CogToM: A Cognitive Architecture Implementation

of the Theory of Mind

Fabio Grassiotto

and Paula Dorhofer Paro Costa

Dept. of Comp. Engineering and Industrial Automation (DCA), School of Electrical and Computer Engineering,

University of Campinas, Campinas, Brazil

Keywords:

Autism, Cognitive Architectures, Artiﬁcial Intelligence.

Abstract:

Mind-blindness, a typical trait of autism, is the inability of an individual to attribute mental states to others.

This cognitive divergence prevents the proper interpretation of the intentions and the beliefs of other individ-

uals in a given scenario, typically resulting in social interaction problems. In this work, we propose CogToM,

a novel cognitive architecture designed to process the output of computer systems and to reason according to

the Theory of Mind. In particular, we present a computational implementation for the psychological model of

the Theory of Mind proposed by Baron-Cohen and we explore the usefulness of the concepts of Affordances

and Intention Detection to augmenting the effectiveness of the proposed architecture. We verify the results by

evaluating both a canonical false-belief and a number of the Facebook bAbI dataset tasks.

1 INTRODUCTION

Autism Spectrum Disorder (ASD) (WHO, 1993) is

a biologically based neurodevelopmental disorder,

characterized by marked and sustained impairment in

social interaction, deviance in communication, and

restricted or stereotyped patterns of behaviors and in-

terests (Klin, 2006). ASD prevalence in Europe, Asia,

and the United States ranges from 1 in 40 to 1 in 500,

according to population and methodology used (Au-

gustyn, 2019).

Surveys on interactive technologies for autism

show that the research in the ﬁeld has had a focus on

diagnosis, monitoring, assessment and intervention

tools, interactive or virtual environments, mobile and

wearable applications, educational devices, games,

and other therapeutic devices or systems (Boucenna

et al., 2014; Picard, 2009; Kientz et al., 2019; Jali-

aawala and Khan, 2020). However, we highlight the

lack of computational systems targeted towards indi-

viduals with autism with the express intent of assist-

ing them in real-time with their impairments in social

interactions.

Our understanding is that these systems should be

designed to analyze environmental and visual social

cues that are not readily interpreted by those indi-

https://orcid.org/0000-0003-1885-842X

https://orcid.org/0000-0002-1534-5744

viduals in the spectrum and provide expert advice on

the best alternative for interaction, improving the out-

comes of the social integration for these individuals.

However, the lack of systems like this can be partly

explained by the limitations of the state-of-the-art ma-

chine learning models, which are capable of translate

languages, recognize objects, and spoken speech, but

still struggles to extract the underlying logical, tem-

poral and causal structure from a massive amount of

training data. In this scenario, neurosymbolic artiﬁ-

cial intelligence have emerged as an approach to the

problem. In CLEVRER, for example, the authors

propose a Neuro-Symbolic Dynamic Reasoning (NS-

DR) model that combines symbolic logic reasoning

and neural nets for pattern recognition (Yi* et al.,

2020).

In this work, we also explore the idea of a spe-

cialized visual recognition system that could be at-

tached to a reasoning system. In particular, we focus

on the modeling of a reasoning system that is inspired

by studies on autism, and we explore existing mecha-

nisms that the human mind uses for facilitating social

interaction.

There are several approaches for explaining the

deﬁcits brought about by ASD, including environ-

mental, genetics and cognitive. In one of the cognitive

approaches, Simon Baron-Cohen proposed the mind-

blindness theory of autism (Baron-Cohen, 1997). His

work proposed the existence of the mindreading sys-

546

Grassiotto, F. and Costa, P.

CogToM: A Cognitive Architecture Implementation of the Theory of Mind.

DOI: 10.5220/0010194205460553

In Proceedings of the 13th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2021) - Volume 2, pages 546-553

ISBN: 978-989-758-484-8

tem and established that the cognitive delays associ-

ated with autism are related to deﬁcits in the develop-

ment of such a system.

The mindreading system is directly related to the

concept of Theory of Mind (ToM) as the innate ability

of attributing mental states to oneself and others and

to understand beliefs and desires that are distinct from

one’s own (Premack and Woodruff, 1978).

Later, research has shown that individuals with

ASD show deﬁcits in ToM (Kimhi, 2014; Baraka

et al., 2019; Baron-Cohen, 2001). The deﬁcits can

be demonstrated in a number of test tasks, in particu-

lar false-belief tasks, which are test tasks designed to

evaluate children capacity to understand other people

mental states (Baron-Cohen, 1990).

This work proposes and evaluates CogToM, a

novel cognitive architecture that models the Theory

of Mind, as hypothesized by Baron-Cohen, that pro-

cess inputs provided by an external specialized visual

recognition system. The paper is organized as fol-

lows. In Section 2, we review key concepts related to

cognitive architectures, false-belief tests and the key-

components of the Baron-Cohen mindreading model.

Following, in Section 2 we describe our proposal in

detail. In Section 6 we evaluate our model, using the

bAbI dataset. The paper is concluded in Section 7.

2 RELATED WORK

2.1 ToM in Cognitive Architectures

Not many cognitive architectures have supported

the implementation of the ToM ability up to this

point. Sigma has demonstrated an application for

simultaneous-move games. Polyscheme explored

perspective-taking for robots interaction with hu-

mans. ACT-R has built models of false-belief tasks

and later implemented them on a mobile robot.

Brian Scasselatti PhD thesis (Scassellati, 2001)

proposed a novel architecture called “Embodied The-

ory of Mind” in which he presented psychological

theories on the development of ToM in children, dis-

cussing the potential application of both in robotics

with the purpose of applying psychological models to

the detection of human faces and identifying agents.

Sigma, Polyscheme, ACT-R and others proposed

integrating principles of the ToM to enable speciﬁc

robotic behavior to simulate human-like capabilities

of social and robotic interaction. However, none of

them proposed an Observer-like implementation as

we seek here with the CogTom cognitive architecture

with the express intent of assistance.

Figure 1: The Sally-Anne test for false-belief. Adapted

from (Baron-Cohen et al., 1985), drawing by Alice Gras-

siotto.

2.2 Autism and False-Belief Tasks

False-Belief tasks are a type of task used in the study

of ToM to check if a child understands that another

person does not possess the same knowledge as her-

self. In (Baron-Cohen et al., 1985), Baron-Cohen

and Frith proposed the Sally-Anne test (Figure 1) as

a mechanism to infer the ability of autistic and non-

autistic children to attribute mental states to other peo-

ple regardless of the IQ level of the children being

tested.

The results presented on the article supported the

hypothesis that autistic children, in general, fail to

employ a ToM, due to the inability of representing

mental states. It is thought that this lack of predicting

ability causes deﬁcits in the social skills for people in

the autism spectrum, making it much harder to face

the challenges of social interaction.

2.3 Mindreading

The mindreading model (Baron-Cohen, 1997) (Fig-

ure 2) seeks to understand the human mindreading

CogToM: A Cognitive Architecture Implementation of the Theory of Mind

547

Intentionality

Detector (ID)

Shared Attention

Mechanism (SAM)

Theory of Mind

Mechanism (ToMM)

Eye Direction

Detector (EDD)

Figure 2: The ToM Model.

process by proposing a set of four separate compo-

nents. Intentionality Detector (ID) is a perceptual

device that is able to interpret movement and identify

agents from objects and assign goals and desires. The

Eye Direction Detector (EDD) is a visual system

able to detect the presence of eyes or eye-like stim-

uli in others, to compute whether eyes are directed

to the self or towards something else and infer that

if the eyes are directed towards something, that the

agent to whom the eyes belong to is seeing that some-

thing. The Shared Attention Mechanism (SAM)

builds internal representations that specify relation-

ships between an agent, the self and a third object.

Finally, the Theory of Mind Mechanism (ToMM)

completes the agent development of mindreading by

representing the agent mental states.

3 AFFORDANCES

The concept of affordances has been extended as de-

scribed by (McClelland, 2017) including applications

for affordances in robotics (Montesano et al., 2008),

(S¸ahin et al., 2007) as a process to encode the rela-

tionships between actions, objects and effects.

For CogToM, the concept of affordances has

found use in the analysis of the environment to as-

sign properties to the objects and the environment it

is currently situated.

4 INTENTION DETECTION

Intention Understanding, a requirement for human-

machine interaction (Yu et al., 2015), allows for

robots to deduce the possible human intention by con-

sidering the relationship between objects and actions.

Processing Blocks

Visual

System

Affordances

Beliefs

CogToM Architecture

Positioning

Figure 3: CogToM Cognitive Architecture.

CogToM architecture proposes using external sys-

tems capable of understanding human intention to

augment the environmental analysis it requires.

5 CogToM PROPOSAL

The CogToM architecture (Figure 3) is designed as a

central processing unit to implement decision-making

functionality with the purpose of implementing an AI

Observer. This Observer has the purpose of passing

a false-belief task by implementing the mindreading

model and integrating it with the processing of affor-

dances and intentions. It relies on inputs as an ex-

ternal system, on the form of a visual camera system

capable of identifying agents and objects as well as

its locations, eye direction of the agents, and human

intention.

Affordances are provided to the system as a

database, or rather a dictionary of known properties

of the objects in the scene.

The outputs of the system (Beliefs) are textual rep-

resentations of the mental state of an agent as per-

ceived by the Observer.

For the purpose of simulating the external visual

and affordances system, the required set of inputs are

modeled through text ﬁles.

The system is simulated by processing a set of in-

puts that are tied to temporal steps. These temporal

steps are related to mind cycles and are deﬁned as

“Mind Steps”.

As an example, we will present here the set of in-

puts the system would require for running two mind

steps in the Sally-Anne test on Table 1.

• Mind Step 1: Sally and Anne are in the room.

Basket, box and ball are on the ﬂoor.

• Mind Step 2: Sally reaches for the ball.

Input ﬁle entities.txt (Table 1a) simulate a camera in-

put identifying a scene. The camera system identiﬁes

a list of entities in the scene and if the entity is an

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

548

Table 1: Input tables for the canonical false-belief test.

(a) entities.txt

t Entity Is agent

1 Sally True

1 Anne True

1 Basket False

1 Box False

1 Ball False

2 Sally True

2 Anne True

2 Basket False

2 Box False

2 Ball False

(b) positioning.txt

t Entity Location

1 Sally Room

1 Anne Room

1 Basket Room

1 Box Room

1 Ball Room

2 Sally Room

2 Anne Room

2 Basket Room

2 Box Room

2 Ball Room

t Agent Object

1 Sally Anne

1 Sally Basket

1 Sally Box

1 Sally Ball

1 Anne Sally

1 Anne Basket

1 Anne Box

1 Anne Ball

2 Sally Anne

2 Sally Basket

2 Sally Box

2 Sally Ball

2 Anne Sally

2 Anne Ball

2 Anne Basket

2 Anne Box

(d) affordances.txt

Object Affordance

Box Contains

Basket Contains

Ball Hides

Anne Exists

Sally Exists

(e) intentions.txt

t Agent Intention Object Target

1 Sally None None None

1 Anne None None None

2 Sally ReachFor Ball None

2 Anne None None None

agent. The column t speciﬁes the mind step for the

camera information.

Input ﬁle positioning.txt (Table 1b) simulate a po-

sitioning system capable of identifying the location

of the agents and objects in the environment. The

column t speciﬁes the mind step for the positioning

system information.

Input ﬁle eye directions.txt (Table 1c) simulates

a visual system capable of identifying eye direc-

tion. The information is provided as a triple <

t, Agent, Ob ject > where t is the simulation mind

step, agent is agent name and object is the entity the

agent is looking at.

Input ﬁle affordances.txt (Table 1d) presents “af-

fordances” for each of the entities in the scene. Affor-

dances are an entity’s properties that show the possi-

ble actions users can take with it. For example, a Box

may contain other objects, and a Ball may be hidden.

Affordances, for the purpose of this system, are im-

mutable properties during the simulation timeline.

Input ﬁle intentions.txt (Table 1e) simulates a

camera input identifying a scene, that could be

achieved with human intention understanding video

analysis. The camera system identiﬁes the intention

of an agent based on movement and posture informa-

tion in the scene. The column t speciﬁes the mind step

for the camera information.

Mindreading Model

Agents,

Objects,

Eye

Detection,

Shared

Attention

Affordance Handler

Intention Handler

Affordances

Intention

Detection

Memory SystemBeliefs

Figure 4: CogToM Processing Blocks.

CogToM Cognitive Architecture consists of four main

processing blocks (Figure 4), the Mindreading Model,

an Affordance Handler, an Intention Handler and a

Memory System. The processing blocks create inter-

nal representations of the input data (Table 2).

The Mindreading Model block implements the

four major modules (ID, EDD, SAM, ToMM) as de-

ﬁned by Baron-Cohen (Baron-Cohen, 1997).

ID is responsible for identifying agents and ob-

jects in the scene and takes as input Table 1a data,

creating an internal representation as the agents Table

2a.

EDD takes as input Table 1b data and creates in-

ternal representations: an Entity Table for the agents

and objects, an Eye Direction Table that reports which

agents are seeing what objects (a value of 1 indicates

CogToM: A Cognitive Architecture Implementation of the Theory of Mind

549

Table 2: Internal model tables for the canonical false-belief test.

(a) ID Agents Table

Agents

Sally

Anne

(b) EDD Entities Table

Entities

Sally

Anne

Basket

Box

Ball

Sally Anne Basket Box Ball

Sally 0 1 1 1 1

Anne 1 0 1 1 1

(d) EDD Agent Registry

Agent Registry

Sally Anne Basket Box Ball

Anne Sally Basket Box Ball

(e) SAM Shared Attention Table

Object Agent 1 Agent 2

Basket Sally Anne

Box Sally Anne

Ball Sally Anne

(f) TOMM Beliefs Table

Agent Belief Object Affordance Target

Sally Believes Anne Exists None

Sally Believes Basket Contains None

Sally Believes Box Contains None

Sally Believes Ball Hides None

Anne Believes Sally Exists None

Anne Believes Basket Contains None

Anne Believes Box Contains None

Anne Believes Ball Hides None

(g) Observer Beliefs Table

Agent Belief Entity IS AT Target

Observer Knows Sally IS AT Room

Observer Knows Anne IS AT Room

Observer Knows Basket IS AT Room

Observer Knows Box IS AT Room

Observer Knows Ball IS AT Room

the object is in the visual ﬁeld of the agent, 0 other-

wise), and an Agent Registry that based in the Eye

Direction Table creates a list of the objects in the vi-

sual ﬁeld (tables 2b, 2c, 2d).

SAM creates three-way relationships between two

agents and one object (or another agent) to assign

a shared attention property to the agents (Table 2e).

SAM takes as input the internal processing results

from ID and EDD.

ToMM creates textual presentations in the form of

Beliefs as a result of the visual processing in the min-

dreading modules (Tables 2f and 2g). There are two

sets of beliefs the Observer generates: beliefs associ-

ated to each of agents in the scene and self-beliefs of

the Observer, such as the location of all entities in the

scene.

The Affordance Handler block is responsible for

processing object affordances and assigning proper-

ties to the objects in a scene from Table 1c data, mod-

ifying the set of beliefs in the ToMM module.

The Intention Handler block is responsible for

assigning outcomes for agent Intentions from Table

1d data and, based on the object affordances, also

modifying the set of beliefs in the ToMM module.

The Memory System block stores the results of

the simulation in the form of textual descriptions, as-

signing the Beliefs of each agent in each simulation

step.

CogToM provides a console system to control pro-

cessing of input for the duration of the simulation.

The console stops the simulation after each mind step

is processed and allows querying the mental states of

the Observer entity.

The mental states of the observer entity are re-

turned as Beliefs, text descriptions of the form:

< AGENT, BELIEV ES/KNOW S, OBJECT,

AFFORDANCE, TARGET OBJECT >

Where:

• AGENT: is the main agent that the mental state

applies to, for example Sally.

• BELIEVES/KNOWS: are the mental states as-

signed to the agent and to the observer. There are

various mental states that could be considered in-

cluding the states of pretending, thinking, know-

ing, believing, imagining, guessing and deceiving.

For the purposes of this system, only the Believe

and Know mental states are initially implemented.

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

550

• OBJECT: is the object of the belief, for example

Ball.

• AFFORDANCE: is the main property, or affor-

dance, of the object, for example a Box may Con-

tain something.

• TARGET OBJECT: is the target object for the

affordance, when applicable. For example, a Box

may contain a Ball.

The console system allows for queries on the mind

state of the Observer entity regarding the agents and

objects of the scene, providing output as the set of

beliefs:

Anne Believes Ball Hides

Anne Believes Basket Contains

Anne Believes Box Contains

Anne Believes Sally Exists

Observer Knows Anne IS AT Room

Observer Knows Ball IS AT Room

Observer Knows Basket IS AT Room

Observer Knows Box IS AT Room

Observer Knows Sally IS AT Room

6 RESULTS

6.1 The Sally-Anne Test

The canonical false-belief test is described by the se-

quence of steps:

Canonical False-Belief Test.

Sally and Anne are in the room. Basket, box

and ball are on the ﬂoor.

Sally reaches for the ball.

Sally puts the ball in the basket.

Sally exits the room.

Anne reaches for the basket.

Anne gets the ball from the basket.

Anne puts the ball in the box.

Anne exits the room, and Sally enters.

Sally searches for the ball in the room.

To assess CogToM’s ability in Sally-Anne test, we

simulated a visual system (Grassiotto and Costa,

2020) that would be capable of generating tables 1a,

1b and 1c (the tables show just the ﬁrst two mind-

steps). Also we simulated a system that is capable of,

given an object, returning typical affordances of the

object, as in Table 1c.

CogToM is able to reply correctly to the belief

question as described in the Sally-Anne test as can

be seen in the console output below, providing the set

of beliefs:

Anne Believes Ball OnHand Of Anne

Anne Believes Basket OnHand Of Anne

Anne Believes Box Contains

Anne Believes Sally Exists

Sally Believes Anne Exists

Sally Believes Ball HiddenIn Basket

Sally Believes Basket Contains

Sally Believes Box Contains

This can be seen in the two mental models for Anne

and Sally:

Anne Believes Ball OnHand of Anne

Sally Believes Ball HiddenIn Basket

Since Sally was not present in the room while Anne

took the ball from the basket and hid it, she still be-

lieves the ball is in the basket. Therefore, the system

we designed is able to pass the false-belief task.

6.2 The bAbI Dataset

In order to measure the performance of the system

with tasks other than the canonical false-belief task,

other scenarios were considered for validation.

Facebook Research proposed The bAbI dataset in

(Weston et al., 2015) as a set of 20 simple toy tasks to

evaluate question answering and reading comprehen-

sion. Two tasks from this set were considered for the

validation of the proposed architecture.

Task 1: Single Supporting Fact.

Mary went to the bathroom.

John moved to the hallway.

Mary travelled to the ofﬁce.

Where is Mary? A:ofﬁce

Task 1: consists of a question to identify the location

of an agent, given one single supporting task (Mary

travelled to the ofﬁce). Input tables for this ﬁrst task

are at Table 3.

In order to handle positioning questions, the sys-

tem uses positioning data at Table 3b to reply cor-

rectly using the Observer beliefs for the location of

agents and objects in the environment.

CogToM was able to respond correctly the location of

the agents, by querying for the Observer beliefs:

Observer Knows John IS AT Hallway

Observer Knows Mary IS AT Office

Task 2: is an extension of task 1 to query the location

of an object that is on hand of an agent. The Observer

CogToM: A Cognitive Architecture Implementation of the Theory of Mind

551

Table 3: Input tables for Facebook bAbI Task 1.

(a) entities.txt

t Entity Is agent

1 Mary True

1 John True

2 Mary True

2 John True

(b) positioning.txt

t Entity Location

1 Mary Bathroom

1 John Hallway

2 Mary Ofﬁce

2 John Hallway

t Agent Object

1 Mary John

1 John Mary

2 Mary John

2 John Mary

(d) affordances.txt

Object Affordance

Mary Move

John Move

(e) intentions.txt

t Agent Intention Object Target

1 Mary Go Self Bathroom

1 John Go Self Hallway

2 Mary Go Self Ofﬁce

2 John None Self None

Task 2: Two Supporting Facts.

John is in the playground.

John picked up the football.

Bob went to the kitchen.

Where is the football? A:playground

Table 4: Input tables for Facebook bAbI Task 2.

(a) positioning.txt

t Entity Location

1 John Playground

1 Bob Playground

1 Football Playground

2 John Playground

2 Bob Kitchen

2 Football Playground

beliefs support the positioning of agents and objects

as can be seen on Table 4. For brevity, only the posi-

tioning table is shown here.

The console output shows the Observer beliefs

with the location of the football.

John Believes Bob Exists

John Believes Football OnHand Of John

Bob Believes Football OnHand Of John

Bob Believes John Exists

Observer Knows Bob IS AT Kitchen

Observer Knows Football IS AT Playground

Observer Knows John IS AT Playground

7 CONCLUSION

CogToM was designed as a platform to validate the

viability for a computational system to pass false-

belief tasks based on implementing a psychological

model of the human mind. The system thus designed

has shown us the need for integrating further informa-

tion about the world in the form of affordances and

human intentions.

The cognitive architecture we proposed is capable

of passing the canonical false-belief task as deﬁned

by researchers in the autism spectrum. The system

was designed in such a way to be generic enough to

allow for testing with simple tasks as described by the

Facebook bAbI dataset.

Even though the main motivation for the design of

CogToM was to explore the viability of designing an

assistant for people in the autism spectrum, our sys-

tem uses text processing at its core for the production

of beliefs. We see that it may ﬁnd applications in the

domain of natural language processing research.

CogToM holds promise as a base system from

which future assistive systems for people in the

autism spectrum can be based on.

ACKNOWLEDGEMENTS

This study was ﬁnanced in part by the Coordenac¸

de Aperfeic¸oamento de Pessoal de Nivel Superior -

Brasil (CAPES) - Finance Code 001.

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