FIELD

[00001] The invention relates broadly to a computerized colour-based, non-verbal neuropsychological testing method.

BACKGROUND

[00002] Experiments in which individuals are required to contemplate psychologically pure-red for varying lengths of time have shown this colour has a decidedly stimulating effect on the nervous system - blood pressure increases, respiration rate and heartbeat both speed up. Red is therefore, “exciting” in its effect on the nervous system, especially on the sympathetic branch of the autonomic nervous system. Similar exposure to psychologically pure-blue on the other hand has the reverse effect - blood pressure fails, heartbeat and breathing both slow down. Dark blue is therefore ‘calming” in its effect and operates chiefly through the parasympathetic branch of the autonomic nervous system.

[00003] The complicated networks of nerves and fibres above by which the body and all its parts are controlled can be grouped under two main headings - the Central Nervous System (CNS) and the Autonomic (or ‘self-regulating”) Nervous System (ANS).

[00004] The CNS can be considered as concerning itself with those physical and sensory functions which occur at, or above the threshold of awareness. The ANS, on the other hand, is concerned primarily with those functions which take place below the threshold of awareness and, for this very reason, must operate on an automatic, self-regulating basis. The beating of the heart, the rise and fall of the lungs, the digestion of food, in fact all complex processes of the body which must continue without any conscious effort are functions of the autonomic nervous system, the fibres from both systems running to each group of organs in which self-regulation is essential.

[00005] The heartbeat, for example, normally occurs at a rate kept within certain bounds by the balance struck between the sympathetic and parasympathetic branches of the ANS; but under the influence of physical effects (e.g., exertion, effort) or emotional effects (e.g. fear, anger, excitement) effects, the sympathetic system will override the parasympathetic and the heart beat will speed up. In general terms, the sympathetic nervous system overrides the parasympathetic nervous system under the influence of excitement, exertion or increased necessity. The parasympathetic nervous system works to restore things to normal when conditions of stress have been removed and is the dominant branch of the autonomic nervous system in conditions of calm, contentment and relaxation. [00006] Even today, the mechanism by which colour is actually “seen” and recognised as colour is imperfectly understood. When a simple question such as, “How do we see colour?” gives rise to so many different theories in the search for an answer, then the chances are that in some way we cannot quite comprehend we are either asking the wrong question or are starting off with some faulty premises. However, the “Contrast Colour Theories” seem to fit most closely with what is actually observed.

[00007] In contrast theories when humans observe “visual purple” ( a substance contained in the rods of the retina within the eye, and also known as “rhodopsin”) is bleached under the influence of bright colours and reconstitutes itself when exposed to dark colours - that is, ‘light’ has a catabolic(breaking down effect), while “dark” has an anabolic (building up, regenerative) effect. According to contrast theory, white subjects visual purple to catabolism and breaks it down; black on the other hand, brings about anabolism and restores visual purple to its original state. The same effects were found to occur with red- green and yellow-blue, resulting in a “contrast effect” applicable to all colours in terms of their brightness or darkness.

[00008] The developing ability to “see” by a newly born child begins by being able to distinguish contrast, that is: “brightness” and “darkness; next comes the ability to distinguish movement, and after that shape and form. The recognition of colour is the last development of all. The distinction of contrast is therefore the earliest and most primitive form of visual perception.

[00009] In humans, the more sophisticated interpretations of what our senses tell us appear to be functions of the more “educated” part of the brain - the cortex. To be able to recognise and distinguish one perfume from another would be a “cortical” function and the result of educating the sense of smell; but the instinctive reaction to a “bad smell” is just that - instinctive reaction to a ‘bad smell” is just that - instinctive and reactive, leading at the least to a nose-wrinkling recoil, and at worst to nausea and vomiting. These are not cortical reactions but arise in centres in the older and more primitive areas of the brain lying more centrally and which are more closely allied to the brains of our evolutionary ancestors.

[00010] Colour vision is similarly related to both educated and primitive brain. Neuroscientists have shown that a network of nerve fibres led directly from a nucleus in the retina to the midbrain (mesencephalon) and to the pituitary system.

[00011] The pituitary is an endocrine, or ductless, gland lying close to the centre of the brain which secretes several important hormones into the blood stream. The importance of this gland is such that it is referred to as “the leader of the endocrine orchestra” and controls the functions of other ductless glands, as well as serving other purposes such as growth control.

[00012] The distinguishing of colour, its identification, naming and any aesthetic reactions to it, are all functions of the cortex; they are therefore the result of development and education rather than of instinct and reactive response. Reflexive and instinctive visual functions on the other hand appear to follow the neural networks to the much more primitive midbrain, operating in terms of contrast and affecting the physical and glandular systems through the pituitary in some way which is still not fully understood.

[00013] It is this last factor - the instinctive response to colour in terms of contrast - which makes colour choice a valid instrument even in cases of defective colour-vision, or even actual colour blindness, since the acceptability of a particular colour is somatically (from Greek “soma” body; somatic therefore means “having to do with the body”) related to the degree to which anabolism or catabolism is needed by the organism. If it is psychically or physically in need of emotional peace, physical regeneration and release from tension or stress, then the instinctive response will be to choose the darker colours. If the organism needs to dissipate energy by outgoing activity or in mental creativeness, then the instinctive response will be for the brighter colours. An examination into the validity of colour choice in the event of colour blindness was carried out using normal controls and individuals suffering from both partial and total red-green colour blindness. The findings of this research show that colour blindness is not an impediment to making colour choices.

[00014] When the light reflected from coloured objects strikes the retinas in our eyes, the wavelengths are converted into electrical impulses. These pass into that part of the brain that rules our hormones and endocrine system, which are instrumental in controlling our moods and emotions. Unconsciously, then, our eyes and bodies constantly adapt to these stimuli, influencing our impulses, perceptions and behaviours (our stored memories). Modern neuropsychology maintains that these stored memories are colour-coded in a colour spectrum and that distinct frequencies of colour and pattern can reactivate synapses in the brain which were previously blocked. Repressed memories of a physical or emotional trauma are held in the hippocampus and amygdala of the limbic system of the brain. These memories can be accessed and treated with the correct colour wavelength, with, for example wearing colour glasses with the recommended colour, and other treatment options. Colours can have two distinct and often opposite effects. Because of the colour coding of emotions, treatment with colour can either trigger the expected colour with a physiological reaction or enable the release of a related colour coded emotion or problem. For example, blue light will usually have a sedative effect, but if a person suffered trauma in connection with someone wearing blue clothing toddler, blue may cause sympathetic arousal (distress) in this person until the trauma is healed. The complexion constantly changes according to the fluctuation of the emotions and the difference in the state of health, for example, blushing or extreme anger may turn the face reddish in colour; cyanosis has a bluish colour, and jaundice has a yellow complexion. Colour influences our emotions, our actions and how we respond to various people, situations and ideas. Reds and yellows stimulate the senses and produce feeling and thoughts of warmth and comfort, whereas blues and violets/purples are associated with coldness. Every colour has its own specific wavelength and frequency, from stimulating and energetic qualities, to calming and more subdued ones. This generates specific biochemical and hormonal responses, which influence the physiology and autonomic nervous system. Certain colours alter mood states and can change patterns of behaviour.

SUMMARY OF THE INVENTION

[00015] Embodiments of the invention employ colour choice testing as the basis of predicting behaviours.

[00016] In an embodiment, there is provided a computerized colour-based psychological testing method comprising: presenting a test interface to a subject candidate via an electronic display device, the test interface comprising a display of a set of colours, and one or more input components to enable candidates to make an ordered selection of at least a subset of the set of colours, the ordered selection comprising a plurality of positions and a colour for each position; receiving, by a computing device, the subject candidate’s ordered selection input via the test interface; processing, with the computing device, the subject candidate’s ordered selection, based on at least one colour pattern derived from colour selections using the test interface by a plurality of candidates matched to at least one test condition for which the subject candidate is being tested; and processing, with the computing device, the allocated values to generate a test outcome for the subject candidate.

[00017] In another embodiment, there is provided a computing device for colourbased psychological testing, the system comprising: a processor; and memory storing computer program instructions which when executed by the processor cause the processor to: control an electronic display device to present a test interface to a subject candidate, the test interface comprising a display of a set of colours, and one or more input components to enable candidates to make an ordered selection of at least a subset of the set of colours, the ordered selection comprising a plurality of positions and a colour for each position; receive the subject candidate’s ordered selection input via the test interface; process the subject candidate’s ordered selection, based on at least one colour pattern derived from colour selections using the test interface by a plurality of candidates matched to at least one test condition for which the subject candidate is being tested; and process the allocated values to generate a test outcome for the subject candidate.

BRIEF DESCRIPTION OF THE DRAWINGS

[00018] Example embodiments are described in relation to the accompanying drawings in which:

[00019] FIG.1 is an example of a system for conducting colour-based testing;

[00020] FIG. 2 is a flow chart of an embodiment;

[00021] FIGs. 3A to 3D show an example test interface;

[00022] FIG.4 is an alternative test interface;

[00023] FIG. 5 is graph illustrating the results of a pool of subject candidate selection using the colour testing;

[00024] FIG. 6 is a graph showing the effect of candidate selection;

[00025] FIG. 7 is an example of a resilience score output; and

[00026] FIGs. 8 to 11 are example extracts from a report generated using team cohesiveness algorithms.

DETAILED DESCRIPTION

[00027] Embodiments of the invention employ colour choice testing as the basis of predicting behaviours. Embodiments of the invention gather/use ordered colour selections from a set of colours or “colour choice spectrum” of individuals matched to desired behaviours in order to set test conditions that can be used to test other individuals to determine if they match the test condition. The colour choice spectrum of an embodiment has 16 colours. Other examples use different numbers colours, for example, 8-16 colours depending on the test with 16 being chosen for the embodiment because of selection fatigue if too many choices are presented.

[00028] FIG. 1 is a block diagram of an example colour-based psychological testing system 100. As shown in FIG. 1 a server computer 110 has a processor 102 and memory storing a candidate database 104; algorithm database 106; and report database 108 stored in memory of the server. As is known in the art, the server computer 110 can be implemented over more than one physical computing device in order to satisfy requirements such as volume, load-balancing, redundancy etc.

[00029] Server 110 is configured to communicate with a plurality of user devices 130A-130C over a network 120 such as the Internet. Each of the user devices 130 has at least one electronic display device on which a test interface can be presented. In some examples, system 100 communicates with at least one administrator device 140 which is also configured to communicate with at least one physiological measurement device 150 such as an electroencephalogram (EEG) device for recording electrical activity of the brain and electrocardiogram (ECG) device for recording the electrical activity generated by the heart. In some examples, such devices 150 may be used to monitor heart rate variability (HRV) during completion of the colour test. In some circumstances, it may be desired to implement all testing via administrator devices in order to have greater control over calibration of the electronic display device. In such implementations, a standalone computing device can be employed rather than using a client server arrangement.

[00030] FIGS. 3A to 3D show an example of a test interface 300 that can be presented via an electronic display device. In an example, test interface is presented to the candidate after they have entered registration details (e.g., name, age, email address, etc) via a user device. In some examples, the candidate is also required to complete other questions, for example, to capture additional information such as whether the candidate has slept well. In some examples such additional information may be used to qualify the candidate’s responses or identify results that are anomalous.

[00031] As shown in FIG. 3A, in this example, the candidate is presented with a test interface 300 in an initial state where it comprises fourteen coloured circles 301 -314 which are different colours. (Note that the numbers 301-314 are labels and not part of the test interface 300.) In this example, the test interface 300 incorporates an input component associated with each circle in that, the test interface is configured to determine which of the circles has been selected by a candidate making an input gesture on the colour (e.g. touching a screen, clicking on the circle with a mouse etc). The colours could be presented in other shapes (e.g., a square), however, circles have the advantage of having the affordance of a button, thus assisting the candidate to comprehend the interface. Above the coloured circles 301 -314 is the instruction “Select the colour you are most drawn to until no colors remain”. Below the coloured circles 301-314 is a submit button 340 to be pressed once all colours are selected. In other examples, alternative input components may be employed, for example, check boxes positioned below each circle.

[00032] In FIGs 3A to 3D the colours are (with hex colour codes in parentheses):

C1 - White (#EBEBEB) 301 ;

C2 - Black (#000000) 302;

C3 - DarkGreen (#808000) 303;

C4 - Grey (#808080) 304;

C5 - Green (#008000) 305;

C6 - DarkBlue (#1 B089B) 306;

C7 - LightGreen (#14E014) 307;

C8 - LightBlue (#42C0FB) 308;

C9 - Yellow (#FFFF00) 309;

C10 - Pink (#FFC0CB) 310;

C11 - Brown (#954A00) 311 ;

C12 - Purple (#B330FF) 312;

C13 - Orange (#FF9E1 F) 313; and

C14 - Red (#E81 B05) 314.

[00033] The above colours were chosen by monitoring subjects using an EEG (HRV) device while observing subjects selecting from a set of 72 Derwent™ colour pencils to complete a task. The subjects were asked to choose any colours they wished to colour an image while being monitored using EEG(HRV) technology. Measurements indicated that colours were stimulating or soothing (relaxing) dependant on their chromatic intensity. If the colour was a deeply saturated colour, it was likely to be stimulating and if it has low saturation, it was likely to be soothing. For example, darker more intensely saturated blues stimulates the mind while lighter less saturated blues can soothe and calm the mind. A strong dark red is physically exhilarating, but a soft light red was found to be physically soothing and comforting. A set of colours were chosen that provided a diverse set of responses were selected by trial and improvement. [00034] FIG. 3B is an example of a first updated state of the test interface 300A following an initial selection. It will be observed that the subject candidate’s first selection was of DarkBlue 306 and that this circle has been removed with the remaining coloured circles 301-305,307-314 re-arranged on the electronic display so that there is no gap between colours. It will be observed that, for example, light green 307 now neighbours Green 305 and Yellow 309 has been moved to the top line. An additional message 331 “Please select all the colours until none remain” is added to remind the candidate of the task.

[00035] In other examples, when a selected colour circle is removed, the other coloured circles are left in place. Alternatively, rather than being removed, selected coloured circles could have their display changed (e.g. by being greyed-out) to indicate that they can no longer be selected.

[00036] FIG. 3C is an example of a second updated state of the test interface 300B after the candidate has made seven selections such that White 301 , Black 302, Dark Green 303, Grey 304, Pink 310, Brown 311 and Purple coloured circles remain.

[00037] FIG. 3D is an example of a final state of the test interface 300C where only the white coloured circle 301 remains.

[00038] In other examples, selection of the final coloured circle (or indeed the second last coloured circle) may act as the submit button.

[00039] As indicated above, alternative test interfaces may be employed. FIG. 4 shows an example test interface 400 with sixteen coloured circles 301-316 comprising the same circles as in FIG. 3A and two additional coloured circles:

C15 - Dark Red (#c70039) 315; and

C16 - Sky Blue (#b1f9ff) 316.

[00040] FIG. 2 is a flow chart of a method 200 of an embodiment. Broadly speaking, the method comprises an algorithm generation phase of steps 205,210 and/or steps 215,220; a candidate testing phase of steps 230-240; and an output phase of step 250 and/or step 255.

[00041] At step 205, the process 200 involves extracting and/or obtaining test data of candidates that match a test condition, such as an observed behaviour. In some examples, the test data can be obtained for candidates that are already know to satisfy the desired test condition. In other examples, all candidates can be tested and have their test data stored in candidate database together with behaviour data so that the candidates can be subsequently matched to test conditions. In some examples, behaviour data may be gathered from other sources (e.g. human resources records) and matched to test data. [00042] In an example, the test interface of FIG.3 was used to obtain results to see if a stable set of colour choices would be shown by individuals. In this example in order to determine whether, at the end of 3 months employment was there a colour pattern of blue collar workers who reported no incidents or accidents (i.e. they remained safe). In some examples, the test interface of FIG. 4 was used with results for the two additional coloured circles discarded for calculation of the algorithm. The use of the two additional colours enabled additional data to be gathered as part of ongoing research. That is, in this example, the colour testing data for candidates who stayed safe was extracted from candidate database 104 and processed at step 210 to obtain an algorithm related to their patterns of colour selection that would be predictive of other candidates that would be likely to stay safe and also of candidates who were unlikely to stay safe.

[00043] In this example, frequency of selection of colours at positions by the “remained safe” cohort was used to assign colours to positions and their relative weighting. In this respect, herein the term “position” refers to the position in the order that the candidate selected a colour so that the first position refers to the first selection made by the candidate (e.g. the Dark Blue 306 in the example of FIG. 3A above).

[00044] In an example algorithm, for each of the first fourteen positions each colour was allocated a variable points score of 0,1 ,2 or 3. The highest variable point score, which is 3, was allocated to the colour or colours which had the highest occurrence at the respective position in the ordered selection. The variable of 2 points is calculated to include the next highest occurrence, and so forth for the last variable point of 1 . Remaining colours are allocated zero points. In this example, a tolerance of one was allowed so that colours that had similar numbers of “hits” were allocated the same score. It will be appreciated that the size of an appropriate tolerance may depend on the size of the sample set. For example, for the variable point of 1 , all possible options were included if the distance between their occurrence is just one. Similarly, if there was a large gap (e.g. 10 hits) between the most common colour selected for a position and the next most common, no colour was allocated two points. Example point allocations are shown below on a position by position basis.

[00045] Position 1 : the analysis showed occurrence as follows:

[00046] Dark Blue- 20 hits in this position (that is, Dark Blue was selected 20 times), Light Blue- 18 hits, Black - 14 hits, Red - 10 hits, White- 6 hits, with all other colours having fewer hits.

[00047] In this case, Var 3(Points=3) was assigned to Dark Blue, Var 2 (Points= 2) for Light Blue and Var 1 (Points=1) for Black. For any other colour occurrence, Var=0 (Points = 0). [00048] Position 2: the analysis showed occurrence as follows:

[00049] Dark Blue- 21 hits in this position, Black- 14hits , Grey- 14 hits, White- 10hits, Red - 10hits, Violet- 9 hits, Orange - 7h its, Yellow- 6 hits , with all other colours having fewer hits.

[00050] In this case, the determined colour pattern was Var 3(Points=3) for Dark Blue,

Var 2 (Points= 2) for Black/Grey and Var 1 (Points=1) for White/Red as well as Violet (since it is just one distance away of 9 hits). For any other colour occurrence, Var=0 (Points = 0).

[00051] Position 3: the analysis showed occurrence as follows:

[00052] Green - 19 hits in this position, Yellow - 17h its , Light Blue- 13 hits , Violet-

12 hits, Pink - 11 hits, White- 10 hits, Light Green - 10 hits , with all other colours having fewer hits.

[00053] In this case, the determined colour pattern was Var 3(Points=3) for Green, Var 2 (Points= 2) for Yellow and Var 1 (Points=1) for Light Blue as well as Violet (since it is just one distance away of 12 hits). For any other colour occurrence, Var=0 (Points = 0).

[00054] Position 4: the analysis showed occurrence as follows:

[00055] Light Blue - 16 hits in this position, Violet- 12 hits , Pink- 11 hits, Dark Blue - 11 hits, Brown - 10 hits, Grey- 10 hits, Black- 7 hits, Red- 6 hits , with all other colours having fewer hits.

[00056] In this case, the determined colour pattern was Var 3(Points=3) for Light Blue, Var 2 (Points= 2) for Violet and Var 1 (Points=1) for Dark Blue/ Pink/ Grey/ Brown (since Grey and Brown are just one distance away of 10 hits). For any other colour occurrence, Var=0 (Points = 0).

[00057] Position 5: the analysis showed occurrence as follows:

[00058] Yellow- 18 hits in this position, Pink- Whits , Violet - Whits, Black- 11 hits,

Red - 10 hits, Green- Whits, Orange-3 hits , with all other colours having fewer hits.

[00059] In this case, the determined colour pattern was Var 3(Points=3) for Yellow/Pink, Var 2 (Points= 2) for Violet and Var 1 (Points=1 ) for Black/Green/Red (since Red and Green are just one distance away of 10 hits). For any other colour occurrence, Var=0 (Points = 0).

[00060] Position 6: the analysis showed occurrence as follows:

[00061] Red- 16 hits in this position, Yellow- 10 hits , Violet- 10 hits, Orange - 10 hits, Dark Blue- 7hits, Pink - 7 hits, Light Green- 6 hits, Brown - 6 hits, LightBlue - 3 hits, with all other colours having fewer hits. [00062] In this case, the determined colour pattern was Var 3(Points=3) for Red, Var 2 (Points= 2) for Violet/Yellow/Orange and Var 1 (Points=1) for Dark Blue/ Pink/ Light Green/Brown (since Light Green/Brown are just one distance away of 6 hits). For any other colour occurrence, Var=0 (Points = 0).

[00063] Position 7: the analysis showed occurrence as follows:

[00064] Dark Green- 17 hits in this position, Light Green- 17 hits , Black- 11 hits, Dark Blue- 11 hits, Pink - 11 hits, Orange- 10 hits, Light Blue-10 hits, Grey- 9 hits, Yellow- 3 hits, Red - 3 hits , with all other colours having fewer hits.

[00065] In this case, the determined colour pattern was Var 3(Points=3) for Dark Green/ Light Green. There is a huge overlap for the next cohort of colours- Black/Grey/ Dark Blue/ Light Blue/ Pink/ Orange which are just one distance away. Hence, they were all selected for Var 1 (Points=1). In this case, no colours were assigned for Var 2 (Points= 2). And for any other colour occurrence, Var=0 (Points = 0).

[00066] Position 8: the analysis showed occurrence as follows:

[00067] Black- 17 hits in this position, Orange- 17hits , Violet- 12 hits, Light Blue - Whits, Yellow- 6 hits , with all other colours having fewer hits.

[00068] In this case, the determined colour pattern was Var 3(Points=3) for Black/ Orange, Var 2 (Points= 2) for Violet and Var 1 (Points=1 ) for Light Blue. For any other colour occurrence, Var=0 (Points = 0).

[00069] Position 9: the analysis showed occurrence as follows:

[00070] Red- 16 hits in this position Grey/ Dark Blue/ Orange - 11 hits, Pink- 7 hits,

Yellow- 6 hits, Brown- 6 hits, Purple- 6 hits, Black-4 hits , with all other colours having fewer hits.

[00071] In this case, the determined colour pattern was Var 3(Points=3) for Red, Var 2 (Points= 2) for Grey/ Dark Blue/ Orange and Var 1 (Points=1 ) for Pink/ Yellow/ Brown/Purple (since Yellow/ Brown/Purple are just one distance away of 6 hits). For any other colour occurrence, Var=0 (Points = 0).

[00072] Position 10: the analysis showed occurrence as follows:

[00073] Dark Green- 17 hits in this position, Brown- 17 hits, Orange- 17hits, Grey- 12 hits, Yellow - 10 hits, Violet- 10 hits, Pink- 7 hits and so on.

[00074] In this case, the determined colour pattern was Var 3(Points=3) for Dark Green/ Brown/ Orange, Var 2 (Points= 2) for Grey and Var 1 (Points=1) for Yellow/ Violet. For any other colour occurrence, Var=0 (Points = 0). [00075] Position 11 : the analysis showed occurrence as follows:

[00076] Dark Green- 17 hits in this position, Orange- 17 hits, Black- 7 hits, Pink-7 hits, Green- 6 hits, Yellow- 6 hits, Brown- 6 hits, Grey - 3 hits , with all other colours having fewer hits.

[00077] In this case, the determined colour pattern was Var 3(Points=3) for Dark Green/ Orange. Clearly, there is a huge overlap for the next cohort of colours- Black/Pink/ Green/ Yellow/Brown which are just one distance away. Hence, they were all selected for Var 1 (Points=1). In this case, no colours were assigned for Var 2 (Points= 2) due to the larger hap of ten hits. And for any other colour occurrence, Var=0 (Points = 0).

[00078] Position 12: the analysis showed occurrence as follows:

[00079] Dark Green - 13 hits in this position, Green- 13 hits, Brown- 13 hits, Light Green- 10 hits , Pink- 7 hits, Grey- 6 hits, Dark Blue- 4 hits , with all other colours having fewer hits.

[00080] In this case, the determined colour pattern was Var 3(Points=3) for Dark Green/ Green/ Brown, Var 2 (Points= 2) for Light Green and Var 1 (Points=1 ) for Grey/ Pink (since Pink is just one distance away of 6 hits). For any other colour occurrence, Var=0 (Points = 0).

[00081] Position 13: the analysis showed occurrence as follows:

[00082] Light Green - 17 hits in this position, Grey- 12 hits, Dark Green- 10 hits, Red- 10 hits , Brown- 10 hits, Black- 7 hits , with all other colours having fewer hits.

[00083] In this case, the determined colour pattern was Var 3(Points=3) for Light Green, Var 2 (Points= 2) for Grey and Var 1 (Points=1 ) for Dark Green/ Brown/ Red. For any other colour occurrence, Var=0 (Points = 0).

[00084] Position 14: the analysis showed occurrence as follows:

[00085] White - 26 hits in this position, Dark Green- 13 hits, Black- 11 hits, Pink- 11 hits, Grey- 11 hits, Yellow- 8 hits, with all other colours having fewer hits.

[00086] In this case, the determined colour pattern was Var 3(Points=3) for White, Var 2 (Points= 2) for Dark Green and Var 1 (Points=1 ) for Black/ Grey/ Pink. For any other colour occurrence, Var=0 (Points = 0).

[00087] As indicated above, in this example, additional two colours are excluded in cases where data is gathered using the interface of FIG. 4. In this example, most positions had one or a small number of colours that were selected with high frequency which is indicative that there is a pattern of selection and hence that the algorithm will be predictive. In other examples, if there are no colours selected with higher frequency or an overlap between different point colours there may be no distinct pattern of selection and hence an indication that a predictive algorithm cannot be derived from the presented colours and/or sample size.

[00088] In this example, these points allocations form an algorithm where subject candidates are scored based on the above allocations of points to determine a total points score. In an example, the total point score is then converted to a percentage. The derived algorithm is stored in algorithm database 106 and is referred to herein as a safety algorithm as it is intended to identify candidates that are more or less likely to stay safe.

[00089] Accordingly, in the test phase described above, a subject candidate is enrolled by entering personal details via one or more screens displayed on the user device 130. The subject candidate’s user device is then controlled to display the test interface. The subject candidate interacts with the test interface in the same manner as described above. When the selections are complete, user device 130 communicates them over Internet 120 to the server 110 where they are received by processor 102 and processed based on the stored safety algorithm.

[00090] Below are the colour choices of a subject candidates that completed the above test is shown in Table 1 (P is an abbreviation for Position):

Table 1

[00091] In this example, this results in the following scoring:

P1 - 0

P2 - 0

P3 - 0

P4 - 1

P5 - 1 P6 - 0

P7 - 1

P8 - 0

P9 - 1

P10 - 0

P11 - 1

P12 - 3

P13 - 2

P14 - 2

[00092] Accordingly, the subject candidate’s total score is 12 points resulting in a safety percentage of 29%.

[00093] As will be apparent from the above, the aim of developing this algorithm was to improve safety using the safety algorithm as a predictor of future behaviour and in particular ‘safety behaviour’. A measure of safety is the Lost Time Injury Percentage (“LTI%”) - a measure of the amount of time lost by workers being absent due to work acquired injuries, specifically LTI is usually derived from occurrences in a work place that resulted in a fatality, permanent disability or time lost from work of one day/shift or more. For the most part, particularly, in blue colour work places, these occurrences are ones that have a physical impact on individuals. That is, physical injuries leading to death, permanent disability or temporary injuries. Accordingly, while LTI is an economic term, it is correlated to real-world, physical injuries. Hence, a reduction in LTI would reflect a reduction in real-world impacts. Another measure of safety is Total Recordable Injury Frequency Rate (“TRIFR”) - a measure of how frequently recordable injuries are occurring.

[00094] In this example, the beta testing organisation tested applicants for jobs and removed all applicants from the rest of the application process whose safety percentage fell below 20%. This is illustrated by the graph 500 of FIG. 5 where the horizontal axis is safety percentage as tested with the safety algorithm and the vertical axis is the count of candidate number at respective safety percentage. Line 530 represents the decision point at 20 percent with all candidates to the left of this line (below this percentage) being de-selected and candidates to the right of the line (above this percentage) being hired. This resulted in the bottom 11% of candidates being excluded.

[00095] FIG. 6 is a plot 600 of LTI 610 over time in months 620 following application of the algorithm starting in December 2020 to exclude entry to all applicants having a score of below 20 percent being excluded. FIG. 6 shows a significant impact on the Lost Time lnjury% (LTI%) as candidates are excluded. Specifically, a 59.2 reduction in LTI% between the months of January and July (from 3.38 to 1 .38) which correlates to a reduction in injuries, permanent disability and fatalities. Indeed, in the studied period there were no injuries in the highest categories. In addition, the cost of paying worker’s compensation was 2.5 times lower than in a corresponding period. Finally, the TRIFR reduced by 19.2% in the same period (from 21.26 to 17.18).

[00096] Accordingly, an advantageous effect is a reduction in the injuries, permanent disability and fatalities across the recruited work force as a result of using the computerized colour-based psychological testing method of an embodiment with resultant benefits for the employing organization in terms of reduced logistical and financial burdens associated with injuries.

[00097] It will be appreciated that the above example related to blue collar workers and that different patterns of colour selection can arise for different types of candidates (or indeed within different categories of blue collar workers) resulting in different algorithms. In some examples, processor 102 selects the algorithm from algorithm database 106 based on worker characteristics. Further as more data is collected, results (like injury data) can be used not only to validate the outcome but to revise the algorithms to feed in more granularity and achieve more precision.

[00098] In an example, an algorithm was developed to measure resilience of candidates. The mental resilience of an individual is biologically defined as the ability of the ANS to return to balance or homeostasis, that is an individual’s ability to bounce back from pressures by engaging actions at the conscious, subconscious and unconscious levels. When this system is out of balance for too long and/or is too large it will stress and become sub-optimal (poor performance both physical and mental).

[00099] It was discovered that bio-markers of this process (adrenalin and dopamine production together with the rate of change between them) could be found using Heart Rate Variation EEG technology. The regulatory effect (resilience) of the ANS was also found to produce a distinct colour choice pattern which would allow a prediction of an individual’s current resilience status in broad terms.

[00100] Accordingly, in this example the method of developing an algorithm involved conducting the colour test contemporaneously with physiological testing 215 and processing the results 220 to obtain an algorithm. [00101] Previous research has shown the relationship of colour choice and physiological responses. These early studies showed strong correlation of darker colours chosen by individuals with negative thoughts and lighter colours with positive thoughts.

[00102] Initially, 3 key categories with 9 dominant colours were identified as being predictive of ANS function as set out in Table 2. That is, frequency of colour selection linked these colours choices to the physiological measure of regulatory effort.

Table 2

[00103] This was subsequently expanded to 7 categories as set out below (see Tables 3A to 3H) using the 16 colour set of FIG 4. Again, these Tables were obtained based on frequency of colour selections by candidates whose physiological measurements fell in the indicated range. The conditions for each of the resilience categories (positioning (P1 to P8) and colour (C1 to C16)) are as follows:

CATEGORY 1 :

VERY HIGH (90%) RESILIENCE CAPABILITY (ECG msec variation +31 to +50)

Table 3A

CATEGORY 2:

HIGH (70%) RESILIENCE CAPABILITY (ECG msec variation +21 to +30)

Table 3B

CATEGORY 3:

ABOVE AVERAGE (55%) RESILIENCE CAPABILITY (ECG msec variation +11 to +20)

Table 3C

OR

Table 3D

CATEGORY 4:

BELOW AVERAGE (45%) RESILIENCE CAPABILITY (ECG msec variation -11 to -20)

Table 3E

OR

Table 3F

CATEGORY 5: LOW (30%) RESILIENCE CAPABILITY (ECG msec variation -21 to -30)

Table 3G

CATEGORY 6:

VERY LOW (10%) RESILIENCE CAPABILITY (ECG msec variation -31 to -50)

Table 3H

[00104] CATEGORY ?:

[00105] NORMAL (50%) RESILIENCE CAPABILITY (ECG msec variation -10 to +10)

[00106] IF NONE OF CATEGORIES 1 TO 6 ARE SATISFIED.

[00107] Accordingly, in this example, the resilience algorithm produced by this process and stored in algorithm database 206 comprises extracting the order of selection of nine defined colours from the colours selected by the subject candidate using the interface of FIG. 4, that is: Red, Yellow, Dark Green, Green, Light Green , Dark Blue , Light Blue, Black and Violet. The processor 102 then determines which of Categories 1 to 7, the subject candidate’s selection matches. Table 4 sets out an example of a subject’s ordered selection of these defined colours using the interface of FIG. 4.

Table 4

[00108] Checking the combination of the colours in this subset as per the algorithm causes processor 102 to determine that it satisfies, the condition of Table 3A and accordingly, the subject’s resilience category is above average with Resilience Percentage = 55. In an example, this outcome is output 250 to the subject and the subject’s employer.

[00109] It will be appreciated that the above example describes resilience testing alongside safety testing. In examples, where resilience testing is carried out independently a testing interface incorporating only the defined colours.

[00110] When the regulatory system fails to maintain balance over longer periods (stressed) the physical consequences of this poor resilience response can include all or some of the following: headache, high blood pressure, irritable bowel syndrome, back pain, heart disease, skin conditions (e.g. eczema), obesity/weight loss, digestive problems, ulcers and frequent illnesses and infections. Accordingly, testing for resilience and taking actions as a result has the potential to mitigate against these outcomes.

[00111] In an example, as well as outputting 250 the outcome, the processor 204 generates a report based on the outcome by populating a report based on the subject candidate’s resilience category and outputting it to the subject candidate, for example, by generating an email and sending it to the subject candidate’s email address captured in the enrollment process. In an example, the report comprises a mixture of general information and information specific to the category.

[00112] An example report is as follows:

[00113] “Resilience report for: XXX XXX on 14/09/2021

[00114] About Resilience:

[00115] Resilience is a measure of your ability to ‘bounce back’ or return to a normal balanced state (called Homeostasis) from the effects of pressure. When we don’t bounce back effectively, this can lead to us carrying increased stress which can manifest in both physical and mental symptoms.

[00116] The Pressure & Stress cycle works like this: Pressure and the stress it produces is normal. However, if the pressure is abnormally high or persistent this can lower your resilience. When your resilience is lowered, it can leave the path open for more elevated stress levels to build which compound and reduce resilience further. If left unchecked, this cycle can spiral into ever increasing levels of stress and lowered resilience ultimately leading to mental states we know as ‘burnout’ or ‘breakdown’.

[00117] Below, please find your current level of resilience and some tailored specific tips created from your colour responses to help you manage pressure and stress which you can achieve through maintaining or improving your resilience.

[00118] Your resilience results:

[00119] Your RESILIENCE to PRESSURE is ABOVE AVERAGE. (See Figure 7.)

[00120] This means that your current level of mental resilience can cope with everyday pressure without turning into stress. Should the pressures on you become even higher or sustained over longer periods of time this level of resilience may not be adequate.

[00121] If your resilience is low, or very low please consider and explore the tools and links provided that there are to help.

[00122] Some of the recommended strategies are easy to implement and it’s often the simple changes we create in our routine that can make a big difference to how we feel.

[00123] Behaviours to watch out for and tips to help you:

[00124] Your early warning triggers for lowered resilience and increased stress for you could mean the following:

• You attempt to force harmony without checking whether people are interested

• Try to champion everyone and solve all problems

• You become overwhelmed by multiple responsibilities to help others

[00125] Following on from this, should stress begin to take over you might:

• Make sweeping and excessive criticisms of self and others

• Engage in all-or-none, rigid, logical thinking

• You seek the ultimate “truth”

[00126] Some common stressors for you in this state to watch out for could be:

[00127] Being forced to conform to unacceptable views

• Discordant relationships or pressure to act impersonally

• Time pressures that interfere with working cooperatively

[00128] Some bigger picture ideas to improve your mental resilience and overall state of wellbeing would be to:

[00129] Arrange time alone to think the situation through

Reconnect with what is important Link with supportive people who are not involved in the negative situation

Engage in other self-care activities

[00130] What's next?

[00131] The Pressure & Stress Cycle is dynamic which means that the pressures and stresses we experience affect us differently over time as things change in our personal and professional lives.

[00132] We all want to know that our resilience is effectively coping with the everescalating pressures and stresses in our lives. However, it’s important we keep an eye on it and not just assume everything will be alright.

[00133] For this reason, the resilience checking is not a one-off measure. Undertaking regular assessments helps you to monitor, maintain and improve your mental health leading to a better sense of general wellbeing.

[00134] Engage with the Neuro Resilience Program by getting in touch with us and try the resilience boosting daily sound session program.”

[00135] With respect to the above example report, it will be appreciated that FIG. 7 would normally be incorporated within the report. FIG. 7 is a graphical representation 700 of an example subject candidate’s resilience score on the basis of the above colour testing algorithm showing a normal distribution curve 710 of resilience scores where the horizontal axis 720 is a resilience percentage and the vertical axis 730 is number of people. The subject candidates individual result 740 is shown on the normal distribution curve 710.

[00136] A further example algorithm was developed to measure cohesiveness. Human development and success has been not only dependant on the success of the individual but also the success of the group. This group success has been shown by neuroscientists to reflect in the cohesiveness of the group and in brain terms ‘the same wavelength’.

[00137] When humans connect in a cohesive manner they literally share the same brain waves in two significant parts of the brain called the pre-frontal cortex and the stratium. It is understood the brain does this to improve learning (transfer among the group) with the eventual outcome of higher success.

[00138] Cohesiveness enables higher success through literally ‘being on the same wavelength’. Quicker learning occurs through mutual shared thinking. This is assessed using compatible colour choices between group members and in the first instance between group leader and followers. [00139] In this example, the Team/Group Algorithm evaluates the brain cohesiveness wavelength of one or more individuals with respect to the other. So, at minimum there will be two subject candidates taken into consideration, say Candidate A and Candidate B, for the sake of example. In an example, data was gathered for the ordered selection of the first fourteen positions using the interface of FIG. 4 as set out in Tables 5 and 6..

[00140] Candidate A’s colour choices:

Table 5

Candidate B’s colour choices:

Table 6

[00141] In this example, the algorithm used by processor 102 from algorithm database 106 is to extract these 8 defined colours (or any shades) from the 14 colours that user choses: Grey, Blue, Yellow, Black, Green, Brown, Red, Violet. In case of multiple shades of colour, the algorithm causes processor 204 to pick the first and ignore the rest. The result of this processing is set out in Tables 7 and 8.

A’s Colour Subset

Table 7

B’s Colour Subset

Table 8

[00142] The algorithm causes processor 102 to compare the position of the respective colours, to evaluate scores for each position to arrive at a cohesion score(%). In this example, as there are eight colours an exact match is allocated 12.5% such that if all positons match the cohesion score is 100%. 10% is allocated for a difference of one position and 5% is allocated for a difference of two position.

[00143] The individual percentage allocations are illustrated in Table 9.

Table 9

Cohesiveness Score % = 52.5% (Sum of all scores evaluated above)

[00144] In this example the team score for A and B falls into the category Average (41-60%) .

[00145] The outcome can be communicated based in a report 255 which includes a Team Cohesion Point of Contact Recommendation for A and B:

• Face to Face: 2 times per week

• Phone: 2 times per week

• Email: 3 times per week

[00146] The report can also incorporate a recommended communication Style for A or B which is determined by processor based on their first colour choice. For example, in this case, both have same first colour choice (Blue). “A’s Communication Style (or B’s Communication Style):

You are usually excellent at communication, especially when you are doing this through writing rather than verbally. You can often end up being the mediator between two people, since you are skilled at finding ways to bridge the gap of communication. You can often see where people are misunderstanding one another and find a simpler way to help them understand. You are not always the most chatty person, but what you do communicate is very well thought out. You like to take a step back and process things sometimes, which can make your written communication much better than verbal. When you are given time to think things through, you will have plenty to say.”

[00147] FIGs. 8 to 11 are example extracts from a report generated by processor 102 using the team cohesiveness algorithms for a team of people lead by a first individual 805, “Justin”. FIG. 8 is a map 800 of relative cohesiveness scores for a team of 5 individuals 811 - 815, with the individuals “Vanessa” 811 and “Rachel” 812 having strong synchronization to Justin 805; the individuals “Nichola” 813 and “Tyler” 814 having strong synchronization to Justin 805; and the individual Carla 815 having poor synchronization.

[00148] FIG. 9 is a graph 900 showing the resilience percentage for ach of individuals 805 and 815 and FIG. 10 is an example of text 1000 included in the report for Justin 805 with recommendations as to how to better relate to Carla 815 based on her cohesiveness and resilience scores.

[00149] FIG. 11 is a table 1100 of recommendation to Justin 805 of points of contact for each of the team members based on the cohesiveness algorithm.

[00150] In some examples, a computer program comprising a series of executable instructions may be supplied which when executed by one or more processors causes the one or more processor to implement the above method. In some examples, the computer program may be supplied as a computer program product for example on a tangible computer readable medium such as a disc, memory device, memory card, etc.

[00151] Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise”, “comprising”, and the like, are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense, that is to say, in the sense of “including, but not limited to”.

[00152] Reference to any prior art in this specification is not, and should not be taken as, an acknowledgement or any form of suggestion that that prior art forms part of the common general knowledge in the field of endeavour in any country in the world. [00153] The invention may also be said broadly to consist in the parts, elements, characteristics and features referred to or indicated in the specification of the application, individually or collectively, in any or all combinations of two or more of said parts, elements, characteristics or features.

[00154] While the invention has been described with respect to the figures, it will be appreciated that many modifications and changes may be made by those skilled in the art without departing from the spirit of the invention. Any variation and derivation from the above description and figures are included in the scope of the present invention as defined by the claims.