggers the emotional response.

The emotional response is modelled by creating a second cortex-basal ganglia-thalamus loop, which alters the emotional state between neutral_or_positive and negative based on what SP or lack thereof is in a harm detection state. A second cortex-basal ganglia-thalamus loop runs condition statements for switching between emotional states. This is used in place of an amygdala model to control emotions, as this has not yet been developed in the SPA (Terrence Stewart, personal communication). The idea of using a secondary information control system to model the amygdala and the functional effects of emotions on deliberate actions associated with it has been suggested by West and Young31. Further, West and Young discuss how one of the functional properties of the amygdala is to work as an alarm system used to detect threats. In the present model, the emotional response uses two condition statements, which can be described as:

If (Harm Detection ≠ prototypical_violence)

Then ((Emotional Response = neutral_or_positive) &

(Predicted Outcome Clean Up’s output ⨂ action >> Judgment))

If (Harm Detection = prototypical_violence

Then ((Emotional Response = negative) &

(Predicted Outcome Clean Up’s output ⨂ action ‖ Judgment))

The symbol >> is used to represent a gated connection that is open where the information can flow from one buffer to the other, whereas ‖ is used to represent a closed gated connection where the information from one buffer cannot be projected to the other. Additionally the term ‘clean up’ refers to associative memories used to reduce noise and strengthen the representation of the SP. The first condition statement occurs when the SP prototypical_violence is not being represented in the harm detection state. This is treated as the baseline emotional state and will activate when the model begins to run. When this condition statement is met it alters the Emotional Response to a neutral or positive state and opens a gated synapse that allows for information to flow from the predicted result to the judgment (more on this in section). This condition statement as a whole represents no emotional response to the dilemma allowing information to flow freely between the utilitarian calculus and the judgments state. The second condition statement rule is used to represent an emotional response to the dilemma and its effects on judgment, by changing the emotional state to negative and closing the gated synapse (blocking) between the utilitarian calculus and the judgment.

Judgment

Finally, to model the emotion-reason competition for moral judgments, action selection mechanisms are used to alter the flow of information to the judgment state through gating synapse connections (gating mechanism). This can be thought of as the cortex basal-ganglia thalamic loop as controlling the flow of information to cortical regions such as the dlpfc and vmpfc based on information in those or other cortical regions. The first gate uses the main cortex basal-ganglia thalamus loop to prevent the utilitarian calculus from affecting the judgment before the end goal of the action chain is reached. This is done because utilitarian ethics falls under the family of consequentialism theories, for which the moral value of the final outcome is what is important for moral decisions. Because the goal state in all of the dilemmas is the same, saving the five people represented as the SP save_five_people. This condition statement takes the form of:If (Primary Causal Chain = save_five_people)

Then (Predicted Outcome’s output ⨂ action >> Predicted Outcome Clean Up)

When the Primary Causal Chain reaches the end of the action (the goal), a gated synapse connects the predicted outcome to a clean up memory. This synaptic connection binds the predicted outcome to the SP action, which is used to identify that this outcome is caused by an action and should be judged as such. This is done to follow the myopic module hypothesis, which includes sensitivity to the action/omission distinction. The second gate is controlled by the emotional response condition statement, as previously mentioned. When the emotional response condition statement that sets the emotional state to neutral_or_positive is active, the gate between the predicted outcome cleanup memory is opened, preventing utilitarian judgments from being affected by emotions. However, when the emotional response is set to negative the condition statement rule simultaneously closes this gate, preventing the utilitarian reasoning from affecting the judgment, simulating an emotional override.

A set of synaptic connections projects from the emotional response to the judgment state, which produces the deontological judgment represented by the SP inappropriate. There is no gating mechanism on this synapse allowing it to automatically affect the judgment when the emotional response is triggered regardless of the predicted outcome. Importantly, even during the deontological judgment the model still produces a predicted outcome. Although this does not affect the judgment it is consistent with Greene’s theory16, which suggests that even when one makes a deontological response to a trolley dilemma, they are aware that the outcome of acting will save more lives.

Methods and results

The data collected in Greene12 show that the typical response to the switch dilemma is 87% approval of the action (it is appropriate to sacrifice the one life for the five). For the footbridge dilemma, 31% approve of acting. While the loop dilemma has 81% approval, this was not statistically different from the switch results. The goal of the MERDJ is to output the most common response to each of the three dilemmas, rather than a distribution of participant responses, through the use of act-token representation and emotion-reason competition. The ideal output for MERDJ should be to respond with approval of acting (represented by the SP ‘appropriate’) for both the switch and loop, while disapproving of the action (represented by the SP ‘inappropriate’) in response to the footbridge dilemma.

When the model is run the working memory buffers and associative memory modules represent each act token in succession as causal chains to form whole actions, following the order each act token appears in Fig. 1. Previously these two modules have been used to model action planning for simple tasks but have not been used to model causal chains of hypothetical actions and their consequences. These causal chains are shown in the top part of Fig. 4. Where the y-axis refers to the cosine similarity of the activity in the working memory buffer and the SP for each act-token. The x-axis shows the simulated time. In the case of the footbridge dilemma, the judgment takes less time to reach relative to the onset of the dilemma. This is likely due to the nature of the myopic emotional response, which activates as soon as harm has been detected in the primary causal chain. This occurs in the second act-token in the causal chain whereas the only morally relevant information in the primary causal chain for the other two dilemmas is saving the people in the last act-token.Fig. 4 The x-axis shows time in ms, the y-axis shows the similarity between the representation of the state and the set of SPs shown on the side of the graph. The larger the value of y the more similar the simulated neurons in the state are to representing the SPs. The top graph displays the similarity of the act-token SPs to the activation of the simulated neurons in the Primary Causal Chain state. The bottom graph shows the similarity between the judgment SP activations in the judgment state. As predicted, both the Switch and Loop Dilemmas resulted in “appropriate” (blue) and the Footbridge Dilemma resulted in “inappropriate” (green). These two graphs are made by projecting both the primary casual chain and the judgment state to a clean up memory used to remove noise along with the similarity to unrelated semantic pointers (approximately 0 for all other SPs) to make it easier to follow. Boxes are placed around each dilemma for clarity.

Discussion

Model output

Based on the results the model worked as it was designed to: MERDJ successfully identifies the morally relevant information from an act-token causal chain form of action representation and makes the same moral decisions most people do in these dilemmas. The method used to represent Mikhail’s causal chains is inspired by work done modelling action planning32. Additionally, the MERDJ model builds on that work by extracting relevant contextual information using the SPA from the causal chain to perform a seemingly separate cognitive task of moral judgment. By doing so the MERDJ model shows how general-purpose and established spiking neuron modules such as working memory and associative memory can be used to model theoretical models of how moral judgment works. The findings here are important for unification, a common goal in cognitive modelling, as being able to use the same working memory system with different additional components to perform two distinct cognitive phenomena supports a generality claim for that system of working memory, the same is true for the associative memory in this model. The work done by MERDJ shows how Mikhail’s and Greene’s theoretical models of how moral judgment occurs cognitively are neurally plausible and that these models do not require significant changes to the current functional neural modelling systems to be modelled. The latter point is important as it expands the scope of cognitive phenomena that neural modelling is capable of modelling. Ideally, with many different theories of moral judgment being modelled in spiking neurons further analysis comparing them to human reaction time and accuracy could be done. Whichever model has the closest reaction time and accuracy will likely be the model that best represents how humans process information and form a moral judgment.

Interestingly, there is a difference in response time between the deontological response (SP ‘inappropriate’) and utilitarian responses (SP ‘appropriate). The deontological response occurs on average 192ms after the stimulus is presented whereas the utilitarian responses occurs at an average of 287ms after onset of the stimulus, despite the footbridge dilemma having a longer Primary Causal Chain (deontological response occurs in response to the footbridge). MERDJ was not designed so that deontological responses would occur faster than the utilitarian responses; this outcome was an emergent property of the model. This is an interesting result because in Greene’s reaction time study11, they found no average difference between the two response times across all participants. However, when participants are divided into groups of high-utilitarian (chose utilitarian response in most dilemmas) and low-utilitarian (chose deontological response in most dilemmas) it was found that the low-utilitarian group made utilitarian choice slower than the deontological option in absence of load. Using the tool PlotDigitizer, which is nearly perfect in its reliability33, a graph found in Greene11 was converted back into its data points. The result of this found that the low-utilitarian RT for deontological judgments was on average 5673ms and the RT for utilitarian judgments was an average of 6258 ms11. The difference in RT between the deontological and utilitarian judgments for MERDJ is 95ms with the difference at a ratio of 0.67. For Greene’s11 low-utilitarian participants the difference was 585ms and the ratio of the difference is 0.91.

The observed difference between MERDJ and Greene’s11 participants is likely rooted in the narrow scope of the MERDJ model focused on replicating the most common responses to each dilemma. With this focus act-token representation only contained simple and morally relevant information. Thus, processes such as the generation of act-tokens via natural language processing, detailed action and circumstance representation, and the possible mental imagery of the dilemma were not modelled with MERDJ. Here, these processes are liable to take up most of the reaction time, and because they apply to both the utilitarian and deontological responses, the ratio for the experiments is likely to be higher the those given by MERDJ.

The narrow scope of the MERDJ model serves as its primary limitation. However, it also provides a starting point for future work. To increase the scope of the model, it is important to expand the act-token representation to include all information, not just what is deemed morally relevant, as well as including rules for act-tokens to affect the circumstance. This could be done by combining MERDJ with the Semantic Pointer Architecture: Unified Network (SPAUN), which is the largest functioning brain model and is capable of performing multiple different cognitive functions20. Additionally, the MERDJ model does not account for the variation in responses to dilemmas. Going forward this could be implemented by modelling additional brain regions (such as the anterior cingulate cortex) which are involved with top-down emotional suppression9,34.

This research aimed to take preliminary steps toward neural modelling of the complexity of moral judgment by focusing on Greene’s16 myopic module hypothesis. To accomplish this MERDJ was proposed to model the how the brain might produce the most common response to each of three trolley dilemmas, the switch, footbridge, and loop, according to12. MERDJ successfully completed the task, by representing actions similarly to the proposed action representation from Mikhail7, while identifying the morally relevant information for a utilitarian judgment and myopically identifying harm to form a deontological judgment, then using an emotional response to determine which framework would affect the judgment as described in16. In addition, the model also displayed RT effects that resembled the low-utilitarian group of participants from Greene11. The success of this research shows that the core features of Greene’s16 myopic module hypothesis modelled in MERDJ are neurally plausible, suggesting that this type or moral judgment could occur in human brains.

Supplementary Information

Supplementary Information 1.

Supplementary Information 2.

Supplementary Information 3.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-68024-3.

Author contributions

T.G. wrote the manuscript and built the model, all authors contributed to the editing of the manuscript.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Code availability

The code for this project can be found at https://github.com/Roothalla/Nengo-Trolley. Additionally, we recommend using the nengo GUI to run this model. Instructions can be found here: https://www.nengo.ai/getting-started/.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Greene J Haidt J How (and where) does moral judgment work? Trends Cognit. Sci. 2002 6 517 523 10.1016/S1364-6613(02)02011-9 12475712
Greene, J. & Haidt, J. How (and where) does moral judgment work?. Trends Cognit. Sci. 6, 517–523 (2002).12475712
2. Kohlberg L Stage and sequence: The cognitive-developmental approach to socialization Handb. Social. Theory Res. 1969 347 480
Kohlberg, L. Stage and sequence: The cognitive-developmental approach to socialization. Handb. Social. Theory Res. 347, 480 (1969).
3. Piaget J The moral judgment of the child 1965 Routledge
Piaget, J. The moral judgment of the child (Routledge, 1965).
4. Greene JD Sommerville RB Nystrom LE Darley JM Cohen JD An fmri investigation of emotional engagement in moral judgment Science 2001 293 2105 2108 10.1126/science.1062872 11557895
Greene, J. D., Sommerville, R. B., Nystrom, L. E., Darley, J. M. & Cohen, J. D. An fmri investigation of emotional engagement in moral judgment. Science 293, 2105–2108 (2001).11557895
5. Foot P The problem of abortion and the doctrine of the double effect Oxf. Rev. 1967 5 5 15
Foot, P. The problem of abortion and the doctrine of the double effect. Oxf. Rev. 5, 5–15 (1967).
6. Thomson JJ Killing, letting die, and the trolley problem The Monist 1976 59 204 217 10.5840/monist197659224 11662247
Thomson, J. J. Killing, letting die, and the trolley problem. The Monist 59, 204–217 (1976).11662247
7. Mikhail J Elements of moral cognition: Rawls’ linguistic analogy and the cognitive science of moral and legal judgment 2011 Cambridge University Press
Mikhail, J. Elements of moral cognition: Rawls’ linguistic analogy and the cognitive science of moral and legal judgment (Cambridge University Press, 2011).
8. Awad E Dsouza S Shariff A Rahwan I Bonnefon J-F Universals and variations in moral decisions made in 42 countries by 70,000 participants Proc. Natl. Acad. Sci. 2020 117 2332 2337 10.1073/pnas.1911517117 31964849
Awad, E., Dsouza, S., Shariff, A., Rahwan, I. & Bonnefon, J.-F. Universals and variations in moral decisions made in 42 countries by 70,000 participants. Proc. Natl. Acad. Sci. 117, 2332–2337 (2020).31964849
9. Greene JD Nystrom LE Engell AD Darley JM Cohen JD The neural bases of cognitive conflict and control in moral judgment Neuron 2004 44 389 400 10.1016/j.neuron.2004.09.027 15473975
Greene, J. D., Nystrom, L. E., Engell, A. D., Darley, J. M. & Cohen, J. D. The neural bases of cognitive conflict and control in moral judgment. Neuron 44, 389–400 (2004).15473975
10. Greene JD Why are vmpfc patients more utilitarian? A dual-process theory of moral judgment explains Trends Cognit. Sci. 2007 11 322 323 10.1016/j.tics.2007.06.004 17625951
Greene, J. D. Why are vmpfc patients more utilitarian? A dual-process theory of moral judgment explains. Trends Cognit. Sci. 11, 322–323 (2007).17625951
11. Greene JD Morelli SA Lowenberg K Nystrom LE Cohen JD Cognitive load selectively interferes with utilitarian moral judgment Cognition 2008 107 1144 1154 10.1016/j.cognition.2007.11.004 18158145
Greene, J. D., Morelli, S. A., Lowenberg, K., Nystrom, L. E. & Cohen, J. D. Cognitive load selectively interferes with utilitarian moral judgment. Cognition 107, 1144–1154 (2008).18158145
12. Greene JD Pushing moral buttons: The interaction between personal force and intention in moral judgment Cognition 2009 111 364 371 10.1016/j.cognition.2009.02.001 19375075
Greene, J. D. et al. Pushing moral buttons: The interaction between personal force and intention in moral judgment. Cognition 111, 364–371 (2009).19375075
13. Cushman F Young L Hauser M The role of conscious reasoning and intuition in moral judgment: Testing three principles of harm Psychol. Sci. 2006 17 1082 1089 10.1111/j.1467-9280.2006.01834.x 17201791
Cushman, F., Young, L. & Hauser, M. The role of conscious reasoning and intuition in moral judgment: Testing three principles of harm. Psychol. Sci. 17, 1082–1089 (2006).17201791
14. Hauser M Cushman F Young L Kang-Xing Jin R Mikhail J A dissociation between moral judgments and justifications Mind Lang. 2007 22 1 21 10.1111/j.1468-0017.2006.00297.x
Hauser, M., Cushman, F., Young, L., Kang-Xing Jin, R. & Mikhail, J. A dissociation between moral judgments and justifications. Mind Lang. 22, 1–21 (2007).
15. Mikhail J Universal moral grammar: Theory, evidence and the future Trends Cognit. Sci. 2007 11 143 152 10.1016/j.tics.2006.12.007 17329147
Mikhail, J. Universal moral grammar: Theory, evidence and the future. Trends Cognit. Sci. 11, 143–152 (2007).17329147
16. Greene, J. D. Moral tribes: Emotion, reason and the gap between us and them. Penguin (2013).
17. Eliasmith C Anderson CH Neural engineering: Computation, representation, and dynamics in neurobiological systems 2003 MIT Press
Eliasmith, C. & Anderson, C. H. Neural engineering: Computation, representation, and dynamics in neurobiological systems (MIT Press, 2003).
18. Eliasmith C How to build a brain: A neural architecture for biological cognition 2013 Oxford University Press
Eliasmith, C. How to build a brain: A neural architecture for biological cognition (Oxford University Press, 2013).
19. Stewart, T. C. A technical overview of the neural engineering framework. Univ. Waterloo (2012).
20. Eliasmith C A large-scale model of the functioning brain Science 2012 338 1202 1205 10.1126/science.1225266 23197532
Eliasmith, C. et al. A large-scale model of the functioning brain. Science 338, 1202–1205 (2012).23197532
21. Stewart, T., Choo, F.-X. & Eliasmith, C. Sentence processing in spiking neurons: A biologically plausible left-corner parser. In Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 36 (2014).
22. Gayler, R. W. Vector symbolic architectures answer Jackendoff’s challenges for cognitive neuroscience. arXiv preprint cs/0412059 (2004).
23. Plate TA Holographic reduced representations IEEE Trans. Neural Netw. 1995 6 623 641 10.1109/72.377968 18263348
Plate, T. A. Holographic reduced representations. IEEE Trans. Neural Netw. 6, 623–641 (1995).18263348
24. Stewart, T. C., Choo, X., & Eliasmith, C. et al. Dynamic behaviour of a spiking model of action selection in the basal ganglia. In Proceedings of the 10th International Conference on Cognitive Modeling, 235–40 (Citeseer, 2010).
25. Choo, F.X. Spaun 2.0: Extending the world’s largest functional brain model. (2018).
26. Goldman AI Theory of human action 2015 Princeton University Press
Goldman, A. I. Theory of human action (Princeton University Press, 2015).
27. Steinberg, D. D. & Jakobovits, L. A. Semantics: An interdisciplinary reader in philosophy, linguistics and psychology (CUP Archive, 1971).
28. Thagard, P. & Schröder, T. Emotions as semantic pointers: Constructive neural mechanisms. Psychological Construction of Emotions 144–167 New York. Guilford (2014).
29. Kajic I Schröder T Stewart TC Thagard P The semantic pointer theory of emotion: Integrating physiology, appraisal, and construction Cognit. Syst. Res. 2019 58 35 53 10.1016/j.cogsys.2019.04.007
Kajic, I., Schröder, T., Stewart, T. C. & Thagard, P. The semantic pointer theory of emotion: Integrating physiology, appraisal, and construction. Cognit. Syst. Res. 58, 35–53 (2019).
30. Greene JD The rat-a-gorical imperative: Moral intuition and the limits of affective learning Cognition 2017 167 66 77 10.1016/j.cognition.2017.03.004 28343626
Greene, J. D. The rat-a-gorical imperative: Moral intuition and the limits of affective learning. Cognition 167, 66–77 (2017).28343626
31. West, R. L. & Young, J. T. Proposal to add emotion to the standard model. In 2017 AAAI Fall Symposium Series (2017).
32. Blouw, P., Eliasmith, C. & Tripp, B. P. A scaleable spiking neural model of action planning. In CogSci (2016).
33. Aydin O Yassikaya MY Validity and reliability analysis of the plotdigitizer software program for data extraction from single-case graphs Perspect. Behav. Sci. 2022 45 239 257 10.1007/s40614-021-00284-0 35342869
Aydin, O. & Yassikaya, M. Y. Validity and reliability analysis of the plotdigitizer software program for data extraction from single-case graphs. Perspect. Behav. Sci. 45, 239–257 (2022).35342869
34. Bryant DJ Wang F Deardeuff K Zoccoli E Nam C The neural correlates of moral thinking: A meta-analysis Int. J. Comput. Neural Eng. 2016 3 28 39
Bryant, D. J., Wang, F., Deardeuff, K., Zoccoli, E. & Nam, C. The neural correlates of moral thinking: A meta-analysis. Int. J. Comput. Neural Eng. 3, 28–39 (2016).
